AUTISM SPECTRUM 
DISORDERS: THE ROLE OF 
GENETICS IN DIAGNOSIS 
AND TREATMENT 
 
Edited by Stephen I. Deutsch 
and Maria R. Urbano 

 

 
 

 
 
 
 
 
 
 
 
 
 
Autism Spectrum Disorders: The Role of Genetics in Diagnosis and Treatment 
Edited by Stephen I. Deutsch and Maria R. Urbano 
 
 
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Contents 
 

Preface 

IX 

Part 1  Early Recognition and Diagnosis  1 

Chapter 1  Early Detection of Autism Spectrum Disorders  3 

Jariya Chuthapisith and Nichara Ruangdaraganon 

Part 2  Nosology and Diagnostic Criteria: 

What Makes Sense and Can Genetics Help?  15 

Chapter 2  Pervasive Developmental Disorder- not 

Otherwise Specified: Specifying and Differentiating  17 
Koray Karabekiroglu 

Chapter 3  Autism and Genetic Syndromes  31 

Willem Verhoeven, Jos Egger and Ilse Feenstra 

Part 3  Genetics and Pathophysiology 

of Autism Spectrum Disorders  49 

Chapter 4  The Genetics of Autism Spectrum Disorders  51 

John J.M. Connolly and Hakon Hakonarson 

Chapter 5  Genetic Heterogeneity of Autism Spectrum Disorders  65 

Catherine Croft Swanwick, 
Eric C. Larsen and Sharmila Banerjee-Basu 

Chapter 6  The Genetic Basis of Phenotypic 
Diversity: Autism as an Extreme 
Tail of a Complex Dimensional Trait  83 
Shinji Ijichi, Naomi Ijichi, Yukina Ijichi, 
Hisami Sameshima and Hirofumi Morioka 

Chapter 7  A New Genetic Mechanism for Autism  103 

Julie Gauthier and Guy A. Rouleau 

VI  Contents 

 

Chapter 8  Common Genetic Etiologies and 

Biological Pathways Shared Between 
Autism Spectrum Disorders and Intellectual Disabilities  125 
Liana Kaufman, Abdul Noor, 
Muhammad Ayub and John B. Vincent 

Part 4  Treatment and Genetic Counseling  159 

Chapter 9  Microgenetic Approach to Therapy of Girls with ASD  161 

Katarzyna Markiewicz and Bożydar L.J. Kaczmarek 

Chapter 10  Genetic Counseling in Autistic Phenotypes  181 

Agnes Cristina Fett-Conte 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 

 

 

 

 

 

 

 

 
 
 
 
 
Preface 
 

The broadening of the definitional criteria of autism spectrum disorders (ASDs) and 
increased recognition of these syndromes have led to dramatic increases in their es-
timated prevalence; prevalence estimates of ASDs in the USA are approximately 1 in 
110 children with a three to four time greater male to female predominance. These 
disorders  occur  commonly  as  co-morbid  conditions  in  several  Mendelian  genetic 
disorders  due  to  the  effects  of  a  single  major  gene  (e.g.,  tuberous  sclerosis).  Im-
portantly, although these Mendelian disorders appear to be unrelated to each other, 
recent advances in bioinformatics and “network analyses” suggest that they may in-
deed  be  related  to  each  other;  the  points  of  convergence  can  include  development 
and  architecture  of  the  synapse,  and  early  developmental  events  in  neurogenesis, 
neuronal  cell  migration  and  synaptogenesis.  Additionally,  areas  along  the  human 
genome  are  emerging  as  “hotspots”  for  microdeletions  and  microduplications,  re-
ferred to as Copy Number Variants (CNVs); the density of these CNVs may contrib-
ute to increased risk of neurodevelopmental syndromes, including ASDs. Remarka-
bly, although the 1970’s was focused on elucidating descriptive differences between 
ASDs and schizophrenia presenting in childhood; the emerging data on CNVs sug-
gest that ASDs and schizophrenia, or at least their genetic mechanisms, may be more 
similar than initially appreciated. In any event, the genetic data are also suggesting 
molecular targets; for example, microdeletions at the 15q13.3 locus suggest that hap-
loinsufficiency of a gene product of this locus (i.e., CHRNA7), which codes for the 
α7 nicotinic acetylcholine receptor (α7 nAChR) subunit, may be causally associated 
with ASDs. Thus, selective nicotinic acetylcholine receptor agonist strategies should 
be explored for their potential therapeutic benefit. The high prevalence of these dis-
orders,  their  impact  on  the  identified  affected  patient  and  the  unrecognized  unaf-
fected family members (including sibs), accessibility of Array Comparative Genomic 
Hybridization  screening  technologies,  elucidation  of  associations  with  candidate 
susceptibility  genes,  along  with  CNVs  and  complex  genetics  are  raising  profound 
ethical questions,  heightening  the challenges of  genetic counseling.  The  staggering 
challenges  of  genetic  counseling  are  further  compounded  by  issues  of  imprinting 
(i.e., homologous maternal and paternal chromosomes may have different patterns 
of cytosine methylations and certain genetic disorders differ depending on genetic 
variations  within  one  of  the  affected  parental  chromosomes  [e.g.,  Angelman  and 

X 

 

Preface 

Prader-Willi  syndromes])  and  variable  “penetrance”  (i.e.,  there  is  a  broad  array  of 
possible phenotypes). The chapters contained in  this  book  highlight some  of  these 
emerging issues. 

Stephen I. Deutsch, M.D., Ph.D. and Maria R. Urbano, M.D. 
Department of Psychiatry and Behavioral Sciences 
Eastern Virginia Medical School 
825 Fairfax Avenue, Suite 710 
Norfolk, Virginia 23507-1912 
USA 

 

 

 

 

Part 1 

Early Recognition and Diagnosis

 

1 

Early Detection of Autism Spectrum Disorders 
Jariya Chuthapisith and Nichara Ruangdaraganon 
Department of Paediatrics, Faculty of Medicine Ramathibodi Hospital 
Mahidol University, Bangkok 
Thailand 

1. Introduction 
Autism  spectrum  disorders  (ASDs)  are  neurodevelopmental  disorders  characterized  by 
distinctive  language  impairments,  social  and  communicative  deficits,  and  patterns  of 
restricted  and  stereotyped  behavior.  In  the  Diagnostic  and  Statistical  Manual  of  Mental 
Disorders,  fourth  edition,  text  revision  (DSM-IV-TR)  (American  Psychiatric  Association, 
2000),  pervasive  developmental  disorders  (PDDs)  are  also  referred  to  as  autistic  disorder 
(AD),  Asperger’s  disorder,  PDD  not  otherwise  specified 
(PDD-NOS),  childhood 
disintegrative  disorder,  and  Rett  Disorder.  However,  the  diagnostic  boundaries  between 
these PDD subtypes remain unclear, the symptoms and behaviours lie on a continuum and 
have considerable clinical heterogeneity (Szatmari, 1999). In this review, therefore, ASDs are 
referred to as the diagnostic category of PDDs.  

2. Diagnosis of ASDs 
The manifestations of ASDs vary from mild to severe and pervasive impairment. Currently, 
the  diagnosis  of  ASDs  is  based  on  the  criteria  developed  in  the  DSM-IV-TR  and  the 
International Classification of Diseases, 10th revision (ICD-10) (World Health Organization 
(WHO), 1992) and is supported by standardized diagnostic instruments. According to the 
DSM-IV-TR  criteria,  the  impairments  of  ASDs  consist  of  three  main  impairments  which 
must all be presented for diagnosis.  
2.1 Impairment in social interaction is defined by various symptoms including impairment 
in the use of nonverbal behaviours (e.g. eye contact, use of gestures and facial expressions); 
lack of showing, bringing or pointing out objects; odd relationships of approaches to others; 
and lack of social or emotional reciprocity.  
2.2  Impairments  in  communication  consist  of  delay  in  or  total  lack  of  spoken  language, 
inability  to  initiate  or  sustain  a  conversation  with  others,  stereotyped  or  repetitive  use  of 
language, and lack of social imitative play.  
2.3  Restricted  repetitive  and  stereotyped  patterns  are  behaviours,  interests  and  activities  as 
manifested  by  an  inability  to  cope  with  change,  a  dislike  for  any  interruption  to  routine, 
preoccupation with specific subjects or activities, repetitive or stereotyped motor mannerisms 
such as hand flapping or twisting, and persistent preoccupation with parts of objects.  
Early  diagnosis  for  ASDs  is  undoubtedly  important  and  is  considered  as  a  clinical  best 
practice.  Early  detection  of  ASDs  leads  to  an  early  intervention  (Rutter  et  al.,  2006). 

4 

 
Autism Spectrum Disorders: The Role of Genetics in Diagnosis and Treatment 

However, diagnosis before the age of 3 years remains a challenge (Baron-Cohen et al., 1996). 
Some symptoms of ASDs may overlap with normal developmental variance. Also, ASDs are 
a  continuum  of  disease  which  has  a  wide  range  of  individual  differences.  Distinctions 
between autistic disorder and PDD-NOS remain unstable. A study reported that up to 50% 
of  PDD-NOS  cases,  who  were  diagnosed  before  age  3  years,  could  have  been 
overdiagnosed,  whereas  around  22%  were  underdiagnosed  (Chawarska  et  al.,  2007).  This 
was due to the fact that diagnosis depends on clinical judgments which sometimes may not 
agree with the DSM-IV-TR diagnostic criteria especially evaluating a young child. Some of 
the criteria in the DSM-IV-TR can not apply to young children. In other words, many of the 
characteristic  behaviours  in  the  DSM-IV-TR  are  not  apparent  before  36  months.  For 
example, a child age less than 16-month-old typically can engage in parallel play but has not 
yet  developed  reciprocal  peer  relationships.  Thus,  the  criteria  of  failure  to  develop  age-
appropriate peer relationships need to be adapted (Martinez-Pedraza & Carter, 2009). The 
criteria  of  stereotyped  and  repetitive  use  of  language  can  be  difficult  to  discriminate 
between repetitions of the last word in young typically developing children and echolalia in 
children with ASDs. Furthermore, the criteria “restricted repetitive and stereotyped patterns 
of behaviour, interests and activities” may not appear in young children. These may appear 
later after the third birthday in some cases (Gray & Tonge, 2001; Turner, 1999). Therefore, 
making a diagnosis in children younger than 2 years of age is very challenging. 

3. Early signs of ASDs 
Many  research  studies  have  concluded  that  the  first  signs  and  symptoms  of  ASDs  are 
evident  by  12  to  18  months  of  age  (De  Giacomo  &  Fombonne,  1998;  Young  et  al.,  2003). 
Research on early signs and symptoms of ASDs in young children have focused on parental 
retrospective reports, early home videos of children later diagnosed with ASDs, and studies 
on siblings of children with ASDs. The emergence of ASDs signs and symptoms involve the 
area  of  social  skill  deficits,  language  skill  deficits  and  unusual  repetitive  or  stereotypical 
behavioural  patterns.  Signs  and  symptoms  that  are  predictive  of  ASDs  in  young  children 
are, namely: 

3.1 Social skills deficits 
Social skills are one of the most important areas in defining ASDs in very young children. In 
typically  developing  children,  social  development 
is  acquired  parallel  to  overall 
development (e.g. language, motor and cognitive development). In the very young children 
whose  language  skills  are  limited,  social  development  depends  very  much  on  clinical 
observations.  The  manifestation  is  a  lack  of  or  a  decreased  drive  to  connect  with  others, 
including  share  feelings,  thoughts  and  actions.  Children  who  have  ASDs  have  limited  or 
reduced  eye  contact,  fail  to  orient  their  name  being  called,  limited  imitation,  limited 
responding  to  reciprocal  social  games,  and  lack  of  showing  or  bringing  an  object  to  a 
caregiver.  
The important characteristic in helping make a diagnosis in very young children is lack of 
“joint attention” (JA) (Charman, 2003; Dawson et al., 2002; Turner et al., 2006). JA refers to 
the capacity of the child to coordinate attention with a social partner in relation to an object 
or event (Rapin & Tuchman, 2008). JA normally appears to develop between 8-16 months. In 
8-10  months  old  typically  developing  children,  the  child  will  follow  the  caregiver’s  gaze 

 

 
Early Detection of Autism Spectrum Disorders 

5 

when the caregiver looks at an object or event. This development milestone is called “gaze 
monitoring”. Around 10-12 months of age the child can follow the caregiver’s point and can 
look back at the caregiver. At approximately 12-14 months the child will request for objects 
by pointing. In detail, the child will look back and forth between the object and caregiver to 
reassure that the caregiver understands his or her need, so called protoimperative pointing. 
At  14-16  months  when  the  protodeclarative  pointing  develops,  the  child  will  look 
alternatively between the object and the caregiver. The goal is to share social experience, not 
the  desired  object  (Johnson  &  Myers,  2007).  Other  nonverbal  gestures,  including  facial 
expression,  usually  help  discriminate  the  difference  between  these  two  types  of  pointing. 
Children with ASDs can not achieve these skills at an age-expected time or some can achieve 
partially but do not qualitatively achieve the skill completely. Some children may have no 
pointing at all but use their caregivers’ hands point to the desired object. Some children look 
at the object but do not look at the caregiver to connect socially. A study in infant siblings of 
children with ASDs stated that the inability to shift one’s attention (between child, parent 
and object) may be the first reliable sign of ASDs (Zwaigenbaum et al., 2005). In brief, lack of 
or delayed JA skill that is discrepant from overall functioning is a core feature of the ASDs 
diagnosis.  
Since JA skills may not be observed in typically developing children younger than 1 year of 
age, responding to their name being called is a skill that the child should achieve. Children 
with ASDs usually fail to respond to their name being called. Some children with ASDs may 
respond to environmental sounds well enough to reassure the caregivers that their children 
can hear. Home videos of 1-year-old children who later were diagnosed with ASDs found 
that orienting to name being called is one of the most consistent deficits for affected children 
at that age (Baranek, 1999; Osterling & Dawson, 1994).  
Delay in play skills is one of the features associated with diagnosis of ASDs. In respective 
order,  play  starts  with  sensory-motor,  functional,  constructive,  and  pretend  or  imaginary 
play.  In  typically  developing  children,  approximately  4  months  old,  sensory-motor  play 
begins. At 12-14 months of age, the child plays in a more functional manner. Pretend play 
starts around 16-18 months of age and increases gradually in complexity. Lack of or delay in 
pretend  play  or  play  that  never  passes  the  sensory-motor  play  stage  serves  as  a 
distinguishing  characteristic  of  ASDs.  Although,  some  children  with  ASDs  progress  to 
functional  play,  the  quality  of  play  is  significantly  different  from  typically  developing 
children  by  around  age  2  years  i.e.  play  is  less  purposeful,  less  symbolic  and  less  in 
complexity (McDonough et al., 1997; Sigman et al., 1999; Stone et al., 1990). Some children 
with ASDs play or manipulate objects in a stereotypic or ritualistic manner such as lining 
up,  banging,  and  mouthing  objects.  They  usually  prefer  playing  alone  and  have  trouble 
incorporating into social play. This sophisticated social play may not develop which further 
worsen social skills development. 
Although, there is a possibility to detect social skills deficits in children younger than 1 year 
of  age,  the  reliability  remain  problematic  before  18  months  (Rutter,  2006).  Special 
consideration should focus on gaze monitoring, joint attention, responding to being called 
by name, and play skills.  

3.2 Early language skills deficits 
Generally, absence of language skills appears at around age 2, which may lead to diagnosis 
of ASDs. In order to diagnose of ASDs earlier, delay in language development should  be 

 

6 

 
Autism Spectrum Disorders: The Role of Genetics in Diagnosis and Treatment 

(Zwaigenbaum  et  al.,  2005).  Regarding 

detected  as  soon  as  possible.  A  study  among  the  siblings  of  children  with  ASDs 
demonstrated that during the first year of life, infants later diagnosed with autism vocalized 
less  than  low-risk  control  infants.  Moreover,  delays  in  verbal  skills  and  early  language 
comprehension  were  evident 
language 
abnormalities,  both  expressive  and  receptive  language  deficits  should  be  monitored. 
Typically, infants start to babble by 6 months of age, followed by advances in complexity 
which includes several phonemes. Later, jargoning (i.e. adds inflection to utterances in an 
attempt to tell a story) develops at approximately 10 -12 months of age. Lack or delay of an 
alternating to-and-fro pattern of vocalizations between infant and parent, delay of onset of 
babbling, and decrease or no use of pre-speech gestures (e.g. pointing, showing, nodding) 
are characteristic of ASDs (Wetherby et al., 2000; Johnson & Myers, 2007).  
Repeating words in particular the last one or two words of a sentence right after being heard 
can be observed in typically developing children under the age of 2 years, which mimicks 
the  ASDs  symptom  of  immediate  echolalia.  However,  the  typically  developing  child  will 
pass through this brief stage and will acquire functional language. In children with ASDs, 
this  imitation  still  persists  as  expressive  language  after  the  age  of  around  2  years  and 
beyond. Furthermore, the children with ASDs mostly repeat words in an odd intonation or 
repeat exactly the same intonation as they heard (Martinez-Pedraza & Carter, 2009). 
In young children with ASDs, receptive language ability is often impaired. They initially do 
not respond to their names when called by a caregiver. After language is present, children 
with  ASDs  are  unable  to 
initiate  or  sustain  conversation.  Some  children  have 
comprehension deficits, particularly in complex sentences or questions. Children with ASDs 
also show deficits in non verbal communication; for example, they look at others less, have 
less social smile, lack appropriate gestures, have less pointing or have difficulty following a 
point,  show  objects  less  and  have  a  lack  of  appropriate  facial  and  emotional  expression. 
These  non  verbal  communication  deficits  are  linked  closely  to  lack  of  social  skills 
development (Martinez-Pedraza & Carter, 2009). 
There  is  approximately  one  fourth  to  one  third  of  children  with  ASDs  whose  parents 
reported  a  significant  loss  or  regression  in  language  development.  The  regression 
characteristically occurs between 15-24 months of age (Lord et al., 2004; Luyster et al., 2005). 
Although, some parents reported normal development prior to regression, studies showed 
that some children with ASDs have subtle language and social impairments before the onset 
of regression (Richler et al., 2006; Werner & Dawson, 2005).  

3.3 Restrictive interests, stereotypic and repetitive patterns of behaviours 
Stereotypies and repetitive behaviours are not specific to children with ASDs. Children who 
have  globally  developmental  delay  (GDD)  and  children  with  sensory  impairment  may 
demonstrate stereotypies. Even in typically developing children, stereotypies may present 
e.g.  flapping  their  hands  when  excited  (Johnson,  2008).  Stereotypies  and  repetitive 
behaviours  in  children  with  ASDs  usually  are  not  common  in  very  young  children 
(Charman & Baird, 2002; Cox et al., 1999; Moore & Goodson, 2003). Children with ASDs are 
preoccupied  with  sameness  and  routines,  so  interruption  or  changes  in  routine  lead  to 
tantrum and emotional disturbance. Some display sensory abnormalities: hypo- or hyper-
responsive  to  sensory  stimuli.  Some  children  show  an  unusual  and  preoccupation  with  a 
topic of interest such as train schedules, solar system, dinosaurs, etc. However, this strong 

 

 
Early Detection of Autism Spectrum Disorders 

7 

interest may not present in young children with ASDs. These patterns of behaviours vary 
among young individuals with ASDs. Therefore, diagnosis of ASDs in very young children 
should  focus  on  social  skills  and  language  skills  deficits  rather  than  stereotypies  and 
repetitive behaviours. 

4. Screening tools for ASDs 
The American Academy of Pediatrics (AAP) recommends ASDs screening in children age 18 
and 24 months as part of developmental surveillance during regular health visits (Johnson & 
Myers, 2007). There are many valuable screening tools designed, such as the Checklist for 
Autism  in  Toddlers  (CHAT)  (Baron-Cohen  et  al.,  1992;  Baron-Cohen  et  al.,  1996),  the 
Modified Checklist for Autism in Toddlers (M-CHAT) (Kleinman et al., 2008; Robins et al., 
2001), the Screening Test for Autism in Two-Year-Olds (STAT) (Stone et al., 2000) and the 
Pervasive Developmental Disorders Screening Test-II (PDDST-II) (Siegel, 2004). All of these 
tools, except the STAT, are designed as first-level screens (i.e. the tools are administered to 
all children to differentiate children who are at risk of ASDs from the general population).  
Baron-Cohen et al conducted a study using the CHAT to administer in a primary health care 
setting  to  identify  18-month-old  children  at  risk  of  ASDs.  The  study  included  both  direct 
observation and a questionnaire for parents. The CHAT focuses on 3 key items which are 
gaze monitoring, protodeclarative pointing and pretend play. Findings from the study in the 
general  population  demonstrated  that,  the  CHAT  had  a  specificity  of  98%-100%  and  a 
sensitivity of 18%-38% (Baird et al., 2000; Baron-Cohen et al., 1992; Baron-Cohen et al., 1996; 
Scambler  et  al.,  2001).  Attempts  to  improve  sensitivity  by  modifying  the  cut-off  criteria 
resulted in decrease in positive predictive value (from 75% to 5%). Overall, use of the CHAT 
as a screening tool remains problematic owing to low sensitivity (Bryson et al., 2003).  
The M-CHAT is a screening tool for children 16 to 48 months and was developed to improve 
prediction of the CHAT. In the M-CHAT, there is no observation component, but includes a 
wider range of signs and symptoms of ASDs. This parental questionnaire consists of 23 (yes-
no) items. Children who fail any three items or two critical items are considered to be at risk 
for ASDs. Items that were found to be the best predictors for ASDs were protodeclarative 
pointing, response to name, interest in peers, bringing things to show parents, following a 
point, and imitation. The reported sensitivity and specificity of the M-CHAT were around 
89%  and  93%,  respectively  (Dumont-Mathieu  &  Fein,  2005).  However,  the  positive 
predictive value (PPV) was low (0.11±0.05) when it was used alone as a screen for ASDs in a 
community-based sample. The follow-up interview was reported to be able to significantly 
increase  the PPV  (Kleinman et  al.,  2008).  Overall,  the  M-CHAT  showed  higher  sensitivity 
than the CHAT and is possibly useful in identifying children in need of further assessments, 
but  should  not  be  used  as  a  screen  to  exclude  the  possibility  of  ASDs  (Eaves  et  al.,  2006; 
Barbaro & Dissanayake, 2009). 
The STAT is a second-level screen (that is, the tool is used to differentiate children who are 
at risk of ASDs from those at risk of other developmental disorders). It was designed to be 
used in children aged 2-3 years. The STAT includes 12 pass/fail items and is administered in 
a  play-like  setting  in  order  to  observe  social-communicative  behaviours.  The  test  lasts 
approximately 20 minutes to complete. The estimated sensitivity and specificity were 95% 
and 73%, respectively (Stone et al., 2008). However, increased validity in larger studies and 
community-based samples are required.  

 

8 

 
Autism Spectrum Disorders: The Role of Genetics in Diagnosis and Treatment 

The PDDST-II has both a first and second level screen versions. It is a parental questionnaire 
that can be used with children under 6 years of age. To date, the clinical validity remains 
unclear  because  it  has  not  yet  been  published  in  a  peer-reviewed  journal  (Volkmar  et  al., 
2005). 

5. Diagnostic instrument for ASDs 
Currently, there are standardized instruments to facilitate diagnosis in ASDs. The Autism 
Diagnostic Interview – Revised (ADI-R) (Le Couteur et al., 2003; Lord et al., 1994) and the 
Autism Diagnostic Observation Schedule (ADOS) (Lord et al., 2000a) are well validated and 
currently  their  combination  with  clinical  judgment  based  on  the  DSM-IV-TR  criteria  are 
considered as the “gold standard” for diagnosis of ASDs (Battaglia, 2007). However, these 
instruments should be used with caution in very young children or children with a mental 
age less than 24 months (Stone et al., 1999).  
The  ADOS 
is  the  most  widely  used  standardized  semistructured  assessment  of 
communication, social interaction and play. The scenarios for interaction with the child are 
used in the ADOS and require a well-trained interviewer. The ADOS consists of 4 modules 
devised for individuals with varying developmental and language level. Each module lasts 
approximately  40  minutes.  The  ADOS  provides  an  algorithm  to  differentiate  between 
autism,  ASD  and  not  ASD.  Alpha  coefficients  are  0.86-0.91  for  the  social  domain  (across 
modules), 0.74-0.84 for communication, and 0.63-0.65 for repetitive behaviours (modules 1 
and 2) (Lord et al., 2000a). In younger children, especially younger than 15 months of age, 
the  sensitivity  is  excellent,  the  specificity  is  doubtful  (Chawarska  et  al.,  2007;  Lord  et  al., 
2000b; Risi et al., 2006). Luyster et al developed the toddler version of the ADOS (ADOS-
Toddler Module or ADOS-T) which can be used for children under 30 months of age who 
have  non-verbal  mental  ages  of  at  least  12  months.  The  ADOS-T  has  acceptable  internal 
consistency  and  excellent  inter-rater  and  test-retest  reliability  (Luyster  et  al.,  2009). 
However, larger samples of children and long follow-up studies need further replication.  
The  ADI-R  is  a  standardized  parental  interview  conducted  by  a  trained  interviewer.  The 
interview covers the past developmental history and current functioning of individuals. The 
tool consists of 111 questions and takes about 2-3 hours. The ADI-R is designed to use in 
children  about  4-5  years  old.  The  ADI-R  provides  an  algorithm  to  differentiate  between 
autism  and  not  autism.  The  ADI-R  is  reliable  and  valid.  The  inter-rater  reliability  on 
individual  algorithm  items  ranges  from  0.63  to  0.89.The  internal  consistency  (alpha 
coefficients)  is  0.69-0.95  (Lord  et  al.,  1994).  However,  the  time  needed  for  administration 
precludes its use in clinical settings. Moreover, further study is needed for identifying ASDs 
in preschool children (Le Couteur et al., 2008; Mazefsky & Oswald, 2006; Risi et al., 2006). 
The  Developmental,  Dimensional  and  Diagnostic  Interview  (3Di)  is  a  new  structured 
computerized  interview  for  the  diagnosis  of  ASDs  and  extends  to  co-morbid  disorders. 
There  are  total  266  questions  on  autistic  spectrum  disorders  (ASD)  symptoms  and  53 
questions  for  an  abbreviated  interview.  The  questions  in  the  interview  are  clustered 
according to domains of function: reciprocal social interaction skills, social expressiveness, 
use of language and other social communication skills, use of gesture and non-verbal play, 
and  repetitive/stereotyped  behaviours  and  routines.  To  reduce  a  risk  of  respondent  bias, 
breaking  down  complex  questions  and  scattering  their  components  throughout  the 
interview  were  done.  A  study  reported  that  test-retest  and  inter-rater  reliabilities  were 

 

 
Early Detection of Autism Spectrum Disorders 

9 

target  behaviours  are  visual 

excellent. The sensitivity and specificity were estimated about 100% and 97%, respectively. 
Both the original 3di and the short version demonstrated high agreement with the ADI-R 
(Santosh  et  al.,  2009;  Skuse  et  al.,  2004).  Moreover,  the  short  version  takes  less  time  to 
perform compared with the ADI-R. However, the study was limited to mild cases of ASDs; 
and so far limited numbers of young children have been tested.  
The  Autism  Observation  Scale  for  Infants  (AOSI)  (Bryson  et  al.,  2008)  is  a  diagnostic 
instrument that was developed for infants aged 6-18 months. The instrument consists of 18-
item direct observational measure. Various activities were developed to assess the infant’s 
target  behaviours.  These 
tracking  and  attentional 
disengagement;  coordination  of  eye  gaze  and  action;  imitation;  early  social-affective  and 
communicative behaviours; behavioural reactivity; and various sensory-motor behaviours. 
The  inter-rater  reliability  ranges  from  0.68  to  0.94  at  6,  12  and  18  months.  Test-retest 
reliability is acceptable. The AOSI takes approximately 20 minutes to administer. Although, 
the AOSI is a useful diagnostic instrument for young children, it is not yet proposed to be 
used. 
In  brief,  although  there  have  been  a  number  of  screening  and  diagnostic  instruments  to 
facilitate  ASDs  diagnosis,  a  comprehensive  evaluation  for  suspected  ASDs  should  be 
performed. Such evaluations include a developmental history, parental interview, thorough 
physical examinations, clinical observations, developmental evaluations, assessment of the 
strengths and weaknesses of the child, assessment of family functioning, administration of 
standardized diagnostic instruments that operationalize the DSM criteria, and measures of 
cognitive  and  adaptive  functions.  Such  comprehensive  approaches  together  with  early 
detection  can  lead  to  early  intervention  and  result  in  improvement  of  the  long-term 
functioning of children with ASDs. 

6. Summary 
Early detection of ASDs provides the best opportunity for early intervention, which results 
in significantly improved outcomes for children with ASDs. Awareness of the importance of 
early  diagnosis  and  treatment  has  increased  attention  on  knowledge  of  the  very  early 
manifestations  of  ASDs.  Early  manifestations  include  abnormalities  in  social  interaction, 
communication and behaviours. Firstly, regarding social interaction, a lack of eye contact, 
orienting to name call, imitation, joint attention and limited responding to reciprocal play 
skills are the markers that should be of concern. Secondly, in the area of communication, any 
lack or delay of communication skills including verbal and non-verbal communication are 
indicative  signs  of  ASDs.  Lastly,  the  abnormal  or  unusual  behaviours  (i.e.  repetitive  and 
stereotypic  behaviours,  restrictive  interests,  preoccupied  with  sameness/  routine  and 
sensory abnormalities) can be apparent in young children, however, these behaviours may 
not serve as important predictors of ASDs as the social and communication impairments.  
Although,  there  are  screening  instruments  to  help  identify  children  with  ASDs  in 
community-based  samples,  there  is  no  screening  instrument  that  provides  adequate 
sensitivity and specificity for universal screening (Barbaro & Dissanayake, 2009). According 
to  standardized  diagnostic  instruments,  there  have  been  many  studies  showing  that  the 
ADI-R  and  the  ADOS  have  been  well  validated  and  are  the  instruments  to  accurately 
diagnose  ASDs  as  early  as  2  years.  The  combination  of  the  ADOS  and  the  ADI-R  in 
conjunction  with  clinical  diagnosis  based  on  the  DSM-IV-TR  are  recommended  when 

 

10

 
Autism Spectrum Disorders: The Role of Genetics in Diagnosis and Treatment 

diagnosing  very  young  children  with  ASDs.  In  clinical  practice  where  diagnostic 
instruments  are  not  applicable,  developmental  surveillance  with  proper  guidance  is  a 
recommended  approach.  Further  prospective  studies  in  young  children  should  be 
conducted to provide evidence-based diagnosis for young children, especially under the age 
of two. Those developing research offer hope for better outcomes for children with ASDs. 

7. Acknowledgments 
We are very grateful to Dr. Suebwong Chuthapisith and Dr. Unchalee Lodin who proofread 
this article. 

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12

 
Autism Spectrum Disorders: The Role of Genetics in Diagnosis and Treatment 

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Autism Spectrum Disorders: The Role of Genetics in Diagnosis and Treatment 

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Part 2 

Nosology and Diagnostic Criteria: 
What Makes Sense and Can Genetics Help?

 

2 

Pervasive Developmental 
Disorder- not Otherwise Specified: 
Specifying and Differentiating 
Koray Karabekiroglu 
Ondokuz Mayis University 
Turkey 

1. Introduction 
Pervasive Developmental Disorders (PDD), also called Autism Spectrum Disorders (ASD), 
are  defined  in  terms  of  abnormalities  in  social  and  communication  development  in  the 
presence  of  marked  repetitive  behaviour  and  narrow  interests  (APA,  1994).  The  DSM-IV 
(APA,  1994)  and  ICD-10  (WHO,  1993)  provide  diagnostic  criteria  for  autism  and  related 
disorders  such  as  Asperger  syndrome  (AS),  Rett’s,  and  childhood  disintegrative  disorder. 
Unfortunately,  the  diagnostic  category  of  pervasive  development  disorder-not  otherwise 
specified (PDD-NOS) does not have specific criteria and is often seen as a catchall diagnosis 
for  children  who  do  not  fit  the  criteria  for  one  of  the  other  pervasive  developmental 
disorders (Filipek et al., 1999).   
According  to  Cohen  &  Volkmar  (2005)  classification  systems  should  aim  at  improving 
communication, through their features (internal consistency, use easiness, good definition of 
categories)  and  being  widely  accepted.  The  accuracy  of  early  diagnosis,  as  well  as 
developmental  pathways  that  are  observed  in  young  children  with  ASD  have  both 
theoretical  and  practical  importance  (Luyster  et  al.,  2005).  An  empirically  developed 
dimensional approach that defines the spectrum on multiple dimensions may offer several 
advantages.  It  may,  for  example,  result  in  more  correspondence  between  the  results  of 
genetic  research  and  the  phenotype  of  autistic  disorders,  provided  the  pathology  can  be 
summarized by empirical and valid behavior dimensions (Volkmar et al., 2004; van Lang et 
al., 2006; Hus et al., 2007).  
It  is  now  well  recognized  that  children  with  PDD  vary  in  the  number  and  severity  of 
symptoms (Szatmari et al., 2002). In DSM-IV, a diagnostic category within PDD, which is 
called  “pervasive  developmental  disorder-not  otherwise  specified”  (PDD-NOS),  defines 
children  with  symptoms  such  as  restricted  social  interaction,  poor  verbal  and  non-verbal 
communication  skills,  strict  and/or  stereotypical  behaviors  but  without  full  diagnostic 
criteria of autism (APA, 1994). Epidemiological data suggest that PDD-NOS is at least twice 
as common as autism in the general community (Chakrabatri & Fombonne, 2001). One or 
more of the following conditions may lead to a PDD-NOS diagnosis (1) onset of the disorder 
after 3 years of age, (2) atypical symptoms with regard to the 12 criteria of autism specified 
in DSM-IV, (3) fewer than 6 criteria and thus subtreshold (Walker et al., 2004). A categorical 
system  like  DSM-IV  can  be  very  useful  for  diagnosing  prototypic  manifestations  of  a 
disorder, but it is less useful in encompassing what may be, in its broader manifestations, a 

18

 
Autism Spectrum Disorders: The Role of Genetics in Diagnosis and Treatment 

“spectrum disorder” (Tanguay, 2004). An assumption of the autism-spectrum model is that 
autism  conditions  lie  on  a  continuum  of  social-communication  skills  (Baron-Cohen  et  al., 
2001; Wakabayashi et al., 2007). A continuum view shifts us away from categorical diagnosis 
and towards a quantitative approach. 
Diagnostic  agreement  for  PDD-NOS  is  generally  considered  to  be  weak  (Tanguay,  2004). 
Walker  and  colleagues  presented  compelling  evidence,  both  from  the  literature  and  from 
their  study,  that  attempting  to  improve  the  DSM-IV  criteria  for  PDD-NOS  can  be  quite 
frustrating (Walker et al., 2004). Many of the symptoms of PDD-NOS can occur in non-PDD 
conditions, such as severe mental retardation or language delay, and they may present with 
similar  developmental  history  (Bishop  et  al.,  2006).  Furthermore,  clinical  presentation  of 
PDD-NOS  may  resemble  presenting  symptoms  in  high  functioning  autism,  Asperger’s 
disorder,  reactive  attachment  disorder,  and  psychotic  disorders,  and  the  differential 
diagnosis may be highly complicated. 
Studies  on  the  distinction  between  Autistic  Disorder  (AD)  and  Pervasive  Developmental 
Disorder Not Otherwise Specified (PDD-NOS) have been inconclusive (Snow & Lecavalier, 
2011). The field is in need of more studies examining subtype differences. As the diagnostic 
validity of PDD-NOS is still open to question, and to explore proposed underlying factors, 
we  have  to  assign  cases  based  on  a  valid  clinical  assessment.  Therefore,  we  still  need  to 
investigate  further  the  clinical  features  of  children  with  PDD-NOS  that  distinguish  them 
from children with autism and other non-PDD conditions. 

2. Autism, PDD-NOS, and ADHD 
Barkley (1990) reported that it is common for children with PDD-NOS to be initially given a 
diagnosis  of  Attention  Deficit/Hyperactivity  Disorder  (ADHD).  Jensen  et  al.,  (1997) 
reported that 74% of the children in their study diagnosed with PDD-NOS were originally 
diagnosed  with  ADHD.  Another  study  showed  that  children  with  PDD-NOS  and  ADHD 
did not differ from each other with respect to total number of autistic symptoms, general 
psychopathology, or attention difficulties (Luteijn et al., 2000). Methods for differentiating 
PDD-NOS  from  the  non-PDD  disorders,  such  as  attention  deficit  hyperactivity  disorder 
(ADHD), are not well established. Several investigators concluded that it is difficult to make 
a distinction between ADHD and PDD by using the present diagnostic criteria in DSM-IV 
(Bryson et al., 2008; Gökler et al., 2004). The characteristics that differentiated children with 
PDD-NOS from those with autism and non-PDD disorders were also explored by Buitelaar 
et al., (1999). Four criteria discriminated autism from PDD-NOS most effectively: children 
with  autism  more  often  demonstrated  restricted  patterns  of  interest,  lacked  varied  make-
believe play, failed to use nonverbal behavior, and had an earlier age of onset. In another 
study (Allen et al., 2001), the PDD-NOS group (including both high- and low-functioning 
children)  did not  differ  significantly  from  the autism  or  non-PDD  groups  on  measures  of 
language or adaptive functioning but did show less restricted stereotyped behaviors than 
the high-functioning autism group. 
In a very recent study (Snow & Lecavalier, 2011), authors examined the validity of PDD 
NOS by comparing it to autistic disorder (AD) and other developmental disorders (DD) 
on  parent-reported  behavior  problems.  Fifty-four  children  with  PDD-NOS  were 
individually matched  on age  and  nonverbal IQ  to  54  children  with AD  and 54 children 

 

 
Pervasive Developmental Disorder- not Otherwise Specified: Specifying and Differentiating 

19 

with DD. The only difference between PDD-NOS and AD groups was higher scores in the 
PDD-NOS  group  on  two  items  measuring  Anxiety/Depression.  Cognitive  functioning 
may  be  a  more  salient  variable  than  subtype  when  studying  psychopathology  in 
individuals with ASDs. 
In  a  study  (Karabekiroglu  &  Akbas,  in  press)  designed  to  explore  whether  PDD-NOS 
encompassed  a  distinct  cluster  of  symptoms  and  clinical  profile  or  not,  we  investigated 
differential features of PDD-NOS such as presenting symptoms, developmental history, and 
comorbidity with respect to autism and ADHD. The study involved 188 children (PDD-NOS 
n=94; ADHD n=47; autism n=47) (male n=150, female n=38) who were 5.5(±2.5) years old on 
average (range 2-11 yrs.). The children with Asperger Syndrome were excluded. Preliminary 
PDD-NOS screening scale (PPSSS) was developed based on the ‘presenting’ symptoms of 
PDD-NOS that were systematically collected in a pilot group of children (Table 1).  
The  clinical  diagnoses  and  comorbidities  were  based  on  the  comprehensive  mental  status 
examination, Schedule for Affective Disorders and Schizophrenia for School Age Children-
Present and Lifetime Version-Turkish Version (K-SADS-PL-T), and the consensus between 
two child and adolescent psychiatry specialists. The prevelance rates of the most common 
presenting  symptoms  in  the  PDD-NOS  and  autism  groups  showed  a  similar  pattern  of 
distribution from most common to the least (Figure 1), even when the results were corrected 
for age. However, almost all of these symptoms are reported significantly less in prevalence 
in the PDD-NOS group. 
In this study, ADHD was also explored as a co-morbid diagnosis; 38.3% of the children in 
the PDD-NOS group and 53.2% of the children with autism fullfilled ADHD criteria (p>.05). 
Compared  with  children  in  the  PDD-NOS  group,  children  in  the  ADHD  group  had 
significantly  higher  rates  of  co-morbid  disruptive  behavior  disorders  (27.6%  vs.  9.6%), 
learning  disorders  (14.9%  vs.  5.3%),  elimination  disorders  (12.8%  vs.  2.1%),  tic  disorders 
(8.5%  vs.  2.1%),  social  anxiety  disorder  (8.5%  vs.  2.1%)  and  lower  rates  of  co-morbid 
obsessive  compulsive  disorder  (2.1%  vs.  23.4%).  The  rates  of  other  co-morbid  disorders, 
such as depression, language disorders, and sleep disorders, were found to be similar across 
diagnostic groups. The findings of this study reveal that the PDD-NOS group had a high 
number  of  features  in  common  with  the  autism  and  the  ADHD  groups,  in  terms  of 
presenting  and/or  reported  symptoms  and  developmental  history.  Similar  to  previous 
studies (Volkmar, et al 1993), gender distribution was similar for all groups (in each group 
more than 75% of the patients were male). A recent study has suggested that approximately 
70% of children with ASDs have at least one comorbid psychiatric disorder (Simonoff et al., 
2008). The most prevalent comorbid disorders were anxiety disorders (42%), oppositional or 
conduct disorders (30%), and ADHD (28%). 
In  our  study  (Karabekiroglu  &  Akbas,  in  press),  as  shown  in  Table  1  and  Figure  1,  the 
prevelance  rates  of  the  most  common  presenting  symptoms  in  the  PDD-NOS  and  autism 
groups had a similar pattern of distribution from more to less common. However, almost all 
of  these symptoms  were  reported  significantly  less  in children  diagnosed  with PDD-NOS 
than  children  with  autism.  The  autism  and  the  PDD-NOS  shared  a  common  clinical 
symptom profile on the first clinical admission. On the other hand, the children with ADHD 
had  a  distinct set  of  symptoms.  The  results  suggest  that  PDD-NOS  may  be  assumed  as  a 
quantitative  partial  subtype  of  autism,  and  it  represents  a  less  severe  form  that  lies  on  a 
continuum of social-communication skills.  

 

20

 
Autism Spectrum Disorders: The Role of Genetics in Diagnosis and Treatment 

Preliminary PDD-NOS Symptom 
Screening Scale (PPSSS) Items  
 

1.  poor social interaction 
2.  hyperactivity 
3.  not speaking/ language 

retardation 

stubbornness 
inattentiveness 

4.  aggressiveness  
5. 
6. 
7.  obsessions 
8.  not responsive to social stimuli
9. 
10.  impatience and/or 

stereotypies 

impulsiveness 

11.  fastidiousness, choosyness 
12.  echolalia 
13.  highly interested in television 
14.  conduct problems 
15.  articulation and/or prosody 

problems  

Presence of the symptoms 

(percentages) 

 

PDD-
NOS 
(1) 
 

Autism

(2) 
 

ADHD

(3) 
 

59.6 
56.4 

53.2 

33.0 
31.9 
30.9 
29.8 
25.5 
24.5 

23.4 

23.4 
22.3 
20.2 
21.3 

18.1 

97.9 
80.9 

97.9 

46.8 
46.8 
66.0 
27.7 
95.7 
59.6 

48.9 

10.6 
14.9 
46.8 
36.2 

8.5 

8.5 
89.4 

6.4 

61.7 
44.7 
91.5 
14.9 
40.4 
6.4 

78.7 

10.6 

- 

17.0 
40.4 

4.3 

Overall 
significan

ce 

(p value)
<.001 
<.001 

<.001 

N.S. 
N.S. 
<.001 
N.S. 
<.001 
<.001 

<.001 

N.S. 
N.S. 
<.001 
N.S. 

N.S. 

 

Source of 
significance 

 

1:2; 1:3; 2:3 

1:2; 1:3 

1:2; 1:3; 2:3 

 
 

1:2; 1:3; 2:3 

 

1:2; 2:3 

1:2; 1:3; 2:3 

1:2; 1:3; 2:3 

 
 

1:2; 2:3 

 

 

- 

17.0 
27.7 
6.4 
- 

16.  lack of eye contact 
17.  multiple fears 
18.  sleep problems 
19.  tactile oversensitivity 
20.  confusing pronouns 
21.  shyness 
22.  emotional lability 
23.  tics 
24.  poor appetite 
25.  inappropriate laughing 
26.  persistence with sameness 
27.  frequent startles 
Table 1. Preliminary PDD-NOS Symptom Screening Scale (PPSSS) item distributions of 
patients in each diagnosis group  

<.001 
N.S. 
N.S. 
N.S. 
N.S. 
N.S. 
<.001 
N.S. 
N.S. 
N.S. 
N.S. 
N.S. 

59.6 
8.5 
10.6 
25.5 
12.8 
6.4 
- 
4.3 
25.5 
17.0 
12.8 
10.6 

14.9 
14.9 
14.9 
12.8 
11.7 
11.7 
11.7 
10.6 
7.4 
4.3 
2.1 
1.1 

17.0 
27.7 
10.6 
21.3 
2.1 
6.4 
8.5 

 
 
 
 
 

 
 
 
 
 

1:2; 1:3; 2:3 

1:3; 2:3 

 

 
Pervasive Developmental Disorder- not Otherwise Specified: Specifying and Differentiating 

21 

e
g
a
t
n
e
c
r
e
P

100
90
80
70
60
50
40
30
20
10
0

language retardation
emotional lability
high TV interest
impulsiveness
not responsiveness
poor social interaction
inattentiveness
stereotypies
hyperactivity
lack of eye contact

Differential symptoms

PDD-NOS
Autism
ADHD

 

Fig. 1. The significantly discriminative symptom percentages of the diagnostic groups 

3. Cluster and factor analysis 
To identify ASD subgroups, several investigators used cluster and factor analysis based on 
social  functioning,  intelligence,  developmental  milestones,  and  so  forth.  Various  clusters 
were reported (Eaves et al., 1994; Prior et al., 1998; Sevin et al., 1995; Waterhouse et al., 1996; 
Wing & Gould, 1979). But these findings were not replicated and the clusters identified were 
not adopted or replicated in later studies. Despite several studies with ASD, clinical validity 
and differential features of PDD-NOS are yet to be consistently established. A very recent 
study (Shumway et al., 2011) examined the relationship between onset status and current 
functioning using a recently proposed onset classification system in 272 young children with 
autism  spectrum  disorder  (ASD).  Participants  were  classified  into  one  of  the  following 
groups,  based  on  parent  report  using  the  Autism  Diagnostic  Interview—Revised:  Early 
Onset (symptoms by 12 months, no loss), Delay and Regression (symptoms by 12 months 
plus  loss),  Plateau  (no  early  symptoms  or  loss),  and  Regression  (no  early  symptoms, 
followed  by  loss).  Findings  indicate  that  current  functioning  does  not  differ  according  to 
onset pattern, calling into question the use of onset categorizations for prognostic purposes 
in children with ASD.  
A previous study performed a factor analysis on a sample of variant categories of PDD, and 
two  factors  emerged.  One  factor  represented  autistic  symptoms  and  another  represented 
level  of  functioning  (Szatmari  et  al.,  2002).  More  recent  studies  used  a  factor  analytic 
approach  based  on  particular  diagnostic  instruments,  such  as  the  Autism  Diagnostic 
Interview-Revised  (ADI-R)  and  the  Autism  Diagnostic  Observation  Schedule  (ADOS) 
(Tadevosyan-Layfer  et  al.,  2003;  Tanguay,  2004).  The  results  suggested  that  there  is  a 
developmental  continuum  from  affective  reciprocity  to  emotional  joint  attention  to  verbal 
joint attention and to intuitive social knowledge (Tanguay, 2004). Tadevosyan-Layfer et al. 
(2003) found six factors: spoken language, compulsions, developmental milestones, savant 
skills, sensory aversion, and social intent. 

 

22

 
Autism Spectrum Disorders: The Role of Genetics in Diagnosis and Treatment 

In  our  study  (Karabekiroglu  &  Akbas,  in  press),  including  all  subjects  in  all  diagnostic 
groups (PDD-NOS n=94; ADHD n=47; autism n=47) (male n=150, female n=38) who were 
5.5(±2.5) years old on average (range 2-11 yrs.), a principal axis factor analysis with Promax 
rotation revealed ten factors; seven were found to be discriminative (Table 2, Figure 2). We 
 

Note. Loadings <.30 are omitted. Adopted items into the factors are shown bold.  
Table 2. PPSSS items and factor loadings for the rotated ten factors 

 

 

 
Pervasive Developmental Disorder- not Otherwise Specified: Specifying and Differentiating 

23 

 retained  all  components  with  eigenvalue  (a  measure  of  explained  variance)  greater  than 
unity. Ten factors had eigenvalues greater than 1.0, which is a common criterion for a factor 
to  be  useful.  When  ten  factors  were  requested,  Kaiser-Meyer-OIkin  (KMO)  measure  was 
adequate (.66), and Bartlett’s Test of Spherity was significant (p<.001). These measures mean 
that  the  variables  are  correlated  highly  enough  to  provide  a  reasonable  basis  for  factor 
analysis.  We considered  all variables  with  factor  loadings  0.3  or  larger  in  the  appropriate 
factor matrices to define the underlying factor and we took these variables as a cluster of 
variables for the factor. The two rotation procedures produced similar results. When there 
were  differences,  we  took  the  Promax  solution  as  the  preferred  one.  After  rotation,  ten 
factors accounted for 66.3% of the variance. 
 

 

Factor 1 includes “lack of eye contact”, “stereotypies”, “inappropriate laughing”, “frequent startles”, 
“highly interestedness in TV”, and “tactile oversensitiveness”;  
Factor 2 includes “poor social interaction”, “language retardation”, and “not responsive”  
Factor 3 includes “inattentiveness”, “hyperactivity”, and “impatiente, impulsiveness” 
Factor 4 includes “aggressiveness” and “conduct problems” 
Factor 5 includes “confusing pronouns”, “echolalia”, and “articulation/ prosody problems” 
Factor 7 includes “fastidiousness, choosiness” and “obsessions” 
Factor 8 includes “poor appetite”, “stubbornness”, and “persistence with sameness” 
Fig. 2. The significantly discriminative factors of the diagnostic groups. 
We found significant differences in the toal number of symptoms between three diagnostic 
groups in the factors 1 (p<.001), 2 (p<.001), 3 (p<.001), 4 (p=.004), 5 (p<.001), 7 (p=.026), and 
8  (p=.006).  The  scores  in  the  factors  1,  2,  3,  and  8  were  significantly  higher  in  the  autism 
group  compared  to  the  PDD-NOS  group.  The  scores  in  the  factors  1,  2,  5,  and  7  were 
significantly higher in the PDD-NOS group compared to the ADHD group. Inversely, the 
scores in the factors 3, 4, and 8 were significantly higher in the ADHD group compared to 
the PDD-NOS group (Figure 2).  

 

24

 
Autism Spectrum Disorders: The Role of Genetics in Diagnosis and Treatment 

Based on the assumption that the items were predicted to index three constructs: symptoms 
related to autism, ADHD, and PDD-NOS, in a further analysis three factors were requested 
(Karabekiroglu & Akbas, In press). The first factor seemed to index core autism spectrum, 
the  second  factor,  disruptive  behaviors  spectrum,  and  the  third  factor  seemed  to  index 
symptoms to be interpreted as anxiety spectrum. Four in twenty-seven items do not seem to 
load with any of the factors. When the total number of the symptoms in each factor were 
compared between the diagnostic groups, the core autism spectrum and the disruptive behavior 
spectrum  factors  revealed  significant  differences  between  the  groups  (p<.001).  Post-hoc 
analysis showed that in the core autism spectrum factor, the autistic group had significantly 
more symptoms than the PDD-NOS group (4.87 vs. 2.14) (p<.001), and the PDD-NOS group 
had  significantly  more  symptoms  than  the  ADHD  group  (2.14  vs.  0.81)  (p<.001).  On  the 
other hand, on the disruptive behavior spectrum factor, both the ADHD (3.62 vs. 2.19) (p<.001) 
and  the  autistic  groups  (4.55  vs.  2.19)  (p<.001)  had  significantly  more  symptoms  than  the 
PDD-NOS group. The anxiety spectrum factor did not reveal a significant difference between 
diagnostic groups. 

4. Discussion 
Because  the  diagnostic  agreement  for  PDD-NOS  was  generally  considered  to  be  weak 
(Tanguay  2004,  Walker  et  al.  2004),  and  differentiation  of  PDD-NOS  from  the  non-PDD 
disorders, such as ADHD was not well-defined, we conducted a factor analysis including 
the data from all three diagnosis groups (Autism, PDD-NOS, and ADHD) (Karabekiroglu & 
akbas,  in  press).  A  factor  analysis  revealed  three  symptom  clusters,  core  autistic  spectrum, 
disruptive behavior spectrum, and anxiety spectrum. As would be expected, the children with 
autism had higher rates of symptoms in the autistic spectrum factor and the children with 
ADHD  had  higher  rates  of  symptoms  in  the  disruptive  behavior  spectrum  factor.  The  PDD-
NOS group had lower rates of symptoms on both factors. 
In a recent study (Kamp-Becker et al., 2009), the dimensional structure of higher functioning 
autism phenotype was investigated by factor analysis. The goal of this study was to identify 
the  degree  to  which  early  symptoms  of  autism  (measured  using  the  ADI-R)  could  be 
predictive  of  the  current  symptoms  of  autism  as  identified  using  the ADOS, the  adaptive 
behavior scales, IQ scores and theory of mind scores. The authors reported that the social 
interaction  and  communication  domains  were  closely  related  to  one  factor  namely:  Social 
communication.  An  additional  factor  implies  anxious  and  compulsive  behavior  which  is 
associated  with  current  social  communication  functioning.  Another  study  compared  the 
behavioral symptomatology in 26 children and adolescents with autism and 25 children and 
adolescents with PDD-NOS (Pearson et al., 2006). Relative to individuals with PDD-NOS, 
those with autism had more symptoms of depression, social withdrawal, atypical behavior, 
and  immature  social  skills,  and  fewer  family  problems.  These  differences  remained  even 
when  group  differences  in  intellectual  ability  were  controlled  statistically.  No  group 
differences  emerged  in  somatization,  anxiety,  or  hyperactivity.  Their  findings  suggested 
that,  although  both  groups  demonstrated  considerable  evidence  of  behavioral  and 
emotional  problems,  those  with  autism  were  at  particularly  high  risk  for  co-morbid 
behavioral and emotional disabilities (Pearson et al., 2006). 
In  a  recent  study  (Mandy  et  al.,  2011)  authors  aimed,  first,  to  improve  the  reliability  and 
replicability of PDD-NOS by operationalizing its DSM-IV-TR description and, second, to test 
its validity through comparison with autistic disorder (AD) and Asperger’s disorder (AsD). 

 

 
Pervasive Developmental Disorder- not Otherwise Specified: Specifying and Differentiating 

25 

In a sample of 256 young people (mean age: 9.1 years) [AD (n:97), AsD (n:93) and PDD-NOS 
(n:66)],  groups  were  compared  on  independent  measures  of  core  PDD  symptomatology, 
associated autistic features, and intelligence. Contrary to the assumption that PDD-NOS is 
heterogeneous, almost all (97%) of those with PDD-NOS had one distinct symptom pattern, 
namely impairments in social reciprocity and communication, without significant repetitive 
and stereotyped behaviors (RSB). Compared to AD and AsD, they had comparably severe 
but more circumscribed social communication difficulties, with fewer non-social features of 
autism,  such  as  sensory,  feeding  and  visuo-spatial  problems.  These  individuals  appear  to 
have a distinct variant of autism that does not merely sit at the less severe end of the same 
continuum of symptoms.  
The symptoms of ASD may change with development (Luyster et al., 2005). PDD-NOS has 
been assumed significantly less stable as a diagnosis (Lord et al., 2006). In a study (Kleinman 
et al., 2008), 77 children received a diagnostic and developmental evaluation between 16 and 
35  months  and  also  between  42  and  82  months.  Diagnoses  based  on  clinical  judgment, 
Childhood  Autism  Rating  Scale,  and  the  Autism  Diagnostic  Observation  Schedule  were 
stable over time. Diagnoses made using the Autism Diagnostic Interview were slightly less 
stable. According to clinical judgment, 15 children (19%) moved off the autism spectrum by 
the second evaluation; none moved onto the spectrum. Results indicate diagnostic stability 
at  acceptable  levels  for  diagnoses  made  at  age  2.  Nevertheless,  diagnoses  of  autism  and 
PDD-NOS  by  experienced  clinicians  on  the  basis  of  multiple  measures  were  valid  and 
reliable over time (Lord et al., 2006). If a child is given an ASD diagnosis (either autism or 
PDD-NOS) at age 2 years, it is highly likely to apply at age 9, although there may be some 
shifting  within  the  range  of  ASD  diagnostic  categories  (Lord  et  al.,  2006).  Generally,  it 
appears that the overall picture of development for autism and PDD-NOS is similar, with 
most children experiencing continued impairment. Based on these two studies, there does 
not appear to be evidence for qualitatively discrete groups (i.e., autism versus PDD-NOS), 
but differences appear to be quantitative (Lord et al., 2006; Turner, et al., 2006).  
A recent meta-analysis (Rondeau et al., 2010) conducted on the eight longitudinal studies on 
PDD-NOS  that  have  been  published  from  1996  to  2009  showed  that  PDD-NOS  diagnosis 
was  less  stable  than  autistic  disorder  diagnosis.  When  established  before  36  months,  the 
overall stability rate was 35% at 3-year follow-up. Consistent with the previous literature on 
the reliability of the PDD-NOS diagnosis in young children, our metaanalysis did not 
support  the  discriminant  and  predictive  validity  of  this  category.  Thus,  from  a  clinical 
standpoint, children whose PDD-NOS diagnosis was established before 36 months should 
be re-assessed at a later age (Rondeau et al., 2010). 
Similar  to  previous  reports  (Allen  et  al.,  2001,  deBruin  et  al.,  2006,  Matson,  et  al.,  2007, 
Szatmari et al. 2002), in our study (Karabekiroglu & Akbas, in press) mental retardation was 
significantly more prevalent in the autism than in the PDD-NOS or ADHD groups. Several 
investigators  suggested  that  exploring  the  presence  of  mental  retardation  may  be  more 
useful in terms of planning treatment and predicting outcome than a classification based on 
symptom number alone (Szatmari et al., 2002). However, IQ may be a poor measure of level 
of  functioning,  based  as  it  is  on  performance  in  a  highly  artificial  setting  (Szatmari  et  al. 
2002). In a study (Scheirs & Timmers, 2009) an attempt was made to distinguish among the 
three groups (ADHD, PDD-NOS, and ADHD plus PDD-NOS) on the basis of intelligence 
(WISC-III)  profiles.  It  was  found  that  the  PDD-NOS  group  had  higher  verbal  and 
performance IQ’s, as well as higher WISC-III index scores than the ADHD group. Subtests 

 

26

 
Autism Spectrum Disorders: The Role of Genetics in Diagnosis and Treatment 

Block  Design  and  Mazes  discriminated  best.  It  was  concluded  that  based  on  intelligence 
scores,  only  PDD-NOS  and  ADHD  emerged  as distinct  categories,  whereas  the  combined 
diagnosis did not. Allen et al. (2001) compared 18 preschool children with PDD-NOS to 176 
children with autistic disorder and 311 non-autistic children with developmental language 
disorders (DLD) (N = 201) or low IQ (N = 110). The children with PDD-NOS did not differ 
significantly from either the children with autism or the children with DLD in verbal and 
adaptive skills. They suggested that the similarity of PDD-NOS children to autistic children 
in maladaptive behaviors and an intermediate position between autistic and DLD groups on 
virtually  all  measures  helped  to  explain  the  difficulty  clinicians  encounter  in  classifying 
children with PDD-NOS (Allen et al., 2001). 
Rates of comorbid psychiatric conditions in children with PDD-NOS are hardly available, 
although  these  conditions  are  often  considered  as  more  responsive  to  treatment  than  the 
core symptoms of PDD-NOS (deBruin  et al., 2007). In our sample (Karabekiroglu & Akbas, 
in  press),  53.2%  of  the  children  with  PDD-NOS  had  at  least  one  co-morbid  psychiatric 
disorder,  including  disruptive  behavior  disorders  (40.4%),  and  anxiety  disorders  (18.0%). 
With respect to the PDD-NOS group, the ADHD group had significantly higher rates of co-
morbid  disruptive  behavior  disorders,  learning  disabilities,  tic  disorders,  elimination 
disorders,  and  social  anxiety  disorder.  On  the  other  hand,  the  PDD-NOS  group  had 
significantly  higher  rates  of  co-morbid  obsessive  compulsive  disorder  with  respect  to  the 
ADHD group. In a previous study, DeBurin et al. (2007) explored the comorbidity in ninety-
four children with PDD-NOS, aged 6-12 years. At least one co-morbid psychiatric disorder 
was present in 80.9% of the children; 61.7% had a co-morbid disruptive behavior disorder, 
and  55.3%  fulfilled  criteria  of  an  anxiety  disorder.  Compared  to  those  without  co-morbid 
psychiatric  disorders,  children  with  a  co-morbid  disorder  had  more  deficits  in  social 
communication.  

features 

including 

5. Conclusion 
The overall results suggest that children with PDD-NOS have a high number of common 
features  with  patients  having  autism  and  ADHD.  The  symptoms  of  all  three  diagnostic 
groups  appeared  to  form  three  clusters,  “autistic  spectrum,”  “ADHD  spectrum,”  and 
“anxiety  spectrum.”  Many 
language  and  motor  development, 
“presenting” and/or “reported” symptom distribution, and gender distribution were found 
to be similar in the PDD-NOS and the autism groups. Mental retardation rate and symptom 
severity  (e.g.,  “poor  social  interaction”,  “lack  of  eye  contact”,  “stereotypies”)  were 
significantly higher in the autism group with respect to the PDD-NOS group. In addition, 
most  of  the  previous  studies  supported  quantitative  discrimination  rather  than  assuming 
that PDD-NOS and autism are qualitatively discrete groups. Therefore, PDD-NOS may be 
assumed  as  a  partial  subtype  of  autism  and  that  it  lies  on  a  continuum  of  social-
communication  skill  deficits.  On  the  other  hand,  some  of  the  studies  suggest  that  these 
individuals appear to have a distinct variant of autism that does not merely sit at the less 
severe end of the same continuum of symptoms. They emphasize that compared to other 
disorders in PDD category, the children diagnosed with PDD-NOS had comparably severe 
but more circumscribed social communication difficulties, with fewer non-social features of 
autism. Therefore, we still need to investigate further the clinical features of children with 
PDD-NOS that distinguish them from children with autism and other non-PDD conditions. 

 

 
Pervasive Developmental Disorder- not Otherwise Specified: Specifying and Differentiating 

27 

6. References 
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Autistic  disorder  versus  other  pervasive  developmental  disorders  in  young 
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American  Psychiatric  Assossiation  (APA)  (1994).  Diagnostic  and  Statistical  Manual  of 
Mental  Disorders,  Fourth  Edition.  Washington  DC:  American  Psychiatric 
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Barkley,  R.A.  (1990),  A  critique  of  current  diagnostic  criteria  for  attention  deficit 
hyperactivity  disorder:  clinical  and  research  implications. Journal  of  Developmental 
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Baron-Cohen, S., Wheelwright, S., Skinner, R., Martin, J., & Clubley, E. (2001). The Autism-
Spectrum  Quotient  (AQ):  Evidence  from  Asperger  Syndrome/high-functioning 
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Bishop,  S.L.,  Richler,  J.,  &  Lord,  C.  (2006).  Association  between  restricted  and  repetitive 
in  children  with  autism  spectrum  disorders.  Child 

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Bryson,  S.A.,  Corrigan,  S.K.,  McDonald,  T.P.,  &  Holmes,  C.  (2008).  Characteristics  of 
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30

 
Autism Spectrum Disorders: The Role of Genetics in Diagnosis and Treatment 

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3 

Autism and Genetic Syndromes 
Willem Verhoeven, Jos Egger and Ilse Feenstra 
Erasmus University Medical Centre, Rotterdam 
Radboud University Nijmegen, Nijmegen 
Vincent van Gogh Institute, Venray 
The Netherlands 

1. Introduction 
Autism  is  a  developmental  disorder  defined  as  a  severe  and  persistent  restriction  in 
communicative  skills,  including  lack  of  social  and  emotional  reciprocity,  as  well  as 
stereotyped and repetitive behaviours. Such an impairment of social interaction was already 
described  in  1919  by  the  Swiss  psychiatrist  Eugen  Bleuler  within  the  framework  of  the 
negative  symptom  complex  of  schizophrenia.  In  the  following  decades in  particularly  the 
German  language  areas  of  Switzerland,  autism  was  viewed  by  Kretschmer  (1921)  as  a 
schizoid temperament whereas later, he viewed it as a special form of schizophrenia. In the 
late  fifties,  Leonhard  (1957)  assigned  this  specific  disturbance  in  communication  to  the  so 
called systematic schizophrenias. In 1943, the immigrated American child psychiatrist from 
Austria, Leo Kanner, originally described early infantile autism as Autistic Disturbance of 
Affective  Contact.  In  a  study  of  11  children,  four  behavioural  characteristics  were 
distinguished:  severe  social  withdrawal  behavior,  obsessive  desire  for  repetitiveness, 
persistent  fascination  with  specific  objects  or  thoughts,  and  severe language  impairments. 
One  year  later,  the  Austrian  pediatrician  Hans  Asperger  reported  comparable  findings 
under  the  title  ‘Die  ‘Autistischen  Psychopathen’  in  Kindesalter’.  Both  Kanner  (1943,  1971) 
and Asperger (1944) considered autism a communication disorder for children with severely 
impoverished relations with the environment (i.e., ‘autistic aloneness’).  
Up  until  the  beginning  of  the  1960s,  under  the  influence  of  the  then  prevalent 
psychodynamic theories, autism was largely attributed to family and environmental factors. 
Rutter  (1968)  placed  autism  in  a  different  perspective  and  demarcated  the  phenotypical 
presentation  of  both  early  infantile  autism  and  schizophrenia  from  their  biological 
underpinnings. Lorna Wing (1981) can be credited for bringing the descriptions of Asperger 
from  1944  back  to  our  attention  in  the  1980s  and,  on  the  basis  of  extensive  childhood 
epidemiological research, for placing autism in a broader diagnostic context and developing 
diagnostic  criteria  (Wing  &  Gould,  1979).  Wing  introduced  the  term  ‘autism  spectrum 
disorder’, which can be described on the basis of information from three domains: (a) social 
reciprocity, (b) verbal and non-verbal communication and imagination, and (c) a restricted, 
stereotyped pattern of interests and activities. These elements still constitute the diagnostic 
criteria from e.g. the DSM-IV category of Pervasive Developmental Disorders that include 
Autistic Disorder, Rett’s Disorder, Childhood Disintegrative Disorder, Asperger’s Disorder, 
and Pervasive Developmental Disorder Not Otherwise Specified (overview: Kumbier et al. 
2010). 

32

 
Autism Spectrum Disorders: The Role of Genetics in Diagnosis and Treatment 

In  her  retrospective  ‘Reflections  on  opening  Pandora’s  box’  presented  a  few  years  back, 
Lorna Wing (2005) warns about stretching the boundaries of the autistic spectrum, which 
presently includes those who have normal to extremely high intelligence at the one end and 
those with a severe intellectual disability and limited social and communicative skills at the 
other end. In such a manner, the diagnostic label of ‘autistic spectrum disorder’ can possibly 
be misused to attain care (Volkmar et al., 2009). This is certainly not  inconceivable in light of 
the quadrupled prevalence of pervasive developmental disorders across a period of 40 years 
(4.1 per 10,000 to 16.8 per 10,000), including somatic/neurological disorders that accompany 
autism  (Fombonne,  2003;  Rice,  2009).  Moreover,  autism  and  Asperger’s  disorder  are 
regularly associated with other syndromes (Gillberg & Gillburg, 1989; Gillberg and Billstedt, 
2000).  As  elegantly  stated  by  Gillberg  already  in  1991  in  his  Emanuel  Miller  Memorial 
Lecture, researchers and clinicians, rather than allow themselves to be guided by stereotypic 
(sub)classifications, should be guided by a more balanced view of autism as belonging to a 
group  of  empathy  disorders  and 
for  autism-relevant 
endophenotypes (Gillberg, 1992). 
In  the  following  sections,  a  brief  overview  of  the  current  neuropsychological  and  genetic 
explanatory models will first be given. Thereafter, a selection of syndromes with a known 
genetic  etiology  that  are  seen  to  accompany  autism  will  be  discussed.  To  conclude,  the 
manner  in  which  modern  genetics  can  be  utilized  in  daily  diagnostic  practice  will  be 
elucidated. 

thereby  start 

the  search 

2. Neuropsychological models 
Roughly three explanatory models can be discerned for the pattern of disorders encountered 
in  cases  of  autism.  The  first  model  is  based  upon  the  weak  central  coherence  (WCC) 
hypothesis,  which  claims  that  patients  with  autism  are  inclined  to  attend  to  details  as 
opposed  to  the  whole  during  the  processing  of  context-based  information  (i.e.,  strictly 
feature-based  perception).  As  a  consequence,  the  information  is  not  understand  as  a 
meaningful whole and thus remains fragmented and confusing (Happé & Frith, 2006; Frith, 
1989). 
The second model draws upon the concept of Theory of Mind, which refers to the capacity to 
attribute thoughts, desires, and intentions to others. This capacity starts to develop around 
the  age  of  three  to  four  years  and  allows  humans  to  take  the  perspectives  of  others  into 
consideration  in  their  own  thinking.  Deficiencies  in  this  domain  can  easily  lead  to  social 
misinterpretations  and  socially  inadequate  behavior,  which  form  the  basis  of  the  severe 
communication  problems  that  people  with  autism  show  (Baron-Cohen,  1989).  In  this 
connection, the more general notion of social cognition may be called upon and a link made 
to, for example, diminished activity of the amygdala and deviant perceptions of one’s own 
emotions (Kethrapal, 2008). 
In  the  last  model,  that  of  the  dysexecutive  functioning  hypothesis,  disturbed  executive 
functions are assumed to play an important role. The executive functions (EF) are of major 
importance  for  the  integration,  steering,  and  control  of  processes  required  to  execute 
purposeful  behavior  in  new  or  complex  situations.  At  a  structural  level,  four  frontal-
subcortical circuits are involved in EF. The dorsolateral-prefrontal circuit has been related to 
executive cognitive dysfunctioning; the ventromedial circuit has been related to activation 
and  motivation  problems;  and  the  medial-  and  lateral-orbitofrontal  circuits  have  been 
related to disturbed affect regulation and disturbed social behavior, respectively (Chow & 

 

 
Autism and Genetic Syndromes 

33 

Cummings,  2007;  Alvarez  &  Emory,  2006).  EF  often  involves  the  overruling  of  automatic 
responses  in  favour  of  more  intentional  behaviour.  A  capacity  to  flexibly  switch  between 
different behavioral repertoires (i.e., monitoring and shifting) is required for such overruling 
and typically found to be a problem in cases of autism. Barnes-Holmes and colleagues view 
EF as rule-governed behaviour and thus behaviour that stands in contrast to contingency-
shaped behaviour, which has been automatized. EF thus defined, is verbal behaviour that 
precedes  other  behaviour  (i.e.,  verbal  antecedent  behavior)  and  therefore  distracts  the 
individual from his usual, automatic reaction pattern. Stated differently, the probability of 
an  alternative  behavior  is  changed  in  the  direction  of  a  particular  objective.  In  such  a 
manner, thus, recent research on rule-governed behaviour connects EF with autism and an 
underlying Theory of Mind (Barnes-Holmes et al., 2004; McHugh et al, 2004). 

3. Genetic models 
Autism  can  be  viewed  as  a  classic  example  of  a  disorder  with  a  strong  genetic  basis.  A 
distinction must nevertheless be made between the Autistic disorder as originally described 
by Kanner and the autism spectrum disorder, which can be viewed as a component of an 
array  of  clinical  pictures  and  syndromes  that  are  sometimes  referred  to  as  secondary  or 
syndromic autism (Benvenuto et al., 2009). Given the complex interplay between genes and 
autism,  a  search  for  at  least  two  types  of  genetic  factors  is  of  importance,  namely:  (rare) 
chromosomal    abnormalities  or  gene  alterations  that  can  be  directly  related  to  core  (i.e., 
classic)  autism  and    genetic  copy  number  variants  that  correlate  with  a  vulnerability  to 
develop an autistic disorder. 
In several studies from the 1980s and 1990s that use a strict definition of autism, a 69% to 
95%  concordance  has  been  demonstrated  in  monozygotic  twins,  while  the  chance  in 
dizygotic  twins  is  only  0%  to  24%.  The  contribution  of  the  hereditary  components  is 
estimated to be 90%. The male-female ratio is between 3-4 to 1 (Brkanac et al., 2008). In order 
to  advance  the  understanding  of  the  genetic  heterogeneity  of  autistic  disorders,  various 
techniques  can  be  used  such  as  (molecular)  cytogenetic  research,  linkage  studies,  and 
association studies.  
Linkage studies search for those parts of a chromosome that are found to be the same for all 
affected individuals in a family but different for the non-affected family members. A gene 
that  contributes  to  the  occurrence  of  a  vulnerability  for  autism  may  lie  in  such  a  shared 
region. These studies have revealed a wide variety of loci, from which a considerable genetic 
heterogeneity can be concluded as well as the absence of single, specific locations for autism 
(Szatmari et al., 2007). Recently, in a very large-scale linkage study, two new locations have 
been  found  on  chromosomes  6  and  20  (6q27  and  20p13,  respectively)  for  which  the 
functional significance has yet to be clarified (Weiss et al., 2009). 
The same holds for candidate genes that have been implicated in a large series of association 
studies.  These  studies  investigate  significant  genetic  differences  between  large  groups  of 
patients, on the one hand, and groups of healthy individuals, on the other hand (Vorstman 
et  al.,  2006a).  The  research  findings  make  it  clear,  however,  that  the  pathophysiology  of 
autism involves genes that code for proteins from the family of neurexins and neuroligins 
that play, in turn, a role in the development and functioning of synaptic and in particular 
glutamatergic and GABA-ergic networks (Lisé & El-Husseini, 2006; Buchsbaum, 2009). The 
first  X-linked  mutations  in  genes  involved  in  the  coding  of  neuroligin  were  revealed  in 
patients with autism in two Swedish families in 2003 (Jamin et al., 2003). 

 

34

 
Autism Spectrum Disorders: The Role of Genetics in Diagnosis and Treatment 

When  the  classic  microscopic  cytogenetic  route  is  followed,  structural  chromosomal 
aberrations are found in 3% to 7% of patients with autism and developmental delay. This 
finding concerns mainly maternal duplications on the long arm of chromosome 15 (q11-13) 
(Hogart et al., 2010) and deletions on the long arm of chromosome 2 (q37) (Falk & Casas, 
2007),  chromosome  7  (q22  and  q31)  (Alarcón  et  al.,  2002)  and  chromosome  22  (q11) 
(Niklasson at al., 2009) and (q13) (overview: Kumar & Christian, 2009). The fluorescence-in-
situ-hybridization (FISH) technique is used to search for specific submicroscopic deletions 
and is used primarily to confirm a clinical diagnosis such as the 22q11 deletion syndrome. 
Disadvantages  of  this  technique  are  its  labor  intensiveness  and  that  only  one  or  a  few 
chromosome regions can be examined per experiment. 
More commonly used these days is the whole-genome microarray technique. Here, details 
more  than  a  hundred  times  smaller  can  be  perceived  when  compared  to  microscopic 
cytogenetic examination (de Vries et al., 2005; Veltman & de Vries, 2006). With the aid of 
such a ‘DNA chip’, the complete genome with a high resolution can be  examined for the 
presence  of  microdeletions  and  duplications  or  so-called  copy-number  variations  (CNVs). 
Of principal concern here are small quantitative, structural variations that are paired with a 
loss or gain of chromosome material. Furthermore, the ‘targeted genomic array’ technique 
can  be  applied  to  study  specific  regions  such  as  the  subtelomeric  chromosome  regions  or 
well-known microdeletion syndrome regions (Lintas & Persico, 2009). 
For  various  neuropsychiatric  disorders  including  autism,  CNVs  possibly  involved  in  the 
vulnerability  for  the  development  of  a  disorder  within  the  autism  spectrum  have  been 
demonstrated using the array technique (Jacquemont et al., 2006; Cook & Scherer, 2008). For 
some of these CNVs, a clear correlation has been demonstrated, e.g., a maternal 15q11-q13 
duplication  was  shown  for  1-3%  of  patients  with  an  autistic  spectrum  disorder.  Another 
frequently  occurring  CNV  is  found  on  the  chromosome  16p11.2  which  present  with  a 
deletion or duplication in approximately 1% of the patients (Weiss et al., 2008; Fernandez et 
al., 2010).  
There are, however, also CNVs found with an, as yet, unknown significance; because, for 
example, the same change can be traced back to a healthy parent. A precise interpretation of 
the array results with the aid of bio-informatics, literature databases, data from the pedigree 
and clinical investigation of affected individuals, is therefore essential. 
Another  interesting  perspective  is  the  neuropeptide  concept.  It  has  been  known  for  quite 
some  time  that  the  nonapeptide  oxytocine  (OXT)  is  involved  in  affiliation  behaviour  and 
social  cognition  via  an  improvement  of  social  memory,  including  the  recall  and 
understanding  of  affective  events  (Hollander  et  al.,  2007;  Insel,  2010;  Green  &  Hollander, 
2010).  For  these  reasons,  research  has  been  performed  on  the  association  between  single 
nucleotide  polymorphisms  (SNPs)  in  the  OXT  gene  and  the  OXT  receptor  gene  (OXTr). 
Relative to the normal population, more SNPs were present in the OXTr for a subgroup of 
patients  with  autism,  which  could  indicate  a  genetically  determined  vulnerability  for  the 
development of autism (Lerer et al., 2008; Lee et al., 2009). In line with these observations, 
Gregory  et  al.  (2009)  found  that  epigenetic  regulation  of  OXTr  is  implicated  in  the 
development of autism. 
To summarize, in linkage and association studies among patients with autistic disorders up 
until now, a large number of candidate genes and gene locations have been found for which 
it can be assumed that they may be involved in the development of functional processes of 
the central nervous system. In Table 1, a selective overview is presented.  In the following 
section, the most well-known genetic disorders associated with autism will be discussed. 

 

 
Autism and Genetic Syndromes 

35 

Candidate Gene 

NRXN1 
DPP10 
OXTr 
CNTN4 

Network/Function 
Synapse formation 
Neurotransmission 
Neurotransmission 
Synapse formation 

GABRG/GABRA 

GABA neurotransmission 

Chromosome 

2p16.3 

2q12.3-q14.2 

3p24-26 
3p26-p25 
4p14-q21.1 

7q31.1 
7q35-q36 

8p23 

15q11-q14 

15q13 
16p11.2 
22q11 
22q13 
Xp22.3 
Xp11.4 

ST7 

CNTNAP2 
DLGAP2 

GABRA/GABRB/GABRG 

APBA2 
DOC2A 
PRODH 
SHANK3 
NLGN4 
TSPAN7 
IL1RAPL1 

Tumor suppression 
Synapse formation 

NMDA neurotransmission 
GABA neurotransmission 

Neurotransmission 
Neurotransmission 
Neuromodulation 
Synapse formation 
Synapse formation 

Neuronal growth and development 

Xp22.1-p21.3 

Interleukin receptor 
Table 1. Selection of genes and functions possibly involved in autism (adapted from 
Guilmatre et al., 2009 and El-Fishawy & State, 2010) 

4. Genetic syndromes and autism 
4.1 Fragile X syndrome 
The fragile X syndrome (FXS; Figure 1) is the most well-known genetic disorder related to 
autism.  Brown  and  colleagues  (1982)  were  the  first  to  report  on  this.  Initially,  FXS  was 
described  by  Lubs  (1969),  who  detected  a  fragile  site  at  the  end  of  the  long  arm  of  the  X 
chromosome  by  using  classical  microscopic  cytogenetic  techniques.  FXS  is  caused  by 
hypermethylation  of  an  expanded  trinucleotide  repeat  (CGG)  in  the  ‘fragile  X  mental 
retardation 1 (FMR1) gene’ (Xq27.3). In normal individuals, the number of CGG repeats is 5 
to  45  which  is  stably  transmitted  to  the  next  generation.  In  case  of  a  FMR1  premutation, 
there is a small expansion of 55 to 200 repeats. In FXS, the number of repeats exceeds 200. As 
a  result  of  this  enlarged  number  of  repeats,  hypermethylation  of  the  FMR1  gene  occurs 
which leads, in turn, to a shortage or complete loss of the FMR1 protein that is essential for 
dendrite  formation,  synapse  formation,  and  experiential  learning  (Marco  &  Skuse,  2006; 
Hernandez et al., 2009). 
FXS is an X-linked disorder with an incidence of about 1 in 4000 newborn males. Affected 
males show a variable degree of developmental delay, behaviour problems, and distinctive 
dysmorphic features such as a long face and large, prominent ears. Female carriers with a 
full  mutation  (>200  repeats)  may  present  with  or  without  impaired  level  of  intelligence. 
Females with FXS and normal intelligence, however, have an increased risk of mood and 
anxiety disorders and a schizotypical personality disorder (Franke et al., 1998).  
In  males  with  a  premutation  (50-200  repeats),  there  is  an  increased  probability  of  the 
development  of  the  so-called  fragile-X-associated  tremor/ataxia  syndrome  (FXTAS).  Its 

 

36

 
Autism Spectrum Disorders: The Role of Genetics in Diagnosis and Treatment 

symptoms emerge at a later age, and the syndrome has a progressive course. The clinical 
picture comprises intention tremor, frequent falling, Parkinsonian symptoms, and disturbed 
cognitive and executive functioning (Verhoeven et al., 2008; Bourgeois et al., 2009).  
 

X-chromosome

a

b

c

Normal FMR1 gene

5-40 CGG

repeats

Fragile X

> 200 CGG

repeats

e
n
e
g
-
1
R
M
F

FMR1 
protein

- Brain 
development, 
dendric formation
-Regulation of 
protein-translation 
in neurons

Hypermethylation

Loss of FMR1-
protein

- Fragile X phenotype
- Abnormal dendric development
- No translational inhibition

 

Fig. 1. Fragile X syndrome and the FMR1 gene 
Schematic representation of the location of the FMR1 gene on the long arm of the X chromosome. (a) 
The FMR1 gene comprises a polymorph repetition of the base pairs cytosine-guanine-guanine (CGG) 
at the start of the gene. In healthy individuals, this number of CGG repeats varies from 5 to about 45 
units. (b) Affected individuals have more than 200 CGG repeats: the full mutation. (c) The expansion 
to a full mutation (>200 repeats is usually associated with hypermethylation of the CGG repetition 
and the adjacent area, which leads to a  transcription stop resulting in the absence of the FMR1 
protein. This results in intellectual disability and other symptoms of the Fragile X syndrome among 
men, and in >50% of the women who are carriers of a full mutation. Carriers with 55 and 200 repeats 
are asymptomatic and are thus called premutation carriers 
For decades, it has been known that the severity of intellectual disability and the intensity of 
related  behaviour  problems 
repeats.  The 
psychopathological  phenotype  of  FXS  includes,  in  addition  to  the  developmental  delay, 
multiform  anxiety  symptoms,  obsessive-compulsive  characteristics,  hyperactivity  / 
impulsivity, and aggression. Epileptic phenomena are frequent. Predominant, however, are 
autism-related symptoms such as social anxiety and withdrawal behaviour, stereotypies like 
flapping or biting of the hands, perseverations, extreme sensitivity to environmental stimuli, 
and, in general, decreased social reciprocity with an avoidance of eye contact (Hagerman, 
2005).  

the  number  of 

is  proportional 

to 

 

 
Autism and Genetic Syndromes 

37 

Using  neuroimaging  techniques,  various  structural  abnormalities  of  the  central  nervous 
system  can  be  demonstrated,  in  particular  enlargement  of  hippocampus,  amygdala, 
caudatus, and thalamus with a reduction of the cerebellar vermis (Hessl et al., 2004). These 
neuronal changes are caused by an overabundance of immature dendritic spines. In normal 
conditions, dendritic spines are essential for the formation of new neuronal connections that, 
in turn, form the basis for learning and memory. In FXS, the cognitive dysfunctions in the 
domains  of  attention,  (working)  memory,  mathematical  skills,  executive  functioning  and 
social  cognition  largely  correspond  to  the  observed  abnormalities  of  the  central  nervous 
system.  
The treatment of patients with FXS is primarily symptomatic and aimed at the reduction of 
the  most  prominent  behavioural  problems  or  psychiatric  symptoms,  such  as  anxiety, 
hyperactivity,  impulsivity  and  distractibility  (Garber  et  al.,  2008).  Since  the  extremely 
heightened sensitivity to environmental stimuli is assumed to underlie the above mentioned 
symptoms, it is essential to reduce excessive environmental sensory activation. This can be 
achieved  with  a  more  structured  daily  program  of  activities,  the  promotion  of  a  realistic 
pattern  of  expectations  among  parents/caregivers,  individualized  instruction  and,  most 
importantly,  the  dissemination  of  knowledge  about  the  persistence  of  the  FXS  behavioral 
phenotype. 

4.2 Rett syndrome 
Rett syndrome (RS) was first described in 1966 by Andreas Rett. This syndrome is inherited 
in  an    X-linked  manner,  caused  by  a  mutation  in  the  Methyl-CpG-Binding  Protein  2 
(MECP2) gene (Xq28). In Figure 2, the location of the MECP2 gene is depicted. RS occurs 
almost exclusively in girls and its prevalence is estimated to be between 1/10,000 - 1/20,000. 
In boys, the disorder is nearly always lethal. In rare male cases, an extra X chromosome or 
mosaicism of the MECP2 mutation has been found.  
RS is characterized by an apparently normal development in the first 6 to 18 months of life 
after which development stagnates, acquired skills get lost and development finally stops. 
This stagnation of development also becomes manifest in a retarded and disproportionate 
growth  in  head  circumference,  decrease  of  eye  contact,  and  both  cognitive  and  motor 
deterioration. Already in the first year of life, autistic behavioural elements are present such 
as social withdrawal, declining speech and communication, limited eye contact, grinding of 
the teeth, and characteristic hand stereotypies (Ben Zeev, 2007; Gonzales & LaSalle, 2010). In 
the  majority  of  the  patients,  the  syndrome  is  associated  with  severe  epilepsy.  The  first 
decade  is  dominated  by  severe  neurological  dysfunctions  and  an  irregular  respiration 
pattern  as  a  result  of  an  immaturely  developed  brainstem.  In  addition,  a  prolonged  QT 
interval is often present with, as a consequence, risk for sudden cardiac arrhythmia. From 
the  age  of  10,  a  developmental  plateau  occurs  and  the  patient  becomes  severely 
neurologically handicapped with profound intellectual disability. 
In 80% to 90% of the patients with RS, a mutation that almost always occurs de novo, can be 
demonstrated in the MECP2 gene. This gene is expressed particularly in neurons and to a 
lesser  extent  in  glial  cells,  and  involved  in  neuronal  maturation  in  the  postnatal  period. 
MECP2 is involved in the expression of the gene that codes for brain derived neurotrophic 
factor 
for  neuronal  maturation  and  plasticity.  The 
pathophysiology  of  RS  thus  lies  conceivably  in  a  MECP2–mediated  disturbance  in  the 
regulation of BDNF. It is assumed that the severity of the disorder and the progression over 
time of the successive stages corresponds with a polymorphism in BDNF (Matijevic et al., 
2009; Ben Zeev et al., 2009). 

(BDNF),  which 

is  essential 

 

38

 
Autism Spectrum Disorders: The Role of Genetics in Diagnosis and Treatment 

RS  is  one  of  the  better  examples  of  an  autism-related  disorder  with  a  proven  genetic 
pathophysiology.  While  there  are  clear  differences  between  classical  autism  and  the 
phenotypic  presentation  of  autism  in  RS  (i.e.,  characteristic  stereotypy  such  as  hand-
wringing  at  chest  level  and  a  relative  maintenance  of  eye  contact),  research  in  RS  could 
nevertheless contribute to a better understanding of involvement of central nervous system 
dysfunctions in autism.  
 

X-chromosome

a

b

e
n
e
g
-
2
P
C
E
M

Mutations 

(~5%)

5’UTR

3’UTR

Exon 1

Exon 2

Exon 3

Exon 4

Mutations 

(>55%)

Small 
deletions
(>10%)

 

Fig. 2. Rett syndrome and the MECP2 Gene 
Schematic representation of the location of the MECP2 gene on the long arm of the X chromosome. 
(a) The MECP2 gene is constituted by 4 coding exons.The majority of mutations among patients with 
Rett syndrome are found in exon 4. In addition, in more than 10% of the patients, a deletion of the 
last part of exon 4 is present. The terms 5’ UTR (UnTranslated Region) and 3’ UTR indicate the 
direction in which the gene is read; from 5’ UTR to 3’ UTR 

4.3 Tuberous sclerosis 
Tuberous  sclerosis  complex  (TSC)  of  Bourneville-Pringle  was  first  described  in  1880  by 
Bourneville  and  is  a  multi-organ  disorder  with  a  autosomal  dominant  inheritance.  The 
prevalence is 1 in the 6,000 - 10,000 births. TSC is caused by mutations in two genes, the TSC 
1 and 2 gene. The TSC1 gene is located on chromosome 9 (9q34.3) and codes for hamartin 
while  the  TSC2  gene  is  located  on  chromosome  16  (16p13.3)  and  codes  for  tuberin.  In 
approximately 85% of patients with a clinically confirmed diagnosis of TSC, a change in one 
of these two genes can be demonstrated. Usually, a de novo mutation is present, although 
30% of the index patients has one or more affected family members. Mutations in both genes 
can lead to abnormal cell growth and differentiation in multiple organ systems. In the brain, 
this is expressed by the formation of cortical and subcortical hamartomas including tubers. 
In  addition,  various  organ  systems  can  be  affected  leading  to  the  development  of  cystic 
kidneys, angiofibromas of the face, and rhabdomyomas. The structural abnormalities of the 

 

 
Autism and Genetic Syndromes 

39 

central  nervous  system  are  associated  with  various  forms  of  epilepsy,  cognitive 
dysfunctions  and  symptoms  of  autism  (Datta  et  al.,  2008).  There  is,  however,  a  great 
variability in the severity of the clinical characteristics across TSC patients, also within one 
single family. 
Already in 1932 and thus before Kanner’s publication, Critshley and Earl described the autistic 
characteristics  associated  with  TSC,  being  decreased  social  contact,  stereotypies,  disturbed 
speech, and withdrawal behaviour. Research during the last few decades has shown autism to 
occur  in  about  25  to  60  percent  of  TSC  patients,  although  its  symptom  profile  differs 
qualitatively from that seen in classical autism. A higher level of social-cognitive functioning 
as well as less pronounced stereotypies are characteristic of patients with TSC. Moreover, in 
contrast to autism, the male-female ratio in TCS is about equal (Wiznitzer, 2004). 
The  neurobiological  substrate  for  autism  in  TSC  is  still  unclear.  For  both  hamartin  and 
tuberin, it is assumed that both proteins modulate cell function and play a role in neuronal 
migration,  differentiation,  and  development  and  that  they  together  form  a  functional 
complex (Asato et al., 2004). The latter can be considered as a type of ’neuronal polarity’ in 
which over expression of the TSC1/TSC2 complex suppresses the formation of axons while 
an  under  expression  is  associated  with  the  formation  of  tubers  (Choi  et  al.,  2008).  This 
TSC1/TSC2 functional integration may explain that a mutation in one of the two genes can 
result in the same phenotype (Orlova & Crino, 2010). Finally, it has been demonstrated that 
the  number  of  tubers  in  the  brain  correlates  with  the  incidence  of  autism  and  that  their 
localization corresponds with the type of epilepsy (Marcotte & Crino, 2006). 
In  sum,  also  for  TSC,  it  is  clear  that  the  presence  of  autistic  behavioural  characteristics 
relates  to  a  well-defined  gene  defect.  This  knowledge  from  TSC  research  may  further 
elucidate the pathophysiology of autism. 

4.4 22q11 deletion syndrome  
The  22q11  deletion  syndrome  (22q11DS)  was  first  described  in  1978  by  Shprintzen  as  velo-
cardio-facial syndrome and is caused by an interstitial deletion of chromosome 22 (22q11.2). In 
Figure 3, a micro-array profile of chromosome 22 from a patient with 22q11DS is depicted. 
This syndrome is associated with, among others, congenital heart and conotruncal defects, 
cleft palate, hypoparathyroidism, and facial dysmorphisms. The prevalence of 22q11DS is 
1:4,000 with an equal male-female distribution. The deletion involved in this syndrome can 
encompass multiple genes, with the T-box 1 (TBX1) gene as the most important. Its encoded 
protein is crucial for the development of specific brain areas, heart, face, and limbs (Paylor et 
al.,  2006).  It  is,  however,  doubtful  whether  this  gene  also  plays  a  role  in  the  etiology  of 
psychiatric disorders that often accompany 22q11DS (Funke et al., 2007). 
During  the  past  decades,  it  has  become  clear  that  psychiatric  disorders  often  occur  in 
22q11DS patients. These include psychoses in particular (Vogels et al., 2002; van Amelsvoort 
et  al.,  2004;  Verhoeven  et  al.,  2007),  but  also  anxiety,  mood,  and  obsessive-compulsive 
disorders  (Shprintzen,  2000).  In  addition,  in  15%  to  30%  of  the  patients  with  22q11DS, 
autistic  features  are  present  such  as  withdrawal  behaviour,  impaired  social  interaction, 
reduced  facial  expression,  and  cognitive  deficits,  e.g.,  perseveration,  reduced  mental 
flexibility, and restricted problem-solving capacities (Fine et al., 2005; Vorstman et al., 2006b; 
Anshel  et  al.,  2007;  Niclasson  et  al.,  2009).  Closer  inspection  of  the  psychopathological 
profile has demonstrated that both the psychotic and the autistic symptoms evolve from a 
diminished  comprehension  of  abstract  and  symbolic  language,  in  addition  to  a  limited 
capacity to correctly estimate the intentions, emotions, and behaviour of others (Sphrintzen, 
2000; Verhoeven et al., 2007).  

 

40

 
Autism Spectrum Disorders: The Role of Genetics in Diagnosis and Treatment 

In  sum,  for  22q11DS,  it  is  obvious  that  detailed  analysis  of  the  cognitive,  emotional,  and 
psychiatric profile is of critical importance for the choice of an individual treatment strategy.  

 

Fig. 3. Microarray profile of chromosome 22 from a patient with 22q11DS  
(a) Representation of a microarray profile of chromosome 22 from a patient with a 22q11 deletion. (b) 
The ideogram  of chromosome 22 with the indication of short p arm and the long q arm is depicted 
underneath.. In the upper portion, every individual clone is represented separately by a red dot on the 
X axis running between the end of the p arm (left) and the end of the q arm (right). On the Y axis, the 
amount of DNA from the patient as compared to that from control samples (CK) can be read. In case 
of an equal amount, the log2 ratio approximates zero. In cases of deletion, this  will be -1 or lower. In 
cases of duplication, this will be +1 or higher. The p arm of chromosome 22 is not represented in the 
microarray profile because it only consists of satellites and non-coding material 

4.5 Metabolic disorders  
While  genetically  determined  metabolic  disorders  are  relatively  rare,  nevertheless,  they 
often  manifest  with  disorders  along  the  autistic  spectrum.  The  establishment  of  such  a 
diagnosis is of importance for not only treatment and prognosis but also for gaining more 
insight  into  the  pathophysiology  of  autism.  Primarily  involved  are  disturbances  in  amino 
acid  metabolism  such  as  phenylketonuria,  disorders  in  purine  metabolism,  creatine 
deficiency  syndromes,  Smith-Lemli-Opitz  syndrome  (i.e.,  an  inherited  defect  in  the 
synthesis  of  cholesterol),  urea  cycle  disorders,  and  mitochondrial  disorders  (Manzi  et  al., 
2008;  Zecavati  &  Spence,  2009;  see  Table  2).  The  latter  may  even  have  its  debut  with 
symptoms from an autism spectrum disorder (Weissman et al., 2008). 
From  the  metabolic  disorders,  the  creatine  deficiency  syndromes  represent  a  recently 
recognized  group  of  diseases  that  are  caused  by  inherited  defects  in  the  biosynthesis  and 
transport  of  creatine.  Two  defects  in  the  biosynthesis  have  been  reported  that  include 
deficiencies  of 
(AGAT)  and 
guanidinoacetate methyltransferase (GAMT). The third is a functional defect involving the 
creatine transporter mechanism. The latter is an X-linked syndrome caused by a defective 
creatine transporter and was first described by Salomons et al. (2001). It appeared to be the 

the  enzymes  L-arginine-glycine  amindinotransferase 

 

 
Autism and Genetic Syndromes 

41 

result  of  a  mutation  in  the  creatine  transporter  gene  called  SLC6A8  that  was  mapped  to 
Xq28. Its prevalence is estimated to be at least 2% of X-linked mental retardation syndromes 
(Rosenberg  et  al.,  2004).  Since  the  SLC6A8  gene  is  expressed  in  most  tissues  (e.g.  skeletal 
muscle, kidney, colon, brain and heart), several organ systems can be affected. 
 

Disorder 

First 
appearance 
Phenylketonuria Neonatal 

Characteristics 

Intellectual disability, autism, 
epilepsy 

Treatment 
option 
Special diet 

From childhood Development delay, autistic 

characteristics, impulsivity, epilepsy

None 

3 months – 2 
years 

Autistic characteristics, 
epilepsy/myoclonic twitches, 
language and developmental delays, 
extrapyramidal symptoms 

Creatine 
substitution 

From childhood Autism, psychomotor retardation 

Hyperactivity, epilepsy, intellectual 
disability, autism 
Loss of acquired skills, autism 

Cholesterol 
suppletion 
Special diet 
measures 
None 

Purine 
metabolism 
disorders 
Creatine 
deficiency 
syndromes 

Cholesterol 
synthesis defects
Urea cycle 
disorders 
Sanfilippo 
syndrome 

Mitochondrial 
disorders 

Neonatal and 
postnatal 
Variable, 
depending on 
subtype 
Variable, 
depending on 
subtype 

Depending on organ system 

None 

Table 2. Metabolic disorders and autism (adapted from Zecavati & Spence, 2009 and Manzi 
et al., 2008) 
The  main  organ  involved  in  creatine  deficiency  syndromes  is  the  central  nervous  system. 
Patients  show  severe  neurodevelopmental  delay  and,  from  early  infancy  on,  mental 
retardation,  epilepsy,  disturbances  in  active  and  comprehensible  speech,  autism  and  self-
injurious behaviour become prominent (Salomons et al., 2003; Béard and Braissant, 2010). In 
patients with GAMT or AGAT deficiency, early oral creatine substitution treatment might 
effectively prevent neurological sequelae. In those with a defect in the creatine transporter 
gene SLC6A8, however, suppletion with L-arginine is not effective at all (Nasrallah et al., 
2009). 
Although metabolic syndromes should always be involved in the differential diagnosis of 
autism spectrum disorder, systematic screening of such patients is only mandatory in case 
of a suggestive actual symptomatology and/or developmental history. An example is the 
Sanfilippo B syndrome, a mucopolysaccharidosis caused by a mutation in the NAGLU gene, 
which leads to an accumulation of heparan sulfate with, as a consequence, damage to the 
central nervous system and various organ systems. This diagnosis was recently determined 

 

42

 
Autism Spectrum Disorders: The Role of Genetics in Diagnosis and Treatment 

for  an  older,  mild  intellectually  disabled  patient  who  was  referred  for  behavior  problems 
with  a  history  of  limited  verbal  and  emotional  communication,  stereotypies,  impulsivity, 
and anxieties (Verhoeven et al., 2010). 

5. Closing remarks 
The majority of patients with autism present with a mild to severe intellectual disability. In a 
substantial number of cases, moreover, the autistic disorder appears to be part of a genetic 
disorder.  It  is  quite  remarkable  therefore,  that  only  one  genetic  disorder from  the  array  of 
genetic  disorders  associated  with  autism,  is  included  in  the  DSM-category  of  autistic 
disorders,  namely  the  neurodegenerative  Rett  syndrome.  It  should,  however,  be 
emphasized  that  the  identification  of  autistic  behaviours  and  the  diagnosis  of  an  autism 
spectrum disorder is extremely difficult in patients with severe intellectual disability in the 
context  of  a  genetic  syndrome  (Moss  &  Howlin,  2009).  It  is  also  evident  that  in  case  of 
exceptionally  high  intelligence,  Asperger’s  disorder  or  atypical  autism  are  usually  the 
autistic disorders involved. For this specific group of patients, however, no information on 
genetics  is  available  as  yet.  These  patients  are  often  subsumed  under  the  general  DSM 
category ’Pervasive Developmental Disorder,Not Otherwise Specified’. 
Apart from the changes of diagnostic concepts over the past decades, research on the genetic 
underpinnings  of  autism  and  related  disorders  confronts  three  major  complexities.  First, 
there  is  the  large  degree  of  genetic  heterogeneity,  which  means  that  different  genes  can 
contribute  in  a  varying  way  to  the  emergence  of  a  disorder.  A  second  difficulty  is  the 
polygenetic  inheritance;  that  is,  the  simultaneous  presence  of  multiple  genetically-
determined  vulnerabilities  that  may  be  responsible  for  the  development  of  a  particular 
syndrome. A third problem lies in the well-known interaction between environmental and 
genetic factors during development from early conception on (Volkmar et al., 2009).  
All mutations that are causative for the aforementioned disorders concern genes involved in 
the early development of the central nervous system. The search for susceptibility genes has 
made it clear that disturbed synaptic transmission in, for example, the neuroligin network is 
involved in the pathophysiology of a certain, albeit small, percentage of cases with autism. 
This  kind  of  knowledge  might  be  relevant  for  the  development  of  putative  future 
pharmacological treatment strategies for a subgroup of patients with autism. In this context, 
the earlier mentioned significance of the neuropeptide OXT could also be noteworthy. 
The  results  from  a  large  number  of  studies  during  the  past  decades  lead  to  several 
conclusions.  It  is  clear  that  autism,  both  phenotypically  and  genotypically,  is  a  very 
heterogeneous  disorder  and  that  the  quest  for  the  grail  of  a  single-high-impact  gene  will 
never  succeed.  In  general,  mutations  or  common  variants  in  genes  are  thought  to  be 
involved  in  the  neuronal  domains,  synaptic  interaction,  neurotransmission,  and  cell 
migration and growth (Freitag et al., 2010). Attention should therefore be shifted to large-
scale screening for de novo mutations and CNVs that can influence the functioning of a gene 
(Sebat et al., 2007; Vissers et al., 2010).  
Recently,  all  information  available  on  the  vulnerability  genes  and  CNVs  associated  with 
autism  has  become  available  via  the  Autism  Genetic  Database  (AGD),  that  can  be  freely 
accessed  at  http://wren.bcf.ku.edu  (Matuszek  &  Talebizadeh,  2009).  In  addition,  modern 
fMRI techniques may be of use to map neuronal endophenotypes that are critical for further 
genetic studies of autism (Losh et al., 2008; Piggot et al., 2009).  
For  daily  clinical  practice,  facial  dysmorphisms  in  patients  with  autism  in  addition  to 
intellectual  disabilities,  constitute  the  initial  indication  for  modern  genetic  investigation. 

 

 
Autism and Genetic Syndromes 

43 

Epilepsy  at  young  age  and  gradual  deterioration  of  previously  acquired  skills  warrant 
further search for a metabolic disorder. Future scientific studies may reveal to which extent 
sets of genes are involved in the pathophysiology of autism and autism  related disorders 
per se, but also of neuropsychiatric disorders in general (Lichtenstein et a., 2010). In all cases 
it  is  clear  that  detailed  information  about  developmental  history,  neuropsychiatric/ 
neuropsychological  profile  as  well  as  an  elaborative  inventory  of  family  characteristics  is 
mandatory  for  appropriate  genetic  search.  This  holds  true  for  both  the  individual  patient 
and for a group of well defined patients. 

6. References 
Alarcón,  M.,  Cantor,  R.M.,  Liu,  J.,  Giliam,  T.C.,  The  Autism  Genetic  Resource  Exchange 
Consortium & Geschwind, D.H. (2002). Evidence for a language quantitative trait 
locus on chromosome 7q in multiplex autism families. American Journal of Human 
Genetics, 70, 60-71. 

Alvarez, J. A., & Emory, E. (2006). Executive function and the Frontal Lobes: a meta-analytic 

review Neuropsychology Review, 16, 17-42. 

Antshel,  M.,  Aneja,  A.,  Strunge,  L.,  Peebles,  J.,  Fremont,  W.P.,  Stallone,  K.,  et  al.  (2007). 
Autistic  spectrum  disorders  in  velo-cardio  facial  syndrome  (22q11.2  deletion). 
Journal of Autism and Developmental Disorders, 37, 1776-1786. 

Asato,  M.R.  &  Hardan,  A.Y.  (2004).  Neuropsychiatric  problems  in  tuberous  sclerosis 

complex. Journal of Child Neurology, 19, 241-249. 

Asperger, H. (1944). Die “Autistischen Psychopathen” im Kindesalter. Archiv für Psychiatrie 

und Nervenkrankheiten, 1, 76-136. 

Barnes-Holmes, Y., Barnes, D., McHugh, L., & Hayes, S. C. (2004). Relational Frame Theory: 
Some  Implications  for  Understanding  and  Treating  Human  Psychopathology. 
International Journal of Psychology and Psychological Therapy, 4, 355-375. 

Baron-Cohen, S. (1989). The autistic child’s theory of mind : A case of specific developmental 

delay. Journal of Child Psychology and Psychiatry, 30, 285-297. 

Béard, E. & Braissant, O. (2010) Synthesis and transport of creatine in the CNS: improtance 

for cerebral functions. Journal of Neurochemistry, 115, 297-313. 

Benvenuto, A., Moavero, R., Alessandrelli, R., Manzi, B. & Curatolo, P. (2009). Syndromic 

autism: causes and pathogenetic pathways. World Journal of Pediatrics, 5, 169-176. 

Ben Zeev Ghidoni, B. (2007). Rett syndrome. Child and adolescent psychiatric clinics of North 

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Part 3 

Genetics and Pathophysiology 
of Autism Spectrum Disorders

 

4 

The Genetics of Autism Spectrum Disorders 
John J.M. Connolly1 and Hakon Hakonarson1,2 
1Center for Applied Genomics 
The Children’s Hospital of Philadelphia, Philadelphia  
2Department of Pediatrics,University of Pennsylvania School of Medicine, Philadelphia 
USA 

1. Introduction 
Autism  is  a  neurodevelopmental  disorder  of  complex  etiology  and  is  amongst  the  most 
heritable  of  neuropsychiatric  disorders  while  sharing  genetic 
liability  with  other 
neurodevelopmental  disorders  such  as  intellectual  disability  (ID).  Autism  spectrum 
disorders  (ASDs)  are  defined  more  broadly  and  include  autism,  Asperger  syndrome, 
childhood  disintegrative  disorder  and  pervasive  developmental  disorder  not  otherwise 
specified.  Under  the  Diagnostic  and  Statistical  Manual  of  Mental  Disorders,  4th  Edition 
Revised  (DSM-IVTR),  these  disorders  are  grouped  together  with  Rett  syndrome  (“Rett’s 
disorder”) as pervasive developmental disorders. However, Rett syndrome has a reportedly 
distinct pathophysiology, clinical course, and diagnostic strategy (Levy & Schultz, 2009) and 
will  likely  be  removed  in  the  impending  publication  of  DSM-V  (APA,  2010).  The  new 
diagnostic manual will formally adopt the single diagnostic category “ASDs”, which is used 
here.  Reported  prevalence  rates  for  ASDs  range  from  20  (Newschaffer  et  al.  2007)  to  116 
(Baird  et  al.,  2006)  per  10,000  children,  and  vary  in accordance  with  diagnostic, sampling, 
and screening criteria. The Centers for Disease Control and Prevention (CDC) suggest that 
in the United States, the prevalence of ASDs is 1 in 110 (1/70 in boys and 1/315 in girls) 
(ADDM, 2009). The three primary characteristics of ASDs are communication impairments, 
social  impairments,  and  repetitive/stereotyped  behaviors.  The  DSM-IVTR,  ICD-10,  and 
many  other  diagnostic  instruments  require  impairment  in  each  of  these  domains  for  a 
diagnosis of autistic disorder.  
Within  the  last  decade,  a  number  of  major  technological  developments  have  transformed 
our understanding of the genetic causes of autism, and the field continues to evolve rapidly. 
In this chapter, we will review three approaches to identifying genetic factors that contribute 
to  the  pathogenesis  of  ASDs:  1)  common  variants  and  genome-wide  association  studies 
(GWAS); 2) rare variants and copy number variation (CNV) studies, and 3) familial forms of 
autism  and  the  role  of  next-generation  sequencing  (NGS)  methods.  Data  from  all  three 
approaches underscores the conclusion that autism is a highly complex and heterogeneous 
disorder, involving a multifactorial etiology. Moreover, it is becoming increasingly apparent 
that autism is not a unitary disorder, and that the spectrum may consist of any number of 
different autisms that share similar symptoms or phenotypes. This conclusion has important 
implications for evaluation and treatment, which are discussed in the conclusion.  

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Autism Spectrum Disorders: The Role of Genetics in Diagnosis and Treatment 

2. Heritability of ASDs  
Although Skuse (2007) cautions that heritability estimates of ASDs may have been skewed 
by the co-inheritance of (low) intelligence or other variables, there is little doubt that genetic 
factors  play  a key  role  in  autism. In  the  most  widely-cited  twin  study, Bailey et  al.  (1995) 
report  that  monozygotic  twins  are  92%  concordant  on  a  broad  spectrum  of  cognitive  or 
social abnormalities, compared with only 10% for dizygotic twins. Parents and siblings of 
individuals with ASDs often exhibit subsyndromal levels of impairment (Piven et al., 1997), 
and having an affected sibling is the single biggest risk factor for developing an ASD. In an 
analysis of 943,664 Danish children (Lauritsen et al., 2005), the strongest predictors of autism 
were  siblings  with  ASDs,  who  conferred  a  22-fold  increased  risk,  while  Fombonne  (2005) 
suggested that this risk may be even greater.  

3. Early insight from Rett syndrome and fragile X 
Early  efforts  to  identify  the  genetic  causes  of  ASDs  utilized  linkage  and  association 
approaches.  Linkage  studies,  more  prominent  in  the  1980s  and  1990s,  typically  focus  on 
families or larger pedigrees and is well powered to identify rare genetic variants. The most 
common linkage approach is the affected sib-pair design (see O’Roak & State, 2008), which 
examines  the  transmission  of  genomic  segments  through  generations.  Linkage  studies 
helped  define  the  locus  containing  FMR1,  which  is  mutated  in  fragile  X  syndrome  (e.g. 
Richards  et  al.,  1991),  and  is  a  common  cause  of  autism  in  fragile  X  syndrome,  affecting 
~30% of children who are diagnosed with fragile X (Rogers et al., 2001; Harris et al., 2008). 
Similarly, this approach has been important to identifying MECP2 as the major cause of Rett 
syndrome (e.g. Curtis et al., 1993).  
Association studies take the opposite approach, scanning the genome from the top down, 
with the goal of determining post-hoc whether identified variants are more or less common 
in  affected  individuals.  Early  association  studies  (i.e.  pre  HapMap)  were  complementary 
with the linkage approach, and in many designs, linkage primed target loci for this more 
fine-grained  analysis.  These  early  insights  have  played  a  significant  role  in  shaping  our 
current  understanding  of  ASDs,  and  functional  studies  of  FMR1  and  MECP2  have 
highlighted the importance of synaptic dysfunction (Ramocki & Zoghbi, 2008) as a unifying 
factor that could extend into the more common forms of autism. This is significant because it 
provides a means of linking neural correlates with genomic data, as well as related clinical 
phenotypes such as seizures and cognitive deficits (Hagerman et al., 2009). The Alzheimer’s 
paradigm,  which  includes  functional  models  of  how  molecular,  biochemical,  and  neural 
systems interact is instructive in this regard (e.g. Cissé et al., 2011).  

4. Genome wide association and common variants  
Aside  from  notable  successes  with  fragile  X  and  Rett  syndrome,  early  linkage  and 
association studies have been inconsistent in resolving more complex genetic correlates of 
ASDs,  with  candidate  genes  often  not  being  replicated  between  studies.  These  challenges 
may  in  part  accounted-for  by  their  relatively  low  resolution,  which  makes  it  difficult  to 
detect  candidate  loci  other  than  those  of  major  effect.  In  the  past  decade  however, 
association  studies  have  become  increasingly  more  sophisticated,  with  the  whole-genome 
approach, allowing us to examine thousands of individuals on a mass scale, using hundreds 
of thousands of markers.  

 

 
The Genetics of Autism Spectrum Disorders 

53 

Genome-wide  association  studies  (GWAS)  examine  the  frequency  of  single  nucleotide 
polymorphisms  (SNPs)  in  cases  versus  control  populations  and  can  adopt  either  a  case-
control or family-based approach. The former allows researchers to avoid the often complex 
process of acquiring diagnostic/phenotype data from a patient’s family, and can incorporate 
very  large  numbers  of  control  datasets  that  may  be  more  readily  available.  The  latter 
controls for the often confounding phenomenon of population stratification, where variants 
more  common  to  specific  racial  groups  may  either  be  erroneously  identified  as causal,  or 
obscure  actual  causal  variants.  A  major  caveat  with  family-based  designs  is  the  often 
unfounded assumption that unaffected family members do not share causal variants.  
GWAS  test  for  common  variants  (>1%  population  frequency),  with  the  assumption  that 
ASDs are at least in part caused by the coinheritance of multiple risk variants, each of small 
individual  effect  (odds  ratios  between  1:1  and  1:5).  This  assumption  is  known  as  the 
common disease-common variant (CDCV) model (Risch and Merikangas, 1996).  
A 2009 paper by Wang et al. (2009) from our laboratory was the first to identify common 
variants  for  ASDs  on  a  genome-wide  scale.  We  examined  780  families  (3,101  individuals) 
with affected children, a second group of 1,204 affected individuals, and 6,491 controls, all of 
whom were of European ancestry. We identified six genetic markers on chromosome 5 in 
the  5p14.1  region  that  confirmed  susceptibility  to  ASDs.  The  region  straddles  two  genes, 
CDH9 and CDH10. Both genes encode type II classical cadherins, transmembrane proteins 
that  promote  cell  adhesion.  The  association  of  cadherins  is  consistent  with  the  cortical-
disconnectivity  model  of  autism  (e.g.  Gepner  &  Féron,  2009),  which  postulates  that  ASDs 
may  result  from  an  increase  or  decrease  in  functional  connectivity  and  neuronal 
synchronization in relevant neural pathways. Functional studies suggest that under-activity 
between  and  within  networks  are  correlated  with  social,  communication,  cognitive,  and 
sensorimotor impairments (Müller et al., 2011).  
The study design utilized two independent replication cohorts and the key SNP at this locus 
has  also  been  replicated  in  two  additional  independent  cohort  studies  (Ma  et  al.,  2009;  St 
Pourcain et al., 2010), lending further support that genetic factors at the 5p14 locus, which is 
flanked by two relevant cadherin genes, represent strong candidates for aligning molecular 
function with known neural deficits in ASDS. The original report by Wang and colleagues, 
also  demonstrated  that  there  was  an  enrichment  in  Catherin  associated  genes  in  ASDs  in 
general,  based  on  gene-pathway  analysis  (Wang  et  al,  2009).  Cadherins  represent  a  large 
family of transmembrane proteins that mediates calcium-dependent cell–cell adhesion and, 
via cell adhesion, has been shown to generate synaptic complexity in the developing brain 
(Redies,  2000).  Other  common  GWAS  variants  reported  have  not  been  replicated  in 
independent studies and will not be covered here. 

4.1 Replicated common variants from candidate gene studies  
Other  common  variants  from  candidate  gene  studies  include  CNTNAP2,  EN2,  and  MET, 
which are reviewed briefly below. A more in depth review of these genes can be derived 
from catalogs at http://www.genome.gov/26525384 and http://w w w.ncb.nlm.nih.gov/o 
mim/209850.  
Located on chromosome 7q35, Contactin Associated Protein 2 (CNTNAP2) was identified by 
Alarcón et al. (2002) as a candidate for the age at first word endophenotype. A subsequent 
follow-up  by  the  same  group  (Alarcón  et  al.  2008)  using  linkage,  association,  and  gene-
expression  analyses,  found  CNTNAP2  to  be  the  only  autism-susceptibility  gene  to  reach 
significance across all approaches. An independent linkage analysis by Arking et al. (2008) 
also  highlighted  CNTNAP2  as  a  significant  ASD  candidate  gene.  CNTNAP2  is  part  of  the 

 

54

 
Autism Spectrum Disorders: The Role of Genetics in Diagnosis and Treatment 

neurexin  family,  which  have  repeatedly  been  associated  with  autism  (see  below). 
Interestingly, Vernes et al. (2008) showed that CNTNAP2 binds to FOXP2, which is a well-
established  correlate  of  language  and  speech  disorders  (Lai  et  al.,  2001)  –  a  common 
phenotype in ASDs.  
Engrailed 2 (EN2) is a homeobox gene that is critical to the development of the midbrain and 
cerebellum. Like other homeobox genes, it regulates morphogenesis. EN2 is a human homolog 
of  the  engrailed  gene,  which  is  found  in  Drosophila.  En2  mouse  mutants  have  anatomic 
phenotypes  in  the  cerebellum  that  resemble  cerebellar  abnormalities  reported  in  autistic 
individuals  (Cheng  et  al.,  2010).  Benayed  et  al.  (2005,  2009)  have  reported  and  replicated  in 
three  separate  datasets  a  significant  association  with  broad  and  narrow  ASD  phenotypes. 
Wang et al. (2008) also found an association between EN2 and ASDs in a Chinese Han sample, 
although Zhong et al. (2003) failed to find evidence of an underlying association.  
The  oncogene  MET  is  also  strongly  linked  to  ASD  etiology,  having  been  supported  by  a 
number of studies in the past decade (e.g. IMGSAC, 2001; Campbell et al., 2006, 2008; Sousa 
et  al.,  2009).  Recently,  Eagleson  et  al.  (2011)  reported  a  role  for  Met  signaling  in  cortical 
interneuron development in vitro in a mouse model. 

4.2 Unexplained variance  
For the most significant discovery SNP identified in the Wang et al. study above (rs4307059), 
the risk allele frequency was 0.65 in cases with an odds ratio of 1.19, which is comparable 
with  common  variant  discoveries  in  other  psychiatric  disorders  including  schizophrenia 
(Glessner & Hakonarson, 2009; Glessner et al., 2010); bipolar disorder (Ferreira, 2008), and 
attention-deficit/hyperactivity disorder (Arcos-Burgos et al. 2010). While it is important not 
to undermine the significance of these findings, it should be noted that the predictive value 
of such ratios is relatively low (Dickson et al. 2010), often explaining less than 5% of the total 
risk (review at http://www.genome.gov/26525384). However, it is also possible that these 
common  SNPs  may  be  tagging  a  more  rare  causative  variant  (i.e.,  synthetic  association), 
where the effect sizes may be markedly underestimated by the GWAS variant as we recently 
reported (Dickson et al. 2010). In one example, Wang et al. (2010), examined the NOD2 locus 
as a cause of Crohn disease. Using resequencing data, they found that three causal variants 
explain  >  5%  of  the  genetic  risk,  where  GWAS  had  estimated  the  risk  at  ~1%.  Careful 
phenotyping  of  cohorts  is  important  to  ensure  that  the  phenotypes  produced  by  rare-
variants  are  not  being  “filtered-out”and  thereby  missed  as  a  consequence.  A  long  range 
haplotype analysis of the GWAS data at the respective loci is therefore recommended in an 
attempt to enrich for individuals with rare-causative variants, who should be picked out of 
the cohort and subsequently sequenced for confirmation (Wang et al., 2010). This is clearly a 
critical point, particularly in relation to psychiatric disorders, where diagnoses can be more 
contingent upon subjective observation than, for example, the genetics of height, which can 
utilize more intrinsically quantitative data.  
The  possibility  that  common  variants  are  not  the  major  cause  of  ASDs  is  also  gaining 
increased support from the preponderance of copy number variation (CNV) studies, which 
are identifying rare variants with a stronger causal impact.  

5. Copy number variation in ASDs 
Copy  number  variations  (CNVs)  are  insertions,  deletions,  or  translocations  in  the  human 
genome  that  are  universal  in  the  general  population  but  more  commonly  found  in  genic 

 

 
The Genetics of Autism Spectrum Disorders 

55 

regions in individuals with neuropsychiatric disorders (e.g. Pinto et al., 2010). CNVs can be 
detected by the same SNP arrays used in GWAS, and vary in length from many megabases 
to 1 kilobase or smaller. They are often not associated with any observable phenotype.  
One  of  the  most  widely-known  CNVs  is  Down  syndrome,  which  is  characterized  by  an 
extra chromosome 21. Rett syndrome is also caused by a CNV, which includes a deletion in 
MECP2. CNVs can be inherited or occur de novo, the cause of which is thus far unknown. 
Common disease-causing CNVs are infrequent but rare CNVs, with a frequency of less than 
1%,  have  been  identified  for  a  range  of  disorders  including  ADHD  (e.g.  Williams  et  al., 
2010), schizophrenia (e.g. Glessner et al., 2010; Levinson et al., 2011), bipolar disorder (e.g. 
Chen et al., 2010) and many others. A substantial portion of autism appears to be caused by 
rare  CNVs.  De  novo  CNVs  that  are  greater  than  100kb  in  size  are  more  common  in  genic 
regions in individuals with ASDs than in the general population.  
Sebat et al. (2007) provided some relevant early insights into the genomic features of CNVs. 
Firstly, they noted that de novo CNVs were individually rare – from 118 ASD cases, none of 
the identified variants were observed more than twice, with the majority seen just once. This 
confirmed the widely-held assumption that many different loci can contribute to the same 
ASD phenotype. Secondly, the authors affirmed the utility of population-study approaches 
that  analyze  sporadic  and  multiplex  (i.e.  more  than  one  family  member  affected)  families 
separately. The rate of de novo mutation in large (mostly genic) loci in multiplex families was 
significantly lower than for the sporadic cases (p = 0.04). While this observation remains to 
be replicated in a larger study, the finding implies two mechanisms of genetic susceptibility 
– spontaneous mutation and inheritance. Finally, the sheer volume of loci identified by this 
approach  (multiple  loci  on  20  chromosomes)  affirms  the  extraordinarily  complexity  of 
ASDs.  
A number of subsequent studies have greatly expanded the number of candidate loci. Our 
laboratory (Bucan et al. (2009)) reported 150+ CNVs in 912 ASD families that were not found 
in 1,488 controls. Critically, 27 of these loci were replicated in an independent cohort of 859 
ASD cases and 1,051 controls. Some of the rare variants we identified had previously been 
associated  with  autism,  including  NRXN1  and  UBE3A,  which  are  established  ASD 
candidate genes (Guilmatre et al., 2009). Samaco et al. (2005) previously identified significant 
deficits  in  ube3a  expression  in  mecp2-deficient  mice,  suggesting  a  shared  pathological 
pathway with Rett syndrome (as well as Angelman syndrome, and autism). Similarly, Kim 
et al. (2008) associated NRXN1 with a balanced chromosomal abnormality at chromosome 
2p16.3 in two unrelated ASD individuals. Rare variants in the coding region included two 
missense changes. 
Glessner et al. (2009) identified and reported CNVs in two major gene networks, including 
neuronal cell adhesion molecules (such as NRXN1) and the ubiquitin gene family (such as 
UBE3A). Interestingly, four of the most prominent genes enriched by CNVs in ASD cases 
(UBE3A,  PARK2,  RFWD2  and  FBXO40)  are  all  part  of  the  ubiquitin  gene  family. 
Ubiquitination  can  alter  protein  function  after  translation,  and  degrade  target  proteins  in 
conjunction with proteasomes. The ubiquitin–proteasome system operates at pre- and post-
synapses, whose functions includes regulating neurotransmitter release, recycling synaptic 
vesicles  in  pre-synaptic  terminals,  and  modulating  changes  in  dendritic  spines  and  post-
synaptic  density  (Yi  &  Ehlers,  2005).  As  well  as  implicating  an  ubiquitination  network  in 
relation to ASDs, we also identified a second pathway involving NRXN1, CNTN4, NLGN1, 
and  ASTN2.  Genes  in  this  group  mediate  neuronal  cell-adhesion,  and  contribute  to 
neurodevelopment  by  facilitating  axon  guidance,  synapse  formation  and  plasticity,  and 
neuron–glial  interactions.  We  also  note  that  ubiquitins  are  involved  in  recycling  cell-

 

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Autism Spectrum Disorders: The Role of Genetics in Diagnosis and Treatment 

adhesion molecules, which is a possible mechanism by which these two networks are cross 
linked.  
In a similar approach, Pinto et al. (2010) further confirmed the importance of rare CNVs as 
causal  factors  for  ASDs.  Interestingly,  the  group  did  not  observe  a  significant  difference 
between  cases  and  controls  in  terms  of  raw  number  of  CNVs  or  estimated  CNV  size. 
However, the number of CNVs in genic regions was significantly greater in ASDs compared 
to controls. Again, loci enriched for CNVs include a number of genes known to be important 
for neurodevelopment and synaptic plasticity, such as SHANK2, SYNGAP1, and DLGAP2. 
Between 5.5% and 5.7% of ASD cases have at least one de novo CNV, further confirming the 
significance of de novo genetic events as risk factors for autism. Similar to the Glessner study, 
the  Pinto  group  mapped  CNVs  to  a  series  of  networks  involved  in  the  development  and 
regulation  of  the  central  nervous  system  functions. Implicated  networks  include  neuronal 
cell adhesion, GTPase regulation (important for signal transduction and biosynthesis), and 
GTPase/Ras signaling, also involved in ubiquitination.  
Finally, Gai et al. (2011) took a slightly different approach, focusing exclusively on inherited 
CNVs.  While  underlying  loci  were  not  necessarily  common  to  those  identified  by  the 
Glessner  and  Pinto  groups,  enrichment  in  pathways  involving  central  nervous  system 
development,  synaptic  functions  and  neuronal  signaling  processes  was  again  confirmed. 
The  Gai  et  al.  study  also  emphasized  the  role  of  glutamate-mediated  neuronal  signals  in 
ASDs.  
Collectively, these CNV studies suggest that certain hotspots on the genome are particularly 
vulnerable to ASDs, which include loci on chromosomes 1q21, 3p26, 15q11-q13, 16p11, and 
22q11. These hotspots are part of large gene networks that are important to neural signaling 
and neurodevelopment and have additionally been associated with other neuropsychiatric 
disorders.  
In  particular,  a  number  of  CNV  studies  in  schizophrenia  have  highlighted  structural 
mutations  incorporating  chromosomes  1q21,  15q13,  and  22q11  (e.g.  McClellan  and  King 
2010;  Glessner  et  al.,  2010), which  are  significantly  enriched  in  cases  versus  controls, with 
NRXN1  being  a  standout  in  this  regard.  From  a  phenotype  perspective,  autism  and 
schizophrenia seem very different, both in behavioral manifestation and age of onset, and it 
may  seem  counter-intuitive  that  associated  loci  should  overlap.  Some  authors  have 
addressed  this  peculiarity  by  proposing  that  schizophrenia  and  autism  may  in  fact  be 
different poles of the same spectrum. Thus, Crespi and Braddock (2008) suggest that social 
cognition is underdeveloped in ASDs and over-developed in the psychotic spectrum, with a 
similar  polarization  of  language  and  behavioral  phenotypes.  Although  speculative,  this 
hypothesis  has  gained  some  traction.  In  the  next  several  years,  genomic,  imaging,  and 
model-systems approaches will likely shed further light on the relationship between autism, 
schizophrenia and other neuropsychiatric disorders.  

6. Sequencing familial forms of ASDs  
To this point, we have focused primarily on the complex interactions of polygenic networks 
as the major cause of ASDs. However, this is not exclusively the case. Paralleling the recent 
spate  of  CNV  is  a  renewed  focus  on  rare  disorders,  including  familial  forms  of  complex 
diseases  that  potentially  are  monogenic  or  with  less  complex  inheritance  pattern.  At  the 
outset of this chapter, we emphasized the overlap with fragile X syndrome, where one third 
of cases are co-morbid for ASD. As mentioned, fragile X is caused by a failure to express the 

 

 
The Genetics of Autism Spectrum Disorders 

57 

protein coded by FMR1. However, mutations in FMR1 do not always result in fragile-X and 
can result in a phenotype more representative of ASDs. Thus, Muhle et al. (2004) found that 
7-8% of idiopathic ASD cases may have mutations at the FMR1 locus. Likewise, although 
mutations in MECP2 are the common cause of Rett syndrome, certain mutations at the same 
locus have been associated with idiopathic autism (Carney et al. (2003).  
X-linked  genes  encoding  neurologins  NLGN3  and  NLGN4  and  SHANK3  (a  neuroligin 
binding partner) are other prominent examples of distinct rare genetic causes, and a parallel 
can be drawn with these studies and mental retardation and epilepsy, which include many 
rare syndromes that collectively account for a substantial proportion of the two disorders 
(Morrow et al., 2008). Indeed it is perhaps more than coincidence that autism is heavily co-
morbid with these two conditions, with >40%( Bölte et al., 2009) and ~40% (Danielsson et al., 
2005)  of  ASD  cases  meeting  diagnostic  criteria  for  mental  retardation  and  epilepsy 
respectively.  It  also  is  noteworthy  that  many  of  these  monogenic-related  genes  are  also 
major  players  in  neurodevelopment  and  synapse  activity.  Other  prominent  examples 
include TSC1, TSC2 (Osborne et al., 1991; Franz, 1998), NF1, and UBE3A (see Morrow et al. 
(2008). 
The identification of monogenic or possibly oligogenic autisms is likely to accelerate in the 
next  several  years  as  next-generation  sequencing  becomes  more  widely  available.  We 
recently  encountered  a  family  of  two  parents,  six  healthy  siblings,  and  two  siblings  with 
severe  autism  suggestive  of  autosomal  recessive  inheritance.  Unsuccessful  attempts  using 
linkage and CNV approaches failed to identify a causal locus, but whole-exome sequencing 
at  20x  coverage  identified  four  genes,  including  one  with  a  non-synonymous  SNP  in  the 
protocadherin  alpha  4  isoform1  precursor  (PCDHA4)  gene,  which  presents  a  strong 
candidate gene, currently under validation. Protocadherins are part of the cadherin family 
that  facilitates  neuronal  cell  adhesion  and  this  discovery  is  consistent  genomically  and 
neurobiologically with the findings addressed above in relation to CDH9 and CDH10.  
Known  syndromes  with  ASD  features  include  fragile-x,  neurofibromatosis  type  1,  down 
syndrome, tuberous sclerosis, neurofibromatosis (which confers a 100-fold increased risk for 
ASDs  Li  et  al.  (2005),  Angelman,  Prader-Willi  and  related  15q  syndromes,  and  at  least 
several dozen others (see Zafeiriou et al., 2007 for a comprehensive review). Table 1 from 
Volkmar et al. (2005) lists the most commonly associated syndromes with median rate and 
range. It is likely that many more unidentified rare syndromes with Mendelian causes have 
ASD  phenotypes.  As  of  March  2011,  the  Online  Mendelian  Inheritance  in  Man  (OMIM) 
database  listed  6,727  known  or  suspected  Mendelian  diseases  (MD),  with  2,993  (44%)  of 
these  having  an  identified  molecular  basis.  Since  OMIM  derives  its  data  from  published 
reports, these figures likely under-represent rare disorders, which may go unreported. It has 
been  proposed  that  as  many  as  30,000  genetic  disorders  may  exist,  suggesting  that  many 
Mendelian  disorders  have  no  genetic  etiology  identified  to  date.  Given  the  large-
representation of autism phenotypes in known syndromes, we can assume a similar trend in 
unreported disease.  
It remains to be determined whether rare variants will account for the majority of autisms. 
Irrespective, as with many other aspects of scientific inquiry, the study of rare variants will 
continue to play an important role in explicating the pathogenesis of ASDs. El-Fishawy and 
State (2010) point to hypercholesterolemia and hypertension (Brown, 1974; Lifton et al., 2001) 
as  examples  where  rare  mutations  have  been  successful  in  driving  a  molecular 
understanding  of  the  disease  as  opposed  to  identifying  risk  factors  in  the  general 

 

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Autism Spectrum Disorders: The Role of Genetics in Diagnosis and Treatment 

population. Rare mutations, particularly when they are Mendelian, carry large effects and 
are  typically  in  genic  regions.  These  characteristics  make  the  resolution  of  underlying 
networks distinctly less complex and, moreover, are amenable to modeling in other systems.  
Recent  groundbreaking  studies  by  Marchetto  et  al.  (2010)  and  Muotri  et  al.  (2010),  who 
created a cell culture model of Rett syndrome, are potentially exciting developments in this 
regard. Here, the researchers used skin biopsies from four Rett’s patients, each carrying a 
different MeCP2 mutation, to culture induced pluripotent stem cells (iPS). Once the iPS cells 
developed into neurons, they showed a decreased number of neurons and dendritic spines, 
consistent  with  neurodevelopmental  disruptions.  Intervention  with  insulin-like  growth 
factor 1 (IGF1), which is known to regulate neurodevelopment, was subsequently shown to 
reverse Rett-like symptoms in a mouse model of the disease. This innovative approach is an 
exciting  model  of  how  rare  gene  approaches  can  stimulate  our  understanding  of  the 
pathophysiology and potential reversibility of ASDs. 
 

11 
9 
12 
6 

Number of Studies  Median Rate 

Syndrome  
Tuberous sclerosis  
Fragile X  
Down syndrome  
Neurofibromatosis 1 
Table 1. Associated disorders and their rate in autism (from Volkmar et al., 2005 in Zafeiriou 
et al. 2007) 

1.1 
0.0 
0.7 
0 

Range % 

0–3.8 
0–8.1 
0–16.7 
0–1.4 

7. Conclusions 
ASDs are clearly highly heritable disorders and advances in gene-finding technology in the 
past  decade  have  rapidly  accelerated  gene  discovery.  As  is  typically  the  case,  successive 
developments  have  made  the  problem  more  complex  such  that  there  are  dozens  of 
candidate genes, many of which remain to be replicated. In spite of this complexity, we can 
observe a number of patterns beginning to unfold 1) the relative scarcity of causal common 
variants,  2)  the  growing  list  of  causal  rare  variants,  and  3)  the  emergence  of  monogenic 
disorders with primary and secondary ASD phenotypes.  
The  monogenic  autisms  are  particularly  interesting  from  a  treatment  perspective,  as  they 
provide a mechanism for studying ASD phenotypes in model systems and an obvious target 
for drug intervention. They are also amenable to clinical testing and the decreasing cost of 
research technologies means that this capacity is more widely available to clinicians. In fact, 
as  the  resolution  of  clinical  instruments  becomes  more  sophisticated,  it  is  likely  that  the 
clinic will become a primary workplace for syndromic discovery.  
A  key  requirement  in  driving  gene  discovery  is  the  necessity  of  high-quality  phenotype 
data. ASDs are notoriously heterogeneous, and are fractionated in terms of symptoms and 
trajectory. Mandy & Skuse (2008) reviewed seven factor analysis studies of ASDs symptoms, 
and found that all but one dissociated social and non-social factors. In a non-clinical sample 
of 3,000 twin pairs, Happé et al. (2006) examined autistic-like traits and found consistently 
low  correlations  (r  =  0.1-0.4)  between  each  of  the  core  deficits  on  the  autism  spectrum. 
Endophenotypes, sub-components or sub-processes of the broader phenotype, may provide 
a productive avenue to disentangling some of this complexity. By filtering out all but a few 
discrete  measures,  we  can  theoretically  increase  the  signal-to-noise  ratio  in  genotype-
phenotype  associations.  A  number  of  endophenotypes  for  ASDs  have  been  identified 

 

 
The Genetics of Autism Spectrum Disorders 

59 

associated  with  disease  genes,  including  head  circumference  (associated  with  the  HOXA1 
A218G  polymorphism,  Conciatori  et  al.,  2004),  age  at  first  word  (associated  with  a 
quantitative  trait  locus  on  7q35,  Alarcón  et  al.  2005),  delayed  magnetoencephalography 
evoked  responses  to  auditory  stimuli  (Roberts  et  al.,  2010),  and  enhanced  perception 
(Mottron et al., 2006). The endophenotype approach is arguably more consistent with rare-
/mono-genic discovery, where a mutated network may not yield a diagnosis of autism per 
se, but nevertheless cause associated abnormalities. Note, this approach does not diminish 
the pleiotropic effects of genes involved in neurodevelopment, and only serves to make the 
point that the relevant genotype may associate with some but not all ASD features.  
The converse, of course, is also true with a large number of candidate genes contributing to 
the majority of known ASDs. With ~80% of genes expressed in the brain it is likely that this 
number will continue to grow, and here again careful phenotyping is critical to identifying 
functional consequences. Ultimately, the primary goal is not to determine the frequency of 
variation/mutation  in  cases  versus  controls,  but  to  determine  the  pathway(s)  and  gene 
networks that lead to pathology. This will be no mean feat, with other major players such as 
epigenetic  factors,  RNA  regulatory  elements,  and  environmental  exposures  also  an 
important  part  of  the  equation.  While  daunting,  the  elucidation  of  these  elements  will 
doubtlessly  take  us  closer  to  developing  effective  treatments  for  ASDs.  Given  the  current 
rate of progress, we have cause for cautious optimism in this regard.  

8. References  
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5 

Genetic Heterogeneity of Autism 
Spectrum Disorders 
Catherine Croft Swanwick, Eric C. Larsen 
and Sharmila Banerjee-Basu 
MindSpec, Inc. 
United States of America 

1. Introduction 
Although autism is considered to be one of the most highly heritable psychiatric disorders, 
molecular  mechanisms  underlying  its  pathogenesis  remain  largely  unresolved.  A  strong 
genetic  component  underlying  autism  spectrum  disorders  (ASD)  has  been  firmly 
established  from  various  lines  of  studies  ranging  from  whole  genome  scans  to  genetic 
association  studies.  Recent  genomic  advances  have  led  to  steep  growth  in  the  number  of 
diverse genetic loci linked to ASD, including candidate genes containing rare or common 
variants,  chromosomal  aberrations,  and  submicroscopic  copy  number  variations. 
Additionally,  autism  is  consistently  associated  with  a  number  of  single  gene  mutation 
disorders  such  as  Fragile  X  Syndrome.  Most  genetic  variations  fail  to  replicate  between 
studies and populations, further complicating our understanding of ASD disease etiology.  
Here we review recent expansion of heterogeneity in the genetic landscape for ASD. First 
we  define  the  types  of  genetic  risk  factors  implicated  in  this  disorder.  We  then 
comparatively analyze the pools of ASD candidate genes identified as of the end of years 
2006  and  2010,  profiling  both  their  distribution  and  molecular  function.  We  highlight 
bioinformatics  tools  for  ASD  which  can  be  used  to  build  and  evaluate  networks  of  ASD 
genes  as  the  number  of  risk  factors  grows.  Finally,  we  discuss  the  impact  of  genetic 
heterogeneity on theories of ASD pathogenesis.  

2. Genes 
In  the  post-genomic  era,  continuous  identification  of  new  ASD  risk  factors  has  rapidly 
expanded the types of candidate genes implicated in the pathogenesis of this disorder. Until 
2003,  single  gene  mutations  in  ASD  were  derived  from  well-characterized  genetic 
syndromes  such  as  Fragile  X  Syndrome  and  Rett  Syndrome,  in  which  subpopulations  of 
individuals  develop  autistic  symptoms.  Later  that  year,  Thomas  Bourgeron’s  group  first 
identified single gene mutations/disruptions in neuroligins in siblings with ASD (Jamain et 
al., 2003). This seminal work opened up the field of ASD research in two major areas: first, a 
strong  genetic  foundation  to  non-syndromic  forms  of  ASD  and,  second,  a  focus  on  the 
synaptic model for the disorder. Since then, high throughput genetic studies have rapidly 
identified additional genetic risk factors, vastly expanding the pool of ASD-linked genes.  

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Autism Spectrum Disorders: The Role of Genetics in Diagnosis and Treatment 

Candidate genes for ASD can currently be defined into four distinct sets:  
1.  Rare:  genes  implicated  in  rare  monogenic  forms  of  ASD.  The  types  of  allelic  variants 
within  this  class  include  rare  polymorphisms  and  single  gene  disruptions/mutations 
directly linked to ASD. Examples include NRXN1 and SHANK3.  

2.  Syndromic:  genes  implicated  in  syndromes  in  which  a  significant  subpopulation 
develops autistic symptoms. Examples include FMR1 (Fragile X Syndrome) and MECP2 
(Rett Syndrome).  

3.  Association:  genes  with  common  polymorphisms  that  confer  small  risk  for  ASD  and 
have  been  identified  from  genetic  association  studies  of  ASD  derived  from  unknown 
cause (known as “idiopathic ASD”). Examples include MET and GABRB1.  

4.  Functional: genes with functions relevant for ASD biology and not included in any of 
the  other  genetic  categories.  Examples  include  CADSP2,  for  which  knockout  mouse 
models exhibit autistic characteristics, but the gene itself has not been directly tied to 
known cases of ASD. 

Of these four gene categories, Rare and Syndromic contain the strongest evidence of links to 
ASD  (for  review,  El-Fishawy  &  State,  2010).  Association  genes  lack  replication  of  their 
relationship to ASD, and Functional genes have no documented direct link to ASD. Over 200 
ASD candidate genes have been reported thus far in the scientific literature (Table 1). These 
genes are distributed at discrete regions throughout the entire genome (Figure 1).  
 

Fig. 1. Ideogram of all currently known ASD candidate genes. To the left of each 
chromosome are genes that fall within either the association category (red) or the functional 
category (black), whereas to the right of each chromosome are genes that fall within either 
the rare mutation category (green) or the syndromic category (blue). * = gene also identified 
by genome-wide association studies. 

 

3. Chromosomes 
Microscopically visible large-scale chromosomal rearrangements have long been implicated 
in the onset and progression of a host of developmental disorders. Deletions of the 15q11-
q13 region on the maternal chromosome lead to Angelman syndrome (Williams et al., 2008), 
whereas the corresponding deletion on the paternal chromosome gives rise to Prader-Willi 
syndrome  (Cassidy  &  Schwartz,  2009).  Deletions,  duplications,  translocations,  and 

 

 
Genetic Heterogeneity of Autism Spectrum Disorders 

67 

inversions larger than 3 Mb responsible for these and other syndromes have traditionally 
been  identified  by  microscopic  techniques  such  as  karyotyping  and,  more  recently, 
fluorescent  in  situ  hybridization  (FISH).  In  recent  years,  technological  and  computational 
advances have provided researchers with the sensitivity and accuracy to identify structural 
variation  in  chromosomes  less  than  3  Mb  in  size,  which  could  not  have  previously  been 
identified by traditional cytogenetic methods such as karyotyping.  
 

Genetic 
Category 

Number of 

Genes 

81 

Rare 

Syndromic 

Association 

Functional 

21 

84 

23 

Genes 

ANKRD11, A2BP1*, APC*, ASTN2, AUTS2, BZRAP1, 
C3orf58, CA6, CACNA1H, CADM1, CENTG2*, CNTN4, 
CNTNAP2*, CNTNAP5, CXCR3, DIAPH3, DLGAP2, DPP10, 
DPP6, DPYD, EIF4E, FABP5*, FABP7*, FBXO40, FHIT, 
FRMPD4, GALNT13, GLRA2, GRPR, HNRNPH2, 
IL1RAPL1, IMMP2L*, JMJD1C, KCNMA1, KIAA1586, 
MBD1, MBD3, MBD4, MCPH1, MDGA2, MEF2C, NBEA, 
NLGN1, NLGN3, NLGN4X, NOS1AP, NRXN1, ODF3L2, 
OPHN1, OR1C1, PARK2, PCDH9, PCDH10, PCDH19, 
PDZD4, PLN, PPP1R3F, PSMD10, PTCHD1, RAB39B*, 
RAPGEF4, RB1CC1, REEP3, RFWD2, RIMS3, RPL10, 
RPS6KA2, SCN1A, SCN2A, SEZ6L2, SH3KBP1, SHANK2, 
SHANK3, SLC4A10, SLC9A9, ST7, SUCLG2, TMEM195, 
TSPAN7, UBE3A*, WNK3 
ADSL, AGTR2, AHI1*, ALDH5A1, ARX, CACNA1C, 
CACNA1F, CDKL5, DHCR7, DMD, DMPK, FMR1, MECP2, 
NF1, NTNG1, PTEN, SLC6A8, SLC9A6, TSC1, TSC2, XPC 
ABAT, ADA, ADORA2A, ADRB2, AR, ARNT2, ASMT, 
ATP10A, AVPR1A, C4B, CACNA1G, CCDC64, CDH10, 
CDH22, CDH9, CTNNA3, CYP11B1, DISC1, DLX1, DLX2, 
DRD3, EN2, ESR1, ESRRB, FBXO33, FEZF2, FOXP2, FRK, 
GABRA4, GABRB1, GABRB3, GLO1, GPX1, GRIK2, 
GRIN2A, GRM8, GSTM1,HLA-A, HLA-DRB1, HOXA1, 
HRAS, HS3ST5, HSD11B1, HTR1B, HTR3A, HTR3C, INPP1, 
ITGA4, ITGB3, LAMB1, LRFN5, LRRC1, LZTS2, MACROD2, 
MARK1, MET, MTF1, MYO16, NOS2A, NPAS2, NRCAM, 
NRP2, NTRK1, NTRK3, OXTR, PER1, PIK3CG, PITX1, 
PON1, PRKCB1, PTGS2, RELN, RHOXF1, SLC1A1, 
SLC25A12, SLC6A4, STK39, SYT17, TDO2, TPH2, UBE2H, 
VASH1, WNT2 
ALOX5AP, ASS, CACNA1D, CADPS2, CBS, CD44, CNR1, 
DAB1, DAPK1, DCUN1D1, DDX11, EGR2, F13A1, FLT1, 
ITGB7, MAOA, MAP2, OPRM1, RAI1, ROBO1, SDC2, 
SEMA5A, TSN 

Table 1. Genetic classification of all currently identified ASD candidate genes. (* = gene 
replicated by independent association studies.)  

 

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Autism Spectrum Disorders: The Role of Genetics in Diagnosis and Treatment 

Of  particular  interest  in  the  field  of  submicroscopic  structural  variants  are  deletions  and 
duplications  collectively  categorized  as  copy  number  variants.  A  copy  number  variant 
(CNV)  is  typically  defined  as  a  ≥1  kb  DNA  segment  that  is  present  at  a  differing  copy 
number compared to a reference genome (Feuk et al., 2006). CNVs can either arise de novo or 
be  inherited  on  the  maternal  and/or  paternal  chromosome.  Much  like  many  single 
nucleotide  polymorphisms,  apparently  benign  CNVs  exist  in  the  general  population  at 
relatively high frequencies; as such, CNVs that exist in the general population at a rate of 1% 
or higher are generally described as CNV polymorphisms (Feuk et al, 2006). Submicroscopic 
copy  number  variants  have  come  under  increased  scrutiny  in  recent  years  as  a  potential 
causative  agent  in  the  onset  and  progression  of  developmental  disorders,  including 
neuropsychiatric disorders such as ASD. 

3.1 Copy number variation in autism spectrum disorders 
As more syndromes were subsequently shown to be associated with both microscopic and 
submicroscopic  chromosomal  structural  variation,  it  became  apparent  that  a  subset  of 
patients diagnosed with some of these syndromes also developed ASD or displayed autistic 
traits. For example, DiGeorge Syndrome (also called Velocardiofacial Syndrome), which is 
frequently  characterized  by  congenital  heart  anomalies,  palatal  abnormalities,  immune 
system deficits and some degree of facial dysmorphism, has been found to result from a ~3 
Mb deletion in chromosome 22 (McDonald-McGinn et al., 2005). Individuals diagnosed with 
this syndrome, also referred to as 22q11.2 deletion syndrome, frequently experience learning 
disabilities; however, approximately 20% of patients with this syndrome also develop ASD. 
Given  that  a  subset  of  patients  with  syndromes  caused  by  chromosomal  structural 
abnormalities  also  display  autistic  traits,  as  well  as  the  high  prevalence  of  ASD  in 
individuals  with  cytogenetically  visible  duplications  of  the  Angelman/Prader-Willi 
syndrome region (15q11-q13) on the maternal chromosome (Cook Jr. et al., 1997; Schroer et 
al.,  1998),  a  number  of  studies  in  the  past  decade  have  focused  on  identifying 
submicroscopic  structural  variants,  in  particular  CNVs,  in  individuals  with  ASD  and 
subsequently determining the importance of these variants in disease pathogenesis. In order 
to  more  fully  ascertain  the  pathogenic  risk  associated  with  copy  number  variants,  only 
patients with idiopathic cases of ASD have typically been used; patients with mutations in 
genes  previously  implicated in  ASD,  such  as  the FMR1  gene,  or  with  gross  chromosomal 
abnormalities have frequently been excluded from these studies.  
The advent of genome-wide scanning technologies has enabled researchers to identify and 
subsequently  confirm  >1200  potentially  pathologically  relevant  CNVs  located  within  over 
490 distinct loci in autistic populations since 2007 (Sebat et al., 2007; Szatmari et al., 2007; 
Marshall et al., 2008; Cuscó et al., 2009; Glessner et al., 2009; Gregory et al., 2009; van der 
Zwaag et al., 2009; Pinto et al., 2010; Bremer et al., 2011). Confirmation  or validation of a 
CNV by an independent approach following its discovery is essential not only to remove 
false  positives,  but  also  to  more  accurately  identify  the  boundaries  of  a  CNV.  Validated 
CNVs in autistic individuals have been located in loci on all 22 somatic chromosomes and 
the X chromosome (Figure 2).  
While many of the CNVs identified by these methods are singletons and require additional 
replication to more accurately assess their potential role in disease, there are rare, recurring 
CNVs at particular loci that have been identified across multiple autistic populations that 
have emerged as strong risk-conferring candidates in ASD pathogenesis. Ten loci that have 
been identified multiple times in autistic case populations are described in Table 2. Perhaps 
the most intensely studied of these recurring CNVs, aside from duplications in the 15q11-13 

 

 
Genetic Heterogeneity of Autism Spectrum Disorders 

69 

loci,  are  ~500  kb  deletions  and  duplications  that  occur  at  the  16p11.2  locus.  A  recently 
published meta-analysis of the 16p11.2 locus in autistic populations discovered that CNVs at 
the 16p11.2 locus have a prevalence of 0.76%, with deletions occurring approximately twice 
as frequently with duplications (Walsh & Bracken, 2011). CNVs in autistic individuals have 
been identified in regions previously associated with other deletion-duplication syndromes, 
such as the 1q21.1, 22q11.21 and 22q13.33 loci (McDonald-McGinn et al., 2005; Phelan, 2007; 
Haldeman-Englert  &  Jewett,  2011).  Other  strong  candidate  CNV  loci  to  emerge  from 
genome-wide scanning assays include 2p16.3, 3p26.3, 6q26, 7q11.22, and 15q13.3. In some 
cases,  CNVs  at  these  “hot-spot”  loci  appear  to  target  genes  that  have  previously  been 
implicated  in  ASD  pathogenesis,  such  as  NRXN1  (2p16.3),  PARK2  (6q26),  and  AUTS2 
(7q11.22). 
 

 

 
Fig. 2. Validated copy number variants (CNVs) identified in genome-wide scanning arrays 
from nine published reports (Sebat et al., 2007; Szatmari et al., 2007; Marhall et al., 2008; 
Cuscó et al., 2009; Glessner et al., 2009; Gregory et al., 2009; van der Zwaag et al., 2009;  
Pinto et al., 2010; Bremer et al., 2011). The red lines to the left of each chromosome represent 
the >1200 validated CNVs identified in these studies. Especially long CNVs that overlap 
smaller CNVs are represented with thinner red lines. The green lines to the right of 
chromosomes 1, 2, 3, 6, 7, 15, 15, and 22 represent ten rare, recurring CNV loci identified in 
at least three of the nine aforementioned publications (described in more detail in Table 2). 
Increasingly,  targeted  assays  using  methods  such  as  quantitative  PCR  are  being  used  to 
characterize  CNVs  at  particular  loci  that  have  been  previously  identified  by  more  global 
scanning  approaches,  given  the  relatively  high  frequency  of  these  CNVs  in  autistic  case 
cohorts. CNVs are now considered one of the most common, genetic causes of ASD, with  
10-20% of ASD cases believed to be the result of submicroscopic deletions and duplications 
(Miles et al., 2010). 

 

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Autism Spectrum Disorders: The Role of Genetics in Diagnosis and Treatment 

CNV locus

Candidate gene(s)

CNV Type

References

1q21.1

2p16.3

3p26.3*

6q26

7q11.22

15q11-q13

15q13.3*

16p11.2

22q11.21

NRXN1

CNTN4

PARK2

AUTS2

UBE3A, GABRB3, 
GABRA5, GABRG3

CHRNA7

Deletion-Duplication

Deletion-Duplication

Deletion-Duplication

Deletion-Duplication

Duplication

Duplication

Deletion-Duplication

Deletion-Duplication

Deletion-Duplication

22q13.33

SHANK3

Deletion-Duplication

Szatmari et al., 2007; Gregory et al., 2009; Pinto et al., 2009
Szatmari et al., 2007; Glessner et al., 2009; Pinto et al., 2010; 

Bremer et al., 2011

Glessner et al., 2009; Pinto et al., 2010; Bremer et al., 2011
Szatmari et al., 2007; Glessner et al., 2009; Pinto et al., 2010; 

Bremer et al., 2011

Cuscó et al., 2009; Glessner et al., 2009, Pinto et al., 2010

Sebat et al., 2007; Marshall et al., 2008; Glessner et al., 2009; 

Pinto et al., 2010

Gregory et al., 2009; Pinto et al., 2010; Bremer et al., 2011
Sebat et al., 2007; Marshall et al., 2008; Glessner et al., 2009; 

Pinto et al., 2010; Bremer et al., 2011

Szatmari et al., 2007; Marshall et al., 2008; Glessner et al., 2009; 

Pinto et al., 2010; Bremer et al., 2011

Szatmari et al., 2007; Marshall et al., 2008; Glessner et al., 2009; 
van der Zwaag et al., 2009; Pinto et al., 2010; Bremer et al., 2011  

Table 2. Examples of loci in which rare, recurring, potentially risk-conferring CNVs are 
frequently observed in autistic populations. Potential candidate genes within the locus, the 
type of CNV that targets each locus, and the articles in which these CNV loci were identified 
are included. *, CNV locus which overlaps with adjacent loci in at least one of the 
publications listed. 
A  more  detailed  analysis  of  the  nine  published  research  articles  used  to  construct  the 
ideogram in Figure 2 reveals that, while the percentage of previously unidentified CNV loci 
has steadily declined since 2007, new CNV loci still constitute a very high percentage of the 
total CNV loci identified and validated in these studies (Table 3). Therefore, while recurring 
CNVs  such  as  16p11.2  and  others  continue  to  be  observed  across  multiple  autistic 
populations and CNV studies, novel CNVs in autistic populations are still being identified, 
indicating  that  there  are  likely  multiple  potential  targets  for  the  pathogenic  properties  of 
CNVs  throughout  the  human  genome.  It  is  likely  that  other  novel  CNVs  in  autistic 
individuals have not yet been identified, and as such their identification will shed new light 
on the pathways adversely affected in ASD. 
 

Year 

2007 

2008 

2009 

2010-11 

# of published reports 

# total CNV loci identified 

# previously unidentified CNV loci 

2 

98 

97 

1 

32 

27 

4 

56 

47 

2 

396 

320 

% of CNV loci previously unidentified 

98.98 

84.38 

83.93 

80.81 

Table 3. Analysis of the nine papers used to construct the ideogram in Figure 2. while the 
overall percentage of previously unidentified CNV loci has decreased from year to year, 
novel CNV loci still constitute the majority of the total CNV loci that have been identified 
and confirmed in these studies. 

 

 
Genetic Heterogeneity of Autism Spectrum Disorders 

71 

3.2 Risk-conferring vs. benign copy number variants 
Although advances in genome-wide and targeted scanning assays have enabled researchers 
to  discover  potentially  risk-conferring  CNVs  in  autistic  individuals,  significant  issues 
remain in the determination of which CNVs are pathologically relevant or benign in nature. 
This  is  of  particular  importance  in  terms  of  potentially  using  genetic  screening  for  risk-
conferring  CNVs  as  a  tool  to  assess  the  risk  of  ASD  in  unborn  children.  The  diagnostic 
accuracy  of  such  a  screening  protocol  would  be  entirely  dependent  on  knowing  which 
CNVs  would  confer  the  greatest  potential  risks  for  ASD  pathogenesis.  In  order  to 
distinguish  between  risk-conferring  and  benign  CNVs  in  an  autistic  population,  a 
comparison  must  be  made  between  both  the  existence  and  frequency  of  CNVs  between 
affected  and  unaffected  individuals.  To  account  for  possible  genetic  differences  between 
ethnic  groups,  it  is  critical  that  a  control  population  of  comparable  size  and  ethnic 
background be included in any CNV study. For example, CNVs at loci thought to confer a 
high risk of ASD susceptibility, such as deletions and duplications at the 16p11.2 locus, have 
also  been  identified  in  healthy  individuals,  although  at  a  much  lower  frequency  than  in 
autistic populations. Given the increased frequency of CNVs at the 16p11.2 loci in autistic 
populations versus control populations, CNVs at this region remain classified as high risk-
conferring CNVs. In addition, there are online databases such as the Database of Genomic 
Variants (http://projects.tcag.ca/variation/) and the Copy Number Variant resource at the 
Children’s  Hospital  of  Philadelphia  (http://cnv.chop.edu/)  available  that  describe 
previously identified CNVs in healthy individuals. These tools provide a means to further 
filter out likely benign CNVs from autistic case studies and enrich for potentially pathogenic 
variants.  However,  it  should  be  noted  that  seemingly  benign  CNVs  may  be  involved  in 
more subtle phenotypes in autistic individuals when occurring in combination with other 
factors.  Likewise,  additional  meta-analysis  studies  of  CNV  loci  across  multiple  published 
autistic  populations,  such  as  that  described  for  the  16p11.2  locus,  will  be  required  to 
compare  frequencies  of  CNV  a  in  order  to  more  fully  determine  the  global  risk  potential 
associated with any given CNV at a particular locus. 

3.3 De novo vs. inherited copy number variants 
As previously stated, CNVs can either arise de novo, or be inherited from the mother and/or 
father. Considerable interest has been placed in the pathogenic importance of de novo CNVs 
as a cause of ASD compared to inherited variants, especially within the context of sporadic 
vs.  familial  ASD  cases. Indeed, some  studies  have  found  that  the  rate  of de  novo  CNVs  is 
higher in sporadic cases compared to familial cases (Sebat et al., 2007; Marshall et al., 2008), 
while Bremer et al. (2011) found that the rate of rare inherited CNVs was higher in familial 
cases  compared  to  sporadic  cases.  These  findings  would  suggest  that  de  novo  CNVs  are 
predominantly  responsible  for  ASD  in  sporadic  cases,  whereas  inherited  CNVs  are 
primarily  responsible  for  familial  cases  of  ASD.  However,  Pinto  et  al.  (2010)  found  no 
significant difference between the frequencies of de novo CNVs in sporadic vs. familial cases. 
It has been reported that validated de novo CNVs strongly associate with ASD (Sebat et al., 
2007). However, there is no firm evidence that de novo CNVs confer a higher probability or 
severity  of  disease  than  inherited  variants.  On  the  other  hand,  the  dynamics  of  CNV 
inheritance and subsequent susceptibility to ASD has its own issues: an autistic individual 
with  a  potential  risk-conferring  CNV  may  inherit  that  CNV  from  a  parent  who  fails  to 
exhibit autistic traits; an autistic individual may have unaffected siblings who have likewise 
inherited the identical CNV; or one affected sibling in a multiple family may have a risk-
conferring CNV, whereas other affected siblings may not.  

 

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Autism Spectrum Disorders: The Role of Genetics in Diagnosis and Treatment 

3.4 Copy number variation and phenotypic heterogeneity 
Detailed studies attempting to correlate genotype with phenotype have demonstrated that 
there is significant phenotypic heterogeneity between individuals with CNVs at a particular 
chromosomal  locus,  both  in  terms  of  disease  presence  and  severity  of  disease.  Studies  in 
autistic populations containing CNVs at the 15q13.3 (Miller et al., 2009; Ben-Shachar et al., 
2009)  and  16p11.2  (Fernandez  et  al.,  2010)  loci,  for  example,  have  shown  that  autistic 
phenotypes,  such  as  the  extent  of  facial  dysmorphism  and  the  extent  of  intellectual 
disability, can vary from one patient to the next with the same CNV. One model that has 
been designed to address some of the issues as to how CNVs contribute to ASD states that 
certain CNVs at particular loci increase the susceptibility of an individual to developing an 
ASD based on a “threshold” of disease severity (Cook & Scherer, 2008). Chief among these 
high susceptibility CNVs are maternal duplications at 15q11-q13, deletions at 16p11.2, and 
deletions  at  the  loci  encoding  for  cell  adhesion  proteins  such  as  neuroligins.  Other  rare 
recurring CNVs that have been identified in autistic populations may confer a lower overall 
risk of ASD pathogenesis, or a decreased severity of disease, such as CNVs at 1q21.1, 2p16.3, 
and  22q11.21. However,  even  these  CNVs  can  result  in  the  onset  of  ASD,  or  more  severe 
disease phenotypes, when in combination with other genetic and non-genetic factors. These 
genetic factors may include additional CNVs (indeed, many autistic individuals have more 
than  one  CNV  within  their  genome)  or  single  gene  mutations,  such  as  those  described 
elsewhere in this chapter, whereas non-genetic factors can be environmental, sex-related, or 
epigenetic in nature. Epigenetic regulation of gene expression may be of particular importance 
with regards to phenotypic heterogeneity in autistic individuals with 15q11-q13 duplications, 
as  this  region  contains  a  number  of  potentially  critical  imprinted  genes.  Further  studies 
involving  more  detailed  analysis  of  genotype-phenotype  correlations  in  autistic  individuals 
with CNVs will be instrumental in determining the role of CNVs in ASD. 

3.5 Mechanism of action of copy number variants 
The  general  mechanism  by  which  a  CNV  might  contribute  to  ASD  pathogenesis  remains 
unclear.  The  simplest  mechanism  of  action  involves  gene  dosage,  by  which  deletion  or 
duplication of a gene or genes within a particular CNV locus, or the deletion or duplication 
of  gene  regulatory  elements,  subsequently  results  in  altered  or  disrupted  levels  of  gene 
product. A deletion at a particular locus might also result in the unmasking of a recessive 
gene  on  the  corresponding  chromosomal  locus,  which  would  then  be  able  to  elicit  a 
deleterious  effect.  Such  a  mechanism  might  be  involved  in  disease  pathogenesis  in  an 
autistic  individual  with  a  10  Mb  maternally  inherited  deletion  in  chromosome  13q  and  a 
point mutation in the DIAPH3 gene on the paternal chromosome (Vorstman et al., 2010). As 
the  proband’s  unaffected  sibling  also  had  the  DIAPH3  mutation,  but  lacked  the 
corresponding  deletion,  it  is  tempting  to  argue  that  the  maternal  deletion  unmasked  a 
recessive  mutation  in  the  paternal DIAPH3 gene,  and  that  in  turn  influenced  the  onset  of 
ASD in the proband. Given that many CNVs are large enough to include up to 50 or more 
genes,  identifying  which  genes  are  of  functional  relevance  in  ASD  pathogenesis  within  a 
particular  CNV  loci  remains  a  challenging  task.  Much  in  the  same  way  that  genes  that 
confer susceptibility to ASD have been found to fall within intriguing functional categories, 
bioinformatic analysis of genes that lie within or adjacent to recurring CNV loci may yield 
similar  results  and  aid  in  both  identifying  new  candidate  genes  and  in  discovering 
conserved  pathways  potentially  targeted  by  copy  number  variation.  Indeed,  analysis 
designed  to  identify  potentially  relevant  functional  pathways  containing  genes  located  in 
copy number variants have been performed (Pinto et al., 2010). 

 

 
Genetic Heterogeneity of Autism Spectrum Disorders 

73 

4. Comparative analysis of ASD genes  
To analyze recent evolution of the ASD molecular landscape, we profiled ASD genes identified 
as of the end of years 2006 and 2010. To define pools of ASD candidate genes existing at these 
time points, we used the ASD database AutDB (www.mindspec.org/autdb.html), a publicly 
available, curated, web-based, searchable genetic database for ASD created by our laboratory 
(see Section 5). We then examined the genetic and functional expansion of these gene sets. 

4.1 Genetic expansion  
To  quantify  the  total  number  of  ASD  candidate  genes  identified  as  of  2006  and  2010,  we 
sorted existing ASD candidate genes according to year of first publication. We discovered 
that  the  total  number  of  ASD  candidate  genes  more  than  doubled  in  the  past  four  years: 
whereas 91 genes were linked to ASD as of 2006, this number rapidly grew to 209 genes in 
2010.  
To  compare  genetic  distribution  within  these  datasets,  we  defined  ASD  candidate  genes 
according to the classification system described in Section 2: rare variants (Rare), syndromic 
genes  (Syndromic),  genes  identified  by  association  studies  (Association),  and  genes  whose 
functions have been implicated in ASD (Functional). We found that expansion of the total 
ASD gene pool was largely due to steep growth of both Rare and Association gene sets, with 
a  slight  increase  in  the  numbers  of  identified  Syndromic  and  Functional  genes  (Figure  3). 
Notably,  the  near  quadrupling  of  the  number  of  rare  mutations  supports  the  Rare  Allele, 
Common Disease as a plausible theory of ASD pathogenesis (see Section 6).  
 

Fig. 3. Genetic distribution of ASD candidate genes identified as of the end of years 2006 and 
2010. 

4.2 Functional expansion 
Recent large-scale ASD studies have used a systems biology approach to translate genetic 
information  into  functional  maps.  For  instance,  Glessner  et  al.  (2009)  showed  that  ASD-
linked  genes  cluster  in  synaptic  processes  such  as  cell  adhesion  and  ubiquitin-mediated 

 

 

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Autism Spectrum Disorders: The Role of Genetics in Diagnosis and Treatment 

degradation. Additionally, in the largest ASD study performed to date, Pinto et al. (2010) 
found  that  genes  affected  by  rare  CNVs  were  enriched  in  functions  such  as  neuronal 
development and GTPase/Ras signaling.  
To  build  upon  these  functional  maps,  we  used  a  well-known  synaptic  proteome 
classification system (Husi et al., 2000), to organize ASD gene sets from 2006 and 2010 into 
eight broad categories of molecular function, defined by corresponding subcategories:  
1.  Cell  Adhesion  (cell  adhesion  molecule,  cell  adhesion/axon  guidance,  extracellular 

matrix, extracellular secreted protein) 

2.  Guidance/Outgrowth 

(axon  guidance,  cell  migration,  cell  surface  glycoprotein, 

cytoskeletal remodeling, dendritic spine morphology, animal model evidence) 

3.  Neurotransmission  (adaptor  protein,  G-protein  coupled  receptors,  ligand-gated  ion 
channel,  neuromodulator  receptor,  neuromodulator  receptor-associated  protein, 
neuromodulator  synthesis,  neurotransmitter  receptor,  neurotransmitter  synthesis, 
presynaptic release, scaffolding protein, sensory receptor, transporter, voltage-gated ion 
channel, voltage-gated ion channel modulator) 

4.  Signaling (glycosylation, kinase, kinase substrate, phosphatase, proteoglycan, small G-

protein or modulator, tyrosine receptor kinase, other signal) 

5.  Degradation (proteasome-related protein, ubiquitin ligase) 
6.  Transcription (circadian protein, cofactor, DNA binding, DNA damage response protein, 
DNA  methylation,  estrogen  receptor,  histone  demethylation  protein,  homeodomain 
protein, preinitiation complex, purine metabolism, transcription factor) 

7.  Translation (ribosomal protein, RNA binding, RNA metabolism, RNA structure) 
8.  Other (antioxidant, endosome regulation, energy production, fatty acid binding protein, 
immune system, membrane biosynthesis, mitochondrial carrier protein, mitochondrial 
targeting protein, oxidation, prostaglandin, unknown function).  

The functional distribution of ASD risk genes vastly expanded from 2006 to 2010 (Figure 4). 
Because  Rare  and  Syndromic  genes  contain  the  strongest  links  to  ASD  (see  Section  2),  we 
examined  this  combined  “Rare/Syndromic”  set  as  one  dataset.  We  comparatively  assessed 
them with Association genes as a separate gene set. Both Rare/Syndromic and Association gene 
datasets followed the same trend: whereas Neurotransmission and Signaling were by far the 
largest functional categories in 2006, the number of genes in all other functional categories 
increased over the past four years such that all are becoming relatively equalized. The most 
dramatic increases occurred in Cell Adhesion, Degradation, Transcription, and Other.  
This  functional  expansion  has  led  to  shifting  theories  of  ASD  pathogenesis.  In  2006,  the 
largest percentage of ASD susceptibility genes resided in the Neurotransmission or Signaling 
categories, supporting specific theories of dysfunction, such as serotonin transport (Cook & 
Leventhal, 1996). However, rapid expansion of nearly all functional categories throughout 
2010  indicates  that  ASD  susceptibility  genes  are  actually  widespread  in  neurobiological 
function.  Such  functional  expansion  supports  broad  theories  of  pathogenesis  such  as  the 
proposed enhancement of brain excitability in ASD (Rubenstein & Merzenich, 2003). Each 
designated  functional  category  includes  neurobiological  factors  that  contribute  to  brain 
excitability,  reinforcing  the  idea  that  mutations  in  vastly  different  genes  may  facilitate 
similar outcomes in brain function by contributing to shared molecular pathways. Together, 
accelerated  identification  of  ASD  risk  genes  with  widespread  neurobiological  functions  is 
leading to a convergent model of ASD pathogenesis.  

 

 
Genetic Heterogeneity of Autism Spectrum Disorders 

75 

  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Fig. 4. Functional profile of ASD candidate genes identified as of the end of years 2006 and 
2010.  

5. Bioinformatics of ASD 
The  enormous  amount  of  data  currently  being  generated  by  large-scale  genomic  studies 
poses  a  critical  challenge  for  its  storage  and  analysis.  To  process  this  information, 
bioinformatics  tools  are  becoming  increasingly  vital  to  the  scientific  community.  Here  we 
highlight several ASD-related databases which researchers can use to navigate this data and 
shed insight into the molecular pathways underlying ASD pathogenesis.  

5.1 AutDB 
Our laboratory created the ASD database AutDB (www.mindspec.org/autdb.html), the first 
publicly  available,  curated,  web-based,  searchable  genetic  database  for  ASD  (Basu  et  al., 
2009;  Kumar  et  al.,  2011).  In  AutDB,  evidence  regarding  ASD  candidate  genes  is 
systematically  extracted  from  peer-reviewed,  primary  scientific  literature  and  manually 
curated by our researchers. To provide high-resolution view of various components linked 
to ASD, we developed detailed annotation rules based on the biology of each data type and 
generated  controlled  vocabulary  for  data  representation.  AutDB  is  widely  used  by 
individual  laboratories  (Crespi  et  al.,  2010;  Elia  et  al.,  2010;  Gillis  et  al.,  2010;  Toro  et  al., 
2010) and consortiums (Simons Foundation) for understanding genetic bases of ASD. 
With  a  systems  biology  approach,  AutDB  integrates  various  modules  encompassing 
different types of data relevant for ASD:  
Human  Gene:  This  original  module  of  AutDB  includes  all  genes  whose  mutations  have 
been  associated  or  implicated  with  ASD,  together  with  all  risk-conferring  candidates 

 

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Autism Spectrum Disorders: The Role of Genetics in Diagnosis and Treatment 

associated with these disorders (Basu et al., 2009). ASD-related genes are classified into the 
four categories described in Section 2: 1) Rare: genes implicated in rare monogenic forms of 
ASD;  2)  Syndromic:  genes  implicated  in  syndromic  forms  of  ASD  where  a  subpopulation 
with  a  specific  genetic  syndrome  develops  autistic  symptoms;  3)  Association:  small  risk-
conferring  candidate  genes  with  common  polymorphisms 
identified  from  genetic 
association  studies  in  idiopathic  ASD;  and,  4)  Functional:  candidates  genes  with  functions 
relevant for ASD biology, not covered by any of the previous genetic categories. All known 
ASD-specific mutations at the DNA sequence level will be available by late 2011.  
Animal  Model:  This  module  provides  a  comprehensive  collection  of  all  mouse  models 
linked  to  ASD  (Kumar  et  al.,  2011).  The  core  behavioral  features  of  ASD  involve  higher 
order human brain functions like social interactions and communications, which can only be 
approximated  in  animal  models,  so  the  annotation  strategy  for  this  module  includes  four 
broad areas: 1) core behavioral features of ASD, 2) ASD-related traits such as seizures and 
circadian  rhythms  that  are  heritable  and  more  easily  quantified  in  animal  models;  3) 
neuroanatomical features, and 4) molecular profiles. To this end, we developed PhenoBase, 
a  classification  table  for  systematically  annotating  models  with  controlled  vocabulary. 
PhenoBase contains 16 major categories and >100 standardized phenotype terms.  
Protein Interaction (PI): This module serves as a repository for all known protein-protein 
interactions of ASD candidate genes. It documents five major types of direct interactions: 1) 
protein  binding,  2)  promoter  binding,  3),  RNA  binding,  4)  protein  modification,  and  5) 
direct regulation. One of the newest additions to AutDB, a beta version of this module was 
released in April 2011, with a full version scheduled for release in late 2011. Its content is 
envisioned  to  have  immediate  application  for  network  biology  analysis  of  molecular 
pathways involved in ASD pathogenesis. 
Copy Number Variant (CNV): This module is a comprehensive, up-to-date reference for all 
known copy number variants (CNVs) implicated in ASD (see Section 3). It originates from a 
multi-level  annotation  model  including  data  such  as  chromosomal  location,  size,  and 
relevance to ASD. Like the PPI module, a beta version of the CNV module was released in 
May 2011, with a full version scheduled for release in late 2011.  

5.2 ASD Chromosome Rearrangement Database  
The ASD Chromosome Rearrangement Database (http://projects.tcag.ca/ASD/) is a web-
based,  searchable  genetic  database  hosted  by  The  Centre  for  Applied  Genomics  at  the 
Hospital for Sick Children in Toronto, Canada (Marshall et al., 2008). The ASD Chromosome 
Rearrangement Database provides information not only on submicroscopic CNVs, that have 
been  identified  by  microarray  studies,  but  also  data  on  microscopic  structural  variants 
identified  by  cytogenetic  studies.  This  database  is  updated  both  from  published  research 
articles and in-house experimental results. 

5.3 ASD Genetic Database  
The  ASD  Genetic  Database  (http://wren.bcf.ku.edu/)  is  another  web-based,  searchable 
genetic  database  developed  by  researchers  at  the  University  of  Kansas  (Matuszek  & 
Talabizadeh, 2009). Much like AutDB and the ASD Chromosome Rearrangement Database, 
the  ASD  Genetic  Database  provides  information  on  genes  and  CNVs  believed  to  impart 
susceptibility  to  ASD.  However,  this  database  also  includes  information  on  known  non-

 

 
Genetic Heterogeneity of Autism Spectrum Disorders 

77 

coding  RNAs  and  chemically-induced  fragile  sites  in  the  human  genome.  Non-coding 
RNAs,  such  as  microRNAs,  have  come  under  increased  scrutiny  with  regards  to  their 
potential pathogenic role in ASD. For example, the 15q11-q13 region contains a number of 
small nucleolar RNAs (snoRNAs). Duplication of a region of mouse chromosome 7 that has 
conserved linkage with human chromosome 15q11-q13 in mouse model of ASD resulted in 
overexpression of the snoRNA MBII52 (the mouse ortholog of the human snoRNA HBII52), 
which  could  potentially  alter  serotonergic  signaling  and  contribute  in  part  to  the  autistic 
traits  exhibited  by  these  mice  (Nakatani  et  al.,  2009).  Spontaneous  breakage  during  DNA 
replication  at  rare  chromosomal  fragile  sites  may  also  play  a  role  in  the  pathogenesis  of 
neuropsychiatric  disorders  such  as  ASD.  The  chromosomal  fragile  site  FRAXA  has  been 
implicated in fragile X syndrome, and other fragile sites have been identified that associate 
with ASD, such as FRA2B, FRA6A, and FRA13A (Smith et al., 2010). 

6. Discussion 
6.1 Rare vs. common alleles 
At the beginning of this decade, few single mutations for ASD had been identified. As of 
2003, single mutations in only two genes were known: neuroligins 3 and 4, published in a 
single report (Jamain et al., 2003).  This led to predominance of the Common Allele Common 
Disease theory, which proposes that ASD is caused by combined effects of multiple common 
polymorphisms.  
However,  evidence  from  two  recent  major  studies  led  to  the  emergence  of  an  alternative 
Rare  Allele  Common  Disease  theory  for  ASD  pathogenesis.  First,  comparative  genomic 
hybridization  with  subsequent  confirmation  showed  a  strong  association  between  de  novo 
CNVs  mutations  and  ASD  (Sebat  et  al.,  2007).  Second,  homozygosity  mapping  identified 
numerous single gene mutations in families with ASD (Morrow et al., 2008).  
According  to  the  Rare  Allele  Common  Disease  theory,  the  genetics  underlying  complex 
neuropsychiatric  disorders  such  as  ASD  is  highly  heterogeneous.  It  proposes  that  ASD  is 
caused by numerous rare, highly penetrant mutations that may even by caused by “private 
mutations” specific to individual families; a similar theory has been proposed to explain the 
genetic  complexity  of  schizophrenia  (McClellan  et  al.,  2007).  The  identification  of  rare 
variants has more than quadrupled in the past four years (see Section 4), lending credibility 
to this theory.  
At present, it appears that the Rare Allele Common Disease  theory is a highly relevant genetic 
paradigm  for  ASD  and  other  complex  disorders.  A  few  recent  papers  have  identified 
common variants associated with ASD (Campbell et al., 2006; Wang et al., 2009; Weiss et al., 
2009; Anney et al., 2010), but these mutations are still far outnumbered by known rare single 
gene mutations. With increased availability of various types of sequencing technologies, it is 
projected that additional rare mutations/variations will be discovered or validated rapidly 
in upcoming years, making clinical genomics of ASD an option for affected families. 

6.2 Prioritization of genetic ASD risk factors 
In future, ASD risk genes should be prioritized based on careful definitions at both genetic 
and  functional  levels.  High  priority  genes  should  show  evidence  for  replication  or 
participate  in  a  molecular  pathway  exhibiting  multiple  ASD-linked  mutations.  Examples 
include  the  cell  adhesion  molecule  CNTNAP2,  a  neurexin  family  member  in  which  both 

 

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Autism Spectrum Disorders: The Role of Genetics in Diagnosis and Treatment 

common and rare variants have been associated with ASD (Arking et al., 2008). CNTNAP2 is 
also  regulated  by  FOXP2,  a  candidate  ASD  gene  highly  relevant  for  human  language 
development (Gong et al., 2004). Additionally, the role of  synaptic scaffolding proteins in 
ASD  has  been  strengthened  by  recent  identification  of  recurrent  mutations  in  SHANK2 
(Berkel  et  al.,  2010;  Pinto  et  al.,  2010).  Furthermore,  if  multiple  genes  contribute  to 
syndromic ASD, each gene should only be considered high priority when accompanied by a 
documented direct genetic link to ASD.  
CNVs should likewise be prioritized based on a number of factors. A high risk-conferring 
structural variant should not only display a high prevalence in autistic populations, but also 
an  enrichment  in  autistic  populations  compared  to  control  populations.  Meta-analysis 
studies,  such  as  that  previously  described  for  the  16p11.2  locus  in  multiple  autistic  case 
studies  (Walsh  &  Bracken,  2011),  will  aid  greatly  in  determining  which  CNVs  meet  these 
criteria. Furthermore, a high priority CNV locus should either contain a gene or genes, or 
the regulatory elements for a gene or genes, which demonstrate potential participation in a 
molecular  pathway  exhibiting  multiple  ASD-linked  single  gene  mutations.  CNV  loci 
containing genes that have already been associated with increased risk of autism, such as 
2p16.3 (NRXN1), are of particular interest in this regard. 

6.3 Synaptic theory of ASD 
A  hypothesis  for  ASD  as  a  synaptic  disorder  is  well  recognized,  largely  based  on  strong 
evidence  from  rare  mutations  in  neuroligins,  neurexins  and  SHANK3  (Bourgeron,  2009). 
Rapid expansion of the ASD risk gene pool has supported this synaptic theory of ASD by 
identifying  rare  mutations  in  numerous  additional  synapse-related  genes,  including 
SHANK2 (Berkel et al., 2010; Pinto et al., 2010) and PTCHD1 (Marshall et al., 2008; Noor et 
al., 2010; Pinto et al., 2010). Additionally, functional maps generated from large-scale studies 
of ASD have enriched this synaptic hypothesis of ASD, identifying categories ranging from 
cell  adhesion  and  ubiquitin-mediated  degradation  (Glessner  et  al.,  2009)  to  neuronal 
development and GTPase/Ras signaling (Pinto et al., 2010). 
Our  functional  profile  of  all  ASD  candidate  genes  identified  as  of  2010  supports  this 
synaptic hypothesis (see Section 4). The majority of ASD-linked genes function in synaptic 
processes  such  as  cell  adhesion,  guidance/outgrowth,  neurotransmission,  signaling, 
degradation, transcription, and translation. A smaller fraction of ASD risk genes possessed 
unknown  functions  or  “Other”  non-synaptic  functions.  Examples  of  synaptically  enriched 
ASD gene functions are modeled in Figure 5. 

7. Conclusion 
In  conclusion,  the  broadened  molecular  landscape  for  ASD  suggests  that  an  integrated 
approach  is  required  to  understand  functional  pathways  underlying  ASD.  An  unbiased 
view of ASD risk gene datasets emphasizes the importance of overall synaptic networks for 
human  cognition.  Higher  order  functions  require  efficient  information  processing,  and 
mutations  in  any  synaptic  component  could  lead  to  the  range  of  impairments  present  in 
ASD. Future spatiotemporal mapping of ASD gene expression patterns may provide clues 
to  how  shared  susceptibility  genes  give  rise  to  different  forms  of  ASD.  Moreover, 
identification of new ASD-associated genes using advanced techniques like deep sequencing 
will increasingly sharpen our functional understanding of ASD synapse biology.  

 

 
Genetic Heterogeneity of Autism Spectrum Disorders 

79 

NLGN 1, 
3, 4X 
NRXN1 

CACNA1C 
CACNA1G 
GABRB3 

SHANK2, 
3
UBE3A 

FMR1 

Fig. 5. Synaptic enrichment of ASD candidate genes. ASD risk genes are concentrated in 
numerous synaptic functions, including cell adhesion (examples: NLGN1, NLGN3,  
NLGN4X, NRXN1), synaptic scaffolds (examples: SHANK2-3), degradation (example: 
UBE3A), translation (example: FMR1), and neurotransmission (examples: CACNA1C, 
CACNA1G, GABRB3).  

 

8. Acknowledgements 
The authors would like to thank the other members of Mindspec, Inc. (Ajay Kumar, Cynthia 
Soderblom,  Nicole  Johnson,  Rachna  Wadhawan,  Rainier  Rodriguez,  and  Sue  Spence),  as 
well as the Simons Foundation. AutDB is licensed to the Simons Foundation as SFARI Gene. 

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Autism Spectrum Disorders: The Role of Genetics in Diagnosis and Treatment 

Smith,  C.L.;  Bolton,  A.  &  Nguyen,  G.  (2010).  Genomic  and  epigenomic  instability,  fragile 
sites, schizophrenia and autism. Current Genomics, Vol. 11, No. 6 (September 2010), 
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Szatmari, P., et al. (2007). Mapping autism risk loci using genetic linkage and chromosomal 
rearrangements.  Nature  Genetics,  Vol.  39,  No.  3  (March  2007),  pp.  319-328,  ISSN 
1061-4036. 

van der Zwaag, B., et al. (2009). Gene-network analysis identifies susceptibility genes related 
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Vorstman,  J.A.S.,  et  al.  (2010).  A  double  hit  implicates  DIAPH3  as  an  autism  risk  gene. 

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Walsh, K.M. & Bracken, M.B. (2011). Copy number variation in the dosage-sensitive 16p11.2 
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6 

The Genetic Basis of Phenotypic Diversity: 
Autism as an Extreme Tail of 
a Complex Dimensional Trait 
Shinji Ijichi1,2, Naomi Ijichi2, Yukina Ijichi2, 
Hisami Sameshima1 and Hirofumi Morioka1 
1Health Service Center, Kagoshima University, Kagoshima  
2Institute for EGT (Externalization of Gifts and Talents), Kagoshima 
Japan 

1. Introduction 
Autism  is  a  developmental  lifelong  condition  of  the  human  brain,  and  a  behavioral 
characterization as a spectrum (autism spectrum disorder: ASD) is the best way to illustrate 
this  complex  trait  (Frith,  2001;  Rapin,  1997;  Wing,  1997).  The  predominant  presence  of 
autistic cases without comorbidity (idiopathic or primary ASD) (Freitag, 2007) clearly means 
that  the  biological  effects  associated  with  the  known  concomitant  medical  conditions 
(cytogenic  abnormalities,  fragile  X  syndrome,  tuberous  sclerosis,  congenital  infections, 
maternal  thalidomide  use,  epilepsy,  etc.)  cannot  be  the  common  prerequisite  for  ASD  at 
least in the majority of the cases. The presence of a strong genetic contribution is evident 
from the results of twin studies, which demonstrated that 70-90% of monozygotic twins are 
concordant for ASD, and the concordance in dizygotic twins and the recurrence rate in the 
proband’s  siblings  are  both  less  than  10%  (Rapin  &  Katzman,  1998).  A  broadening  of  the 
criteria of diagnosis leads the monozygotic concordance ratio to more than 90%, but 100% 
concordance is never obtained (Rapin & Katzman, 1998). Therefore, it is claimed that genetic 
factors contribute about 90% to ASD with environmental factors contributing no more than 
10%  (Garber,  2007).  Although  a  flood  of  genetic  information  in  the  field  of  ASD  is 
continuously  growing,  even  the  newest  genome-wide  molecular  studies cannot  detect  the 
universal genetic prerequisite for idiopathic cases with ASD, compelling some researchers to 
speculate that ASD has a huge inter-case heterogeneity of the related gene variants.  
Many gene variants, which seem to affect brain development and synaptic functions, have 
been  reported  in  association  with  the  autistic  development  (Betancur,  2011;  Garber,  2007; 
Persico & Bourgeron, 2006; Pinto et al., 2010). In families with the candidates for autism gene 
variants,  however,  the  strict  co-segregation,  in  which  the  gene  variant  is  found  only  in 
individuals with ASD among family members including parents, is still exceptional (Table 
1).  To  explain  this  fact,  the  broader  distribution  of  the  more  primary  phenotype  or  pre-
behavioral phenotype (endophenotype) beyond the categorical border is introduced as the 
speculative solution through this research maze (Viding & Blakemore, 2007). It may be quite 
difficult  to  detect  and  evaluate  such  endophenotypes  because  of  the  configurational  or 
hierarchical structures of human cognitions and behaviors. Even if such speculations were 

84

 
Autism Spectrum Disorders: The Role of Genetics in Diagnosis and Treatment 

all true, it is too early to conclude that “a single gene variant causes a small percentage of 
cases with this complex trait” (Garber, 2007; Beaudet, 2007). As clearly demonstrated in the 
case of human disease-associated mutations found as wild-type alleles in normal chimpanzee 
(L.  Azevedo  et  al.,  2006),  a  deleted  or  mutated  allele  does  not  necessarily  contribute  to  the 
disease  development.  Because  evidence  consistent  with  a  theory  is  not  proof  of  that  theory 
(Cannell,  2010),  until  one  could  delineate  the  molecular  or  biological  trajectory  underlying 
autistic development which is quantitatively different from the  parents, there is still a huge 
black box  between  the de  novo variant allele  and complex  human behaviors in  the sporadic 
cases with idiopathic ASD. The reported gene variants are, at present, nothing but one of the 
concomitants  in  a  small  percentage  of  cases  (5-7%,  in  Table  1).  The  possibility  that  the 
variantsare mere relative risk factors remains to be elucidated (Jones & Szatmari, 2002). As a 
general  rule,  a  genetic  link  does  not  necessarily  imply  neurological  damage  (Simpson, 
  

Prevalence 

References 

Variants 

 

SHANK3 variants 
 
 
SHANK3 variants 
SHANK3 variants 
 
SHANK3 deletion 
 
SHANK3 deletion 
 
SHANK2 de novo deletion  In ASD individuals 
 
NLGN3 variants 
NLGN3 duplication 
 
NLGN4 variants 
 
NRXN1 deletion
NRXN1α variants
 
NRXN1 deletion
 
NRXN1 de novo variants
 
NRXN1β variants
 
NRXN2β variants
NRXN3β variants
CNTN4 deletion
 
CNTN4 duplication
 

In ASD families 
15 / 227 (6.6%)a 
Co-segregated cases  3 / 227 (1.3%)b 
In controls 
5 / 270 (1.9%)a 
3 / 400 (0.8%)c 
In ASD families 
34 / 427 (8.0%) 
In ASD individuals 
16 / 190 (8.4%) 
In controls 
1 / 427 (0.2%)c 
In ASD individuals 
In controls 
0 / 190 
2 / 2,195  (0.1%) 
In ASD individuals 
2 / 2,519 (0.1%) 
In controls 
2 / 996 (0.2%) 
0 / 1,287, 0 / 3,677
0 / 96 
1 / 2,195 (0.05%) 
0 / 2,519 
4 / 148 (2.7%)d 
0 / 336 
1 / 1,181 (0.1%) 
5 / 116 (4.3%)
1 / 192 (0.5%)
10 / 2,195 (0.5%)
0 / 2,519
4 / 996 (0.4%)
5 / 1,287 (0.4%)
4 / 203 (2.0%)d,e 
0 / 535
0 / 72
0 / 72
10 / 2,195 (0.5%)
0 / 2,519
9 / 2,195 (0.4%)c 
1 / 2,519 (0.04%) 

In controls 
In ASD individuals 
In ASD individuals 
In controls 
In ASD individuals 
In controls 
In ASD families 
In ASD individuals 
In controls
In ASD individuals
In controls
In ASD families
In controls
In ASD individuals
In controls
In ASD individuals
In ASD individuals
In ASD individuals
In controls
In ASD individuals
In controls 

(Durand et al., 2007) 

(Moessner et al., 2007) 
(Gauthier et al., 2009) 

(Gauthier et al., 2009) 

(Glessner et al., 2009) 

(Pinto et al., 2010) 

(Yan et al., 2005) 
(Glessner et al., 2009) 

(Yan et al., 2005) 

(AGPC, 2007) 
(Yan et al., 2008) 

(Glessner et al., 2009) 

(Pinto et al., 2010) 

(Feng et al., 2006) 

(Feng et al., 2006) 
(Feng et al., 2006) 
(Glessner et al., 2009) 

(Glessner et al., 2009) 

 

The Genetic Basis of Phenotypic Diversity: 
Autism as an ExtremeTail of a Complex Dimensional Trait 

85 

Variants 

 

Prevalence 

References 

(Glessner et al., 2009) 

(Pinto et al., 2010) 

(Glessner et al., 2009) 

In ASD individuals 
In controls 

(Glessner et al., 2009) 

(Glessner et al., 2009) 

(Depienne et al., 2009) 
(Weiss et al., 2008) 

(AGPC, 2007) 
(AGPC, 2007) 
(AGPC, 2007) 
(Glessner et al., 2009) 

1 / 2,195 (0.05%) 
0 / 2,519 
7 / 996 (0.7%) 
In controls 
0 / 1,287, 0 / 3,677
In ASD individuals 
15 / 2,195 (0.7%)f 
In controls 
0 / 2,519 
In ASD individuals 
4 / 522 (0.8%)g 
In ASD families 
12 / 751 (1.6%) 
In controls 
5 / 4,234 (0.1%) 
In ASD individuals 
3 / 299 (1.0%) 
In controls 
7 / 18,834 (0.04%) 
In ASD individuals 
9 / 2,195 (0.4%)c 
In controls 
4 / 2,519 (0.2%) 
In ASD individuals 
8 / 2,195 (0.4%)h 
In controls 
4 / 2,519 (0.2%) 
In ASD families 
3 / 1,181 (0.3%)d 
In ASD families 
3 / 1,181 (0.3%)i 
In ASD families 
2 / 1,181 (0.2%)d 
In ASD individuals 
9 / 2,195 (0.4%) 
In controls 
0 / 2,519 
In ASD families 
10 / 1,181 (0.8%) 
Co-segregated cases  3 / 1,181 (0.3%)j 
14 / 195 (7.2%)k 
In ASD individuals 
In sporadic cases 
12 / 118 (10.2%) 
In multiplex families 2 / 77 (2.6%) k 
2 / 196 (1.0%) 
In controls 
27 / 427 (6.3%) 
In ASD families 
In sporadic cases 
4 / 56 (7.1%) 
In multiplex families 1 / 49 (2.0%) 
50 / 876 (5.7%)l 
In ASD families 
In simplex families 
22 / 393 (5.6%) 
In multiplex families 19 / 348 (5.5%) 

AUTS2 
 
DDX53/PTCHD1 deletion In ASD cases 
(maternally inherited) 
CNVs at 15q11-13 
(UBE3A) 
CNVs at 15q11-13 
CNVs at 16p11.2 
 
 
 
16p11.2  duplication 
 
16p11.2 deletion 
 
CNV gain at 1q21 
CNV at 17p12 
CNV gain at 22q11.2 
22q11.2 duplication 
 
De novo CNVs 
 
De novo CNVs 
 
 
 
De novo CNVs 
 
 
De novo CNVs 
 
 
ASDs:  autism  spectrum  disorders;  NLGN:  neuroligin  gene;  NRXN:  neurexin  gene;  CNTN:  contactin 
gene;  AUTS:  autism  susceptibility  candidate  gene;  CNV:  copy  number  variation;  AGPC:  the  Autism 
Genome  Project  Consortium.  aTwo  nonsynonymous  SHANK3  mutations  were  revealed  in  4  ASD 
families and 2 control individuals. bIn the SHANK3 study, de novo truncating mutations in two families 
and a chromosomal rearrangement in one family were demonstrated as the strict co-segregated cases 
whose  gene  variants  were  found  only  in  individuals  with  ASD  among  family  members  including 
parents. cOne de novo case is included. dStrict co-segregation was not shown. eTwo cases with mild facial 
dysmorphism are included. fTwo de novo cases are included. gThree de novo cases are included. hFive de 
novo cases are included.  iOne case is included as a co-segregated family.  jIn ASD families with two or 
more affected individuals (multiplex families), three de novo CNVs were found in both ASD sibs. kTwo 
multiplex families whose variant-phenotype co-segregation is not mentioned are included. l>0.6% cases 
are carrying two or more de novo events. 
Table 1. The prevalence of variants in gene regions recently implicated in idiopathic ASD 

(AGPC, 2007) 

(Sebat et al., 2007) 

(Marchall et al., 2008) 

(Pinto et al., 2010) 

 

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Autism Spectrum Disorders: The Role of Genetics in Diagnosis and Treatment 

2003), and high heritability does not vindicate the condition as a diagnostic category (Keller 
&  Miller,  2006).  There  is  as  yet  no  qualitative  biological  marker  including  microscopic 
lesions  that  can  reliably  help  to  categorize  a  genetically  homogeneous  autism  subtype 
(Amaral  et  al.,  2008;  Moldin  et  al.,  2006;  Santangelo  &  Tsatsanis,  2005;  Schmitz  &  Rezaie, 
2008). In this article, the significance of gene variants which have currently been detected in 
autistic individuals is carefully reconsidered and the outstanding questions are addressed 
from multidisciplinary points of view. Such an attempt may highlight the importance of the 
notion that the evolutionally survived trait is the phenotypic diversity itself, in which ASD 
is  included  as  an  extreme  tail.  In  addition,  important  concepts  and  mechanisms  for  the 
genetic basis of phenotypic diversity are also reviewed. 

2. Facts and questions 
Although  some  authorities  appreciated  the  smooth  behavioral  continuum  between 
individuals with ASD and the non-autistic majority (Frith, 2001; Happé, 1999; Rapin, 1997; 
Wing,  1997),  idiopathic  ASD  has  sometimes  been  misinterpreted  as  a  qualitative  disorder 
which  can  be  clearly  distinguished  from  normal  development.  The  boundary  between 
individuals with low-functioning ASD and a communicative subtype (Asperger syndrome) 
has  also  been  misrepresented  as  to  be  qualitatively  distinct  (Simpson,  2003).  Even  the 
differentiation between Asperger syndrome and high-functioning ASD could be made with 
authority  (Kamp-Becker  et  al.,  2010).  These  biased  constructions  may  be  attributable  to 
referral  bias  in  general  practice  or  increased  probability  of  clinical  ascertainment  in 
individuals with low achievement (Skuse, 2007). Although ASD can still be documented as a 
categorical  entity  in  clinically  ascertained  samples  (Frazier  et  al.,  2010),  the  fact  that  the 
autistic  phenotype  extends  beyond  its  formal  diagnostic  boundaries  has  underscored  the 
significance of quantitative evaluations (Lamb et al., 2000; Maestrini et al., 2000), and many 
population  studies  revealed  that  ASD  including  high-functioning  subtypes  are  best 
characterized  as  an  extreme  of  some  bell-shaped  behavioral  dimensions  that  distribute 
quantitatively  (Constantino  &  Todd,  2000;  Constantino  &  Todd,  2003;  Happé  et  al.,  2006; 
Hoekstra et al., 2007; Posserud et al., 2006; Ronald et al., 2005; Ronald et al., 2006a, 2006b; 
Skuse et al., 2005). The description ‘qualitative’ in the autism criteria in the Diagnostic and 
Statistical  Manual  of  Mental  Disorders  (DSM)  is  removed  and  Asperger’s  disorder 
(Asperger syndrome) is subsumed into ASD in the draft of DSM-5 (http://www.dsm5.org 
/Pages/Default.aspx).  The 
sociability, 
communication,  and  rigid/repetitive  behavior  correlate  modestly  to  each  other  in  the 
population (Dworzynski et al., 2007; Ronald et al., 2005, 2006a, 2006b), and the coincidence 
of  these  phenotypic  extremes  is  also  observed  in  hyperactive  individuals  with  attention-
deficit/hyperactivity disorder (AD/HD) (Hattori et al., 2006; Ijichi & Ijichi, 2007; Reiersen et 
al., 2007; Ronald et al., 2008). The diagnosis of autism is highly affected by the circumstantial 
consequence of social adaptability and autistic recognition and behavior sometimes does not 
become fully manifest until social demands exceed the individual’s limited capacities (the 
draft  of  DSM-5).  The  clinical  picture  can  change  with  increasing  age  and  in  different 
circumstances (Wing, 1997), and the behavioral plasticity or clinical improvement is evident 
in  supportive  circumstances  by  structured  behavioral  interventions,  mentoring,  and/or 
social  involvement  with  appropriate  accommodation  (Garcia-Villamisar  &  Hughes,  2007; 
Ijichi & Ijichi, 2007; McGovern & Sigman, 2005; Tonge et al., 1994). 
The most unique and potentially meaningful property of autistic cognition is savant skill. 
The estimated prevalence of the cognitive superiority in ASD varies from 10% to surprising 

quantitative 

three 

domains 

including 

 

The Genetic Basis of Phenotypic Diversity: 
Autism as an ExtremeTail of a Complex Dimensional Trait 

87 

numbers  (Dawson  et  al.,  2007;  Happé,  1999;  Rapin  &  Katzman,  1998).  The  supposed 
common ‘high intelligence’ in autistic individuals with low IQ may involve high processing 
speed,  prodigious  memory  capacities,  and  heightened  primary  sensory  processing 
(Boddaert  et  al.,  2005;  McCleery  et  al.,  2007;  Scheuffgen  et  al.,  2000).  These  cognitive 
superiorities are believed to have the same origin as the social difficulties in ASD (Brosius & 
Kreitman), and the term, ‘autistic savant skills’, is used to describe one of the core cognitive 
features of ASD (Badcock & Crespi, 2006; Scheuffgen et al., 2000). As a unifying explanation 
which covers the manifold autistic characteristics, excessive neuronal processing (a hyper-
functionality model) is also implicated as opposed to usual hypo-functionality explanations 
(Markram et al., 2007). 
The  ratio  of  sibling  recurrence  risk  to  population  prevalence  is  approximately  50  with  an 
overwhelming predominance of sporadic cases, suggesting the multifactorial nature of ASD 
(AGPC, 2007). The high monozygotic concordance rate in twins and the modest recurrence 
risk in dizygotic twins and among siblings may also suggest that the genetic architecture for 
ASD  has  the  same  complexity  as  those  for  human  physical  appearances  including  facial 
characteristics and brain gray matter volume (Ijichi & Ijichi, 2004). In traditional views, the 
modest correlation between autistic behavioral domains in population studies implies that 
there  is  no  single  (genetic  or  endophenotypical)  cause  for  the  three  autistic  extreme 
characteristics and a mere coincidence of the phenotypic extremes might be the true nature 
of autistic social difficulties (Happé et al., 2006). Although positive assortative mating might 
cause phenotypic anticipation and a negative assortative mating between the couple might 
gather  the  non-overlapping  genetic  components  in  a  baby  (Ijichi  et  al.,  2008),  there  is  no 
evidence for such assortative mating (Hoekstra et al., 2007). 
As  exemplified  in  Table  1,  there  is,  so  far,  no  universal  genetic  marker  which  is  co-
segregated  with  ASD  in  the  affected  families.  In  contrast  to  the  early  prediction  (30-40%) 
(Beaudet,  2007),  no  more  than  5-7%  of  ASD  cases  may  be  traceable  to  single  or  multiple 
genetic  concomitant(s)  (Table  1).  Although  many  whole-genome  scans  for  autism 
susceptibility loci have identified a lot of linkage peaks, the reproduction of the results is 
exceptional and association studies have failed to identify the gene variants (Sykes & Lamb, 
2007). The regions of structural variants including copy number variations (CNVs) seldom 
conform to the linkage peaks (Sebat, 2007). The lack of an unambiguous pathophysiological 
marker is also one of the important characteristics of idiopathic autism (Amaral et al., 2008; 
Moldin  et  al.,  2006;  Santangelo  &  Tsatsanis,  2005;  Schmitz  &  Rezaie,  2008).  The  only 
anatomical candidate which can be consistently co-segregated with ASD including masked 
autistic savants may be a quantitative increase in the number of processing units of cortex 
(minicolumns) (Casanova, et al., 2002, 2007). The increase in the number of minicolumns is 
thought to be associated with mammalian brain evolution, and the finding can explain other 
apparent tendencies revealed in some autistic individuals, including increases in the volume 
of  brain  structures  and  the  prevalence  of  epilepsy  (Casanova  et  al.,  2006).  Recent 
preliminary  findings  suggest  that  the  tendency  of  brain  overgrowth  originates  prenatally 
(Hobbs et al., 2007; Leonard et al., 2008). Furthermore, there is no biological deficit including 
chemical and molecular findings which is universal in individuals with ASD or can reliably 
help to identify putative subgroups that are genetically homogeneous (Lauritsen & Ewald, 
2001).  Over-expression  of  neuron-associated  genes  is  still  one  of  the  candidates  for 
molecular markers (Lepagnol-Bestel et al., 2008; Maussion et al., 2008; Rinaldi et al., 2007). 
The  scientific  puzzle,  which  is  metaphorically  described  as  “myopic  investigators  are  still 
patting the elephant” (Rapin, 1999) remains to be solved (Baron, 2008). Why is the male to 

 

88

 
Autism Spectrum Disorders: The Role of Genetics in Diagnosis and Treatment 

female  ratio  biased  (3-4  to  1)?  Why  cannot  the  behavioral  uniformity  with  strong  genetic 
contribution be interpreted by common gene variant alleles? Why is the disparity between 
monozygotic and dizygotic concordances so large? Why do the autistic behavioral domains 
correlate  modestly?  Although  these  questions  may  provide  very  important  clues  and  are 
encouraging  researchers  to  speculate  on  reasons,  the  jigsaw  is  still  incomplete.  Missing 
puzzle pieces include the solution of the evolutionary mystery of autism prevalence. Human 
conditions  can  be  selected  and  survive  when  it  is  somehow  associated  with  increased 
reproductive success (Nesse & Williams, 1994). However, in spite of the hypo-reproductive 
tendency  of  behaviors  in  extreme  cases  with  ASD  (Lord  et  al.,  2000),  the  estimated  high 
prevalence has never declined (Baird et al., 2006; CDC, 2009; Fombonne, 2009). 

3. Genetic and environmental explanations 
It is recently recognized that ASD has the highest prevalence (more than 0.5%) in childhood 
neurodevelopmental conditions (CDC, 2009; Fombonne, 2009). In traditional frameworks, in 
which researchers are searching the human genome for the condition-specific genetic variants, 
three  genetic  models  should  be  considered  as  the  genetic  mechanism  for  such  a  common 
phenotypic condition (Gibson, 2009). The quite low effect size of each ASD-related variant is 
suggested to be the cause of difficulty in replication of the positive findings in the common 
disease-common variant (CD-CV) model (Anney, 2010). Although a rare alleles of major effect 
(RAME) model is one of the core principles for recent genome-wide association studies in ASD 
(Gibson,  2009),  the  replication  may  also  be  complicated  by  chance  findings,  as  well  as 
differences  in  ascertainment,  because  of  the  modest  relative  risk  of  the  rare  alleles  (Anney, 
2010). The third model, the infinitesimal model, can make an excuse for the situation of genetic 
studies,  because  it  is  very  hard  to  identify  rare  variants  of  small  effect  by  genetic  means 
(Manolio  et  al.,  2009).  It  is,  anyhow,  clear  that  it’s  time  to  reconsider  and  question  simple 
intuitive models that link a human complex condition to mutation (Gibson, 2009). 

3.1 Genetic factors 
The non-universality of the candidate gene variants which have previously been implicated 
in ASD may be consistent with the speculation that heterogeneous sets of gene variants can 
contribute to ASD (Betancur, 2011; Beaudet, 2007; Garber, 2007). Furthermore, in order to 
explain the modest correlations between the three autistic behavioral domains, the presence 
of  domain-specific  heterogeneous  sets  of  gene  variants  are  also  suggested  (Happé  et  al., 
2006).  However,  even  novel  genetic  means  including  whole  genome  screening  using 
microarray-based  hybridization  cannot  fully  confirm  these  speculations  (Table  1).  The 
frequent absence of diagnostic history of ASD in the parents of an idiopathic ASD proband 
may suggest that the supposed variants should be carried by a non-ASD parent (incomplete 
penetrance) or the proband should have de novo mutations (Beaudet, 2007; Constantino & 
Todd, 2005; Zhao et al., 2007) (Table 2). However again, such genetic transmission is still one 
of the hypotheses and the concomitant de novo variants can be detected only in a minor part 
of  the  cases  (Table  1).  The  number  of  candidate  gene  regions  is  still  increasing  without  a 
convincing and comprehensive demonstration of the link between such variants and autistic 
developmental trajectory (Glessner et al., 2009). 
The genetic contribution to a quantitative trait may be attributable to the cumulative effect 
of a set of quantitative trait loci (QTLs) (Plomin et al., 1994, 2009; Plomin & Kosslyn, 2001). 
Each QTL is neither necessary nor sufficient for the overall phenotypic outcomes, the effect 
size  of  each  QTL  may  fluctuate  according  to  other  genetic  backgrounds  (epistasis,  non-

 

The Genetic Basis of Phenotypic Diversity: 
Autism as an ExtremeTail of a Complex Dimensional Trait 

89 

additive gene-gene interactions) and the environment (gene-environment interactions), and a 
QTL  may  affect  more  than  one  phenotypic  trait  (pleiotropy).  The  concept  of  epistasis  had 
initially been introduced for ASD as an alternative explanation of the incomplete penetrance or 
as a risk factor model (Bradford et al., 2001; Folstein & Rosen-Sheidley, 2001; Jones & Szatmari, 
2002).  Because  natural  chromosomal  and  segmental  shuffling  during  normal  meiosis  is  a 
strong random modifier of epstatic effects among QTLs in a sib-pair and dizygotic twins, the 
big disparity between monozygotic and dizygotic concordances in autism may be explained 
by the presence of epistatic QTLs. Pleiotropy can account for the presence of autistic savants. 
The  modest  correlation  among  autistic  behavioral  domains  can  also  illustrated  by 
unsynchronized epistatic pleiotropy (Ijichi et al., 2008). To explain the sporadic manner of the 
prevalence  and  the  survival  of  hypo-reproductive  autistic  extremes,  the  implication  of 
epistasis-associated  intergenerational  oscillation  of  phenotypic  outcomes  was  introduced 
(Ijichi et al., 2008). Some candidates for autism QTLs have been reported (Ashley-Koch et al., 
2006;  Coutinho  et  al.,  2007;  Jiang  et  al.,  2004;  Weiss  et  al.,  2007),  linkage  analysis  with 
quantitative  measures  of  some  autistic  characteristics  revealed  QTL  signals  (Alarcón  et  al., 
2002, 2005; Chen et al., 2006; Duvall et al., 2007), and a quantitative covariance analysis can 
confirm 
‘range  of 
interest/flexibility’ (Sung et al., 2005). Although the supposed contribution of QTLs ought to 
be traced in family studies or genome scans according to a traditional logic, “the causal gene 
variant can be cosegregated with the phenotypic variant”, the delay and difficulty in detecting 
the  causal  variant  alleles  at  QTLs  is  strangely  common  to  all  idiopathic  quantitative  traits 
including autism, physical and physiological characteristics, and personalities (de Geus et al., 
2001; Fullerton, 2006; Palmert & Hirschhorn, 2003; Willis-Owen & Flint, 2006). 
 

the  high  genetic  correlation  between 

‘social  motivation’  and 

Facts and questions 

Penetrance 

Explanations 

De novo 

Environment 

－ 
－ 
－ 
○ 
○ 
－ 
○ 
－ 
－ 
－ 
(○) 
－ 

(○) 
－ 
－ 
○ 
(○) 
－ 
○ 
－ 
－ 
－ 
(○) 
－ 

The quantitative feature 
Partial behavioral plasticity 
The presence of autistic savants 
Strong genetic contribution 
Usually sporadic without family history 
Domain-specific genetic factors 
Lack of the common genetic marker 
Lack of the common pathological lesion 
Lack of the common chemical marker 
Lack of the common molecular marker 
Why is the male to female ratio biased? 
Why is it so difficult to detect autism 
genes? 
Why do hypo-reproductive extremes 
survive? 
Penetrance: Poor penetrance of heterogeneous gene variants; De novo: De novo involvement of 
heterogeneous gene variants; QTLs: Quantitative trait loci; Environment: Environmental contribution; ○: 
explainable; (○): unexplainable by itself but explainable with some further speculation; －: hard to explain 
Table 2. Genetic and environmental explanations for the facts and outstanding questions in 
idiopathic autism researches 

(○) 
○ 
－ 
－ 
○ 
－ 
－ 
－ 
－ 
－ 
(○) 
－ 

(○) 

－ 

○ 

QTLs 
○ 
－ 
(○) 
○ 
(○) 
(○) 
○ 
－ 
－ 
－ 
(○) 
－ 

－ 

 

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Autism Spectrum Disorders: The Role of Genetics in Diagnosis and Treatment 

3.2 Epigenetic factors and ASD 
Phenotypic outcomes with robustness or plasticity cannot be exclusively determined by the 
DNA sequence itself which looks like the core genetic factor of the phenotype (Goldberg et 
al., 2007). Epigenetics is the study of changes in gene expression that occur without a change 
in DNA sequence and the epigenotype is meiotically and mitotically transmissible (Morris, 
2005; van Vliet et al., 2007). Although the significance of the contribution made by epigenetic 
factors to human complex traits remains unclear, it is speculated that epigenetic factors can 
influence gene-environment interactions and the liability/outcomes of the traits (van Vliet et 
al.,  2007).  Epigenetic  changes  in  gene  expression  are  achieved  through  RNA-associated 
silencing,  DNA  methylation,  and  histone  modifications  (Morris,  2005),  and  cis-acting 
expansion  of  the  epigenetic  influences  on  the  flanking  genes  is  referred  to  as  genomic 
imprinting  which  results  in  parent  of  origin-specific  gene  expression  (Pauler  et  al.,  2007). 
The  epigenetic  factors  and  genomic  imprinting  may  be  implicated  in  syndromic  autistic 
individuals with some single gene/chromosomal disorders including Rett syndrome, fragile 
X  syndrome,  Prader-Willi  syndrome,  and  Angelman  syndrome,  and  a  variety  of  factors 
associated  with  epigenetic  modifications  have  been  considered  as  candidates  for  autism 
genes (Badcock & Crespi, 2006; Jiang et al., 2004; Persico & Bourgeron, 2006; Schanen, 2006; 
Skuse,  2000;  van  Vliet  et  al.,  2007).  These  factors,  however,  cannot  be  the  common 
prerequisite for idiopathic ASD at least in the majority of the cases (Jiang et al., 2004; Persico 
& Bourgeron, 2006), and the power of epigenetic factors are recognized as an accidental cue 
to shift the quantitative distribution of the autistic traits in a threshold model (Skuse, 2000). 
If  the  epigenetic  factors  act  only  in  gene-environment  interactions  in  idiopathic  cases,  the 
epigenetic contribution should be modest in the overall underpinnings. Given an unforeseen 
transmissible  powerful  architecture  connecting  genotype  and  phenotype  for  phenotypic 
diversity independent of genetic diversity, the epigenetic mechanism should be referred to 
as merely one of the molecular-level environments derived from gene networks. 

3.3 Environmental factors 
Environmental factors contribute no more than 10% to ASD (Garber, 2007). However, the 
environmental factors including rubella, thalidomide, and valproic acid embryopathies may 
still be important as additive triggers of the clinical manifestation (Jones & Szatmari, 2002; 
Persico & Bourgeron, 2006). Environmental contributions including behavioral experiences 
are originally misunderstood to explain the patterns of familial recurrence risks observed in 
autism studies (Jorde et al., 1991). Because the genetic components affecting autistic traits 
seem to be the same across the sexes (Constantino & Todd; Hoekstra et al., 2007), it can be 
speculated  that  the  lower  prevalence  of  autistic  traits  in  girls  is  the  result  of  increased 
sensitivity  to  early  environmental  influences  that  operate  to  promote  social  competency 
(Constantino & Todd, 2003). The minimal contribution of shared environmental influences 
(Ronald  et  al.,  2006a)  may  be  associated  with  the  autistic  behavioral  manifestations 
including resistance to change or insistence on sameness. 
Combinations of the traditional theories (poor penetrance, de novo mutations, and QTLs and 
the  environmental  contribution)  may  answer  not  a  few  of  the  outstanding  questions  in 
idiopathic  autism  research  (Table  2).  However,  in  spite  of  the  presence  of  a  big  genetic 
contribution to the autistic development, the question, “Why is it difficult to detect autism 
gene  variants?”,  still  remains  to  be  resolved.  In  addition,  the  significance  of  both  de  novo 
mutations  and  the  environmental  modification  is  just  a  speculation  in  a  part  of  the  ASD 
cases. 

 

The Genetic Basis of Phenotypic Diversity: 
Autism as an ExtremeTail of a Complex Dimensional Trait 

91 

4. Evolutionary explanations 
Does  idiopathic  ASD  really  represent  many  distinct  conditions  with  numerous  etiologies 
(Geschwind,  2007)?  Is it  really  time  to  give up  on  a  single  explanation  for  autism  (Baron, 
2008; Happé et al., 2006)? A variety of qualitative concomitants, including gene variants and 
environmental  factors,  have  already  been  demonstrated  in  part  of  autistic  cases  as 
exemplified above. However, it may be still too early to reach the conclusion even in such 
frameworks,  because  no  single  qualitative  process  associated  with  the  concomitants  can 
indicate the molecular or chemical differences between the autistic developmental extremes 
and  the  non-autistic  majority.  In  order  to  understand  human  complex  traits,  genetic, 
molecular,  and  biochemical  explanations  should  be  combined  with  evolutionary 
explanations (Nesse & Williams, 1994). In autistic individuals, ASD per se does not shorten 
the span of life (Gillberg et al., 2010). Although high or preserved androgenic competence is 
suspected in ASD (Tordjman et al., 1997), the extreme cases almost never marry (Lord et al., 
2000).  The  hypo-fertility  results  from  reduced  opportunity  or  behavioral  ability  in  the 
mating arena. Therefore, we must probe into who is enjoying the reproductive benefits of 
the genetic architecture for ASD in the evolutionary framework (Table 3). 
 

Who gets the reproductive 

benefits? 
None (an inevitable 
outcome) 
Unaffected carriers of genetic 
factors 

All of the non-autistic 
majority 

Hypotheses or mechanisms 

References 

Mutation-selection balance theory (Keller & Miller, 2006) 

Hyper-systemizing theory 
(extreme male brain theory) 
Extreme imprinted brain theory 
Population benefit theory 
Monomorphic loci theory 

(Baron-Cohen, 2002) 
 
(Badcock & Crespi, 2006) 
(Fitzgerald, 2002) 
(Ijichi et al., 2011) 

Table 3. Evolutionary explanations for the survival of autistic extremeness 

4.1 Mutation-selection balance theory 
In  the  mutation-selection  balance  theory,  individuals  with  a  high  load  of  mutations  are 
postulated to be at higher chance of passing risk on to their offspring, and it is not necessary 
that  there  are  individuals  with  the  reproductive  benefits  (Keller  &  Miller,  2006). 
Importantly, according to the proposed model, everyone alive has minor brain deviations 
that cause them to be a little bit abnormal in behavioral and cognitive dimensions (Keller & 
Miller, 2006). The non-autistic majority in the population is regarded as the genetic carrier-
state  for  ASD  and  the  mutation  load  and  the  risk  of  having  autistic  offspring  may  vary 
quantitatively.  In  the  mutation-selection  balance  theory,  balancing  selection  for  genetic 
diversity  is  recognized  to  be  unsuitable  to  explain  persistent  heritability  in  human 
conditions (Keller & Miller, 2006; Zhang & Hill, 2005). One of the grounds of this exclusion 
of balancing selection is the absence of an ongoing homeostatic mechanism that counteracts 
the  homogenizing  effect  of  genetic  drift  and  stabilizing  selection,  and  the  reproductive 
benefits  of  the  genetic  burden  for  autism  are  not  addressed  (Keller  &  Miller,  2006).  The 
mutation-selection perspective can be an evolutionary interpretation of a cumulative effect 

 

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Autism Spectrum Disorders: The Role of Genetics in Diagnosis and Treatment 

of de novo mutations and is at least consistent with the quantitative distribution of autistic 
domains. 

4.2 Extreme male brain theory 
The second is a group of theories in which only a part of the population is regarded as the 
genetic carrier-state for ASD. The prevalence or maintenance of positive assortative mating 
between the non-autistic carriers is critical to accumulate genetic factors in these theories, 
and the remaining non-autistic majority does not have the genetic components for ASD. In 
the  hyper-systemizing  theory,  the  unaffected  carriers  of  the  genetic  factors  are  high 
systemizers and ASD is the result of both parents being the high systemizers (Baron-Cohen, 
2002,  2004,  2006).  Systemizing  is  the  drive  to  understand  and  predict  the  next  step  of 
inanimate events and acts contrary to empathizing. In males, the systemizing mechanism is 
set at a slightly higher level than non-autistic males (Baron-Cohen, 2004). This extreme male 
brain  theory  of  autism  had  originally  been  proposed  by  Asperger  in  1944.  Individuals 
including both parents of individuals with autism, who are placed in the adjacent part to the 
autistic extremeness, systemize at a higher level than average (above average systemizers) 
and account for approximately a half of the vast majority. Over successive generations, the 
above  average  systemizers  carry  the  genetic  components  for  ASD  and  might  enjoy  the 
reproductive  benefits.  As  one  of  the  genetic  bases  of  the  hyper-systemizing  theory,  the 
extreme imprinted brain theory had been proposed (Badcock & Crespi, 2006). 

4.3 Population benefit theory and individual benefit theory 
In  the  third  framework,  it  is  suggested  that  the  evolutionarilly  selected  and  conserved 
phenotype is not the hypo-reproductive extremeness but the whole quantitative distribution 
itself.  A  group  selection  theory  has  been  introduced  to  bring  sense  into  the  link  between 
autism  and  exceptional  creativity  (Fitzgerald,  2003).  In  this  population  benefit  theory,  the 
creativity,  which  can  be  concomitant  with  autism,  benefits  all  members  of  the  human 
community and the community can survive. On the other hand, the third framework can 
also include individual benefit concepts (the monomorphic loci theory) (Ijichi et al., 2011). In 
the individual benefit concepts, everybody has both the genetic architecture for ASD and the 
possibility  to  enjoy  the  reproductive  benefits  of  autism  genes.  Each  phenotypic  outcome, 
however,  varies  individually  mainly  according  to  the  differences  in  genetic  background 
noise  and  environmental  factors,  whose  functions  are  not  necessarily  related  to  ASD 
phenotypes directly. In the process of reaching the monomorphic loci theory, the epistasis-
mediated intergenerational oscillation of phenotypic outcomes has been advanced in a QTL 
model (Ijichi et al., 2008). The monomorphic loci theory does not dismiss the comprehensive 
view of the known genetic contributions, including major gene effects and additive genetic 
networks (Ijichi et al., 2011). The postulated involvement of monomorphic loci can be valid 
as  merely  one  of  the  genetic  constituents  in  complex  (additive  and/or  non-additive) 
interactions with polymorphic loci.  

4.4 The monomorphic loci theory and gene networks 
Because both positive and negative epistasis may be byproducts of evolution (L. Azevedo et 
al., 2006; R.B.R. Azevedo et al., 2006; Harrison et al., 2007), the invisibility of the contribution 
of monomorphic epistatic loci from the traditional genetic view is an attractive candidate for 
the explanation of the black box between polymorphic genotype and phenotypic diversity 
(Ijichi  et  al.,  2011).  Complex  phenotypes  have  hierarchical  structures,  including  RNA 

 

The Genetic Basis of Phenotypic Diversity: 
Autism as an ExtremeTail of a Complex Dimensional Trait 

93 

(transcript traits), protein, metabolite, and functional levels. It has been suggested that less 
heritability of metabolite traits than transcript traits is associated with the difference in the 
quantity  of  biological  noise  between  the  genetic  determinants  and  the  trait  (Rowe  et  al., 
2008).  The  more  steps  that  are  involved  between  genotype  and  the  trait  level,  the  more 
biological noise may reside in the process. Such biological noise originates from inter-locus 
interactions  and  gene-environment  interactions,  and  the  inter-locus  interactions  may  have 
an  important  role  in  the  biological  noise.  Additive  and/or  non-additive  inter-locus 
interactions with other loci are available in a variety of processes including cis-, trans-, and 
inter-cellular  interactions  (Figure  1).  The  presence  of  gene-environment-gene  circuits  may 
make it difficult to distinguish inter-locus interactions from gene-environment interactions 
in the biological noise (Ijichi et al., 2011). In these interactions, an intergenerational change in 
the number or property of factors (environment and/or other related loci) in the regulatory 
circuit  may  easily  individualize  the  balance  of  each  hierarchical  trajectory  (coding  RNA, 
non-coding RNA, translation, autocrine, paracrine, and endocrine levels) and individually 
determine the developmental outcomes. The net non-additive effects of the biological noise 
are metaphorically interpreted as hub-and-spoke structures of regulatory networks among 
polymorphic loci (Benfey & Mitchell-Olds, 2008). 
 

 

Fig. 1. Cellular and molecular interactions of biological noise in regulatory networks around a 
gene locus (A). Additive and/or non-additive phenomena can be involved in each interaction 
(Ijichi et al., 2011). In this explanation, an arrow represents the net contribution between loci 
and the gene-environment relationship. The locus A can interact with other loci in association 
with coding RNA and/or non-coding RNA level in cis-acting manner (①, ②) and trans-acting 
manner (③, ④). The cis-acting interactions are involved in genetic imprinting. After 
translation, interactions can be mediated through autocrine, paracrine, and endocrine 
mechanisms (⑤, ⑥). Gene-environment interactions can modify penetrance of the outcomes 
affected by the locus A. The network constituents can change the sensitivity to environmental 
influences (⑦), that can provide gene-environment-gene circuits. In the monomorphic loci 
theory, the gene A can be monomorphic and the link between monomorphic A and the A-
associated polymorphic noise is usually invisible in the context of traditional genetics. 

 

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Autism Spectrum Disorders: The Role of Genetics in Diagnosis and Treatment 

5. Quantitative domains and genetic factors 
The distributional shift of a bell-shaped curve and the change in the curve shape illustrates 
the  mean  value  change  and  the  variance  alteration  of  the  quantitative  dimension, 
respectively  (Gibson,  2009).  These  changes  can  affect  the  proportion  of  individuals  with 
autism to those without as determined by a liability threshold. The biased male to female 
ratio  (3-4  to  1)  in  ASD  is  plausibly  interpreted  as  a distributional  shift of  the  quantitative 
bell-shaped curve as a gender gap. In the hyper-systemizing theory, the male systemizing 
mechanism  is  set  at  a  slightly  higher  level  than  in  females  (Baron-Cohen,  2004).  In  an 
imprinted-X liability threshold model, actions of some X-linked genes, which are expressed 
only  from  paternal  X-chromosome,  are  suggested  to  be  associated  with  the  male 
predisposition to ASD (Skuse, 2000). The gender is a bimorphic genetic variation and there 
is a gender gap in sensitivity or vulnerability to environmental factors (Constantino & Todd, 
2003).  The  relationship  between  a  bell-shaped  quantitative  distribution  and  the  genetic 
factors underlying the complex phenotype still remains to be elucidated. 

5.1 Polygenic liability model 
The traditional concept of polygenic liability supposes a normal distribution of frequencies 
of  susceptibility  variant  alleles  (Gibson,  2009).  The  manner  of  the  allele  contribution  is 
additive, and each allele contribution usually results in a positive or negative effect on the 
phenotype in the carrier individual and the quantitative population dimension results from 
such  additive  allele  contributions.  To  explain  the  smooth  normal  distribution,  an 
environmental variance of each allele contribution is addressed in this model. 
In  a  genetic  model,  oligogenicity  with  epistasis,  the  contributing  genes  are  likely  to  be 
common ones in the population (Folstein & Rosen-Sheidley, 2001). There is no evidence that 
the  genetic  causative  processes  affecting  the  autistic  extreme  are  different  from  those 
contributing  the  autistic  dimension  including  individuals  without  autism  (Ronald  et  al., 
2006a).  If  the  presence  of  epistasis,  pleiotropy,  and  gene-environment  interactions  are  all 
supposed, the polymorphic genetic underpinning is referred to as QTLs (Plomin et al., 1994, 
2009; Plomin & Kosslyn, 2001). However, it is also the fact that the delay and difficulty in 
detecting the causal variant alleles at QTLs is common to all idiopathic quantitative traits 
including ASD, physical and physiological characteristics, and personalities (de Geus et al., 
2001; Fullerton, 2006; Palmert & Hirschhorn, 2003; Willis-Owen & Flint, 2006).  
If  the  genetic  factors  for  a  tail  of  the  bell-shaped  curve  are  different  from  those  for  the 
majority  and  have  extremity-specific  properties  including  serious  involvement  of  coding 
gene segments (Mitchison, 2000), the variant alleles should be more detectable. Because the 
genetic contribution in ASD is the biggest in human complex traits and the environmental 
influence on ASD is quite minimal as described above, the difficulty in finding the universal 
genetic marker for ASD warrants the necessity of a paradigm shift. 

5.2 Additive and non-additive interactions between mono- and poly-morphic loci 
It has been emphasized that the three behavioral domains of ASD modestly correlate to each 
other and the set of genes for each domain may be partly different (Dworzynski et al., 2007; 
Happé et al., 2006; Ronald et al., 2005, 2006a, 2006b). The speculated modest genetic overlap 
among  autistic  domains  may  be  indistinguishable  from  that  among  human  complex 
phenotypes  including  ASD,  bipolar  disorder,  and  schizophrenia  (Rzhetsky  et  al.,  2007), 
suggesting that the autistic domains and these psychiatric conditions might share the same 

 

The Genetic Basis of Phenotypic Diversity: 
Autism as an ExtremeTail of a Complex Dimensional Trait 

95 

genetic  architecture  at  least  in  part  (Craddock  &  Owen,  2010).  In  an  argument  about 
domain-specific genes for cognitive functions, it is expected that the domain-general genes 
are responsible for the brain infrastructure including receptors, neurotransmitters, dendritic 
spines,  synapse  vesicles,  and  axonal  filaments  (Marcus  &  Rabagliati,  2006).  Although  the 
universality  of  the  domain-general  genes  for  cognitive  functions  among  other  human 
complex  phenotypes  is  controversial,  genes  for  the  brain  infrastructure  are  also  current 
topics in the field of ASD (Garber, 2007; Persico & Bourgeron, 2006). Both the heterogeneity 
of genetic markers for ASD and the modest correlation among autistic core domains can be 
explained  by  epistasis-mediated  oscillation  of  the  domain-general  effect  values  and 
unsynchronized epistatic pleiotropy in the monomorphic loci theory, which never dismiss 
the  comprehensive view  of  the  known  genetic contributions,  including major  gene  effects 
and additive genetic networks (Ijichi et al., 2011). The assumption of the random outcomes 
mediated  by  the  non-additive  interactions  between  functional  monomorphic  loci  and 
polymorphic  backgrounds  may  transform  the  traditional  complementary  roles  of  some 
monomorphic  loci  (Gjuvsland  et  al.,  2007)  to  active  and  leading  roles  for  the  phenotypic 
diversity (Ijichi et al., 2011). However, the controversy concerning the importance of non-
additive  effects  in  phenotypic  diversity  still  exists  (Gale  et  al.,  2009;  Hill  et  al.,  2008; 
Malmberg & Mauricio, 2005). 

5.3 Social environmental changes and decanalization 
The  decanalization  concept  may  have  sizable  significance  in  searching  the  cause  of  the 
maintained or increasing prevalence of ASD. Canalization is an evolutionary phenomenon 
characterized by robustness to genetic or environmental perturbation, and most individuals 
tend to cluster around the optimal phenotype in canalized populations (Gibson, 2009). If the 
phenotypic  dimension  consists  of  multiple  endophenotypic  vectors  which  have  nonlinear 
relationships  to  each  other  and  are  partially  determined  by  genetic  factors,  overt 
environmental  perturbations  for  one  of  the  endophenotypes  can  be  the  cue  of 
decanalization,  which  changes  the  shape  of  the  phenotypic  demensional  distribution 
(Gibson, 2009). Social environmental perturbations may also shift the entire distribution of 
ASD liability, or move the liability threshold. 

6. Conclusions 
The  difficulty  in  detecting  the  universal  biological  marker  for  the  predisposition  to  ASD 
presents  significant  challenges  and  conflicts  to  researchers  in  related  fields.  The  reported 
gene  variants  in  some  sporadic  cases  with  idiopathic  ASD  are  nothing  but  one  of  the 
concomitants, until the molecular or biological trajectory underlying autistic development is 
clearly  delineated  or  association  studies  reproduce  the  causal  relationship.  Before  the 
speculation  that  idiopathic  ASD  represents  many  distinct  conditions  with  numerous 
etiologies, the quantitative manner of the distribution of the behavioral domains and the fact 
that  ASD  is  a  mere  tail  of  the  behavioral  dimensions  should  strictly  be  considered  and 
emphasized. Even combinations of traditional theories including poor penetrance, de novo 
mutations, quantitative trait loci, and environmental contribution cannot fully account for 
the  entire  genetic  underpinning.  Importantly,  the  almost  monolithic  insight  into  the 
prevalence of ASD can only be obtained in an evolutionary framework on the assumption 
that the complex genetic networks are responsible not for the individual cases but for the 
human  behavioral  diversity  itself.  Gender  differences,  environmental  factors,  epigenetic 

 

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Autism Spectrum Disorders: The Role of Genetics in Diagnosis and Treatment 

mechanisms including genetic imprinting, and major gene effects may all be mere accidental 
modifiers of the relationship between the diversity and the liability threshold. 

7. Acknoledgment 
The authors greatly thank Professor Bryan H. King for his helpful suggestions. 

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7 

A New Genetic Mechanism for Autism 
Julie Gauthier and Guy A. Rouleau 
Université de Montréal 
Canada 

1. Introduction  
It  has  been  almost  half  a  century  since  Leo  Kanner  first  described  the  clinical  phenotype 
associated  with  Autism1.  Since  Kanner's  descriptions,  much  effort  has  been  devoted  to 
understanding and identifying the factors which may contribute to Autism. It was not until 
the early 80’s that compelling evidence started to accumulate suggesting that Autism is a 
disorder of abnormal brain development. It is now generally accepted that both genetic and 
environmental  factors  are  implicated  in  the  etiology  of  this  intriguing  condition.  In  the 
current chapter, we will focus on the role of genetic factors involved in the pathogenesis of 
Autism. While multiple hypotheses have been proposed to account for the genetic origin of 
Autism, some have received more empirical support than others. While it is likely that more 
than one genetic mechanisms is involved in the pathogenesis of this disorder, this chapter 
will  focus  on  a  recent  hypothesis  implicating  de  novo  mutations  in  synaptic  genes.  This 
hypothesis is based on the proposition that rare, highly penetrant mutations affecting any of 
many different genes which  code for synaptic  molecules, and which are specific to single 
families,  predispose  to  Autism.  Empirical  lines  of  evidence  for  this  hypothesis  will  be 
presented, along with examples, some of which are derived from work by our group.  

1.1 The role of genetics in autism 
Over the last two decades many studies aimed at identifying the genetic causes of Autism 
have  shown  that  genetic  factors  play  a  predominant  role  in  the  genesis  of  this  disorder. 
Twin studies predict that the heritability (i.e. the degree to which a given trait is controlled 
by  inheritance)  of  Autism  is  between  70-90%  (Bailey  et  al.,  1995;  Marco  &  Skuse,  2006; 
Lichtenstein et al., 2010). The relatively low concordance rate in dizygotic twins, the sharp 
decrease  in  recurrence  risk  of  Autism  in  second-  and  third-degree  relatives  of  autistic 
subjects  (0.18%  and  0.12%  respectively)  as  well  as  a  low  risk  in  first-degree  relatives  of 
autistic  subjects  (3-7%)  (Chakrabarti  &  Fombonne,  2001;  Muhle  et  al.,  2004)  predicts  two 
different  genetic  scenarios:  1)  Autism  could  be  explained  by  the  co-inheritance  in  one 
individual  of  multiple  disease-predisposing  alleles,  each  with  a  small  but  additive  effect, 
resulting in disease, or 2) by de novo mutations (i.e. not inherited from either parent). The 
first  scenario  is  a  polygenic  inheritance,  where  the  effect  of  multiple  risk  genes  acting 
additively  or  multiplicatively  results  in  disease.  The  second,  very  recently  considered  for 
Autism, argues that a fraction of the cases would result from incompletely penetrant new 
                                                 
1 In this chapter the term Autism refers to the autism spectrum disorders which include Autism 
disorder, Asperger syndrome and Pervasive developmental disorder not otherwise specified  

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Autism Spectrum Disorders: The Role of Genetics in Diagnosis and Treatment 

mutations  or  de  novo  mutations,  such  that  only  identical  twins  would  share  the  genetic 
predisposition to Autism, hence the much higher monozygotic concordance than dizygotic 
concordance. Familial aggregation studies demonstrate that the risk of developing Autism is 
greater in offspring than in the parents. There are many possible explanations, one of which 
is that new mutations cause a fraction of Autism cases.  
Dozens  of  genome-wide  genetic  linkage  studies  have  been  conducted  in  Autism  kindred, 
identifying  genetic  signals  of  various  significance  levels  on  almost  every  arm  of  every 
human chromosome. There are few instances of consistent replication of linkage to any one 
site. Fine-mapping of these loci has been difficult, mainly because of genetic heterogeneity, 
so  researchers  have  frequently  opted  to  look  at  candidate  genes  directly  based  on  the 
linkage results and/or on relevant gene functions. Association studies have also been used 
to search for genes in Autism. Although, in general, most studies have not been replicated, a 
few have been yielding a crop of possible susceptibility genes. Although the few replicated 
positive  association  studies  are  promising,  it  is  surprising  that  no  causative  mutations  or 
sequence variants have been identified in any of the loci associated with the disorder. In the 
absence of such mutations, the role of these genes in Autism remains unproven.  
A number of genes have been strongly associated with Autism. For example, the X-linked 
neuroligin  genes,  NLGN3  and  NLGN4,  two  synaptic  molecules,  have  been  found  to  be 
associated with Autism. In the original report, two families with affected brothers (one with 
autism  disorder  and  the  other  with  Asperger  syndrome)  have  a  frameshifting  and  a 
missense mutation within the coding region of NLGN4 and NLGN3, respectively (Jamain et 
al., 2003). Interestingly, both were new mutations that occurred in the mother of the affected 
brothers. Other mutations have since been described in these genes further supporting their 
role in the pathogenesis of Autism. NLGN3 and NLGN4 belong to the neuroligin family of 
postsynaptic cell adhesion molecules that are widely expressed in the brain (Philibert et al., 
2000). The products of these genes are involved in late steps of synaptogenesis, mediating 
the  specific  recruitment  of  pre-  and  postsynaptic  proteins  to  the  site  of  initial  synaptic 
contact.  Two  independent  studies  have  shown  in  vitro  that  the  frameshifting  and  the 
missense mutations described in humans alter the formation of presynaptic terminals (Chih 
et al., 2004; Comoletti et al., 2004). Since this discovery, mutations in other genes have been 
linked to Autism (discussed below). These findings support our hypothesis, which will be 
discussed in section below, that Autism is mainly a synaptic disorder largely caused by de 
novo mutations in synaptic genes. 

2. The concept of “de novo” mutations 
Recent studies on the direct measurement of human mutation rate have revealed that in any 
single conceptus there is approximately 1.1 x 10-8 (0.76 x 10-8 to 2.2 x 10-8) mutation per base per 
generation (Awadalla et al., 2010; Lynch, 2010; Roach et al., 2010). A newborn is thought to 
have  acquired  about  sixty  new  mutations  in  his/her  genome.  Among  these,  approximately 
0.86  new  deleterious  mutation  will  lead  to  an  altered  amino  acid,  which  corresponds  to  an 
average  of  about  1  new  coding  mutation  per  conceptus  (Eyre-Walker  &  Keightley,  1999; 
Giannelli  et  al.,  1999;  Crow,  2000).  De  novo  or  spontaneous  germline  mutations  can  lead  to 
serious clinical consequences, such as a disease, when affecting critical genes. 

2.1 Common disease and common variants  
Classical linkage and association studies, as mentioned earlier, have largely failed to identify 
predisposing genes for Autism as well as a number of other psychiatric disorders. The main 

 

 
A New Genetic Mechanism for Autism 

105 

reason  for  this  lack  of  success  is  likely  to  be  allelic  and  non-allelic  genetic  heterogeneity, 
with dozens to perhaps hundreds of genes predisposing to Autism, with each gene having 
many allelic variants. Such heterogeneity would require an enormous sample size to detect 
predisposing genes using population genetic approaches. It is likely that this heterogeneity 
results  mostly  from  our  limited  ability  to  sub-phenotype  brain  disorders,  particularly 
behavioural disabilities. The diagnosis of most psychiatric disorders remains largely based 
on  clinical  criteria,  which  define  broad  categories  of  dysfunction  that  may  or  may  not  be 
biologically  linked.  To  date,  there  is  no  consistent,  biologically  validated  method  for 
defining these sub-phenotypes. Simply stated, Autism, as currently defined, probably result 
from so many different genes and alleles that classical genetic methods will prove inefficient 
in the identification of susceptibility genes for this disorder. 
The  hypothesis  that  a  common  disease  may  be  caused  by  common  variants  was  the 
favoured  model  for  the  genetic  architecture  of  Autism  until  recently.  Indeed,  the 
constellation  of  published  association  studies  reflects  the  widespread  belief  of  the 
involvement  of  common  variants  in  Autism.  This  hypothesis  was  appealing  to  many 
investigators  since  the  common  variants  should  be  identifiable  using  methods  such  as 
linkage disequilibrium (Reich & Lander, 2001). Unfortunately, there are very few examples 
to support this hypothesis, particularly for brain disorders. Clinicians argue that Autism is a 
highly heterogeneous group of disorders, and none of them can be explained by single or 
even a few common variants. If this were the case, the plethora of genetic studies performed 
over the years should already have identified some of these variants. The widely distributed 
linkage and association positive signals scattered all over the genome rejects the existence of 
one  or  a  few major  predisposing  common  variants  in  this  disorder.  Furthermore,  the  few 
genes that have been found to definitely predispose to Autism explain only a small fraction 
of  cases.  This  is  not  to  say  that  some  common  variants  will  not  be  found  for  some 
predisposing  genes,  but  this  mechanism  is  unlikely  to  explain  all  the  genetics  of  this 
condition.  
It  has  recently  been  recognized  that  many  complex  disorders  may  result  from  a  mix  of 
common and rare variants. Let us consider breast cancer as an example (Nathanson et al., 
2001):  BRCA1  and  BRCA2  genes  contribute  to  a  relatively  common  genetic  disorder,  but 
have many different rare mutations, even in a founder population (the Ashkenazim). For a 
complex  trait  such  as  Autism,  the  occurrence  of  many  rare  variants  in  many  different 
disease predisposing genes seems to better predict the genetic architecture of the disorders 
(Pritchard, 2001; Pritchard & Cox, 2002; Smith & Lusis, 2002).  
As a final point on the common disease common variant hypothesis, most studies looking at 
disease-causative  mutations  for  Autism  report  mutations  that  are  not  recurrent,  i.e.  not 
observed more than once and specific to one individual. Again this suggests that mutations 
at many different loci may contribute to Autism, a result consistent with the failure to find 
common  heritable  variants  with  a  major  effect  on  disease  risk.  Lack  of  recurrence  of 
mutations may in fact reflect the possibility that autistic traits can result from many different 
genetic defects. 

2.2 Rare variants and new mutations 
While not all amino acid substitutions will be deleterious, a significant fraction will be and 
may lead to disease. Therefore, for a disorder that may result from dysfunction in any one of 
hundreds of different genes, new mutations may be responsible for a significant fraction of 
cases.  For  example,  should  dysfunction  in  any  of  100  different  genes  potentially  lead  to 

 

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Autism Spectrum Disorders: The Role of Genetics in Diagnosis and Treatment 

Autism, and assuming amino acid changes lead to gene dysfunction in one fifth of instances, 
new mutations could cause Autism in one case out of 1,000 births, which would correspond 
to over 10% of cases based on the overall population incidence.  
Looking  at  simple  Mendelian  traits,  we  can  see  that  new  mutations  are  common.  For 
example, 1 in 6,000 live births harbour a novel mutation causing neurofibromatosis type 1  
(Stephens et al., 1992; Grimm et al., 1994; Hudson et al., 1997). The frequency of new point 
mutations in Duchenne Muscular Dystrophy is similar, 1 in 10,500 live births (Grimm et al., 
1994).  One  can  argue  that  these  are  large  genes,  allowing  for  high  mutation  rate.  
Nonetheless,  these  are  surprisingly  high  numbers  of  novel  deleterious  mutations.  Let  us 
consider Rett syndrome, which is closely related to Autism, and results from mutations in 
the relatively small MECP2 gene (4 exons, 498 amino acids). The incidence of Rett syndrome 
is  one  in  10,000-15,000  females.  Because  99-99.5%  of  all  cases  are  sporadic  with  new 
mutations, this represents a new mutation rate of one in 5,000-7,500 live births for this small 
gene  of  498  amino  acids  (Hagberg,  1985;  Hagberg  &  Hagberg,  1997;  Van  den  Veyver  & 
Zoghbi,  2001).  This  example  clearly  shows  that,  for  neurodevelopmental  disorders,  new 
mutations can act dominantly and can occur with a high enough frequency to explain the 
relatively high incidence of Autism. A high rate of new mutations can in part explain why 
genetic  studies  have  so  far  failed  to  identify  many  Autism  genes,  and  why  diseases  have 
been identified for a mere 3% of genes in the human genome. Mutations in genes leading to 
severe  outcome  where  there  is  a  strong  negative  selection  against  the  phenotype,  such  as 
lethality  in  embryonic  stages  or  reduced  reproductive  fitness,  will  not  be  transmitted  to 
multiple family members, and therefore will not be detected by linkage gene mapping.  

3. The role of de novo mutation in autism 
Though  we  predict  that  de  novo  mutations will  be  a  frequent  cause  of  Autism,  we do not 
think  that  it  will  be  the  only  genetic  explanation.  The  alternative  genetic  hypothesis  for 
complex  traits,  mentioned  previously,  predicts  that  disease  results  from  a  combination  or 
pattern  of  genotypes  at  different  susceptibility  loci.  In  recent  years,  statisticians  have 
developed  analytical  methods  that  capture  contributions  from  multiple  susceptibility  loci, 
and provide evidence for the localization of disease genes on human chromosomes (Sherriff 
& Ott, 2001; Hoh & Ott, 2003; Carlson et al., 2004). However, such analyses are very complex 
and  yield  few  successful  examples,  even  considering  the  simplest  scenarios  (Tiret  et  al., 
1994;  Bolk  et  al.,  2000;  Zetterberg  et  al.,  2003).  It  seems  that  genome-wide  searches  using 
realistic  sample  sizes  may  not  have  the  power  to  detect  potential  multi-gene  interactions. 
The  existence  of  new  mutations,  which  contribute  to  this  heterogeneity,  makes  classical 
genetic  approaches  even  more  difficult.  Thus,  the  failure  of  conventional  linkage  and 
genome-wide association studies to identify but a few causative Autism genes is most likely 
due  to  two  main  confounding  factors:  phenotypic  and  genetic  heterogeneity.  Phenotypic 
heterogeneity  is  due  to  the  inability  to  distinguish  closely  related  clinical  subtypes  in  the 
autism spectrum of behavioural disturbances. Genetic heterogeneity refers to fact that many 
different genes (and/or alleles of the same genes) lead to the same phenotype.  

3.1 Monozygotic and dizygotic concordance  
De  novo  mutations  in  identical  twins  would  result  in  their  sharing  the  same  genetic 
predisposition to Autism. These alleles would be highly but not completely penetrant; hence 

 

 
A New Genetic Mechanism for Autism 

107 

the high monozygotic concordance and the low dizygotic concordance, as in the latter case 
the unaffected twin would not share the novel disease predisposing allele. Instances of non-
penetrance  would  explain  the  fact  that  monozygotic  twin  concordance  is  not  100%.  A  de 
novo or spontaneous mutation can arise from different mechanisms and in different periods 
in the development of an individual. This kind of mutation can occur in the gametes (sperm 
or eggs), very early in the developing foetus or later in life as observed in cancer.  The partial 
phenotypic  concordance  in  monozygotic  twin  could  also  be  in  part  explained  by  the 
occurrence of a de novo mutation early in the development of one twin, but not the other.  

3.2 Reduced reproductive fitness  
In  the  general  population,  the  mutational  load  can  be  thought  of  as  a  balance  between 
selection  against  a  deleterious  gene  and  its  acquisition  of  new  mutations.  Lower  rates  of 
reproduction constitute a negative selection factor that should reduce the number of mutant 
alleles in the population, ultimately leading to decreased disease prevalence. These selective 
pressures tend to be of different intensity in different environments. In the case of Autism, 
only  rarely  do  individuals  with  Autism  have  children,  particularly  the  more  severely 
affected  individuals  (Nicolson  &  Szatmari,  2003).  Thus,  Autism  has  a  lower  reproductive 
fitness  (which  is  the  ability  to  pass  on  genes  by  having  offspring)  due  to  an  early  age  of 
onset  and  severely  impaired  cognitive  and  social  functions.  This  observation  should 
influence the disorders incidence and prevalence; but this is not what we observe. Autism 
incidence and prevalence seems to be relatively constant worldwide..  
Studies  of  monogenic  diseases2  indicate  that  rare  diseases  with  strong  negative  selection 
generally exhibit very large allelic diversity, hence many different mutations (Smith & Lusis, 
2002). One exception to this pattern of high allelic diversity occurs when disease alleles also 
provide  protection  from  negative  environmental  selective  pressures.  One  example  is  the 
thalassemias, which confer resistance to malaria. Once arisen, the strong positive selective 
pressure conferred by these alleles allowed their relatively rapid spread through a specific 
population. However, such phenomena are usually regional, in response to specific regional 
environmental pressures. There are no examples of such phenomena occurring with equal 
strength in all cultural and geographical parts of the world, which needs to be the case to 
have a uniform incidence of Autism throughout the world. De novo mutations could explain 
this  relatively  uniform  high  incidence  of  disease,  as  new  disease  predisposing  alleles  will 
continually be introduced at a similar rate in all parts of the world. 

3.3 Effects of paternal age 
The male-to-female ratio of de novo mutations is estimated at about 4–6:1, presumably due to 
a higher number of germ-cell divisions with age in males(Crow, 2000). Therefore, one would 
predict that de novo mutations would more frequently come from males, particularly older 
men  (Li  et  al.,  2002).    At  the  genetic  level,  increased  risk  for  a  disease  with  increasing 
paternal  age  can  be  explained  by  spermatogonial  stem  cell  divisions  that  occur  over  the 
lifetime of males contributing to higher mutational rates in the sperm of older men. A higher 
paternal  origin  of  de  novo  mutations  has  been  shown  for  many  diseases,  including  Apert 
syndrome (Moloney et al., 1996), Crouzon syndrome (Glaser et al., 2000), Multiple endocrine 
neoplasia type II (Carlson et al., 1994) and neurofibromatosis type 1  (Jadayel et al., 1990).  
                                                 
2 Disorders caused by the inheritance of a single defective gene 

 

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Autism Spectrum Disorders: The Role of Genetics in Diagnosis and Treatment 

Rett  syndrome,  a  neurodevelopmental  disease  closely  related  to  Autism,  results  almost 
entirely  from  new  mutations  which  are  exclusively  of  paternal  origin  (Girard  et  al.,  2001; 
Trappe et al., 2001). A role for de novo mutations in Autism would predict that the incidence 
of  disease  should  increase  with  increasing  paternal  age.  Indeed,  multiple  recent  studies 
reported advancing paternal age as a significant risk factor for Autism (Miller, 2006; Cantor 
et  al.,  2007;  Croen  et  al.,  2007;  Puleo  et  al.,  2008).  Some  authors  have  predicted  a  high 
incidence of male-derived novel mutations in many mental disorders (Preuss et al., 2004). 
Similar  observation  has  been  reported  for  schizophrenia  (Malaspina  et  al.,  2001)  and  
intellectual  disabilities  (Malaspina  et  al.,  2005),  two  conditions  phenotypically  related  to 
Autism. These observations provide strong evidence that accumulation of de novo mutations 
in paternal sperm contributes to the overall risk of Autism. 

3.4 Worldwide incidence  
Data  from  a  worldwide  amalgam  of  studies  show  that  the  incidence  of  Autism  has  been 
maintained at a constant, relatively high prevalence in the worldwide population across a 
wide range of cultures and countries (McDonald & Paul, 2010). This occurs despite a strong 
negative selection against this condition. Indeed and with the exception of variants which 
date back to speciation, one would expect that common variants would result in a detectable 
uneven  disease  incidence  across  different  populations  due  to  migration,  different 
population  growth  and  isolation.  This  is  not  the  case  for  Autism.  In  addition,  this  is  not 
what one would predict in diseases with reduced reproductive fitness like Autism, unless 
there  was  a  high  new  mutation  rate.  These  observations  emphasize  the  importance  of  de 
novo mutations in the pathogenicity of Autism.  
Taken together, the high prevalence, the high monozygotic twin concordance, the predicted 
high  level  of  allelic  and  non-allelic  genetic  heterogeneity,  the  uniform  worldwide  high 
incidence  despite  significantly  reduced  reproductive  fitness,  constitute  evidences  that 
Autism may result at least in part, from de novo mutations.  

4. De novo mutations in genes associated with autism  
The fact that a growing number of studies, several from our group, report the association of 
rare  genetic  variants  with  Autism  constitutes  strong  evidence  for  the  de  novo  hypothesis. 
Indeed, among causal genes identified for Autism, Rett syndrome and intellectual disability 
(three closely related disorders), the predisposing mutations, whether they be copy number 
variations, insertions/deletions or point mutations, are very frequently of de novo origin. A 
good example of such a gene is SHANK3 encoding a synaptic scaffolding protein. Two of 
the first three mutations reported in the first manuscript linking SHANK3 to Autism were 
actually of de novo origin; one a deletion of the terminal 22q13 and the other a G insertion 
leading to a frameshift that was carried by two affected brothers (Durand et al., 2007). None 
of  these  mutations  were  found  in  the  parents.  Many  subsequent  reports  on  mutation 
screening  of  SHANK3  gene  in  Autim  also  find  novel  de  novo  mutations  (Moessner  et  al., 
2007;  Gauthier  et  al.,  2009).  Other  examples,  such  as  the  neuroligins  genes,  NLGN3  and 
NLGN4, also clearly demonstrate the importance of de novo mutations in Autism. Jamain et 
al. found a single nucleotide insertion in two affected brothers, one with typical autism and 
the other with Asperger that arose de novo in the mother (Jamain et al., 2003). The NRXN1 
gene has also been found to harbour de novo pathogenic mutations in persons with Autism, 

 

 
A New Genetic Mechanism for Autism 

109 

as well as in intellectual disabilities and in schizophrenia (Ching et al., 2010). Ching et al. 
found  twelve  deletions  in  NRXN1  in  patients  with  Autism  and  four  were  de  novo  copy 
number variations not identified in either parent (Ching et al., 2010). SYNGAP1 (Hamdan et 
al.)  and  IL1RAPL1  (Piton  et  al.,  2008)  are  two  other  examples  where  we  found  de  novo 
mutations  in  individuals  with  Autism  and/or  intellectual  disability.  Actually,  de  novo 
mutations are also a common cause of intellectual disability (Hamdan et al.; Vissers et al., 
2010).  As  expected  and  as  recently  observed  in  Autism,  de  novo  mutations  have  all  been 
identified in different genes. For example, our group found six de novo deleterious mutations 
in females individuals with intellectual disability in SYNGAP1 gene (encoding synaptic Ras 
GTPase activating protein 1) (Hamdan et al.; Piton et al., 2008; Hamdan et al., 2009).  
In our recent study on the direct measurement of the de novo mutation rate in Autism and 
schizophrenia, we found a significant excess of potentially deleterious de novo mutations in 
individuals  with  Autism  and  schizophrenia  (Awadalla  et  al.,  2010).  In  this  study,  we 
examined variants identified by direct re-sequencing of 401 genes in a cohort of 285 autistic 
or  schizophrenic  individuals  and  for  a  subset  of  these  genes  in  population  control 
individuals.  For  the  analysis, we  distinguished  functional  from  non-functional  sites  based 
on  the  effect  of  a  mutation  on  the  transcription  or  translation  of  the  protein  at  a  given 
position.  Among  trios3  without  family  history  of  Autism,  we  observed  a  significant 
enrichment  of  functional  de  novo  mutations  (p  =  0.003  in  one-tail  binomial  test;  p  =  0.022 
Fisher’s exact test). Using a binomial test, our observed number of missense to nonsense de 
novo  mutations  was  also  significantly  higher  than  the  neutral  expectation  (p  =  0.04), 
suggesting  that  some  of  the  mutations  are  likely  to  be  pathogenic.  All  of  our  reported 
observations suggest an excess of potentially disease-predisposing de novo mutations in the 
Autism and schizophrenia cohorts. Indeed, in this study, from sequencing only 8% of genes 
of the human genome, functional de novo mutations were found in 5% of individuals with no 
family  history  of  Autism,  exhibiting  a  wide  range  of  clinical  phenotypes.  These  few 
examples  and  many  others  recently  published  collectively  provide  strong  evidence  for  a 
major role of de novo mutations in Autism. 

5. Altered synaptic connectivity in autism  
The synapse is the locus of neural communication which is critical for human brain function. 
Defects  in  synaptic  transmission  are  thought  to  underlie  many  common  developmental 
brain  disorders  that  are  characterized  by  grossly  normal  brain  structure  (Zoghbi,  2003; 
Levitt et al., 2004). At a cellular level, there are presynaptic nerve endings specialized for the 
activity-dependent  release  of  transmitter  into  the  synaptic  cleft,  which  is  encapsulated  by 
glial cells and contains adhesive molecules that keep presynaptic endings in register with 
postsynaptic specializations (“densities”) on neural cell bodies and branches. In the mature 
nervous  system  these  structures  signal  by  chemical  transmission  and  thus  integrate  and 
propagate the electrical signals that communicate through the brain. Synapses are thought 
to  form  in  the  embryo  largely  by  genetically  pre-programmed,  activity-independent  and 
evolutionarily  conserved  mechanisms  (Goodman  &  Shatz,  1993).  During  post-natal 
development, which is the period during which many developmental brain diseases start to 
manifest  themselves,  synaptic  activity  is  required  to  select,  refine  and  stabilize  mature 
connectivity patterns (Katz & Shatz, 1996). Thus cells that fire together wire together.  
                                                 
3 A trio constitutes an affected individual and both his/her biological parents 

 

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Autism Spectrum Disorders: The Role of Genetics in Diagnosis and Treatment 

Multiple indirect lines of evidence support the hypothesis of altered synaptic connectivity in 
Autism.  These  come  in  part  from  brain-imaging  and  neuropathological  studies  showing 
numerous  alterations  to  both  gross  and  microscopic  structures  of  the  brain  of  autistic 
individuals.  For  instance,  an  increased  brain  volume  (Piven  et  al.,  1996),  increased  brain 
weight (Bailey et al., 1998), abnormal neuronal morphology, with decreased complexity of 
dendritic  branching  and  underdeveloped  neuronal  arbors  (Bauman  &  Kemper,  1985; 
Raymond et al., 1996) have all been observed in autistic individuals. An abnormal neuronal 
density  in  the  cerebellar  hemispheres  has  also  been  observed  (Bauman  &  Kemper,  1985). 
Notably,  several  components  of  the  limbic  system,  including  the  amygdala  (Lotspeich  & 
Ciaranello,  1993)  and  the  hippocampus  (Raymond  et  al.,  1996),  have  been  found  to  be 
abnormal at the microscopic level.  Cytoarchitectural features that are frequently abnormal 
include  reduced  numbers  of  Purkinje  neurons  in  the  cerebellum  and  vermis  and  small 
tightly packed neurons in regions of the limbic system, especially in the entorhinal cortex 
and  in  the  medially  placed  nuclei  of  the  amygdala.  The  reduced  neuronal  size  and 
shortened  dendritic  pattern  found  in  post-mortem  studies  are  consistent  with  synaptic 
alterations. This synaptic deficiency hypothesis has been also proposed for schizophrenia, a 
neurodevelopmental  disorder  that  is  also  characterized  by  marked  disruptions  of 
information processing and cognition (Glantz & Lewis, 2000). More recently, in an effort to 
directly  determine  if  spine  densities,  or  the  synaptic  connectivity,  are  altered  in  autistic 
subjects,  Hussler  and  Zhang  examined  the  structural  microcircuitry  within  the  cerebral 
cortex i.e. dendritic spine densities on cortical pyramidal cells from autistic subjects and age-
matched  control  cases,  on  neurons  located  within  both  the  superficial  and  deep  cortical 
layers of frontal (BA 9), temporal (BA 21), and parietal lobe (BA 7). They observed several 
alterations in spine density in autistic subjects; for example the average spine densities in 
Autism  were  higher  than  those  found  in  control  cases,  supporting  altered  synaptic 
connectivity  and  plasticity  in  the  brains  of  individuals  affected  with  Autism  (Hutsler  & 
Zhang, 2009).  
Other  evidence  suggesting  impaired  synaptic  function  in  autistic  individuals  includes  the 
discovery  of  mutations  in  different  synaptic  genes,  such  as  the  neuroligins,  the  neurexins 
and  SHANK3  (see  examples  below  in  section  5.1).    As  mentioned  earlier,  Rett  syndrome 
shares many features with Autism. Mutations in the coding region of the MECP2 gene (a 
transcription repressor factor expressed by neurons and preferentially abundant in mature 
neurons) are known to be implicated in this severe disorder, with the vast majority of cases 
resulting  from  new  mutations.  Mutations  in  MECP2  gene  have  also  been  identified  in  3 
females who meet the full diagnostic criteria for Autism, underscoring the similarity of these 
diseases (Carney et al., 2003). While the target genes for MECP2 protein remain unknown, 
the small brain size and the reduced neuronal and dendrite sizes in Rett Syndrome patients 
suggest that MECP2 may play a role in synaptic processes (Shahbazian et al., 2002; Balmer et 
al., 2003). Recent findings using the olfactory system as a model to study MECP2 expression 
during development suggest that it may be involved in the formation of synaptic contacts 
(Cohen et al., 2003). These data further support the possibility that Autism results mainly 
from synaptic dysfunction. 

5.1 Synaptic genes as candidates for autism 
At a molecular level, synapses are organized as macromolecular “machines” (Grant, 2003). 
These synaptic machines consist of a presynaptic release apparatus and a signalling device 
at the postsynaptic density held together in quasi-crystalline registry at the adhesive cleft. 

 

 
A New Genetic Mechanism for Autism 

111 

Many  of  the  proteins  constituting  these  various  components  have  been  identified  by 
decades  of  synaptic  biochemistry  and  physiological  genetics,  and  their  macromolecular 
assemblies  have  been  characterized  by  proteomic  analysis.  The  presynaptic  release 
apparatus  consists  of  proteins  that  include  those  for  the  structural  cytoskeleton,  vesicular 
membrane  and  trafficking  components,  vesicle  fusion  grid  and  nerve  terminal  membrane 
(Phillips et al., 2001; Blondeau et al., 2004). The postsynaptic density consists of structural 
proteins as well as signalling components such as tyrosine kinases and phosphatases, while 
both  pre-  and  post-synaptic  membranes  contain  fast  voltage-gated  channels  and 
neurotransmitter-gated  receptors,  channels,  transporters  and  G-protein  coupled  receptors 
mediating neuromodulation (Walikonis et al., 2000; Satoh et al., 2002)  Rapid and selective 
communication across the synapse is ensured by the firm adhesion of each compartment at 
the  cleft  by cell  surface  as  well  as  secreted extracellular  matrix  components  (Huber  et al., 
2003). It is therefore not surprising that synaptic genes constitute the largest class of genes 
associated to developmental brain disorders – with many more to be discovered.  Likewise, 
since  many  of  these  proteins  are  exposed  at  the  extracellular  surface,  they  could  provide 
excellent “druggable” targets. 
The discovery of genes clinically relevant to Autism is accelerating, with many involved in 
the synapse including several neuroligands, as well as genes involved in the glutamatergic 
pathway  (Betancur  et  al.,  2009).  Of  particular  interest  is  the  example  of  the  synaptic  cell 
adhesions  and  associated  molecules  including  the  neuroligins-neurexins-SHANK3  genes. 
Mutations in the X-linked neuroligin-3 (NLGN3) and neuroligin-4 (NLGN4X and NLGN4Y) 
genes have been identified in brothers with autism. Laumonnier et al. identified a two base-
pair  deletion  in  NLGN4  in  12  affected  members  of  a  French  family  with  X-linked  mental 
retardation,  some  of  whom  were  also  autistic  (Laumonnier  et  al.,  2004).  Jamain  et  al. 
identified a C-to-T transition in the NLGN3 gene, in two brothers, one with autism and the 
other with Asperger syndrome (Jamain et al., 2003). The SHANK3 gene, which codes for a 
synaptic  protein  that  binds  directly  to  neuroligins,  seems  crucial  for  the  development  of 
language  and  social  cognition.  SHANK3  mutations  and  small  cytogenetic  rearrangements 
have been implicated with the Autism phenotype (Durand et al., 2007; Gauthier et al., 2009). 
Other genes involved in this pathway have been found to be mutated in autistic individuals. 
Indeed,  variants  in  SHANK2  and  LRRTM1  are  reported  in  schizophrenia  and  Autism 
(Francks et al., 2007; Berkel et al., 2010). Other synaptic molecules implicated in Autism are 
the  protocadherin  family  genes,  which  have  been  shown  to  be  associated  with  Autism. 
Marshall  et  al.  detected  causative  copy  number  variations  in  PCDH9  gene  and  a  de  novo 
translocation  deleting  the  CDH18  genes  in  Autism  (Marshall  et  al.,  2008).  Moreover,  in  a 
study  of  consanguineous  families  of  Autism,  a  large  homozygous  deletion  implicating 
PCDH10 was detected (Morrow et al., 2008). All of these examples emphasized the role of 
impaired synaptic pathways in the pathogenesis of Autism. 

6. Similar genetic architecture in other neurodevelopmental disorders  
Autism,  schizophrenia,  and  intellectual  disability  are  all  severe  neurodevelopmental 
disorders that have childhood or early adulthood onset with a lifetime disability. Clinical 
manifestations  of  these  disorders  are  diverse  and  complex,  and  include  abnormalities  in 
neuronal  excitability,  processing  of  complex  information,  as  well  as  behaviors  such  as 
anxiety  and  impaired  social  interactions.  Pathological  studies,  neuroimaging  and  other 
clinical observations predict that these disorders result from disrupted neurodevelopment 

 

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Autism Spectrum Disorders: The Role of Genetics in Diagnosis and Treatment 

caused by genetic and environmental factors (Lewis & Levitt, 2002). There is a significant 
overlap  in  clinical  manifestations  in  these  mental  disorders,  such  as  episodic  psychosis 
and/or  seizures,  impaired  cognitive  functions,  and  language  problems.  Fifteen  to  thirty 
percent  of  Autism  patients  present  with  seizures  and  20%  of  psychotic  patients  were 
diagnosed as having pervasive developmental disorders (Matese et al., 1994). Also, there is 
no clear clinical or neurobiological distinction between childhood schizophrenia, pervasive 
developmental  disorder  and  autism  (Mouridsen  et  al.,  2000).  Furthermore,  these 
neurodevelopmental  disorders  can  be  included  within  the  allelic  spectrum  of  the  same 
candidate  gene.  These  observations  strongly  suggest  that  Autism,  schizophrenia  and 
intellectual disability may share similar pathogenic pathways and, thus, potential candidate 
genes.  In  addition,  Autism,  schizophrenia  and  intellectual  disability  have  also  a  high 
prevalence, a high monozygotic twin concordance, a predicted high level of allelic and non-
allelic genetic heterogeneity and a uniform worldwide high incidence despite significantly 
reduced reproductive fitness. All of these observations support the notion that the de novo 
mutations model be involved in all these disorders.  

that  Autism, 

schizophrenia  and 

intellectual  disability,  all 

6.1 One gene, three phenotypic conditions 
severe 
We  believe 
neurodevelopmental  disorders,  can  be  studied  in  a  similar  manner,  which  focuses  on  the 
synaptic gene de novo mutation model. In addition, in some instances mutations in the same 
gene  can  lead  to  Autism,  intellectual  disability  or  schizophrenia,  three  clinically  distinct 
phenotypes,  as  defined  in  the  Diagnostic  and  Statistical  Manual  of  Mental  Disorders,  the 
reference manual for the classification of mental disorders. A recent example is our findings 
with the SHANK3 gene. Disruption of this synaptic gene was originally associated with the 
22q13.3  deletion  syndrome  [OMIM  606232]  characterized  by  neonatal  hypotonia,  global 
developmental  delay,  normal  to  accelerated  growth,  absent  to  severely  delayed  speech, 
autistic  behavior  (OMIM  209850),  and  minor  dysmorphic  features.  In  2007,  Durand  et  al. 
and  Moessner  et  al.,  showed  that  abnormal  gene  dosage  of  SHANK3  is  associated  with 
Autism  (Durand  et  al.,  2007;  Moessner  et  al.,  2007).  In  addition,  we  identified  a  de  novo 
splicing  mutation  in  SHANK3  in  a  patient  with  non-syndromic  intellectual  disability 
without  Autism  (Hamdan  et  al.).  We  also  found  deleterious  de  novo  mutations  in  the 
SHANK3  gene  in  a  patient  diagnosed  with  schizophrenia  plus  cognitive  impairment. 
Similarly, mutations in the NRXN1 gene can lead to rare forms of Autism and schizophrenia 
(Kim et al., 2008; Rujescu et al., 2009). This phenomenon has been observed in other diseases 
and, as stressed by Zoghbi and Warren in their recent paper (Zoghbi & Warren, 2010), other 
examples  include  the  ARX  gene  which  causes  X-linked  lissencephaly,  agenesis  of  corpus 
callosum with abnormal genitalia, cognitive deficits with or without seizures, or cognitive 
deficits,  dystonia,  and  seizures;  LMNA  gene,  which  can  lead  to  a  diversity  of  disorders 
including  Emery-Dreifuss  muscular  dystrophy  Type  2,  Charcot-Marie-Tooth  axonal 
neuropathy  limb  girdle  muscular  dystrophy  Type  1B,  Hutchinson-Gilford  progeria 
syndrome, and many other different clinical manifestations. 
Altogether, these findings suggest that the neuroanatomical and physiological disturbances 
resulting  from  dysfunction  of  mutant  genes  may  be  influenced  by  the  effect  of  genetic 
modifiers, the nature of the gene’s role in the human brain and the effect of environmental 
experiences  of  the  affected  individuals,  leading  to  different  clinical  outcomes  in  different 
patients.  Differences  in  the  mutation  types  (for  example,  point  mutation  vs.  large  gene 
disruptions)  must  certainly  also  contribute  to  the  phenotypic  variability.  Although  this 

 

 
A New Genetic Mechanism for Autism 

113 

observation is intriguing,  multiple  phenotypic  manifestations  from  mutations  of  the same 
single gene have been described for many other diseases. Finally, the observation that one 
gene  can  lead  to  many  phenotypes  raised  the  question  of  whether  Autism,  schizophrenia 
and intellectual disability are different entities or part of a same phenotypic continuum.  

7. Gene hunting approaches and the impact of the development of new 
technologies   
In the last few years, a new generation of technologies, referred to as the next-generation 
DNA  sequencing  technologies  have  been  developed  which  allow  screening  of  the  entire 
genome  (i.e.  >  20,000  genes)  of  single  individuals  within  a  matter  of  days.  This  new 
technology  has  revolutionized  genetic  research  and  has  allowed  new  approaches  in  the 
search  for  diseases-causative  genes.  Before  the  advent  of  the  next-generation  DNA 
sequencing technologies, gene screening for the identification of disease-causative mutation 
was  done  one  gene  at  a  time.  Next-generation  DNA  sequencing  enable  the  parallel 
sequencing  of  all  the  20,000  genes,  leading  to  faster  identification  of  mutations.    These 
technologies therefore constitute the ideal method of screening for rare causative variants in 
all  genes  simultaneously.  In  the  context  of  Autism  and  of  the  above  mentioned  de  novo 
mutation genetic mechanism, the major advantage of these technologies is to make possible 
the  identification  of  very  rare  de  novo  mutations,  by  comparing  the  genetic  variants  in  an 
affected subject to those in both of his/her parents (a family trio). Given sufficient coverage 
and quality in next-generation DNA sequencing datasets, identifying de novo mutations in 
trios is highly feasible.   
Sequencing of  entire genomes is still rather expensive, so many  groups now focus on  the 
sequencing  of  the  entire  “exome”  (i.e.,  the  various  coding  regions  of  the  genome)  of  an 
individual. Focusing on the exome is a reasonable approach as the vast majority of disease-
causing  mutations  identified  to  date  disrupt  the  protein-coding  regions  of  genes.  Such 
mutations  include  nonsense,  small  insertion/deletions,  frameshifts,  splicing  and  missense 
mutations, whose consequences can be predicted in silico based on well-annotated reference 
datasets  (e.g.  the  human  consensus  CDS  (CCDS)  subset  of  the  NCBI  RefSeq  database 
includes  23,339  consistently  annotated  protein-coding  transcripts).    These  coding  regions 
constitute less than 3% of the entire genome. Oligonucleotide hybridization-based methods 
that  permit  the  capture  and  amplification  of  virtually  all  human  exons  at  once,  i.e.  the 
human “exome”, are now commercially available (NimbleGen, Agilent, Illumina). Limiting 
the  analysis  to  the  exome  significantly  increases  the  number  of  samples  that  can  be 
sequenced,  and  is  more  likely  to  identify  causative  genes.  Since  it  is  likely  that  highly 
penetrant  alleles  (i.e.  protein-truncating  or  missense)  in  different  autistic  cases  will  result 
from mutations in dozens of different genes,  it is preferable to concentrate on re-sequencing 
only the coding regions of the genome, or the “exome”,  using targeted microarray capture 
followed by next generation sequencing. In addition mutations in coding regions are most 
easily interpreted thus making the link to the disorder easier to establish. The availability of 
these  technologies  is  accelerating  all  aspects  of  the  gene  hunting  process  e.g.  increasing 
number of genes that can be now analysed in a shorter period of time and the number of 
subjects being studied.  

7.1 Challenges for the next-generation approaches 
As  next-generation  DNA  sequencing  technologies  improve,  and  as  it  becomes  possible  to 
rapidly  produce  detailed  lists  of  variants  per  individual  genome,  the  challenge  will  be  to 

 

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Autism Spectrum Disorders: The Role of Genetics in Diagnosis and Treatment 

discriminate  the  pathogenic  variants  from  the  benign  ones  and  establish  the  link  to  the 
disorder. Three major challenges can be identified. The first and most technical one is the 
ability to handle very large datasets in the order of tens or hundreds of terabytes in size, and 
access  to  powerful  computing  platforms  that  can  process  these  datasets,  and  adequate 
resources for storage, retrieval and archiving. The second is the capacity to develop robust 
yet  comprehensive  methods  to  identify  variants  from  next-generation  DNA  sequencing 
datasets. Choosing the correct sequence coverage and quality filters will ensure a maximum 
of true variants to be identified, with as few false positives or false negatives as possible. 
Several programs are available that can align short sequence reads to a reference genomic 
sequence  and  call  potential  homozygous  or  heterozygous  variants.  However,  the  total 
number  of  variants  identified,  even  using  different  parameters  within  the  same  program, 
can vary largely.  Intuitive paradigms and empirically determined cut off points need to be 
implemented.  Furthermore,  the  accurate  annotation  of  genomic  variants  is  critical  to 
classifying  different  variants  for  their  potential  impact  on  transcription,  splicing  or 
translation. This step must be comprehensive so that potential protein-truncating variants 
are not missed given that alternate splicing can lead to different transcripts with different 
open reading frames within a single gene. Finally, developing an experimental design based 
on the known or anticipated genetic mechanisms underlying the disease or condition, and 
on high quality diagnostic procedures, requires that affected and unaffected individuals be 
carefully selected from family groups to help prioritize variants for further analysis, and to 
maximize the chances of finding causative genes. In our case, identifying de novo mutations 
by analysing trios can very quickly lead to the identification of causative mutations and risk 
genes.  Successful mastery of these key strategic and technical competencies is necessary to 
identify potentially pathogenic variants. 

including  deletions  and  duplications,  translocations,  and 

7.2 Copy number variations and autism 
The  use  of  microarray  approaches  for  the  detection  of  copy  number  variations,  which  its 
continuously  improving  resolution,  provides  additional  evidence  for  the  occurrence  of  de 
novo genomic events in the pathogenesis of Autism. In the last decade, studies  linking copy 
number  variation,  and  Autism  have  revealed  that  de  novo  and  inherited  copy  number 
variations, 
inversions  of 
chromosomes,  all  may  significantly  contribute  to  the  pathogenesis  of  Autism,  usually  as 
penetrant  rare  variants  (Sebat  et  al.,  2007;  Walsh  et  al.,  2008).  For  example  Sebat  et  al.  
showed that de novo copy number variations are more common in autistic patients than in 
non-autistic  individuals  (Sebat  et  al.,  2007).  They  found  that  10%  of  their  patients  with 
sporadic Autism (i.e. individuals with no history of the disorder) harboured a de novo copy 
number variation, while the frequency was only 3 % in patients with an affected first-degree 
relative and 1% in controls.  
Other  similar  examples  include  the  study  from  Marshall  et  al.  (Marshall  et  al.,  2008), 
Christian et al. (Christian et al., 2008) and Szatmari et al. (Szatmari et al., 2007) and more 
recently  the  report  of  Bremer  et  al.  (Bremer  et  al.),  which  are  all  consistent  with  the 
hypothesis that de novo or weakly recurrent copy number variations seem to be significant 
contributing factor in the pathogenesis of Autism.  Interestingly, based on the results of their 
copy  number  variations  analyses,  Marshall  et  al,  concluded  that  “structural  de  novo  were 
found  in  sufficiently  high  frequency  in  Autism  subjects  suggesting  that  cytogenetic  and 
microarray  analyses  be  considered  in  routine  clinical  workup”  (Marshall  et  al.,  2008). 
Although this is not the focus of this chapter, the genes identified by copy number variation 

 

 
A New Genetic Mechanism for Autism 

115 

analysis  also  support  the  notion  that  there  are  shared  biological  pathways  in  Autism, 
intellectual disability and schizophrenia (Guilmatre et al., 2009)  

8. Conclusion and directions for future studies  
As  outlined  throughout  this  chapter,  several  lines  of  evidence  support  the  role  of de  novo 
mutations in the pathogenesis of Autism. De novo mutations are a well-established genetic 
mechanism  for  the  development  of  a  number  of  disorders  such  as  Rett  Syndrome  and 
certain types of cancers but have been poorly explored for common diseases like Autism. In 
general,  the  development  of  technologies  often  brings  new  challenges,  but  mostly  allows 
research to accelerate. Indeed, this technological progress is already starting to provide data 
supporting the role of de novo mutations in Autism. This hypothesis is gaining acceptance in 
the scientific community, as reflected in the growing number of recent publications on this 
subject. Although the focus of this chapter is on the role of de novo mutations in Autism, we 
acknowledge  the  fact  that  many  other  genetic  or  non-genetic  mechanisms  certainly 
contribute to this disorder.  
The  accessibility  of  next-generation  DNA  sequencing  methodologies  have  enabled 
researchers to analyse a large amount of DNA and has had an important impact on gene 
hunting strategies , which have shifted from a tendency to look at single genes, one at the 
time,  to  multiple  genes  simultaneously.  One  interesting  consequence  of  next-generation 
DNA sequencing is that it recently permitted to directly estimate the rate of de novo germline 
base substitution mutations in humans (Awadalla et al., 2010; Durbin et al., 2010). Based on 
these data, the challenge will be to determine if the observed de novo mutation rate detected 
in a disease is greater than the baseline rate.  
In the last few years, researchers have identified several genes contributing to Autism, and 
most encode for proteins that are part of the synaptic machinery. An important concern for 
future  research,  where  there  will  be  rapid  identification  of  many  potential  Autism  gene 
mutations, will be to determine if they have a functional relevance to the disorder. Indeed, 
this question needs to be judiciously examined for most of the variants discovered. This will 
require to study  model organism systems as proposed in our current Synapse to disease 
project  (S2D;  http://www.synapse2disease.ca),  a  large-scale  medical  research  project 
launched in 2006 aiming to identify genes involved in several neurological and psychiatric 
diseases  caused  by  defects  in  the  development  and  functioning  of  the  brain  and  nervous 
system.  This  project’s  philosophy  is  that  once  the  base  changes  are  discovered  and 
considered  likely to be  “pathogenic mutations”, biological validation must be conducted in 
vitro and in vivo in different model organism (e.g. fly, worm, fish, etc.) to determine their 
functional effects. Biological validation is an essential step often missing from most genetic 
studies and has thus severely limited data interpretation in the context of disease pathology. 
This  validation  will  consequently  help  understanding  pathways  in  neurodevelopmental 
disorders  and  ultimately  give  insights  for  the  development  of  targeted  therapeutic 
strategies.  
Another challenge for future research in the field is the issue of whether genomic variants 
beyond the coding regions of a gene contribute to the etiology of the disorder. As mentioned 
earlier, the majority of mutations identified in Autism are located within coding regions but 
it should not be forgotten that variants in the non-coding regions, particularly the regulatory 
gene region, can also lead to disease.  

 

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Autism Spectrum Disorders: The Role of Genetics in Diagnosis and Treatment 

Finally, the accessibility of the next-generation DNA sequencing technologies is facilitating 
the  gene  hunting  process  for  researchers,  whereas  its  application  for  clinical  diagnostic 
testing  seems  to  be  inevitable,  particularly  as  the  cost  per  base  continues  to  decrease.  
Although, the clinical tests based on these technologies represent particular challenges and 
will  need  careful  validation,  the  connection  between  research  findings  in  the  genetics  of 
Autism or any other neurodevelopmental disorders and clinical applications is closer than 
ever.  All  these  research  and  technological  advancements  are  for  the  greatest  benefit  of 
families. 

9. Acknowledgments  
We  wish  to  thank  Anna  Bonnel  and  Fadi  F.  Hamdan  for  their  critical  reading  of  this 
manuscript. 

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8 

Common Genetic Etiologies and Biological 
Pathways Shared Between Autism Spectrum 
Disorders and Intellectual Disabilities 
Liana Kaufman1, Abdul Noor1, Muhammad Ayub2,3 and John B. Vincent1,4 
1Neurogenetics Section, The Centre for Addiction & Mental Health, Toronto, Ontario 
2Tees, Esk and Wear Valleys NHS Foundation Trust  
3School for Medicine and Health, University of Durham 
Wolfson Research Institute Queen’s Campus, Stockton on Tees 
4Department of Psychiatry, University of Toronto, Toronto, Ontario 
1,4Canada 
2,3United Kingdom 

1. Introduction 
1.1 Definitions of ID and autism 
Intellectual disability (ID) is a common neurodevelopmental disorder that is characterized 
by an intelligence quotient (IQ) lower than 70, and deficits in at least two behaviors related 
to  adaptive  functioning  diagnosed  by  18  years  of  age  (American  Psychiatric  Association, 
2000). Adaptive functioning behaviours can be defined as the ability to acquire skills that 
help an individual to live independently and to cope with everyday life, and involves skills 
such  as  language/communication,  social  skills,  home  living  and  safety.  ID  ranges  in  its 
severity and may either be present co-morbidly with many congenital syndromes, or may 
present alone. 
Autism is a severe, lifelong neurodevelopmental disorder characterized by impairments in 
three  major  domains:  communication,  socialization,  and  repetitive  behavior.  Leo  Kanner 
first described this developmental disorder in 1943 as a social disorder featuring the innate 
inability to form typical, affective contact with others (Kanner, 1943). It is now known that 
the  Autism  Spectrum  Disorder  (ASD)  includes  a  widely  variable  range  of  clinical 
phenotypes  that  have  been  grouped  into  individual  disorders.  Autistic  disorder  (autism), 
Asperger syndrome, pervasive developmental disorder not otherwise specified (PDD-NOS), 
Rett syndrome and child disintegrative disorder (CDD) are all currently separate diagnoses 
in  the  DSM-IV  under  the  umbrella  title  “Pervasive  Developmental  Disorders”(American 
Psychiatric Association, 2000). These disorders differ from each other with regard to severity 
of  symptoms,  early  development  of  language,  deterioration  in  skills  once  they  have 
developed, and cognitive development.  
Individuals  with  autism  show  severe  deficits  in  all  three  major  phenotypic  domains  and 
present with abnormal development before age 3 years. In addition, cognitive functioning is 
frequently delayed. Individuals with Broad Autism Phenotype (BAP) have some symptoms 
of autism, but do not meet the full criteria for autism or ASD (Hurley et al., 2007). BAP is 

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Autism Spectrum Disorders: The Role of Genetics in Diagnosis and Treatment 

currently  not  a  recognized  diagnosis  within  the  DSM-IV,  however,  it  can  be  useful  when 
looking at familiality in ASDs.  
Asperger  syndrome  is  characterized  by  qualitative  impairment  in  social  interaction  and 
restricted repetitive and stereotyped patterns of behavior, interests and activities. Language 
and cognitive development is relatively unaffected in these individuals, however pragmatic 
or  social  language  is  often  delayed  (McConachie  &  Diggle,  2007).  Individuals  with  PDD-
NOS  meet  autism  criteria  and  have  later  age  of  onset.  These  individuals  may  also  show 
severe and pervasive impairment in one or two of the three core domains with or without 
cognitive or language delay. Rett syndrome occurs almost exclusively occurs in females and 
it is characterized by developmental arrest between 6 and 18 months of age, followed by loss 
of  speech,  stereotypical  movements,  microcephaly,  seizures,  and  intellectual  disability 
(Hagberg et al., 1983). CDD is characterized by normal development for at least the first two 
years  (but  up  to  10  years)  of  life,  followed  by  clinically  significant  loss  of  acquired  skills. 
Individuals  with  CDD  must  not  meet  the  diagnostic  criteria  for  Rett  syndrome  or 
schizophrenia.  They  also  must  display  at  least  two  of  the  three  diagnostic  domains  for 
autism.  
While ASDs are often associated with ID, it should be noted that not all individuals with 
ASD have cognitive deficits. The current DSM-IV statistic states that 75% of individuals with 
ASD have some degree of intellectual disability. However, this may be an outdated over-
estimate. More recent studies suggest an increasingly modest level; approximately 50-60% 
[71%(Chakrabarti  &  Fombonne,  2001)  63%(Bertrand  et  al.,  2001);  40%(Baird  et  al.,  2006), 
50%(Charman  et  al.,  2011].  With  increased  awareness  in  the  general  population  and  a 
greater understanding of the problem by professionals, an increasing number of people with 
high  functioning  Autism  and  Asperger  syndrome  are  diagnosed  with  the  condition.  This 
has  probably  changed  the  proportion  of  cases  with  identifiable  ID  among  the  ASD 
population.  It  is  also  possible  that  expanding  the  diagnostic  criteria  for  ASDs,  which 
historically were much more limited, might explain this decrease (Charman et al., 2011).  
The  new  DSM-V,  which  will  be  available  in  2013,  will  define  ASDs  differently  than  the 
current  version.  The  three  behavioural  domains  will  be  condensed  into  two:  Social 
communication  and  repetitive  behaviours/narrow  interests.  In  addition,  the  separate 
diagnoses  within  the  spectrum  will  be  removed.  Autism,  Rett  syndrome,  Asperger 
syndrome, PDD-NOS and CDD will all be classified as “autism spectrum disorder” with a 
specified  etiology/syndrome  if  known  (e.g.  ASD  with  Rett  syndrome;  DSM-5  Proposed 
Revisions, 2010). In addition, a level of severity will be assigned to each diagnosis, which 
will reflect the functional level of the individual. The alteration in nomenclature reflects the 
clinical and interventional needs of the individuals and is intended to be more reflective of 
what is known of the pathology of the disorder (DSM-5 Proposed Revisions, 2010). 

1.2 Classification of ID by IQ and syndromic vs. nonsyndromic 
ID  is  currently  subdivided  into  5  categories  based  on  intelligence  quotient  (IQ):  mild, 
moderate, severe, profound and unable to classify (American Psychiatric Association, 2000; 
Table  1).  However,  epidemiological  studies  often  use  a  simplified  classification,  grouping 
their subjects into mild ID (IQ50-70) and severe ID (IQ<50; Ropers & Hamel, 2005). IQ tests 
are a set of tasks which are administered to representative population samples for creation 
of norms. An IQ two standard deviations below the mean or lower is indicative of ID. The 
distribution of IQ in the population is normal in the main, apart from an increased number 

 

Common Genetic Etiologies and Biological 
Pathways Shared Between Autism Spectrum Disorders and Intellectual Disabilities 

127 

of cases in the tail on the lower end of IQ. On that basis, the population prevalence of ID 
should  be  close  to  3%  at  least.  The  studies  looking  at  prevalence  of  ID  were  recently 
reviewed systematically. The range reported varied between 0.93 per 1000 and 156.03 per 
1000 (Maulik et al., 2011). Differences in rates of mild ID mainly account for this variation. 
The  reasons  are  differences  in  definition  of  ID  criteria,  characteristics  and  setting  of  the 
sample  studied  and  differences  in  methodology.  The  prevalence  of  severe  ID  is  relatively 
stable (3-4 per 1000; (Roeleveld et al., 1997; Leonard & Wen, 2002; Emerson, 2007).  
 

Severity 

IQ 

Proportion of ID 

Functional Level 

Borderline 

70-84 

Mild 

50-69 

Moderate 

35-49 

Severe 

20-34 

Profound/ 
Unspecified 

<20 

N/A 

85% 

10% 

3-4% 

1-2% 

Can live independently; May require 

low level support 

Can often live independently with 

social support 

Acquire some communication and self-

help skills, require moderate 

supervision 

Acquire only basic self-help and 
communication skills, require 

supervision 

Require highly structured and 
supervised living conditions 

Table 1. This table indicates the categories of intellectual disabilities by IQ and ability to 
function in society as indicated by the DSM-IV-TR.  
The  new  DSM-5  will  likely  be  changing  the  severity  criteria  to  encompass  functional 
behavioural  deficits  and  the  level  of  interference  that  these  have  in  the  lives  of  affected 
individuals (DSM-5 Proposed Revisions, 2010). Although the new criteria have not yet been 
established, it will likely echo the changes to the criteria for autism, focusing on functional 
level as opposed to strict IQ cut-offs. The manual will also be changing the wording of the 
ID  diagnostic  criteria  to  encourage  cultural  sensitivity  and  relevance,  and  to  ensure  that 
culturally validated psychometric tests are used to evaluate IQ and level of functioning.  
In addition to categorization by severity/IQ level, ID can also be grouped into syndromic 
intellectual  disability  (S-ID)  and  non-syndromic  intellectual  disability  (NS-ID).  In  S-ID, 
individuals present with an identifiable constellation of clinical features or co-morbidities in 
addition to ID. While S-ID has a clear definition, there is debate over the classification of NS-
ID.  Traditionally,  NS-ID  has  been  defined  by  the  presence  of  intellectual  disability  as  the 
sole clinical feature. However, it has been a challenge to rule out the presence of more subtle 
physical  signs,  neurological  anomalies  and  psychiatric  disorders  in  these  individuals,  as 
they  may  be  less  apparent,  or  difficult  to  diagnose  due  to  the  cognitive  impairment. 
Additionally, the symptoms of some syndromes may be so subtle that they are extremely 
difficult to diagnose unless the features are looked for specifically in the context of a known 
genetic defect previously associated with these features (Ropers, 2006). Thus the distinction 
between S-ID and NS-ID is often blurred. 

 

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Autism Spectrum Disorders: The Role of Genetics in Diagnosis and Treatment 

1.3 Endophenotypes and “essential autism” versus “complex autism” 
The extreme phenotypic heterogeneity of ASD poses a challenge for the study of underlying 
etiologies. It has been argued that delineation of the clinical heterogeneity of ASD may help 
in the identification of more homogeneous sub-groups for the study of etiological factors, 
and  to  predict  the  outcome  and  treatment  choices.  While  ASD  has  three  core  phenotypic 
domains, it can also be sub-grouped on the basis of presence or absence of certain clinical 
features,  termed  endophenotypes.  Endophenotypes  of  ASDs  include  IQ  level,  seizures, 
brain malformations, dysmorphology and head circumference (Viding & Blakemore, 2007).  
Historically,  based  on  non-verbal  IQ  testing,  ~70%  of  autistic  children  were  reported  as 
having some form of ID (Fombonne, 2003). While it is now thought that this number may be 
lower, it is essential to look at IQ as an endophenotype of autism when predicting outcomes. 
Previously published longitudinal studies report that IQ scores can strongly predict long-
term outcomes of ASD and are directly associated with the psychopathology of autism, even 
in  young  children  (Howlin  et  al.,  2004).  In  addition,  preschool  cognitive  functioning  has 
been found to be a strong predictor of school-age functioning, and high IQ has been shown 
to be necessary but not sufficient for optimal outcome in the presence of severe language 
impairment (Stevens et al., 2000).  
Another common central nervous system (CNS) dysfunction associated with autism is the 
high  risk  of  epilepsy  (Spence  &  Schneider,  2009).  The  prevalence  of  seizures  in  autism  is 
estimated to be up to 46% (Hughes & Melyn, 2005) and it has been estimated that as many 
as 32% of individuals with epilepsy may meet the diagnostic criteria for ASD (Clarke et al., 
2005).  Notably,  the  prevalence  of  seizures  is  higher  among  autistic  individuals  with 
moderate to severe ID and individuals with overt motor abnormalities (Tuchman & Rapin, 
2002).  Furthermore,  individuals  with  autism  plus  epilepsy  on  average  have  lower  IQs. 
Epilepsy 
is  one  of  the  negative  factors  contributing  to  cognitive,  adaptive  and 
behavioral/emotional outcomes for autistic individuals (Hara, 2007). 
Structural  brain  malformations,  including  accentuated  Virchow–Robin  space,  acrocallosal 
syndrome and polymicrogyria have been reported to be associated with autism (Steiner et 
al., 2004; Schifter et al., 1994; Zeegers et al., 2006), however, until recently, MRIs have been 
considered to be of indeterminate value and they are not included in the standard clinical 
evaluation of autism. A recent study has revealed an unexpectedly high prevalence of brain 
abnormalities (48%) in autism patients. Some common abnormalities include white-matter 
signal  abnormalities,  severely  dilated  Virchow-Robin  space  and  temporal  lobe  structural 
abnormalities (Boddaert et al., 2009). 
Generalized dysmorphology, an indicator of insult in early development, has been reported 
in 15% to 20% of individuals with autism (Miles & Hillman, 2000) and has been proposed to 
be a predictor of a poor response to early intensive behavioral intervention. According to the 
Autism  Dysmorphology  Measure  (ADM)  guidelines,  the  12  body  areas  assessed  for 
dysmorphology  are:  height,  hair  growth  pattern,  structure  and  size  of  ear,  nose  size  and 
shape,  face  size  and  structure,  philtrum,  mouth  and  lips,  teeth,  hand  size,  fingers  and 
thumbs, nails and feet. The ADM was developed by the Miles laboratory at the University of 
Missouri to aid clinicians who are not extensively trained in medical genetics to distinguish 
between individuals with ASD with and without dysmorphisms (Miles & Hillman, 2000). 
Besides  generalized  dysmorphology,  head  size  abnormalities 
(microcephaly  and 
individuals.  Microcephaly,  head 
macrocephaly)  have  also  been  found 
circumference <2nd centile, occurs in 5 to 15% of children with autism and is a predictor of 
poor  outcome  (Miles  et  al.,  2005).  On  the  other  hand,  macrocephaly,  head  circumference 

in  autistic 

 

Common Genetic Etiologies and Biological 
Pathways Shared Between Autism Spectrum Disorders and Intellectual Disabilities 

129 

>97th  centile  has  been  observed  in  ~30%  of  children  with  autism  (Miles  et  al.,  2000). 
Generalized dysmorphology and microcephaly have been proposed as good predictors of 
clinical outcome and may be used to classify the autism phenotype into subgroups: complex 
autism  and  essential  autism.  Complex  autism  consists  of  autistic  individuals  who  show 
evidence  of  some  abnormality  in  early  morphogenesis,  manifested  by  either  significant 
dysmorphology  or  microcephaly.  The  remainder  without  dysmorphic  features  or 
microcephaly are classified as having essential autism (Miles et al., 2005), and make up 70-
80% of the autism population. 

1.4 Diagnostic approaches 
The  symptoms  of  most  ASDs  are  usually  present  by  the  age  of  three  and  may  persist 
throughout  the  lifespan;  however,  CDD  and  PDD-NOS  may  present  later.  A  number  of 
checklists and diagnostic tools are available for diagnosis of autism. The Childhood Autism 
Rating  Scale  (CARS;  Schopler  et  al.,  1980) is  a  commonly  used  diagnostic  checklist  which 
consists of 15 questions scored by the parents and the examiner. It is a reliable and efficient 
tool that is commonly used in clinics. Another autism screening tool widely used in clinical 
settings is the Checklist for Autism in Toddlers-modified (M-CHAT). This checklist consists 
of 23 yes/no questions and is a promising tool for the early diagnosis of autism (Robins et 
al.,  2001).  Other  such  checklists  include  the  Autism  Behaviour  Checklist  (ABC;  Witwer  & 
Lecavalier, 2007) and Gilliam Autism Rating Scale (GARS; South et al., 2002).   
The  Autism  Diagnostic  Observation  Schedule  (ADOS)  and  Autism  Diagnostic  Interview-
Revised  (ADI-R)  are  two  widely  accepted  instruments  used  for  diagnosis  of  ASD  in  both 
clinical  and  research  settings.  ADI-R,  a  revised  version  of  Autism  Diagnostic  Interview 
(ADI), is a semi-structured, investigator-based interview for the caregivers of children with 
autism and adults for whom autism or ASD is a possible diagnosis (Lord et al., 1994). The 
ADOS is a semi-structured, standardized assessment of social interactions, communication, 
play, and imaginative use of objects for children suspected of having ASD (Lord et al., 2000). 
It is an observational assessment of the child’s behaviour, often performed by a psychologist 
or another trained professional. Checklist tools are widely used in clinical practice because 
of their ease and efficiency; however, ADI-R and ADOS have been adapted, particularly in 
recent  years,  to  make  them  more  appropriate  for  use  in  clinical  settings,  as  well  as  for 
diagnosis  of  toddlers  and  patients  with  intellectual  disabilities.  In  particular,  the  shorter 
version of ADOS is becoming increasingly popular in clinics.  
All  of  these  diagnostic  tools  have  their  own  advantages  and  disadvantages.  For  example, 
ADI-R and ADOS are lengthy, require elaborate training and are suitable for use in more 
specialized settings. The Checklist for Autism in Toddlers-modified (M-CHAT), described 
earlier in this section, has been particularly useful in the frontline clinical world, as well as 
for early diagnosis of autism (Robins et al., 2001), and has overcome some of the challenges 
faced  by  ADI-R  and  ADOS.  M-CHAT  includes  a  checklist  of  23  items  to  be  filled  out  by 
parents and it can be administered at a much earlier stage to identify toddlers who are at  
risk  of  autism.  A  recent  study  has  confirmed  the  validity  of  this  instrument  in  detecting 
possible ASD at 16-30 months of age (Kleinman et al., 2008). However, the high sensitivity of 
this checklist means that some children without autism will fail the screening. It has been 
suggested  that  children  who  fail  and  do  not  have  autism  are  at  increased  risk  for  other 
developmental  disorders  or  delays  and  should  be  monitored  accordingly  (Robins  et  al., 
2001).  

 

130 

 
Autism Spectrum Disorders: The Role of Genetics in Diagnosis and Treatment 

Over  last  two  decades  the  diagnosis  of  syndromes  based  on  behavioural  symptoms  has 
become  relatively  standardized,  although  there  are  some  issues  around  the  potential  for 
over diagnosis of higher functioning autism and Asperger syndrome. However, the etiology 
of  ASDs  is  largely  unknown,  and  few  genetic  tests  or  biomarkers  have  been  found  to 
confirm  an  autism  diagnosis.  In  other  words,  no  laboratory-based  means  of  testing  for 
autism is yet widely available. While various genes have been identified that cause autism, 
none of them are common, and justification for performing individual testing is debatable. 
However,  tests  are  currently  being  offered  commercially  for  a  number  of  these  known 
genes, including CNTNAP2, PTEN and SHANK3 . Recently, microarray technology has also 
been proposed as a potential diagnostic tool, or a method to determine etiology. Because of 
the growing number of genes known to cause ASDs, an autism microarray, containing only 
verified autism causing genes, could be custom made to assess if these genes are aberrant in 
affected individuals.  The proportion of cases with known etiology at this stage will be very 
small  (less  than  5%)  and  a  negative  test  would  not  mean  an  exclusion  of  the  diagnosis. 
Knowing the root cause of autism may not lead to an alteration in treatment or intervention 
at this stage, but may be useful for family planning and genetic counseling, as well as for the 
emotional well being of concerned family members. Additionally, with further phenotype 
profiling and analysis of individuals with particular mutations, it may be possible to tailor 
interventions based on genetic diagnoses in the future.  

2. Causes of ID 
ID can be caused by environmental and/or genetic factors. However, for up to 60% of cases, 
there  is  no  identifiable  cause  (Rauch  et  al.,  2006).  Environmental  exposure  to  certain 
teratogens, viruses or radiation can cause ID, as can severe head trauma or injury causing 
lack of oxygen to the brain (Chelly et al., 2006). While these factors explain some cases of ID, 
it is also important to consider genetic etiology. 
Genetic  causes  of  ID  are  thought  to  be  present  in  25-50%  of  cases,  although  this  number 
increases  proportionally  with  severity 
(McLaren  &  Bryson,  1987).  Chromosomal 
abnormalities  have  been  reported  in  ID,  with  a  broad  range  of  prevalence,  and  many 
different  types  of  aberrations  have  been  identified  (Rauch  et  al.,  2006).  Over  the  past  15 
years many single gene causes of ID have been identified as well. Many of these genes cause 
a  broad  range  of  phenotypes  including  syndromic  ID  (S-ID),  non-syndromic  ID  (NS-ID), 
autism and other neurodevelopmental and psychiatric phenotypes. This suggests that it is 
likely  that  other  genetic  modifiers  or  environmental  factors  may  be  involved  in  disease 
etiology, and that similar biological pathways, when disturbed, have the potential to lead to 
a range of these conditions. This illustrates the need for detailed study and descriptions of 
phenotypes  for  each  gene  and  mutation.  Most  of  the  mutations  that  cause  ID  are  highly 
penetrant and  are inherited in a Mendelian fashion. Many known ID genes are on the X-
chromosome,  however  the  number  of  autosomal  genes  associated  with  ID  is  growing 
rapidly  (Kaufman  et  al.,  2010).  This  is  due  to  advances  in  technology  which  allow  us  to 
study the autosomes more efficiently, along with a shift in focus from the X-chromosome to 
the autosomes resulting from the realization that the X-linked genes may only account for 
~10%  of  ID  cases,  while  autosomal  genes  may  account  for  many  more  (Ropers  &  Hamel, 
2005). 

 

Common Genetic Etiologies and Biological 
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131 

3. Causes of autism 
3.1 Genetic contributions 
The  causes  of  autism  are  likely  far  more  complex  than  the  causes  of  ID.  Genetic  factors 
clearly  play  a  prominent  role.  The  evidence  for  the  involvement  of  genetic  factors  in  the 
etiology of autism comes primarily from family and twin studies and is further supported 
by cytogenetic and molecular studies. Several sibling concordance studies have provided a 
strong  indication  that  autism  has  a  significant  genetic  component.  Studies  over  the  years, 
including  analyses  of  autism  probands  with  severe  ID,  evaluations  of  the  developmental, 
social,  and  psychiatric  histories  of  siblings  of  autistic  individuals  and  reviews  of  familial 
aggregation  in  large  autism  cohorts,  have  all  displayed  higher  than  expected  familiality 
(Baird & August, 1985; Piven et al., 1990; Bolton et al., 1994). A recent study examined the 
cognitive, adaptive, social, imitation, play, and language abilities of 42 non-autistic siblings 
(of autism probands) and 20 toddlers with no family history of autism. The siblings were 
below  average  in  expressive  language  abilities  and  IQ.  They  had  lower  mean  receptive 
language,  adaptive  behavior,  and  social  communication  skills.  They  used  fewer  words, 
distal gestures, and social smiles than children with no familial history of autism (Toth et al., 
2007).  
On  the  other  hand,  the  familiality  of  autism  does  not  imply  that  genetic  factors  are 
exclusively responsible for the disease, and role of the environmental factors, which are also 
shared  by  family  members  who  live  together,  cannot  be  excluded  by  these  studies  alone. 
Twin  studies  provide  an  alternative  approach  for  investigating  the  relative  magnitude  of 
genetic and/or environmental factors on the autism phenotype and penetrance. In 1977, a 
landmark  study  by  Folstein  and  Rutter  demonstrated  a  significant  difference  between 
monozygotic  (MZ)  and  dizygotic  (DZ)  twins  in  their  concordance  for  autism  (Folstein  & 
Rutter, 1977). This difference in concordance suggested a major role for genes in the etiology 
of autism and this was confirmed by subsequent studies (Ritvo et al., 1985; Steffenburg et al., 
1989). Most recently, a large scale study of 277 twin pairs (210 DZ and 67 MZ) reported 88% 
concordance of autism for MZ twins and 31% concordance of autism for DZ twins. In MZ 
twins,  the  authors  also  observed  a  higher  prevalence  of  bipolar  disorder  and  Asperger 
syndrome  with  a  higher  concordance  of  the  latter  (Rosenberg  et  al.,  2009).  The  finding  of 
increased  bipolar  disorder  in  twins  is  interesting  because  the  co-morbidity  of  psychiatric 
disorders (eg. anxiety disorder, ADHD, etc) in children with autism is up to 70% (Charman 
et  al.,  2011),  and  suggests  significant  overlap  in  genetic  etiology  between  different 
psychiatric disorders. Specific genetic causes for autism will be outlined more thoroughly 
later in this chapter.  

3.2 Epigenetic contributions 
It is widely accepted that genetic factors play a major role in the etiology of ASD, however, 
epigenetic  factors  may  also  be  an  important  determinant  of  the  autism  phenotype. 
Epigenetic  modifications  include  cytosine  methylation  and  post-translational  modification 
of histone proteins, and act as a mechanism to control gene expression (Samaco et al., 2005). 
The  epigenetic  regulation  of  gene  expression  can  be 
influenced  by  exposure  to 
environmental factors and can show parent of origin effects. Notably, epigenetic factors play 
a  central  role  in  pathogenesis  of  two  single  gene  disorders,  Rett  syndrome  and  Fragile  X 
syndrome  (FXS),  that  are  commonly  associated  with  autism  (Brown  et  al.,  1982;  Gillberg, 
1986).  Rett  syndrome,  a  progressive  neurodevelopmental  disorder,  is  classified  among 

 

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Autism Spectrum Disorders: The Role of Genetics in Diagnosis and Treatment 

ASDs. It is caused by mutation in the MECP2 gene that encodes the methyl-CpG-binding 
protein 2, which is involved in epigenetic regulation of gene expression (Amir et al., 1999). 
JARID1C, a relatively common ID gene associated with some cases of autism (Adegbola et 
al., 2008) has been shown to regulate the methylation of histones and function in epigenetic 
transcriptional repression (Tahiliani et al., 2007). 
In addition, some autism (and ID) genes are regulated by known epigenetic mechanisms. 
FXS is caused by the expansion of a tract of CGG repeats in the 5’ untranslated region of the 
FMR1  gene.  This  expansion  results  in  epigenetic  silencing  of  the  region,  causing  loss  of 
expression  of  the  gene,  thus,  FXS  is  caused  by  a  genetic  mutation  resulting  in  epigenetic 
dysregulation  (Hagerman  et  al.,  2005).  The  RELN  gene  is  another  interesting  example  of 
possible  contributions  of  epigenetic  factors  to  ASD.  The  RELN  gene  encodes  a  large 
extracellular matrix protein that organizes neuronal positioning during corticogenesis and is 
regulated  by  epigenetic  mechanisms.  Several  independent  studies  have  shown  an 
association between RELN and ASD (Skaar et al., 2005; Ashley-Koch et al., 2007; Holt et al., 
2010). Interestingly, reduced levels of reelin and its isoforms have been previously shown in 
autistic twins and their first degree relatives (Fatemi et al., 2005).  
Genomic  imprinting  is  another  mode  of  regulation  of  gene  expression  by  epigenetic 
modifications  and  it  results  in  parent  of  origin-specific  gene  expression.  Genomic 
duplications  of  an  imprinted  region  on  the  proximal  long  arm  of  chromosome  15  (15q11-
q13) are associated with 0.5-3.0% of autism cases (Hogart et al., 2010).  

3.3 Other factors 
Support  for  possible  environmental  factors  contributing  to  the  causation  of  autism  comes 
from the incomplete concordance in monozygotic twins. Additionally, at this point in time, 
known  genetic  defects  only  explain  a  small  proportion  of  autism  patients.  Furthermore, 
there is evidence that in utero exposure to valproic acid and thalidomide may increase the 
risk of ASD (Arndt et al., 2005). One long-term study of 632 children exposed to antiepileptic 
drugs during gestation, found that 6.3% of the children exposed to valproic acid in utero had 
ASD or some features of ASD. This incidence is seven times higher than the control group 
(0.9%;  (Bromley  et  al.,  2008)).  Similarly,  a  higher  incidence  of  autism  has  been  reported 
among  children  prenatally  exposed  to  thalidomide.  In  a  population  of  100  Swedish 
thalidomide  embryopathy  cases,  at  least  four  met  full  diagnostic  criteria  for  autism 
(Stromland  et  al.,  1994).  Animal  models  have  also  demonstrated  that  early  serotonergic 
neural  development  is  disrupted  in  rats  exposed  to  thalidomide  or  valproic  acid  on  the 
ninth  day  of  gestation,  conferring  increased  risk  for  the  development  of  ASD-related 
behaviours (Narita et al., 2010).  
Mercury (Hg), because of its known neurotoxicity, has drawn particular attention in relation 
to  the  neurodevelopment  of  individuals  with  autism,  and  a  number  of  studies  have 
compared the  level of Hg in  blood, hair, or urine in children with autism versus without 
autism.  However,  none  of  these  studies  have  shown  any  substantial  evidence  of 
involvement of Hg in autism. Recently, a study conducted on 452 autism patients failed to 
demonstrate  any  difference  in  blood  Hg  level  of  autism  patients  compared  to  controls 
(Hertz-Picciotto et al., 2010). 
Childhood immunization is an environmental factor that has been popularized in the media 
as a potential cause of autism. The use of mercury in vaccines has been one of the prime 
sources of concern surrounding vaccines and their role in autism (Baker, 2008). However, 

 

Common Genetic Etiologies and Biological 
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133 

there  is  no  consistent  evidence  in  support  of  the  theory  that  vaccines  are  related  to  the 
etiology of autism. In the late 1990s, a link between vaccines and autism was reported by 
clinical observation of the onset of autism soon after vaccination of children (Wakefield et 
al., 1998). These observations triggered a series of studies in the US, UK, Europe and Japan, 
however, none of these studies found any compelling evidence for a link between vaccines 
and  autism.  The  original  study  has  since  been  retracted  (Murch  et  al.,  2004;  Anonymous, 
2010) 
Although  the  majority  of  research  to  date  has  focused  on  genetic  factors  involved  in  the 
etiology of autism, non-genetic factors are also likely to contribute. Our knowledge of these 
factors  is,  however,  currently  very  limited.  It  has  been  suggested  that  distinct  genetic 
features/pathways may cause distinct domains of autistic behaviour, but this has yet to be 
tested at the molecular level (Happe & Ronald, 2008). It does, however, resonate with the 
idea  that  autism  is  a  genetically  heterogeneous  spectrum,  and  that  multiple  genetic 
aberrations  may  be  necessary  to  reach  the  autism  phenotype  threshold  (Cook  &  Scherer, 
2008).  The  threshold  theory  postulates  that  the  cumulative  effect  of  several  genetic 
aberrations, for instance a copy number variant together with one or more single nucleotide 
variants,  and  possibly  in  combination  with  environmental  factors,  in  a  single  individual, 
may  result  in  an  autism  phenotype.  These  genetic  aberrations  may  include  chromosomal, 
single  nucleotide  or  epigenetic  abnormalities.  It  has  also  been  noted  that  some  genetic 
aberrations are more penetrant than others and may be more likely to result in a phenotype. 
In contrast with ID genetics, which are relatively straightforward, autism presents us with a 
convoluted, likely multigenic/multifactorial disorder for which it may be more difficult to 
delineate causes.   

4. Epidemiology 
4.1 Autism & autism spectrum disorders 
In published literature the incidence of autism is variable, and a worldwide trend of increase 
in prevalence of autism has been reported. During the 1980s, autism was thought to be rare, 
with  a  prevalence  of  less  than  5  per  10,000  persons  (Gillberg  et  al.,  1991)  and  was  not 
categorized as major public health problem. During the 1990s, the prevalence of autism was 
estimated to be 21 to 31 per 10,000 in preschool children (Fombonne, 1999). A recent review 
of epidemiologic studies reported a prevalence of 20 per 10,000 for classic autism and 60-70 
per 10,000 (1 in 150) for all ASDs (Fombonne, 2009). In addition it reported a prevalence of 
30-40 per 10,000 for PDD-NOS and 2 per 10,000 for CDD (Fombonne, 2009). Epidemiologic 
studies of Asperger syndrome have been more rare, and although current numbers estimate 
a  prevalence  of  6  per  10,000,  there  are  severe  limitations  to  calculating  this  prevalence 
accurately (Fombonne, 2009). Other recent large scale epidemiological studies have shown 
that as many as 1 in 100, or 1% of school age children have an ASD (Baird et al., 2006; Baron-
Cohen et al., 2009; Kogan et al., 2009). It is noteworthy that some of these studies are based 
on parents’ reporting of ASD and it could be argued that these estimates might be falsely 
high (Kogan et al., 2009). On the other hand, it has been argued that the increasing incidence 
of autism might be due to increased awareness of public and professionals coupled with the 
broadening of the diagnostic criteria (Fombonne et al., 2006). Today, the prevalence of ASDs 
is  believed  to  be  very  high  and  this  condition  is  now  thought  to  be  second  only  to 
Intellectual  Disability  (ID)  among  the  most  common  developmental  disabilities  in  the 
United States (Yeargin-Allsopp et al., 2003).  

 

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Autism Spectrum Disorders: The Role of Genetics in Diagnosis and Treatment 

It is also noteworthy that there is a gender bias in autism. Among children with autism, the 
ratio of affected males to females is estimated to be 4:1 (Volkmar et al., 1993) and the male to 
female ratio for Asperger syndrome is even higher. In contrast, Rett syndrome occurs almost 
exclusively in females.  

4.2 Intellectual disabilities 
ID is the most common neurodevelopmental disorder in the United States (Yeargin-Allsopp, 
et al., 2003). The prevalence of ID is between 1 and 3% (Roeleveld et al., 1997; Leonard & 
Wen, 2002) and is present in every social class and culture (Leonard & Wen, 2002). Despite 
its  universal  occurrence,  there  tends  to  be  higher  prevalence  of  ID  in  areas  of  lower 
socioeconomic  status  and  developing  countries,  particularly  for  mild  cases  (Drews  et  al., 
1995; Roeleveld et al., 1997; Durkin et al., 1998; Durkin, 2002; Emerson, 2007). The variability 
of prevalence is more pronounced for mild ID than for severe forms. It has been suggested 
that this discrepancy is likely due to environmental factors (Roeleveld et al., 1997; Durkin et 
al., 1998; Emerson, 2007). 
Approximately 30% more males are diagnosed with ID than females (McLaren & Bryson, 
1987; American Psychiatric Association, 2000). However, despite a higher ratio of males to 
females among milder cases of ID, the ratio decreases as IQ decreases (McLaren & Bryson, 
1987; American Psychiatric Association, 2000). Some studies suggest that severe ID may be 
more  prevalent  among  females  (Katusic  et  al.,  1996;  Bradley  et  al.,  2002),  however  these 
studies were performed in quite specific geographic locations and populations, and may not 
necessarily be generalizable to other regions. Some of this gender bias can be accounted for 
by  mutations  on  the  X-chromosome.  In  most  cases  of  X-linked  ID  (ie.  X-linked  mental 
retardation;  XLMR)  or  X-linked  autism,  more  males  are  affected  due  to  hemizygosity. 
However, in some disorders, such as Rett syndrome, this ratio is reversed because mutations 
in the Rett syndrome gene, MECP2, are generally lethal in haploid genomes. In addition, a 
rare phenomenon is apparent in female-restricted epilepsy and mental retardation (EFMR), 
in which heterozygous mutations in the gene PCDH19 cause the disease in females and in 
which  there  is  reprieve-in-males  with  hemizygous  PCDH19  mutations,  who  remain 
unaffected (Dibbens et al., 2008; Hynes et al., 2009). A possible explanation for this reprieve-
in-males phenomenon could be that carrier males have a homogenous population of mutant 
PCDH19-containing cells, whereas affected females would possess a mosaic population of 
mutant and wild-type PCDH19-containing cells. It is postulated that this mosaicism, rather 
than the effect of the mutated protein alone, may disrupt cell-cell communication, resulting 
in the clinical presentation (Dibbens et al., 2008). 

4.3 Co-morbidity of autism spectrum disorders & intellectual disability (syndromic 
and non-syndromic) 
It is often necessary to look at ASDs and ID together, as there is significant overlap between 
them  both  in  terms  of  phenotype  and  in  genetic  causation.  As  previously  noted,  ID  is 
present in ~50-60% of individuals with autism. Additionally, in a study performed on an ID 
population, 28% met the criteria for an autism diagnosis on the ADI-R scale and only half of 
these cases had been previously diagnosed (Bryson et al., 2008). Similar studies have also 
shown  that  within  ID  populations,  the  prevalence  of  autism  is  8-20%,  and  that  it  is  more 
likely for individuals with severe ID to meet criteria for ASD (Wing & Gould, 1979; Deb & 

 

Common Genetic Etiologies and Biological 
Pathways Shared Between Autism Spectrum Disorders and Intellectual Disabilities 

135 

Prasad, 1994; Nordin & Gillberg, 1996; Stromme & Diseth, 2000; de Bildt et al., 2005; Bryson 
et al., 2008).  
ID and autism have multiple overlapping phenotypic domains. The three major phenotypic 
domains  that  characterize  autism—language  deficits,  social  deficits  and  stereotypies/ 
repetitive  behaviours—can  often  be  seen  to  varying  degrees  in  individuals  with  ID. 
Individuals with ID often display stereotypies, which tend to become more pronounced and 
often self-injurious with decreasing IQ (Symons et al., 2005). Studies have found that 30-60% 
of individuals with ID display some form of stereotypy (Bodfish et al., 1995; Bodfish et al., 
2000;  Goldman  et  al.,  2009).  Language  deficits  are  often  particularly  pronounced  in 
individuals with severe and profound ID.  
Many  ID  syndromes  have  an  occurrence  of  autism  that  is  significantly  higher  than  the 
occurrence  for  the  general  population.  For  example,  a  current  review  of  the  literature 
indicates that up to 25-47%  of individuals with Fragile X syndrome, 5-10% of individuals 
with Down syndrome, and 16-48% of individuals with tuberous sclerosis (TSC) also have an 
autism/PDD  diagnosis,  compared  to  0.6-1%  in  the  general  population  (Fombonne,  2009; 
Molloy et al., 2009). Other ID syndromes that have high  occurrence of concordant autism 
include Angelman syndrome, Joubert Syndrome and Cohen syndrome.  

5. Shared genetics of autism and ID 
5.1 Shared genetic causes of autism and ID: Syndromic 
As previously noted, there is evidence that several syndromic forms of ID are more likely to 
present with autism than would be expected in the general population. The observation of 
overlap  in  phenotype  between  autism  and  the  most  common  XLMR  disorder,  fragile  X 
mental  retardation  syndrome  (FXS)  is  a  long-standing,  albeit  controversial  one.  The 
frequency of molecular diagnosis of FXS among autistic patients has been reported as high 
as 12.4% and as low as 0%, averaging at 7.25% (Smalley et al., 1988; Gurling et al., 1997). 
More recent studies indicate a more conservative rate of FXS of 2-4% (Wassink et al., 2001). 
Two  studies  of  young  FXS  individuals  demonstrated  that  25%  and  33%  met  criteria  for 
autism and a review suggests that as many as 47% may have an ASD (Bailey et al., 1998; 
Rogers  et  al.,  2001;  Molloy  et  al.,  2009);  however,  it  has  also  been  argued  that  autistic 
features are not more common among individuals with FXS than among other individuals 
with ID (Bardoni et al., 2000). Mutations in MECP2 have also been found among individuals 
with autism (Kim & Cook, 2000; Orrico et al., 2000; Beyer et al., 2002; Hammer et al., 2002). 
Autistic features are also frequently present in other ID syndromes such as Down syndrome 
and phenylketonuria (PKU).  
These disorders are rarely mistaken for autism, as other syndromic features assist with the 
correct diagnostic assignment. On the other hand, there is evidence that even for these well-
characterized syndromic forms of XLMR, there is a very broad phenotypic expression of the 
disease. For instance, mutations within the aristaless gene, ARX, are responsible for several 
distinct  forms  of  XLMR  and  neurological  phenotypes:  West  syndrome  (infantile  spasms 
with  hypsarrhythmia;  Stromme  et  al.  2002a),  Partington  syndrome  (XLMR  with  dystonic 
hand movements; Stromme et al. 2002b), XLAG (Kitamura et al. 2002), XLMR (Bienvenu et 
al. 2002), Proud syndrome (Kato et al. 2004), and various forms of epilepsy (Stromme et al. 
2002a, b; Scheffer et al. 2002). A 24 base pair duplication in ARX has been found in families 
with West syndrome and Partington syndrome, and recently in several families previously 
identified  as  having  non-syndromic  XLMR.  In  these  families  several  individuals  were 

 

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Autism Spectrum Disorders: The Role of Genetics in Diagnosis and Treatment 

reported  as  having  only  mild  intellectual  impairment  along  with  autism  or  autistic-like 
behaviours (Turner et al., 2002).  

5.2 Shared genetic causes of autism and ID: Non-syndromic  
The Neuroligin 4 (NLGN4) gene has been linked to autism by several studies (Jamain et al., 
2003;  Laumonnier  et  al.,  2004;  Marshall  et  al., 2008).  However,  in  2004,  Laumonnier  et  al. 
identified  a  family  containing  individuals  with  NS-ID,  with  or  without  ASD,  segregating 
with a NLGN4 mutation (Laumonnier et al., 2004). This study was the first to suggest that 
NS-ID  and  autism  may  have  overlapping  genetic  etiologies.  A  similar  finding  was  noted 
with NRXN1, which interacts with NLGN4. Heterozygous copy number variants (CNV) in 
this gene have been found in autism while homozygous mutations of NRXN1 cause the S-ID 
disorder Pitt-Hopkins-like syndrome-2 (PTHSL2; Zweier et al., 2009). 
More recently, a truncating mutation was found in SHANK3 in an individual with NS-ID 
(Hamdan et al., 2011). SHANK3 has been found to cause autism in several studies (Durand 
et  al.,  2007;  Marshall  et  al.,  2008).  In  addition,  SHANK2  CNVs  and  sequencing  mutations 
have been found in several cases of both autism and NS-ID, displaying a significant level of 
etiological overlap between the two disorders (Berkel et al., 2010; Pinto et al., 2010). CNVs, 
or structural variation within the genome, appear to contribute significantly to the etiology 
of ID and autism. 
PTCHD1 is another X-linked gene that has been implicated in autism and NS-ID. A CNV 
which  deletes  PTCHD1  entirely  causes  NS-ID  in  one  family  (Noor  et  al.,  2010).  Another 
CNV, which results in a loss of the first exon and upstream region of PTCHD1, results in 
autism in another family (Noor et al., 2010). In addition, one CNV upstream of the gene was 
found in an individual with ADHD, suggesting that it may play a role in this phenotype as 
well (Noor et al., 2010). IL1RAPL1, which was initially identified as a cause of NS-ID, and 
has been shown to cause NS-ID in several individuals, has also been implicated in autism 
(Carrie et al., 1999; Bhat et al., 2008; Marshall et al., 2008; Piton et al., 2008; Pinto et al., 2010). 
Similarly, a missense mutation in the NS-ID gene JARID1C was found in an individual with 
autism  (Adegbola  et  al.,  2008).  Most  recently,  a  de  novo  CNV  deletion  overlapping 
SYNGAP1,  a  gene  previously  implicated  in  NS-ID,  was  identified  in  a  female  autism 
proband (Pinto et al., 2010). These genetic links are of much interest, particularly due to the 
strong  phenotypic  overlap  seen  in  NS-ID  and  autism.  These  common  genes  will  be  an 
important factor in teasing out which biochemical processes are disturbed in different forms 
of developmental delay, and why a particular mutation in an individual may lead to one 
condition rather than the other.  

6. Common biological pathways (see Table 2) 
6.1 The mTOR pathway (see Figure 1) 
The common pathways shared by autism genes are also particularly interesting with respect 
to ID. While it cannot necessarily be argued that all autism genes will fall into distinct and 
neat categories, there are certain pathways which are overrepresented among autism-related 
genes identified so far. One review of the literature suggested that there appear to be two 
major pathways that known autism genes are a part of: 1. excitation and inhibition at the 
synaptic  junction  and  2.  cellular  and  synaptic  growth—i.e.  participation  in  the  mTOR 
pathway (Bourgeron, 2009). This categorization is valid but does not encompass all autism 

 

Common Genetic Etiologies and Biological 
Pathways Shared Between Autism Spectrum Disorders and Intellectual Disabilities 

137 

genes,  and  as  our  knowledge  of  genes  that  contribute  to  the  autism  phenotype  increases, 
more common pathways may be elucidated. 
 

Gene Name  ASD or ID* Chromosomal 

Protein Product 

Potential Pathogenic 

Locus 

Common Gene Function: Cell Adhesion 

Mechanism 

CDH8 

ASD 

16q22.1 

Cadherin 8 

CDH9 
CDH10 
CDH15 

CNTNAP2 

NLGN3 

ASD 
ASD 
ID 
ASD 

ASD 

5p14 
5p14.2 
16q24.3 
7q35 

Xq13.1 

Cadherin 9 
Cadherin 10 
Cadherin 15 

Contactin-associated 

protein-like 2 
Neuroligin 3 

NLGN4 
NRXN1 

ASD/ID 
ASD/ID 

Xp22.33 
2p16.3 

Neuroligin 4 
Neurexin 1 

PCHD10 
PCDH19 
TSPAN7 

ASD 
ID 

ASD/ID 

4q28.3 
Xq13.3 

 

Protocadherin 10 
Protocadherin 19 

 

Disruption of neuronal cell 

adhesion leading to 

aberrant synaptogenesis or 

plasticity 
As above 
As above 
As above 
As above 

Disruption of neuronal cell 
adhesion specifically at the 
excitatory synapse leading 
to aberrant synaptogenesis 

or plasticity 
As above 

Disruption of neuronal cell 

adhesion leading to 

aberrant synaptogenesis or 

plasticity 
As above 
As above 
As above 

Common Gene Function: Receptors at Inhibitory Synapse 
ASD 

GABA receptors α2, 

4p14 

GABA 
receptors 
(Chr 4) 
GABA 
receptors 
(Chr 15) 

ASD/ID 

15q11.2-q12  GABA receptors α5, 

β1, γ1 and α4 

β3, and γ3 

Disruption of GABA 
receptor activity at the 

inhibitory synapse 
Disruption of GABA 
receptor activity at the 

inhibitory synapse 
Common Gene Function: Regulation and Organization at the Excitatory Synapse 

FMR1 

ASD/ID 

Xq27.3 

Fragile X mental 

retardation 1 protein

GRIK2 

ASD/ID 

6q16.3-q21  Glutamate receptor, 
ionotropic, kainate 2

 

Aberrant translational 
regulation of important 

synaptic genes 

Disruption of Kainate 

Receptors at the excitatory 

synapse 

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Autism Spectrum Disorders: The Role of Genetics in Diagnosis and Treatment 

Gene Name  ASD or ID* Chromosomal 

Protein Product 

Potential Pathogenic 

IL1RAPL1  ASD/ID 

Xp22.1-p21.3 

Locus 

Common Gene Function: Transcriptional Control 

Interleukin 1 

receptor accessory 

protein-like 1 

SH3 and multiple 
ankyrin repeat 

domains 2 

SH3 and multiple 
ankyrin repeat 

domains 3 
Synaptic Ras 

GTPase activating 

protein 1 

Mechanism 

Disruption of activity and 

organization at the 
excitatory synapse 

As Above 

As Above 

Disruption of NMDA and 
AMPA receptors via down 

regulation of Ras/ERK 

signalling 

Aristaless related 

homeobox 

Autism 

susceptibility  
gene 2 protein 

Jumonji, AT rich 
interactive domain 

1C 

Methyl CpG  

binding protein 2 

Disruption of 

transcriptional regulation 
leading to alterations in 
dosage of multiple genes 

Neuronal nuclear 
expression; Putative 

regulator of transcription 
(Kalscheuer et al., 2007) 

Disruption of 

transcriptional regulation 
leading to alterations in 
dosage of multiple genes 

As above 

neurofibromin  Neuronal cell overgrowth 

due to the down  

regulation of the mTOR 

pathway or down 

regulation of Ras/ERK 

signaling 

phosphatidylinositol
-3,4,5-trisphosphate 

Neuronal cell overgrowth 
due to the down regulation 

of the mTOR pathway 

3-phosphatase  

and dual-specificity 

protein 

 phosphatase 

Tuberous Sclerosis 1 
protein (hamartin)

As above 

SHANK2  ASD/ID 

1q41 

SHANK3  ASD/ID 

22q13.3 

SYNGAP1  ASD/ID 

6p21.3 

ARX 

ASD/ID 

Xp21.3 

AUTS2 

ASD 

7q11.2 

JARID1C  ASD/ID  Xp11.22-p11.21

MECP2 

ASD/ID 

Xq28 

NF1 

ASD/ID 

17q11.2 

PTEN 

ASD 

10q23.3 

TSC1 

ASD/ID 

9q34 

 

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Common Genetic Etiologies and Biological 
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139 

Gene Name  ASD or ID* Chromosomal 

Protein Product 

TSC2 

ASD/ID 

Locus 
16p13.3 

Common Gene Function: Varying Functionalities 

Tuberous Sclerosis 2 

protein (tuberin) 

IQ motif and SEC7 
domain-containing 

protein 2 

IQSEC2 

ASD/ID 

Xp11.22 

PTCHD1 

ASD/ID 

Xp22.11 

Patched domain 1  Potential disruption of the 

Potential Pathogenic 

Mechanism 
As above 

Disruption of the ARF 

signaling pathway 

Hedgehog signaling 

pathway 

Abnormal neuronal cell 
migration and cell-cell 

interaction 

RELN 

ASD/ID 

7q22 

Reelin 

SLC6A8 

ASD/ID 

Xq28 

UPF3B 

ASD/ID 

Xq25-q26 

Solute carrier family 

Creatine deficiency 

6 member 8 

UPF3 regulator of 
nonsense transcripts 

Dysfunction of nonsense 

mediated decay and 
mRNA surveillance 

homolog B 
*associated with autism spectrum disorder or intellectual disability 
Table 2. Genes associated with ASD, ID or both and chromosomal location, along with 
protein product and function, and the potential route by which the gene results in 
neurodevelopmental phenotypes. 
NF1,  TSC1  and  TSC2  are  genes  that,  when  mutated,  may  result  in  disorders  -
neurofibromatosis and tuberous sclerosis respectively- in which there is high incidence of 
autism-  neurofibromatosis  and  tuberous  sclerosis  respectively.  These  genes  are  negative 
regulators  of  the  mTOR-raptor  complex  (mTORC1)—a  complex  that  is  important  in 
regulation and cell growth during mitosis and likely plays an active role in synaptogenesis 
(Bourgeron,  2009).  Furthermore,  in  hippocampus  of  fmr1  knockout  mice,  upregulation  of 
mTOR activity has also been reported (Sharma et al., 2010). It can be speculated that when 
deleterious mutations occur in these genes, the mTOR pathway would become more active 
due to loss of down regulation. PTEN, another negative regulator of the mTOR pathway has 
also  been  implicated  in  autism.  PTEN  mutations  can  lead  to  PTEN  Hamartoma-Tumor 
Syndrome  (PHTS)  which  includes  Cowden  Syndrome  and  Bannayan-Riley-Ruvalcaba 
Syndrome, but are also present in autism probands without these syndromes (McBride et 
al., 2010). The mutations found in autism probands appear to be less penetrant than other 
PTEN mutations (McBride et al., 2010). 
With  the  finding  that  mTOR  genes  are  involved  in  autism  susceptibility  we  can  propose 
several possibilities, the first of which is that regulators of the mTORC1 complex may also 
be good candidates for ID genes, based on overlapping phenotype between autism and ID. 
However,  individuals  with  neurofibromatosis  do  not  typically  present  with  ID  despite 
having  a  higher  incidence  of  learning  disabilities,  ADHD  and  autism  (Hsueh,  2007). 
Tuberous  Sclerosis  is  associated  with  learning  disabilities,  developmental  delay  including 
autism and epilepsy in about 85% of cases (Curatolo et al., 2008). This specificity for autism 
susceptibility  in  the  mTOR  pathway  may  help  us  to  delineate  the  complex  association 
between autism and ID genes, and understand how autism specific phenotypes are caused.  

 

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Autism Spectrum Disorders: The Role of Genetics in Diagnosis and Treatment 

 
The mTOR pathway has been implicated as a potential major contributor to the etiology of autism. The 
mTOR pathway is negatively regulated by NF1, TSC1/2, and PTEN (and possibly FMRP), all of which 
are involved in the etiology of autism. mTOR activation results in cell survival and proliferation. 
Negative regulation of this pathway prevents overgrowth. Some cancer syndromes are also associated 
with these genes. mTOR is a target of rapamycin, which inhibits its functioning. This is a potential 
pharmaceutical candidate for the treatment of syndromes caused by mutations in these genes, as well as 
autism resulting from mutations in these genes. This figure shows the role of NF1, TSC1/2 and PTEN in 
the mTOR pathway.  
Fig. 1. mTor Pathway in autism and ID 

 

 

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6.2 Synaptic proteins and synaptogenesis (see Figures 2 & 3) 
Most of the known genes that overlap both autism and ID are present at the synapse and are 
involved  in  the  excitatory/inhibitory  pathway,  with  an  emphasis  on  excitation.  For 
example,  the  SHANK  family  of  genes  appears  to  make  quite  a  significant  contribution  to 
autism and ID etiologies. Both SHANK2 and SHANK3 encode scaffolding proteins present at 
the post-synaptic density and in dendrites. They are important for scaffolding in the post-
synaptic density—connecting ion channels, neurotransmitter receptors and other membrane 
proteins to the actin cytoskeleton—and act as a structural framework at this site (Boeckers et 
al.,  2002;  Hayashi  et  al.,  2009).  They  are  also  likely  to  play  a  role  in  neuronal  plasticity 
(Boeckers et al., 2002; Hayashi et al., 2009). 
 

Many autism genes, in particular those that overlap with intellectual disabilities, are present at 
excitatory synapses. Many of these genes encode protein products which are present in the postsynaptic 
density (PSD) including SYNGAP1, SHANK2, SHANK3, NLGN4, NLGN3, NRXN1, and IL1RAPL1. 
Glutamatergic synapses contain NMDA receptors, AMPA receptors and Kainate receptors.  Mutations 
in additional synaptic genes have been implicated in ID, e.g. CASK and STXBP1. Aberrant function at 
this synapse has been postulated to be part of both autism and ID etiology. 

 

 
Fig. 2. Glutamatergic Synapse in autism and ID 

 

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GABA is the major inhibitory neurotransmitter in the human brain. GABA receptor genes have been 
postulated as candidates for autism etiology.  Most GABA receptors cluster in certain chromosomal 
regions  some of which are associated with autism and/or ID, such as 15q11.2-q13 which contains 
GABRα5, GABRβ3, and GABRγ3, and 4p14 which contains the genes GABRα2, GABRβ1, GABRγ1 and 
GABRα4. Duplications of the 15q11.2-q13 region are present in 0.5-3% of autism cases.   
Fig. 3. GABAergic Synapse in autism and ID 
Another  important  gene  involved  in  both  ID  and  autism  is  SYNGAP1  which  has  been 
identified  as  a  dominant  cause  of  ID  in  several  individuals  with  truncating  mutations 
(Hamdan et al, 2009; Hamdan et al, 2011), as well a cause of autism in an individual with a 
CNV  deletion  overlapping  the  gene  (Pinto  et  al.  2010).  SYNGAP1  encodes  SynGAP—a 
GTPase activating protein that is part of the NMDA receptor complex (NMDAR), and binds 
to  the  NR2B  subunit  (Kim  et  al.,  2005).  NMDARs  play  a  role  in  glutamate-activated 

 

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143 

excitation  of  postsynaptic  neurons,  and  have  been  implicated  in  memory  formation  and 
synaptic  plasticity.  SynGAP  is  a  negative  regulator  of  NMDAR  mediated  ERK  activation 
and  causes  inhibition  of  the  Ras/ERK  pathway  (Kim  et  al.,  2005).  Over-expression  of 
SynGAP  has  also  been  shown  to  down  regulate  GLuR1,  a  subunit  of  AMPA  receptors 
(AMPAR), a class of excitatory ionotropic glutamate receptors which are regulated by the 
Ras/ERK  pathway  (Kim  et  al.,  2005;  Rumbaugh  et  al.,  2006).  Syngap  knockout  mice 
implicate SynGAP in the regulation of long term potentiation (LTP) and AMPAR expression 
(Komiyama et al., 2002). While both ID and autism probands with mutations in this gene 
were heterozygous for the mutation, individuals with ID have truncating single nucleotide 
mutations, while the individual with autism had a deleterious CNV, which is an example of 
how different aberrations within the same gene can have differential effects on phenotypic 
manifestation, i.e. allelism (Hamdan et al., 2009; Pinto et al., 2010; Hamdan et al., 2011). This 
has been shown for several other autism/ID genes as well, including IL1RAPL1, JARID1C, 
and SHANK2 (Adegbola et al., 2008; Piton et al., 2008; Berkel et al., 2010; Pinto et al., 2010).  
Other autism-related genes also bind to the NMDAR and are involved in its function. NF1 
described earlier as a down regulator of the mTOR pathway also plays a role in the negative 
regulation  of  the  Ras  signaling  pathway  and  is  known  to  bind  directly  to  the  NMDAR 
complex (Hsueh, 2007). This suggests an alternate mechanism for its role in autism. Within 
the context of Bourgeron’s review, this gene is actually affiliated with both “major” autism 
gene pathways.  
In addition IL1RAPL1 also appears to have an impact on NMDAR function. Mutations in 
IL1RAPL1, a gene with several known mutations in NS-ID and autism, result in the incorrect 
localization  of  the  MAGUK  family  protein  PSD-95  (DLG4),  which  is  important  for 
organization  and  function  of  NMDARs,  ion  channels  and  other  signaling  proteins  at  the 
post-synapse  (Carrie  et  al.,  1999;  Gardoni,  2008;  Pavlowsky  et  al.,  2010).  The  IL1RAPL1 
protein has been shown to interact with PSD-95, and knockout of this gene decreased the 
post-synaptic density (PSD) and the localization of PSD-95 at excitatory synapses. Loss of 
IL1RAPL1 also results in a decrease of activity in the JNK pathway, which was found to lead 
to decreased phosphorylation of PSD-95 (Pavlowsky et al., 2010). IL1RAPL1 has also been 
shown  to  be  important  for  the  formation  of  excitatory  synapses  in  vivo  (Pavlowsky  et  al., 
2010).  PSD-95  directly  interacts  with  several  known  NS-ID  associated  proteins  including: 
CASK, SynGAP, GLuR6 and neuroligins (Kim & Sheng, 2004). 
GRIK2 is a gene that is mutated in NS-ID (Motazacker et al., 2007) and has been linked by 
association studies to autism (Jamain et al., 2002; Shuang et al., 2004; Kim et al., 2007) . It 
encodes a protein called GLuR6, which is a subunit of a kainate receptor (KAR). KARs are 
ionotropic  glutamate  receptors  which  respond  to  the  excitatory  neurotransmitter  l-
glutamate, similar to NMDA or AMPA receptors. They are expressed at a high level in the 
brain,  particularly  in  the  hippocampal  mossy  fibers,  where  GLuR6  has  been  found  to 
modulate  LTP  in  mouse  models  (Bortolotto  et  al.,  1999;  Contractor,  Swanson  and 
Heinemann., 2001). GLuR6 knockout mice show decreased LTP in mossy fibers. LTP in the 
hippocampus  has  been  implicated  as  a  mechanism  for  memory  formation  and  learning 
(Bliss & Collingridge, 1993; Fedulov et al., 2007). While polymorphisms in GRIK2 are likely 
not  responsible  for  an  autism  phenotype  alone,  the  association  suggests  that  this  locus 
confers susceptibility to the phenotype and may contribute to the disorder in concert with 
other genetic aberrations and environmental factors.  
Additionally, FMRP, encoded for by FMR1, the fragile X syndrome (FXS) gene, is thought to 
play a role in neuronal plasticity by acting as a suppressor of local protein translation (as 

 

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Autism Spectrum Disorders: The Role of Genetics in Diagnosis and Treatment 

reviewed  by  Jin  et  al.  2004).  It  binds  mRNA  in  the  nucleus  and  carries  the  mRNA  to  its 
target destination in the cytoplasm (Bagni & Greenough, 2005). The transcripts targeted by 
FMRP include some that are relevant to autism and ID, including PSD-95, DLG1, SHANK1, 
DLGAP1-4, Grin1, Grin2b, GluR1, and GluR2 (Bassell & Warren, 2008; Schutt et al., 2009). It 
also  binds  other  important  neuronal  transcripts  such  as  itself  (FMR1),  SEMA3F,  CamKIIα, 
GABRD,  ARC,  MAP1B  and  APP  (Bassell  &  Warren,  2008).  While  FXS  is  an  X-linked 
syndrome  of  variable  phenotype,  it  is  a  very  common  cause  of  ID,  and  often  is  present 
together with autistic behaviours (de Vries et al., 1998). In addition, the FMR2 gene, which is 
similar  to  FMR1  in  structure  but  has  a  poorly  defined  function,  is  also  involved  in  the 
genetics  of  ID  and  autism  (Gecz  et  al.,  1996).  Mutations  in  this  gene  result  in  intellectual 
disabilities with or without autistic behaviours.  
Some important synaptic genes that are present at inhibitory synaptic junctions appear to be 
associated  with  autism  and  some  ID  syndromes.  GABA  is  the  major  inhibitory 
neurotransmitter  in  the  human  brain,  and  dysfunction  of  its  receptors  could  result  in  a 
decrease of the inhibitory response. A significant amount of research has been done to study 
the  role  of  GABA  and  GABA  receptors  in  autism.  In  particular,  GABA  receptor  subunits 
residing  in  clusters  on  4p  and  15q  have  been  implicated  in  autism.  As  indicated  earlier, 
genomic duplications of an imprinted region on the long arm of chromosome 15 (15q11-q13) 
are present in 0.5-3.0% of autistic individuals (Hogart et al., 2010). Deletions and uniparental 
disomy  (UPD)  in  this  region  are  responsible  for  Angelman  syndrome  and  Prader-Willi 
syndrome depending on the parent of origin of the mutation, and both of these syndromes 
present with ID (Dykens et al., 2004), and frequently also with autism (Hogart et al., 2010). 
This region contains a cluster of three GABA receptor subunit genes: GABRα5, GABRβ3, and 
GABRγ3  (Dykens  et  al.,  2004).  Based  on  the  frequency  of  this  duplication  in  autism,  it  is 
possible that these genes are involved in the autism phenotype, however there are ~10 genes 
in  the  duplicated  region,  all  of  which  may  play  a  major,  minor  or  no  role  in  the  autism 
phenotype. GABRα2, GABRβ1, GABRγ1 and GABRα4 on 4p14 (Ma et al., 2005; Vincent et al., 
2006; Kakinuma et al., 2008) have also been implicated in autism. Molecular work specific to 
some of these GABA receptors supports their role in autism (Ma et al., 2005; Collins et al., 
2006;  Fatemi  et  al.,  2010).  It  is  also  of  note  that  GABA  neurotransmission  is  strongly 
implicated  in  fragile  X  syndrome,  and  knockout  of  the  fmr1  gene  in  mice  has  a  hugely 
disruptive  effect  on  the  GABAergic  system,  and  is  a  potential  target  for  the  treatment  of 
symptoms for both Fragile-X syndrome and autism (Hagerman et al., 2005).  
Several  studies  have  found  significant  genetic  associations  between  the  chromosome  4 
GABA receptor cluster and autism. In addition, specific GABA receptor genes within the 4p 
cluster  have  been  implicated  as  likely  contributors  to  autism.  GABRα4  was  found  to  be 
involved  in  the  etiology  of  autism  independently  and  through  interactions  with  GABRβ1 
(Ma  et  al.,  2005)  and  both  of  these  genes  have  been  linked  to  the  autism  phenotype  by 
association (Collins et al., 2006). Recently, a study by Fatemi et al. indicated that the levels of 
GABA receptor mRNAs in autism brains are significantly different from controls (Fatemi et 
al., 2010). The study shows that in the BA9 region of brains acquired from individuals with 
autism, levels of GABRα4, GABRα5 and GABRβ1 mRNAs are significantly decreased, while 
in  the  cerebella  of  these  brains  mRNA  for  these  same  genes  are  increased  compared  to 
normal  controls  after  normalization  with  housekeeping  genes  (Fatemi  et  al.,  2010).  In 
addition, several small studies and cases have shown that the levels of GABA in peripheral 
blood and plasma are altered in individuals with autism (Dhossche et al., 2002, Rolf et al., 
1993) but these findings are inconsistent, and more thorough and larger scale studies need 
to be done to determine whether GABA could act as stable biomarker for ASD.  

 

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6.3 Neuronal cell adhesion 
Neuronal  cell  adhesion  is  an  interesting  common  pathway  between  ID  and  autism.  As 
previously noted, NLGN4 and NRXN1 are both examples of genes that are mutated in cases 
of autism and ID. NLGN4 presents us with a particularly interesting genetic link between 
autism  and  ID,  as  it  displayed  pleiotropy  within  a  single  family,  with  the  same  mutation 
causing autism in some individuals, and NS-ID in others. The NLGN4 protein, located on 
the  postsynaptic  membrane,  interacts  with  NRXN1  on  the  presynaptic  membrane. 
Heterozygous  mutations  in  NRXN1  appear  to  result  in  autism  as  well  (Autism  Genome 
Project Consortium et al., 2007; Kim et al., 2008). Similar disruptions in NRXN1 have also 
been documented in schizophrenia (Rujescu et al., 2009), ID, and language delays (Ching et 
al., 2010). The NLGN4 protein acts as an important element in postsynaptic differentiation, 
forming  complexes  with  β-neurexins  and  PSD-95  (Ichtchenko  et  al.,  1995;  Irie  et  al.,  1997; 
Scheiffele  et  al.,  2000).  NLGN4  is  linked  to  glutamatergic  postsynaptic  proteins  and 
neuroligin/neurexin complexes, which appears to be sufficient for synaptogenesis (Graf et 
al.,  2009).  These  genes  play  a  major  role  in  both  cell  adhesion  as  well  as  synaptogenesis 
showing a role for overlap between these pathways.  
Additionally, it has been postulated that TSPAN7, an X-linked NS-ID gene, is involved in a 
complex of ß-integrins, which are involved in cell-cell and cell-matrix interactions (Zemni et 
al.,  2000)  and  that  this  gene  may  cause  autism  in  individuals  with  deleterious  CNVs 
(Marshall et al., 2008). There are several other neuronal cell adhesion genes that have been 
implicated in autism, with and without ID, including NLGN3 and CNTNAP2 (Jamain et al., 
2003;  Alarcon  et  al.,  2008).  This  suggests  that  neuronal  cell  adhesion  is  a  common 
mechanism  by  which  both  autism  and  ID  occur,  and  may  be  helpful  in  elucidating  the 
biological mechanism of these highly related, albeit different, disorders. 
In  addition  to  these  neuronal  cell  adhesion  genes,  several  cadherins  and  protocadherins 
have  been  implicated  in  autism  as  well.  CDH8  has  been  found  to  be  disrupted  by 
microdeletions in individuals with learning disabilities and autism, and is not disrupted in 
over 5000 controls (Pagnamenta et al., 2011). Additionally, a genome-wide association study 
of individuals with autism identified significant peaks at CDH9 and CDH10 (Wang et al., 
2009).  PCDH10  has  also  been  suggested  as  a  candidate  gene  for  autism  based  on  a 
homozygous CNV deletion overlapping the gene in an affected individual (Morrow et al., 
2008).  
Several similar genes have been implicated in ID as well. PCDH19, a protocadherin, has 
also  been  implicated  in  epilepsy  with  mental  retardation  limited  to  females  (EFMR; 
Dibbens et al., 2008; Hynes et al., 2009). Additionally CDH15 is an autosomal dominant 
cause  of  S-ID  and  NS-ID  in  several  individuals.  CDH15  encodes  a  cadherin  that  is 
expressed mainly in brain and skeletal muscle (Bhalla et al., 2008). Mutations of this gene 
in individuals with ID were found to decrease cell adhesion by greater than 80% (Bhalla et 
al.,  2008).  It  is  clear  from  these  genetic  links  that  neuronal  cell  adhesion  is  a  common 
pathway in both ID and autism. Interestingly, while some of these genes overlap both ID 
and ASD, some are unique to one condition or the other. It is possible that some of these 
apparently unique genes may actually be involved in both disorders but no examples have 
been  identified  because  mutations  are  so  rare.  It  is  also  possible  that  these  genes  could 
only  cause  specific  endophenotypes  and  may  be  useful  for  helping  us  tease  out  the 
intricate web of connections between the two disorders. 

 

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6.4 Transcriptional control 
Disruption  of  transcriptional  control  can  have  far  reaching  implications  in  terms  of 
phenotypic manifestation. Genes involved in transcriptional control, when disrupted, may 
in  turn  affect  the  expression  of  many  other  genes.  For  example,  ARX  is  one  of  the  most 
frequently mutated genes in X-linked NS-ID. It is a homeobox-containing gene that is part of 
the Aristaless-related gene family. This is a family of transcription factors that are required 
for various essential events during vertebrate embryogenesis, including CNS development 
(Meijlink et al. 1999). Based on experimental data and gene structure it has been speculated 
that ARX regulates transcription by both gene activation and suppression and it is essential 
for normal development of the CNS (as reviewed by Friocourt et al. 2006). Mutations in this 
gene  have  been  implicated  in  many  ID  syndromes,  XLMR  and  autism/autistic  features 
(Turner et al., 2002; Friocourt et al., 2006).  
Another  example  of  a  transcription  factor  involved  in  both  autism  and  ID  is  MECP2,  the 
causative gene for Rett syndrome: a regressive syndrome described earlier in this chapter. 
MECP2  mutations  may  result  in  various  alternative  phenotypic  manifestations  including 
MRXS13, LUBS X-linked ID syndrome and NS-ID. While genotype/phenotype data is often 
ambiguous, some studies have demonstrated a genotype/phenotype correlation in terms of 
severity,  as  well  as  for  specific  phenotypic  measures  (Ham  et  al.,  2005;  Bebbington  et  al., 
2008). MECP2 encodes the methyl CpG binding protein 2 (MECP2), which is believed to act 
as  a  transcriptional  modulator  that  is  capable  of  long-range  chromatin  re-organization 
resulting in repression or activation of genes through binding to methylated CpG DNA (As 
reviewed by Gonzales and LaSalle, 2010).  
Additionally, JARIDIC, also known as KMD5C, is another relatively common gene related to 
X-linked ID with over twenty mutations known in XLMR individuals (Tzschach et al., 2006; 
Tahiliani et al., 2007). In addition, it has been identified as a causative gene in an individual 
with autism (Adegbola et al., 2008). JARIDIC is a histone demethylase containing a PHD-
finger domain that is characteristic of zinc finger proteins and specifically demethylates di- 
and  tri-methylated  histone  3  lysine  4  (H3K4me2/me3)  residues  (Christensen  et  al.,  2007; 
Tahiliani et al., 2007; Cloos et al., 2008). Trimethylation at this residue is extremely important 
for  transcriptional  regulation  and  chromatin  structure.  JARID1C  is  likely  involved  in 
repressor element silencing transcription factor (REST)-mediated transcriptional repression. 
It  has  been  shown  to  regulate  the  expression  of  several  REST-mediated  genes,  as  well  as 
regulate  the  H3K4me2/H3K4me3  levels  at  their  promoters  (Tahiliani  et  al.,  2007). 
Determining which genes these transcriptional regulators control may give insight into the 
biological pathways involved in disease and the mechanisms by which they occur. 
The  gene  AUTS2  was  originally  identified  through  the  mapping  of  a  translocation 
breakpoint on chromosome 7 in a pair of autistic monozygotic twins (Sultana et al., 2002), 
and subsequently identified in a number of studies of other autism patients with cytogenetic 
aberrations  (Bakkaloglu  et  al.,  2008;  Huang  et  al.,  2010).  AUTS2  has  more  recently  been 
identified  at  the  breakpoint  for  de  novo  translocations  in  three  unrelated  ID  individuals 
(Kalscheuer et al., 2007), as well as at breakpoints of CNVs in individuals with ADHD (Elia 
et al., 2010) and epilepsy (Mefford et al.. 2010). Although the function of the protein encoded 
by  AUTS2  is  unknown,  in  silico  analysis  suggest  the  protein  has  similarity  to  known 
transcription factors (Kalscheuer et al., 2007), and expression studies show that the protein is 
highly  expressed  within  the  nucleus  of  neurons  and  neuronal  progenitors  during 
development of the cerebral cortex and cerebellum, as well as other regions (Bedogni et al., 
2010). As such, it is likely that AUTS2 also functions as a regulator of gene transcription. 

 

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7. Other genes displaying ID/autism overlap (see Table 2) 
While it is very helpful to look at overlapping autism and ID genes in common pathways, it 
is  not  always  possible.  Autism  and  ID  both  have  highly  variable  genetic  causes.  Some  of 
these  causal  genes  cannot  be  grouped  together  in  common  pathways  but  are  still  very 
important  and  have  been  widely  implicated  in  autism  and  ID.  PTCHD1  is  an  example  of 
such  a  gene.  This  gene  is  estimated  to  explain  ~1%  of  cases  of  autism,  as  well  as  several 
cases of NS-ID (Noor et al., 2010). PTCHD1 is thought to encode a receptor for the hedgehog 
signaling  pathway,  however  no  definitive  role  for  the  PTCHD1  protein  has  yet  been 
established, and as the patched-like domain present in PTCHD1 also has a potential sterol-
sensing function, there may be sterol transporting pathways implicated (Noor et al., 2010). 
Mutations  at  the  PTCHD1  locus  can  occur  either  within  the  gene  itself  or  in  the  region 
upstream  of  the  gene,  which  are  thought  to  disrupt  PTCHD1  regulation.  Additionally, 
UPF3B,  a  component  of  the  nonsense  mediated  decay  surveillance  machinery,  has  been 
implicated  in  various  ID  cases  across  four  families,  as  well  as  in  several  individuals  with 
autism (Tarpey et al., 2007; Addington et al., 2010; Laumonnier et al., 2010). It has also been 
implicated in childhood onset schizophrenia and ADHD (Addington et al., 2010). IQSEC2, a 
gene  that  encodes  a  GTPase  for  the  ARF  family  of  proteins,  is  also  mutated  in  several 
families with ID and varying degrees of ASD and epilepsy (Shoubridge et al., 2010).  

8. Summary 
In this chapter, we have explored the relationship between ASD and ID: two separate but 
often co-morbid forms of developmental disorder. They are both relatively common in the 
general  population,  however  the  incidence  of  ASD  appears  to  be  on  the  rise,  while  ID  is 
relatively stable. Both can be impacted by environmental factors, however ASD appears to 
have  a  more  complex  etiology  and  may  require  a  combination  of  genetic,  epigenetic  and 
environmental factors to manifest phenotypically. Meanwhile, genes that cause ID tend to 
be either de novo or passed down in a Mendelian fashion, and are highly penetrant.  
While  ASD  is  co-morbid  with  ID  in  40-70%  of  cases,  ASD  can  also  present  with  normal 
intelligence.  Certain  endophenotypes,  such  as  IQ,  head  size,  presence  of  dysmorphisms, 
seizures  and  MRI  abnormalities,  may  help  to  predict  the  effectiveness  of  early  intensive 
behavioural  intervention.  Understanding  the  fundamental  differences  between  “essential” 
and “complex” autism may be the key to creating personalized behavioural programming 
that is specific not only for the skills of a particular child, but for the phenotypic specificity 
conferred by a narrower diagnosis.  
It  is  clear  that  many  of  the  genes  implicated  in  both  ASD  and  ID  cause  variable 
developmental, intellectual and psychiatric phenotypes, with and without additional clinical 
symptoms. Understanding the molecular mechanisms that result in developmental delays 
will  be  useful 
these  disorders. 
Understanding more about the phenotypes conferred by aberrations in different genes may 
lead  us  to  develop  differential  interventions  based  on  these  genotypes.  Additionally, 
classification  of  aberrant  molecular  pathways  may  help  us  to  identify  biomarkers,  which 
could  be  used  for  early  diagnosis  of  these  disorders.  Individuals  with  ASD  classically 
respond  best  to  early  intensive  intervention,  thus  the  earlier  we  can  diagnose  ASD,  the 
earlier we can act to help ensure the best outcomes.  

for  potential  management  and 

interventions 

in 

 

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Autism Spectrum Disorders: The Role of Genetics in Diagnosis and Treatment 

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Part 4 

Treatment and Genetic Counseling

 

9 

Microgenetic Approach to 
Therapy of Girls with ASD 
Katarzyna Markiewicz1 and Bożydar L.J. Kaczmarek1, 2 
1University of Maria Curie Sklodowska 
2Szczecin University 
Poland 

long-term 

1. Introduction 
The  term  Autism  Spectrum  Disorders  (ASD)  came  into  use  in  1988  to  stress  the  fact  that 
symptoms of autism may appear with various intensity – from very mild to very severe. The 
matter  gets  further  complicated  due  to  the  fact  that  the  symptoms  occur  in  different 
combinations. Autistic deficits include not only “autistic triad”, that is disorders of speech, 
behaviour and social interactions but disturb also cognitive, and motor abilities as well as 
emotional  functioning.  Despite  a 
interdisciplinary  studies  and  detailed 
descriptions  of  autistic  symptoms  diagnosis  of  ASD  still  causes  a  number  of  diagnostic 
difficulties.  The  diagnostic  procedure  has  been  made  easier  thanks  to  the  introduction  of 
two systems of classification - International Statistical Classification of Diseases and Related 
Health  Problems  (ICD-10)1  and  DSM-IV-TR2  -  but  it  still  remains  a  long  and  arduous 
process. It happens quite often that the final diagnosis is made in the course of therapy. 
Assessment of children with ASD must takes into account their spontaneous and reactive 
behaviours  as  well  as  information  gained  during  the  interview  with  their  parents.  As 
mentioned above, dysfunctions occurring in autistic spectrum vary to a considerable degree 
in their intensity. Moreover, they are of a dynamic character and are apt to undergo changes 
as  a  result  of  a  course  of  general  development,  individual  experience,  social  conditions, 
undertaken  therapy,  and  efficacy  of  stimulation.  All  these  may  hamper  the  process  of 
evaluation, especially at the early stages of a child development. Hence, autistic children are 
often made a diagnosis of mental disability, behaviour disorders, hearing problems as well 
as strange and eccentric behaviours. To complicate the matter further the above disorders 
frequently coexist with autism. It is imperative, therefore, to be able to discriminate autism 
and  other  developmental  disorders  since  the  early  diagnosis  provides  basis  for  creating 
effective therapeutic and educational programs.  

2. Frequency of ASD occurrence 
Recent  epidemiological  studies  show  a  dramatic  increase  of  the  number  of  persons  with 
autistic symptoms. Thus, the frequency of occurrence of ASD was rated at a level of 4 to 10,000 
                                                 
1 Proposed by World Heath Organization – WHO, 10th revision, 1992 
2 Diagnostic and Statistical Manual of Mental Disorders developed by American Psychiatric Association 
 - APA, 2000; a new version V is to be published in May 2013 

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Autism Spectrum Disorders: The Role of Genetics in Diagnosis and Treatment 

cases  in  the  years  of  1980-90,  while  in  the  last  decade  the  number  of  autistic  persons  is 
considered to reach the level of 2 to 1000 persons, and 2 to 1000 persons in the case of Asperger 
syndrome. At the same time, the population of persons with ASD is rated at the level of 1-2 to 
100 cases in the U.S.A., the country in which the most advanced epidemiological studies are 
carried out. In addition, statistical data show that the increase of a frequency rate of ASD is 10 
– 17% a year (Newschaffer et. al., 2007). It may be due to a real increase of the occurrence of 
autism but might as well reflect refinement of assessment techniques as well as a wider scope 
of diagnostic criteria, such as lowering down the age of assessment, broadening the scope of 
diagnostic criteria as well as development of diagnostic tools and techniques. In addition, the 
awareness of the parents of disabled children has also changed lately. 
An analysis of statistical data shows also that ASD disorders are more frequent in boys than 
in girls. It is estimated as 4,3 to 1, while in the case of Asperger syndrome the occurrence of 
the disorder in boys rises to 8 in comparison to 1 girl (ICD-10, 1997). There are a number of 
theories  trying  to  explain  such  a  state  of  affairs.  One  of  frequently  cited  is  the  theory  of 
neurotoxic  testosterone.  It  presumes  that  due  to  the  unequal  development  of  brain 
hemispheres in the prenatal stage a very high level of testosterone brings about disturbances 
in the development of the left hemisphere, which results in a very high occurrence of autistic 
disorders in boys (Geschwind, Galaburda, 1985a, 1985b, 1985c).  
The idea was further elaborated by Baron-Cohen and collaborators (2005). They conducted a 
longitudinal study, which aimed at revealing a possibility of the influence of so called foetal 
testosterone (FT) upon the development of a child in the prenatal stage of life. The study 
included  pregnant  women  in  whom  level  of  testosterone  was  measured  in  amniotic  fluid 
obtained via amniocentesis in order to evaluate the influence of difference in a level of FT 
upon  a  subsequent  development  of  the  child.  The  results  described  also  in  the  following 
paper  (Auyeung  et  al.,  2009)  revealed  a  negative  correlation  between  FT  and  the 
development  of  language  and  social  skills,  and  positive  correlation  with  specific  traits  of 
autism,  such  as  excessive  concentration  on  details,  stereotyped  movements,  and 
perseverations. The studies on hiper-mascunalization in ASD are carried on, since the role of 
the influence of testosterone upon the foetal development needs further verification. Yet, an 
analysis  of  androgens  influence  on  the  appearance  of  autistic  disorders  seems  to  bring 
promising results (Chakrabarti et al., 2009).  
The above presented data raises a question whether a clinical picture of autistic symptoms in 
girls differs from that observed in boys, and what - if any - is a specificity of autism occurring 
in girls. Kopp (2010) noted that autistic symptoms in girls are often neglected by professionals 
despite  the  reports  of  anxious  parents.  It  creates  the  need  of  presenting  a  more  detailed 
analysis  of  autistic  disorders  observed  in  girls.  All  the  more  that  the  most  available 
descriptions concentrate upon the characteristics of autism based upon an analysis of boys are 
of a rather general nature. Therefore, we decided to perform an analysis of autistic symptoms 
in autistic girls from the perspective of microgenetic and microdevelopment approaches. We 
were motivated by the fact that those theories make possible the evaluation of developmental 
potential of autistic persons. In order to make the clinical picture of autism in girls as complete 
as possible the description of individual cases will be presented in some detail. 

3. Autism spectrum disorder in girls 
As  mentioned  above,  most  studies  published  so  far  give  general  information,  which  is 
mostly based upon an analysis of autistic boys. As a rule the sex of examined subjects is not 

 

 
Microgenetic Approach to Therapy of Girls with ASD 

163 

taken into consideration, especially as ASD is prevailing in boys. Lately, specificity of ASD 
occurring  in  girls  has  been  noted,  yet  most  authors  still  concentrate  upon  symptoms 
characteristic of autism in general, such as tendency to routine behaviours, lack of interest in 
fantasy  games,  difficulties  in  social  interactions  and/or  distractibility  leading  to  learning 
problems (Knopp, 2010). On the other hand, McLennan and collaborators (1993) report that 
Loveland observed lowering of general IQ in boys (N = 700), while it was not stated in girls 
(N = 300). At the same time, it was noted that no use is made of the developmental potential 
of autistic girls, which may result in lower social and communication capacities and greater 
difficulties in establishing relations with peers in girls (Lord & Schopler, 1985). In addition, 
girls  score  worse  than  boys  both  in  verbal  abilities  and  visual-spatial  tests.  According  to 
Nichols (2009) it reflects higher level of expectations concerning social and communication 
skills  in  girls,  and  hence  more  negative  evaluation  of  observed  deviations.  Inappropriate 
behaviours of girls are often interpreted as a way to make others to pay attention to them, 
while  in  the  case  of  boys  such  behaviours  are  believed  to  reflect  their  attempts  to  get  a 
desired object.  
The  differences  between  sexes  are  also  believed  to  be  a  result  of  differences  in 
developmental trajectories. Some authors believe that boys exhibit more difficulties at the 
early stage of their life, while the difficulties in girls increase in the period of adolescence 
(Nichols, 2009; Nichols et al., 2008). Symptoms of brain damage, however, are more frequent 
in  girls  than  in  boys,  which  finds  its  confirmation  in  EEG  records,  which  show  more 
irregularities in girls. On the other hand, the autistic girls are better in performing games 
that  require  using  rules  and  also  show  weaker  tendency  to  stereotyped  movements  than 
boys (Lord et al., 1982; Nichols, 2009).  
Lord  and  collaborators  (1985)  point  out  that  longitudinal  studies  revealed  that  girls  with 
ASD did not establish any friendly relationships during a period of ten years, while several 
boys did accomplish it. According to these authors it may be due to the fact that girls tend to 
be more short-tempered and tearful (see also McLennan et al., 1993). Those discrepancies in 
behavioural and neurological functioning tend to disappear if the autistic girls are offered 
appropriate stimulants for their development. Yet, the autistic symptoms often appear again 
as the years go.  
Kopp and colleagues (2010) compared the quality of life of 100 girls with ASD and ADHD 
aged from 3 to 18 years, and it made them believe that those two types of disorders tend to 
co-occur, since ADHD was stated in 95% of autistic girls. At the same time, a higher level of 
fear, sleeping problems as well as a higher risk of depression was noted in both ASD and 
ADHD. Moreover, comparative studies of girls with ASD and ADHD revealed a regression 
in development in comparison to healthy subjects of the same age. The dysfunctions were 
observed  both  in  psychological,  motor,  and  social  abilities  so  they  included  all  aspects  of 
behaviour.  It  needs  to  be  stressed  that  a  positive  influence  of  environmental  factors, 
especially  of  appropriate  education  and  family  conditions,  proved  to  stimulate  the 
development  of  autistic  girls.  Hambrook  and  collaborators  (2008)  observed  that  anorexic 
girls exhibit lack of empathy and of an ability to systemize as well as other autistic traits. It 
is emphasized that a distorted pattern of information processing characteristic of anorexia 
shows a significant similarity to the autistic spectrum. Those difficulties may take various 
forms such as a lack of cognitive flexibility or stereotyped behaviours. A good example of 
rigid patterns of response noted both in ASD and anorexia provides an inability to shift a 
plan of action. 

 

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Autism Spectrum Disorders: The Role of Genetics in Diagnosis and Treatment 

4. Microgenetic approach form developmental point of view  
A  traditional  approach  to  the  study  of  developmental  processes  concentrates  either  on  a 
long  period  of  time  (longitudinal  studies)  or  upon  groups  including  as  big  numbers  as 
possible (cross-sectional studies). In recent years many scholars emphasize the usefulness of 
research taking into account the scores gained by the same group of children evaluated in 
short  time  intervals.  Such  an  approach  has  derived  also  from  the  microgenetic  theory.  In 
other words, it enables an observer to monitor the specific moments of transformations in 
thought  and  behaviour  in  contradistinction  to  classic  longitudinal  studies,  which  provide 
only a general pattern of a change of behaviour in examined subject (Levelly et al., 2005).  
Flynn  and  co-workers  (2006)  enumerate  three  aspects  pointing  to  the  usefulness  of  a 
microgenetic approach: 
1. 

It makes possible delineation of a whole range of a mechanism underlying a process of 
changes. 

2.  Observations are conducted while the factor causing a change is at work and not only 

before and after it took place. 
It is possible to control a moment of passing from applying a stimulus and initiating a 
change. 

Is the instruction understood by the child?  

At  the  same  time,  microgenetic  approach  enables  getting  answers  to  the  following 
questions: 
1. 
2.  Does the child use innovative strategies in solving a given problem? 
3. 
4.  What  is  the  efficiency  of  actions  undertaken  by  the  child  while  looking  for  a  proper 

Is the child able to discover a new more effective strategy in the course of action? 

3. 

 

solution? 

Is the child able to generalize an acquired strategy to solve other similar problems? 
In what way was a new experience acquired? 

5.  What amount of time does a child need to solve a particular problem? 
6. 
7. 
Microgenetic  approach  aims  at  an  analysis  of  changes  occurring  during  solving  a  given 
problem  that  takes  into  account  five  dimensions  of  cognitive  growth.  They  include  path, 
rate, breadth, source, and variability (Siegler & Svetina, 2002; Calais, 2008)).  
• 

The path of change involves the sequence of problem solving attempts performed by 
children  in  their  way  to  gaining  required  competence.  It  also  shows  if  the  change  is 
qualitative or quantitative. 
The rate of change concerns the time or amount of experience the children needed to 
start using a new strategy in a consistent way. It also includes an analysis of the nature 
of a change - whether it occurred gradually or suddenly.  
The breadth of change reflects children’s ability widely to generalize a new approach to 
other problems and contexts.  
The source of change takes into account factors that evoked observed changes.  
The variability of change enables evaluation of individual traits of a child in acquiring 
other dimensions of change. In other words, it enables creating a characteristics of an 
individual child as well as comparing a pattern of change across individuals (Siegler & 
Svetina, 2002; Flynn et al. 2006; Calais, 2008)). 

• 

• 

• 
• 

It is due to the fact that concentration upon the process of change as it is occurring reveals 
what mechanisms underpin it. It thus makes possible identifying both detrimental changes 

 
Microgenetic Approach to Therapy of Girls with ASD 

165 

(areas  of  dysfunctions)  as  well  as  positive  ones  reflecting  a  developmental  potential  of  a 
given child.  

5. Microgenesis from neuropsychological point of view 
A  neuropsychological  perspective  of  the  microgenetic  theory  points  to  the  fact  that  each 
action starts at lower levels of the brain and unfolds to the higher more specialized levels. It 
enables  a  fresh  look  upon  the  nature  of  symptoms  observed  in  brain  dysfunctions.  As 
pointed  out  by  Brown  &  Pachalska  (2003)  “the  lesion  displays  phases  in  a  transitional 
sequence  from  depth  to  surface”  (p.  4).  Two  important  notions  are  introduced  here: 
parcelation  and  heterochrony.  Parcelation  means  the  elimination  of  cells  and connections, 
which occur in over-abundance at birth, in order to achieve specificity. It is connected both 
with maturation and cognitive experience. Hence, sensory deprivation results in a diffuse 
and  redundant  connectivity  and  a  loss  of  the  ability  to  discriminate  among  perceived 
stimuli.  
In  the  case  of  function  the  same  role  as  elimination  is  played  by  inhibition.  Brown  and 
Pachalska state: “Inhibition occurs in the development of action, in newborns, which goes 
from  global  movement  of  the  hand  or  face  (the  cherubic  face  of  the  infant)  to  one  that  is 
more finely individuated” (2003, p. 6). In other words, the basic pattern of each system are 
elimination,  inhibition  and  specification,  which  means  sculpting  away  constraints  at 
successive  phases  of  cognitive  activity.  The  authors  point  out  that  in  pathology  re-
generalization through disinhibition leads to a number of deviant behaviours.  
Another  important  notion  of  microgenesis  is  heterochrony.  It  assumes  that  the  fact  that 
different brain systems develop at different rates can result not only in adaptations to the 
environment but also in malfunctions and aberrations. It is connected with the phenomenon 
of neoteny, which means selective prolongation of an immature phase of development. It 
makes possible refinement of structures and functions allowing mastering higher cognitive 
processes of which verbal communication is a good example.  
As pointed out by Brown (1998, 2001) microgenesis assumes that phyletic and ontogenetic 
growth  patterns  are  retraced  in  microgeny  but  the  processes  are  collapsed  here  over  a 
second  or  in  a  fraction  of  a  second.  Moreover,  both  mental  and  motor  processes  have  a 
hierarchical structure as the later levels unfold out of earlier ones. It is, therefore, possible to 
analyze  the  changes  in  children’s  behaviour  when  they  attempt  to  solve  a  given  problem 
passing from one level (or stage) to another. And that is of much help in making a course of 
therapy as effective as possible.  

6. Procedure  
The aim of our study was to delineate the cognitive abilities of autistic girls in the context of 
the microgenetic approach. The following question was asked:  
What are the characteristic traits of the cognitive development in girls?  
In  order  to  find  an  answer  to  the  above  formulated  question  a  detailed  description  of 
changing  competence  of  three  girls  with  ASD  will  be  presented.  All  the  three  girls 
represented similar level of autistic features and of cognitive abilities. All of them were able 
to  communicate  verbally  and  all  of  them  were  diagnosed  in  accordance  with  ICD–10 
(International  Statistical  Classification  of  Diseases  and  Related  Health  Problems)  criteria. 

 

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Autism Spectrum Disorders: The Role of Genetics in Diagnosis and Treatment 

The  control  variables  were:  depth  of  autistic  deficits,  and  the  level  of  social,  and 
communicative competence as well as interest in playing and reacting to applied incentives. 
The  elapse  of  time  between  individual  examinations  was  also  controlled.  The  boys 
development  was  presented  in  other  works  (Markiewicz,  2008,  2009;  Markiewicz  & 
Pachalska, 2007; Markiewicz & Grochmal, 2008; Markiewicz & Mc Queen, 2008;; Markiewicz 
et al., 2009), therefore their results will be used as a background for the description of girls. 

6.1 Case one 
Ola B. was first diagnosed at the age of three with the suggestion of middle stage mental 
disability. No neurological dysfunctions were noted. But her environmental conditions were 
very  bad  since  her  mother  died  when  the  girl  was  3  months  old  and  her  father  was  an 
alcoholic. His parental rights were juristically limited when the girl was 2 years old, and she 
was at the custody of her grandmother (the mother of mother). The first verification of the 
initial  diagnosis  was  after  the  girl  was  4  years  old  at  our  clinic  this  time.  The  assessment 
performed by our team indicated dysfunctions in all spheres of developmental development 
(F84) suggesting Asperger syndrome (F84.5). The results of psychological examination are 
presented below. 

6.1.1 Results from ICD-10 
The evaluation of behaviours performed with ICD-10 revealed: 
1.  Qualitative impairments in social interactions, which was manifested by: 

a.  Difficulties  in  accepting  new  situations  (e.g.  signs  of  frustration  if  a  sequence  of 
known  activities  was  changed),  limited  social  activity  (she  did  not  undertake 
interactions on her own but undertook simple forms of activity if initiated by an 
adult) 

b.  Weak adaptability to surrounding stimuli, mainly social ones (e.g. she was entirely 

indifferent to the new persons in her environment) 

c.  Emotional  distance,  lack  of  spontaneous  expression  of  feelings  with  the  use  of 

speech, gestures, and facial expressions. 

2.  Communication disabilities revealing in: 

a.  Lack of initiating verbal contacts, and limited readiness to communicate 
b.  Weak  reactions  to  questions  and  commands  with  preserved  understanding  (she 

performed simple verbal commands such as ‘come here’) 

c.  Weak  reactions  to  visual  and/or  auditory  stimuli,  and  limited  reactivity  to  non-

d.  Speech limited to simple sentences that often were constructed against grammatical 

linguistic messages 

rules 

e.  Limited  inventory  of  instrumental  gestures  and  emblems  (which  commonly  are 

used in place of speech). 

3.  Behaviour disorders that revealed in: 

a.  Many  stereotyped  reactions  (turning  round  on  tiptoes,  swinging,  beating  a  floor 

with an object, and non-functional use of objects) 

b.  Lack of correct reaction to stimuli as well as inability to differentiate reactions in 
response to various character of stimuli, odd treatment of objects, making dices and 
blocks move.  

 

 
Microgenetic Approach to Therapy of Girls with ASD 

167 

c.  Lack  of  emotional  reactions  to  new  toys  characteristic  of  young  children  (lack  of 

curiosity and pleasure connected with receiving a toy, interest in new objects) 

d.  Lack of initiative to start playing. 

6.1.2 Results from PEP-R (psychoeducational profile – revised) 
The scores of Ola on the developmental scale are typical of 30-34 months of age (she was 48 
months  old  at  that  time).  After  taking  into  account  emerging  scores  they  rise  to  43-47 
months of age, which makes it closer to the age of 3years and 9 months. The highest scores 
the  girl  gained  in  gross  motility.  At  a  similar  level,  yet  significantly  below  her  age,  were 
skills of imitation, perception, fine motor, and an ability to come into relationships (relating 
and  effect),  sensory  responses,  and  eye-hand  coordination.  Among  them  most  promising 
proved  to  be  sensory  responses,  eye-hand  coordination,  and  cognitive  processes  with  the 
exclusion of language. Therefore, those three spheres were regarded as the zone of proximal 
development. 
 
 

35

30

25

20

15

10

5

0

relating and affect play and interest
in materials

sensory
responses

language

imitation

perception

fine motor

gross motor

eye-hand
coordination

cognitive
performance

cognitive verbal

obtained

emerging

 

 
Fig. 1. Obtained and emerging scores of Ola on PEP-R (Psychoeducational Profile – Revised) 
Consequent examinations confirmed occurrence of the so called autistic triad, that is trouble 
getting  along  with  others  in  a  social  circle,  especially  when  working  together,  sharing 
feelings and thoughts, and making friends; impairments of communication; impairments of 
flexible imaginative functions of which restricted and repetitive behaviours and interests as 
well  as  difficulties  in  coping  with  changes  are  most  characteristic.  A  significant  variable, 
which  might  have  influenced  the  development  and  functioning  of  the  girl,  was  her 
traumatic  babyhood  experience.  Yet,  the  character  of  developmental  changes  allows  the 
conclusion that it had been rather a secondary factor, though the experiences of her early 
childhood might have stimulated the appearance of ASD dysfunctions.  

 

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Autism Spectrum Disorders: The Role of Genetics in Diagnosis and Treatment 

6.1.3 Results from WISC-R (Wechsler intelligence scale for children – revised) 
The next examination connected with a need to assess girl’s school readiness at the age of 6;9 
revealed a considerable range of scores. They ranged form moderate intellectual disability to 
average scores. Her general IQ was in the range of <48 – 76>; the scores of verbal IQ were 
within <40 – 58>; while those of performance scale rose to <73 – 98> (p = 0.05). The highest 
scores in the verbal scale the girl got in arithmetic – they were within the average. Other 
scores were at the range of low and very low. In the case of the performance scale it were the 
block design, object assembly, and coding, which were scored at the average level. Close to 
average were also her scores on picture completion and picture assembly.  
It is also worth pointing out that the girl was able to comprehend simple commands while 
fulfilling tasks connected with examination, and she also came into verbal contact with the 
examiner.  A  three-way  analysis  revealed  that  only  perceptual  organization  scored  at 
average  level  (7.75),  resistance  to  distracters  scored  below  average  (5),  while  scores  of 
comprehension were very low (1).  
Thus,  the  scores  gained  by  the  examined  girl  revealed  average  abilities  in  visual  analysis 
and  synthesis  as  well  as  good  perception  of  abstract  stimuli,  and  good  long-term  visual 
memory.  At  the  same  time  they  suggest  correct  imagination,  visual  orientation,  and 
visualization as well as a preserved ability to create abstract concepts. Moreover, the scores 
reflect a good level of planning skills and of simultaneous processing. The girl’s ability to 
concentrate attention upon a particular task, and an auditory perception of simple stimuli 
was below average. But most impaired were the abilities to communicate while performing 
particular  tasks.  This  might  suggest  that  her  auditory  short-memory  was  disordered  (She 
was able to repeat only two digits). Auditory perception of complex verbal stimuli, verbal 
expression,  verbal  memory,  and  ability  to  remember  previously  learned  utterances  also 
proved to be on a very low level.  

6.2 Case two 
Monika J. was first diagnosed before she was 3 years old. An intellectual retardation of a 
moderate level (F71) with “some traits of autistic behaviours” was stated at the other clinics. 
The  verification  of  this  diagnosis  performed  by  our  team  two  years  later  pointed  to 
persuasive developmental disorders (F84), autism in particular (F84.0).  

6.2.1 Results from ICD-10 
The evaluation of her behaviour in accordance with the criteria of ICD-10 showed:  
1.  Qualitative impairment of social interactions:  

a. 

Inadequate  evaluation  of  social  and  emotional  signals,  incorrect  reactions  to  the 
emotional states of others, weak modulation of behaviour in response to a given 
social context. 

b.  Low level of social skills, weak integration of social, emotional, and communicative 

behaviours 

c.  Disturbances in reciprocal social interactions. 

2.  Restricted, stereotyped , and repetitive interests and activities: 

a.  Routine and inflexibility in everyday behaviours, and making others stick to such 

actions 

b.  Stereotyped movements 

 

 
Microgenetic Approach to Therapy of Girls with ASD 

169 

c.  Attachment  to  the  computer  appeared  with  age,  including  the  interest  in  its 

construction. 

3.  Disorders of communication: 

a.  Low level of social use of language, weak synchronization and lack of reciprocation 

in dialogue  

b.  Weak  changeability  of  verbal  expression;  limited  ability  emotionally  to  modulate 
utterances,  and  weak  reactions  to  questions  and  instructions  as  well  as  to 
nonverbal clues. 

c.  Difficulties in differentiation of rhythm and accent to modulate communication 
d.  Limited ability to use facial expressions and gestures in communication. 

6.2.2 Results from PEP-R 
The scores of PEP-R show that the actual level of the girl’s abilities is typical of the age range 
22  –  29  month  both  in  the  developmental  and  behavioural  scale.  It  means  that  they  are 
significantly  below  her  chronological  age  as  she  was  52  months  old  at  the  moment  of 
examination.  She  scored  best  in  gross  motor,  fine  motor,  cognitive  performance,  and 
perception, while the scores of imitation, language, cognitive verbal, relating and affect as 
well as play and interest in materials were significantly below average. At the same time, 
her emerging scores indicated abilities typical of the age range 46 to 51 months so they were 
close  to  her  chronological  age.  It  was  observed  in  accomplishing  such  tasks  as  cognitive 
performance,  imitation,  relating  and  affect  as  well  as  play  and  interest  in  materials, 
providing  for  her  zone  of  proximal  development.  Scores  gained  by  Monika  during  an 
examination with the use of PEP-R are presented in figure 2. 
 
 

 
Fig. 2. Obtained and emerging scores of Monika on PEP-R (Psychoeducational Profile – 
Revised) 

 

 

170 

 
Autism Spectrum Disorders: The Role of Genetics in Diagnosis and Treatment 

6.2.3 Results from WISC-R 
The examination administered at the age of 6 years 11 months for the needs of evaluating 
her school readiness revealed a wide range of scores form moderate mental retardation to an 
average level. Her general IQ was within the range of <40 - 68> , the verbal IQ was within 
<34 – 53>, and the performance scores were within the range of <65 – 85>. She accomplished 
the  following  subtests  of  Wechsler  Scale  on  the  average  level:  object  assembly,  picture 
arrangement, and block design, while on similarities, vocabulary, and arithmetic subtest she 
scored  very  low.  It  should  be  pointed  out  here  that  despite  her  difficulties  with  defining 
terms the girl was quite good in communicating with others. She was able to understand 
simple commands and to carry on conversation. She was also surprisingly good at reading 
messages displayed on a computer screen. She was also able to solve simple puzzles and to 
put  together  mixed  verses  of  a  poem  into  a  meaningful  whole.  Moreover,  she  arranged 
individual  words  into  sentences,  and  knew  the  meaning  of  road  signs.  Her  arithmetic 
abilities were also quite good as she recognized and wrote down digits starting from 0 to 
100, and she was able to compare digits within the range of ten with the use of signs: =, <, >. 
Three-way analysis revealed that her perceptual organization was at an average level, while 
the other factors, such as reasoning (1.5) and resistance to distracters (1) were on a very low 
level.  Her  scores  suggest  average  level  of  visual  analysis  and  synthesis,  and  of  visual 
perception  as  well  as  a  correct  long-term  memory.  They  also  indicate  good  visual 
orientation  and  visualization  as  well  as  preserved  ability  to  create  abstract  concepts.  Her 
abilities to plan and simultaneous processing were also preserved.  
However, her abilities to concentrate upon a particular task and to interact verbally while 
performing  it  as  well  as  her  auditory  perception  of  simple  stimuli  were  limited.  Very 
severely disturbed was the perception of complex stimuli, verbal expression, durability of 
verbal memory, and an ability to remember previously learned expressions.  

6.3 Case three 
Gabriel  R.  was  first  diagnosed  when  she  was  3  years  old.  It  suggested  delay  and 
disharmony of her development. The verification of initial diagnosis was done by our team 
when she reached the age of 4 years and 4 months. It indicated persuasive developmental 
disorders (F84) suggesting autism (F84.0).  

6.3.1 Results from ICD-10  
The evaluation of her behaviour performed in accordance with ICD-10 revealed: 
1. 

Impairment of social interactions:  
a.  Lack of reciprocal social interactions 
b.  Weak modulation of behaviour in response to a given social context 
c.  Lack of interest in social aspects of play and/or performing tasks 
d.  Lack of spontaneous interactions with the closest. 

2.  Restricted repertoire of interests and activities: 

 Sniffing at the surrounding, liking of sharp odours 
 Stereotyped behaviours, turning round on tiptoes, very quick walking on tiptoes  

a. 
b. 
c.  Obsessive interest in maps and diagrams of technical devices.  

3.  Decreased communicative competence 
a.  Lack of ability to use the language in various social interactions 
b. 

Inadequate reactions to verbal cues 

 

 
Microgenetic Approach to Therapy of Girls with ASD 

171 

c.  Monotonous non-modulated utterances with agrammatisms 
d.  Difficulties with understanding complex utterances  
e.  Difficulties  with  synchronization  of 

linguistic  and  non-linguistic  aspects  of 

communication. 

6.3.2 Results from PEP-R 
The girl’s scores are at the age range of 37-42 month of life. They are, therefore, below her 
chronological  age.  If  we  take  into  account  emerging  scores  the  age  range  rises  to  50-54 
months of life, which means that her potential abilities are close to the chronological age. A 
detailed  analysis  reveals  that  the  girl  scored  best  in  gross  motor,  fine  motor,  cognitive 
performance, eye-hand coordination, and perception in developmental subscales, while in 
the case of behavioural scale she scored best on play and interest in materials, and sensory 
responses.  She  obtained  significantly  low  scores  on  language,  and  relating  and  affect 
(behavioural scale) as well as on cognitive verbal, and imitation (developmental scale). The 
zone  of  proximal  development  included  relating  and  affect,  language,  imitation,  and 
cognitive verbal subscales. The scores of PEP-R test administered at the age of 4 years and 4 
months are presented in figure 3. 
 

30

25

20

15

10

5

0

relating and affect

play and interest in
materials

sensory responses

language

imitation

perception

fine motor

gross motor

eye-hand
coordination

cognitive
performance

cognitive verbal

Fig. 3. Obtained and emerging scores of Gabriel on PEP-R (Psychoeducational Profile – 
Revised) 

obtained

emerging

 

6.3.3 Results from WISC-R 
Next stages of diagnostic-therapeutic procedure confirmed the existence of an autistic triad. 
At  the  same  time,  an  examination  of  school  readiness  at  the  age  of  6  years  11  months 

 

172 

 
Autism Spectrum Disorders: The Role of Genetics in Diagnosis and Treatment 

indicated the distribution of scores within the range from moderate mental retardation to 
below  average.  The  scores  were  within  <56  –  74>  for  the  verbal  scale,  <63  –  83>  for  the 
performance scale, and <52 – 80> in the case of the full scale IQ (confidence level p = 0.05 ). 
The highest verbal IQ scores (ranging within average results) the girl gained on arithmetic 
and  information.  The  remaining  scores  were  very  low.  As  far  as  the  performance  IQ  is 
concerned  she  scored  on  average  level  on  picture  completion,  block  design,  and  object 
assembly  subtests.  On  the  remaining  subtests  her  scores  were  below  average  (picture 
arrangement)  or  very  low  (coding).  She  also  exhibited  difficulty  in  defining  concepts  (on 
vocabulary  subtest)  in  a  way  similar  to  the  two  above  described  autistic  girls.  She  was, 
however, quite good at comprehending simple commands and instructions.  
To  give  the  reader  some  insight  into  the  nature  of  the  girl’s  difficulties  we  give  some 
samples of her responses appearing in the vocabulary subtest: ‘umbrella’ – like rain; ‘clock’ – 
bim-bam; ‘alphabet’ –a,b,c…(she made an attempt to enumerate the letters of the alphabet); 
‘nail’ – it is cut or not cut; ‘bicycle’ – she lied down on the floor and showed how to ride a 
bicycle; ‘knife’ – she performed the movements of cutting.  
Three-way analysis revealed a correct level of development of perceptual organization (7.5) 
and of resistance to distracters (8.7). Yet, her score on verbal reasoning was very low (2.7). It 
may reflect her low level of linguistic and communicative competences. On the other hand, 
the above scores point to good visual processing, and preserved ability to concentrate on a 
given  task.  Her  abilities  to  create  abstract  concepts  as  well  as  planning  and  simultaneous 
processing were also quite good.  

 The child demonstrates the toy to the parent 

7. An example of a task on developing social interactions 
The  microgenetic  model  makes  it  possible  to  directly  observe  the  acquisition  of  cognitive 
abilities in the process of training. Below we give an example of a task that made possible 
the evaluation to what extent the child is able to comprehend the visual perspective of both 
her  and  of  the  parent.  The  girls  were  to  demonstrate  to  the  parent  a  toy  that  they  had 
constructed by themselves. The following elements we assessed: 
1.  The child approaches the parent 
2.  The child tells the parent: “Look what I have done” 
3. 
4.  The child moves her eyes from the toy to the parent  
5.  The child moves her eyes from the parent to the toy  
6.  The child demands a commendable: ”Do you like it”  
During the experiment the examiner and the child were seated at a table, and the parent was 
seated  at  another  table  at  the  opposite  corner  of  the  room.  The  child  was  able  to  see  the 
whole  room,  while  the  parent  was  seated  diagonally  at  the  other  side.  The  examiner 
presented the child a puppet consisting of blocks strung on a stick with a stand. The blocks 
could be arranged in various configurations so that the shape of the puppet was changed. 
The girl constructed the puppet, and then the examiner said: 
1.  Show me what you have done 
2.  Look at the puppet 
3.  Do I look at the puppet? 
4.  Do you like the puppet? 
5.  Do I like the puppet? 

 

 
Microgenetic Approach to Therapy of Girls with ASD 

173 

After a short period of joint play, the examiner handed the child the puppet and said: “Show 
the  puppet  to  your  mum/daddy”.  The  command  was  accompanied  by  pointing  to  the 
parent with a hand of the examiner. The parents had been instructed to: (1) put their hands 
over  their  eyes,  (2)  sit  with  their  backs  to  the  child.  Hence,  in  order  to  demonstrate  the 
puppet the child had to approach the parent and make her look at the puppet, for example 
by pulling her hand and saying “Look what I have done”. The task was evaluated in the 
following way: 

0 points - no demonstrating the puppet to the parent 
1 point - the child approaches the parent but keeps the puppet and looks at it without 
demonstrating it to the parent  
2  points  -  the  child  demonstrates  the  puppet  to  the  parent  with  an  appropriate 
statement. 

Altogether six tasks were performed during one session, and the possible maximum score 
was  12  points.  The  same  pattern  of  six  tasks  was  then  repeated  during  three  consecutive 
sessions presented at two weeks intervals, and the analysis took into account a total of all 
scores gained by the examined girls during all sessions. 

8. Results 
The scores of the girls described in the present chapter are presented in table 1. They show 
that all of them mastered a particular schema of action. During the first session only one girl 
approached her parent in the second task but she did not encourage her/him to take interest 
in  the  toy.  The  remaining  two  girls  reached  those  criteria  only  in  the  fifth  task.  The 
qualitative  analysis,  however,  will  make  it  possible  to  delineate  the  strategy  of  forming 
reciprocal social interactions in a task schema by girls with ASD. After the first session the 
examiner demonstrated the flow diagram. The demonstration was to show the child what 
she was expected to do. During the second session the girls were shown pictures, in which 
consecutive stages of actions to be done were presented. The pictures were shown before 
each task. 
 

 
Session I  Ola  

 

 
Session II  Ola  

Monika  
Gabriel  

Monika  
Gabriel  

Monika  
Gabriel  

 
Session III Ola  

Task 1 
0 
0 
0 

Task 2 
0 
0 
0 

Task 3 
0 
1 
0 

Task 4 
0 
1 
0 

Task 5 
1 
1 
1 

Task 6 
1 
1 
1 

Total 
2 
4 
2 

1 
1 
1 

1 
2 
2 

1 
1 
1 

2 
2 
2 

1 
2 
2 

2 
2 
2 

1 
2 
2 

2 
2 
2 

1 
2 
2 

2 
2 
2 

1 
2 
2 

2 
2 
2 

6 
10 
10 

11 
12 
12 

Table. 1. Scores obtained by the examined girls in consecutive sessions. 
Below we present the scores of boys in accomplishing the above described task (see table 2). 
Their  results  from  Wechsler  Scale  were  within  average  and  lower  than  average  range.  It 

 

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Autism Spectrum Disorders: The Role of Genetics in Diagnosis and Treatment 

means that the intellectual level of the boys corresponded with intellectual abilities of the 
above  described  girls.  It  must  be  stressed,  however,  that  all  boys  exhibited  aggressive 
behaviours,  such  as  screams,  squeaks,  throwing  objects,  tearing  paper,  psychomotor 
agitation, disobedience, and ignoring commands and prohibitions.  
 

Task 1 
0 
0 
0 

Task 2 
0 
0 
0 

Task 3 
0 
0 
0 

Task 4 
1 
0 
0 

Task 5 
0 
0 
1 

Task 6 
1 
0 
0 

Total 
2 
0 
1 

 

 
Session I  Paul  
Jarek 
Martin 

 
Session II  Paul 
Jarek 
Martin 

 
Session III Paul 
Jarek 
Martin 

0 
0 
1 

1 
1 
1 

0 
1 
1 

1 
1 
1 

1 
0 
1 

1 
1 
1 

1 
1 
1 

2 
1 
2 

1 
1 
1 

2 
1 
1 

2 
1 
1 

2 
2 
2 

5 
4 
6 

9 
7 
8 

Table 2. Scores obtained by boys in consecutive sessions. 
Comparison of scores obtained by girls an boys shows that :  
1.  Results of the first sessions were similar to those of girls. 
2.  Difference appeared in the third session since the performance of the boys was much 
worse  than  of  girls.  They  did  not  support  the  demonstration  of  the  puppet  with  an 
appropriate statement. 

3.  Negativistic  and/or  aggressive  behaviours  were  much  more  frequent  in  boys.  They 
exhibited strong tendency to scream, to squeak, and were apt to break accomplishing 
the task.  

8.1 Microgenetic analysis 
The  developmental  microgenetic  model  (Siegler,  Svetina,  2002;  Calais,  2008)  makes  it 
possible to take into consideration five mentioned earlier dimensions reflecting the manner 
of  solving  a  given  problem.  They  are  the  path,  rate,  breadth,  source,  and  variability  of 
changes that occurred in consequence of accomplishing a particular task.  
1.  An  analysis  of  the  path  revealed  that  the  changes  appearing  at  consecutive  stages  of 
performing  a  task  were  mainly  of  quantitative  nature:  starting  from  the  lack  of 
demonstrating the puppet to the parent in the initial tasks of sessions, then approaching 
the parent without demonstrating it, and finally demonstration with a verbal message. 
It allows the conclusion that the change of competence took place there resulting in an 
ability  to  come  into  social  interaction  with  parents,  and  to  understand  their  point  of 
view.  As  can  be  noted  in  table  1,  two  of  the  girls  were  able  to  improve  their  action 
already in the third task. The girls approached their parents, and made them look at the 
puppet.  It  is  worth  to  remind  here  that  PEP-R  examination  indicated  high  emerging 
scores in imitation subscale in all the three girls. Therefore, the above tasks made use of 
a significant zone of proximal development. 

2.  An  analysis  of  the rate  of  change  showed  that  a  difference  between  the  primary  and 
consequent strategy of solving a problem came into being only in the second session of 

 

 
Microgenetic Approach to Therapy of Girls with ASD 

175 

4. 

5. 

our study two weeks after the first session. However, it was not possible to state if the 
new  strategy  appeared  gradually  or  all  of  a  sudden.  Stating  it  would  require  a  more 
frequent, everyday, repetition of experimental sessions.  

3.  As  far  as  the  breadth  of  change  is  concerned  it  seems  that  the  girls  were  not  able  to 
modify once learned schema of behaviour. They were not acting in a spontaneous way, 
and did not express any emotional commitment. Perhaps the use of other experimental 
strategy would enable a deeper insight into the problems encountered by the examined 
girls. 
It  was  also  stated  that  a  carefully  designed  experimental  procedure  enables  forming 
desired,  and  at  the  same  time  quite  complex, actions  in  autistic  girls.  In  this  case  the 
source of change was demonstration of expected behaviours accompanied by verbal as 
well as nonverbal clues.  
 It should be stressed, however, that the girls acted in an automatic way. They did not 
express any satisfaction with their work or a will to boast about their success. At the 
same  time,  they  tended  to  stick  to  the  once  learned  schema  of  action.  It  allows  the 
conclusion that there was no variability in their behaviour as no personal traits could 
be noted in their manner of accomplishing the tasks as well.  

Yet, it was experimentally stated that an appropriate therapeutic approach makes it possible 
developing schemas of social interactions. It results in developing independence and a sense of 
agency so important for creating a sense of identity in a given child. It is also worth to remind 
that all the three girls had a high level of perceptual organization. And it is connected with 
simultaneous  and  global  processing  of  information  based  upon  visual  perception,  which 
enables integration of perceived elements and combining them into meaningful wholes. The 
rate of mental processes as well as eye-hand coordination were also good. They might have 
difficulties  due  to  the  necessity  to  work  under  time  pressure  and  their  weak  resistance  to 
distracters. On the other  hand, all of them  scored very low on verbal perception  tasks, and 
exhibited difficulties with comprehension of verbal commands and instructions.  
It does not need reminding that communication impairment is the main diagnostic feature 
of autistic triad. Yet, the majority of studies relay heavily upon verbal instructions given to 
the  examined  children.  The  authors  seem  to  forget  that  autistic  children  may  have 
difficulties  with  understanding  verbal  commands  or  need  some  time  to  process  the 
information  included  there.  Hence,  if  the  scores  of  sequential  processing  of  abstract 
nonverbal concepts are within average range, it allows the conclusion that a given child is 
also  able  to  perform  logical  operations  on  verbal  material.  Especially,  if she  is  able  to see 
cause  and  effect  relationships  in  everyday  situations.  Beside  communication  problems 
difficulties  with  performing  experimental  tasks  may  also  be  due  to  the  necessity  of 
combining  two  types  of  clues:  verbal  (instruction)  and  nonverbal  (demonstration).  So  the 
actions of an examiner, who intends to explain the rules of a given task, may in effect lead to 
a confusion as was the case with well known Piaget’s experiments (see Donaldson, 1978). It 
is also reflected in the phenomenon of “horizontal decaláge”. That is an inconsistency in the 
tasks  healthy  children  can  perform.  For  instance,  they  can  solve  Piaget’s  conservation 
problem  on  numbers  at  age  six,  but  they  are  not  able  to  solve  it  on  mass  until  age  eight, 
while  the  ability  to  perform  conservation  of  weight  task  appears  only  at  the  age  of  ten 
(Wortman, Loftus and Marshall, 1988).  
Bearing all this in mind we have used a number of repetitions in order to make sure that the 
child  had  understood  what was  required  from her  in  a  given  experimental  situation. The 

 

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Autism Spectrum Disorders: The Role of Genetics in Diagnosis and Treatment 

aim of the above described task was to make it the child understand the visual perspective 
of another person. While showing her work to the parent the child established a common 
field of sharing attention. Awareness of the existence of two different points of view is an 
important indicator of developing social interactions (Meltzoff, 1995; Repacholi & Gopnik, 
1997; Gopnik, Meltzoff & Kuhl 1997). A sequence of behaviours used in the above described 
experiment was to create an awareness of different points of view in autistic children. As 
pointed out above the girls were much better in performing those tasks than the boys we 
were examining during a ten year period.  

8.2 Possible sources of differences between girls and boys 
It  is  often  stressed  in  autistic  literature  that  the  symptoms  of  ASD  exhibited  by  girls  are 
more difficult to diagnose than those occurring in boys (Attwood, 2007; Kopp et al., 2010; 
Skuse,  2009).  One  of  the  reasons  is  higher  than  in  boys’  level  of  social  skills,  such  as  an 
ability  to  come  into  social  interactions  and  a  style  of  behaviour  in  general.  Moreover, 
deviant behaviours of children with ASD influence not only their own development but also 
the life of the whole family. At the same time, they have impact upon their relations with 
peers and adults from outside the family. It concerns their attitudes towards such children 
in particular since autistic children were believed - and often still are – to be dangerous for 
others due to their bizarre and odd behaviours. Since boys are generally believed to be more 
aggressive, another significant factor discriminating functioning of girls and boys with ASD 
may  be  a  difference  in  educational  treatment  (Constantino  et  al.,  2009:  Jonson-Reid  et  al., 
2010). Beside social learning biological factors may also play an important role. One of them 
is the testosterone impact upon the foetus (Baron-Cohen et al., 2005).  
It  may  also  be  worth  to  point  to  another  factor  that  might  cause  differences  between 
symptoms observed in boys and girls. Namely, a well known clinical fact that female brain 
is less localized than the male brain (Moir and Jessel, 1992). In consequence, brain lesions in 
women are less disastrous than in men what may be best observed in aphasia recovery. As 
pointed  out  earlier,  the  process  of  brain  development  is  connected  with  elimination  of 
unnecessary  connections,  which  leads  to  refinement  of  behaviour  (see  also  Kaczmarek, 
Markiewicz, 2008). It is highly plausible that in the case of autism the selection is delayed, 
which  results  in  over-abundance  of  connections,  and  in  disinhibition  leading  to  sensory 
overload.  
It  was  noted  in  other  works  (Kaczmarek,  2003;  Kaczmarek,  Markiewicz,  2008)  that  while 
creating our own image of the world we concentrate upon matters that are important for us 
and  leave  out  less  significant.  Therefore,  our  world  image  is  simplified  to  a  considerable 
degree,  and  thanks  to  it  the  surroundings  seem  predictable.  And  it  is  that  presumed 
predictability that gives us a feeling of safety. Thus, meeting a mentally ill person makes us 
feel a bit nervous because we do not know what to expect from him. The autistic child is not 
able to single out significant stimuli from the non-significant ones, hence her world becomes 
unpredictable, incomprehensible, and terrifying. It may lead not only to fits of aggression 
and self-aggression but also to stereotyped repetitive behaviours so characteristic of autism. 

9. Conclusion 
Our own clinical practice as well as other studies show that early diagnosis and therapeutic 
procedures  connected  with  it  facilitate  socialization  of  children  with  ASD.  Of  particular 

 

 
Microgenetic Approach to Therapy of Girls with ASD 

177 

significance is the individual approach to each autistic child. It enables evaluating not only 
level  of  actual  skills  but  also  developmental  potential  of  a  given  child,  which  in  turn 
improves efficacy of treatment. If we know a zone of proximal development of a given child, 
we are able to concentrate on the areas in which the child is prone to succeed. Therefore, the 
microgenetic  analysis  of  the  manners  in  which  the  child  strides  to  overcome  difficulties 
while solving particular tasks proves to be of great significance. It gives a therapist tools for 
developing potential abilities of the child, and not to concentrate on her disabilities as it is 
often  the  case.  Such  an  individualized  and  progressive  approach  increases  the  efficacy  of 
therapy and gives the child a feeling of success stimulating her to work.  
Taking into account developmental profile of a particular child is a necessity since there is a 
considerable differentiation  among  autistic persons  both in  the  inter-  and  intra-individual 
dimensions. One of them is the difference between the clinical picture of symptoms in boys 
and  girls.  Moreover,  studies  of  Constantino  and  collaborators  (2009)  revealed  subtle 
difficulties in communicating with others in 20 per cent of families of children with ASD. 
They  were  observed  mainly  in  siblings  in  whom  some  traits  of  autism  appear  about  ten 
times  more  often  than  in  healthy  population.  The  authors  are  of  the  opinion  that  at  least 
some  of  specific  traits  of  autistic  spectrum  may  be  hereditary.  Therefore,  they  stress  the 
necessity of taking into consideration sex differences while making a diagnosis of autism.  
Our study has shown that it is possible to develop a schema of action, or a script, in autistic 
girls.  Moreover,  the  techniques  we  applied  made  it  possible  to  analyze  a  given  change, 
while  it  was  actually  happening,  and  not  comparing  behavioral  patterns  from  a  pre-  and 
post-change  as  it  is  often  done.Yet,  the  script  remains  a  rigid  unchangeable  sequence  of 
actions, while healthy people change it in accordance with the requirements of environment. 
It was also noted that the autistic symptoms in girls are less pronounced due mainly to their 
better  communicative  competence.  For  that  reason  their  disorders  are  often  neglected 
despite the fact that their qualitative character is not much different from the disturbances 
observed in boys. The differences are mainly of a quantitative nature. It is quite probable 
that refinement of diagnostic methods will lead to better understanding of their problems 
causing a dramatic increase of the number of autistic girls as was the case with other clinical 
syndromes.  

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10 

Genetic Counseling in Autistic Phenotypes 
Agnes Cristina Fett-Conte 
Medical School - FAMERP/FUNFARME, São José do Rio Preto, São Paulo 
Brazil 

1. Introduction 
Autism  is  a  neurobehavioural  disorder  that  includes impairment  in  social  interaction  and 
language  development  and  communication  deficits  accompanied  by  repetitive  and 
stereotyped  behaviours.  More  recently  this  term  has  been  used  to  define  a  very  broad 
behavioural phenotype which is classified as different disorders that comprise the Pervasive 
Developmental  Disorders  (PDD)  according  to  the  Diagnostic  and  Statistical  Manual  of 
Mental  Disorders,  4th  Edition-DSM-IV  (American  Psychiatric  Association  [APA],  1994).  It 
contains  the  criteria  for  diagnosis  and  specific  characteristics  of  each  disease,  including 
Autism,  Asperger’s  syndrome,  Childhood  Disintegrative  Disorder,  Rett  syndrome  and 
Pervasive Developmental Disorder Not Otherwise Specified (PDD-NOS).  
However, with the exception of Rett syndrome, the others make up a continuous spectrum 
rather than clinically defined diagnostic categories due to the wide variation of clinical signs 
and symptoms and the subjectivity of the criteria for differential diagnosis. For this reason 
these  disorders  have  been  included  in  a  general  conceptual  category,  Autism  Spectrum 
Disorders  (ASDs)  (Snow  &  Lecavalier,  2011;  Witwer  &  Lecavalier,  2008).  Hence,  the 
proposals for DSM-V, being prepared by the APA, which is scheduled to be published in 
2012 or 2013, recommend that Rett syndrome is not considered among the ASDs, that the 
designation  PDD  is  no  longer  used  and  that  ASD  is  considered  a  single  category  that 
includes Autism, Asperger's syndrome, Childhood Disintegrative Disorder and PDD-NOS. 
That  is,  the  disorders  that  compose  the  autistic  spectrum  would  no  longer  have  specific 
names (APA, 2011).  
Rett syndrome almost exclusively affects girls and is characterized by normal development 
until about six months followed by regression of motor and social skills. The triad dementia-
ataxia-autism  is  observed  as  is  a  characteristic  pattern  of  deceleration  in  the  rate  of  head 
growth, loss of acquired manual skills, poorly coordinated gait, involuntary movements of 
the  hands  and  the  trunk  and  autistic  features.  Epilepsy  may  be  present  and  a  abnormal 
respiratory  pattern  is  typical  .  The  prevalence  among  women  is  between  1:10,000  and 
1:15,000  with  most  cases  caused  by  a  sporadic  mutation  in  the  MECP2  gene,  located  on 
Xq28.  In  some  cases,  the  etiology  is  due  to  mitochondrial  DNA  mutations  (Gonzales  & 
LaSalle, 2010; Nissenkorn et al., 2010; Temudo et al., 2010). The peculiar nature and specific 
etiology, linked to a genetic defect with consequent brain damage, are among the reasons 
for not being considered within the ASDs. 
Childhood  Disintegrative  Disorder  is  basically  characterized  by  normal  development  of 
children until at least two years of age followed by a process of loss of previously acquired 
intellectual and behavioural skills, which results in autistic behaviour (Homan et al., 2011). 

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Autism Spectrum Disorders: The Role of Genetics in Diagnosis and Treatment 

Asperger’s  syndrome  differs  from  other  diagnoses  because  of  the  absence  of  delay  in 
language  development, 
in  general  a  preserved  cognitive  development,  frequently 
prodigious  memory,  as  well  as  "pedantic"  speech,  inadequate  social  interaction  and,  in 
many cases, disinterest in interpersonal relationships (Koyama & Kurita, 2008). 
Since  the  reports  of  Kanner  in  1943  on  "autistic disturbances  of  affective  contact", Autism 
has  been  extensively  discussed  and  investigated.  Currently  regarded  as  a  developmental 
disorder  that  manifests  before  thirty  months  of  age,  it  is  characterized  by  abnormal 
responses  to  auditory  and  visual  stimuli  and  underdeveloped  or  absent  speech.  Serious 
communication  and  social  interaction  problems  occur  and  behaviour  is  ritualistic, 
aggregating abnormal routines with resistance to change. Approximately 75% of cases are 
associated  with  mental  retardation,  15  to  40%  with  seizures  and  20  to  50%  with 
electroencephalographic abnormalities (Tuchman et al., 2010). 
PDD-NOS is a diagnosis of exclusion made when an individual presents severe impairment 
of reciprocal social interaction and verbal or nonverbal communication skill development, 
but does not satisfy the criteria for other PDDs. Atypical Autism, for which the etiology and 
prevalence  remain  unknown,  is  also  included  in  this  category  (Chiappedi  et  al.,  2010; 
Koyama & Kurita, 2008). 
ASDs occur in approximately 1:150 live births and in all ethnic groups and social classes, 
and  thus  can  be  considered a  public  health  problem.  There  are  discussions  as  to  whether 
there is a real progressive increase in the prevalence of these diseases in the population and 
it is speculated that there are several risk factors. However, this increase appears to result 
from the fact that diagnosis is being made earlier as education and healthcare staff are more 
attentive to the symptoms, besides the diffusion of information, leading to the identification 
of a greater number of cases (Liu et al., 2010; Shen et al., 2010; Nassar et al., 2009). There are 
several diagnostic scales that use "checklists" and are effective in the rapid identification of 
possible cases of ASD. The degree of behavioral and cognitive functioning is highly variable 
and  early  diagnosis  is  of  paramount  importance  because  stimulation  programs  achieve 
much more significant results when interventions occur in the early development stages. If 
diagnosis and intervention are delayed, the results are not very promising (Biederman et al., 
2010; Marteleto et al., 2008). 
But if the classification of the autism phenotype is so difficult and so discussed, the etiology 
is  even  more  so.  Knowledge  about  the  etiology  of  ASDs  is  increasing,  but  causes  remain 
elusive for most cases. The truth is that autism has many etiologies. 
ASD associated with a known cause is called syndromic autism. There is an expanding list 
of medical conditions in the literature associated with autistic manifestations, ranging from 
disruptions caused by varying environmental agents to several mutations and well-defined 
syndromes, chromosomal abnormalities and metabolic diseases. In cases where the cause is 
identified,  the  autistic  manifestation  is  considered  secondary  (Benvenuto  et  al.,  2009). 
Among  these,  prenatal  infections,  prenatal  exposure  to  physical  and  chemical  agents  and 
genetic disorders may be cited (Ratajczak, 2011; Zhang et al., 2010). However, the biological 
mechanisms involved in these associations are unclear.  
The clinical heterogeneity of  ASDs probably reflects the complexity of the genetic profile. 
There is no doubt that different genetic mechanisms contribute to the pathogenesis of ASDs. 
Thus, when the many different etiologies of autistic phenotype are referred to, the principal 
focus  is  on  genetic  aspects.  Heritability  is  estimated  in  90%  and  the  monozygotic  twin 
concordance  rate  is  as  high  as  95%.  The  situation  is  complicated  by  significant  inter-
individual  heterogeneity,  the  numerous  loci  involved  and  gene-environment  interactions 
(Caglayan, 2010).  

 

 
Genetic Counseling in Autistic Phenotypes 

183 

Another interesting aspect is related to the phenomenon of genetic anticipation. Since the 
first descriptions by Kanner, particular personality traits in relatives of autistic patients have 
been recognized. The findings of familial aggregation of minor variants suggest that genes 
confer  susceptibility  at  variable  severity,  which  is  often  "light",  known  as  broad  phenotype, 
and independently segregates among relatives (Losh et al., 2008; Schmidt et al., 2008).  
This suggests that this complex combination of genetic and environmental factors, is what 
really defines the risk for ASDs. The commonly accepted empirical risk estimate for a couple 
with one affected child is 2–8%, in the absence of a definablecondition (Selkirk et al., 2009).  
Karyotype  analysis  shows  changes  involving  all  chromosomes  in  3  to  6%  of  ASD  cases. 
However,  the  functional  significance  of  these  changes  also  remains  unknown  given  the 
variation in the size of the regions involved and the diversity of loci. Moreover, the majority 
of rearrangements are sporadic, some are detected in other asymptomatic family members 
or are de novo in individuals with a positive family history of ASDs (Marshall et al., 2008; 
Sykes & Lamp, 2007). 
Many  genes  are  likely  to  contribute  to  the  etiology  of  ASDs,  especially  in  cases  of  non-
syndromic  autism,  as  they  present  mutations  or  polymorphisms.  The  identification  is 
becoming  easier  as  a  result  of  advances  in  genetic  technology.  It  is  believed  that  the 
emergence  of  the  autistic  phenotype  in  most  cases  depends  on  a  small  additive  effect  of 
multiple genes, but all with expressions in the central nervous system. Among these are the 
CENTG2 gene mapped at 2q37.2, the SHANK3 gene mapped at 22q13.3, the GABRB3 gene 
mapped at 15q11-13, the SLC6A4 gene located at 17q11.2 and the NLGN3 gene mapped at 
Xq13.1 (Cuscó et al., 2009). 
Many  studies  recommend  that  the  laboratory  evaluation  of  ASD  cases  should  initially 
include  an  analysis  of  G  banded  karyotype,  preferably  high  resolution  and  a  molecular 
evaluation of the FMR1 gene. But even at high resolution, abnormalities smaller than ~5Mb 
cannot  be  detected  by  karyotyping,  which  is  problematic,  particularly  in  subtelomeric 
chromosomal regions that are rich in genes susceptible to rearrangements. For this reason, 
karyotypic evaluation by the Fluorescent in situ Hybridization (FISH) technique is indicated 
to  overcome  some  limitations  and  clarify  certain  karyotypic  findings.  However,  negative 
results obtained with these techniques have not ruled out other types of genetic alterations. 
Genetic  screens  represent  a  powerful  tool  when  dealing  with  monogenic  disorders 
characterized  by  direct  genotype-phenotype  correlations.  Current  guidelines  for  clinical 
genetic  evaluation  of  patients  recommends  carrying  out  a  detailed  physical  examination, 
hearing evaluation, obtaining a detailed personal and family history, screening for inborn 
errors  of  metabolism  and  neuroimaging  studies,  as  well  as  karyotype  and  fragile-X  DNA 
testing (Lintas & Persico, 2008; Wassink et al., 2007). The identification of genes linked to 
susceptibility and investigation of pathogenic mechanisms is crucial in clinical practice and 
for adequate genetic counseling of families, but the specifications and limitations of each test 
should be considered. Some genetic testing, even as part of research protocols for ASDs, can 
only be time consuming and not appropriate in many cases. 
More  recently  tests  to  identify  cryptic  genomic  changes  have  been  proposed.  The 
development  of  array-based  CGH  (Comparative  Genomic  Hybridization)  and  MLPA 
analysis  (Multiplex  Ligation-dependent  Probe  Amplification)  has  enabled  detection  of 
microdeletions and microduplications in patients with ASDs. These have been referred to as 
copy number variants (CNVs) and seem to play a key role in the etiology of many cases, 
more  commonly  among  patients  with  non-dysmorphic  ASDs  (Benvenuto  et  al.,  2009; 
Christian  et  al.,  2008).  But  despite  the  promising  genetic  findings,  the  data  are  still 

 

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Autism Spectrum Disorders: The Role of Genetics in Diagnosis and Treatment 

inconclusive which is due to genetic heterogeneity, the likely involvement of many genes 
that  interact,  epistatic  interactions,  gene-environment  interactions,  variability  in  gene 
expression, the influence of epigenetic mechanisms and the fact that the expression of some 
genes is influenced by specific regulatory regions located at relatively long distances, even 
on  other  chromosomes,  which  makes  the  selection  of  candidate  genes  difficult  (Zahir  & 
Brown,  2011;  Vorstman  et  al.,  2006).  The  fact  that  the  cost  of  these  tests  is  high  and  the 
availability is low has to be considered as this makes access for many patients difficult. 
The  high  prevalence  and  complexity  of  the  ASDs  have  motivated  several  studies  using 
different research strategies. Genetic factors are the most studied and its potential cause in 
many  cases  has  resulted  in  a  significant  increase  in  the  number  of  referrals  to  clinical 
geneticists and genetic counselors. 
Genetic counselors are able to help families that have children with syndromic autism and 
even  in  cases  with  uncertainty  regarding  etiology.  But,  genetic  counseling  for  families  of 
ASD  individuals  is  a  difficult  procedure.  The  most  important  aspect  is  that  genetic 
counseling  is  not  only  a  question  of  giving  technical  information  related  to  all  the 
complexity of the aforementioned features. Even so, technical information can be offered in 
several contexts such as healthcare and educational booklets and even in television shows. 
Genetic counseling is the process of providing information to individuals and families about 
the nature, inheritance, and implications of genetic disorders to help them make informed 
medical  and  personal  decisions.  It  is  a  communication  process.  As  such  it  should  be 
understood  as  a  "two-way  street",  i.e.  as  a  situation  of  "exchange".  Counselors  have  no 
guarantee or control that their "message" to counselees is understood as intended, nor even 
about the consequences of the process. Thus, besides the communication of biological and 
clinical information, counselors must prioritize the educational and psychological aspects of 
the  process,  so  that  all  the  information  and  emotional  support  given  to  counselees  can 
support  their  decision  making  and  help  to  reduce  anxiety  and  guilt.  It  is  essential  to 
remember that nondirectiveness is crucial in this process (Kessler, 2001) as is the context, the 
environment chosen for the process to develop. 
Nondirectiveness  is  not  a  question  of  whether  to  give  advice  or  not  or  to  say  what  the 
counselor thinks is best or not. It is a form to promote and to enhance the autonomy and 
self-directedness of counselees. It is necessary to provide accurate, complete and unbiased 
information  and  to  have  an  empathic  relationship  between  those  involved,  professionals 
and family. Nondirectiveness and ethical principles applied to genetic counseling are very 
well documented in a publication of the World Health Organization in 1998 (World Health 
Organization [WHO], 1998). 
There are different methods to promote the identification of the most relevant aspects that 
must be addressed with the families. The counselor should convey all the useful information 
requested, as well as information that should have been requested by counselees, but was 
not. 
All  circumstances  of  genetic  counseling  are  in  a  complex  context  that  involves  personal 
dynamics  and  social  interactions  with  the  meaning  and  perception  being  very  different 
among  those  involved  (i.e.,  counselees  and  counsellor).  Invariably,  however,  stress  and 
anxiety are present in this context. It should be noted that coping strategies differ between 
individuals, ranging from seeking information or avoiding new information to reactions of 
anger  or  indifference,  which  are  psychological  defences  against  an  aversive  event  that 
causes  pain.  Often,  the  cascade  of  psychological  effects  that  begins  is  unpredictable. 
Certainly, at least in the first session of genetic counseling, the counselor is faced by shocked 

 

 
Genetic Counseling in Autistic Phenotypes 

185 

and very vulnerable individuals with a great sense of loss, guilt and shame. Family issues, 
structure,  emotional  dynamics,  religion,  patterns  of  communication,  kinds  of  interactions, 
ethnicity and social support must be considered during the counseling process because all 
these issues will influence the counseling. 
The diagnosis of autism is really a major stressor for families who have to adapt to a reality 
that, in addition to being new, is very heterogeneous, complex, and difficult and can result 
in conflict. This requires professionals working in this area to invest more in psychosocial 
genetic  counseling  skills.  These  issues  and  their  applications  to  genetic  counseling  were 
detailed by Weil (2000).  
Genetic counseling generally involves a chronicity situation which is absolutely true in the 
case of autism. Chronic diseases can produce consequences such as pain, discomfort, low 
self-esteem, uncertainty about the future, suicidal thoughts, fear, panic, general and specific 
disorders of conduct, academic performance deficits, difficulties in interpersonal and family 
relationships, anxiety, and depression among others. The emotional distress associated with 
these diseases, if ignored, can lead to a significant reduction in the quality of life of patients 
and  their  families  and  negatively  affect  the  absorption  of  important  information  and 
adherence  to  treatment.  Thus,  family  members  should  always  be  considered  at  risk  for 
developing some kind of emotional disorder. These considerations underscore the nature of 
genetic  counseling  as  something  far  beyond  the  process  of  medical  diagnosis  and  the 
establishment  of  the  risk  of  occurrence/recurrence.  Hence,  skilled  and  experienced 
professionals  are  needed  to  perform  this  task,  giving  priority  to  communications  and 
humanization  of  care.  In  the  past,  communication  skills  were  not  considered  a  priority. 
However,  today  these  skills  have  become  a  professional  demand  and  even  the  legal 
obligation of every professional in healthcare. 
Genetic counseling is developed in a continuous and integrated manner. Division in phases 
is only for teaching purposes and can be summarized as: the reception and identification of 
patient/family, understanding of the problem/complaint, the identification of antecedents, 
establishment  and  confirmation  of  the  diagnosis,  assessment  of  genetic  risk,  discussion 
about  options  and  decisions,  and  follow  up.  Psychological  support  from  the  counselor  is 
essential for each phase, whether in a single session or several. 
There are not a great number of reports about genetic counseling in ASDs. Maybe one of the 
reasons  is  related  to  the  misunderstanding  about  the  heritability  of  these  disorders  as 
mentioned  above.  Like  other  diseases,  some  cases  are  inherited  and  others  are  not.  Then, 
what to do in each case since genetic causes may play an important role in the etiology of 
ASDs?  
The  clinicians  have  to  identify  specific  causes  or  exclude  them  to  provide  effective 
counseling and this is not always an easy job. Two situations must always be considered: 
syndromic autism and non-syndromic autism (idiopathic or primary). In the first one, there 
is a known cause related to the behavioural phenotype and that often can be identified by 
dysmorphic  features.  It  may  be  associated  with  well-known  monogenic  disorders, 
chromosomal alterations and environmental events. Genetic counseling should be directed 
to information related to the cause, genetic or not. Someone could say that if the cause is not 
genetic, the patient certainly will not go to a genetic counseling service, but in practice this is 
not true. In non-syndromic cases, determined after detailed investigations, the approach will 
be  different,  discussing  in  particular  the  polygenic  predisposition  and  the  environmental 
contribution to the autistic phenotype.  

 

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Autism Spectrum Disorders: The Role of Genetics in Diagnosis and Treatment 

2. Genetic counseling in genetic disease associated to autism 
Approximately  10%  of  autistic  patients  have  a  diagnosis  of  single  gene  diseases  or 
chromosomal  abnormalities; there  are  several molecular  pathways  potentially  involved  in 
the  alterations  that  affect  normal  neurodevelopmental  events.  In  cases  of  chromosomal 
defects,  for  example,  these  can  cause  alterations  in  neuronal  migration  and  brain  growth, 
with subsequent altered cortical organization, synaptic and dendritic changes and the ASD 
phenotype.  Metabolic  disorders  produce  an  accumulation  of  toxic  metabolites  which  can 
cause  a  reduction  of  myelin,  neuronal  loss,  alterations  in  dopaminergic  or  serotonergic 
neurotransmission and ASD (Benvenuto et al., 2009). 
The  most  common  genetic  diseases  associated  with  ASDs  include  Fragile-X  syndrome, 
Tuberous  Sclerosis,  invdup(15)  or  idic(15),  Prader-Willi  and  Angelman  syndromes,  Down 
syndrome, Joubert syndrome, macrocephaly and overground syndromes, Turner syndrome, 
Williams  syndrome,  Timothy  syndrome,  Smith-Magenis  syndrome,  Phelan-McDermid 
(22q13.3  deletion)  syndrome,  Cohen  syndrome,  Sanfilippo  syndrome,  mitochondrial 
cytopathies, among others (Caglayan, 2010; Moss & Howlin, 2009). And this list just keeps 
growing! 
A Fragile X syndrome is diagnosed in almost 5% of the children with ASDs. Faced with this, 
it is considered the most common genetic etiology of the autistic phenotype. It is the most 
common cause of inherited intellectual deficiency in men. It results from a full mutation that 
affects approximately 1 in 2500 males and 1 in 8000 females. The molecular basis involves a 
dynamic and unstable mutation characterized by the repeat expansion of the trinucleotide 
(CGG)n in the 5’ untranslated region of the first exon of the FMR-1 gene (Fragile-X Mental 
Retardation 1) mapped at Xq27.3. When the number of CGG repeats is greater than 200, the 
allele is classified as a full mutation. CpG island hypermethylation of the promoter causes 
gene inactivation. Persons with the syndrome produce little or no detectable expression of 
the encoded protein called Fragile X Mental Retardation Protein or FMRP which is essential 
for  normal  brain  function.  It  is  involved  in  synaptic  maturation  and  its  loss  may  alter 
neuronal  plasticity.  Brain  damage  results  in,  among  other  things,  autistic  behaviour. 
Individuals with intermediate CGG expansions in the range of 55–200 repeats are known as 
fragile  X  premutation  carriers  and  are  at  increased  risk  for  a  related  disorder  known  as 
Fragile X-Associated Tremor and Ataxia Syndrome (FXTAS) that affects primarily men over 
the age of 50. The presence of the premutation in women can also cause premature ovarian 
failure (Hampson et al, 2011). 
All the diseases associated with autism have specific molecular biological mechanisms with 
different  genes  involved  and  different  types  of  inheritance  pattern.  The  fact  is  that  the 
autism phenotype is one of the clinical manifestations of the disease itself, which in one way 
or another, changes the structure and/or function of the brain. For this reason, it is spoken 
of  as  association  rather  than  comorbidity  because  the  events  are  not  random  in  the  same 
patient. Given this scenario, genetic counseling should be directed according to information 
relevant  to  that  specific  syndromic  diagnosis  with  explanations  of  the  causes,  risks  and 
consequences. Conduct is not so differentwhen the etiology of autism arises from the action 
of a toxic environmental agent. 
The  autism  phenotype,  however,  is  a  major  complicating  factor  when  combined  with  a 
genetic disease because the parents, who are usually very distressed, can confuse the risk of 
disease recurrence with risk of autism among those affected by it. Not all of those affected 
have the autism phenotype. It is important that counselors, in addition to background and 

 

 
Genetic Counseling in Autistic Phenotypes 

187 

needs, identify the expectations of their genetic counseling clients. However, this scenario 
includes hundreds of possibilities of events and different strategies to solve them. Some of 
the genetic conditions are inherited while others are not. In most cases there is an important 
variability in clinical manifestations. If the disease is genetic it is incurable although often 
there may be symptomatic and palliative treatments.  
Autism  phenotype  can  be  associated  with  autosomal  recessive  disease,  which  was 
originated from parents who carry the deleterious gene. This is one of situations of genetic 
counseling that inherently evoke guilt. The dominant culture of the family, especially if its 
members are Latino, produces the feeling of being punished for some sin. In this case the 
guilt can be a response to new and adverse reality over which one has no control. This can 
be exacerbated if during the explanation the counselor emphasize features such as that the 
probability of the outcome was very low. 
Sometimes  it  is  difficult  for  parents  to  'see'  the  genetic  disease  of  their  children  since  the 
autistic  symptoms  appear  more  strongly  than  the  dysmorphic  features.  By  a  lack  of 
standardized  diagnostic  procedures  in  many  syndromes  and  the  absence  of  laboratory 
markers, the diagnostic process often stems from interpretation of a set of clinical signs and 
the  experience  of  the  geneticist.  For  some  families,  accustomed  to  different  clinical 
procedures, this can also cause anxiety. 
In  some  cases  there  is  a  probable  diagnostic  hypothesis,  however,  the  test(s)  required  to 
arrive at an accurate diagnosis may not be accessible to the family. This can greatly hinder 
the  process  of  genetic  counseling  and  create  stress  in  the  family  and  counselor.  The 
molecular  revolution  observed  in  the  last  three  decades  has  introduced  many  procedures 
that are not still available in public health programs of several countries and only the most 
economically  advantaged  families  can  access  them,  which  does  not  correspond  with  the 
reality  of  most  people.  While  it  lasts,  intercountry  collaboration  programmes  should  be 
stimulated (WHO, 2010). 
A large number of families consult the Internet before the counseling to obtain information 
about the diagnoses, treatment, and tests and so many clients arrive for genetic counseling 
with notions of the condition for which they are to have counseling (Peters & Petrill, 2011). 
This  creates  a  series  of  expectations.  Not  always,  however,  information  is  obtained  from 
reliable  sources  and 
is  for  the  counselor  to  clarify  false  beliefs  or  possible 
misinterpretation. 
Also,  it  must  be  emphasized  that  the  search  for  a  solution  makes  the  Internet  a  tool  that 
frequently causes more harm than good. The demand for treatment has increased gradually 
and  cognitive  behavioural  intervention  programmes  aimed  at  trying  to  improve  social 
interaction  and  communications  are  encouraged  (Wood  et  al.,  2009).  The  design  of  these 
interventions is to act during the critical period of postnatal neuronal plasticity (within the 
first three years of life). But there are other not empirically proven therapies; for this reason, 
sites  selling  solutions  for  autism  have  proliferated.  Couples  come  to  genetic  counseling 
requesting an opinion and explanation from the counselor on "magic formulas";they become 
anguished and even feel guilty when they realize that this solution is unfeasible, especially 
as some of them have very high costs. It is for the counselor to reduce the anxiety of parents 
and explain that this is not about being for or against any type of alternative therapy, but 
that most have no scientific basis and some may even pose health hazards. Families need to 
understand  the  evidence  for  efficacy  (or  lack  thereof)  and  potential  side  effects.  More 
accurate  and  earlier  diagnosis  or  the  elucidation  of  etiological  factors  does  not  mean 
effective therapies in the short term. 

it 

 

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3. Genetic counseling in autism of unknown etiology  
On  taking  into  account  all  technologies,  an  underlying  genetic  diagnosis  is  identified  in 
around  10–15%  of  ASDs  cases  while  cytogenetically  visible  chromosomal  rearrangements 
are  found  in  2–6%  of  ASDs  individuals  (Bremer  et  al,  2011;  Kumar  &  Christian,  2009). 
Hence, for most individuals (90%) with the autistic phenotype, there is no known genetic or 
environmental  cause,  which  defines  them  as  non-syndromic  or  "idiopathic'  as  previously 
mentioned. Often this condition is established after negative resultsobtained from a medical 
evaluation to identify medical issues that affect the development and behavior of nonverbal 
children,  physical  examinationabout  metabolic,  medical,  or  neurologic  conditions,  careful 
examinationof  personal  history,  a  detailed  investigation  of  gestational  antecedents  and 
dysmorphic signs and after performing an odysseyof multiple testing.  
Genome-wide  studies  have  implicated  numerous  minor  risk  alleles  with  low  and  high 
penetrance  but  few  common  variants  and  with  many  contributing  loci.  Among  the 
candidates  are  genes  that  code  for  important  proteins  in  synaptic  structure,  function  and 
maintenance. Genetic mutations in these genes result in an aberrant synaptic process that 
could produce the ASDs phenotypes. However, the frequency of these mutations is so low 
that widespread screening does not seem to be clinically justified. Some, however, deserve 
to  be  investigated  because  of  clinical  findings  such  as  mutations  in  the  PTEN  gene  in 
children with macrocephaly (Lintas & Persico, 2008). 
As etiological factors are progressively being discovered, it is natural to think that the number 
of idiopathic cases will also gradually decrease. The increased resolution of CGH array testing 
in combination with new technologies, such as whole genome sequencing and bioinformatics 
programs, will play an important role in helping us to further understand the complex genetic 
basis of autism. The implementation of these high resolution techniques in the genetic research 
of ASDs may discover specific genotypes and subtypes of ASDs for which new diagnostic and 
therapeutic  strategies  can  be  developed.  For  this  reason  the  identification  of  genetic 
abnormalities is a high priority in the study of ASD (Bremer et al., 2011). 
For now, non-syndromic cases are much more common than other forms with estimates in 
the general population reported at approximately 1 in 100. In these cases, ASD is considered 
a  complex  disease  of  multifactorial  pattern  inheritance  (Harrington,  2010;  Maenner  & 
Durkin, 2010)[4] M.J. Maenner and M.S. Durkin, Trends in the prevalence of autism on the 
basis of special education data, Pediatrics 126 (2010), pp. e1018–e1025. Full Text via CrossRef 
| View Record in Scopus | Cited By in Scopus (1). About 70% of probands with autism of 
unknown cause has a first- or second-degree relative with autistic symptoms, and 15% has 
fathers  with  Asperger  syndrome.  The  empiric  aggregate  risk  to  sibs  of  individuals  with 
autism of unknown cause varies across studies but is generally considered to range from 5% 
to  10%  for  autism  and  10%  to  15%  for  milder  symptoms,  including  language,  social,  and 
psychiatric disorders. For families with two or more affected children, the recurrence risk 
approaches 35% (Miles et al., 2010).  
All this information should be thoroughly discussed with the members of the family at their 
level  of  understanding.  Obviously,  faced  with  such  uncertainty  and  heterogeneity,  the 
counselor  may  feel  uncomfortable  to  report  these  risks.  It  is  essential  that  the  family 
understands that when a child is diagnosed with an ASD, a range of etiological options are 
involved, which means the possibility of many different diseases. An aggravating factor is 
that  the  information  may  generate  anxiety;  most  families  have  social  and  institutional 
barriers to carrying out more sophisticated tests. 

 

 
Genetic Counseling in Autistic Phenotypes 

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in 

this  condition  does  not  only  change 

4. Psychosocial aspects of genetic counseling in autism 
Throughout its development, the family goes through many changes. Each phase of the so-
called life cycle (acquisition, adolescence, maturity and final) has its own peculiarities and 
difficulties inherent to the transformations that occur. During the acquisition phase, with the 
arrival of children, accepting parenting is already difficult. The family system "grows" as a 
whole  and  new  links  and  forms  of  communication  are  needed.  Moreover,  the  "myth  of 
happy motherhood" is common, influenced by sociocultural aspects. This myth may become 
unreachable  and  a  crisis  may  result  from  this  expectation,  as  the  ideal  social  value  is  not 
achieved. If motherhood is culturally associated with well-being and achieving, when the 
son  or  daughter  is  not  compatible  with  the  one  desired  by  the  parents,  as  is  the  case  of 
children  born  with  a  vulnerability, 
the 
psychophysiological functioning of the mother and her quality of life, but can also result in 
negative  consequences  for  the  whole  family.  Parenthood  is  a  relational  experience  of 
profound  psychological  meaning,  experienced 
family  relationships,  which  are 
transformed over the entire life and that are restructured with the normal cycles of family 
development and, occasionally, by unforeseen events (Cerveny & Berthoud, 1997). 
The arrival of a child with ASD can be considered an unexpected contingency at any stage 
that  the  family  is  going  through,  because  these  are  serious  psychiatric  illnesses,  which 
require special needs and require much understanding and patience due to the peculiarity 
of the symptoms. Given this reality, some authors have reported that mothers of children 
with  disabilities  tend  to  depression,  which  may  be  associated  with  hopelessnessand 
worsened quality of life. This is also observed in the fathers and siblings of individuals with 
ASDs,  with  the  degree  of  symptoms  reflecting  the  severity  of  the  autism  of  the  affected 
relative  (Orsmond  et  al.,  2009;  Orsmond  &  Seltzer,  2009).  Carter  and  collaborators  (2009) 
studied  stability  and  individual  change  in  depressive  symptoms  among  mothers  raising 
young  children  with  ASD.  They  observed  that  child  problem  behaviors  and  delayed 
competence,  maternal  anxiety  symptoms  and  angry/hostile  mood,  low  parenting  efficacy 
and  social  supports,  and  coping  styles  were  associated  with  depression  severity.  Only 
maternal anxiety and parenting efficacy predicted individual change. Many mothers do not 
appear to adapt, supporting the need for early intervention for maternal well-being. 
In  particular,  mothers  experience  the  reality  of  having  an  autistic  child  permeated  by 
feelings of nullity, loneliness and solitude. They also stop living their daily lives to live the 
everyday life of the child. Brothers and sisters have more stressful conditions of life, which 
include early responsibilities, anxiety and feelings of inferiority (Benderix & Sivberg, 2007).  
Pearson  et  al.  (2006)  found  that  autistic  individuals  have  more  symptoms  of  depression, 
withdrawal from social life, atypical behaviour and immature social skills. Besides, they are 
at particularly high risk of comorbidities involving emotional and behaviour disorders, with 
direct  consequences  on  their  family.  Family  members  have  to  adapt  to  a  reality  that,  in 
addition to being new, is very heterogeneous, complex and difficult and that can result in 
conflicts  that  require  intervention  (Kelly  et  al.,  2008).The  disease  eventually  becomes  the 
focus and other problems become unimportant; family members live only the disease and 
end up getting sick too (Balieiro & Cerveny, 2004).  
What is observed in practice is that when a child is diagnosed with ASD, parents experience 
a variety of very complicated feelings that are often unrelated to interventions involving the 
child, but related to the parents particular vision of the world(Wachtel & Carter, 2008). After 
all, few other diseases can pose such a great threat to the family as these do, because autism 

 

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Autism Spectrum Disorders: The Role of Genetics in Diagnosis and Treatment 

is still seen as an intense "stressor" (Woodgate et al.,2008; King et al., 2006). But when, for 
example,  a  better  relationship  is  established  between  the  mother  and  child,  the  autistic 
symptoms may reduce (Smith et al., 2008). 
As ASDs are related to a great need for care that directly affects the development not only of 
the  individual  but  also  of  their  families,  the  resources  available  to  families  must  be 
evaluated  very  well  (Montalbano  &Roccella,  2009;  Montes  &  Halterman  ,  2008).  It  is 
important to strengthen social networks and the availability of resources such as specialized 
schools, stimulation therapy clinics and family psychotherapy(Smith & Elder, 2010; Cahill & 
Glidden, 1996). Family support is associated with increased optimism that, in turn, predict 
higher  levels  of  positive  feelings.  Even  the  child  psychiatrist  should  be  encouraged  to 
participate in  the  social support  network  of  parents,  helping  them  on  the  long  journey  of 
raising their children (Wachtel & Carter, 2008).  
The paediatrician’s role is crucial, because with more frequent contact with the child and the 
bond of trust with the family, the doctor will able to detect symptoms early and to guide the 
investigation and treatment. Most important, according to De Ocampo and Jacobs (2006), is 
to  establish  close  cooperation  and  communication  between  the  family  and  all  the  experts 
who care for the child. 
There are many gaps in the scientific knowledge which justifies the need to define future 
research  on  families  of  children  with  these  diseases.  Health  professionals  must  strive  to 
study them and create effective support strategies. 

5. Genetic counselor and counselee: a model and an example of case 
Genetic  counseling,  although  governed  by  traditional  guidelines  that  recommend  certain 
actions, phases and intentions, varies much in the way it is developed, from centre to centre, 
region  to  region  and  from  country  to  country.  Not  only  the  emphasis  on  some  particular 
goal may vary but the composition of the team and the different forms of participation of 
each of its members may change. 
Many  kinds  of  questions  can  be  used  in  different  ways  to  increase  the  understanding, 
respect and empathy on both sides, counselor and counselees. The counselor is part of the 
system  in  which  he  acts  and  his  personality  is  a  determinant  of  how  the  process  will  be 
conducted  within  the  basic  goals  of  genetic  counseling.  Some  counselors  are  more 
paternalistic (I suffer with you and if I could do anything for the situation to be different ...), 
and  some  are  more  authoritarian  (You  have  to  understand  that  I  am  experienced  in  this 
matter and definitely can help you…). There are also the many peculiarities of each team; 
never will the counseling given by one counselor in one situation be the same as that given 
by another. Also, counseling performed by one team for one family with a particular type of 
problem will not be identical to that for another family with exactly the same problem. The 
process is so dynamic that it cannot be predicted. 
We  will  briefly  describe  a  model  of  genetic  counseling  which  occurred  in  a  community 
genetic  service  of  a  low-income  country  (Brazil).  It  involves  a  context  characterized  by 
certain cultural, legal and religious limitations such as the cultural fear of genetic disorders 
due  to  stigma  and  legal  restrictions  in  respect  to  selective  abortion,  among  others.  The 
service in questionis located in a referral centre for health in a city of the most developed 
state  of  the  country  (São  Paulo).  It  has  an  interdisciplinary  team  comprised  of  three 
counselors, three psychologists, physicians of different specialties, a social worker and two 
nurses. One of its peculiarities is that the genetic counselor and psychologist work together 
during counseling sessions of families, in a transdisciplinary way. 

 

 
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Briefly, the model can be described by the different phases through which the family passes 
after its arrival in the service: 
-  After presenting at the reception, the family is asked to stay in a waiting room. There 
the family is approached by a psychologist, who presents himself, establishes a rapport 
(contact, dialogue) and investigates the characteristics, expectations and basic needs of 
the  family.  The  psychologist  makes  observations  about  the  emotional  state  (anger, 
sadness,  anxiety,  etc.),  the  main  coping  strategies  (emotional,  cognitive  and 
behavioural), psychological functions (guidance, judgement, attention, language, mood, 
level  of  understanding,  etc.)  and  beliefs  or  fears.  Questions  such  as  these  are  used: 
"What is the reason for your referral to this service?", "Who referred you?" "What do 
you know about genetic counseling?", "How do you feel?", and "What do you expect 
from genetic counseling?". The family should be guided and informed on the practical, 
structural and dynamic operation of genetic counseling, its meaning, as well as the role 
of  the  different  professionals  involved.  During  the  psychological  approach  a  more 
relaxed atmosphere should be created. 
Before the counselor has contact with the family, he is informed by the psychologist on 
the data collected in the waiting room. The counselor has elements to promote a more 
focused and effective intervention, using a more targeted and personalized approach. 

-  On  being  called  for  counseling,  the  psychologist  who  established  the  initial  rapport 
with the family in the waiting room, introduces the family members to the counselor, 
enters the room and participates in the genetic counseling process. Everyone sits in a 
circle,  with  a  small  table  moved  to  the  side,  just  for  the  counselor’s  note  taking.  The 
central table is considered an "obstacle" to establishing a relationship as it may suggest 
difference  in  level/hierarchical  which  always  causes  awkwardness.  The  psychologist 
accompanies  the  discussion,  observes  and  only  intervenes  quickly  and  objectively  on 
psychological  aspects  when  requested  or  when  he  believes  it  is  absolutely  necessary. 
The phases of genetic counseling develop. It is up to the counselor to give psychological 
support inherent to the process. It is important to motivate the family to return for a 
follow up consultation, to perform exams, comply with treatment and to offer supports 
linked 
the  most  urgent  difficulties,  contacting  a  social  assistant  and 
professionals/support institutions. When necessary, refer members of the family for a 
more detailed psychological assessment or for psychotherapy. 
In all consultations, the counselor and the psychologist caring for the family should be 
the same as the first visit and even when the process is completed the team should be 
available  to  explain  future  doubts  that  may  arise  through  further  meetings  or  by 
telephone. 

All the professionals involved in the care of families of individuals with ASDs surely pass 
through  difficult  situations  of  intense  learning  that  require  much  skill  and  compassion. 
Perhaps I can illustrate what this means using a true case. 
On one day in November 2010 ... The psychologist informed the counselor that the family 
that she was about to meet comprised of a father, a mother, a three-year-old child with a 
diagnosis of autism made one week previously, and another five-year-old apparently health 
son. He said that the family was psychologically very weak. The mother, aged 32, expressed 
much sadness and spoke only when questioned. The father, 39 years old, expressed great 
anger, was extremely anxious and said that he did not know why they had been referred for 
genetic counseling, which he thought was a waste of time. Both were well educated; she is a 
computer engineer and university professor, and he is a judge. They had already researched 

to 

- 

- 

 

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Autism Spectrum Disorders: The Role of Genetics in Diagnosis and Treatment 

on the Internet many details about the problem and were very shocked and confused. In the 
waiting room the psychologist explained to them about the dynamics of the process and the 
benefits they might obtain with the clearing up of their doubts and specific guidance. The 
father rejected obstinately attempts of contact and the mother reported that she was feeling 
very lonely. As they spoke, the eldest son always listened in silence. When asked how he felt 
by the psychologist, the son answered "tired".  
When called and led by the psychologist to the consultation room, the counselor noted the 
seemingly arrogant and cold attitude of the father, who entered the room in front of his wife 
that was holding the hands of both children, and sat down before anyone else. The children 
were seated in the centre of the circle where some toys had been placed so that they could 
play and so they would stay there. The counselor noted the autism phenotype of the child 
with  repetitive  stereotypic  movements,  isolation,  lack  of  speech,  among  other  things, 
without dysmorphic signs. The mother reported that the diagnosis was made by the team of 
psychiatrists and neurologists that had requested exams, including biochemistry, imaging, 
hearing  evaluation,  among  other  tests,  which  were  normal.  The  pregnancy  and  delivery 
occurred  without  complications.  Also,  there  was  no  parental  consanguinity  or  other  risk 
factors involved. She said that before the completion of the diagnosis of childhood autism, 
other professionals had partial or wrong diagnoses, which left her very confused. 
Both  the  father  and  mother  started  giving  much  information  without  being  requested, 
including  some  technical  information  about  autism.  They  started  a  kind  of  "competition", 
both on involving who spoke first and on the level of knowledge that each one had. Thus, 
the genetic counselor had an opportunity to observe and evaluate the couple's dynamics. At 
one  point  the  counselor  interrupted  them  and  said,  in  an  attempt  to  move  on  directly  to 
emotional issues, "I am realizing how much you are frightened by the diagnosis that you 
received. Before I explain to you about the diagnosis, I would like to know more about your 
feelings. What made you so upset? Do you think it is very hard for you to talk about this 
now?" The couple, as they were caught by surprise, agreed to talk about it and the counselor 
asked the mother to speak first. Crying a lot, she reported that she was trying to understand 
everything  that  was  happening  and  that  she  was  not  able  to  concentrate  on  her  work 
anymore. She felt very guilty because her family was no longer the same, her eldest son was 
in trouble at school and that she felt very lonely. She did not know anyone with a child with 
the same problem and that, initially, the worst that she thought was that her son was deaf. 
She confessed that she always only wanted to have one child and that the second pregnancy 
was not planned. She had rejected the child and she felt that was being punished for this. 
She even felt that she was being punished too for an abortion she had as a teenager. She 
would like to talk to other people about their son but she had made a deal with her husband 
that they would not reveal the child's diagnosis to anyone; not before trying to help him to 
get better. 
The counselor told her she had some mistaken ideas and meanings but it was good to see 
that she was seeking help. Those feelings, though difficult, were natural and expected, as in 
general, no person is prepared to have a child that is different to what they expected and 
very few people are ready for this possibility. The counselor continued saying that much of 
the information that they would receive starting from the first session, might certainly help 
in  this  difficult  emotional  period  that  the  entire  family  was  going  through.  The  father 
interrupted saying "Speaking of information, I need you to tell me why my son is autistic!" 
The  counselor  felt  upset  with  the  authoritative  behavior  of  the  father  and  his  attempts  to 

 

 
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hide his emotions. The mother broke in with the phrase "It is impossible to live with him" to 
which the father replied "You cannot talk about us because is our son who needs help!"  
Then the counselor told the father that she noted that he was also seeking help albeit in a 
different  way.  The  counselor  explained  the  relative  proportions  of  autism  cases  in  the 
population  that  might  be  attributable  to  various  mechanisms  of  genetic  transmission  and 
that the vast majority of cases of autism remain idiopathic. The counselor asked the father 
how  possible information  about  the  cause  of  the  autism  of  their  son  could  help  him, and 
why he preferred not to reveal the diagnosis to the child's relatives and friends. He replied 
that by discovering the cause there would certainly be drugs/specific therapies that would 
improve their son’s condition and that people would look less and would not feel sorry for 
their  son.  The  anxiety  about  the  manifestations  of  the  autistic  child  was  clear.  He  added 
saying  that  the  child  was  very  "stubborn";  he  was  being  seen  by  a  speech  therapist  and 
occupational therapist and was taking psychiatric drugs and did not improve much except 
for being less aggressive. In a possible attempt to justify their ways, the father said many 
members  of  his  family  were  stubborn,  especially  his  father.  He  had  been  educated  in  a 
traditional  manner.  His  father  was  very  angry  and  never  admitted  that  his  children,  all 
male, were weak. 
The  counselor  noticed  that  the  psychological  defenses  of  father  were  not  entirely 
unconscious. He was being “defensive” and his behavioral probably was related to a great 
sense of loss.  
The counselor provided some practical explanations about autism and coping with affected 
children, explained the importance of knowing other families, some support institutions and 
the etiology of ASDs. In 90% of the cases, the etiologies of ASDs are not known. 
As the parents had some technical knowledge from other sources, but did not understand it 
very well, the counselor re-organized the information and clearly explained it, in particular, 
in respect to idiopathic cases. It was explained that some more sophisticated genetic testing 
methods that the child had not been done, but they also had a low probability of identifying 
the  cause  of  the  disease.  The  counselor  congratulated  the  parents  because  they  were 
adhering to the proposedtreatment plan and explained the lack of specific remedies linked 
to  a  possible  cause  in  this  case  and  in  most  others.  Finally,  that  she  understood  the 
frustration  of  the  father,  his  difficulties  in  understanding  the  behaviour  of  his  child,  who 
was not stubborn, but he just could not "understand" what his father wanted from him. 
At this point the father began to cry copiously and the psychologist intervened saying that 
he was among friends who wanted to help him, and that he was in the right place to express 
his emotions without shame or fear of being judged. The counselor asked his wife to hold 
the hand of her husband and in so doing the eldest son stood up and hugged his father, a 
move which, to everyone's surprise, was followed by the autistic son, who sat on the father’s 
lap. 
After this time, the challenges and clashes that marked the start of the session were replaced 
by interest to explore and discuss all information. They expressed interest in performing the 
tests  that  were  missing  and  in  doing  psychotherapy.  The  counselor  reiterated  that  she 
perceived  the  sense  of  responsibility  and  parental  love,  fundamental  for  the  family's 
adjustment to the new reality. The session was adjourned with the family thanking the team 
for their help and patience, who thanked them for their trust. A return visit was set for 45 
days. 
At the next session the parents came back hand in hand, the mother was more confident and 
the  father  more  pleasant.  The  new  tests  also  showed  normal  results  and  the  counselor 

 

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Autism Spectrum Disorders: The Role of Genetics in Diagnosis and Treatment 

restated  some  information.  The  parents  were  very  satisfied  with  psychotherapy  and  had 
chosen couples therapy. The autistic child had begun equine therapy and the parents were 
excited  and  hopeful.  They  said  that  they  had  organized  a  lunch  for  relatives  and  close 
friends,  where  they  would  tell  their  child’s  diagnosis  and  how  they  counted  on  the 
understanding and help of all.  
The counselor and psychologist expressed their admiration and congratulated the mother 
and  father  for  their  initiative  and  expressed  their  satisfaction  with  the  many  positive 
developments.  The  team  of  professionals  knew  the  family’s  feelings  of  love  and  of 
commitment to each other would support them through what lay ahead. At the end of the 
session,  the  psychologist  could  not  contain himself  and  asked  their  eldest  son: "And  you, 
how are you feeling?" He just smiled and hugged his autistic brother... 

6. Conclusion 
ASDs have become a public health problem but there are many misunderstandings about 
the heritability of these disorders. The detection of genetic alterations may contribute to the 
diagnosis, allow an understanding of biological mechanisms involved in the pathogenesis, 
assist  in  genetic  counseling  of  families  and  guide  prevention  and  educational  planning. 
Health care practitioners need to be able to provide information about general principles of 
human genetics as well as the epidemiological and molecular aspects of genetics regarding 
Autism Spectrum Disorders. In addition, they need to understand the limitations of genetic 
testing  and  the  psychological  conditions  of  the  families. Knowledge  of  the  genetic  factors 
involved and of the psychological effects of these diseases is crucial for the establishment of 
intervention strategies that promote the bio-, psycho- and social well being of those affected 
and their families. Besides providing technical information necessary for the family to have 
a  better  understanding  about  the  disease,  genetic  counseling  can  alleviate  some  of  the 
common mistaken beliefs and provide support to families, assisting in the transformation 
and adaptation of the members. It is very important that psychoeducation programmes be 
created  for  parents,  focused  on  handling  stress  and  emotions,  modifying  false  beliefs and 
solving the daily problems that arise from ASDs. 

7. References  
APA. American Psychiatric Association. (1994). DSM-IV - Diagnostic and Statistical Manual of 
MentalRetardation.  American  Psychiatric  Association,  4th  edn.  Washington  DC, 
USA. 

APA.  American  Psychiatric  Association  (March  2011).  DSM-V  Development,  In:  Proposed 

Revision, 02.03.2011, Avalaible from: 
http:<//www.dsm5.org/Pages/Default.aspx/>. 

 
Balieiro,  C.R.B.  &  Cerveny,  C.M.O.  (2004).  Família  e  Doença,  In:  Família  e...,  C.  M.  O. 

Cerveny, pp.155-166, Casa do Psicólogo, ISBN85-7396-325-5, São Paulo, Brazil. 

Benderix,  Y.  &  Sivberg,  B.  (2007).  Siblings’  Experiences  of  Aving  a  Brother  or  Sister  with 
Autism and Mental Retardation: A Case Study of 14 Sibling From Five Families. J 
Pediatr Nurs, Vol.22, No.5, pp. 410-418. 

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