@article{ahrens21_regardingfword,
  title = {Regarding the {{F}}-word: {{The}} Effects of Data Filtering on Inferred Genotype-environment Associations},
  shorttitle = {Regarding the {{F}}-word},
  author = {Ahrens, Collin W. and Jordan, Rebecca and Bragg, Jason and Harrison, Peter A. and Hopley, Tara and Bothwell, Helen and Murray, Kevin and Steane, Dorothy A. and Whale, John W. and Byrne, Margaret and Andrew, Rose and Rymer, Paul D.},
  year = 2021,
  month = jul,
  journal = {Molecular Ecology Resources},
  volume = {21},
  number = {5},
  pages = {1460--1474},
  issn = {1755-098X, 1755-0998},
  doi = {10.1111/1755-0998.13351},
  urldate = {2026-01-15},
  abstract = {Abstract             Genotype-environment association (GEA) methods have become part of the standard landscape genomics toolkit, yet, we know little about how to best filter genotype-by-sequencing data to provide robust inferences for environmental adaptation. In many cases, default filtering thresholds for minor allele frequency and missing data are applied regardless of sample size, having unknown impacts on the results, negatively affecting management strategies. Here, we investigate the effects of filtering on GEA results and the potential implications for assessment of adaptation to environment. We use empirical and simulated data sets derived from two widespread tree species to assess the effects of filtering on GEA outputs. Critically, we find that the level of filtering of missing data and minor allele frequency affect the identification of true positives. Even slight adjustments to these thresholds can change the rate of true positive detection. Using conservative thresholds for missing data and minor allele frequency substantially reduces the size of the data set, lessening the power to detect adaptive variants (i.e., simulated true positives) with strong and weak strengths of selection. Regardless, strength of selection was a good predictor for GEA detection, but even some SNPs under strong selection went undetected. False positive rates varied depending on the species and GEA method, and filtering significantly impacted the predictions of adaptive capacity in downstream analyses. We make several recommendations regarding filtering for GEA methods. Ultimately, there is no filtering panacea, but some choices are better than others, depending on the study system, availability of genomic resources, and desired objectives.},
  langid = {english},
  file = {/home/kevin/work/bibliography/zotdir/storage/NDBY2WQ4/Ahrens et al. - 2021 - Regarding the F‐word The effects of data filtering on inferred genotype‐environment associations.pdf}
}

@article{alonso-blanco16_1001genomes,
  title = {1,135 {{Genomes Reveal}} the {{Global Pattern}} of {{Polymorphism}} in {{Arabidopsis}} Thaliana},
  author = {{Alonso-Blanco}, Carlos and Andrade, Jorge and Becker, Claude and Bemm, Felix and Bergelson, Joy and Borgwardt, Karsten M. and Cao, Jun and Chae, Eunyoung and Dezwaan, Todd M. and Ding, Wei and Ecker, Joseph R. and {Exposito-Alonso}, Moises and Farlow, Ashley and Fitz, Joffrey and Gan, Xiangchao and Grimm, Dominik G. and Hancock, Angela M. and Henz, Stefan R. and Holm, Svante and Horton, Matthew and Jarsulic, Mike and Kerstetter, Randall A. and Korte, Arthur and Korte, Pamela and Lanz, Christa and Lee, Cheng-Ruei and Meng, Dazhe and Michael, Todd P. and Mott, Richard and Muliyati, Ni Wayan and N{\"a}gele, Thomas and Nagler, Matthias and Nizhynska, Viktoria and Nordborg, Magnus and Novikova, Polina Yu and Pic{\'o}, F. Xavier and Platzer, Alexander and Rabanal, Fernando A. and Rodriguez, Alex and Rowan, Beth A. and Salom{\'e}, Patrice A. and Schmid, Karl J. and Schmitz, Robert J. and Seren, {\"U}mit and Sperone, Felice Gianluca and Sudkamp, Mitchell and Svardal, Hannes and Tanzer, Matt M. and Todd, Donald and Volchenboum, Samuel L. and Wang, Congmao and Wang, George and Wang, Xi and Weckwerth, Wolfram and Weigel, Detlef and Zhou, Xuefeng},
  year = 2016,
  month = jul,
  journal = {Cell},
  volume = {166},
  number = {2},
  pages = {481--491},
  issn = {0092-8674, 1097-4172},
  doi = {10.1016/j.cell.2016.05.063},
  urldate = {2016-07-12},
  abstract = {Arabidopsis thaliana serves as a model organism for the study of fundamental physiological, cellular, and molecular processes. It has also greatly advanced our understanding of intraspecific genome variation. We present a detailed map of variation in 1,135 high-quality re-sequenced natural inbred lines representing the native Eurasian and North African range and recently colonized North America. We identify relict populations that continue to inhabit ancestral habitats, primarily in the Iberian Peninsula. They have mixed with a lineage that has spread to northern latitudes from an unknown glacial refugium and is now found in a much broader spectrum of habitats. Insights into the history of the species and the fine-scale distribution of genetic diversity provide the basis for full exploitation of A.~thaliana natural variation through integration of genomes and epigenomes with molecular and non-molecular phenotypes.},
  langid = {english},
  pmid = {27293186},
  keywords = {1001 Genomes,Arabidopsis thaliana,glacial refugia,GWAS,population expansion}
}

@article{chang15_secondgenerationplink,
  title = {Second-Generation {{PLINK}}: Rising to the Challenge of Larger and Richer Datasets},
  shorttitle = {Second-Generation {{PLINK}}},
  author = {Chang, Christopher C. and Chow, Carson C. and Tellier, Laurent CAM and Vattikuti, Shashaank and Purcell, Shaun M. and Lee, James J.},
  year = 2015,
  month = dec,
  journal = {GigaScience},
  volume = {4},
  number = {1},
  doi = {10.1186/s13742-015-0047-8},
  urldate = {2019-09-11},
  abstract = {AbstractBackground.  PLINK 1 is a widely used open-source C/C++ toolset for genome-wide association studies (GWAS) and research in population genetics. However,},
  langid = {english}
}

@article{czech25_analysisreadyvcf,
  title = {Analysis-Ready {{VCF}} at {{Biobank}} Scale Using {{Zarr}}},
  author = {Czech, Eric and Tyler, Will and White, Tom and Jeffery, Ben and Millar, Timothy R and Elsworth, Benjamin and Guez, J{\'e}r{\'e}my and Hancox, Jonny and Karczewski, Konrad J and Miles, Alistair and Tallman, Sam and Unneberg, Per and Wojdyla, Rafal and Zabad, Shadi and Hammerbacher, Jeff and Kelleher, Jerome},
  year = 2025,
  month = jan,
  journal = {GigaScience},
  volume = {14},
  pages = {giaf049},
  issn = {2047-217X},
  doi = {10.1093/gigascience/giaf049},
  urldate = {2026-01-15},
  abstract = {Abstract                            Background               Variant Call Format (VCF) is the standard file format for interchanging genetic variation data and associated quality control metrics. The usual row-wise encoding of the VCF data model (either as text or packed binary) emphasizes efficient retrieval of all data for a given variant, but accessing data on a field or sample basis is inefficient. The Biobank-scale datasets currently available consist of hundreds of thousands of whole genomes and hundreds of terabytes of compressed VCF. Row-wise data storage is fundamentally unsuitable and a more scalable approach is needed.                                         Results               Zarr is a format for storing multidimensional data that is widely used across the sciences, and is ideally suited to massively parallel processing. We present the VCF Zarr specification, an encoding of the VCF data model using Zarr, along with fundamental software infrastructure for efficient and reliable conversion at scale. We show how this format is far more efficient than standard VCF-based approaches, and competitive with specialized methods for storing genotype data in terms of compression ratios and single-threaded calculation performance. We present case studies on subsets of 3 large human datasets (Genomics England: \$n\$=78,195; Our Future Health: \$n\$=651,050; All of Us: \$n\$=245,394) along with whole genome datasets for Norway Spruce (\$n\$=1,063) and SARS-CoV-2 (\$n\$=4,484,157). We demonstrate the potential for VCF Zarr to enable a new generation of high-performance and cost-effective applications via illustrative examples using cloud computing and GPUs.                                         Conclusions               Large row-encoded VCF files are a major bottleneck for current research, and storing and processing these files incurs a substantial cost. The VCF Zarr specification, building on widely used, open-source technologies, has the potential to greatly reduce these costs, and may enable a diverse ecosystem of next-generation tools for analysing genetic variation data directly from cloud-based object stores, while maintaining compatibility with existing file-oriented workflows.},
  copyright = {https://creativecommons.org/licenses/by/4.0/},
  langid = {english}
}

@article{danecek11_variantcall,
  title = {The Variant Call Format and {{VCFtools}}},
  author = {Danecek, Petr and Auton, Adam and Abecasis, Goncalo and Albers, Cornelis A. and Banks, Eric and DePristo, Mark A. and Handsaker, Robert E. and Lunter, Gerton and Marth, Gabor T. and Sherry, Stephen T. and McVean, Gilean and Durbin, Richard and {1000 Genomes Project Analysis Group}},
  year = 2011,
  month = aug,
  journal = {Bioinformatics},
  volume = {27},
  number = {15},
  pages = {2156--2158},
  issn = {1367-4811, 1367-4803},
  doi = {10.1093/bioinformatics/btr330},
  urldate = {2026-01-15},
  abstract = {Abstract             Summary: The variant call format (VCF) is a generic format for storing DNA polymorphism data such as SNPs, insertions, deletions and structural variants, together with rich annotations. VCF is usually stored in a compressed manner and can be indexed for fast data retrieval of variants from a range of positions on the reference genome. The format was developed for the 1000 Genomes Project, and has also been adopted by other projects such as UK10K, dbSNP and the NHLBI Exome Project. VCFtools is a software suite that implements various utilities for processing VCF files, including validation, merging, comparing and also provides a general Perl API.             Availability: ~http://vcftools.sourceforge.net             Contact: ~rd@sanger.ac.uk},
  copyright = {http://creativecommons.org/licenses/by-nc/2.5},
  langid = {english},
  file = {/home/kevin/work/bibliography/zotdir/storage/GUXAG22N/Danecek et al. - 2011 - The variant call format and VCFtools.pdf}
}

@article{danecek21_bcftools12years,
  title = {Twelve Years of {{SAMtools}} and {{BCFtools}}},
  author = {Danecek, Petr and Bonfield, James K. and Liddle, Jennifer and Marshall, John and Ohan, Valeriu and Pollard, Martin O. and Whitwham, Andrew and Keane, Thomas and McCarthy, Shane A. and Davies, Robert M. and Li, Heng},
  year = 2021,
  month = feb,
  journal = {GigaScience},
  volume = {10},
  number = {2},
  pages = {giab008},
  issn = {2047-217X},
  doi = {10.1093/gigascience/giab008},
  abstract = {BACKGROUND: SAMtools and BCFtools are widely used programs for processing and analysing high-throughput sequencing data. They include tools for file format conversion and manipulation, sorting, querying, statistics, variant calling, and effect analysis amongst other methods. FINDINGS: The first version appeared online 12 years ago and has been maintained and further developed ever since, with many new features and improvements added over the years. The SAMtools and BCFtools packages represent a unique collection of tools that have been used in numerous other software projects and countless genomic pipelines. CONCLUSION: Both SAMtools and BCFtools are freely available on GitHub under the permissive MIT licence, free for both non-commercial and commercial use. Both packages have been installed {$>$}1 million times via Bioconda. The source code and documentation are available from https://www.htslib.org.},
  langid = {english},
  pmcid = {PMC7931819},
  pmid = {33590861},
  keywords = {bcftools,data analysis,Genome,Genomics,High-Throughput Nucleotide Sequencing,high-throughput sequencing,next generation sequencing,samtools,Software,variant calling},
  file = {/home/kevin/work/bibliography/zotdir/storage/AQREBSVT/danecek21_twelve__twelve_years_of_samtools_and_bcftools.pdf}
}

@misc{knaus16_vcfrpackage,
  title = {{{VcfR}}: A Package to Manipulate and Visualize {{VCF}} Format Data in {{R}}},
  shorttitle = {{{VcfR}}},
  author = {Knaus, Brian J. and Gr{\"u}nwald, Niklaus J.},
  year = 2016,
  month = feb,
  publisher = {Bioinformatics},
  doi = {10.1101/041277},
  urldate = {2026-01-15},
  abstract = {Abstract           Software to call single nucleotide polymorphisms or related genetic variants has converged on the variant call format (VCF) as the output format of choice. This has created a need for tools to work with VCF files. While an increasing number of software exists to read VCF data, many only extract the genotypes without including the data associated with each genotype that describes its quality. We created the R package vcfR to address this issue. We developed a VCF file exploration tool implemented in the R language because R provides an interactive experience and an environment that is commonly used for genetic data analysis. Functions to read and write VCF files into R as well as functions to extract portions of the data and to plot summary statistics of the data are implemented. VcfR further provides the ability to visualize how various parameterizations of the data affect the results. Additional tools are included to integrate sequence (FASTA) and annotation data (GFF) for visualization of genomic regions such as chromosomes. Conversion functions translate data from the vcfR data structure to formats used by other R genetics packages. Computationally intensive functions are implemented in C++ to improve performance. Use of these tools is intended to facilitate VCF data exploration, including intuitive methods for data quality control and easy export to other R packages for further analysis. VcfR thus provides essential, novel tools currently not available in R.},
  archiveprefix = {Bioinformatics},
  copyright = {http://creativecommons.org/licenses/by/4.0/},
  langid = {english},
  file = {/home/kevin/work/bibliography/zotdir/storage/PQVJBYPZ/Knaus and Grünwald - 2016 - VcfR a package to manipulate and visualize VCF format data in R.pdf}
}

@article{knaus17_vcfrpackage,
  title = {{\textsc{Vcfr}} : A Package to Manipulate and Visualize Variant Call Format Data in {{R}}},
  author = {Knaus, Brian J. and Gr{\"u}nwald, Niklaus J.},
  year = 2017,
  month = jan,
  journal = {Molecular Ecology Resources},
  volume = {17},
  number = {1},
  pages = {44--53},
  issn = {1755-098X, 1755-0998},
  doi = {10.1111/1755-0998.12549},
  urldate = {2026-01-15},
  abstract = {Abstract                            Software to call single-nucleotide polymorphisms or related genetic variants has converged on the variant call format (VCF) as the output format of choice. This has created a need for tools to work with               VCF               files. While an increasing number of software exists to read               VCF               data, many only extract the genotypes without including the data associated with each genotype that describes its quality. We created the               r               package               vcfr               to address this issue. We developed a               VCF               file exploration tool implemented in the               r               language because               r               provides an interactive experience and an environment that is commonly used for genetic data analysis. Functions to read and write               VCF               files into               r               as well as functions to extract portions of the data and to plot summary statistics of the data are implemented.               vcfr               further provides the ability to visualize how various parameterizations of the data affect the results. Additional tools are included to integrate sequence (               fasta               ) and annotation data (               GFF               ) for visualization of genomic regions such as chromosomes. Conversion functions translate data from the               vcfr               data structure to formats used by other               r               genetics packages. Computationally intensive functions are implemented in C++ to improve performance. Use of these tools is intended to facilitate               VCF               data exploration, including intuitive methods for data quality control and easy export to other               r               packages for further analysis.               vcfr               thus provides essential, novel tools currently not available in               r               .},
  copyright = {http://onlinelibrary.wiley.com/termsAndConditions\#vor},
  langid = {english}
}

@article{marees18_tutorialconducting,
  title = {A Tutorial on Conducting Genome-wide Association Studies: {{Quality}} Control and Statistical Analysis},
  shorttitle = {A Tutorial on Conducting Genome-wide Association Studies},
  author = {Marees, Andries T. and {de Kluiver}, Hilde and Stringer, Sven and Vorspan, Florence and Curis, Emmanuel and Marie-Claire, Cynthia and Derks, Eske M.},
  year = 2018,
  month = feb,
  journal = {International Journal of Methods in Psychiatric Research},
  volume = {27},
  number = {2},
  pages = {e1608},
  issn = {1049-8931},
  doi = {10.1002/mpr.1608},
  urldate = {2024-08-02},
  abstract = {Objectives Genome-wide association studies (GWAS) have become increasingly popular to identify associations between single nucleotide polymorphisms (SNPs) and phenotypic traits. The GWAS method is commonly applied within the social sciences. However, statistical analyses will need to be carefully conducted and the use of dedicated genetics software will be required. This tutorial aims to provide a guideline for conducting genetic analyses. Methods We discuss and explain key concepts and illustrate how to conduct GWAS using example scripts provided through GitHub (https://github.com/MareesAT/GWA\_tutorial/ )., In addition to the illustration of standard GWAS, we will also show how to apply polygenic risk score (PRS) analysis. PRS does not aim to identify individual SNPs but aggregates information from SNPs across the genome in order to provide individual-level scores of genetic risk. Results The simulated data and scripts that will be illustrated in the current tutorial provide hands-on practice with genetic analyses. The scripts are based on PLINK, PRSice, and R, which are commonly used, freely available software tools that are accessible for novice users. Conclusions By providing theoretical background and hands-on experience, we aim to make GWAS more accessible to researchers without formal training in the field.},
  pmcid = {PMC6001694},
  pmid = {29484742},
  file = {/home/kevin/work/bibliography/zotdir/storage/RZ7WB5SR/mareesTutorialConductingGenome2018__a_tutorial_on_conducting_genome‐wide_association_studies_quality_control_and_statistical_analysis.pdf}
}

@article{obenchain14_variantannotationbioconductor,
  title = {{{VariantAnnotation}} : A {{Bioconductor}} Package for Exploration and Annotation of Genetic Variants},
  shorttitle = {{{VariantAnnotation}}},
  author = {Obenchain, Valerie and Lawrence, Michael and Carey, Vincent and Gogarten, Stephanie and Shannon, Paul and Morgan, Martin},
  year = 2014,
  month = jul,
  journal = {Bioinformatics},
  volume = {30},
  number = {14},
  pages = {2076--2078},
  issn = {1367-4811, 1367-4803},
  doi = {10.1093/bioinformatics/btu168},
  urldate = {2026-01-15},
  abstract = {Abstract             Summary: ~VariantAnnotation is an R / Bioconductor package for the exploration and annotation of genetic variants. Capabilities exist for reading, writing and filtering variant call format (VCF) files. VariantAnnotation allows ready access to additional R / Bioconductor facilities for advanced statistical analysis, data transformation, visualization and integration with diverse genomic resources.             Availability and implementation: This package is implemented in R and available for download at the Bioconductor Web site ( http://bioconductor.org/packages/2.13/bioc/html/ VariantAnnotation .html ). The package contains extensive help pages for individual functions and a `vignette' outlining typical work flows; it is made available under the open source `Artistic-2.0' license. Version 1.9.38 was used in this article.             Contact: ~vobencha@fhcrc.org},
  langid = {english},
  file = {/home/kevin/work/bibliography/zotdir/storage/U6TW52V8/Obenchain et al. - 2014 - VariantAnnotation  a Bioconductor package for exploration and annotation of genetic variants.pdf}
}

@article{zheng12_highperformancecomputing,
  title = {A High-Performance Computing Toolset for Relatedness and Principal Component Analysis of {{SNP}} Data},
  author = {Zheng, Xiuwen and Levine, David and Shen, Jess and Gogarten, Stephanie M. and Laurie, Cathy and Weir, Bruce S.},
  year = 2012,
  month = jan,
  journal = {Bioinformatics},
  volume = {28},
  number = {24},
  pages = {3326--3328},
  issn = {1367-4803, 1460-2059},
  doi = {10.1093/bioinformatics/bts606},
  urldate = {2016-09-27},
  abstract = {Summary: Genome-wide association studies are widely used to investigate the genetic basis of diseases and traits, but they pose many computational challenges. We developed gdsfmt and SNPRelate (R packages for multi-core symmetric multiprocessing computer architectures) to accelerate two key computations on SNP data: principal component analysis (PCA) and relatedness analysis using identity-by-descent measures. The kernels of our algorithms are written in C/C++ and highly optimized. Benchmarks show the uniprocessor implementations of PCA and identity-by-descent are {$\sim$}8--50 times faster than the implementations provided in the popular EIGENSTRAT (v3.0) and PLINK (v1.07) programs, respectively, and can be sped up to 30--300-fold by using eight cores. SNPRelate can analyse tens of thousands of samples with millions of SNPs. For example, our package was used to perform PCA on 55 324 subjects from the `Gene-Environment Association Studies' consortium studies. Availability and implementation: gdsfmt and SNPRelate are available from R CRAN (http://cran.r-project.org), including a vignette. A tutorial can be found at https://www.genevastudy.org/Accomplishments/software. Contact: zhengx@u.washington.edu},
  langid = {english},
  pmid = {23060615}
}
