Metadata-Version: 2.1
Name: pyfizi
Version: 0.7
Summary: Impute GWAS summary statistics using reference genotype data
Home-page: https://github.com/bogdanlab/fizi
Author: Nicholas Mancuso, Megan Roytman
Author-email: nick.mancuso@gmail.com, meganroytman@gmail.com
License: UNKNOWN
Description: # Functionally-informed Z-score Imputation (FIZI)
        FIZI leverages functional information together with reference linkage-disequilibrium (LD) to
        impute GWAS summary statistics (Z-score).
        
        This README is a working draft and will be expanded soon.
        
        [//]: # (This repository serves as the home for the python implementation of the algorithm described in XX.)
        
        Installation
        ----
        0. Make sure that setuptools is up-to-date by typing the following command
        
            `pip install setuptools --upgrade --user`
            
        1. First grab the latest version of FIZI using git as
        
            `git clone https://github.com/bogdanlab/fizi`
            
        2. FIZI can be installed using setuptools as 
        
            `cd fizi` then
            
            `python setup.py install --user` or optionally as
            
            `sudo python setup.py install` if you have root access and wish to install for all users
            
        3. Check that FIZI was installed by typing
        
            `fizi --help`
        
        4. If that did not work, and `--user` was specified, please check that your local user path is included in
        `$PATH` environment variable. `--user` location and can be appended to `$PATH`
        by executing
        
            `` export PATH=`python -m site --user-base`/bin/:$PATH ``
            
            which can be saved in `.bashrc` or `.bash_profile`. To reload the environment type
            
            `source ~/.bashrc` or `source .bash_profile` depending where you entered it.
        
        
        Incorporating functional data to improve summary statistics imputation
        -----
        Usage consists of several steps. We outline the general workflow here when the intention to perform imputation on
        chromosome 1 of our data:
        
        1. Munge/clean _all_ GWAS summary data before imputation
        
            `fizi munge gwas.sumstat.gz --out cleaned.gwas`
        
        2. Partitioning cleaned GWAS summary data into chr1 and everything else (loco-chr1).
        3. Run LDSC on locoChr to obtain tau estimates
        4. Perform functionally-informed imputation on chr1 data using tau estimates from loco-chr
        
        Imputing summary statistics using only reference LD
        ------
        When functional annotations and LDSC estimates are not provided to FIZI, it will fallback to the classic ImpG
        algorithm described in ref[1]. To impute missing summary statistics using the ImpG algorithm simply enter the
        command 
        
            fizi impute cleaned.gwas.sumstat.gz plink_data_path --chr 1 --out imputed.cleaned.gwas.sumstat
        
        Software and support
        -----
        If you have any questions or comments please contact nmancuso@mednet.ucla.edu and/or meganroytman@gmail.com
        
        For performing various inferences using summary data from large-scale GWASs please find the following useful software:
        
        1. Association between predicted expression and complex trait/disease [FUSION](https://github.com/gusevlab/fusion_twas)
        2. Estimating local heritability or genetic correlation [HESS](https://github.com/huwenboshi/hess)
        3. Estimating genome-wide heritability or genetic correlation [UNITY](https://github.com/bogdanlab/UNITY)
        4. Fine-mapping using summary-data [PAINTOR](https://github.com/gkichaev/PAINTOR_V3.0)
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Operating System :: OS Independent
Requires-Python: >=3
Description-Content-Type: text/markdown
