Metadata-Version: 2.1
Name: pyfizi
Version: 0.7.1
Summary: Impute GWAS summary statistics using reference genotype data
Home-page: https://github.com/bogdanlab/fizi
Author: Nicholas Mancuso, Megan Roytman
Author-email: nicholas.mancuso@med.usc.edu
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
        ----
        The easiest way to install `fizi` and `pyfizi` is through conda and conda-forge:
        
            conda config --add channels conda-forge
            conda install pyfizi
            
        Alternatively you can use pip for installation:
        
            pip install pyfizi
        
        Or directly from the github repository:
        
            git clone git@github.com:bogdanlab/fizi.git
            cd fizi
            pip install .
            
        Check that FIZI was installed by typing
        
            fizi --help
        
        If that did not work, and `pip install pyfizi --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.
        
        *We currently only support Python3.6+. [Python2.7 and below is not supported](https://pythonclock.org/)*
        
        Overview
        --------
        `fizi` has two main functions: `munge` and `impute`. The `munge` subcommand is a pruned down version of the LDSC munge_sumstats software with a few bells and whistles needed for our imputation algorithm. The `impute` subcommand performs summary statistic imputation using either the functionally informed algorithm (i.e. `fizi`) or using only reference-LD-only algorithm (i.e. ImpG). For a full list of features please refer to the help command: `fizi munge -h` or `fizi impute -h`. 
        
        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 only for chromosome 1 using the ImpG algorithm 
        simply enter the commands
        
            1. fizi munge gwas.sumstat.gz --out cleaned.gwas
            2. fizi impute cleaned.gwas.sumstat.gz plink_data_path --chr 1 --out imputed.cleaned.gwas.chr1.sumstat
        
        By default `fizi` requires that at least 50% of SNPs to be observed for imputation at a region. This can be changed with the `--min-prop PROP` flag in step 2.
        
        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
        
        Software and support
        -----
        If you have any questions or comments please contact nicholas.mancuso@med.usc.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)
        
        [1]: https://academic.oup.com/bioinformatics/article/30/20/2906/2422225
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)
Classifier: Operating System :: OS Independent
Requires-Python: >=3
Description-Content-Type: text/markdown
