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    "info": {
        "author": "Nicholas Mancuso, Megan Roytman",
        "author_email": "nick.mancuso@gmail.com, meganroytman@gmail.com",
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        "classifiers": [
            "License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3"
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        "description": "# Functionally-informed Z-score Imputation (FIZI)\nFIZI leverages functional information together with reference linkage-disequilibrium (LD) to\nimpute GWAS summary statistics (Z-score).\n\nThis README is a working draft and will be expanded soon.\n\n[//]: # (This repository serves as the home for the python implementation of the algorithm described in XX.)\n\nInstallation\n----\n0. Make sure that setuptools is up-to-date by typing the following command\n\n    `pip install setuptools --upgrade --user`\n\n1. First grab the latest version of FIZI using git as\n\n    `git clone https://github.com/bogdanlab/fizi`\n\n2. FIZI can be installed using setuptools as \n\n    `cd fizi` then\n\n    `python setup.py install --user` or optionally as\n\n    `sudo python setup.py install` if you have root access and wish to install for all users\n\n3. Check that FIZI was installed by typing\n\n    `fizi --help`\n\n4. If that did not work, and `--user` was specified, please check that your local user path is included in\n`$PATH` environment variable. `--user` location and can be appended to `$PATH`\nby executing\n\n    `` export PATH=`python -m site --user-base`/bin/:$PATH ``\n\n    which can be saved in `.bashrc` or `.bash_profile`. To reload the environment type\n\n    `source ~/.bashrc` or `source .bash_profile` depending where you entered it.\n\n\nIncorporating functional data to improve summary statistics imputation\n-----\nUsage consists of several steps. We outline the general workflow here when the intention to perform imputation on\nchromosome 1 of our data:\n\n1. Munge/clean _all_ GWAS summary data before imputation\n\n    `fizi munge gwas.sumstat.gz --out cleaned.gwas`\n\n2. Partitioning cleaned GWAS summary data into chr1 and everything else (loco-chr1).\n3. Run LDSC on locoChr to obtain tau estimates\n4. Perform functionally-informed imputation on chr1 data using tau estimates from loco-chr\n\nImputing summary statistics using only reference LD\n------\nWhen functional annotations and LDSC estimates are not provided to FIZI, it will fallback to the classic ImpG\nalgorithm described in ref[1]. To impute missing summary statistics using the ImpG algorithm simply enter the\ncommand \n\n    fizi impute cleaned.gwas.sumstat.gz plink_data_path --chr 1 --out imputed.cleaned.gwas.sumstat\n\nSoftware and support\n-----\nIf you have any questions or comments please contact nmancuso@mednet.ucla.edu and/or meganroytman@gmail.com\n\nFor performing various inferences using summary data from large-scale GWASs please find the following useful software:\n\n1. Association between predicted expression and complex trait/disease [FUSION](https://github.com/gusevlab/fusion_twas)\n2. Estimating local heritability or genetic correlation [HESS](https://github.com/huwenboshi/hess)\n3. Estimating genome-wide heritability or genetic correlation [UNITY](https://github.com/bogdanlab/UNITY)\n4. Fine-mapping using summary-data [PAINTOR](https://github.com/gkichaev/PAINTOR_V3.0)\n\n\n",
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