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Impute GWAS summary statistics using reference genotype data

Project 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.


  1. Make sure that setuptools is up-to-date by typing the following command

    pip install setuptools --upgrade --user

  2. First grab the latest version of FIZI using git as

    git clone

  3. FIZI can be installed using setuptools as

    cd fizi then

    python install --user or optionally as

    sudo python install if you have root access and wish to install for all users

  4. Check that FIZI was installed by typing

    fizi --help

  5. 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 and/or

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
  2. Estimating local heritability or genetic correlation HESS
  3. Estimating genome-wide heritability or genetic correlation UNITY
  4. Fine-mapping using summary-data PAINTOR

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