suraj-adewale/finemapping

PolyFun

PolyFun (POLYgenic FUNctionally-informed fine-mapping) described in Weissbrod et al. 2020 Nat Genet.

This page contains the code of the methods PolyFun for functionally-informed fine-mapping

PolyFun estimates prior causal probabilities for SNPs, which can then be used by fine-mapping methods like SuSiE or FINEMAP. Unlike previous methods for functionally-informed fine-mapping, PolyFun can aggregate polygenic data from across the entire genome and hundreds of functional annotations.

1 Installation

Create an Anaconda environment

The easiest way to install polyfun is by creating a dedicated environment through the Anaconda Python distribution. To do this, please install Anaconda on your machine and then type the following commands:

mkdir polyfun
cd PGWAS
conda env create -f polyfun.yml
conda activate polyfun

This will install all the dependencies except for SuSiE and FINEMAP You can use PolyFun without these packages to compute prior causal probabilities, but you won't be able to apply the actual fine-mapping. Please see installation instructions for these two packages below.

After the installation, you can always invoke the PolyFun environment with the command conda activate pGWAS.

Install option 2: Manually install packages

PolyFun and PolyLoc are designed for Python >=3.6 and require the following freely available Python packages:

• numpy and scipy
• scikit-learn
• pandas (version >=0.25.0)
• tqdm
• pyarrow
• bitarray
• networkx (only required for HESS-based estimation of effect size variance)
• pandas-plink

It is recommended (but not required) to also install the following:

• rpy2 (a Python package)
• R version 3.5.1 or higher
• Ckmeans.1d.dp (a package for R, that will be invoked from python via the rpy2 package).

If rpy2 or Ckmeans.1d.dp are not installed, PolyFun will fallback to suboptimal clustering via scikit-learn.

The finemapper script also requires the following:

1. A fine-mapping package you'd like to use. At the moment we support susieR and FINEMAP v1.4. Please see installation instructions for these packages below.
2. (optional) The program LDstore 2.0 for computing LD directly from .bgen files (imputed genotypes)

Installing SuSiE

To install SuSiE, please start an R shell (usually by typing R) and then type:

devtools::install_github("stephenslab/[email protected]",build_vignettes=FALSE)

If this doesn't work, please refer to the SuSiE website for more information, or contact the SuSiE authors through the SuSiE Github page.

Installing FINEMAP v1.4

To install FINEMAP v1.4, please type one of the following two commands:
If you use Linux:

wget http://www.christianbenner.com/finemap_v1.4_x86_64.tgz
tar xvf finemap_v1.4_x86_64.tgz

If you use Mac OS X :

wget http://www.christianbenner.com/finemap_v1.4_MacOSX.tgz
tar xvf finemap_v1.4_MacOSX.tgz

TO RUN THE PIPELINE

./pipeline.sh ~/pGWAS/data/AFR/boltlmm_sumstats.gz susie Null 327209 ~/data AFR 1 46000001 49000001 5

#Teting with small file

#~/pGWAS/./pipeline.sh ~/pGWAS/data/test_data/boltlmm_sumstats.gz finemap Null 327209 test_data 1 46000001 49000001 5