marislab/tad_pathways_pipeline

Pipeline to implement a "TAD_Pathways" analysis. Discover candidate genes based on association signals in TADs

TAD_Pathways

Leveraging TADs to identify candidate genes at GWAS signals

Gregory P. Way, Casey S. Greene, and Struan F.A. Grant - 2017

Summary

The repository contains data and instructions to implement a "TAD_Pathways" analysis for over 300 different trait/disease GWAS or custom SNP lists.

TAD_Pathways uses the principles of topologically association domains (TADs) to define where an association signal (typically a GWAS signal) can most likely impact gene function. We use TAD boundaries as defined by Dixon et al. 2012 and hg19 Gencode genes to identify which genes may be implicated. We then perform an overrepresentation pathway analysis to identify significantly associated pathways implicated by the input TAD-defined geneset.

For more specific details about our method, refer to our short report at the European Journal of Human Genetics.

We also present a 6 minute video introducing the method and discussing the experimental validation at EJHG-tube.

Setup

First, clone the repository and navigate into the top directory:

git clone https://github.com/marislab/tad_pathways_pipeline.git
cd tad_pathways_pipeline

Before you begin, download the necessary TAD based index files and GWAS curation files and setup python environment:

bash initialize.sh

# Using conda version 4.4.11
conda activate tad_pathways

Now, a TAD_Pathways analysis can proceed. Follow an example pipeline to work from an existing GWAS or the custom pipeline example for insight on how to run TAD_Pathways on user curated SNPs.

Examples

We provide an example for a TAD pathways analysis pipeline. To run this example:

source activate tad_pathways

# Example using custom input SNPs
bash example_pipeline_custom.sh

General Usage

To perform a TAD_Pathways analysis, uses need to spicify 3 inputs:

1. name of the tad cell:

   E.g.: 'hESC'

2. path to the TAD domain file:

   The TAD domain file is a 3-column tab-separated bed file. The first column is the chromsome number. The second column is the start position of the tad. And the third position is the end position of the tad.

   E.g.: hESC_domains_hg19.bed

3. path to the SNPs file

   The SNPs file is a comma separated text file. The first row of the text file should have group names and subsequent rows should list the rs numbers of interest. There can be manycolumns with variable length rows.

   E.g.: custom_example.csv

   Group 1	Group 2
   rs12345	rs67891
   rs19876	rs54321
   ...	...

Then, perform the following steps:

source activate tad_pathways

bash run_pipeline.sh --TAD-Boundary hESC \
                     --TAD-File hESC_domains_hg19.bed \
                     --SNP-File custom_example.csv

The output of these steps are Group specific text files with all genes in TADs harboring an input SNP. See example_pipeline_custom.sh for more details.

Contact

For all questions and bug reporting please file a GitHub issue

For all other questions contact Casey Greene at csgreene@mail.med.upenn.edu or Struan Grant at grants@email.chop.edu