https://github.com/greenelab/tad_pathways_pipeline
Pipeline to implement a "TAD_Pathways" analysis. Discover candidate genes based on association signals in TADs
https://github.com/greenelab/tad_pathways_pipeline
analysis candidate-genes gwas methodology pathways tad tads workflow
Last synced: 27 days ago
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Pipeline to implement a "TAD_Pathways" analysis. Discover candidate genes based on association signals in TADs
- Host: GitHub
- URL: https://github.com/greenelab/tad_pathways_pipeline
- Owner: greenelab
- License: mit
- Created: 2017-01-05T19:33:21.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2019-12-21T15:21:58.000Z (over 5 years ago)
- Last Synced: 2023-10-20T20:05:53.403Z (over 1 year ago)
- Topics: analysis, candidate-genes, gwas, methodology, pathways, tad, tads, workflow
- Language: Python
- Homepage:
- Size: 2.24 MB
- Stars: 10
- Watchers: 7
- Forks: 5
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# TAD_Pathways
## Leveraging TADs to identify candidate genes at GWAS signals
**Gregory P. Way, Casey S. Greene, and Struan F.A. Grant - 2017**
[](https://doi.org/10.5281/zenodo.254190)
### 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](https://doi.org/10.1038/nature11082) and
[hg19 Gencode genes](ftp://ftp.sanger.ac.uk/pub/gencode/Gencode_human/release_19/)
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](https://doi.org/10.1038/ejhg.2017.108 "Implicating candidate genes at GWAS signals by leveraging topologically associating domains")
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](http://www.nature.com/ejhg/videos/index.html).
### Setup
First, clone the repository and navigate into the top directory:
```bash
git clone [email protected]:greenelab/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
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 three different examples for a TAD pathways analysis pipeline. To run
each of the analyses:
```bash
source activate tad_pathways
# Example using Bone Mineral Density GWAS
bash example_pipeline_bmd.sh
# Example using Type 2 Diabetes GWAS
bash example_pipeline_t2d.sh
# Example using custom input SNPs
bash example_pipeline_custom.sh
```
### General Usage
There are two ways to implement a TAD_Pathways analysis:
1. GWAS
2. Custom
#### GWAS
To perform a `TAD_Pathways` analysis on publicly available GWAS results, simply
browse the `data/gwas_catalog/` directory to select a valid GWAS file. These
files contain a curation of all significant SNPs mapped to specific traits as
distributed by the [NHGRI-EBI GWAS Catalog](https://www.ebi.ac.uk/gwas/).
Each file in this directory is a tab separated text file of genome-wide
significant SNPs and their genomic location along with their reported nearest
gene and associated PUBMED id. For complete information on how these files were
constructed, refer to https://github.com/greenelab/tad_pathways.
Each GWAS has 3 associated files, including files in `data/gwas_catalog/`. The
other files are located in `data/gwas_tad_snps/` and `data/gwas_tad_genes/`.
All files are important for performing a `TAD_Pathways` analysis. See the
GWAS example files for instructions on how to implement the necessary scripts.
#### Custom
To perform a `TAD_Pathways` analysis on a list of custom SNPs, generate 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 many
columns with variable length rows.
E.g.: [`custom_example.csv`](custom_example.csv)
| Group 1 | Group 2 |
| ------- | ------- |
| rs12345 | rs67891 |
| rs19876 | rs54321 |
| ... | ... |
Then, perform the following steps:
```bash
source activate tad_pathways
# Map custom SNPs to genomic locations
Rscript --vanilla scripts/build_snp_list.R \
--snp_file "custom_example.csv" \
--output_file "mapped_results.tsv"
# Build TAD based genelists for each group
python scripts/build_custom_TAD_genelist.py \
--snp_data_file "mapped_results.tsv" \
--output_file "custom_tad_genelist.tsv"
```
The output of these steps are Group specific text files with all genes in TADs
harboring an input SNP. See
[`example_pipeline_custom.sh`](example_pipeline_custom.sh) for more details.
### Contact
For all questions and bug reporting please file a
[GitHub issue](https://github.com/greenelab/tad_pathways/issues)
For all other questions contact Casey Greene at [email protected] or
Struan Grant at [email protected]