To investigate trait or phenotype multimorbidity by integrating chromatin interaction and expression quantitative trait loci data.
- GWAS Catalog all associations v1.0.1 from here
- Rao et al (2014)'s Hi-C libraries of GM12878, IMR90, HMEC, NHEK, K562, HUVEC, and KBM7 cell lines from the GEO repository
- GTEx multi-tissue eQTLs data analysis v4
- OMIM's genemap2 data
The figures generated in this study (and their underlying code) can be viewed in visualisation.html or the visualisation.Rmd R Markdown file.
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To reproduce this work, clone this repository with:
git clone https://github.com/Genome3d/multimorbidity-atlas.git
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Install the [CoDeS3D][https://github.com/alcamerone/codes3d] pipeline:
cd scripts/python/ git clone https://github.com/alcamerone/codes3d.git
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To quickly find multimorbid traits without running the entire methods, download and unzip the spatial eQTL data for traits into the results directory (1.59 GB). Then run step 9 in Method overview section below.
- Obtaining SNPs from GWAS Catalog associations file from the command line:
cd scripts/python/ ./extract_gwas_snps.py
- Identification of spatial eQTLs from GWAS SNPs:
cd ../bash/ ./batch_codes3d.sh
- Saving eQTLs to database:
cd ../python/ ./init_db.py
- Get eQTLs associated with complex traits:
./get_trait_eqtls.py
- Obtain significant tissue-specific spatial eQTLs:
cd ../bash/ ./produce_summaries.sh
- Get eQTL-eGene interactions in tissues
cd ../python/ ./get_eqtl_interactions.py
- Construct matrices of shared eQTLs, eGenes ratios among complex traits. Plus control analysis:
./control_analysis.py
- Cluster complex traits by shared eGenes and eQTLs
cd ../R/ ./convex_biclustering.R
- Find traits that share eGenes. Run command with -h for help:
cd ../python/ ./get_cluster.py