Year: 2022
Type: article
Abstract: SUMMARY: Identifying genomic features responsible for genome-wide association study (GWAS) signals has proven to be a difficult challenge; many researchers have turned to colocalization analysis of GWAS signals with expression quantitative trait loci (eQTL) and splicing quantitative trait loci (sQTL) to connect GWAS signals to candidate causal genes. The ColocQuiaL pipeline provides a framework to perform these colocalization analyses at scale across the genome and returns summary files and locus visualization plots to allow for detailed review of the results. As an example, we used ColocQuiaL to perform colocalization between a recent type 2 diabetes GWAS and Genotype-Tissue Expression (GTEx) v8 single-tissue eQTL and sQTL data. AVAILABILITY AND IMPLEMENTATION: ColocQuiaL is primarily written in R and is freely available on GitHub: https://github.com/bvoightlab/ColocQuiaL.
Source: Bioinformatics
Authors: Brian Y Chen, William P Bone, Kim Lorenz, Michael Levin, Marylyn D Ritchie, Benjamin F Voight
Institutions: University of Pennsylvania, Philadelphia VA Medical Center
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $3,618
Awards: Algorithms to identify non-coding mutational burden and disease-relevant pathways, Algorithms to identify non-coding mutational burden and disease-relevant pathways, Leveraging genome-wide pleiotropy to improve cardiometabolic and neurological genetic association methods, Functional Interrogation of T2D-associated genes in human stem cell-derived models and mice, G9000180154, Training Program in Cardiovascular Biology and Medicine
OpenAlex ID: W4288068401