ColocQuiaL: a QTL-GWAS colocalization pipeline

Author:

Chen Brian Y1,Bone William P2ORCID,Lorenz Kim345,Levin Michael567,Ritchie Marylyn D489,Voight Benjamin F345810

Affiliation:

1. School of Arts and Sciences, University of Pennsylvania , Philadelphia, PA 19104, USA

2. Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania , Philadelphia, PA 19104, USA

3. Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania , Philadelphia, PA 19104, USA

4. Department of Genetics, University of Pennsylvania , Philadelphia, PA 19104, USA

5. Corporal Michael J. Crescenz VA Medical Center , Philadelphia, PA 19104, USA

6. Department of Surgery, Perelman School of Medicine, University of Pennsylvania , Philadelphia, PA 19104, USA

7. Division of Cardiovascular Medicine, Department of Medicine, University of Pennsylvania Perelman School of Medicine , Philadelphia, PA 19104, USA

8. Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania , Philadelphia, PA 19104, USA

9. Center for Precision Medicine, Perelman School of Medicine, University of Pennsylvania , Philadelphia, PA 19104, USA

10. Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania , Philadelphia, PA 19104, USA

Abstract

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.

Funder

American Heart Association

National Institutes of Health

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

Reference14 articles.

1. TIGER: the gene expression regulatory variation landscape of human pancreatic islets;Alonso;Cell Rep,2021

2. The NHGRI-EBI GWAS catalog of published genome-wide association studies, targeted arrays and summary statistics 2019;Buniello;Nucleic Acids Res,2019

3. A novel approach to high-quality postmortem tissue procurement: the GTEx project;Carithers;Biopreserv. Biobank,2015

4. A fast and efficient colocalization algorithm for identifying shared genetic risk factors across multiple traits;Foley;Nat. Commun,2021

5. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics;Giambartolomei;PLoS Genet,2014

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