Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics

Alvaro N. Barbeira, Scott P. Dickinson, Rodrigo Bonazzola, Jiamao Zheng, Heather E. Wheeler, Jason M. Torres, Eric S. Torstenson, Kaanan P. Shah, Tzintzuni Garcia, Todd L. Edwards, Eli A. Stahl, Laura M. Huckins, Dan L. Nicolae, Nancy J. Cox and Hae Kyung Im ()
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Alvaro N. Barbeira: The University of Chicago
Scott P. Dickinson: The University of Chicago
Rodrigo Bonazzola: The University of Chicago
Jiamao Zheng: The University of Chicago
Heather E. Wheeler: Loyola University Chicago
Jason M. Torres: The University of Chicago
Eric S. Torstenson: Vanderbilt University Medical Center
Kaanan P. Shah: The University of Chicago
Tzintzuni Garcia: The University of Chicago
Todd L. Edwards: Vanderbilt University Medical Center
Eli A. Stahl: Icahn School of Medicine at Mount Sinai
Laura M. Huckins: Icahn School of Medicine at Mount Sinai
Dan L. Nicolae: The University of Chicago
Nancy J. Cox: Vanderbilt University Medical Center
Hae Kyung Im: The University of Chicago

Nature Communications, 2018, vol. 9, issue 1, 1-20

Abstract: Abstract Scalable, integrative methods to understand mechanisms that link genetic variants with phenotypes are needed. Here we derive a mathematical expression to compute PrediXcan (a gene mapping approach) results using summary data (S-PrediXcan) and show its accuracy and general robustness to misspecified reference sets. We apply this framework to 44 GTEx tissues and 100+ phenotypes from GWAS and meta-analysis studies, creating a growing public catalog of associations that seeks to capture the effects of gene expression variation on human phenotypes. Replication in an independent cohort is shown. Most of the associations are tissue specific, suggesting context specificity of the trait etiology. Colocalized significant associations in unexpected tissues underscore the need for an agnostic scanning of multiple contexts to improve our ability to detect causal regulatory mechanisms. Monogenic disease genes are enriched among significant associations for related traits, suggesting that smaller alterations of these genes may cause a spectrum of milder phenotypes.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-03621-1

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DOI: 10.1038/s41467-018-03621-1

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