Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics
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- dc.contributor.author Barbeira, Alvaro N.
- dc.contributor.author Dickinson, Scott P.
- dc.contributor.author Bonazzola, Rodrigo
- dc.contributor.author Zheng, Jiamao
- dc.contributor.author Wheeler, Heather E.
- dc.contributor.author Torres, Jason M.
- dc.contributor.author Torstenson, Eric S.
- dc.contributor.author Shah Kaanan P.
- dc.contributor.author Garcia, Tzintzuni
- dc.contributor.author Edwards, Todd L.
- dc.contributor.author Stahl, Eli A.
- dc.contributor.author Huckins, Laura M.
- dc.contributor.author GTEx Consortium
- dc.contributor.author Nicolae, Dan L.
- dc.contributor.author Cox, Nancy J.
- dc.contributor.author Im, Hae Kyung
- dc.date.accessioned 2018-11-13T12:08:35Z
- dc.date.available 2018-11-13T12:08:35Z
- dc.date.issued 2018
- dc.description.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.
- dc.format.mimetype application/pdf
- dc.identifier.citation Barbeira AN, Dickinson SP, Bonazzola R, Zheng J, Wheeler HE, Torres JM et al. Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics. Nat Commun. 2018;9(1):1825. DOI: 10.1038/s41467-018-03621-1
- dc.identifier.doi http://dx.doi.org/10.1038/s41467-018-03621-1
- dc.identifier.issn 2041-1723
- dc.identifier.uri http://hdl.handle.net/10230/35743
- dc.language.iso eng
- dc.publisher Nature Publishing Group
- dc.relation.ispartof Nature Communications. 2018;9(1):1825
- dc.rights © The Author(s) 2018. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
- dc.rights.accessRights info:eu-repo/semantics/openAccess
- dc.rights.uri http://creativecommons.org/licenses/by/4.0/
- dc.subject.keyword Data integration
- dc.subject.keyword Genome-wide association studies
- dc.title Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics
- dc.type info:eu-repo/semantics/article
- dc.type.version info:eu-repo/semantics/publishedVersion