Multitrait GWAS to connect disease variants and biological mechanisms

Hanna Julienne, Vincent Laville, Zachary R McCaw, Zihuai He, Vincent Guillemot, Carla Lasry, Andrey Ziyatdinov, Cyril Nerin, Amaury Vaysse, Pierre Lechat, Hervé Ménager, Wilfried Le Goff, Marie-Pierre Dube, Peter Kraft, Iuliana Ionita-Laza, Bjarni J Vilhjálmsson and Hugues Aschard

PLOS Genetics, 2021, vol. 17, issue 8, 1-36

Abstract: Genome-wide association studies (GWASs) have uncovered a wealth of associations between common variants and human phenotypes. Here, we present an integrative analysis of GWAS summary statistics from 36 phenotypes to decipher multitrait genetic architecture and its link with biological mechanisms. Our framework incorporates multitrait association mapping along with an investigation of the breakdown of genetic associations into clusters of variants harboring similar multitrait association profiles. Focusing on two subsets of immunity and metabolism phenotypes, we then demonstrate how genetic variants within clusters can be mapped to biological pathways and disease mechanisms. Finally, for the metabolism set, we investigate the link between gene cluster assignment and the success of drug targets in randomized controlled trials.Author summary: Genome-wide association studies (GWAS) established numerous associations between genetic variants and human traits. The anonymized summary of GWAS results is generally made publicly available to the scientific community and can be explored further. Amongst the many possible secondary analyses, one is to study the effect of a genetic variant on several traits (multi-trait GWAS) rather than a unique trait. We compared several tests to conduct multi-trait GWAS on simulated and real data. We detected 322 new associations that were not previously reported by standard GWAS. We then detected clusters of genetic variants having a similar effect across several traits. Focusing on two subsets of immunity and metabolism traits, we demonstrate how genetic variants within clusters can be mapped to biological pathways and disease mechanisms. Finally, for the metabolism set, we investigate the link between gene cluster and success of drug targets in random control trials. We propose this method for improving the functional interpretation of GWAS results.

Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1009713 (text/html)
https://journals.plos.org/plosgenetics/article/fil ... 09713&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pgen00:1009713

DOI: 10.1371/journal.pgen.1009713

Access Statistics for this article

More articles in PLOS Genetics from Public Library of Science
Bibliographic data for series maintained by plosgenetics ().