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Multitrait GWAS to connect disease variants and biological mechanisms

Hanna Julienne 1, * Vincent Laville 1 Zachary Mccaw 2 Zihuai He 3, 4 Vincent Guillemot 1 Carla Lasry 1 Andrey Ziyatdinov 2 Cyril Nerin 1 Amaury Vaysse 1 Pierre Lechat 1 Hervé Ménager 1 Wilfried Le Goff 5 Marie-Pierre Dube 6 Peter Kraft 2 Iuliana Ionita-Laza 7 Bjarni Vilhjálmsson 8 Hugues Aschard 1, * 
* Corresponding author
1 Département de Biologie Computationnelle - Department of Computational Biology
2 Harvard T.H. Chan School of Public Health
3 Stanford University School of Medicine [CA, USA]
4 Stanford University
5 Unité de Recherche sur les Maladies Cardiovasculaires, du Métabolisme et de la Nutrition = Research Unit on Cardiovascular and Metabolic Diseases [IHU ICAN]
6 Montreal Heart Institute - Institut de Cardiologie de Montréal
7 Columbia University [New York]
8 Aarhus University [Aarhus]
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.
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Submitted on : Friday, January 14, 2022 - 12:31:52 PM
Last modification on : Thursday, October 27, 2022 - 2:36:32 PM
Long-term archiving on: : Friday, April 15, 2022 - 6:43:30 PM

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Hanna Julienne, Vincent Laville, Zachary Mccaw, Zihuai He, Vincent Guillemot, et al.. Multitrait GWAS to connect disease variants and biological mechanisms. PLoS Genetics, 2021, 17 (8), pp.e1009713. ⟨10.1371/journal.pgen.1009713⟩. ⟨pasteur-03526210⟩

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