Components of genetic associations across 2,138 phenotypes in the UK Biobank highlight adipocyte biology

Yosuke Tanigawa; Jiehan Li; Johanne M. Justesen; Heiko Horn; Matthew Aguirre; Christopher DeBoever; Chris Chang; Balasubramanian Narasimhan; Kasper Lage; Trevor Hastie; Chong Y. Park; Gill Bejerano; Erik Ingelsson; Manuel A. Rivas

doi:10.1038/s41467-019-11953-9

Nature Communications (Sep 2019)

Components of genetic associations across 2,138 phenotypes in the UK Biobank highlight adipocyte biology

Yosuke Tanigawa,
Jiehan Li,
Johanne M. Justesen,
Heiko Horn,
Matthew Aguirre,
Christopher DeBoever,
Chris Chang,
Balasubramanian Narasimhan,
Kasper Lage,
Trevor Hastie,
Chong Y. Park,
Gill Bejerano,
Erik Ingelsson,
Manuel A. Rivas

Affiliations

Yosuke Tanigawa: Department of Biomedical Data Science, School of Medicine, Stanford University
Jiehan Li: Department of Medicine, Division of Cardiovascular Medicine, Stanford University
Johanne M. Justesen: Department of Biomedical Data Science, School of Medicine, Stanford University
Heiko Horn: Department of Surgery, Massachusetts General Hospital, Harvard Medical School
Matthew Aguirre: Department of Biomedical Data Science, School of Medicine, Stanford University
Christopher DeBoever: Department of Biomedical Data Science, School of Medicine, Stanford University
Chris Chang: Grail, Inc.
Balasubramanian Narasimhan: Department of Biomedical Data Science, School of Medicine, Stanford University
Kasper Lage: Department of Surgery, Massachusetts General Hospital, Harvard Medical School
Trevor Hastie: Department of Biomedical Data Science, School of Medicine, Stanford University
Chong Y. Park: Department of Medicine, Division of Cardiovascular Medicine, Stanford University
Gill Bejerano: Department of Biomedical Data Science, School of Medicine, Stanford University
Erik Ingelsson: Department of Medicine, Division of Cardiovascular Medicine, Stanford University
Manuel A. Rivas: Department of Biomedical Data Science, School of Medicine, Stanford University

DOI: https://doi.org/10.1038/s41467-019-11953-9
Journal volume & issue: Vol. 10, no. 1
pp. 1 – 14

Abstract

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While many pleiotropic genetic loci have been identified, how they contribute to phenotypes across traits and diseases is unclear. Here, the authors propose decomposition of genetic associations (DeGAs), which uses singular value decomposition, to characterize the underlying latent structure of genetic associations of 2,138 phenotypes.

Published in Nature Communications

ISSN: 2041-1723 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science
Website: https://www.nature.com/ncomms/

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