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 () and Manuel A. Rivas ()
Additional contact information
Yosuke Tanigawa: Stanford University
Jiehan Li: Stanford University
Johanne M. Justesen: Stanford University
Heiko Horn: Harvard Medical School
Matthew Aguirre: Stanford University
Christopher DeBoever: Stanford University
Chris Chang: Grail, Inc.
Balasubramanian Narasimhan: Stanford University
Kasper Lage: Harvard Medical School
Trevor Hastie: Stanford University
Chong Y. Park: Stanford University
Gill Bejerano: Stanford University
Erik Ingelsson: Stanford University
Manuel A. Rivas: Stanford University

Nature Communications, 2019, vol. 10, issue 1, 1-14

Abstract: Abstract Population-based biobanks with genomic and dense phenotype data provide opportunities for generating effective therapeutic hypotheses and understanding the genomic role in disease predisposition. To characterize latent components of genetic associations, we apply truncated singular value decomposition (DeGAs) to matrices of summary statistics derived from genome-wide association analyses across 2,138 phenotypes measured in 337,199 White British individuals in the UK Biobank study. We systematically identify key components of genetic associations and the contributions of variants, genes, and phenotypes to each component. As an illustration of the utility of the approach to inform downstream experiments, we report putative loss of function variants, rs114285050 (GPR151) and rs150090666 (PDE3B), that substantially contribute to obesity-related traits and experimentally demonstrate the role of these genes in adipocyte biology. Our approach to dissect components of genetic associations across the human phenome will accelerate biomedical hypothesis generation by providing insights on previously unexplored latent structures.

Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
https://www.nature.com/articles/s41467-019-11953-9 Abstract (text/html)

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:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-11953-9

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-019-11953-9

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().