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

Author

Listed:
  • Yosuke Tanigawa

    (Stanford University)

  • Jiehan Li

    (Stanford University
    Stanford University)

  • Johanne M. Justesen

    (Stanford University
    Stanford University
    University of Copenhagen)

  • Heiko Horn

    (Harvard Medical School
    Broad Institute of MIT and Harvard)

  • Matthew Aguirre

    (Stanford University
    Stanford University School of Medicine, Stanford University)

  • Christopher DeBoever

    (Stanford University
    Stanford University)

  • Chris Chang

    (Grail, Inc.)

  • Balasubramanian Narasimhan

    (Stanford University
    Stanford University)

  • Kasper Lage

    (Harvard Medical School
    Broad Institute of MIT and Harvard
    University of Copenhagen)

  • Trevor Hastie

    (Stanford University
    Stanford University)

  • Chong Y. Park

    (Stanford University)

  • Gill Bejerano

    (Stanford University
    Stanford University School of Medicine, Stanford University
    Stanford University
    Stanford University)

  • Erik Ingelsson

    (Stanford University
    Stanford University)

  • Manuel A. Rivas

    (Stanford University)

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.

Suggested Citation

  • 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 , 2019. "Components of genetic associations across 2,138 phenotypes in the UK Biobank highlight adipocyte biology," Nature Communications, Nature, vol. 10(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-11953-9
    DOI: 10.1038/s41467-019-11953-9
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    Cited by:

    1. Yosuke Tanigawa & Junyang Qian & Guhan Venkataraman & Johanne Marie Justesen & Ruilin Li & Robert Tibshirani & Trevor Hastie & Manuel A Rivas, 2022. "Significant sparse polygenic risk scores across 813 traits in UK Biobank," PLOS Genetics, Public Library of Science, vol. 18(3), pages 1-21, March.
    2. Ewa Bielczyk-Maczynska & Meng Zhao & Peter-James H. Zushin & Theresia M. Schnurr & Hyun-Jung Kim & Jiehan Li & Pratima Nallagatla & Panjamaporn Sangwung & Chong Y. Park & Cameron Cornn & Andreas Stahl, 2022. "G protein-coupled receptor 151 regulates glucose metabolism and hepatic gluconeogenesis," Nature Communications, Nature, vol. 13(1), pages 1-15, December.

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