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Variance-quantitative trait loci enable systematic discovery of gene-environment interactions for cardiometabolic serum biomarkers

Author

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  • Kenneth E. Westerman

    (Massachusetts General Hospital
    Broad Institute of Harvard and MIT
    Harvard Medical School)

  • Timothy D. Majarian

    (Broad Institute of Harvard and MIT)

  • Franco Giulianini

    (Brigham and Women’s Hospital)

  • Dong-Keun Jang

    (Broad Institute of Harvard and MIT)

  • Jenkai Miao

    (Boston Children’s Hospital)

  • Jose C. Florez

    (Broad Institute of Harvard and MIT
    Harvard Medical School
    Massachusetts General Hospital)

  • Han Chen

    (The University of Texas Health Science Center at Houston
    The University of Texas Health Science Center at Houston)

  • Daniel I. Chasman

    (Brigham and Women’s Hospital
    Brigham and Women’s Hospital
    Medical and Population Genetics Program, Broad Institute
    Harvard T.H. Chan School of Public Health)

  • Miriam S. Udler

    (Broad Institute of Harvard and MIT
    Harvard Medical School
    Massachusetts General Hospital)

  • Alisa K. Manning

    (Massachusetts General Hospital
    Broad Institute of Harvard and MIT
    Harvard Medical School)

  • Joanne B. Cole

    (Broad Institute of Harvard and MIT
    Boston Children’s Hospital
    Massachusetts General Hospital)

Abstract

Gene-environment interactions represent the modification of genetic effects by environmental exposures and are critical for understanding disease and informing personalized medicine. These often induce differential phenotypic variance across genotypes; these variance-quantitative trait loci can be prioritized in a two-stage interaction detection strategy to greatly reduce the computational and statistical burden and enable testing of a broader range of exposures. We perform genome-wide variance-quantitative trait locus analysis for 20 serum cardiometabolic biomarkers by multi-ancestry meta-analysis of 350,016 unrelated participants in the UK Biobank, identifying 182 independent locus-biomarker pairs (p

Suggested Citation

  • Kenneth E. Westerman & Timothy D. Majarian & Franco Giulianini & Dong-Keun Jang & Jenkai Miao & Jose C. Florez & Han Chen & Daniel I. Chasman & Miriam S. Udler & Alisa K. Manning & Joanne B. Cole, 2022. "Variance-quantitative trait loci enable systematic discovery of gene-environment interactions for cardiometabolic serum biomarkers," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-31625-5
    DOI: 10.1038/s41467-022-31625-5
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    References listed on IDEAS

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    Cited by:

    1. Robert F. Hillary & Danni A. Gadd & Zhana Kuncheva & Tasos Mangelis & Tinchi Lin & Kyle Ferber & Helen McLaughlin & Heiko Runz & Riccardo E. Marioni & Christopher N. Foley & Benjamin B. Sun, 2024. "Systematic discovery of gene-environment interactions underlying the human plasma proteome in UK Biobank," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    2. Chen Wang & Tianying Wang & Krzysztof Kiryluk & Ying Wei & Hugues Aschard & Iuliana Ionita-Laza, 2024. "Genome-wide discovery for biomarkers using quantile regression at biobank scale," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    3. Peter Zhukovsky & Earvin S. Tio & Gillian Coughlan & David A. Bennett & Yanling Wang & Timothy J. Hohman & Diego A. Pizzagalli & Benoit H. Mulsant & Aristotle N. Voineskos & Daniel Felsky, 2024. "Genetic influences on brain and cognitive health and their interactions with cardiovascular conditions and depression," Nature Communications, Nature, vol. 15(1), pages 1-11, December.

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