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Genome-wide association study of medication-use and associated disease in the UK Biobank

Author

Listed:
  • Yeda Wu

    (The University of Queensland)

  • Enda M. Byrne

    (The University of Queensland)

  • Zhili Zheng

    (The University of Queensland
    Wenzhou Medical University)

  • Kathryn E. Kemper

    (The University of Queensland)

  • Loic Yengo

    (The University of Queensland)

  • Andrew J. Mallett

    (The University of Queensland
    Royal Brisbane and Women’s Hospital)

  • Jian Yang

    (The University of Queensland
    Wenzhou Medical University
    The University of Queensland)

  • Peter M. Visscher

    (The University of Queensland
    The University of Queensland)

  • Naomi R. Wray

    (The University of Queensland
    The University of Queensland)

Abstract

Genome-wide association studies (GWASs) of medication use may contribute to understanding of disease etiology, could generate new leads relevant for drug discovery and can be used to quantify future risk of medication taking. Here, we conduct GWASs of self-reported medication use from 23 medication categories in approximately 320,000 individuals from the UK Biobank. A total of 505 independent genetic loci that meet stringent criteria (P

Suggested Citation

  • Yeda Wu & Enda M. Byrne & Zhili Zheng & Kathryn E. Kemper & Loic Yengo & Andrew J. Mallett & Jian Yang & Peter M. Visscher & Naomi R. Wray, 2019. "Genome-wide association study of medication-use and associated disease in the UK Biobank," Nature Communications, Nature, vol. 10(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-09572-5
    DOI: 10.1038/s41467-019-09572-5
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    Cited by:

    1. Mathias Seviiri & Matthew H. Law & Jue-Sheng Ong & Puya Gharahkhani & Pierre Fontanillas & Catherine M. Olsen & David C. Whiteman & Stuart MacGregor, 2022. "A multi-phenotype analysis reveals 19 susceptibility loci for basal cell carcinoma and 15 for squamous cell carcinoma," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    2. Robert Chen & Áine Duffy & Ben O. Petrazzini & Ha My Vy & David Stein & Matthew Mort & Joshua K. Park & Avner Schlessinger & Yuval Itan & David N. Cooper & Daniel M. Jordan & Ghislain Rocheleau & Ron , 2024. "Expanding drug targets for 112 chronic diseases using a machine learning-assisted genetic priority score," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    3. James P. Pirruccello & Paolo Achille & Seung Hoan Choi & Joel T. Rämö & Shaan Khurshid & Mahan Nekoui & Sean J. Jurgens & Victor Nauffal & Shinwan Kany & Kenney Ng & Samuel F. Friedman & Puneet Batra , 2024. "Deep learning of left atrial structure and function provides link to atrial fibrillation risk," Nature Communications, Nature, vol. 15(1), pages 1-17, December.

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