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Genome-wide analyses of behavioural traits are subject to bias by misreports and longitudinal changes

Author

Listed:
  • Angli Xue

    (The University of Queensland)

  • Longda Jiang

    (The University of Queensland)

  • Zhihong Zhu

    (The University of Queensland)

  • Naomi R. Wray

    (The University of Queensland
    The University of Queensland)

  • Peter M. Visscher

    (The University of Queensland)

  • Jian Zeng

    (The University of Queensland)

  • Jian Yang

    (The University of Queensland
    Westlake University
    Westlake Laboratory of Life Sciences and Biomedicine)

Abstract

Genome-wide association studies (GWAS) have discovered numerous genetic variants associated with human behavioural traits. However, behavioural traits are subject to misreports and longitudinal changes (MLC) which can cause biases in GWAS and follow-up analyses. Here, we demonstrate that individuals with higher disease burden in the UK Biobank (n = 455,607) are more likely to misreport or reduce their alcohol consumption levels, and propose a correction procedure to mitigate the MLC-induced biases. The alcohol consumption GWAS signals removed by the MLC corrections are enriched in metabolic/cardiovascular traits. Almost all the previously reported negative estimates of genetic correlations between alcohol consumption and common diseases become positive/non-significant after the MLC corrections. We also observe MLC biases for smoking and physical activities in the UK Biobank. Our findings provide a plausible explanation of the controversy about the effects of alcohol consumption on health outcomes and a caution for future analyses of self-reported behavioural traits in biobank data.

Suggested Citation

  • Angli Xue & Longda Jiang & Zhihong Zhu & Naomi R. Wray & Peter M. Visscher & Jian Zeng & Jian Yang, 2021. "Genome-wide analyses of behavioural traits are subject to bias by misreports and longitudinal changes," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-020-20237-6
    DOI: 10.1038/s41467-020-20237-6
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    Cited by:

    1. Tabea Schoeler & Doug Speed & Eleonora Porcu & Nicola Pirastu & Jean-Baptiste Pingault & Zoltán Kutalik, 2023. "Participation bias in the UK Biobank distorts genetic associations and downstream analyses," Nature Human Behaviour, Nature, vol. 7(7), pages 1216-1227, July.
    2. Gianmarco Mignogna & Caitlin E. Carey & Robbee Wedow & Nikolas Baya & Mattia Cordioli & Nicola Pirastu & Rino Bellocco & Kathryn Fiuza Malerbi & Michel G. Nivard & Benjamin M. Neale & Raymond K. Walte, 2023. "Patterns of item nonresponse behaviour to survey questionnaires are systematic and associated with genetic loci," Nature Human Behaviour, Nature, vol. 7(8), pages 1371-1387, August.
    3. Bryan R. Gorman & Sun-Gou Ji & Michael Francis & Anoop K. Sendamarai & Yunling Shi & Poornima Devineni & Uma Saxena & Elizabeth Partan & Andrea K. DeVito & Jinyoung Byun & Younghun Han & Xiangjun Xiao, 2024. "Multi-ancestry GWAS meta-analyses of lung cancer reveal susceptibility loci and elucidate smoking-independent genetic risk," Nature Communications, Nature, vol. 15(1), pages 1-13, December.

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