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

Angli Xue, Longda Jiang, Zhihong Zhu, Naomi R. Wray, Peter M. Visscher, Jian Zeng and Jian Yang ()
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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
Peter M. Visscher: The University of Queensland
Jian Zeng: The University of Queensland
Jian Yang: The University of Queensland

Nature Communications, 2021, vol. 12, issue 1, 1-11

Abstract: 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.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-020-20237-6

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DOI: 10.1038/s41467-020-20237-6

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