Participation bias in the UK Biobank distorts genetic associations and downstream analyses

Tabea Schoeler (), Doug Speed, Eleonora Porcu, Nicola Pirastu, Jean-Baptiste Pingault and Zoltán Kutalik ()
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Tabea Schoeler: University of Lausanne
Doug Speed: Aarhus University
Eleonora Porcu: Lausanne University Hospital and University of Lausanne
Nicola Pirastu: Human Technopole
Jean-Baptiste Pingault: University College London
Zoltán Kutalik: University of Lausanne

Nature Human Behaviour, 2023, vol. 7, issue 7, 1216-1227

Abstract: Abstract While volunteer-based studies such as the UK Biobank have become the cornerstone of genetic epidemiology, the participating individuals are rarely representative of their target population. To evaluate the impact of selective participation, here we derived UK Biobank participation probabilities on the basis of 14 variables harmonized across the UK Biobank and a representative sample. We then conducted weighted genome-wide association analyses on 19 traits. Comparing the output from weighted genome-wide association analyses (neffective = 94,643 to 102,215) with that from standard genome-wide association analyses (n = 263,464 to 283,749), we found that increasing representativeness led to changes in SNP effect sizes and identified novel SNP associations for 12 traits. While heritability estimates were less impacted by weighting (maximum change in h2, 5%), we found substantial discrepancies for genetic correlations (maximum change in rg, 0.31) and Mendelian randomization estimates (maximum change in βSTD, 0.15) for socio-behavioural traits. We urge the field to increase representativeness in biobank samples, especially when studying genetic correlates of behaviour, lifestyles and social outcomes.

Date: 2023
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DOI: 10.1038/s41562-023-01579-9

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