Genetic analyses identify widespread sex-differential participation bias

Nicola Pirastu, Mattia Cordioli, Priyanka Nandakumar, Gianmarco Mignogna, Abdel Abdellaoui, Benjamin Hollis, Masahiro Kanai, Veera M. Rajagopal, Pietro Della Briotta Parolo, Nikolas Baya, Caitlin E. Carey, Juha Karjalainen, Thomas D. Als, Matthijs D. Van der Zee, Felix R. Day, Ken K. Ong, Takayuki Morisaki, Eco de Geus, Rino Bellocco, Yukinori OkadaAnders D. Børglum, Peter Joshi, Adam Auton, David Hinds, Benjamin M. Neale, Raymond K. Walters, Michel G. Nivard, John R. B. Perry, Andrea Ganna

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Genetic association results are often interpreted with the assumption that study participation does not affect downstream analyses. Understanding the genetic basis of participation bias is challenging as it requires genotypes of unseen individuals. Here we demonstrate that it is possible to estimate comparative biases by performing a genome-wide association study (GWAS) contrasting one subgroup versus another. For example, we show that sex exhibits artefactual autosomal heritability in the presence of sex-differential participation bias. By performing a GWAS of sex in ∼3.3 million males and females, we identify over 158 autosomal loci spuriously associated with sex and highlight complex traits underpinning differences in study participation between sexes. For example, the body mass index-increasing allele at FTO was observed at higher frequency in males compared to females (OR 1.02, P = 4.4 × 10-36). Finally, we demonstrate how these biases can potentially lead to incorrect inferences in downstream analyses and propose a conceptual framework for addressing such biases. Our findings highlight a new challenge that genetic studies may face as sample sizes continue to grow.
Original languageEnglish
Article number53
Pages (from-to)663
Number of pages671
JournalNature Genetics
Volume53
DOIs
Publication statusPublished - 22 May 2021

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