If two traits influence participation (and therefore contribute to selection), selection bias amounts to implicitly conditioning on their common effect (i.e. participation).1,14 This can in principle lead to biased associations between these two traits. There are exceptions to this, depending on the distribution of the outcome and the parametric analysis model used. For example, if the outcome (Y) is a binary phenotype, and logistic regression is used, then the odds ratio for the association between the single nucleotide polymorphism (SNP) and outcome may be unbiased even when the outcome causes selection (as is true of case-control studies).15 We have previously argued that these effects may be greater in case-control studies than prospective studies, and that since genetic associations have been similar across study designs, the impact of selection bias may in fact be modest.12 We have also previously argued that because conventional confounding is typically low for single genetic variants, problems of selection bias will be less in this context.10 However, given the rapid growth in studies using data from highly selected samples such as UK Biobank, and the use
