Study design and the sampling of deleterious rare variants in biobank-scale datasets

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Abstract
One key component of study design in population genetics is the “geographic breadth” of a sample (i.e., how broad a region across which individuals are sampled). How the geographic breadth of a sample impacts observations of rare, deleterious variants is unclear, even though such variants are of particular interest for biomedical and evolutionary applications. Here, in order to gain insight into the effects of sample design on ascertained genetic variants, we formulate a stochastic model of dispersal, genetic drift, selection, mutation, and geographically concentrated sampling. We use this model to understand the effects of the geographic breadth of sampling effort on the discovery of negatively selected variants. We find that samples which are more geographically broad will discover a greater number of variants as compared to geographically narrow samples (an effect we label “discovery”); though the variants will be detected at lower average frequency than in narrow samples (e.g., as singletons, an effect we label “dilution”). Importantly, these effects are amplified for larger sample sizes and fitness effects. We validate these results using both population genetic simulations and empirical analyses in the UK Biobank. Our results are particularly important in two contexts: the association of large-effect rare variants with particular phenotypes and the inference of negative selection from allele frequency data. Overall, our findings emphasize the importance of considering geographic breadth when designing and carrying out genetic studies, especially at biobank scale.
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Funding Information
  • National Science Foundation (DGE1746045)
  • HHS | NIH | National Institute of General Medical Sciences (R01 GM132383)
  • HHS | NIH | National Institute of General Medical Sciences (R35 GM149521)

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