The Linked Selection Signature of Rapid Adaptation in Temporal Genomic Data

Abstract

Populations can adapt over short, ecological timescales via standing genetic variation. Genomic data collected over tens of generations in both natural and lab populations is increasingly used to find selected loci underpinning such rapid adaptation. Although selection on large effect loci may be detectable in such data, often the fitness differences between individuals have a polygenic architecture, such that selection at any one locus leads to allele frequency changes that are too subtle to distinguish from genetic drift. However, one promising signal comes from the fact that selection on polygenic traits leads to heritable fitness backgrounds that neutral alleles can become stochastically associated with. These associations perturb neutral allele frequency trajectories, creating autocovariance across generations that can be directly measured from temporal genomic data. We develop theory that predicts the magnitude of these temporal autocovariances, showing that it is determined by the level of additive genetic variation, recombination, and linkage disequilibria in a region. Furthermore, by using analytic expressions for the temporal variances and autocovariances in allele frequency, we demonstrate one can estimate the additive genetic variation for fitness and the drift-effective population size from temporal genomic data. Finally, we also show how the proportion of total variation in allele frequency change due to linked selection can be estimated from temporal data. Temporal genomic data offers strong opportunities to identify the role linked selection has on genome-wide diversity over short timescales, and can help bridge population genetic and quantitative genetic studies of adaptation.