Data from: Uncovering the genetic signature of quantitative trait evolution with replicated time series data

The genetic architecture of adaptation in natural populations has not yet been resolved: it is not clear to what extent the spread of beneficial mutations (selective sweeps) or the response of many quantitative trait loci drive adaptation to environmental changes. While much attention has been given to the genomic footprint of selective sweeps, the importance of selection on quantitative traits is still not well studied since the associated genomic signature is extremely difficult to detect. We propose Evolve and Resequence (E&R) as a promising tool, to study polygenic adaptation of quantitative traits in evolving populations. Simulating replicated time series data we show that adaptation to a new intermediate trait optimum has three characteristic phases that are reflected on the genomic level: 1) directional frequency changes towards the new trait optimum, 2) plateauing of allele frequencies when the new trait optimum has been reached, and 3) subsequent divergence between replicated trajectories ultimately leading to the loss or fixation of alleles while the trait value does not change. We explore these 3 phase characteristics for relevant population genetic parameters to provide expectations for various experimental evolution designs. Remarkably, over a broad range of parameters the trajectories of selected alleles display a pattern across replicates, which differs both from neutrality and directional selection. We conclude that replicated time series data from experimental evolution studies provide a promising framework to study polygenic adaptation from whole genome population genetics data.