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Evolve and Resquence (E&R) studies are a useful tool to study genomic processes during rapid adaptation, e.g., in the framework of adaptive responses to global climate change. We applied different thermal regimes to a natural Chironomus riparius (Diptera) population in an E&R framework to infer its evolutionary potential for rapid thermal adaptation. We exposed two replicates to three temperatures each (14°C, 20°C and 26°C) for more than two years, the experiment thus lasting 22, 44 or 65 generations, respectively. The two higher temperatures presented a priori moderate, respectively strong selection pressures. Life-cycle fitness tests revealed no appreciable adaptation to thermal regimes but a common adaptation of all six replicates probably due to the rearing conditions, presumably increased larval density and water quality. Genomic analyses showed a strong, genome-wide selective response in all replicates (mean s of selected SNPs = 0.305). This genomic response was significantly similar at all genomic levels among all replicates (SNPs, 10 kb windows, genes, exons, regions of elevated allele frequency change [REA]). The intersections among the replicates exposed to the same temperature were either insignificant or underrepresented. This confirmed a selective response to identical selection pressure(s), however, not to thermal regime. Genes closest to the SNP with the highest selection coefficient per REA were enriched for GO terms related to ion transport, regulation of transcription and signal transduction, which supported the presumed acting selection pressures. Our study showed the evolutionary potential for rapid adaptation by genome-wide and probably polygenic selection on standing genetic variation in C. riparius. However, because of the impossibility to accurately predict the acting selective regime in evolutionary experiments, we discuss the sobering perspectives for inferring the evolutionary potential of natural populations with this approach.

The identification of signatures of natural selection in genomic surveys has become an area of intense research, stimulated by the increasing ease with which genetic markers can be typed. Loci identified as subject to selection may be functionally important, and hence (weak) candidates for involvement in disease causation. They can also be useful in determining the adaptive differentiation of populations, and exploring hypotheses about speciation. Adaptive differentiation has traditionally been identified from differences in allele frequencies among different populations, summarised by an estimate of FST. Low outliers relative to an appropriate neutral population-genetics model indicate loci subject to balancing selection, whereas high outliers suggest adaptive (directional) selection. However, the problem of identifying statistically significant departures from neutrality is complicated by confounding effects on the distribution of FST estimates, and current methods have not yet been tested in large-scale simulation experiments. Here, we simulate data from a structured population at many unlinked, diallelic loci that are predominantly neutral but with some loci subject to adaptive or balancing selection. We develop a hierarchical-Bayesian method, implemented via Markov chain Monte Carlo (MCMC), and assess its performance in distinguishing the loci simulated under selection from the neutral loci. We also compare this performance with that of a frequentist method, based on moment-based estimates of FST. We find that both methods can identify loci subject to adaptive selection when the selection coefficient is at least five times the migration rate. Neither method could reliably distinguish loci under balancing selection in our simulations, even when the selection coefficient is twenty times the migration rate.