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Dissecting the molecular basis of adaptation remains elusive despite our ability to sequence genomes and transcriptomes. At present, most genomic research on selection focusses on signatures of selective sweeps in patterns of heterozygosity. Other research has studied changes in patterns of gene expression in evolving populations but has not usually identified the genetic changes causing these shifts in expression. Here we attempt to go beyond these approaches by using machine learning tools to explore interactions between the genome, transcriptome, and life-history phenotypes in two groups of 10 experimentally evolved Drosophila populations subjected to selection for opposing life history patterns. Our findings indicate that genomic and transcriptomic data have comparable power for predicting phenotypic characters. Looking at the relationships between the genome and the transcriptome, we find that the expression of individual transcripts is influenced by many sites across the genome that are differentiated between the two types of populations. We find that single-nucleotide polymorphisms (SNPs), transposable elements, and indels are powerful predictors of gene expression. Collectively, our results suggest that the genomic architecture of adaptation is highly polygenic with extensive pleiotropy.

Background: Studies combining experimental evolution and next-generation sequencing have found that adaptation in sexually reproducing populations is primarily fueled by standing genetic variation. Consequently, the response to selection is rapid and highly repeatable across replicate populations. Some studies suggest that the response to selection is highly repeatable at both the phenotypic and genomic levels, and that evolutionary history has little impact. Other studies suggest that even when the response to selection is repeatable phenotypically, evolutionary history can have significant impacts at the genomic level. Here we test two hypotheses that may explain this discrepancy. Hypothesis 1: Past intense selection reduces evolutionary repeatability at the genomic and phenotypic levels when conditions change. Hypothesis 2: Previous intense selection does not reduce evolutionary repeatability, but other evolutionary mechanisms may. We test these hypotheses using D. melanogaster populations that were subjected to 260 generations of intense selection for desiccation resistance and have since been under relaxed selection for the past 230 generations., Results: We find that, with the exception of longevity and to a lesser extent fecundity, 230 generations of relaxed selection has erased the extreme phenotypic differentiation previously found. We also find no signs of genetic fixation, and only limited evidence of genetic differentiation between previously desiccation resistance selected populations and their controls., Conclusion: Our findings suggest that evolution in our system is highly repeatable even when populations have been previously subjected to bouts of extreme selection. We therefore conclude that evolutionary repeatability can overcome past bouts of extreme selection in Drosophila experimental evolution, provided experiments are sufficiently long and populations are not inbred.

Experimental evolutionary genomics now allows biologists to test fundamental theories concerning the genetic basis of adaptation. We have conducted one of the longest laboratory evolution experiments with any sexually-reproducing metazoan, Drosophila melanogaster. We used next-generation resequencing data from this experiment to examine genome-wide patterns of genetic variation over an evolutionary time-scale that approaches 1,000 generations. We also compared measures of variation within and differentiation between our populations to simulations based on a variety of evolutionary scenarios. Our analysis yielded no clear evidence of hard selective sweeps, whereby natural selection acts to increase the frequency of a newly-arising mutation in a population until it becomes fixed. We do find evidence for selection acting on standing genetic variation, as independent replicate populations exhibit similar population-genetic dynamics, without obvious fixation of candidate alleles under selection. A hidden-Markov model test for selection also found widespread evidence for selection. We found more genetic variation genome-wide, and less differentiation between replicate populations genome-wide, than arose in any of our simulated evolutionary scenarios.

Drosophila experimental evolution, with its well-defined selection protocols, has long supplied useful genetic material for the analysis of functional physiology. While there is a long tradition of interpreting the effects of large-effect mutants physiologically, identifying and interpreting gene-to-phenotype relationships has been challenging in the genomic era, with many labs not resolving how physiological traits are affected by multiple genes throughout the genome. Drosophila experimental evolution has demonstrated that multiple phenotypes change because of the evolution of many loci across the genome, creating the scientific challenge of sifting out differentiated but noncausal loci for individual characters. The fused lasso additive model method allows us to infer some of the differentiated loci that have relatively greater causal effects on the differentiation of specific phenotypes. The experimental material that we use in the present study comes from 50 populations that have been selected for different life histories and levels of stress resistance. Differentiation of cardiac robustness, starvation resistance, desiccation resistance, lipid content, glycogen content, water content, and body masses was assayed among 40–50 of these experimentally evolved populations. Through the fused lasso additive model, we combined physiological analyses from eight parameters with whole-body pooled-seq genomic data to identify potentially causally linked genomic regions. We have identified approximately 2,176 significantly differentiated 50-kb genomic windows among our 50 populations, with 142 of those identified genomic regions that are highly likely to have a causal effect connecting specific genome sites to specific physiological characters. [ABSTRACT FROM AUTHOR]