Genetic differentiation across populations that is maintained in the presence of gene flow is a hallmark of spatially varying selection. In Drosophila melanogaster, the latitudinal clines across the eastern coasts of Australia and North America appear to be examples of this type of selection, with recent studies showing that a substantial portion of the D. melanogaster genome exhibits allele frequency differentiation with respect to latitude on both continents. As of yet there has been no genome-wide examination of differentiated copy-number variants (CNVs) in these geographic regions, despite their potential importance for phenotypic variation in Drosophila and other taxa. Here, we present an analysis of geographic variation in CNVs in D. melanogaster. We also present the first genomic analysis of geographic variation for copy-number variation in the sister species, D. simulans, in order to investigate patterns of parallel evolution in these close relatives. In D. melanogaster we find hundreds of CNVs, many of which show parallel patterns of geographic variation on both continents, lending support to the idea that they are influenced by spatially varying selection. These findings support the idea that polymorphic CNVs contribute to local adaptation in D. melanogaster In contrast, we find very few CNVs in D. simulans that are geographically differentiated in parallel on both continents, consistent with earlier work suggesting that clinal patterns are weaker in this species.

Evidence for adaptation to different climates in the model species Arabidopsis thaliana is seen in reciprocal transplant experiments, but the genetic basis of this adaptation remains poorly understood. Field-based quantitative trait locus (QTL) studies provide direct but low-resolution evidence for the genetic basis of local adaptation. Using high-resolution population genomic approaches, we examine local adaptation along previously identified genetic trade-off (GT) and conditionally neutral (CN) QTLs for fitness between locally adapted Italian and Swedish A. thaliana populations [Ågren J, et al. (2013) Proc Natl Acad Sci USA 110:21077-21082]. We find that genomic regions enriched in high F_ST SNPs colocalize with GT QTL peaks. Many of these high F_ST regions also colocalize with regions enriched for SNPs significantly correlated to climate in Eurasia and evidence of recent selective sweeps in Sweden. Examining unfolded site frequency spectra across genes containing high F_ST SNPs suggests GTs may be due to more recent adaptation in Sweden than Italy. Finally, we collapse a list of thousands of genes spanning GT QTLs to 42 genes that likely underlie the observed GTs and explore potential biological processes driving these trade-offs, from protein phosphorylation, to seed dormancy and longevity. Our analyses link population genomic analyses and field-based QTL studies of local adaptation, and emphasize that GTs play an important role in the process of local adaptation.

Simulation is a key tool in population genetics for both methods development and empirical research, but producing simulations that recapitulate the main features of genomic datasets remains a major obstacle. Today, more realistic simulations are possible thanks to large increases in the quantity and quality of available genetic data, and the sophistication of inference and simulation software. However, implementing these simulations still requires substantial time and specialized knowledge. These challenges are especially pronounced for simulating genomes for species that are not well-studied, since it is not always clear what information is required to produce simulations with a level of realism sufficient to confidently answer a given question. The community-developed framework stdpopsim seeks to lower this barrier by facilitating the simulation of complex population genetic models using up-to-date information. The initial version of stdpopsim focused on establishing this framework using six well-characterized model species (Adrion et al., 2020). Here, we report on major improvements made in the new release of stdpopsim (version 0.2), which includes a significant expansion of the species catalog and substantial additions to simulation capabilities. Features added to improve the realism of the simulated genomes include non-crossover recombination and provision of species-specific genomic annotations. Through community-driven efforts, we expanded the number of species in the catalog more than threefold and broadened coverage across the tree of life. During the process of expanding the catalog, we have identified common sticking points and developed the best practices for setting up genome-scale simulations. We describe the input data required for generating a realistic simulation, suggest good practices for obtaining the relevant information from the literature, and discuss common pitfalls and major considerations. These improvements to stdpopsim aim to further promote the use of realistic whole-genome population genetic simulations, especially in non-model organisms, making them available, transparent, and accessible to everyone.