Fast and robust detection of ancestral selective sweeps

There are few methods tailored for detecting signals of positive selection in populations directly ancestral to multiple descendent populations. We introduce the ancestral branch statistic (ABS), a four‐population summary statistic for identifying selective sweeps occurring in the direct ancestor of a pair of populations. Simulations show that ABS performs at least as well as, and often better under model violations, than the complementary likelihood approach of 3P‐CLR across diverse selection scenarios and parameter values. We first applied ABS to contemporary human genomic data to identify genes that may have been adaptive in ancestral East Asian populations, uncovering the well‐established candidate EDAR, as well as a novel candidate SLC35F3, which encodes a putative thiamine transporter that may have been involved in adaptation to eating polished grains. Next, we performed scans with ancient European genomic data to reexamine evidence of recent positive selection in ancestral Europeans. The MCM6/LCT cluster and the SLC45A2 and HERC2 genes are strong outliers, agreeing with previous studies. Novel candidates, such as SLC30A9 and CYP1A2, may have been involved in adaptation to local nutrient sufficiency and lifestyle changes. Finally, we provide open‐source software, CalcABS, which can perform genomic scans of ancestral sweeps with ABS from population allele frequency data.     

Signatures of soft sweeps across the Dt1 locus underlying determinate growth habit in soya bean [Glycine max (L.) Merr.]

Determinate growth habit is an agronomically important trait associated with domestication in soya bean. Previous studies have demonstrated that the emergence of determinacy is correlated with artificial selection on four nonsynonymous mutations in the Dt1 gene. To better understand the signatures of the soft sweeps across the Dt1 locus and track the origins of the determinate alleles, we examined patterns of nucleotide variation in Dt1 and the surrounding genomic region of approximately 800 kb. Four local, asymmetrical hard sweeps on four determinate alleles, sized approximately 660, 120, 220 and 150 kb, were identified, which constitute the soft sweeps for the adaptation. These variable‐sized sweeps substantially reflected the strength and timing of selection and indicated that the selection on the alleles had been completed rapidly within half a century. Statistics of EHH, iHS, H12 and H2/H1 based on haplotype data had the power to detect the soft sweeps, revealing distinct signatures of extensive long‐range LD and haplotype homozygosity, and multiple frequent adaptive haplotypes. A haplotype network constructed for Dt1 and a phylogenetic tree based on its extended haplotype block implied independent sources of the adaptive alleles through de novo mutations or rare standing variation in quick succession during the selective phase, strongly supporting multiple origins of the determinacy. We propose that the adaptation of soya bean determinacy is guided by a model of soft sweeps and that this model might be indispensable during crop domestication or evolution.     

Soft Shoulders Ahead: Spurious Signatures of Soft and Partial Selective Sweeps Result from Linked Hard Sweeps

Characterizing the nature of the adaptive process at the genetic level is a central goal for population genetics. In particular, we know little about the sources of adaptive substitution or about the number of adaptive variants currently segregating in nature. Historically, population geneticists have focused attention on the hard-sweep model of adaptation in which a de novo beneficial mutation arises and rapidly fixes in a population. Recently more attention has been given to soft-sweep models, in which alleles that were previously neutral, or nearly so, drift until such a time as the environment shifts and their selection coefficient changes to become beneficial. It remains an active and difficult problem, however, to tease apart the telltale signatures of hard vs. soft sweeps in genomic polymorphism data. Through extensive simulations of hard- and soft-sweep models, here we show that indeed the two might not be separable through the use of simple summary statistics. In particular, it seems that recombination in regions linked to, but distant from, sites of hard sweeps can create patterns of polymorphism that closely mirror what is expected to be found near soft sweeps. We find that a very similar situation arises when using haplotype-based statistics that are aimed at detecting partial or ongoing selective sweeps, such that it is difficult to distinguish the shoulder of a hard sweep from the center of a partial sweep. While knowing the location of the selected site mitigates this problem slightly, we show that stochasticity in signatures of natural selection will frequently cause the signal to reach its zenith far from this site and that this effect is more severe for soft sweeps; thus inferences of the target as well as the mode of positive selection may be inaccurate. In addition, both the time since a sweep ends and biologically realistic levels of allelic gene conversion lead to errors in the classification and identification of selective sweeps. This general problem of “soft shoulders” underscores the difficulty in differentiating soft and partial sweeps from hard-sweep scenarios in molecular population genomics data. The soft-shoulder effect also implies that the more common hard sweeps have been in recent evolutionary history, the more prevalent spurious signatures of soft or partial sweeps may appear in some genome-wide scans.     

Discovery of Ongoing Selective Sweeps within Anopheles Mosquito Populations Using Deep Learning

Identification of partial sweeps, which include both hard and soft sweeps that have not currently reached fixation, provides crucial information about ongoing evolutionary responses. To this end, we introduce partialS/HIC, a deep learning method to discover selective sweeps from population genomic data. partialS/HIC uses a convolutional neural network for image processing, which is trained with a large suite of summary statistics derived from coalescent simulations incorporating population-specific history, to distinguish between completed versus partial sweeps, hard versus soft sweeps, and regions directly affected by selection versus those merely linked to nearby selective sweeps. We perform several simulation experiments under various demographic scenarios to demonstrate partialS/HIC’s performance, which exhibits excellent resolution for detecting partial sweeps. We also apply our classifier to whole genomes from eight mosquito populations sampled across sub-Saharan Africa by the Anopheles gambiae 1000 Genomes Consortium, elucidating both continent-wide patterns as well as sweeps unique to specific geographic regions. These populations have experienced intense insecticide exposure over the past two decades, and we observe a strong overrepresentation of sweeps at insecticide resistance loci. Our analysis thus provides a list of candidate adaptive loci that may be relevant to mosquito control efforts. More broadly, our supervised machine learning approach introduces a method to distinguish between completed and partial sweeps, as well as between hard and soft sweeps, under a variety of demographic scenarios. As whole-genome data rapidly accumulate for a greater diversity of organisms, partialS/HIC addresses an increasing demand for useful selection scan tools that can track in-progress evolutionary dynamics.     

Detecting Signatures of Selection Through Haplotype Differentiation Among Hierarchically Structured Populations

The detection of molecular signatures of selection is one of the major concerns of modern population genetics. A widely used strategy in this context is to compare samples from several populations and to look for genomic regions with outstanding genetic differentiation between these populations. Genetic differentiation is generally based on allele frequency differences between populations, which are measured by F_ST or related statistics. Here we introduce a new statistic, denoted hapFLK, which focuses instead on the differences of haplotype frequencies between populations. In contrast to most existing statistics, hapFLK accounts for the hierarchical structure of the sampled populations. Using computer simulations, we show that each of these two features—the use of haplotype information and of the hierarchical structure of populations—significantly improves the detection power of selected loci and that combining them in the hapFLK statistic provides even greater power. We also show that hapFLK is robust with respect to bottlenecks and migration and improves over existing approaches in many situations. Finally, we apply hapFLK to a set of six sheep breeds from Northern Europe and identify seven regions under selection, which include already reported regions but also several new ones. We propose a method to help identifying the population(s) under selection in a detected region, which reveals that in many of these regions selection most likely occurred in more than one population. Furthermore, several of the detected regions correspond to incomplete sweeps, where the favorable haplotype is only at intermediate frequency in the population(s) under selection.     

Recent Selective Sweeps in North American Drosophila melanogaster Show Signatures of Soft Sweeps

Adaptation from standing genetic variation or recurrent de novo mutation in large populations should commonly generate soft rather than hard selective sweeps. In contrast to a hard selective sweep, in which a single adaptive haplotype rises to high population frequency, in a soft selective sweep multiple adaptive haplotypes sweep through the population simultaneously, producing distinct patterns of genetic variation in the vicinity of the adaptive site. Current statistical methods were expressly designed to detect hard sweeps and most lack power to detect soft sweeps. This is particularly unfortunate for the study of adaptation in species such as Drosophila melanogaster, where all three confirmed cases of recent adaptation resulted in soft selective sweeps and where there is evidence that the effective population size relevant for recent and strong adaptation is large enough to generate soft sweeps even when adaptation requires mutation at a specific single site at a locus. Here, we develop a statistical test based on a measure of haplotype homozygosity (H12) that is capable of detecting both hard and soft sweeps with similar power. We use H12 to identify multiple genomic regions that have undergone recent and strong adaptation in a large population sample of fully sequenced Drosophila melanogaster strains from the Drosophila Genetic Reference Panel (DGRP). Visual inspection of the top 50 candidates reveals that in all cases multiple haplotypes are present at high frequencies, consistent with signatures of soft sweeps. We further develop a second haplotype homozygosity statistic (H2/H1) that, in combination with H12, is capable of differentiating hard from soft sweeps. Surprisingly, we find that the H12 and H2/H1 values for all top 50 peaks are much more easily generated by soft rather than hard sweeps. We discuss the implications of these results for the study of adaptation in Drosophila and in species with large census population sizes.     

Limited Evidence for Classic Selective Sweeps in African Populations

While hundreds of loci have been identified as reflecting strong-positive selection in human populations, connections between candidate loci and specific selective pressures often remain obscure. This study investigates broader patterns of selection in African populations, which are underrepresented despite their potential to offer key insights into human adaptation. We scan for hard selective sweeps using several haplotype and allele-frequency statistics with a data set of nearly 500,000 genome-wide single-nucleotide polymorphisms in 12 highly diverged African populations that span a range of environments and subsistence strategies. We find that positive selection does not appear to be a strong determinant of allele-frequency differentiation among these African populations. Haplotype statistics do identify putatively selected regions that are shared across African populations. However, as assessed by extensive simulations, patterns of haplotype sharing between African populations follow neutral expectations and suggest that tails of the empirical distributions contain false-positive signals. After highlighting several genomic regions where positive selection can be inferred with higher confidence, we use a novel method to identify biological functions enriched among populations’ empirical tail genomic windows, such as immune response in agricultural groups. In general, however, it seems that current methods for selection scans are poorly suited to populations that, like the African populations in this study, are affected by ascertainment bias and have low levels of linkage disequilibrium, possibly old selective sweeps, and potentially reduced phasing accuracy. Additionally, population history can confound the interpretation of selection statistics, suggesting that greater care is needed in attributing broad genetic patterns to human adaptation.     

Detecting selection signatures in three Iranian sheep breeds

The objective of genome mapping is to achieve valuable insight into the connection between gene variants (genotype) and observed traits (phenotype). Part of that objective is to understand the selective forces that have operated on a population. Finding links between genotype–phenotype changes makes it possible to identify selective sweeps by patterns of genetic variation and linkage disequilibrium. Based on Illumina 50KSNP chip data, two approaches, XP‐EHH (cross‐population extend haplotype homozygosity) and F_ST (fixation index), were carried out in this research to identify selective sweeps in the genome of three Iranian local sheep breeds: Baluchi (n = 86), Lori‐Bakhtiari (n = 45) and Zel (n = 45). Using both methods, 93 candidate genomic regions were identified as harboring putative selective sweeps. Bioinformatics analysis of the genomic regions showed that signatures of selection related to multiple candidate genes, such as HOXB9, HOXB13, ACAN, NPR2, TRIL, AOX1, CSF2, GHR, TNS2, SPAG8, HINT2, ALS2, AAAS, RARG, SYCP2, CAV1, PPP1R3D, PLA2G7, TTLL7 and C20orf10, that play a role in skeletal system and tail, sugar and energy metabolisms, growth, reproduction, immune and nervous system traits. Our findings indicated diverse genomic selection during the domestication of Iranian sheep breeds.     

Deep Learning for Population Genetic Inference

Given genomic variation data from multiple individuals, computing the likelihood of complex population genetic models is often infeasible. To circumvent this problem, we introduce a novel likelihood-free inference framework by applying deep learning, a powerful modern technique in machine learning. Deep learning makes use of multilayer neural networks to learn a feature-based function from the input (e.g., hundreds of correlated summary statistics of data) to the output (e.g., population genetic parameters of interest). We demonstrate that deep learning can be effectively employed for population genetic inference and learning informative features of data. As a concrete application, we focus on the challenging problem of jointly inferring natural selection and demography (in the form of a population size change history). Our method is able to separate the global nature of demography from the local nature of selection, without sequential steps for these two factors. Studying demography and selection jointly is motivated by Drosophila, where pervasive selection confounds demographic analysis. We apply our method to 197 African Drosophila melanogaster genomes from Zambia to infer both their overall demography, and regions of their genome under selection. We find many regions of the genome that have experienced hard sweeps, and fewer under selection on standing variation (soft sweep) or balancing selection. Interestingly, we find that soft sweeps and balancing selection occur more frequently closer to the centromere of each chromosome. In addition, our demographic inference suggests that previously estimated bottlenecks for African Drosophila melanogaster are too extreme.     

The classical hitchhiking model with continuous mutational pressure and purifying selection

Detecting selective sweeps driven by strong positive selection and localizing the targets of selection in the genome play a major role in modern population genetics and genomics. Most of these analyses are based on the classical model of genetic hitchhiking proposed by Maynard Smith and Haigh (1974, Genetical Research, 23, 23). Here, we consider extensions of the classical two‐locus model. Introducing mutation at the strongly selected site, we analyze the conditions under which soft sweeps may arise. We identify a new parameter (the ratio of the beneficial mutation rate to the selection coefficient) that characterizes the occurrence of multiple‐origin soft sweeps. Furthermore, we quantify the hitchhiking effect when the polymorphism at the linked locus is not neutral but maintained in a mutation‐selection balance. In this case, we find a smaller relative reduction of heterozygosity at the linked site than for a neutral polymorphism. In our analysis, we use a semi‐deterministic approach; i.e., we analyze the frequency process of the beneficial allele in an infinitely large population when its frequency is above a certain threshold; however, for very small frequencies in the initial phase after the onset of selection we rely on diffusion theory.     

The genomic basis of adaptation to high‐altitude habitats in the eastern honey bee (Apis cerana)

The eastern honey bee (Apis cerana) is of central importance for agriculture in Asia. It has adapted to a wide variety of environmental conditions across its native range in southern and eastern Asia, which includes high‐altitude regions. eastern honey bees inhabiting mountains differ morphologically from neighbouring lowland populations and may also exhibit differences in physiology and behaviour. We compared the genomes of 60 eastern honey bees collected from high and low altitudes in Yunnan and Gansu provinces, China, to infer their evolutionary history and to identify candidate genes that may underlie adaptation to high altitude. Using a combination of F_ST‐based statistics, long‐range haplotype tests and population branch statistics, we identified several regions of the genome that appear to have been under positive selection. These candidate regions were strongly enriched for coding sequences and had high haplotype homozygosity and increased divergence specifically in highland bee populations, suggesting they have been subjected to recent selection in high‐altitude habitats. Candidate loci in these genomic regions included genes related to reproduction and feeding behaviour in honey bees. Functional investigation of these candidate loci is necessary to fully understand the mechanisms of adaptation to high‐altitude habitats in the eastern honey bee.     

Genomewide patterns of variation in genetic diversity are shared among populations,species and higher‐order taxa

Genomewide screens of genetic variation within and between populations can reveal signatures of selection implicated in adaptation and speciation. Genomic regions with low genetic diversity and elevated differentiation reflective of locally reduced effective population sizes (N_e) are candidates for barrier loci contributing to population divergence. Yet, such candidate genomic regions need not arise as a result of selection promoting adaptation or advancing reproductive isolation. Linked selection unrelated to lineage‐specific adaptation or population divergence can generate comparable signatures. It is challenging to distinguish between these processes, particularly when diverging populations share ancestral genetic variation. In this study, we took a comparative approach using population assemblages from distant clades assessing genomic parallelism of variation in N_e. Utilizing population‐level polymorphism data from 444 resequenced genomes of three avian clades spanning 50 million years of evolution, we tested whether population genetic summary statistics reflecting genomewide variation in N_e would covary among populations within clades, and importantly, also among clades where lineage sorting has been completed. All statistics including population‐scaled recombination rate (ρ), nucleotide diversity (π) and measures of genetic differentiation between populations (F_ST, PBS, d_xy) were significantly correlated across all phylogenetic distances. Moreover, genomic regions with elevated levels of genetic differentiation were associated with inferred pericentromeric and subtelomeric regions. The phylogenetic stability of diversity landscapes and stable association with genomic features support a role of linked selection not necessarily associated with adaptation and speciation in shaping patterns of genomewide heterogeneity in genetic diversity.     

Genetic changes in a novel breeding population of Brassica napus synthesized from hundreds of crosses between B. rapa and B. carinata

Introgression of genomic variation between and within related crop species is a significant evolutionary approach for population differentiation, genome reorganization and trait improvement. Using the Illumina Infinium Brassica 60K SNP array, we investigated genomic changes in a panel of advanced generation new‐type Brassica napus breeding lines developed from hundreds of interspecific crosses between 122 Brassica rapa and 74 Brassica carinata accessions, and compared them with representative accessions of their three parental species. The new‐type B. napus population presented rich genetic diversity and abundant novel genomic alterations, consisting of introgressions from B. rapa and B. carinata, novel allelic combinations, reconstructed linkage disequilibrium patterns and haplotype blocks, and frequent deletions and duplications (nonrandomly distributed), particularly in the C subgenome. After a much shorter, but very intensive, selection history compared to traditional B. napus, a total of 15 genomic regions with strong selective sweeps and 112 genomic regions with putative signals of selective sweeps were identified. Some of these regions were associated with important agronomic traits that were selected for during the breeding process, while others were potentially associated with restoration of genome stability and fertility after interspecific hybridization. Our results demonstrate how a novel method for population‐based crop genetic improvement can lead to rapid adaptation, restoration of genome stability and positive responses to artificial selection.     

SPEED OF ADAPTATION AND GENOMIC FOOTPRINTS OF HOST–PARASITE COEVOLUTION UNDER ARMS RACE AND TRENCH WARFARE DYNAMICS

Coevolution between hosts and their parasites is expected to follow a range of possible dynamics, the two extreme cases being called trench warfare (or Red Queen) and arms races. Long‐term stable polymorphism at the host and parasite coevolving loci is characteristic of trench warfare, and is expected to promote molecular signatures of balancing selection, while the recurrent allele fixation in arms races should generate selective sweeps. We compare these two scenarios using a finite size haploid gene‐for‐gene model that includes both mutation and genetic drift. We first show that trench warfare do not necessarily display larger numbers of coevolutionary cycles per unit of time than arms races. We subsequently perform coalescent simulations under these dynamics to generate sequences at both host and parasite loci. Genomic footprints of recurrent selective sweeps are often found, whereas trench warfare yield signatures of balancing selection only in parasite sequences, and only in a limited parameter space. Our results suggest that deterministic models of coevolution with infinite population sizes do not predict reliably the observed genomic signatures, and it may be best to study parasite rather than host populations to find genomic signatures of coevolution, such as selective sweeps or balancing selection.     

Whole‐genome resequencing uncovers molecular signatures of natural and sexual selection in wild bighorn sheep

The identification of genes influencing fitness is central to our understanding of the genetic basis of adaptation and how it shapes phenotypic variation in wild populations. Here, we used whole‐genome resequencing of wild Rocky Mountain bighorn sheep (Ovis canadensis) to >50‐fold coverage to identify 2.8 million single nucleotide polymorphisms (SNPs) and genomic regions bearing signatures of directional selection (i.e. selective sweeps). A comparison of SNP diversity between the X chromosome and the autosomes indicated that bighorn males had a dramatically reduced long‐term effective population size compared to females. This probably reflects a long history of intense sexual selection mediated by male–male competition for mates. Selective sweep scans based on heterozygosity and nucleotide diversity revealed evidence for a selective sweep shared across multiple populations at RXFP2, a gene that strongly affects horn size in domestic ungulates. The massive horns carried by bighorn rams appear to have evolved in part via strong positive selection at RXFP2. We identified evidence for selection within individual populations at genes affecting early body growth and cellular response to hypoxia; however, these must be interpreted more cautiously as genetic drift is strong within local populations and may have caused false positives. These results represent a rare example of strong genomic signatures of selection identified at genes with known function in wild populations of a nonmodel species. Our results also showcase the value of reference genome assemblies from agricultural or model species for studies of the genomic basis of adaptation in closely related wild taxa.     

Adaptation in isolated populations: when does it happen and when can we tell?

Isolated populations with novel phenotypes present an exciting opportunity to uncover the genetic basis of ecologically significant adaptation, and genomic scans have often, but not always, led to candidate genes directly related to an adaptive phenotype. However, in many cases these populations were established by a severe bottleneck, which can make identifying targets of selection problematic. Here, we simulate severe bottlenecks and subsequent selection on standing variation, mimicking adaptation after establishment of a new small population, such as an island or an artificial selection experiment. Using simulations of single loci under positive selection and population genetics theory, we examine how population size and age of the population isolate affect the ability of outlier scans for selection to identify adaptive alleles using both single‐site measures and haplotype structure. We find and explain an optimal combination of selection strength, starting frequency, and age of the adaptive allele, which we refer to as a Goldilocks zone, where adaptation is likely to occur and yet the adaptive variants are most likely to derive from a single ancestor (a ‘hard’ selective sweep); in this zone, four commonly used statistics detect selection with high power. Real‐world examples of both island colonization and experimental evolution studies are discussed. Our study provides concrete considerations to be made before embarking on whole‐genome sequencing of differentiated populations.     

Detection of hard and soft selective sweeps from Drosophila melanogaster population genomic data

Whether hard sweeps or soft sweeps dominate adaptation has been a matter of much debate. Recently, we developed haplotype homozygosity statistics that (i) can detect both hard and soft sweeps with similar power and (ii) can classify the detected sweeps as hard or soft. The application of our method to population genomic data from a natural population of Drosophila melanogaster (DGRP) allowed us to rediscover three known cases of adaptation at the loci Ace, Cyp6g1, and CHKov1 known to be driven by soft sweeps, and detected additional candidate loci for recent and strong sweeps. Surprisingly, all of the top 50 candidates showed patterns much more consistent with soft rather than hard sweeps. Recently, Harris et al. 2018 criticized this work, suggesting that all the candidate loci detected by our haplotype statistics, including the positive controls, are unlikely to be sweeps at all and that instead these haplotype patterns can be more easily explained by complex neutral demographic models. They also claim that these neutral non-sweeps are likely to be hard instead of soft sweeps. Here, we reanalyze the DGRP data using a range of complex admixture demographic models and reconfirm our original published results suggesting that the majority of recent and strong sweeps in D. melanogaster are first likely to be true sweeps, and second, that they do appear to be soft. Furthermore, we discuss ways to take this work forward given that most demographic models employed in such analyses are necessarily too simple to capture the full demographic complexity, while more realistic models are unlikely to be inferred correctly because they require a large number of free parameters.     

Soft sweeps: molecular population genetics of adaptation from standing genetic variation

A population can adapt to a rapid environmental change or habitat expansion in two ways. It may adapt either through new beneficial mutations that subsequently sweep through the population or by using alleles from the standing genetic variation. We use diffusion theory to calculate the probabilities for selective adaptations and find a large increase in the fixation probability for weak substitutions, if alleles originate from the standing genetic variation. We then determine the parameter regions where each scenario-standing variation vs. new mutations-is more likely. Adaptations from the standing genetic variation are favored if either the selective advantage is weak or the selection coefficient and the mutation rate are both high. Finally, we analyze the probability of "soft sweeps," where multiple copies of the selected allele contribute to a substitution, and discuss the consequences for the footprint of selection on linked neutral variation. We find that soft sweeps with weaker selective footprints are likely under both scenarios if the mutation rate and/or the selection coefficient is high.     

Footprints of adaptive evolution revealed by whole Z chromosomes haplotypes in flycatchers

Detecting positive selection using genomic data is critical to understanding the role of adaptive evolution. Of particular interest in this context is sex chromosomes since they are thought to play a special role in local adaptation and speciation. We sought to circumvent the challenges associated with statistical phasing when using haplotype‐based statistics in sweep scans by benefitting from that whole chromosome haplotypes of the sex chromosomes can be obtained by resequencing of individuals of the hemizygous sex. We analyzed whole Z chromosome haplotypes from 100 females from several populations of four black and white flycatcher species (in birds, females are ZW and males ZZ). Based on integrated haplotype score (iHS) and number of segregating sites by length (nSL) statistics, we found strong and frequent haplotype structure in several regions of the Z chromosome in each species. Most of these sweep signals were population‐specific, with essentially no evidence for regions under selection shared among species. Some completed sweeps were revealed by the cross‐population extended haplotype homozygosity (XP‐EHH) statistic. Importantly, by using statistically phased Z chromosome data from resequencing of males, we failed to recover the signals of selection detected in analyses based on whole chromosome haplotypes from females; instead, what likely represent false signals of selection were frequently seen. This highlights the power issues in statistical phasing and cautions against conclusions from selection scans using such data. The detection of frequent selective sweeps on the avian Z chromosome supports a large role of sex chromosomes in adaptive evolution.     

Adaptation,ancestral variation and gene flow in a ‘Sky Island’ Drosophila species

Over time, populations of species can expand, contract, fragment and become isolated, creating subpopulations that must adapt to local conditions. Understanding how species maintain variation after divergence as well as adapt to these changes in the face of gene flow is of great interest, especially as the current climate crisis has caused range shifts and frequent migrations for many species. Here, we characterize how a mycophageous fly species, Drosophila innubila, came to inhabit and adapt to its current range which includes mountain forests in south‐western USA separated by large expanses of desert. Using population genomic data from more than 300 wild‐caught individuals, we examine four populations to determine their population history in these mountain forests, looking for signatures of local adaptation. In this first extensive study, establishing D. innubila as a key genomic "Sky Island" model, we find D. innubila spread northwards during the previous glaciation period (30–100 KYA) and have recently expanded even further (0.2–2 KYA). D. innubila shows little evidence of population structure, consistent with a recent establishment and genetic variation maintained since before geographic stratification. We also find some signatures of recent selective sweeps in chorion proteins and population differentiation in antifungal immune genes suggesting differences in the environments to which flies are adapting. However, we find little support for long‐term recurrent selection in these genes. In contrast, we find evidence of long‐term recurrent positive selection in immune pathways such as the Toll signalling system and the Toll‐regulated antimicrobial peptides.