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When natural populations split and migrate to different environments, they may experience different selection pressures that can lead to local adaptation. To capture the genomic patterns of a local selective sweep, we develop XP‐nSL, a genomic scan for local adaptation that compares haplotype patterns between two populations. We show that XP‐nSL has power to detect ongoing and recently completed hard and soft sweeps, and we then apply this statistic to search for evidence of adaptation to high altitude in rhesus macaques. We analyze the whole genomes of 23 wild rhesus macaques captured at high altitude (mean altitude > 4000 m above sea level) to 22 wild rhesus macaques captured at low altitude (mean altitude < 500 m above sea level) and find evidence of local adaptation in the high‐altitude population at or near 303 known genes and several unannotated regions. We find the strongest signal for adaptation at EGLN1, a classic target for convergent evolution in several species living in low oxygen environments. Furthermore, many of the 303 genes are involved in processes related to hypoxia, regulation of ROS, DNA damage repair, synaptic signaling, and metabolism. These results suggest that, beyond adapting via a beneficial mutation in one single gene, adaptation to high altitude in rhesus macaques is polygenic and spread across numerous important biological systems.

Loci responsible for local adaptation are likely to have more genetic differentiation among populations than neutral loci. However, neutral loci can vary widely in their amount of genetic differentiation, even over the same geographic range. Unfortunately, the distribution of differentiation—as measured by an index such as FST—depends on the details of the demographic history of the populations in question, even without spatially heterogeneous selection. Many methods designed to detect FST outliers assume a specific model of demographic history, which can result in extremely high false positive rates for detecting loci under selection. We develop a new method that infers the distribution of FST for loci unlikely to be strongly affected by spatially diversifying selection, using data on a large set of loci with unknown selective properties. Compared to previous methods, this approach, called OutFLANK, has much lower false positive rates and comparable power, as shown by simulation.

Detecting recent selected 'genomic footprints' applies directly to the discovery of disease genes and in the imputation of the formative events that molded modern population genetic structure. The imprints of historic selection/adaptation episodes left in human and animal genomes allow one to interpret modern and ancestral gene origins and modifications. Current approaches to reveal selected regions applied in genome-wide selection scans (GWSSs) fall into eight principal categories: (I) phylogenetic footprinting, (II) detecting increased rates of functional mutations, (III) evaluating divergence versus polymorphism, (IV) detecting extended segments of linkage disequilibrium, (V) evaluating local reduction in genetic variation, (VI) detecting changes in the shape of the frequency distribution (spectrum) of genetic variation, (VII) assessing differentiating between populations (FST), and (VIII) detecting excess or decrease in admixture contribution from one population. Here, we review and compare these approaches using available human genome-wide datasets to provide independent verification (or not) of regions found by different methods and using different populations. The lessons learned from GWSSs will be applied to identify genome signatures of historic selective pressures on genes and gene regions in other species with emerging genome sequences. This would offer considerable potential for genome annotation in functional, developmental and evolutionary contexts.

Comparing genome scans among species is a powerful approach for investigating the patterns left by evolutionary processes. In particular, this offers a way to detect candidate genes that drive convergent evolution. We compared genome scan results to investigate if patterns of genetic diversity and divergence are shared among divergent species within the stickleback order (Gasterosteiformes): the threespine stickleback (Gasterosteus aculeatus), ninespine stickleback (Pungitius pungitus), and tubesnout (Aulorhynchus flavidus). Populations were sampled from the southern and northern edges of each species’ range, to identify patterns associated with latitudinal changes in genetic diversity. Weak correlations in genetic diversity (FST and expected heterozygosity) and three different patterns in the genomic landscape were found among these species. Additionally, no candidate genes for convergent evolution were detected. This is a counterexample to the growing number of studies that have shown overlapping genetic patterns, demonstrating that genome scan comparisons can be noisy due to the effects of several interacting evolutionary forces. Genome scans are popular tools for detecting shared evolutionary events among species. However, not all scans will show overlapping results. This article detects three different outcomes of genome scans, and few shared genetic patterns.

We examined genome-wide patterns of DNA sequence diversity and divergence among six species of the important tree genus Eucalyptus and investigated their relationship with genomic architecture. Using c. 90 range-wide individuals of each Eucalyptus species (E. grandis, E. urophylla, E. globulus, E. nitens, E. dunnii and E. camaldulensis), genetic diversity and divergence were estimated from 2840 polymorphic diversity arrays technology markers covering the 11 chromosomes. Species differentiating markers (SDMs) identified in each of 15 pairwise species comparisons, along with species diversity (HHW) and divergence (FST), were projected onto the E. grandis reference genome. Across all species comparisons, SDMs totalled 1.1–5.3% of markers and were widely distributed throughout the genome. Marker divergence (FST and SDMs) and diversity differed among and within chromosomes. Patterns of diversity and divergence were broadly conserved across species and significantly associated with genomic features, including the proximity of markers to genes, the relative number of clusters of tandem duplications, and gene density within or among chromosomes. These results suggest that genomic architecture influences patterns of species diversity and divergence in the genus. This influence is evident across the six species, encompassing diverse phylogenetic lineages, geography and ecology.

Uncovering the genetic and evolutionary basis of local adaptation is a major focus of evolutionary biology. The recent development of cost-effective methods for obtaining high-quality genome-scale data makes it possible to identify some of the loci responsible for adaptive differences among populations. Two basic approaches for identifying putatively locally adaptive loci have been developed and are broadly used: one that identifies loci with unusually high genetic differentiation among populations (differentiation outlier methods) and one that searches for correlations between local population allele frequencies and local environments (genetic-environment association methods). Here, we review the promises and challenges of these genome scan methods, including correcting for the confounding influence of a species’ demographic history, biases caused by missing aspects of the genome, matching scales of environmental data with population structure, and other statistical considerations. In each case, we make suggestions for best practices for maximizing the accuracy and efficiency of genome scans to detect the underlying genetic basis of local adaptation. With attention to their current limitations, genome scan methods can be an important tool in finding the genetic basis of adaptive evolutionary change.

In the marine environment differential gene exchange between partially reproductively isolated taxa can result in introgression that extends over long distances due to high larval dispersal potential. However, the degree to which this process contributes to interlocus variance of genetic differentiation within introgressed populations remains unclear. Using a genome-scan approach in the Indo-Pacific eel Anguilla marmorata, we investigated the degree of interpopulation genetic differentiation, the rate of introgression, and within-population genetic patterns at 858 AFLP markers genotyped in 1117 individuals. Three divergent populations were identified based on clustering analysis. Genetic assignments of individuals revealed the existence of different types of hybrids that tended to co-occur with parental genotypes in three population contact zones. Highly variable levels of genetic differentiation were found between populations across the AFLP markers, and reduced rates of introgression were shown at some highly differentiated loci. Gene flow across semipermeable genetic barriers was shown to generate spatial introgression patterns at some loci which define within-population structure over long distances. These results suggest that differential introgression in subdivided populations may be relevant when interpreting spatial variation patterns displayed by outlying loci in other marine fish populations.

Latitudinal and elevational gradients provide valuable experimental settings for studies of the potential impact of global warming on forest tree species. The availability of long-term phenological surveys in common garden experiments for traits associated with climate, such as bud flushing for sessile oaks (Quercus petraea), provide an ideal opportunity to investigate this impact. We sequenced 18 sessile oak populations and used available sequencing data for three other closely related European white oak species (Quercus pyrenaica, Quercus pubescens, and Quercus robur) to explore the evolutionary processes responsible for shaping the genetic variation across latitudinal and elevational gradients in extant sessile oaks. We used phenotypic surveys in common garden experiments and climatic data for the population of origin to perform genome-wide scans for population differentiation and genotype-environment and genotype-phenotype associations. The inferred historical relationships between Q. petraea populations suggest that interspecific gene flow occurred between Q. robur and Q. petraea populations from cooler or wetter areas. A genome-wide scan of differentiation between Q. petraea populations identified single nucleotide polymorphisms (SNPs) displaying strong interspecific relative divergence between these two species. These SNPs followed genetic clines along climatic or phenotypic gradients, providing further support for the likely contribution of introgression to the adaptive divergence of Q. petraea populations. Overall, the results indicate that outliers and associated SNPs are Q. robur ancestry-informative. We discuss the results of this study in the framework of the postglacial colonization scenario, in which introgression and diversifying selection have been proposed as essential drivers of Q. petraea microevolution.

In population genomics studies, accounting for the neutral covariance structure across population allele frequencies is critical to improve the robustness of genome-wide scan approaches. Elaborating on the BayEnv model, this study investigates several modeling extensions (i) to improve the estimation accuracy of the population covariance matrix and all the related measures, (ii) to identify significantly overly differentiated SNPs based on a calibration procedure of the XtX statistics, and (iii) to consider alternative covariate models for analyses of association with population-specific covariables. In particular, the auxiliary variable model allows one to deal with multiple testing issues and, providing the relative marker positions are available, to capture some linkage disequilibrium information. A comprehensive simulation study was carried out to evaluate the performances of these different models. Also, when compared in terms of power, robustness, and computational efficiency to five other state-of-the-art genome-scan methods (BayEnv2, BayScEnv, BayScan, flk, and lfmm), the proposed approaches proved highly effective. For illustration purposes, genotyping data on 18 French cattle breeds were analyzed, leading to the identification of 13 strong signatures of selection. Among these, four (surrounding the KITLG, KIT, EDN3, and ALB genes) contained SNPs strongly associated with the piebald coloration pattern while a fifth (surrounding PLAG1) could be associated to morphological differences across the populations. Finally, analysis of Pool-Seq data from 12 populations of Littorina saxatilis living in two different ecotypes illustrates how the proposed framework might help in addressing relevant ecological issues in nonmodel species. Overall, the proposed methods define a robust Bayesian framework to characterize adaptive genetic differentiation across populations. The BayPass program implementing the different models is available at http://www1.montpellier.inra.fr/CBGP/software/baypass/.

Adaptation to local environments often occurs through natural selection acting on a large number of loci, each having a weak phenotypic effect. One way to detect these loci is to identify genetic polymorphisms that exhibit high correlation with environmental variables used as proxies for ecological pressures. Here, we propose new algorithms based on population genetics, ecological modeling, and statistical learning techniques to screen genomes for signatures of local adaptation. Implemented in the computer program “latent factor mixed model” (LFMM), these algorithms employ an approach in which population structure is introduced using unobserved variables. These fast and computationally efficient algorithms detect correlations between environmental and genetic variation while simultaneously inferring background levels of population structure. Comparing these new algorithms with related methods provides evidence that LFMM can efficiently estimate random effects due to population history and isolation-by-distance patterns when computing gene-environment correlations, and decrease the number of false-positive associations in genome scans. We then apply these models to plant and human genetic data, identifying several genes with functions related to development that exhibit strong correlations with climatic gradients.