Progress and prospects in mapping recent selection in the genome

One of the central goals of evolutionary biology is to understand the genetic basis of adaptive evolution. The availability of nearly complete genome sequences from a variety of organisms has facilitated the collection of data on naturally occurring genetic variation on the scale of hundreds of loci to whole genomes. Such data have changed the focus of molecular population genetics from making inferences about adaptive evolution at single loci to identifying which loci, out of hundreds to thousands, have been recent targets of natural selection. A major challenge in this effort is distinguishing the effects of selection from those of the demographic history of populations. Here we review some current progress and remaining challenges in the field.     

Temperature-related natural selection in a wind-pollinated tree across regional and continental scales

Adaptive genetic variation is a key factor in evolutionary biology, but the detection of signatures of natural selection remains challenging in nonmodel organisms. We used a genome scan approach to detect signals of natural selection in the Black alder (Alnus glutinosa), a widespread wind-pollinated tree. Gene flow through pollen dispersal is believed to be high in this species, and we therefore expected to find a clear response to natural selection. In combination with two different landscape genetic approaches, we determined which environmental variables were most associated with the inferred selection. This analysis was performed on a regional scale (northern Belgium) and on a continental scale (Europe). Because climate-related differences are much more pronounced at the continental scale, we expected to find more selection-sensitive genetic markers across Europe than across northern Belgium. At both spatial scales, a substantial number of genetic loci were considered outliers, with respect to neutral expectations, and were therefore identified as selective. Based on results from our combined approach, four putative selective loci (or 2.5%) were recovered with high statistical support. Although these loci seemed to be associated with different environmental variables, they were mainly temperature-related. Our study demonstrates that the use of complementary methods in landscape genetics allows the discovery of selective loci which otherwise might stay hidden. In combination with a genome scan, the selective loci can be verified and the nature of the selection pressure can be identified.     

INTEGRATING LANDSCAPE GENOMICS AND SPATIALLY EXPLICIT APPROACHES TO DETECT LOCI UNDER SELECTION IN CLINAL POPULATIONS

Uncovering the genetic basis of adaptation hinges on the ability to detect loci under selection. However, population genomics outlier approaches to detect selected loci may be inappropriate for clinal populations or those with unclear population structure because they require that individuals be clustered into populations. An alternate approach, landscape genomics, uses individual‐based approaches to detect loci under selection and reveal potential environmental drivers of selection. We tested four landscape genomics methods on a simulated clinal population to determine their effectiveness at identifying a locus under varying selection strengths along an environmental gradient. We found all methods produced very low type I error rates across all selection strengths, but elevated type II error rates under “weak” selection. We then applied these methods to an AFLP genome scan of an alpine plant, Campanula barbata, and identified five highly supported candidate loci associated with precipitation variables. These loci also showed spatial autocorrelation and cline patterns indicative of selection along a precipitation gradient. Our results suggest that landscape genomics in combination with other spatial analyses provides a powerful approach for identifying loci potentially under selection and explaining spatially complex interactions between species and their environment.     

A regression-based approach to selection mapping

Selection mapping applies the population genetics theory of hitchhiking to the localization of genomic regions containing genes under selection. This approach predicts that neutral loci linked to genes under positive selection will have reduced diversity due to their shared history with a selected locus, and thus, genome scans of diversity levels can be used to identify regions containing selected loci. Most previous approaches to this problem ignore the spatial genomic pattern of diversity expected under selection. The regression-based approach advocated in this paper takes into account the expected pattern of decreasing genetic diversity with increased proximity to a selected locus. Simulated data are used to examine the patterns of diversity under different scenarios, in order to assess the power of a regression-based approach to the identification of regions under selection. Application of this method to both simulated and empirical data demonstrates its potential to detect selection. In contrast to some other methods, the regression approach described in this paper can be applied to any marker type. Results also suggest that this approach may give more precise estimates of the location of the selected locus than alternative methods, although the power is slightly lower in some cases.     

High performance computation of landscape genomic models including local indicators of spatial association

With the increasing availability of both molecular and topo‐climatic data, the main challenges facing landscape genomics – that is the combination of landscape ecology with population genomics – include processing large numbers of models and distinguishing between selection and demographic processes (e.g. population structure). Several methods address the latter, either by estimating a null model of population history or by simultaneously inferring environmental and demographic effects. Here we present sam βada , an approach designed to study signatures of local adaptation, with special emphasis on high performance computing of large‐scale genetic and environmental data sets. sam βada identifies candidate loci using genotype–environment associations while also incorporating multivariate analyses to assess the effect of many environmental predictor variables. This enables the inclusion of explanatory variables representing population structure into the models to lower the occurrences of spurious genotype–environment associations. In addition, sam βada calculates local indicators of spatial association for candidate loci to provide information on whether similar genotypes tend to cluster in space, which constitutes a useful indication of the possible kinship between individuals. To test the usefulness of this approach, we carried out a simulation study and analysed a data set from Ugandan cattle to detect signatures of local adaptation with sam βada , bayenv , lfmm and an F_ST outlier method (FDIST approach in arlequin ) and compare their results. sam βada – an open source software for Windows, Linux and Mac OS X available at http://lasig.epfl.ch/sambada – outperforms other approaches and better suits whole‐genome sequence data processing.     

Population, quantitative and comparative genomics of adaptation in forest trees

High-throughput DNA sequencing and genotyping technologies have enabled a new generation of research in plant genetics where combined quantitative and population genetic approaches can be used to better understand the relationship between naturally occurring genotypic and phenotypic diversity. Forest trees are highly amenable to such studies because of their combined undomesticated and partially domesticated state. Forest geneticists are using association genetics to dissect complex adaptive traits and discover the underlying genes. In parallel, they are using resequencing of candidate genes and modern population genetics methods to discover genes under natural selection. This combined approach is identifying the most important genes that determine patterns of complex trait adaptation observed in many tree populations.     

Alzheimer disease susceptibility loci: evidence for a protein network under natural selection

Recent genome-wide association studies have identified a number of susceptibility loci for Alzheimer disease (AD). To understand the functional consequences and potential interactions of the associated loci, we explored large-scale data sets interrogating the human genome for evidence of positive natural selection. Our findings provide significant evidence for signatures of recent positive selection acting on several haplotypes carrying AD susceptibility alleles; interestingly, the genes found in these selected haplotypes can be assembled, independently, into a molecular complex via a protein-protein interaction (PPI) network approach. These results suggest a possible coevolution of genes encoding physically-interacting proteins that underlie AD susceptibility and are coexpressed in different tissues. In particular, PICALM, BIN1, CD2AP, and EPHA1 are interconnected through multiple interacting proteins and appear to have coordinated evidence of selection in the same human population, suggesting that they may be involved in the execution of a shared molecular function. This observation may be AD-specific, as the 12 loci associated with Parkinson disease do not demonstrate excess evidence of natural selection. The context for selection is probably unrelated to AD itself; it is likely that these genes interact in another context, such as in immune cells, where we observe cis-regulatory effects at several of the selected AD loci.     

Comparison of F(ST) outlier tests for SNP loci under selection

Genome scans with many genetic markers provide the opportunity to investigate local adaptation in natural populations and identify candidate genes under selection. In particular, SNPs are dense throughout the genome of most organisms and are commonly observed in functional genes making them ideal markers to study adaptive molecular variation. This approach has become commonly employed in ecological and population genetics studies to detect outlier loci that are putatively under selection. However, there are several challenges to address with outlier approaches including genotyping errors, underlying population structure and false positives, variation in mutation rate and limited sensitivity (false negatives). In this study, we evaluated multiple outlier tests and their type I (false positive) and type II (false negative) error rates in a series of simulated data sets. Comparisons included simulation procedures (FDIST2, ARLEQUIN v.3.5 and BAYESCAN) as well as more conventional tools such as global F(ST) histograms. Of the three simulation methods, FDIST2 and BAYESCAN typically had the lowest type II error, BAYESCAN had the least type I error and Arlequin had highest type I and II error. High error rates in Arlequin with a hierarchical approach were partially because of confounding scenarios where patterns of adaptive variation were contrary to neutral structure; however, Arlequin consistently had highest type I and type II error in all four simulation scenarios tested in this study. Given the results provided here, it is important that outlier loci are interpreted cautiously and error rates of various methods are taken into consideration in studies of adaptive molecular variation, especially when hierarchical structure is included.     

Genome‐wide analyses suggest parallel selection for universal traits may eclipse local environmental selection in a highly mobile carnivore

Ecological and environmental heterogeneity can produce genetic differentiation in highly mobile species. Accordingly, local adaptation may be expected across comparatively short distances in the presence of marked environmental gradients. Within the European continent, wolves (Canis lupus) exhibit distinct north–south population differentiation. We investigated more than 67‐K single nucleotide polymorphism (SNP) loci for signatures of local adaptation in 59 unrelated wolves from four previously identified population clusters (northcentral Europe n = 32, Carpathian Mountains n = 7, Dinaric‐Balkan n = 9, Ukrainian Steppe n = 11). Our analyses combined identification of outlier loci with findings from genome‐wide association study of individual genomic profiles and 12 environmental variables. We identified 353 candidate SNP loci. We examined the SNP position and neighboring megabase (1 Mb, one million bases) regions in the dog (C. lupus familiaris) genome for genes potentially under selection, including homologue genes in other vertebrates. These regions included functional genes for, for example, temperature regulation that may indicate local adaptation and genes controlling for functions universally important for wolves, including olfaction, hearing, vision, and cognitive functions. We also observed strong outliers not associated with any of the investigated variables, which could suggest selective pressures associated with other unmeasured environmental variables and/or demographic factors. These patterns are further supported by the examination of spatial distributions of the SNPs associated with universally important traits, which typically show marked differences in allele frequencies among population clusters. Accordingly, parallel selection for features important to all wolves may eclipse local environmental selection and implies long‐term separation among population clusters.     

Ecologically and evolutionarily important SNPs identified in natural populations

Evolution by natural selection acts on natural populations amidst migration, gene-by-environmental interactions, constraints, and tradeoffs, which affect the rate and frequency of adaptive change. We asked how many and how rapidly loci change in populations subject to severe, recent environmental changes. To address these questions, we used genomic approaches to identify randomly selected single nucleotide polymorphisms (SNPs) with evolutionarily significant patterns in three natural populations of Fundulus heteroclitus that inhabit and have adapted to highly polluted Superfund sites. Three statistical tests identified 1.4-2.5% of SNPs that were significantly different from the neutral model in each polluted population. These nonneutral patterns in populations adapted to highly polluted environments suggest that these loci or closely linked loci are evolving by natural selection. One SNP identified in all polluted populations using all tests is in the gene for the xenobiotic metabolizing enzyme, cytochrome P4501A (CYP1A), which has been identified previously as being refractory to induction in the three highly polluted populations. Extrapolating across the genome, these data suggest that rapid evolutionary change in natural populations can involve hundreds of loci, a few of which will be shared in independent events.     

Regional environmental pressure influences population differentiation in turbot (Scophthalmus maximus)

Unravelling the factors shaping the genetic structure of mobile marine species is challenging due to the high potential for gene flow. However, genetic inference can be greatly enhanced by increasing the genomic, geographical or environmental resolution of population genetic studies. Here, we investigated the population structure of turbot (Scophthalmus maximus) by screening 17 random and gene‐linked markers in 999 individuals at 290 geographical locations throughout the northeast Atlantic Ocean. A seascape genetics approach with the inclusion of high‐resolution oceanographical data was used to quantify the association of genetic variation with spatial, temporal and environmental parameters. Neutral loci identified three subgroups: an Atlantic group, a Baltic Sea group and one on the Irish Shelf. The inclusion of loci putatively under selection suggested an additional break in the North Sea, subdividing southern from northern Atlantic individuals. Environmental and spatial seascape variables correlated marginally with neutral genetic variation, but explained significant proportions (respectively, 8.7% and 10.3%) of adaptive genetic variation. Environmental variables associated with outlier allele frequencies included salinity, temperature, bottom shear stress, dissolved oxygen concentration and depth of the pycnocline. Furthermore, levels of explained adaptive genetic variation differed markedly between basins (3% vs. 12% in the North and Baltic Sea, respectively). We suggest that stable environmental selection pressure contributes to relatively strong local adaptation in the Baltic Sea. Our seascape genetic approach using a large number of sampling locations and associated oceanographical data proved useful for the identification of population units as the basis of management decisions.     

Genome-wide SNP discovery and genetic linkage map construction in sunflower (<Emphasis Type="Italic">Helianthus annuus</Emphasis> L.) using a genotyping by sequencing (GBS) approach

Recently developed plant genomics approaches (LD mapping and genome-wide selection) require many molecular markers distributed throughout the plant genome. As a result, the availability of an increasing number of markers is essential for maintaining highly efficient and accurate plant breeding programs. In this study, we identified SNP loci in sunflower using a genotyping by sequencing (GBS) approach in an intraspecific F₂ mapping population. A total of 271,445,770 reads were generated by the Genome Analyzer II next-generation sequencing platform and 29.2 % of the reads were aligned to unique locations in the genome. A total of 46,278 SNP loci were identified and 7646 SNP loci were validated in an F₂ population. In addition, a SNP-based linkage map was constructed. This is the first report of SNP discovery in sunflower by GBS. The SNP markers and SNP-based linkage map will be valuable molecular genetics tools for sunflower breeding.     

Molecular population genetics and the search for adaptive evolution in plants

The first papers on plant molecular population genetics were published approximately 10 years ago. Since that time, well over 50 additional studies of plant nucleotide polymorphism have been published, and many of these studies focused on detecting the signature of balancing or positive selection at a locus. In this review, we discuss some of the theoretical and statistical issues surrounding the detection of selection, with focus on plant populations, and we also summarize the empirical plant molecular population genetics literature. At face value, the literature suggests that a history of balancing or positive selection in plant genes is rampant. In two well-studied taxa (maize and Arabidopsis) over 20% of studied genes have been interpreted as containing the signature of selection. We argue that this is probably an overstatement of the prevalence of natural selection in plant genomes, for two reasons. First, demographic effects are difficult to incorporate and have generally not been well integrated into the plant population genetics literature. Second, the genes studied to date are not a random sample, so selected genes may be overrepresented. The next generation of studies in plant molecular population genetics requires additional sampling of local populations, explicit comparisons among loci, and improved theoretical methods to control for demography. Eventually, candidate loci should be confirmed by explicit consideration of phenotypic effects.     

Origin, fate, and architecture of ecologically relevant genetic variation

Recent advances in molecular genetics combined with field manipulations are yielding new insight into the origin, evolutionary fate, and genetic architecture of phenotypic variation in natural plant populations, with two surprising implications for the evolution of plant genomes. First, genetic loci exhibiting antagonistic pleiotropy across natural environments appear rare relative to loci that are adaptive in one or more environments and neutral elsewhere. These 'conditionally neutral' alleles should sweep to fixation when they arise, yet genome comparisons find little evidence for such selective sweeps. Second, genes under biotic selection tend to be of larger effect than genes under abiotic selection. Recent theory suggests this may be a consequence of high gene flow among populations under selection for local adaptation.     

From population genetics to population genomics of forest trees: integrated population genomics approach

Early works by Altukhov and his associates on pine and spruce laid the foundation for Russian population genetic studies on tree species with the use of molecular genetic markers. In recent years, these species have become especially popular as nontraditional eukaryotic models for population and evolutionary genomic research. Tree species with large, cross-pollinating native populations, high genetic and phenotypic variation, growing in diverse environments and affected by environmental changes during hundreds of years of their individual development, are an ideal model for studying the molecular genetic basis of adaptation. The great advance in this field is due to the rapid development of population genomics in the last few years. In the broad sense, population genomics is a novel, fast-developing discipline, combining traditional population genetic approaches with the genomic level of analysis. Thousands of genes with known function and sometimes known genomic localization can be simultaneously studied in many individuals. This opens new prospects for obtaining statistical estimates for a great number of genes and segregating elements. Mating system, gene exchange, reproductive population size, population disequilibrium, interaction among populations, and many other traditional problems of population genetics can be now studied using data on variation in many genes. Moreover, population genomic analysis allows one to distinguish factors that affect individual genes, alleles, or nucleotides (such as, for example, natural selection) from factors affecting the entire genome (e.g., demography). This paper presents a brief review of traditional methods of studying genetic variation in forest tree species and introduces a new, integrated population genomics approach. The main stages of the latter are : (1) selection of genes, which are tentatively involved in variation of adaptive traits, by means of a detailed examination of the regulation and the expression of individual genes and genotypes, with subsequent determination of their complete allelic composition by direct nucleotide sequencing; (2) examination of the phenotypic effects of individual alleles by, e.g., association mapping; and (3) determining the frequencies of the selected alleles in natural population for identification of the adaptive variation pattern in the heterogeneous environment. Through decoding the phenotypic effects of individual alleles and identification of adaptive variation patterns at the population level, population genomics in the future will serve as a very helpful, efficient, and economical tool, essential for developing a correct strategy for conserving and increasing forests and other commercially valuable plant and animal species.     

From population genetics to population genomics of forest trees: Integrated population genomics approach

Early works by Altukhov and his associates on pine and spruce laid the foundation for Russian population genetic studies on tree species with the use of molecular genetic markers. In recent years, these species have become especially popular as nontraditional eukaryotic models for population and evolutionary genome-wide research. Tree species with large, cross-pollinating native populations, high genetic and phenotypic variation, growing in diverse environments and affected by environmental changes during hundreds of years of their individual development, are an ideal model for studying the molecular genetic basis of adaptation. The great advance in this field is due to the rapid development of population genomics in the last few years. In the broad sense, population genomics is a novel, fast-developing discipline, combining traditional population genetic approaches with the genome-wide level of analysis. Thousands of genes with known function and sometimes known genome-wide localization can be simultaneously studied in many individuals. This opens new prospects for obtaining statistical estimates for a great number of genes and segregating elements. Mating system, gene exchange, reproductive population size, population disequilibrium, interaction among populations, and many other traditional problems of population genetics can be now studied using data on variation in many genes. Moreover, population genome-wide analysis allows one to distinguish factors that affect individual genes, allelles, or nucleotides (such as, for example, natural selection) from factors affecting the entire genome (e.g., demography). This paper presents a brief review of traditional methods of studying genetic variation in forest tree species and introduces a new, integrated population genomics approach. The main stages of the latter are: (1) selection of genes, which are tentatively involved in variation of adaptive traits, by means of a detailed examination of the regulation and the expression of individual genes and genotypes, with subsequent determination of their complete allelic composition by direct nucleotide sequencing; (2) examination of the phenotypic effects of individual alleles by, e.g., association mapping; and (3) determining the frequencies of the selected alleles in natural population for identification of the adaptive variation pattern in the heterogeneous environment. Through decoding the phenotypic effects of individual alleles and identification of adaptive variation patterns at the population level, population genomics in the future will serve as a very helpful, efficient, and economical tool, essential for developing a correct strategy for conserving and increasing forests and other commercially valuable plant and animal species.     

A tight balance between natural selection and gene flow in a southern African arid-zone endemic bird

Gene flow is traditionally thought to be antagonistic to population differentiation and local adaptation. However, recent studies have demonstrated that local adaptation can proceed provided that selection is greater than the homogenizing effects of gene flow. We extend these initial studies by combining ecology (climate), phenotype (body size), physiological genetics (oxidative phosphorylation genes), and neutral loci (nuclear microsatellites and introns) to test whether selection can counter-balance gene flow and hence promote local adaptation in a bird whose distribution spans an aridity gradient. Our results show that the Karoo scrub-robin's climatic niche is spatially structured, providing the potential for local adaptation to develop. We found remarkably discordant patterns of divergence among mtDNA, morphology, and neutral loci. For the mitochondrial genes, two amino acid replacements, strong population structure and reduced gene flow were associated with the environmental gradient separating western coastal sites from the interior of southern Africa. In contrast, morphology and the neutral loci exhibited variation independent of environmental variables, and revealed extensive levels of gene flow across the aridity gradient, 50 times larger than the estimates for mitochondrial genes. Together, our results suggest that selective pressures on physiology, mediated by the mitochondrial genome, may well be a common mechanism for facilitating local adaptation to new climatic conditions.     

Riverscape genomics of a threatened fish across a hydroclimatically heterogeneous river basin

Understanding how natural selection generates and maintains adaptive genetic diversity in heterogeneous environments is key to predicting the evolutionary response of populations to rapid environmental change. Detecting selection in complex spatial environments remains challenging, especially for threatened species where the effects of strong genetic drift may overwhelm signatures of selection. We carried out a basinwide riverscape genomic analysis in the threatened southern pygmy perch (Nannoperca australis), an ecological specialist with low dispersal potential. High‐resolution environmental data and 5162 high‐quality filtered SNPs were used to clarify spatial population structure and to assess footprints of selection associated with a steep hydroclimatic gradient and with human disturbance across the naturally and anthropogenically fragmented Murray–Darling Basin (Australia). Our approach included F_ST outlier tests to define neutral loci, and a combination of spatially explicit genotype–environment association analyses to identify candidate adaptive loci while controlling for the effects of landscape structure and shared population history. We found low levels of genetic diversity and strong neutral population structure consistent with expectations based on spatial stream hierarchy and life history. In contrast, variables related to precipitation and temperature appeared as the most important environmental surrogates for putatively adaptive genetic variation at both regional and local scales. Human disturbance also influenced the variation in candidate loci for adaptation, but only at a local scale. Our study contributes to understanding of adaptive evolution along naturally and anthropogenically fragmented ecosystems. It also offers a tangible example of the potential contributions of landscape genomics for informing in situ and ex situ conservation management of biodiversity.