Detecting clinal and balanced selection using spatial autocorrelation analysis under kin-structured migration

Recently spatial autocorrelation has been employed to infer microevolutionary processes from patterns of genetic variation. In theory, different processes should show characteristic signature correlograms; e. g., clinal selection should produce correlograms decreasing from positive to negative autocorrelation, whereas uniform balanced selection should lead to no spatial autocorrelation. The ability of a statistical method such as spatial autocorrelation analysis to distinguish between these selective regimes or even to detect departures from neutrality is dependent on the strength of the evolutionary force and the population structure. Weak selection or migration will not be apparent against the expected background of stochastic noise. Moreover, the population structure may generate sufficient stochastic variation such that even strong evolutionary forces may fail to be detected. This study uses computer simulation to examine the effects of kin-structured migration and three different selective regimes on the shape of spatial correlograms to assess the ability of this technique to detect different microevolutionary processes. Genetic variation among 8 loci is simulated in a linear set of 25 artificial populations. Kin-structured stepping-stone migration among adjacent populations is modeled; directional, balanced, and clinal selection, as well as neutral loci are considered. These experiments show that strong selection produces correlograms of the predicted shape. However, with an anthropologically reasonable population structure, considerable stochastic variation among correlograms for different alleles may still exist. This suggests the need for caution in inferring genetic process from spatial patterns. © 1994 Wiley-Liss, Inc.     

Signatures of hybridization and speciation in genomic patterns of ancestry*

Genomes sampled from hybrid zones between nascent species provide important clues into the speciation process. With advances in genome sequencing and single nucleotide polymorphism (SNP) genotyping, it is now feasible to measure variation in gene flow with high genomic resolution. This progress motivates the development of conceptual and analytical frameworks for hybrid zones that complement well‐established cline approaches. We extend the perspective that genomic distributions of ancestry are sensitive indicators of hybridization history. We use simulations to examine the behavior of the number of ancestry junctions—a simple summary of genomic patterns—in hybrid zones under increasingly realistic scenarios. Neutral simulations revealed that ancestry junction number is shaped by population structure, migration rate, and population size. Modeling multiple genetic architectures of hybrid dysfunction, with an emphasis on epistatic hybrid incompatibilities, showed that selection reduces junction number near loci that confer reproductive barriers. The magnitude of this signature was affected by the form of selection, dominance, and genomic location (autosome vs. sex chromosome) of incompatible loci. Our results suggest that researchers can identify loci involved in reproductive isolation by scanning hybrid genomes for local reductions in junction number. We outline necessary directions for future theory and method development to realize this goal.     

The potential of a population genomics approach to analyse geographic mosaics of plant--insect coevolution

A central issue in the evolutionary ecology of species interactions is coevolution, which involves the reciprocal selection between individuals of interacting species. Understanding the importance of coevolution in shaping species interactions requires the consideration of spatial variation in their strength. This is exactly what the, recently developed, geographic mosaic theory of coevolution addresses. Another major development in the study of population ecology is the introduction of the population genomics approach in this field of research. This approach addresses spatial processes through molecular methods. It is of particular interest that population genomics is especially applicable to natural populations of non-model species. We describe how population genomics can be used in the context of the geographic mosaic of coevolution, specifically to identify coevolutionary hot-spots, and to attribute genetic variation found at specific loci to processes of selection versus trait remixing. The proposed integration of the population genomics approach with the conceptual framework of the geographic mosaic of coevolution is illustrated with a few selected, particularly demonstrative, examples from the realm of insect--plant interactions.     

Neutral and adaptive genomic signatures of rapid poleward range expansion

Many species are expanding their range polewards, and this has been associated with rapid phenotypic change. Yet, it is unclear to what extent this reflects rapid genetic adaptation or neutral processes associated with range expansion, or selection linked to the new thermal conditions encountered. To disentangle these alternatives, we studied the genomic signature of range expansion in the damselfly Coenagrion scitulum using 4950 newly developed genomic SNPs and linked this to the rapidly evolved phenotypic differences between core and (newly established) edge populations. Most edge populations were genetically clearly differentiated from the core populations and all were differentiated from each other indicating independent range expansion events. In addition, evidence for genetic drift in the edge populations, and strong evidence for adaptive genetic variation in association with the range expansion was detected. We identified one SNP under consistent selection in four of the five edge populations and showed that the allele increasing in frequency is associated with increased flight performance. This indicates collateral, non‐neutral evolutionary changes in independent edge populations driven by the range expansion process. We also detected a genomic signature of adaptation to the newly encountered thermal regimes, reflecting a pattern of countergradient variation. The latter signature was identified at a single SNP as well as in a set of covarying SNPs using a polygenic multilocus approach to detect selection. Overall, this study highlights how a strategic geographic sampling design and the integration of genomic, phenotypic and environmental data can identify and disentangle the neutral and adaptive processes that are simultaneously operating during range expansions.     

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.     

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.     

Using genome scans of DNA polymorphism to infer adaptive population divergence

Elucidating the genetic basis of adaptive population divergence is a goal of central importance in evolutionary biology. In principle, it should be possible to identify chromosomal regions involved in adaptive divergence by screening genome-wide patterns of DNA polymorphism to detect the locus-specific signature of positive directional selection. In the case of spatially separated populations that inhabit different environments or sympatric populations that exploit different ecological niches, it is possible to identify loci that underlie divergently selected traits by comparing relative levels of differentiation among large numbers of unlinked markers. In this review I first address the question of whether diversifying selection on polygenic traits can be expected to produce predictable patterns of allelic variation at the underlying quantitative trait loci (QTL), and whether the locus-specific effects of selection can be reliably detected against the genome-wide backdrop of stochastic variability. I then review different approaches that have been developed to identify loci involved in adaptive population divergence and I discuss the relative merits of model-based approaches that rely on assumptions about population structure vs. model-free approaches that are based on empirical distributions of summary statistics. Finally, I consider the evolutionary and functional insights that might be gained by conducting genome scans for loci involved in adaptive population divergence.     

Isolation by environment in White‐breasted Nuthatches (Sitta carolinensis) of the Madrean Archipelago sky islands: a landscape genomics approach

Understanding landscape processes driving patterns of population genetic differentiation and diversity has been a long‐standing focus of ecology and evolutionary biology. Gene flow may be reduced by historical, ecological or geographic factors, resulting in patterns of isolation by distance (IBD) or isolation by environment (IBE). Although IBE has been found in many natural systems, most studies investigating patterns of IBD and IBE in nature have used anonymous neutral genetic markers, precluding inference of selection mechanisms or identification of genes potentially under selection. Using landscape genomics, the simultaneous study of genomic and ecological landscapes, we investigated the processes driving population genetic patterns of White‐breasted Nuthatches (Sitta carolinensis) in sky islands (montane forest habitat islands) of the Madrean Archipelago. Using more than 4000 single nucleotide polymorphisms and multiple tests to investigate the relationship between genetic differentiation and geographic or ecological distance, we identified IBE, and a lack of IBD, among sky island populations of S. carolinensis. Using three tests to identify selection, we found 79 loci putatively under selection; of these, seven matched CDS regions in the Zebra Finch. The loci under selection were highly associated with climate extremes (maximum temperature of warmest month and minimum precipitation of driest month). These results provide evidence for IBE – disentangled from IBD – in sky island vertebrates and identify potential adaptive genetic variation.     

Genetic variation in the acorn barnacle from allozymes to population genomics

Understanding the patterns of genetic variation within and among populations is a central problem in population and evolutionary genetics. We examine this question in the acorn barnacle, Semibalanus balanoides, in which the allozyme loci Mpi and Gpi have been implicated in balancing selection due to varying selective pressures at different spatial scales. We review the patterns of genetic variation at the Mpi locus, compare this to levels of population differentiation at mtDNA and microsatellites, and place these data in the context of genome-wide variation from high-throughput sequencing of population samples spanning the North Atlantic. Despite considerable geographic variation in the patterns of selection at the Mpi allozyme, this locus shows rather low levels of population differentiation at ecological and trans-oceanic scales (F(ST)?～?5%). Pooled population sequencing was performed on samples from Rhode Island (RI), Maine (ME), and Southwold, England (UK). Analysis of more than 650 million reads identified approximately 335,000 high-quality SNPs in 19 million base pairs of the S. balanoides genome. Much variation is shared across the Atlantic, but there are significant examples of strong population differentiation among samples from RI, ME, and UK. An F(ST) outlier screen of more than 22,000 contigs provided a genome-wide context for interpretation of earlier studies on allozymes, mtDNA, and microsatellites. F(ST) values for allozymes, mtDNA and microsatellites are close to the genome-wide average for random SNPs, with the exception of the trans-Atlantic F(ST) for mtDNA. The majority of F(ST) outliers were unique between individual pairs of populations, but some genes show shared patterns of excess differentiation. These data indicate that gene flow is high, that selection is strong on a subset of genes, and that a variety of genes are experiencing diversifying selection at large spatial scales. This survey of polymorphism in S. balanoides provides a number of genomic tools that promise to make this a powerful model for ecological genomics of the rocky intertidal.     

The population genomic signature of environmental selection in the widespread insect-pollinated tree species Frangula alnus at different geographical scales

The evaluation of the molecular signatures of selection in species lacking an available closely related reference genome remains challenging, yet it may provide valuable fundamental insights into the capacity of populations to respond to environmental cues. We screened 25 native populations of the tree species Frangula alnus subsp. alnus (Rhamnaceae), covering three different geographical scales, for 183 annotated single-nucleotide polymorphisms (SNPs). Standard population genomic outlier screens were combined with individual-based and multivariate landscape genomic approaches to examine the strength of selection relative to neutral processes in shaping genomic variation, and to identify the main environmental agents driving selection. Our results demonstrate a more distinct signature of selection with increasing geographical distance, as indicated by the proportion of SNPs (i) showing exceptional patterns of genetic diversity and differentiation (outliers) and (ii) associated with climate. Both temperature and precipitation have an important role as selective agents in shaping adaptive genomic differentiation in F. alnus subsp. alnus, although their relative importance differed among spatial scales. At the ‘intermediate'' and ‘regional'' scales, where limited genetic clustering and high population diversity were observed, some indications of natural selection may suggest a major role for gene flow in safeguarding adaptability. High genetic diversity at loci under selection in particular, indicated considerable adaptive potential, which may nevertheless be compromised by the combined effects of climate change and habitat fragmentation.     

A molecular perspective: biology of the emerging pathogen Batrachochytrium dendrobatidis

Ten years after the first discovery of the chytrid pathogen Batrachochytrium dendrobatidis (Bd), the catastrophic effect of Bd on wild amphibian populations is indisputable. However, a number of persistent questions remain about Bd's origin and mechanisms of pathogenicity. Here we discuss the promise of genetic and genomic tools for answering these previously intractable questions about the biology and evolutionary history of Bd. Full genomes of 2 Bd strains have recently been sequenced, and Bd research on this species using population genetics, phylogenetics, proteomics, comparative genomics and functional genomics is already underway. We review some of the insights gleaned from the first studies using these genome-scale approaches focusing particularly on Bd's genomic architecture, patterns of global genetic variation, virulence factors and genetic interactions with hosts. Avenues of future research promise to be particularly fruitful and highlight the need for integrative studies that unite genetic, ecological and spatial data in both Bd and its amphibian hosts.     

Characterising functionally important and ecologically meaningful genetic diversity using a candidate gene approach

Over the past two decades the fields of molecular ecology and population genetics have been dominated by the use of putatively neutral DNA markers, primarily to resolve spatio-temporal patterns of genetic variation to inform our understanding of population structure, gene flow and pedigree. Recent emphasis in comparative functional genomics, however, has fuelled a resurgence of interest in functionally important genetic variation that underpins phenotypic traits of adaptive or ecological significance. It may prove a major challenge to transfer genomics information from classical model species to examine functional diversity in non-model species in natural populations, but already multiple gene-targeted candidate loci with major effect on phenotype and fitness have been identified. Here we briefly describe some of the research strategies used for isolating and characterising functional genetic diversity at candidate gene-targeted loci, and illustrate the efficacy of some of these approaches using our own studies on red grouse (Lagopus lagopus scoticus). We then review how candidate gene markers have been used to: (1) quantify genetic diversity among populations to identify those depauperate in genetic diversity and requiring specific management action; (2) identify the strength and mode of selection operating on individuals within natural populations; and (3) understand direct mechanistic links between allelic variation at single genes and variance in individual fitness.     

Ecological genomics in full colour

Colour patterns in animals have long offered an opportunity to observe adaptive traits in natural populations. Colour plays myriad roles in interactions within and among species, from reproductive signalling to predator avoidance, leading to multiple targets of natural and sexual selection and opportunities for diversification. Understanding the genetic and developmental underpinnings of variation in colour promises a fuller understanding of these evolutionary processes, but the path to unravelling these connections can be arduous. The advent of genomic techniques suitable for nonmodel organisms is now beginning to light the way. Two new studies in this issue of Molecular Ecology use genomic sequencing of laboratory crosses to map colour traits in cichlid fishes, a remarkably diverse group in which coloration has played a major role in diversification. They illustrate how genomic approaches, specifically RAD sequencing, can rapidly identify both simple and more complex genetic variation underlying ecologically important traits. In the first, Henning et al. ( 2014 ) detect a single locus that appears to control in a Mendelian fashion the presence of horizontal stripes, a trait that has evolved in numerous cichlid lineages. In the second, Albertson et al. ( 2014 ) identify several genes and epistatic interactions affecting multiple colour traits, as well as a novel metric describing integration across colour traits. Albertson et al. ( 2014 ) go further, by quantifying differential expression of parental alleles at a candidate locus and by relating differentiation among natural populations at mapped loci to trait divergence. Herein lies the promise of ecological genomics – efficiently integrating genetic mapping of phenotypes with population genomic data to both identify functional genes and unravel their evolutionary history. These studies offer guidance on how genomic techniques can be tailored to a research question or study system, and they also add to the growing body of empirical examples addressing basic questions about how ecologically important traits evolve in natural populations.     

Genetic perspectives on northern population cycles: bridging the gap between theory and empirical studies

Many key species in northern ecosystems are characterised by high‐amplitude cyclic population demography. In 1924, Charles Elton described the ecology and evolution of cyclic populations in a classic paper and, since then, a major focus has been the underlying causes of population cycles. Elton hypothesised that fluctuations reduced population genetic variation and influenced the direction of selection pressures. In concordance with Elton, present theories concern the direct consequences of population cycles for genetic structure due to the processes of genetic drift and selection, but also include feedback models of genetic composition on population dynamics. Most of these theories gained mathematical support during the 1970s and onwards, but due to methodological drawbacks, difficulties in long‐term sampling and a complex interplay between microevolutionary processes, clear empirical data allowing the testing of these predictions are still scarce. Current genetic tools allow for estimates of genetic variation and identification of adaptive genomic regions, making this an ideal time to revisit this subject. Herein, we attempt to contribute towards a consensus regarding the enigma described by Elton almost 90 years ago. We present nine predictions covering the direct and genetic feedback consequences of population cycles on genetic variation and population structure, and review the empirical evidence. Generally, empirical support for the predictions was low and scattered, with obvious gaps in the understanding of basic population processes. We conclude that genetic variation in northern cyclic populations generally is high and that the geographic distribution and amount of diversity are usually suggested to be determined by various forms of context‐ and density‐dependent dispersal exceeding the impact of genetic drift. Furthermore, we found few clear signatures of selection determining genetic composition in cyclic populations. Dispersal is assumed to have a strong impact on genetic structuring and we suggest that the signatures of other microevolutionary processes such as genetic drift and selection are weaker and have been over‐shadowed by density‐dependent dispersal. We emphasise that basic biological and demographical questions still need to be answered and stress the importance of extensive sampling, appropriate choice of tools and the value of standardised protocols.     

Ecological genomics of mutualism decline in nitrogen-fixing bacteria

Anthropogenic changes can influence mutualism evolution; however, the genomic regions underpinning mutualism that are most affected by environmental change are generally unknown, even in well-studied model mutualisms like the interaction between legumes and their nitrogen (N)-fixing rhizobia. Such genomic information can shed light on the agents and targets of selection maintaining cooperation in nature. We recently demonstrated that N-fertilization has caused an evolutionary decline in mutualistic partner quality in the rhizobia that form symbiosis with clover. Here, population genomic analyses of N-fertilized versus control rhizobium populations indicate that evolutionary differentiation at a key symbiosis gene region on the symbiotic plasmid (pSym) contributes to partner quality decline. Moreover, patterns of genetic variation at selected loci were consistent with recent positive selection within N-fertilized environments, suggesting that N-rich environments might select for less beneficial rhizobia. By studying the molecular population genomics of a natural bacterial population within a long-term ecological field experiment, we find that: (i) the N environment is indeed a potent selective force mediating mutualism evolution in this symbiosis, (ii) natural variation in rhizobium partner quality is mediated in part by key symbiosis genes on the symbiotic plasmid, and (iii) differentiation at selected genes occurred in the context of otherwise recombining genomes, resembling eukaryotic models of adaptation.     

A hierarchical Bayesian model for next-generation population genomics

The demography of populations and natural selection shape genetic variation across the genome and understanding the genomic consequences of these evolutionary processes is a fundamental aim of population genetics. We have developed a hierarchical Bayesian model to quantify genome-wide population structure and identify candidate genetic regions affected by selection. This model improves on existing methods by accounting for stochastic sampling of sequences inherent in next-generation sequencing (with pooled or indexed individual samples) and by incorporating genetic distances among haplotypes in measures of genetic differentiation. Using simulations we demonstrate that this model has a low false-positive rate for classifying neutral genetic regions as selected genes (i.e., Φ(ST) outliers), but can detect recent selective sweeps, particularly when genetic regions in multiple populations are affected by selection. Nonetheless, selection affecting just a single population was difficult to detect and resulted in a high false-negative rate under certain conditions. We applied the Bayesian model to two large sets of human population genetic data. We found evidence of widespread positive and balancing selection among worldwide human populations, including many genetic regions previously thought to be under selection. Additionally, we identified novel candidate genes for selection, several of which have been linked to human diseases. This model will facilitate the population genetic analysis of a wide range of organisms on the basis of next-generation sequence data.     

Adapting legume crops to climate change using genomic approaches

Our agricultural system and hence food security is threatened by combination of events, such as increasing population, the impacts of climate change, and the need to a more sustainable development. Evolutionary adaptation may help some species to overcome environmental changes through new selection pressures driven by climate change. However, success of evolutionary adaptation is dependent on various factors, one of which is the extent of genetic variation available within species. Genomic approaches provide an exceptional opportunity to identify genetic variation that can be employed in crop improvement programs. In this review, we illustrate some of the routinely used genomics‐based methods as well as recent breakthroughs, which facilitate assessment of genetic variation and discovery of adaptive genes in legumes. Although additional information is needed, the current utility of selection tools indicate a robust ability to utilize existing variation among legumes to address the challenges of climate uncertainty.