The genetic differentiation at quantitative trait loci under local adaptation

Detecting gene subnetworks under selection in biological pathways

Advances in high throughput sequencing technologies have created a gap between data production and functional data analysis. Indeed, phenotypes result from interactions between numerous genes, but traditional methods treat loci independently, missing important knowledge brought by network-level emerging properties. Therefore, detecting selection acting on multiple genes affecting the evolution of complex traits remains challenging. In this context, gene network analysis provides a powerful framework to study the evolution of adaptive traits and facilitates the interpretation of genome-wide data. We developed a method to analyse gene networks that is suitable to evidence polygenic selection. The general idea is to search biological pathways for subnetworks of genes that directly interact with each other and that present unusual evolutionary features. Subnetwork search is a typical combinatorial optimization problem that we solve using a simulated annealing approach. We have applied our methodology to find signals of adaptation to high-altitude in human populations. We show that this adaptation has a clear polygenic basis and is influenced by many genetic components. Our approach, implemented in the R package signet, improves on gene-level classical tests for selection by identifying both new candidate genes and new biological processes involved in adaptation to altitude.

Investigating the genomic basis of discrete phenotypes using a Pool-Seq-only approach: New insights into the genetics underlying colour variation in diverse taxa

While large-scale genomic approaches are increasingly revealing the genetic basis of polymorphic phenotypes such as colour morphs, such approaches are almost exclusively conducted in species with high-quality genomes and annotations. Here, we use Pool-Seq data for both genome assembly and SNP frequency estimation, followed by scanning for F_ST outliers to identify divergent genomic regions. Using paired-end, short-read sequencing data from two groups of individuals expressing divergent phenotypes, we generate a de novo rough-draft genome, identify SNPs and calculate genomewide F_ST differences between phenotypic groups. As genomes generated by Pool-Seq data are highly fragmented, we also present an approach for super-scaffolding contigs using existing protein-coding data sets. Using this approach, we reanalysed genomic data from two recent studies of birds and butterflies investigating colour pattern variation and replicated their core findings, demonstrating the accuracy and power of a Pool-Seq-only approach. Additionally, we discovered new regions of high divergence and new annotations that together suggest novel parallels between birds and butterflies in the origins of their colour pattern variation.

Genetics of dispersal

Dispersal is a process of central importance for the ecological and evolutionary dynamics of populations and communities, because of its diverse consequences for gene flow and demography. It is subject to evolutionary change, which begs the question, what is the genetic basis of this potentially complex trait? To address this question, we (i) review the empirical literature on the genetic basis of dispersal, (ii) explore how theoretical investigations of the evolution of dispersal have represented the genetics of dispersal, and (iii) discuss how the genetic basis of dispersal influences theoretical predictions of the evolution of dispersal and potential consequences.

Dispersal has a detectable genetic basis in many organisms, from bacteria to plants and animals. Generally, there is evidence for significant genetic variation for dispersal or dispersal‐related phenotypes or evidence for the micro‐evolution of dispersal in natural populations. Dispersal is typically the outcome of several interacting traits, and this complexity is reflected in its genetic architecture: while some genes of moderate to large effect can influence certain aspects of dispersal, dispersal traits are typically polygenic. Correlations among dispersal traits as well as between dispersal traits and other traits under selection are common, and the genetic basis of dispersal can be highly environment‐dependent.

By contrast, models have historically considered a highly simplified genetic architecture of dispersal. It is only recently that models have started to consider multiple loci influencing dispersal, as well as non‐additive effects such as dominance and epistasis, showing that the genetic basis of dispersal can influence evolutionary rates and outcomes, especially under non‐equilibrium conditions. For example, the number of loci controlling dispersal can influence projected rates of dispersal evolution during range shifts and corresponding demographic impacts. Incorporating more realism in the genetic architecture of dispersal is thus necessary to enable models to move beyond the purely theoretical towards making more useful predictions of evolutionary and ecological dynamics under current and future environmental conditions. To inform these advances, empirical studies need to answer outstanding questions concerning whether specific genes underlie dispersal variation, the genetic architecture of context‐dependent dispersal phenotypes and behaviours, and correlations among dispersal and other traits.

Substantial variation in the timing of pollen production reduces reproductive synchrony between distant populations of Pinus sylvestris L. in Scotland

The ability of a population to genetically adapt to a changing environment is contingent not only on the level of existing genetic variation within that population, but also on the gene flow received from differently adapted populations. Effective pollen-mediated gene flow among plant populations requires synchrony of flowering. Therefore differences in timing of flowering among genetically divergent populations may reduce their ability to adapt to environmental change. To determine whether gene flow among differently adapted populations of native Scots pine (Pinus sylvestris) in Scotland was restricted by differences in their flowering phenology, we measured timing of pollen release among populations spanning a steep environmental gradient over three consecutive seasons (2014-2016). Results showed that, over a distance of 137 km, there were as many as 15.8 days' difference among populations for the predicted timing of peak pollen shedding, with the earliest development in the warmer west of the country. There was much variation between years, with the earliest development and least synchrony in the warmest year (2014) and latest development and greatest synchrony in the coolest year (2015). Timing was negatively correlated with results from a common-garden experiment, indicative of a pattern of countergradient variation. We conclude that the observed differences in reproductive synchrony were sufficient to limit gene flow via pollen between populations of P. sylvestris at opposite ends of the environmental gradient across Scotland. We also hypothesize that continually warming, or asymmetrically warming spring temperatures will decrease reproductive synchrony among pine populations.

Water availability drives signatures of local adaptation in whitebark pine (Pinus albicaulis Engelm.) across fine spatial scales of the Lake Tahoe Basin, USA

Patterns of local adaptation at fine spatial scales are central to understanding how evolution proceeds, and are essential to the effective management of economically and ecologically important forest tree species. Here, we employ single and multilocus analyses of genetic data (n = 116 231 SNPs) to describe signatures of fine-scale adaptation within eight whitebark pine (Pinus albicaulis Engelm.) populations across the local extent of the environmentally heterogeneous Lake Tahoe Basin, USA. We show that despite highly shared genetic variation (F_ST  = 0.0069), there is strong evidence for adaptation to the rain shadow experienced across the eastern Sierra Nevada. Specifically, we build upon evidence from a common garden study and find that allele frequencies of loci associated with four phenotypes (mean = 236 SNPs), 18 environmental variables (mean = 99 SNPs), and those detected through genetic differentiation (n = 110 SNPs) exhibit significantly higher signals of selection (covariance of allele frequencies) than could be expected to arise, given the data. We also provide evidence that this covariance tracks environmental measures related to soil water availability through subtle allele frequency shifts across populations. Our results replicate empirical support for theoretical expectations of local adaptation for populations exhibiting strong gene flow and high selective pressures and suggest that ongoing adaptation of many P. albicaulis populations within the Lake Tahoe Basin will not be constrained by the lack of genetic variation. Even so, some populations exhibit low levels of heritability for the traits presumed to be related to fitness. These instances could be used to prioritize management to maintain adaptive potential. Overall, we suggest that established practices regarding whitebark pine conservation be maintained, with the additional context of fine-scale adaptation.

What can genome-wide association studies tell us about the evolutionary forces maintaining genetic variation for quantitative traits?

Contents 21 I. 21 II. 22 III. 24 IV. 25 V. 29 30 References 30 SUMMARY: Understanding the evolutionary forces that shape genetic variation within species has long been a goal of evolutionary biology. Integrating data for the genetic architecture of traits from genome-wide association mapping studies (GWAS) along with the development of new population genetic methods for identifying selection in sequence data may allow us to evaluate the roles of mutation-selection balance and balancing selection in shaping genetic variation at various scales. Here, we review the theoretical predictions for genetic architecture and additional signals of selection on genomic sequence for the loci that affect traits. Next, we review how plant GWAS have tested for the signatures of various selective scenarios. Limited evidence to date suggests that within-population variation is maintained primarily by mutation-selection balance while variation across the landscape is the result of local adaptation. However, there are a number of inherent biases in these interpretations. We highlight these challenges and suggest ways forward to further understanding of the maintenance of variation.

Landscape genetic approaches to guide native plant restoration in the Mojave Desert

Restoring dryland ecosystems is a global challenge due to synergistic drivers of disturbance coupled with unpredictable environmental conditions. Dryland plant species have evolved complex life-history strategies to cope with fluctuating resources and climatic extremes. Although rarely quantified, local adaptation is likely widespread among these species and potentially influences restoration outcomes. The common practice of reintroducing propagules to restore dryland ecosystems, often across large spatial scales, compels evaluation of adaptive divergence within these species. Such evaluations are critical to understanding the consequences of large-scale manipulation of gene flow and to predicting success of restoration efforts. However, genetic information for species of interest can be difficult and expensive to obtain through traditional common garden experiments. Recent advances in landscape genetics offer marker-based approaches for identifying environmental drivers of adaptive genetic variability in non-model species, but tools are still needed to link these approaches with practical aspects of ecological restoration. Here, we combine spatially explicit landscape genetics models with flexible visualization tools to demonstrate how cost-effective evaluations of adaptive genetic divergence can facilitate implementation of different seed sourcing strategies in ecological restoration. We apply these methods to Amplified Fragment Length Polymorphism (AFLP) markers genotyped in two Mojave Desert shrub species of high restoration importance: the long-lived, wind-pollinated gymnosperm Ephedra nevadensis, and the short-lived, insect-pollinated angiosperm Sphaeralcea ambigua. Mean annual temperature was identified as an important driver of adaptive genetic divergence for both species. Ephedra showed stronger adaptive divergence with respect to precipitation variability, while temperature variability and precipitation averages explained a larger fraction of adaptive divergence in Sphaeralcea. We describe multivariate statistical approaches for interpolating spatial patterns of adaptive divergence while accounting for potential bias due to neutral genetic structure. Through a spatial bootstrapping procedure, we also visualize patterns in the magnitude of model uncertainty. Finally, we introduce an interactive, distance-based mapping approach that explicitly links marker-based models of adaptive divergence with local or admixture seed sourcing strategies, promoting effective native plant restoration.

The genetics of adaptation to discrete heterogeneous environments: frequent mutation or large-effect alleles can allow range expansion

Range expansions are complex evolutionary and ecological processes. From an evolutionary standpoint, a populations' adaptive capacity can determine the success or failure of expansion. Using individual-based simulations, we model range expansion over a two-dimensional, approximately continuous landscape. We investigate the ability of populations to adapt across patchy environmental gradients and examine how the effect sizes of mutations influence the ability to adapt to novel environments during range expansion. We find that genetic architecture and landscape patchiness both have the ability to change the outcome of adaptation and expansion over the landscape. Adaptation to new environments succeeds via many mutations of small effect or few of large effect, but not via the intermediate between these cases. Higher genetic variance contributes to increased ability to adapt, but an alternative route of successful adaptation can proceed from low genetic variance scenarios with alleles of sufficiently large effect. Steeper environmental gradients can prevent adaptation and range expansion on both linear and patchy landscapes. When the landscape is partitioned into local patches with sharp changes in phenotypic optimum, the local magnitude of change between subsequent patches in the environment determines the success of adaptation to new patches during expansion.

Spatial patterns of immunogenetic and neutral variation underscore the conservation value of small, isolated American badger populations

Small and isolated populations often exhibit low genetic diversity due to drift and inbreeding, but may simultaneously harbour adaptive variation. We investigate spatial distributions of immunogenetic variation in American badger subspecies (Taxidea taxus), as a proxy for evaluating their evolutionary potential across the northern extent of the species' range. We compared genetic structure of 20 microsatellites and the major histocompatibility complex (MHC DRB exon 2) to evaluate whether small, isolated populations show low adaptive polymorphism relative to large and well-connected populations. Our results suggest that gene flow plays a prominent role in shaping MHC polymorphism across large spatial scales, while the interplay between gene flow and selection was stronger towards the northern peripheries. The similarity of MHC alleles within subspecies relative to their neutral genetic differentiation suggests that adaptive divergence among subspecies can be maintained despite ongoing gene flow along subspecies boundaries. Neutral genetic diversity was low in small relative to large populations, but MHC diversity within individuals was high in small populations. Despite reduced neutral genetic variation, small and isolated populations harbour functional variation that likely contribute to the species evolutionary potential at the northern range. Our findings suggest that conservation approaches should focus on managing adaptive variation across the species range rather than protecting subspecies per se.

On the maintenance of genetic variation and adaptation to environmental change: considerations from population genomics in fishes

The first goal of this paper was to overview modern approaches to local adaptation, with a focus on the use of population genomics data to detect signals of natural selection in fishes. Several mechanisms are discussed that may enhance the maintenance of genetic variation and evolutionary potential, which have been overlooked and should be considered in future theoretical development and predictive models: the prevalence of soft sweeps, polygenic basis of adaptation, balancing selection and transient polymorphisms, parallel evolution, as well as epigenetic variation. Research on fish population genomics has provided ample evidence for local adaptation at the genome level. Pervasive adaptive evolution, however, seems to almost never involve the fixation of beneficial alleles. Instead, adaptation apparently proceeds most commonly by soft sweeps entailing shifts in frequencies of alleles being shared between differentially adapted populations. One obvious factor contributing to the maintenance of standing genetic variation in the face of selective pressures is that adaptive phenotypic traits are most often highly polygenic, and consequently the response to selection should derive mostly from allelic co-variances among causative loci rather than pronounced allele frequency changes. Balancing selection in its various forms may also play an important role in maintaining adaptive genetic variation and the evolutionary potential of species to cope with environmental change. A large body of literature on fishes also shows that repeated evolution of adaptive phenotypes is a ubiquitous evolutionary phenomenon that seems to occur most often via different genetic solutions, further adding to the potential options of species to cope with a changing environment. Moreover, a paradox is emerging from recent fish studies whereby populations of highly reduced effective population sizes and impoverished genetic diversity can apparently retain their adaptive potential in some circumstances. Although more empirical support is needed, several recent studies suggest that epigenetic variation could account for this apparent paradox. Therefore, epigenetic variation should be fully integrated with considerations pertaining to role of soft sweeps, polygenic and balancing selection, as well as repeated adaptation involving different genetic basis towards improving models predicting the evolutionary potential of species to cope with a changing world.

Detecting polygenic selection in marine populations by combining population genomics and quantitative genetics approaches

Highly fecund marine species with dispersive life-history stages often display large population sizes and wide geographic distribution ranges. Consequently, they are expected to experience reduced genetic drift, efficient selection fueled by frequent adaptive mutations, and high migration loads. This has important consequences for understanding how local adaptation proceeds in the sea. A key issue in this regard, relates to the genetic architecture underlying fitness traits. Theory predicts that adaptation may involve many genes but with a high variance in effect size. Therefore, the effect of selection on allele frequencies may be substantial for the largest effect size loci, but insignificant for small effect genes. In such a context, the performance of population genomic methods to unravel the genetic basis of adaptation depends on the fraction of adaptive genetic variance explained by the cumulative effect of outlier loci. Here, we address some methodological challenges associated with the detection of local adaptation using molecular approaches. We provide an overview of genome scan methods to detect selection, including those assuming complex demographic models that better describe spatial population structure. We then focus on quantitative genetics approaches that search for genotype-phenotype associations at different genomic scales, including genome-wide methods evaluating the cumulative effect of variants. We argue that the limited power of single locus tests can be alleviated by the use of polygenic scores to estimate the joint contribution of candidate variants to phenotypic variation.

Marine invasions enter the genomic era: three lessons from the past, and the way forward

The expanding scale and increasing rate of marine biological invasions have been documented since the early 20th century. Besides their global ecological and economic impacts, non-indigenous species (NIS) also have attracted much attention as opportunities to explore important eco-evolutionary processes such as rapid adaptation, long-distance dispersal and range expansion, and secondary contacts between divergent evolutionary lineages. In this context, genetic tools have been extensively used in the past 20 years. Three important issues appear to have emerged from such studies. First, the study of NIS has revealed unexpected cryptic diversity in what had previously been assumed homogeneous entities. Second, there has been surprisingly little evidence of strong founder events accompanying marine introductions, a pattern possibly driven by large propagule loads. Third, the evolutionary processes leading to successful invasion have been difficult to ascertain due to faint genetic signals. Here we explore the potential of novel tools associated with high-throughput sequencing (HTS) to address these still pressing issues. Dramatic increase in the number of loci accessible via HTS has the potential to radically increase the power of analyses aimed at species delineation, exploring the population genomic consequences of range expansions, and examining evolutionary processes such as admixture, introgression, and adaptation. Nevertheless, the value of this new wealth of genomic data will ultimately depend on the ability to couple it with expanded "traditional" efforts, including exhaustive sampling of marine populations over large geographic scales, integrated taxonomic analyses, and population level exploration of quantitative trait differentiation through common-garden and other laboratory experiments.

Uncovering the genetic signature of quantitative trait evolution with replicated time series data

The genetic architecture of adaptation in natural populations has not yet been resolved: it is not clear to what extent the spread of beneficial mutations (selective sweeps) or the response of many quantitative trait loci drive adaptation to environmental changes. Although much attention has been given to the genomic footprint of selective sweeps, the importance of selection on quantitative traits is still not well studied, as the associated genomic signature is extremely difficult to detect. We propose 'Evolve and Resequence' as a promising tool, to study polygenic adaptation of quantitative traits in evolving populations. Simulating replicated time series data we show that adaptation to a new intermediate trait optimum has three characteristic phases that are reflected on the genomic level: (1) directional frequency changes towards the new trait optimum, (2) plateauing of allele frequencies when the new trait optimum has been reached and (3) subsequent divergence between replicated trajectories ultimately leading to the loss or fixation of alleles while the trait value does not change. We explore these 3 phase characteristics for relevant population genetic parameters to provide expectations for various experimental evolution designs. Remarkably, over a broad range of parameters the trajectories of selected alleles display a pattern across replicates, which differs both from neutrality and directional selection. We conclude that replicated time series data from experimental evolution studies provide a promising framework to study polygenic adaptation from whole-genome population genetics data.

The challenge of separating signatures of local adaptation from those of isolation by distance and colonization history: The case of two white pines

Accurately detecting signatures of local adaptation using genetic‐environment associations (GEAs) requires controlling for neutral patterns of population structure to reduce the risk of false positives. However, a high degree of collinearity between climatic gradients and neutral population structure can greatly reduce power, and the performance of GEA methods in such case is rarely evaluated in empirical studies. In this study, we attempted to disentangle the effects of local adaptation and isolation by environment (IBE) from those of isolation by distance (IBD) and isolation by colonization from glacial refugia (IBC) using range‐wide samples in two white pine species. For this, SNPs from 168 genes, including 52 candidate genes for growth and phenology, were genotyped in 133 and 61 populations of Pinus strobus and P. monticola, respectively. For P. strobus and using all 153 SNPs, climate (IBE) did not significantly explained among‐population variation when controlling for IBD and IBC in redundancy analyses (RDAs). However, 26 SNPs were significantly associated with climate in single‐locus GEA analyses (Bayenv2 and LFMM), suggesting that local adaptation took place in the presence of high gene flow. For P. monticola, we found no evidence of IBE using RDAs and weaker signatures of local adaptation using GEA and F_ST outlier tests, consistent with adaptation via phenotypic plasticity. In both species, the majority of the explained among‐population variation (69 to 96%) could not be partitioned between the effects of IBE, IBD, and IBC. GEA methods can account differently for this confounded variation, and this could explain the small overlap of SNPs detected between Bayenv2 and LFMM. Our study illustrates the inherent difficulty of taking into account neutral structure in natural populations and the importance of sampling designs that maximize climatic variation, while minimizing collinearity between climatic gradients and neutral structure.

Finding the Genomic Basis of Local Adaptation: Pitfalls, Practical Solutions, and Future Directions

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.

Modern Approaches to Local Adaptation

The study of local adaptation has been very important for the development of evolutionary biology broadly. This tradition continues; through new developments in diverse areas, we have more insight into the nature of the ongoing evolutionary process in a heterogeneous spatial context than ever before. This special issue follows from the 2014 Vice-Presidential Symposium of the American Society of Naturalists, bringing together outstanding researchers doing theoretical and empirical work, laboratory work, and fieldwork, with new data and new nodes of analysis. Through study of local adaptation, we learn about a tremendous source of biodiversity, both genetic and phenotypic, within and among species.

Population genomic scans suggest novel genes underlie convergent flowering time evolution in the introduced range of Arabidopsis thaliana

A long-standing question in evolutionary biology is whether the evolution of convergent phenotypes results from selection on the same heritable genetic components. Using whole-genome sequencing and genome scans, we tested whether the evolution of parallel longitudinal flowering time clines in the native and introduced ranges of Arabidopsis thaliana has a similar genetic basis. We found that common variants of large effect on flowering time in the native range do not appear to have been under recent strong selection in the introduced range. We identified a set of 38 new candidate genes that are putatively linked to the evolution of flowering time. A high degree of conditional neutrality of flowering time variants between the native and introduced range may preclude parallel evolution at the level of genes. Overall, neither gene pleiotropy nor available standing genetic variation appears to have restricted the evolution of flowering time to high-frequency variants from the native range or to known flowering time pathway genes.

The genetics of extreme microgeographic adaptation: an integrated approach identifies a major gene underlying leaf trichome divergence in Yellowstone Mimulus guttatus

Microgeographic adaptation provides a particularly interesting context for understanding the genetic basis of phenotypic divergence and may also present unique empirical challenges. In particular, plant adaptation to extreme soil mosaics may generate barriers to gene flow or shifts in mating system that confound simple genomic scans for adaptive loci. Here, we combine three approaches - quantitative trait locus (QTL) mapping of candidate intervals in controlled crosses, population resequencing (PoolSeq) and analyses of wild recombinant individuals - to investigate one trait associated with Mimulus guttatus (yellow monkeyflower) adaptation to geothermal soils in Yellowstone National Park. We mapped a major QTL causing dense leaf trichomes in thermally adapted plants to a <50-kb region of linkage Group 14 (Tr14) previously implicated in trichome divergence between independent M. guttatus populations. A PoolSeq scan of Tr14 region revealed a cluster of six genes, coincident with the inferred QTL peak, with high allele frequency differences sufficient to explain observed phenotypic differentiation. One of these, the R2R3 MYB transcription factor Migut.N02661, is a plausible functional candidate and was also strongly associated (r²  = 0.27) with trichome phenotype in analyses of wild-collected admixed individuals. Although functional analyses will be necessary to definitively link molecular variants in Tr14 with trichome divergence, our analyses are a major step in that direction. They point to a simple, and parallel, genetic basis for one axis of Mimulus guttatus adaptation to an extreme habitat, suggest a broadly conserved genetic basis for trichome variation across flowering plants and pave the way for further investigations of this challenging case of microgeographic incipient speciation.

Population genomic footprints of fine-scale differentiation between habitats in Mediterranean blue tits

Linking population genetic variation to the spatial heterogeneity of the environment is of fundamental interest to evolutionary biology and ecology, in particular when phenotypic differences between populations are observed at biologically small spatial scales. Here, we applied restriction-site associated DNA sequencing (RAD-Seq) to test whether phenotypically differentiated populations of wild blue tits (Cyanistes caeruleus) breeding in a highly heterogeneous environment exhibit genetic structure related to habitat type. Using 12 106 SNPs in 197 individuals from deciduous and evergreen oak woodlands, we applied complementary population genomic analyses, which revealed that genetic variation is influenced by both geographical distance and habitat type. A fine-scale genetic differentiation supported by genome- and transcriptome-wide analyses was found within Corsica, between two adjacent habitats where blue tits exhibit marked differences in breeding time while nesting < 6 km apart. Using redundancy analysis (RDA), we show that genomic variation remains associated with habitat type when controlling for spatial and temporal effects. Finally, our results suggest that the observed patterns of genomic differentiation were not driven by a small proportion of highly differentiated loci, but rather emerged through a process such as habitat choice, which reduces gene flow between habitats across the entire genome. The pattern of genomic isolation-by-environment closely matches differentiation observed at the phenotypic level, thereby offering significant potential for future inference of phenotype-genotype associations in a heterogeneous environment.

Single-Locus versus Multilocus Patterns of Local Adaptation to Climate in Eastern White Pine (Pinus strobus, Pinaceae)

Natural plant populations are often adapted to their local climate and environmental conditions, and populations of forest trees offer some of the best examples of this pattern. However, little empirical work has focused on the relative contribution of single-locus versus multilocus effects to the genetic architecture of local adaptation in plants/forest trees. Here, we employ eastern white pine (Pinus strobus) to test the hypothesis that it is the inter-genic effects that primarily drive climate-induced local adaptation. The genetic structure of 29 range-wide natural populations of eastern white pine was determined in relation to local climatic factors using both a reference set of SSR markers, and SNPs located in candidate genes putatively involved in adaptive response to climate. Comparisons were made between marker sets using standard single-locus outlier analysis, single-locus and multilocus environment association analyses and a novel implementation of Population Graphs. Magnitudes of population structure were similar between the two marker sets. Outlier loci consistent with diversifying selection were rare for both SNPs and SSRs. However, genetic distances based on the multilocus among population covariances (cGD) were significantly more correlated to climate, even after correcting for spatial effects, for SNPs as compared to SSRs. Coalescent simulations confirmed that the differences in mutation rates between SSRs and SNPs did not affect the topologies of the Population Graphs, and hence values of cGD and their correlations with associated climate variables. We conclude that the multilocus covariances among populations primarily reflect adaptation to local climate and environment in eastern white pine. This result highlights the complexity of the genetic architecture of adaptive traits, as well as the need to consider multilocus effects in studies of local adaptation.

Identifying Genetic Signatures of Natural Selection Using Pooled Population Sequencing in Picea abies

The joint inference of selection and past demography remain a costly and demanding task. We used next generation sequencing of two pools of 48 Norway spruce mother trees, one corresponding to the Fennoscandian domain, and the other to the Alpine domain, to assess nucleotide polymorphism at 88 nuclear genes. These genes are candidate genes for phenological traits, and most belong to the photoperiod pathway. Estimates of population genetic summary statistics from the pooled data are similar to previous estimates, suggesting that pooled sequencing is reliable. The nonsynonymous SNPs tended to have both lower frequency differences and lower F_ST values between the two domains than silent ones. These results suggest the presence of purifying selection. The divergence between the two domains based on synonymous changes was around 5 million yr, a time similar to a recent phylogenetic estimate of 6 million yr, but much larger than earlier estimates based on isozymes. Two approaches, one of them novel and that considers both F_ST and difference in allele frequencies between the two domains, were used to identify SNPs potentially under diversifying selection. SNPs from around 20 genes were detected, including genes previously identified as main target for selection, such as PaPRR3 and PaGI.

Microevolution of European temperate oaks in response to environmental changes

This review reconstructs microevolutionary processes that allowed long-lived species as temperate oaks (Quercus petraea and Q. robur) to cope with climate change since the last glacial maximum, by assembling insights from complementary synchronic and allochronic approaches. Paleobotanical and genetic investigations show that oaks migrated at larger velocities than expected, thanks to long-distance rare events and most likely human interferences. Hybridization was a key mechanism accelerating migration and enhancing species succession. Common garden experiments and genome wide association studies demonstrated that diversifying selection across large environmental gradients contributed to rapid local adaptation. Finally the review explores how lessons taken from past evolutionary scenarios may help to predict future responses of oaks to ongoing climate change.

What drivers phenotypic divergence in Leymus chinensis (Poaceae) on large-scale gradient, climate or genetic differentiation?

Elucidating the driving factors among-population divergence is an important task in evolutionary biology, however the relative contribution from natural selection and neutral genetic differentiation has been less debated. A manipulation experiment was conducted to examine whether the phenotypic divergence of Leymus chinensis depended on climate variations or genetic differentiations at 18 wild sites along a longitudinal gradient from 114 to 124°E in northeast China and at common garden condition of transplantation. Demographical, morphological and physiological phenotypes of 18 L. chinensis populations exhibited significant divergence along the gradient, but these divergent variations narrowed significantly at the transplantation. Moreover, most of the phenotypes were significantly correlated with mean annual precipitation and temperature in wild sites, suggesting that climatic variables played vital roles in phenotypic divergence of the species. Relative greater heterozygosity (H_E), genotype evenness (E) and Shannon-Wiener diversity (I) in western group of populations suggested that genetic differentiation also drove phenotypic divergence of the species. However, neutral genetic differentiation (F_ST = 0.041) was greatly lower than quantitative differentiation (Q_ST = 0.199), indicating that divergent selection/climate variable was the main factor in determining the phenotypic divergence of the species along the large-scale gradient.

Population genetic evidence for cold adaptation in European Drosophila melanogaster populations

We studied Drosophila melanogaster populations from Europe (the Netherlands and France) and Africa (Rwanda and Zambia) to uncover genetic evidence of adaptation to cold. We present here four lines of evidence for genes involved in cold adaptation from four perspectives: (i) the frequency of SNPs at genes previously known to be associated with chill-coma recovery time (CCRT), startle reflex (SR) and resistance to starvation stress (RSS) vary along environmental gradients and therefore among populations; (ii) SNPs of genes that correlate significantly with latitude and altitude in African and European populations overlap with SNPs that correlate with a latitudinal cline from North America; (iii) at the genomewide level, the top candidate genes are enriched in gene ontology (GO) terms that are related to cold tolerance; (iv) GO enriched terms from North American clinal genes overlap significantly with those from Africa and Europe. Each SNP was tested in 10 independent runs of Bayenv2, using the median Bayes factors to ascertain candidate genes. None of the candidate genes were found close to the breakpoints of cosmopolitan inversions, and only four candidate genes were linked to QTLs related to CCRT. To overcome the limitation that we used only four populations to test correlations with environmental gradients, we performed simulations to estimate the power of our approach for detecting selection. Based on our results, we propose a novel network of genes that is involved in cold adaptation.

Landscape genomic analysis of candidate genes for climate adaptation in a California endemic oak, Quercus lobata

PREMISE OF THE STUDY

The ability of California tree populations to survive anthropogenic climate change will be shaped by the geographic structure of adaptive genetic variation. Our goal is to test whether climate-associated candidate genes show evidence of spatially divergent selection in natural populations of valley oak, Quercus lobata, as preliminary indication of local adaptation.

METHODS

Using DNA from 45 individuals from 13 localities across the species' range, we sequenced portions of 40 candidate genes related to budburst/flowering, growth, osmotic stress, and temperature stress. Using 195 single nucleotide polymorphisms (SNPs), we estimated genetic differentiation across populations and correlated allele frequencies with climate gradients using single-locus and multivariate models.

RESULTS

The top 5% of FST estimates ranged from 0.25 to 0.68, yielding loci potentially under spatially divergent selection. Environmental analyses of SNP frequencies with climate gradients revealed three significantly correlated SNPs within budburst/flowering genes and two SNPs within temperature stress genes with mean annual precipitation, after controlling for multiple testing. A redundancy model showed a significant association between SNPs and climate variables and revealed a similar set of SNPs with high loadings on the first axis. In the RDA, climate accounted for 67% of the explained variation, when holding climate constant, in contrast to a putatively neutral SSR data set where climate accounted for only 33%.

CONCLUSIONS

Population differentiation and geographic gradients of allele frequencies in climate-associated functional genes in Q. lobata provide initial evidence of adaptive genetic variation and background for predicting population response to climate change.

Conifer genomics and adaptation: at the crossroads of genetic diversity and genome function

Conifers have been understudied at the genomic level despite their worldwide ecological and economic importance but the situation is rapidly changing with the development of next generation sequencing (NGS) technologies. With NGS, genomics research has simultaneously gained in speed, magnitude and scope. In just a few years, genomes of 20-24 gigabases have been sequenced for several conifers, with several others expected in the near future. Biological insights have resulted from recent sequencing initiatives as well as genetic mapping, gene expression profiling and gene discovery research over nearly two decades. We review the knowledge arising from conifer genomics research emphasizing genome evolution and the genomic basis of adaptation, and outline emerging questions and knowledge gaps. We discuss future directions in three areas with potential inputs from NGS technologies: the evolutionary impacts of adaptation in conifers based on the adaptation-by-speciation model; the contributions of genetic variability of gene expression in adaptation; and the development of a broader understanding of genetic diversity and its impacts on genome function. These research directions promise to sustain research aimed at addressing the emerging challenges of adaptation that face conifer trees.

Time to get moving: assisted gene flow of forest trees

Geographic variation in trees has been investigated since the mid-18th century. Similar patterns of clinal variation have been observed along latitudinal and elevational gradients in common garden experiments for many temperate and boreal species. These studies convinced forest managers that a 'local is best' seed source policy was usually safest for reforestation. In recent decades, experimental design, phenotyping methods, climatic data and statistical analyses have improved greatly and refined but not radically changed knowledge of clines. The maintenance of local adaptation despite high gene flow suggests selection for local adaptation to climate is strong. Concerns over maladaptation resulting from climate change have motivated many new genecological and population genomics studies; however, few jurisdictions have implemented assisted gene flow (AGF), the translocation of pre-adapted individuals to facilitate adaptation of planted forests to climate change. Here, we provide evidence that temperate tree species show clines along climatic gradients sufficiently similar for average patterns or climate models to guide AGF in the absence of species-specific knowledge. Composite provenancing of multiple seed sources can be used to increase diversity and buffer against future climate uncertainty. New knowledge will continue to refine and improve AGF as climates warm further.

Detecting spatial genetic signatures of local adaptation in heterogeneous landscapes

The spatial structure of the environment (e.g. the configuration of habitat patches) may play an important role in determining the strength of local adaptation. However, previous studies of habitat heterogeneity and local adaptation have largely been limited to simple landscapes, which poorly represent the multiscale habitat structure common in nature. Here, we use simulations to pursue two goals: (i) we explore how landscape heterogeneity, dispersal ability and selection affect the strength of local adaptation, and (ii) we evaluate the performance of several genotype-environment association (GEA) methods for detecting loci involved in local adaptation. We found that the strength of local adaptation increased in spatially aggregated selection regimes, but remained strong in patchy landscapes when selection was moderate to strong. Weak selection resulted in weak local adaptation that was relatively unaffected by landscape heterogeneity. In general, the power of detection methods closely reflected levels of local adaptation. False-positive rates (FPRs), however, showed distinct differences across GEA methods based on levels of population structure. The univariate GEA approach had high FPRs (up to 55%) under limited dispersal scenarios, due to strong isolation by distance. By contrast, multivariate, ordination-based methods had uniformly low FPRs (0-2%), suggesting these approaches can effectively control for population structure. Specifically, constrained ordinations had the best balance of high detection and low FPRs and will be a useful addition to the GEA toolkit. Our results provide both theoretical and practical insights into the conditions that shape local adaptation and how these conditions impact our ability to detect selection.

Evolutionary Quantitative Genomics of Populus trichocarpa

Forest trees generally show high levels of local adaptation and efforts focusing on understanding adaptation to climate will be crucial for species survival and management. Here, we address fundamental questions regarding the molecular basis of adaptation in undomesticated forest tree populations to past climatic environments by employing an integrative quantitative genetics and landscape genomics approach. Using this comprehensive approach, we studied the molecular basis of climate adaptation in 433 Populus trichocarpa (black cottonwood) genotypes originating across western North America. Variation in 74 field-assessed traits (growth, ecophysiology, phenology, leaf stomata, wood, and disease resistance) was investigated for signatures of selection (comparing Q_ST -F_ST) using clustering of individuals by climate of origin (temperature and precipitation). 29,354 SNPs were investigated employing three different outlier detection methods and marker-inferred relatedness was estimated to obtain the narrow-sense estimate of population differentiation in wild populations. In addition, we compared our results with previously assessed selection of candidate SNPs using the 25 topographical units (drainages) across the P. trichocarpa sampling range as population groupings. Narrow-sense Q_ST for 53% of distinct field traits was significantly divergent from expectations of neutrality (indicating adaptive trait variation); 2,855 SNPs showed signals of diversifying selection and of these, 118 SNPs (within 81 genes) were associated with adaptive traits (based on significant Q_ST). Many SNPs were putatively pleiotropic for functionally uncorrelated adaptive traits, such as autumn phenology, height, and disease resistance. Evolutionary quantitative genomics in P. trichocarpa provides an enhanced understanding regarding the molecular basis of climate-driven selection in forest trees and we highlight that important loci underlying adaptive trait variation also show relationship to climate of origin. We consider our approach the most comprehensive, as it uncovers the molecular mechanisms of adaptation using multiple methods and tests. We also provide a detailed outline of the required analyses for studying adaptation to the environment in a population genomics context to better understand the species’ potential adaptive capacity to future climatic scenarios.

Population size, habitat fragmentation, and the nature of adaptive variation in a stream fish

Whether and how habitat fragmentation and population size jointly affect adaptive genetic variation and adaptive population differentiation are largely unexplored. Owing to pronounced genetic drift, small, fragmented populations are thought to exhibit reduced adaptive genetic variation relative to large populations. Yet fragmentation is known to increase variability within and among habitats as population size decreases. Such variability might instead favour the maintenance of adaptive polymorphisms and/or generate more variability in adaptive differentiation at smaller population size. We investigated these alternative hypotheses by analysing coding-gene, single-nucleotide polymorphisms associated with different biological functions in fragmented brook trout populations of variable sizes. Putative adaptive differentiation was greater between small and large populations or among small populations than among large populations. These trends were stronger for genetic population size measures than demographic ones and were present despite pronounced drift in small populations. Our results suggest that fragmentation affects natural selection and that the changes elicited in the adaptive genetic composition and differentiation of fragmented populations vary with population size. By generating more variable evolutionary responses, the alteration of selective pressures during habitat fragmentation may affect future population persistence independently of, and perhaps long before, the effects of demographic and genetic stochasticity are manifest.

Genetic Adaptation to Climate in White Spruce Involves Small to Moderate Allele Frequency Shifts in Functionally Diverse Genes

Understanding the genetic basis of adaptation to climate is of paramount importance for preserving and managing genetic diversity in plants in a context of climate change. Yet, this objective has been addressed mainly in short-lived model species. Thus, expanding knowledge to nonmodel species with contrasting life histories, such as forest trees, appears necessary. To uncover the genetic basis of adaptation to climate in the widely distributed boreal conifer white spruce (Picea glauca), an environmental association study was conducted using 11,085 single nucleotide polymorphisms representing 7,819 genes, that is, approximately a quarter of the transcriptome.

Linear and quadratic regressions controlling for isolation-by-distance, and the Random Forest algorithm, identified several dozen genes putatively under selection, among which 43 showed strongest signals along temperature and precipitation gradients. Most of them were related to temperature. Small to moderate shifts in allele frequencies were observed. Genes involved encompassed a wide variety of functions and processes, some of them being likely important for plant survival under biotic and abiotic environmental stresses according to expression data. Literature mining and sequence comparison also highlighted conserved sequences and functions with angiosperm homologs.

Our results are consistent with theoretical predictions that local adaptation involves genes with small frequency shifts when selection is recent and gene flow among populations is high. Accordingly, genetic adaptation to climate in P. glauca appears to be complex, involving many independent and interacting gene functions, biochemical pathways, and processes. From an applied perspective, these results shall lead to specific functional/association studies in conifers and to the development of markers useful for the conservation of genetic resources.

Standing and flowing: the complex origins of adaptive variation

A population faced with a new selection pressure can only adapt if appropriate genetic variation is available. This genetic variation might come from new mutations or from gene exchange with other populations or species, or it might already segregate in the population as standing genetic variation (which might itself have arisen from either mutation or gene flow). Understanding the relative importance of these sources of adaptive variation is a fundamental issue in evolutionary genetics (Orr & Betancourt ; Barrett & Schluter ; Gladyshev et al. ) and has practical implications for conservation, plant and animal breeding, biological control and infectious disease prevention (e.g. Robertson ; Soulé & Wilcox ; Prentis et al. ; Pennings ). In this issue of Molecular Ecology, Roesti et al. () make an important contribution to this longstanding debate.

Integration of Random Forest with population-based outlier analyses provides insight on the genomic basis and evolution of run timing in Chinook salmon (Oncorhynchus tshawytscha)

Anadromous Chinook salmon populations vary in the period of river entry at the initiation of adult freshwater migration, facilitating optimal arrival at natal spawning. Run timing is a polygenic trait that shows evidence of rapid parallel evolution in some lineages, signifying a key role for this phenotype in the ecological divergence between populations. Studying the genetic basis of local adaptation in quantitative traits is often impractical in wild populations. Therefore, we used a novel approach, Random Forest, to detect markers linked to run timing across 14 populations from contrasting environments in the Columbia River and Puget Sound, USA. The approach permits detection of loci of small effect on the phenotype. Divergence between populations at these loci was then examined using both principle component analysis and FST outlier analyses, to determine whether shared genetic changes resulted in similar phenotypes across different lineages. Sequencing of 9107 RAD markers in 414 individuals identified 33 predictor loci explaining 79.2% of trait variance. Discriminant analysis of principal components of the predictors revealed both shared and unique evolutionary pathways in the trait across different lineages, characterized by minor allele frequency changes. However, genome mapping of predictor loci also identified positional overlap with two genomic outlier regions, consistent with selection on loci of large effect. Therefore, the results suggest selective sweeps on few loci and minor changes in loci that were detected by this study. Use of a polygenic framework has provided initial insight into how divergence in a trait has occurred in the wild.

Fifteen years of genomewide scans for selection: trends, lessons and unaddressed genetic sources of complication

Genomewide scans for natural selection (GWSS) have become increasingly common over the last 15 years due to increased availability of genome-scale genetic data. Here, we report a representative survey of GWSS from 1999 to present and find that (i) between 1999 and 2009, 35 of 49 (71%) GWSS focused on human, while from 2010 to present, only 38 of 83 (46%) of GWSS focused on human, indicating increased focus on nonmodel organisms; (ii) the large majority of GWSS incorporate interpopulation or interspecific comparisons using, for example F(ST), cross-population extended haplotype homozygosity or the ratio of nonsynonymous to synonymous substitutions; (iii) most GWSS focus on detection of directional selection rather than other modes such as balancing selection; and (iv) in human GWSS, there is a clear shift after 2004 from microsatellite markers to dense SNP data. A survey of GWSS meant to identify loci positively selected in response to severe hypoxic conditions support an approach to GWSS in which a list of a priori candidate genes based on potential selective pressures are used to filter the list of significant hits a posteriori. We also discuss four frequently ignored determinants of genomic heterogeneity that complicate GWSS: mutation, recombination, selection and the genetic architecture of adaptive traits. We recommend that GWSS methodology should better incorporate aspects of genomewide heterogeneity using empirical estimates of relevant parameters and/or realistic, whole-chromosome simulations to improve interpretation of GWSS results. Finally, we argue that knowledge of potential selective agents improves interpretation of GWSS results and that new methods focused on correlations between environmental variables and genetic variation can help automate this approach.

Reliable Detection of Loci Responsible for Local Adaptation: Inference of a Null Model through Trimming the Distribution of F(ST)

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 F(ST)--depends on the details of the demographic history of the populations in question, even without spatially heterogeneous selection. Many methods designed to detect F(ST) 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 F(ST) 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.