THE SIGNATURE OF POSITIVE SELECTION ON STANDING GENETIC VARIATION

Evaluating ARG-estimation methods in the context of estimating population-mean polygenic score histories

Scalable methods for estimating marginal coalescent trees across the genome present new opportunities for studying evolution and have generated considerable excitement, with new methods extending scalability to thousands of samples. Benchmarking of the available methods has revealed general tradeoffs between accuracy and scalability, but performance in downstream applications has not always been easily predictable from general performance measures, suggesting that specific features of the ancestral recombination graph (ARG) may be important for specific downstream applications of estimated ARGs. To exemplify this point, we benchmark ARG estimation methods with respect to a specific set of methods for estimating the historical time course of a population-mean polygenic score (PGS) using the marginal coalescent trees encoded by the ARG. Here, we examine the performance in simulation of seven ARG estimation methods: ARGweaver, RENT+, Relate, tsinfer+tsdate, ARG-Needle, ASMC-clust, and SINGER, using their estimated coalescent trees and examining bias, mean squared error, confidence interval coverage, and Type I and II error rates of the downstream methods. Although it does not scale to the sample sizes attainable by other new methods, SINGER produced the most accurate estimated PGS histories in many instances, even when Relate, tsinfer+tsdate, ARG-Needle, and ASMC-clust used samples 10 or more times as large as those used by SINGER. In general, the best choice of method depends on the number of samples available and the historical time period of interest. In particular, the unprecedented sample sizes allowed by Relate, tsinfer+tsdate, ARG-Needle, and ASMC-clust are of greatest importance when the recent past is of interest-further back in time, most of the tree has coalesced, and differences in contemporary sample size are less salient.

Negative global-scale association between genetic diversity and speciation rates in mammals

Genetic diversity is critical for species evolution and their adaptability to global changes, while speciation rate is critical for explaining large-scale patterns of species richness. Exploring correlates of variation in genetic diversity and speciation rates across species is a major interest of evolutionary biologists, but these two questions have mostly been investigated independently. Here, we assess the relationship between intra-specific genetic diversity and speciation rate for 1897 mammal species (~one third of the total diversity) covering all mammalian orders. We find a negative association between mitochondrial genetic diversity and speciation rate across mammalian clades globally. This association is not accounted for by differences in the ecological attributes of species. Our findings suggest a systematic link between micro- and macroevolutionary processes that need to be better understood and considered when investigating determinants of either genetic diversity or speciation rates.

Evaluating ARG-estimation methods in the context of estimating population-mean polygenic score histories

Scalable methods for estimating marginal coalescent trees across the genome present new opportunities for studying evolution and have generated considerable excitement, with new methods extending scalability to thousands of samples. Benchmarking of the available methods has revealed general tradeoffs between accuracy and scalability, but performance in downstream applications has not always been easily predictable from general performance measures, suggesting that specific features of the ARG may be important for specific downstream applications of estimated ARGs. To exemplify this point, we benchmark ARG estimation methods with respect to a specific set of methods for estimating the historical time course of a population-mean polygenic score (PGS) using the marginal coalescent trees encoded by the ancestral recombination graph (ARG). Here we examine the performance in simulation of seven ARG estimation methods: ARGweaver, RENT+, Relate, tsinfer+tsdate, ARG-Needle, ASMC-clust, and SINGER, using their estimated coalescent trees and examining bias, mean squared error (MSE), confidence interval coverage, and Type I and II error rates of the downstream methods. Although it does not scale to the sample sizes attainable by other new methods, SINGER produced the most accurate estimated PGS histories in many instances, even when Relate, tsinfer+tsdate, ARG-Needle and ASMC-clust used samples ten or more times as large as those used by SINGER. In general, the best choice of method depends on the number of samples available and the historical time period of interest. In particular, the unprecedented sample sizes allowed by Relate, tsinfer+tsdate, ARG-Needle, and ASMC-clust are of greatest importance when the recent past is of interest-further back in time, most of the tree has coalesced, and differences in contemporary sample size are less salient.

Tree Sequences as a General-Purpose Tool for Population Genetic Inference

As population genetic data increase in size, new methods have been developed to store genetic information in efficient ways, such as tree sequences. These data structures are computationally and storage efficient but are not interchangeable with existing data structures used for many population genetic inference methodologies such as the use of convolutional neural networks applied to population genetic alignments. To better utilize these new data structures, we propose and implement a graph convolutional network to directly learn from tree sequence topology and node data, allowing for the use of neural network applications without an intermediate step of converting tree sequences to population genetic alignment format. We then compare our approach to standard convolutional neural network approaches on a set of previously defined benchmarking tasks including recombination rate estimation, positive selection detection, introgression detection, and demographic model parameter inference. We show that tree sequences can be directly learned from using a graph convolutional network approach and can be used to perform well on these common population genetic inference tasks with accuracies roughly matching or even exceeding that of a convolutional neural network-based method. As tree sequences become more widely used in population genetic research, we foresee developments and optimizations of this work to provide a foundation for population genetic inference moving forward.

The impacts of positive selection on genomic variation in Drosophila serrata: Insights from a deep learning approach

This study explores the impact of positive selection on the genetic composition of a Drosophila serrata population in eastern Australia through a comprehensive analysis of 110 whole genome sequences. Utilizing an advanced deep learning algorithm (partialS/HIC) and a range of inferred demographic histories, we identified that approximately 14% of the genome is directly affected by sweeps, with soft sweeps being more prevalent (10.6%) than hard sweeps (2.1%), and partial sweeps being uncommon (1.3%). The algorithm demonstrated robustness to demographic assumptions in classifying complete sweeps but faced challenges in distinguishing neutral regions from partial sweeps and linked regions under demographic misspecification. The findings reveal the indirect influence of sweeps on nearly two-thirds of the genome through linkage, with an over-representation of putatively deleterious variants suggesting that positive selection drags deleterious variants to higher frequency due to hitchhiking with beneficial loci. Gene ontology enrichment analysis further supported our confidence in the accuracy of sweep detection as several traits expected to be under positive selection due to evolutionary arms races (e.g. immunity) were detected in hard sweeps. This study provides valuable insights into the direct and indirect contributions of positive selection in shaping genomic variation in natural populations.

No evidence that ACE2 or TMPRSS2 drive population disparity in COVID risks

Early in the SARS-CoV2 pandemic, in this journal, Hou et al. (BMC Med 18:216, 2020) interpreted public genotype data, run through functional prediction tools, as suggesting that members of particular human populations carry potentially COVID-risk-increasing variants in genes ACE2 and TMPRSS2 far more often than do members of other populations. Beyond resting on predictions rather than clinical outcomes, and focusing on variants too rare to typify population members even jointly, their claim mistook a well known artifact (that large samples reveal more of a population's variants than do small samples) as if showing real and congruent population differences for the two genes, rather than lopsided population sampling in their shared source data. We explain that artifact, and contrast it with empirical findings, now ample, that other loci shape personal COVID risks far more significantly than do ACE2 and TMPRSS2-and that variation in ACE2 and TMPRSS2 per se unlikely exacerbates any net population disparity in the effects of such more risk-informative loci.

Signatures of selective sweeps in continuous-space populations

Selective sweeps describe the process by which an adaptive mutation arises and rapidly fixes in the population, thereby removing genetic variation in its genomic vicinity. The expected signatures of selective sweeps are relatively well understood in panmictic population models, yet natural populations often extend across larger geographic ranges where individuals are more likely to mate with those born nearby. To investigate how such spatial population structure can affect sweep dynamics and signatures, we simulated selective sweeps in populations inhabiting a two-dimensional continuous landscape. The maximum dispersal distance of offspring from their parents can be varied in our simulations from an essentially panmictic population to scenarios with increasingly limited dispersal. We find that in low-dispersal populations, adaptive mutations spread more slowly than in panmictic ones, while recombination becomes less effective at breaking up genetic linkage around the sweep locus. Together, these factors result in a trough of reduced genetic diversity around the sweep locus that looks very similar across dispersal rates. We also find that the site frequency spectrum around hard sweeps in low-dispersal populations becomes enriched for intermediate-frequency variants, making these sweeps appear softer than they are. Furthermore, haplotype heterozygosity at the sweep locus tends to be elevated in low-dispersal scenarios as compared to panmixia, contrary to what we observe in neutral scenarios without sweeps. The haplotype patterns generated by these hard sweeps in low-dispersal populations can resemble soft sweeps from standing genetic variation that arose from substantially older alleles. Our results highlight the need for better accounting for spatial population structure when making inferences about selective sweeps.

Allelic gene conversion softens selective sweeps

The prominence of positive selection, in which beneficial mutations are favored by natural selection and rapidly increase in frequency, is a subject of intense debate. Positive selection can result in selective sweeps, in which the haplotype(s) bearing the adaptive allele "sweep" through the population, thereby removing much of the genetic diversity from the region surrounding the target of selection. Two models of selective sweeps have been proposed: classical sweeps, or "hard sweeps", in which a single copy of the adaptive allele sweeps to fixation, and "soft sweeps", in which multiple distinct copies of the adaptive allele leave descendants after the sweep. Soft sweeps can be the outcome of recurrent mutation to the adaptive allele, or the presence of standing genetic variation consisting of multiple copies of the adaptive allele prior to the onset of selection. Importantly, soft sweeps will be common when populations can rapidly adapt to novel selective pressures, either because of a high mutation rate or because adaptive alleles are already present. The prevalence of soft sweeps is especially controversial, and it has been noted that selection on standing variation or recurrent mutations may not always produce soft sweeps. Here, we show that the inverse is true: selection on single-origin de novo mutations may often result in an outcome that is indistinguishable from a soft sweep. This is made possible by allelic gene conversion, which "softens" hard sweeps by copying the adaptive allele onto multiple genetic backgrounds, a process we refer to as a "pseudo-soft" sweep. We carried out a simulation study examining the impact of gene conversion on sweeps from a single de novo variant in models of human, Drosophila, and Arabidopsis populations. The fraction of simulations in which gene conversion had produced multiple haplotypes with the adaptive allele upon fixation was appreciable. Indeed, under realistic demographic histories and gene conversion rates, even if selection always acts on a single-origin mutation, sweeps involving multiple haplotypes are more likely than hard sweeps in large populations, especially when selection is not extremely strong. Thus, even when the mutation rate is low or there is no standing variation, hard sweeps are expected to be the exception rather than the rule in large populations. These results also imply that the presence of signatures of soft sweeps does not necessarily mean that adaptation has been especially rapid or is not mutation limited.

Power of neutrality tests for detecting natural selection

Detection of natural selection is one of the main interests in population genetics. Thus, many tests have been developed for detecting natural selection using genomic data. Although it is recognized that the utility of tests depends on several evolutionary factors, such as the timing of selection, strength of selection, frequency of selected alleles, demographic events, and initial frequency of selected allele when selection started acting (softness of selection), the relationships between such evolutionary factors and the power of tests are not yet entirely clear. In this study, we investigated the power of 4 tests: Tajiama's D, Fay and Wu's H, relative extended haplotype homozygosity (rEHH), and integrated haplotype score (iHS), under ranges of evolutionary parameters and demographic models to quantitatively expand the understanding of approaches for detecting selection. The results show that each test detects selection within a limited parameter range, and there are still wide ranges of parameters for which none of these tests work effectively. In addition, the parameter space in which each test shows the highest power overlaps the empirical results of previous research. These results indicate that our present perspective of adaptation is limited to only a part of actual adaptation.

Mutation and selection processes regulating short tandem repeats give rise to genetic and phenotypic diversity across species

Short tandem repeats (STRs) are units of 1-6 bp that repeat in a tandem fashion in DNA. Along with single nucleotide polymorphisms and large structural variations, they are among the major genomic variants underlying genetic, and likely phenotypic, divergence. STRs experience mutation rates that are orders of magnitude higher than other well-studied genotypic variants. Frequent copy number changes result in a wide range of alleles, and provide unique opportunities for modulating complex phenotypes through variation in repeat length. While classical studies have identified key roles of individual STR loci, the advent of improved sequencing technology, high-quality genome assemblies for diverse species, and bioinformatics methods for genome-wide STR analysis now enable more systematic study of STR variation across wide evolutionary ranges. In this review, we explore mutation and selection processes that affect STR copy number evolution, and how these processes give rise to varying STR patterns both within and across species. Finally, we review recent examples of functional and adaptive changes linked to STRs.

Ecological Speciation Promoted by Divergent Regulation of Functional Genes Within African Cichlid Fishes

Rapid ecological speciation along depth gradients has taken place repeatedly in freshwater fishes, yet molecular mechanisms facilitating such diversification are typically unclear. In Lake Masoko, an African crater lake, the cichlid Astatotilapia calliptera has diverged into shallow-littoral and deep-benthic ecomorphs with strikingly different jaw structures within the last 1,000 years. Using genome-wide transcriptome data, we explore two major regulatory transcriptional mechanisms, expression and splicing-QTL variants, and examine their contributions to differential gene expression underpinning functional phenotypes. We identified 7,550 genes with significant differential expression between ecomorphs, of which 5.4% were regulated by cis-regulatory expression QTLs, and 9.2% were regulated by cis-regulatory splicing QTLs. We also found strong signals of divergent selection on differentially expressed genes associated with craniofacial development. These results suggest that large-scale transcriptome modification plays an important role during early-stage speciation. We conclude that regulatory variants are important targets of selection driving ecologically relevant divergence in gene expression during adaptive diversification.

The roles of balancing selection and recombination in the evolution of rattlesnake venom

The origin of snake venom involved duplication and recruitment of non-venom genes into venom systems. Several studies have predicted that directional positive selection has governed this process. Venom composition varies substantially across snake species and venom phenotypes are locally adapted to prey, leading to coevolutionary interactions between predator and prey. Venom origins and contemporary snake venom evolution may therefore be driven by fundamentally different selection regimes, yet investigations of population-level patterns of selection have been limited. Here, we use whole-genome data from 68 rattlesnakes to test hypotheses about the factors that drive genomic diversity and differentiation in major venom gene regions. We show that selection has resulted in long-term maintenance of genetic diversity within and between species in multiple venom gene families. Our findings are inconsistent with a dominant role of directional positive selection and instead support a role of long-term balancing selection in shaping venom evolution. We also detect rapid decay of linkage disequilibrium due to high recombination rates in venom regions, suggesting that venom genes have reduced selective interference with nearby loci, including other venom paralogues. Our results provide an example of long-term balancing selection that drives trans-species polymorphism and help to explain how snake venom keeps pace with prey resistance.

Genetics of adaptation

The rediscovery of Mendel’s work showing that the heredity of phenotypes is controlled by discrete genes was followed by the reconciliation of Mendelian genetics with evolution by natural selection in the middle of the last century with the Modern Synthesis. In the past two decades, dramatic advances in genomic methods have facilitated the identification of the loci, genes, and even individual mutations that underlie phenotypic variants that are the putative targets of natural selection. Moreover, these methods have also changed how we can study adaptation by flipping the problem around, allowing us to first examine what loci show evidence of having been under selection, and then connecting these genetic variants to phenotypic variation. As a result, we now have an expanding list of actual genetic changes that underlie potentially adaptive phenotypic variation. Here, we synthesize how considering the effects of these adaptive loci in the context of cellular environments, genomes, organisms, and populations has provided new insights to the genetic architecture of adaptation.

Selection and demography drive range-wide patterns of MHC-DRB variation in mule deer

BACKGROUND

Standing genetic variation is important especially in immune response-related genes because of threats to wild populations like the emergence of novel pathogens. Genetic variation at the major histocompatibility complex (MHC), which is crucial in activating the adaptive immune response, is influenced by both natural selection and historical population demography, and their relative roles can be difficult to disentangle. To provide insight into the influences of natural selection and demography on MHC evolution in large populations, we analyzed geographic patterns of variation at the MHC class II DRB exon 2 locus in mule deer (Odocoileus hemionus) using sequence data collected across their entire broad range.

RESULTS

We identified 31 new MHC-DRB alleles which were phylogenetically similar to other cervid MHC alleles, and one allele that was shared with white-tailed deer (Odocoileus virginianus). We found evidence for selection on the MHC including high dN/dS ratios, positive neutrality tests, deviations from Hardy-Weinberg Equilibrium (HWE) and a stronger pattern of isolation-by-distance (IBD) than expected under neutrality. Historical demography also shaped variation at the MHC, as indicated by similar spatial patterns of variation between MHC and microsatellite loci and a lack of association between genetic variation at either locus type and environmental variables.

CONCLUSIONS

Our results show that both natural selection and historical demography are important drivers in the evolution of the MHC in mule deer and work together to shape functional variation and the evolution of the adaptive immune response in large, well-connected populations.

Understanding signatures of positive natural selection in human zinc transporter genes

Zinc is an essential micronutrient with a tightly regulated systemic and cellular homeostasis. In humans, some zinc transporter genes (ZTGs) have been previously reported as candidates for strong geographically restricted selective sweeps. However, since zinc homeostasis is maintained by the joint action of 24 ZTGs, other more subtle modes of selection could have also facilitated human adaptation to zinc availability. Here, we studied whether the complete set of ZTGs are enriched for signals of positive selection in worldwide populations and population groups from South Asia. ZTGs showed higher levels of genetic differentiation between African and non-African populations than would be randomly expected, as well as other signals of polygenic selection outside Africa. Moreover, in several South Asian population groups, ZTGs were significantly enriched for SNPs with unusually extended haplotypes and displayed SNP genotype-environmental correlations when considering zinc deficiency levels in soil in that geographical area. Our study replicated some well-characterized targets for positive selection in East Asia and sub-Saharan Africa, and proposes new candidates for follow-up in South Asia (SLC39A5) and Africa (SLC39A7). Finally, we identified candidate variants for adaptation in ZTGs that could contribute to different disease susceptibilities and zinc-related human health traits.

Comparative population genomics in Tabebuia alliance shows evidence of adaptation in Neotropical tree species

The role of natural selection in shaping spatial patterns of genetic diversity in the Neotropics is still poorly understood. Here, we perform a genome scan with 24,751 probes targeting 11,026 loci in two Neotropical Bignoniaceae tree species: Handroanthus serratifolius from the seasonally dry tropical forest (SDTF) and Tabebuia aurea from savannas, and compared with the population genomics of H. impetiginosus from SDTF. OutFLANK detected 29 loci in 20 genes with selection signal in H. serratifolius and no loci in T. aurea. Using BayPass, we found evidence of selection in 335 loci in 312 genes in H. serratifolius, 101 loci in 92 genes in T. aurea, and 448 loci in 416 genes in H. impetiginosus. All approaches evidenced several genes affecting plant response to environmental stress and primary metabolic processes. The three species shared no SNPs with selection signal, but we found SNPs affecting the same gene in pair of species. Handroanthus serratifolius showed differences in allele frequencies at SNPs with selection signal among ecosystems, mainly between Caatinga/Cerrado and Atlantic Forest, while H. impetiginosus had one allele fixed across all populations, and T. aurea had similar allele frequency distribution among ecosystems and polymorphism across populations. Taken together, our results indicate that natural selection related to environmental stress shaped the spatial pattern of genetic diversity in the three species. However, the three species have different geographical distribution and niches, which may affect tolerances and adaption, and natural selection may lead to different signatures due to the differences in adaptive landscapes in different niches.

Evolutionary Genetic Signatures of Selection on Bone-Related Variation within Human and Chimpanzee Populations

Bone strength and the incidence and severity of skeletal disorders vary significantly among human populations, due in part to underlying genetic differentiation. While clinical models predict that this variation is largely deleterious, natural population variation unrelated to disease can go unnoticed, altering our perception of how natural selection has shaped bone morphologies over deep and recent time periods. Here, we conduct the first comparative population-based genetic analysis of the main bone structural protein gene, collagen type I α 1 (COL1A1), in clinical and 1000 Genomes Project datasets in humans, and in natural populations of chimpanzees. Contrary to predictions from clinical studies, we reveal abundant COL1A1 amino acid variation, predicted to have little association with disease in the natural population. We also find signatures of positive selection associated with intron haplotype structure, linkage disequilibrium, and population differentiation in regions of known gene expression regulation in humans and chimpanzees. These results recall how recent and deep evolutionary regimes can be linked, in that bone morphology differences that developed among vertebrates over 450 million years of evolution are the result of positive selection on subtle type I collagen functional variation segregating within populations over time.

Detecting Selection in Multiple Populations by Modeling Ancestral Admixture Components

One of the most powerful and commonly used approaches for detecting local adaptation in the genome is the identification of extreme allele frequency differences between populations. In this article, we present a new maximum likelihood method for finding regions under positive selection. It is based on a Gaussian approximation to allele frequency changes and it incorporates admixture between populations. The method can analyze multiple populations simultaneously and retains power to detect selection signatures specific to ancestry components that are not representative of any extant populations. Using simulated data, we compare our method to related approaches, and show that it is orders of magnitude faster than the state-of-the-art, while retaining similar or higher power for most simulation scenarios. We also apply it to human genomic data and identify loci with extreme genetic differentiation between major geographic groups. Many of the genes identified are previously known selected loci relating to hair pigmentation and morphology, skin, and eye pigmentation. We also identify new candidate regions, including various selected loci in the Native American component of admixed Mexican-Americans. These involve diverse biological functions, such as immunity, fat distribution, food intake, vision, and hair development.

Sporadic occurrence of recent selective sweeps from standing variation in humans as revealed by an approximate Bayesian computation approach

During their dispersals over the last 100,000 years, modern humans have been exposed to a large variety of environments, resulting in genetic adaptation. While genome-wide scans for the footprints of positive Darwinian selection have increased knowledge of genes and functions potentially involved in human local adaptation, they have globally produced evidence of a limited contribution of selective sweeps in humans. Conversely, studies based on machine learning algorithms suggest that recent sweeps from standing variation are widespread in humans, an observation that has been recently questioned. Here, we sought to formally quantify the number of recent selective sweeps in humans, by leveraging approximate Bayesian computation and whole-genome sequence data. Our computer simulations revealed suitable ABC estimations, regardless of the frequency of the selected alleles at the onset of selection and the completion of sweeps. Under a model of recent selection from standing variation, we inferred that an average of 68 (from 56 to 79) and 140 (from 94 to 198) sweeps occurred over the last 100,000 years of human history, in African and Eurasian populations, respectively. The former estimation is compatible with human adaptation rates estimated since divergence with chimps, and reveals numbers of sweeps per generation per site in the range of values estimated in Drosophila. Our results confirm the rarity of selective sweeps in humans and show a low contribution of sweeps from standing variation to recent human adaptation.

Selective Sweeps and Polygenic Adaptation Drive Local Adaptation along Moisture and Temperature Gradients in Natural Populations of Coast Redwood and Giant Sequoia

Dissecting the genomic basis of local adaptation is a major goal in evolutionary biology and conservation science. Rapid changes in the climate pose significant challenges to the survival of natural populations, and the genomic basis of long-generation plant species is still poorly understood. Here, we investigated genome-wide climate adaptation in giant sequoia and coast redwood, two iconic and ecologically important tree species. We used a combination of univariate and multivariate genotype-environment association methods and a selective sweep analysis using non-overlapping sliding windows. We identified genomic regions of potential adaptive importance, showing strong associations to moisture variables and mean annual temperature. Our results found a complex architecture of climate adaptation in the species, with genomic regions showing signatures of selective sweeps, polygenic adaptation, or a combination of both, suggesting recent or ongoing climate adaptation along moisture and temperature gradients in giant sequoia and coast redwood. The results of this study provide a first step toward identifying genomic regions of adaptive significance in the species and will provide information to guide management and conservation strategies that seek to maximize adaptive potential in the face of climate change.

Eco-Evolutionary Dynamics in Microbial Communities from Spontaneous Fermented Foods

Eco-evolutionary forces are the key drivers of ecosystem biodiversity dynamics. This resulted in a large body of theory, which has partially been experimentally tested by mimicking evolutionary processes in the laboratory. In the first part of this perspective, we outline what model systems are used for experimental testing of eco-evolutionary processes, ranging from simple microbial combinations and, more recently, to complex natural communities. Microbial communities of spontaneous fermented foods are a promising model system to study eco-evolutionary dynamics. They combine the complexity of a natural community with extensive knowledge about community members and the ease of manipulating the system in a laboratory setup. Due to rapidly developing sequencing techniques and meta-omics approaches incorporating data in building ecosystem models, the diversity in these communities can be analysed with relative ease while hypotheses developed in simple systems can be tested. Here, we highlight several eco-evolutionary questions that are addressed using microbial communities from fermented foods. These questions relate to analysing species frequencies in space and time, the diversity-stability relationship, niche space and community coalescence. We provide several hypotheses of the influence of these factors on community evolution specifying the experimental setup of studies where microbial communities of spontaneous fermented food are used.

Threespine Stickleback: A Model System For Evolutionary Genomics

The repeated adaptation of oceanic threespine sticklebacks to fresh water has made it a premier organism to study parallel evolution. These small fish have multiple distinct ecotypes that display a wide range of diverse phenotypic traits. Ecotypes are easily crossed in the laboratory, and families are large and develop quickly enough for quantitative trait locus analyses, positioning the threespine stickleback as a versatile model organism to address a wide range of biological questions. Extensive genomic resources, including linkage maps, a high-quality reference genome, and developmental genetics tools have led to insights into the genomic basis of adaptation and the identification of genomic changes controlling traits in vertebrates. Recently, threespine sticklebacks have been used as a model system to identify the genomic basis of highly complex traits, such as behavior and host-microbiome and host-parasite interactions. We review the latest findings and new avenues of research that have led the threespine stickleback to be considered a supermodel of evolutionary genomics.

Parallel adaptation in autopolyploid Arabidopsis arenosa is dominated by repeated recruitment of shared alleles

Relative contributions of pre-existing vs de novo genomic variation to adaptation are poorly understood, especially in polyploid organisms. We assess this in high resolution using autotetraploid Arabidopsis arenosa, which repeatedly adapted to toxic serpentine soils that exhibit skewed elemental profiles. Leveraging a fivefold replicated serpentine invasion, we assess selection on SNPs and structural variants (TEs) in 78 resequenced individuals and discover significant parallelism in candidate genes involved in ion homeostasis. We further model parallel selection and infer repeated sweeps on a shared pool of variants in nearly all these loci, supporting theoretical expectations. A single striking exception is represented by TWO PORE CHANNEL 1, which exhibits convergent evolution from independent de novo mutations at an identical, otherwise conserved site at the calcium channel selectivity gate. Taken together, this suggests that polyploid populations can rapidly adapt to environmental extremes, calling on both pre-existing variation and novel polymorphisms.

Relative contributions of pre-existing versus de novo genomic variation to adaptation remain unclear. Here, the authors address this problem by examining the adaptation of autotetraploid Arabidopsis arenosa to serpentine soils and find that both types of variations contribute to rapid adaptation.

Natural variation in the consequences of gene overexpression and its implications for evolutionary trajectories

Copy number variation through gene or chromosome amplification provides a route for rapid phenotypic variation and supports the long-term evolution of gene functions. Although the evolutionary importance of copy-number variation is known, little is understood about how genetic background influences its tolerance. Here, we measured fitness costs of over 4000 overexpressed genes in 15 Saccharomyces cerevisiae strains representing different lineages, to explore natural variation in tolerating gene overexpression (OE). Strain-specific effects dominated the fitness costs of gene OE. We report global differences in the consequences of gene OE, independent of the amplified gene, as well as gene-specific effects that were dependent on the genetic background. Natural variation in the response to gene OE could be explained by several models, including strain-specific physiological differences, resource limitations, and regulatory sensitivities. This work provides new insight on how genetic background influences tolerance to gene amplification and the evolutionary trajectories accessible to different backgrounds.

Human adaptation, demography and cattle domestication: an overview of the complexity of lactase persistence in Africa

Lactase persistence (LP) is a genetically-determined trait that is prevalent in African, European and Arab populations with a tradition of animal herding and milk consumption. To date, genetic analyses have identified several common variants that are associated with LP. Furthermore, data have indicated that these functional alleles likely have been maintained in pastoralist populations due to the action of recent selection, exemplifying the ongoing evolution of anatomically modern humans. Additionally, demographic history has also played a role in the geographic distribution of LP and associated alleles in Africa. In particular, the migration of ancestral herders and their subsequent admixture with local populations were integral to the spread of LP alleles and the culture of pastoralism across the continent. The timing of these demographic events was often correlated with known major environmental changes and/or the ability of domesticated cattle to resist/avoid infectious diseases. This review summarizes recent advances in our understanding of the genetic basis and evolutionary history of LP, as well as the factors that influenced the origin and spread of pastoralism in Africa.

Genomic Targets of Positive Selection in Giant Mice from Gough Island

A key challenge in understanding how organisms adapt to their environments is to identify the mutations and genes that make it possible. By comparing patterns of sequence variation to neutral predictions across genomes, the targets of positive selection can be located. We applied this logic to house mice that invaded Gough Island (GI), an unusual population that shows phenotypic and ecological hallmarks of selection. We used massively parallel short-read sequencing to survey the genomes of 14 GI mice. We computed a set of summary statistics to capture diverse aspects of variation across these genome sequences, used approximate Bayesian computation to reconstruct a null demographic model, and then applied machine learning to estimate the posterior probability of positive selection in each region of the genome. Using a conservative threshold, 1,463 5-kb windows show strong evidence for positive selection in GI mice but not in a mainland reference population of German mice. Disproportionate shares of these selection windows contain genes that harbor derived nonsynonymous mutations with large frequency differences. Over-represented gene ontologies in selection windows emphasize neurological themes. Inspection of genomic regions harboring many selection windows with high posterior probabilities pointed to genes with known effects on exploratory behavior and body size as potential targets. Some genes in these regions contain candidate adaptive variants, including missense mutations and/or putative regulatory mutations. Our results provide a genomic portrait of adaptation to island conditions and position GI mice as a powerful system for understanding the genetic component of natural selection.

Polygenic Selection within a Single Generation Leads to Subtle Divergence among Ecological NichesINc

Selection on standing genetic variation may be effective enough to allow for adaptation to distinct niche environments within a single generation. Minor allele frequency changes at multiple, redundant loci of small effect can produce remarkable phenotypic shifts. Yet, demonstrating rapid adaptation via polygenic selection in the wild remains challenging. Here we harness natural replicate populations that experience similar selection pressures and harbor high within-, yet negligible among-population genetic variation. Such populations can be found among the teleost Fundulus heteroclitus that inhabits marine estuaries characterized by high environmental heterogeneity. We identify 10,861 single nucleotide polymorphisms in F. heteroclitus that belong to a single, panmictic population yet reside in environmentally distinct niches (one coastal basin and three replicate tidal ponds). By sampling at two time points within a single generation, we quantify both allele frequency change within as well as spatial divergence among niche subpopulations. We observe few individually significant allele frequency changes yet find that the "number" of moderate changes exceeds the neutral expectation by 10-100%. We find allele frequency changes to be significantly concordant in both direction and magnitude among all niche subpopulations, suggestive of parallel selection. In addition, within-generation allele frequency changes generate subtle but significant divergence among niches, indicative of local adaptation. Although we cannot distinguish between selection and genotype-dependent migration as drivers of within-generation allele frequency changes, the trait/s determining fitness and/or migration likelihood appear to be polygenic. In heterogeneous environments, polygenic selection and polygenic, genotype-dependent migration offer conceivable mechanisms for within-generation, local adaptation to distinct niches.

Genomic signatures of selection in bats surviving white-nose syndrome

Rapid evolution of advantageous traits following abrupt environmental change can help populations recover from demographic decline. However, for many introduced diseases affecting longer-lived, slower reproducing hosts, mortality is likely to outpace the acquisition of adaptive de novo mutations. Adaptive alleles must therefore be selected from standing genetic variation, a process that leaves few detectable genomic signatures. Here, we present whole genome evidence for selection in bat populations that are recovering from white-nose syndrome (WNS). We collected samples both during and after a WNS-induced mass mortality event in two little brown bat populations that are beginning to show signs of recovery and found signatures of soft sweeps from standing genetic variation at multiple loci throughout the genome. We identified one locus putatively under selection in a gene associated with the immune system. Multiple loci putatively under selection were located within genes previously linked to host response to WNS as well as to changes in metabolism during hibernation. Results from two additional populations suggested that loci under selection may differ somewhat among populations. Through these findings, we suggest that WNS-induced selection may contribute to genetic resistance in this slowly reproducing species threatened with extinction.

Population Genomics in the Great Apes

The great apes play an important role as model organisms. They are our closest living relatives, allowing us to identify the genetic basis of phenotypic traits that we think of as characteristically human. However, the most significant asset of great apes as model organisms is that they share with humans most of their genetic makeup. This means that we can extend our vast knowledge of the human genome, its genes, and the associated phenotypes to these species. Comparative genomic studies of humans and apes thus reveal how very similar genomes react when exposed to different population genetic regimes. In this way, each species represents a natural experiment, where a genome highly similar to the human one, is differently exposed to the evolutionary forces of demography, population structure, selection, recombination, and admixture/hybridization. The initial sequencing of reference genomes for chimpanzee, orangutan, gorilla, the bonobo, each provided new insights and a second generation of sequencing projects has provided diversity data for all the great apes. In this chapter, we will outline some of the findings that population genomic analysis of great apes has provided, and how comparative studies have helped us understand how the fundamental forces in evolution have contributed to shaping the genomes and the genetic diversity of the great apes.

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

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

Selective Sweeps Lead to Evolutionary Success in an Amazonian Hyperdominant Palm

Despite the global importance of tropical ecosystems, few studies have identified how natural selection has shaped their megadiversity. Here, we test for the role of adaptation in the evolutionary success of the widespread, highly abundant Neotropical palm Mauritia flexuosa. We used a genome scan framework, sampling 16,262 single-nucleotide polymorphisms (SNPs) with target sequence capture in 264 individuals from 22 populations in rainforest and savanna ecosystems. We identified outlier loci as well as signal of adaptation using Bayesian correlations of allele frequency with environmental variables and detected both selective sweeps and genetic hitchhiking events. Functional annotation of SNPs with selection footprints identified loci affecting genes related to adaptation to environmental stress, plant development, and primary metabolic processes. The strong differences in climatic and soil variables between ecosystems matched the high differentiation and low admixture in population Bayesian clustering. Further, we found only small differences in allele frequency distribution in loci putatively under selection among widespread populations from different ecosystems, with fixation of a single allele in most populations. Taken together, our results indicate that adaptive selective sweeps related to environmental stress shaped the spatial pattern of genetic diversity in M. flexuosa, leading to high similarity in allele frequency among populations from different ecosystems.

Polygenic adaptation: a unifying framework to understand positive selection

Most adaption processes have a polygenic genetic basis, but even with the recent explosive growth of genomic data we are still lacking a unified framework describing the dynamics of selected alleles. Building on recent theoretical and empirical work we introduce the concept of adaptive architecture, which extends the genetic architecture of an adaptive trait by factors influencing its adaptive potential and population genetic principles. Because adaptation can be typically achieved by many different combinations of adaptive alleles (redundancy), we describe how two characteristics - heterogeneity among loci and non-parallelism between replicated populations - are hallmarks for the characterization of polygenic adaptation in evolving populations. We discuss how this unified framework can be applied to natural and experimental populations.

Identifying adaptive alleles in the human genome: from selection mapping to functional validation

The suite of phenotypic diversity across geographically distributed human populations is the outcome of genetic drift, gene flow, and natural selection throughout human evolution. Human genetic variation underlying local biological adaptations to selective pressures is incompletely characterized. With the emergence of population genetics modeling of large-scale genomic data derived from diverse populations, scientists are able to map signatures of natural selection in the genome in a process known as selection mapping. Inferred selection signals further can be used to identify candidate functional alleles that underlie putative adaptive phenotypes. Phenotypic association, fine mapping, and functional experiments facilitate the identification of candidate adaptive alleles. Functional investigation of candidate adaptive variation using novel techniques in molecular biology is slowly beginning to unravel how selection signals translate to changes in biology that underlie the phenotypic spectrum of our species. In addition to informing evolutionary hypotheses of adaptation, the discovery and functional annotation of adaptive alleles also may be of clinical significance. While selection mapping efforts in non-European populations are growing, there remains a stark under-representation of diverse human populations in current public genomic databases, of both clinical and non-clinical cohorts. This lack of inclusion limits the study of human biological variation. Identifying and functionally validating candidate adaptive alleles in more global populations is necessary for understanding basic human biology and human disease.

VolcanoFinder: Genomic scans for adaptive introgression

Recent research shows that introgression between closely-related species is an important source of adaptive alleles for a wide range of taxa. Typically, detection of adaptive introgression from genomic data relies on comparative analyses that require sequence data from both the recipient and the donor species. However, in many cases, the donor is unknown or the data is not currently available. Here, we introduce a genome-scan method—VolcanoFinder—to detect recent events of adaptive introgression using polymorphism data from the recipient species only. VolcanoFinder detects adaptive introgression sweeps from the pattern of excess intermediate-frequency polymorphism they produce in the flanking region of the genome, a pattern which appears as a volcano-shape in pairwise genetic diversity. Using coalescent theory, we derive analytical predictions for these patterns. Based on these results, we develop a composite-likelihood test to detect signatures of adaptive introgression relative to the genomic background. Simulation results show that VolcanoFinder has high statistical power to detect these signatures, even for older sweeps and for soft sweeps initiated by multiple migrant haplotypes. Finally, we implement VolcanoFinder to detect archaic introgression in European and sub-Saharan African human populations, and uncovered interesting candidates in both populations, such as TSHR in Europeans and TCHH-RPTN in Africans. We discuss their biological implications and provide guidelines for identifying and circumventing artifactual signals during empirical applications of VolcanoFinder.

The process by which beneficial alleles are introduced into a species from a closely-related species is termed adaptive introgression. We present an analytically-tractable model for the effects of adaptive introgression on non-adaptive genetic variation in the genomic region surrounding the beneficial allele. The result we describe is a characteristic volcano-shaped pattern of increased variability that arises around the positively-selected site, and we introduce an open-source method VolcanoFinder to detect this signal in genomic data. Importantly, VolcanoFinder is a population-genetic likelihood-based approach, rather than a comparative-genomic approach, and can therefore probe genomic variation data from a single population for footprints of adaptive introgression, even from a priori unknown and possibly extinct donor species.

Distinct Patterns of Selective Sweep and Polygenic Adaptation in Evolve and Resequence Studies

In molecular population genetics, adaptation is typically thought to occur via selective sweeps, where targets of selection have independent effects on the phenotype and rise to fixation, whereas in quantitative genetics, many loci contribute to the phenotype and subtle frequency changes occur at many loci during polygenic adaptation. The sweep model makes specific predictions about frequency changes of beneficial alleles and many test statistics have been developed to detect such selection signatures. Despite polygenic adaptation is probably the prevalent mode of adaptation, because of the traditional focus on the phenotype, we are lacking a solid understanding of the similarities and differences of selection signatures under the two models. Recent theoretical and empirical studies have shown that both selective sweep and polygenic adaptation models could result in a sweep-like genomic signature; therefore, additional criteria are needed to distinguish the two models. With replicated populations and time series data, experimental evolution studies have the potential to identify the underlying model of adaptation. Using the framework of experimental evolution, we performed computer simulations to study the pattern of selected alleles for two models: 1) adaptation of a trait via independent beneficial mutations that are conditioned for fixation, that is, selective sweep model and 2) trait optimum model (polygenic adaptation), that is adaptation of a quantitative trait under stabilizing selection after a sudden shift in trait optimum. We identify several distinct patterns of selective sweep and trait optimum models in populations of different sizes. These features could provide the foundation for development of quantitative approaches to differentiate the two models.

The evolutionary history and genomics of European blackcap migration

Seasonal migration is a taxonomically widespread behaviour that integrates across many traits. The European blackcap exhibits enormous variation in migration and is renowned for research on its evolution and genetic basis. We assembled a reference genome for blackcaps and obtained whole genome resequencing data from individuals across its breeding range. Analyses of population structure and demography suggested divergence began ~30,000 ya, with evidence for one admixture event between migrant and resident continent birds ~5000 ya. The propensity to migrate, orientation and distance of migration all map to a small number of genomic regions that do not overlap with results from other species, suggesting that there are multiple ways to generate variation in migration. Strongly associated single nucleotide polymorphisms (SNPs) were located in regulatory regions of candidate genes that may serve as major regulators of the migratory syndrome. Evidence for selection on shared variation was documented, providing a mechanism by which rapid changes may evolve.

Selective Sweeps Under Dominance and Inbreeding

A major research goal in evolutionary genetics is to uncover loci experiencing positive selection. One approach involves finding 'selective sweeps' patterns, which can either be 'hard sweeps' formed by de novo mutation, or 'soft sweeps' arising from recurrent mutation or existing standing variation. Existing theory generally assumes outcrossing populations, and it is unclear how dominance affects soft sweeps. We consider how arbitrary dominance and inbreeding via self-fertilization affect hard and soft sweep signatures. With increased self-fertilization, they are maintained over longer map distances due to reduced effective recombination and faster beneficial allele fixation times. Dominance can affect sweep patterns in outcrossers if the derived variant originates from either a single novel allele, or from recurrent mutation. These models highlight the challenges in distinguishing hard and soft sweeps, and propose methods to differentiate between scenarios.

Genomic signatures of host-associated divergence and adaptation in a coral-eating snail, Coralliophila violacea (Kiener, 1836)

The fluid nature of the ocean, combined with planktonic dispersal of marine larvae, lowers physical barriers to gene flow. However, divergence can still occur despite gene flow if strong selection acts on populations occupying different ecological niches. Here, we examined the population genomics of an ectoparasitic snail, Coralliophila violacea (Kiener 1836), that specializes on Porites corals in the Indo-Pacific. Previous genetic analyses revealed two sympatric lineages associated with different coral hosts. In this study, we examined the mechanisms promoting and maintaining the snails' adaptation to their coral hosts. Genome-wide single nucleotide polymorphism (SNP) data from type II restriction site-associated DNA (2b-RAD) sequencing revealed two differentiated clusters of C. violacea that were largely concordant with coral host, consistent with previous genetic results. However, the presence of some admixed genotypes indicates gene flow from one lineage to the other. Combined, these results suggest that differentiation between host-associated lineages of C. violacea is occurring in the face of ongoing gene flow, requiring strong selection. Indeed, 2.7% of all SNP loci were outlier loci (73/2,718), indicative of divergence with gene flow, driven by adaptation of each C. violacea lineage to their specific coral hosts.

Genomic Epidemiology in Filarial Nematodes: Transforming the Basis for Elimination Program Decisions

Onchocerciasis and lymphatic filariasis are targeted for elimination, primarily using mass drug administration at the country and community levels. Elimination of transmission is the onchocerciasis target and global elimination as a public health problem is the end point for lymphatic filariasis. Where program duration, treatment coverage, and compliance are sufficiently high, elimination is achievable for both parasites within defined geographic areas. However, transmission has re-emerged after apparent elimination in some areas, and in others has continued despite years of mass drug treatment. A critical question is whether this re-emergence and/or persistence of transmission is due to persistence of local parasites-i.e., the result of insufficient duration or drug coverage, poor parasite response to the drugs, or inadequate methods of assessment and/or criteria for determining when to stop treatment-or due to re-introduction of parasites via human or vector movement from another endemic area. We review recent genetics-based research exploring these questions in Onchocerca volvulus, the filarial nematode that causes onchocerciasis, and Wuchereria bancrofti, the major pathogen for lymphatic filariasis. We focus in particular on the combination of genomic epidemiology and genome-wide associations to delineate transmission zones and distinguish between local and introduced parasites as the source of resurgence or continuing transmission, and to identify genetic markers associated with parasite response to chemotherapy. Our ultimate goal is to assist elimination efforts by developing easy-to-use tools that incorporate genetic information about transmission and drug response for more effective mass drug distribution, surveillance strategies, and decisions on when to stop interventions to improve sustainability of elimination.

Multiple selective sweeps of ancient polymorphisms in and around LTα located in the MHC class III region on chromosome 6

BACKGROUND

Lymphotoxin-α (LTα), located in the Major Histocompatibility Complex (MHC) class III region on chromosome 6, encodes a cytotoxic protein that mediates a variety of antiviral responses among other biological functions. Furthermore, several genotypes at this gene have been implicated in the onset of a number of complex diseases, including myocardial infarction, autoimmunity, and various types of cancer. However, little is known about levels of nucleotide variation and linkage disequilibrium (LD) in and near LTα, which could also influence phenotypic variance. To address this gap in knowledge, we examined sequence variation across ~ 10 kilobases (kbs), encompassing LTα and the upstream region, in 2039 individuals from the 1000 Genomes Project originating from 21 global populations.

RESULTS

Here, we observed striking patterns of diversity, including an excess of intermediate-frequency alleles, the maintenance of multiple common haplotypes and a deep coalescence time for variation (dating > 1.0 million years ago), in global populations. While these results are generally consistent with a model of balancing selection, we also uncovered a signature of positive selection in the form of long-range LD on chromosomes with derived alleles primarily in Eurasian populations. To reconcile these findings, which appear to support different models of selection, we argue that selective sweeps (particularly, soft sweeps) of multiple derived alleles in and/or near LTα occurred in non-Africans after their ancestors left Africa. Furthermore, these targets of selection were predicted to alter transcription factor binding site affinity and protein stability, suggesting they play a role in gene function. Additionally, our data also showed that a subset of these functional adaptive variants are present in archaic hominin genomes.

CONCLUSIONS

Overall, this study identified candidate functional alleles in a biologically-relevant genomic region, and offers new insights into the evolutionary origins of these loci in modern human populations.

Polygenic Adaptation to an Environmental Shift: Temporal Dynamics of Variation Under Gaussian Stabilizing Selection and Additive Effects on a Single Trait

Predictions about the effect of natural selection on patterns of linked neutral variation are largely based on models involving the rapid fixation of unconditionally beneficial mutations. However, when phenotypes adapt to a new optimum trait value, the strength of selection on individual mutations decreases as the population adapts. Here, I use explicit forward simulations of a single trait with additive-effect mutations adapting to an "optimum shift." Detectable "hitchhiking" patterns are only apparent if (i) the optimum shifts are large with respect to equilibrium variation for the trait, (ii) mutation rates to large-effect mutations are low, and (iii) large-effect mutations rapidly increase in frequency and eventually reach fixation, which typically occurs after the population reaches the new optimum. For the parameters simulated here, partial sweeps do not appreciably affect patterns of linked variation, even when the mutations are strongly selected. The contribution of new mutations vs. standing variation to fixation depends on the mutation rate affecting trait values. Given the fixation of a strongly selected variant, patterns of hitchhiking are similar on average for the two classes of sweeps because sweeps from standing variation involving large-effect mutations are rare when the optimum shifts. The distribution of effect sizes of new mutations has little effect on the time to reach the new optimum, but reducing the mutational variance increases the magnitude of hitchhiking patterns. In general, populations reach the new optimum prior to the completion of any sweeps, and the times to fixation are longer for this model than for standard models of directional selection. The long fixation times are due to a combination of declining selection pressures during adaptation and the possibility of interference among weakly selected sites for traits with high mutation rates.

The combined use of raw and phylogenetically independent methods of outlier detection uncovers genome-wide dynamics of local adaptation in a lizard

Local adaptation is a dynamic process by which different allele combinations are selected in different populations at different times, and whose genetic signature can be inferred by genome-wide outlier analyses. We combined gene flow estimates with two methods of outlier detection, one of them independent of population coancestry (CIOA) and the other one not (ROA), to identify genetic variants favored when ecology promotes phenotypic convergence. We analyzed genotyping-by-sequencing data from five populations of a lizard distributed over an environmentally heterogeneous range that has been changing since the split of eastern and western lineages ca. 3 mya. Overall, western lizards inhabit forest habitat and are unstriped, whereas eastern ones inhabit shrublands and are striped. However, one population (Lerma) has unstriped phenotype despite its eastern ancestry. The analysis of 73,291 SNPs confirmed the east-west division and identified nonoverlapping sets of outliers (12 identified by ROA and 9 by CIOA). ROA revealed ancestral adaptive variation in the uncovered outliers that were subject to divergent selection and differently fixed for eastern and western populations at the extremes of the environmental gradient. Interestingly, such variation was maintained in Lerma, where we found high levels of heterozygosity for ROA outliers, whereas CIOA uncovered innovative variants that were selected only there. Overall, it seems that both the maintenance of ancestral variation and asymmetric migration have counterbalanced adaptive lineage splitting in our model species. This scenario, which is likely promoted by a changing and heterogeneous environment, could hamper ecological speciation of locally adapted populations despite strong genetic structure between lineages.

Selective Sweep at a QTL in a Randomly Fluctuating Environment

Adaptation is mediated by phenotypic traits that are often near continuous, and undergo selective pressures that may change with the environment. The dynamics of allelic frequencies at underlying quantitative trait loci (QTL) depend on their own phenotypic effects, but also possibly on other polymorphic loci affecting the same trait, and on environmental change driving phenotypic selection. Most environments include a substantial component of random noise, characterized both by its magnitude and its temporal autocorrelation, which sets the timescale of environmental predictability. I investigate the dynamics of a mutation affecting a quantitative trait in an autocorrelated stochastic environment that causes random fluctuations of an optimum phenotype. The trait under selection may also exhibit background polygenic variance caused by many polymorphic loci of small effects elsewhere in the genome. In addition, the mutation at the QTL may affect phenotypic plasticity, the phenotypic response of given genotype to its environment of development or expression. Stochastic environmental fluctuations increase the variance of the evolutionary process, with consequences for the probability of a complete sweep at the QTL. Background polygenic variation critically alters this process, by setting an upper limit to stochastic variance of population genetics at the QTL. For a plasticity QTL, stochastic fluctuations also influences the expected selection coefficient, and alleles with the same expected trajectory can have very different stochastic variances. Finally, a mutation may be favored through its effect on plasticity despite causing a systematic mismatch with optimum, which is compensated by evolution of the mean background phenotype.

Evolutionary transcriptomics reveals the origins of olives and the genomic changes associated with their domestication

The olive (Olea europaea L. subsp. europaea) is one of the oldest and most socio-economically important cultivated perennial crop in the Mediterranean region. Yet, its origins are still under debate and the genetic bases of the phenotypic changes associated with its domestication are unknown. We generated RNA-sequencing data for 68 wild and cultivated olive trees to study the genetic diversity and structure both at the transcription and sequence levels. To localize putative genes or expression pathways targeted by artificial selection during domestication, we employed a two-step approach in which we identified differentially expressed genes and screened the transcriptome for signatures of selection. Our analyses support a major domestication event in the eastern part of the Mediterranean basin followed by dispersion towards the West and subsequent admixture with western wild olives. While we found large changes in gene expression when comparing cultivated and wild olives, we found no major signature of selection on coding variants and weak signals primarily affected transcription factors. Our results indicated that the domestication of olives resulted in only moderate genomic consequences and that the domestication syndrome is mainly related to changes in gene expression, consistent with its evolutionary history and life history traits.

Divergent Fine-Scale Recombination Landscapes between a Freshwater and Marine Population of Threespine Stickleback Fish

Meiotic recombination is a highly conserved process that has profound effects on genome evolution. At a fine-scale, recombination rates can vary drastically across genomes, often localized into small recombination "hotspots" with highly elevated rates, surrounded by regions with little recombination. In most species studied, the location of hotspots within genomes is highly conserved across broad evolutionary timescales. The main exception to this pattern is in mammals, where hotspot location can evolve rapidly among closely related species and even among populations within a species. Hotspot position in mammals is controlled by the gene, Prdm9, whereas in species with conserved hotspots, a functional Prdm9 is typically absent. Due to a limited number of species where recombination rates have been estimated at a fine-scale, it remains unclear whether hotspot conservation is always associated with the absence of a functional Prdm9. Threespine stickleback fish (Gasterosteus aculeatus) are an excellent model to examine the evolution of recombination over short evolutionary timescales. Using a linkage disequilibrium-based approach, we found recombination rates indeed varied at a fine-scale across the genome, with many regions organized into narrow hotspots. Hotspots had highly divergent landscapes between stickleback populations, where only ∼15% of these hotspots were shared. Our results indicate that fine-scale recombination rates may be diverging between closely related populations of threespine stickleback fish. Interestingly, we found only a weak association of a PRDM9 binding motif within hotspots, which suggests that threespine stickleback fish may possess a novel mechanism for targeting recombination hotspots at a fine-scale.

Selective Sweeps

For almost 20 years, many inference methods have been developed to detect selective sweeps and localize the targets of directional selection in the genome. These methods are based on population genetic models that describe the effect of a beneficial allele (e.g., a new mutation) on linked neutral variation (driven by directional selection from a single copy to fixation). Here, I discuss these models, ranging from selective sweeps in a panmictic population of constant size to evolutionary traffic when simultaneous sweeps at multiple loci interfere, and emphasize the important role of demography and population structure in data analysis. In the past 10 years, soft sweeps that may arise after an environmental change from directional selection on standing variation have become a focus of population genetic research. In contrast to selective sweeps, they are caused by beneficial alleles that were neutrally segregating in a population before the environmental change or were present at a mutation-selection balance in appreciable frequency.

Inferring the model and onset of natural selection under varying population size from the site frequency spectrum and haplotype structure

A fundamental question about adaptation in a population is the time of onset of the selective pressure acting on beneficial alleles. Inferring this time, in turn, depends on the selection model. We develop a framework of approximate Bayesian computation (ABC) that enables the use of the full site frequency spectrum and haplotype structure to test the goodness-of-fit of selection models and estimate the timing of selection under varying population size scenarios. We show that our method has sufficient power to distinguish natural selection from neutrality even if relatively old selection increased the frequency of a pre-existing allele from 20% to 50% or from 40% to 80%. Our ABC can accurately estimate the time of onset of selection on a new mutation. However, estimates are prone to bias under the standing variation model, possibly due to the uncertainty in the allele frequency at the onset of selection. We further extend our approach to take advantage of ancient DNA data that provides information on the allele frequency path of the beneficial allele. Applying our ABC, including both modern and ancient human DNA data, to four pigmentation alleles in Europeans, we detected selection on standing variants that occurred after the dispersal from Africa even though models of selection on a new mutation were initially supported for two of these alleles without the ancient data.

A genome-wide scan shows evidence for local adaptation in a widespread keystone Neotropical forest tree

The role of natural selection in shaping patterns of diversity is still poorly understood in the Neotropics. We carried out the first genome-wide population genomics study in a Neotropical tree, Handroanthus impetiginosus (Bignoniaceae), sampling 75,838 SNPs by sequence capture in 128 individuals across 13 populations. We found evidences for local adaptation using Bayesian correlations of allele frequency and environmental variables (32 loci in 27 genes) complemented by an analysis of selective sweeps and genetic hitchhiking events using SweepFinder2 (81 loci in 47 genes). Fifteen genes were identified by both approaches. By accounting for population genetic structure, we also found 14 loci with selection signal in a STRUCTURE-defined lineage comprising individuals from five populations, using Outflank. All approaches pinpointed highly diverse and structurally conserved genes affecting plant development and primary metabolic processes. Spatial interpolation forecasted differences in the expected allele frequencies at loci under selection over time, suggesting that H. impetiginosus may track its habitat during climate changes. However, local adaptation through natural selection may also take place, allowing species persistence due to niche evolution. A high genetic differentiation was seen among the H. impetiginosus populations, which, together with the limited power of the experiment, constrains the improved detection of other types of soft selective forces, such as background, balanced, and purifying selection. Small differences in allele frequency distribution among widespread populations and the low number of loci with detectable adaptive sweeps advocate for a polygenic model of adaptation involving a potentially large number of small genome-wide effects.

Spatial soft sweeps: Patterns of adaptation in populations with long-range dispersal

Adaptation in extended populations often occurs through multiple independent mutations responding in parallel to a common selection pressure. As the mutations spread concurrently through the population, they leave behind characteristic patterns of polymorphism near selected loci—so-called soft sweeps—which remain visible after adaptation is complete. These patterns are well-understood in two limits of the spreading dynamics of beneficial mutations: the panmictic case with complete absence of spatial structure, and spreading via short-ranged or diffusive dispersal events, which tessellates space into distinct compact regions each descended from a unique mutation. However, spreading behaviour in most natural populations is not exclusively panmictic or diffusive, but incorporates both short-range and long-range dispersal events. Here, we characterize the spatial patterns of soft sweeps driven by dispersal events whose jump distances are broadly distributed, using lattice-based simulations and scaling arguments. We find that mutant clones adopt a distinctive structure consisting of compact cores surrounded by fragmented “haloes” which mingle with haloes from other clones. As long-range dispersal becomes more prominent, the progression from diffusive to panmictic behaviour is marked by two transitions separating regimes with differing relative sizes of halo to core. We analyze the implications of the core-halo structure for the statistics of soft sweep detection in small genomic samples from the population, and find opposing effects of long-range dispersal on the expected diversity in global samples compared to local samples from geographic subregions of the range. We also discuss consequences of the standing genetic variation induced by the soft sweep on future adaptation and mixing.

When a species is spread out over a large geographic range, different regions may adapt to the same selection pressure by acquiring distinct beneficial mutations. The resulting pattern of genetic variation in the population is called a soft sweep. Dispersal strongly influences soft sweep patterns, as it determines how a mutation that arose in one region might spread to others. Although most plant and animal populations experience some amount of dispersal over very long distances, the impact of such long-range dispersal events on soft sweep patterns remains poorly understood. We use computer simulations and mathematical analysis to study patterns of genetic variation in a model of soft sweeps including long-range dispersal. We show that long-range dispersal leaves distinct signatures in the genetic makeup of the population, which can be detected in genetic samples from individuals across the range. Our results are important for correctly interpreting patterns of genetic diversity in populations that have undergone recent adaptation.