How reliable are empirical genomic scans for selective sweeps?

Signatures of Selection in the Genomes of Chinese Chestnut (Castanea mollissima Blume): The Roots of Nut Tree Domestication

Chestnuts (Castanea) are major nut crops in East Asia and southern Europe, and are unique among temperate nut crops in that the harvested seeds are starchy rather than oily. Chestnut species have been cultivated for three millennia or more in China, so it is likely that artificial selection has affected the genome of orchard-grown chestnuts. The genetics of Chinese chestnut (Castanea mollissima Blume) domestication are also of interest to breeders of hybrid American chestnut, especially if the low-growing, branching habit of Chinese chestnut, an impediment to American chestnut restoration, is partly the result of artificial selection. We resequenced genomes of wild and orchard-derived Chinese chestnuts and identified selective sweeps based on pooled whole-genome SNP datasets. We present candidate gene loci for chestnut domestication and discuss the potential phenotypic effects of candidate loci, some of which may be useful genes for chestnut improvement in Asia and North America. Selective sweeps included predicted genes potentially related to flower phenology and development, fruit maturation, and secondary metabolism, and included some genes homologous to domestication candidates in other woody plants.

Detecting Recent Positive Selection with a Single Locus Test Bipartitioning the Coalescent Tree

Many population genomic studies have been conducted in the past to search for traces of recent events of positive selection. These traces, however, can be obscured by temporal variation of population size or other demographic factors. To reduce the confounding impact of demography, the coalescent tree topology has been used as an additional source of information for detecting recent positive selection in a population or a species. Based on the branching pattern at the root, we partition the hypothetical coalescent tree, inferred from a sequence sample, into two subtrees. The reasoning is that positive selection could impose a strong impact on branch length in one of the two subtrees while demography has the same effect on average on both subtrees. Thus, positive selection should be detectable by comparing statistics calculated for the two subtrees. Simulations demonstrate that the proposed test based on these principles has high power to detect recent positive selection even when DNA polymorphism data from only one locus is available, and that it is robust to the confounding effect of demography. One feature is that all components in the summary statistics ([Formula: see text]) can be computed analytically. Moreover, misinference of derived and ancestral alleles is seen to have only a limited effect on the test, and it therefore avoids a notorious problem when searching for traces of recent positive selection.

The Demographic and Adaptive History of the African Green Monkey

Relatively little is known about the evolutionary history of the African green monkey (genus Chlorocebus) due to the lack of sampled polymorphism data from wild populations. Yet, this characterization of genetic diversity is not only critical for a better understanding of their own history, but also for human biomedical research given that they are one of the most widely used primate models. Here, I analyze the demographic and selective history of the African green monkey, utilizing one of the most comprehensive catalogs of wild genetic diversity to date, consisting of 1,795,643 autosomal single nucleotide polymorphisms in 25 individuals, representing all five major populations: C. a. aethiops, C. a. cynosurus, C. a. pygerythrus, C. a. sabaeus, and C. a tantalus. Assuming a mutation rate of 5.9 × 10-9 per base pair per generation and a generation time of 8.5 years, divergence time estimates range from 523 to 621 kya for the basal split of C. a. aethiops from the other four populations. Importantly, the resulting tree characterizing the relationship and split-times between these populations differs significantly from that presented in the original genome paper, owing to their neglect of within-population variation when calculating between population-divergence. In addition, I find that the demographic history of all five populations is well explained by a model of population fragmentation and isolation, rather than novel colonization events. Finally, utilizing these demographic models as a null, I investigate the selective history of the populations, identifying candidate regions potentially related to adaptation in response to pathogen exposure.

Identifying genetic variants that affect viability in large cohorts

A number of open questions in human evolutionary genetics would become tractable if we were able to directly measure evolutionary fitness. As a step towards this goal, we developed a method to examine whether individual genetic variants, or sets of genetic variants, currently influence viability. The approach consists in testing whether the frequency of an allele varies across ages, accounting for variation in ancestry. We applied it to the Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort and to the parents of participants in the UK Biobank. Across the genome, we found only a few common variants with large effects on age-specific mortality: tagging the APOE ε4 allele and near CHRNA3. These results suggest that when large, even late-onset effects are kept at low frequency by purifying selection. Testing viability effects of sets of genetic variants that jointly influence 1 of 42 traits, we detected a number of strong signals. In participants of the UK Biobank of British ancestry, we found that variants that delay puberty timing are associated with a longer parental life span (P~6.2 × 10⁻⁶ for fathers and P~2.0 × 10⁻³ for mothers), consistent with epidemiological studies. Similarly, variants associated with later age at first birth are associated with a longer maternal life span (P~1.4 × 10⁻³). Signals are also observed for variants influencing cholesterol levels, risk of coronary artery disease (CAD), body mass index, as well as risk of asthma. These signals exhibit consistent effects in the GERA cohort and among participants of the UK Biobank of non-British ancestry. We also found marked differences between males and females, most notably at the CHRNA3 locus, and variants associated with risk of CAD and cholesterol levels. Beyond our findings, the analysis serves as a proof of principle for how upcoming biomedical data sets can be used to learn about selection effects in contemporary humans.

Our global understanding of adaptation in humans is limited to indirect statistical inferences from patterns of genetic variation, which are sensitive to past selection pressures. We introduced a method that allowed us to directly observe ongoing selection in humans by identifying genetic variants that affect survival to a given age (i.e., viability selection). We applied our approach to the GERA cohort and parents of the UK Biobank participants. We found viability effects of variants near the APOE and CHRNA3 genes, which are associated with the risk of Alzheimer disease and smoking behavior, respectively. We also tested for the joint effect of sets of genetic variants that influence quantitative traits. We uncovered an association between longer life span and genetic variants that delay puberty timing and age at first birth. We also detected detrimental effects of higher genetically predicted cholesterol levels, body mass index, risk of coronary artery disease (CAD), and risk of asthma on survival. Some of the observed effects differ between males and females, most notably those at the CHRNA3 gene and variants associated with risk of CAD and cholesterol levels. Beyond this application, our analysis shows how large biomedical data sets can be used to study natural selection in humans.

Heritable gene expression differences between lake and stream stickleback include both parallel and antiparallel components

The repeated phenotypic patterns that characterize populations undergoing parallel evolution provide support for a deterministic role of adaptation by natural selection. Determining the level of parallelism also at the genetic level is thus central to our understanding of how natural selection works. Many studies have looked for repeated genomic patterns in natural populations, but work on gene expression is less common. The studies that have examined gene expression have found some support for parallelism, but those studies almost always used samples collected from the wild that potentially confounds the effects of plasticity with heritable differences. Here we use two independent pairs of lake and stream threespine stickleback (Gasterosteus aculeatus) raised in common garden conditions to assess both parallel and antiparallel (that is, similar versus different directions of lake–stream expression divergence in the two watersheds) heritable gene expression differences as measured by total RNA sequencing. We find that more genes than expected by chance show either parallel (22 genes, 0.18% of expressed genes) or antiparallel (24 genes, 0.20% of expressed genes) lake–stream expression differences. These results correspond well with previous genomic studies in stickleback ecotype pairs that found similar levels of parallelism. We suggest that parallelism might be similarly constrained at the genomic and transcriptomic levels.

Genetic loci associated with coronary artery disease harbor evidence of selection and antagonistic pleiotropy

Traditional genome-wide scans for positive selection have mainly uncovered selective sweeps associated with monogenic traits. While selection on quantitative traits is much more common, very few signals have been detected because of their polygenic nature. We searched for positive selection signals underlying coronary artery disease (CAD) in worldwide populations, using novel approaches to quantify relationships between polygenic selection signals and CAD genetic risk. We identified new candidate adaptive loci that appear to have been directly modified by disease pressures given their significant associations with CAD genetic risk. These candidates were all uniquely and consistently associated with many different male and female reproductive traits suggesting selection may have also targeted these because of their direct effects on fitness. We found that CAD loci are significantly enriched for lifetime reproductive success relative to the rest of the human genome, with evidence that the relationship between CAD and lifetime reproductive success is antagonistic. This supports the presence of antagonistic-pleiotropic tradeoffs on CAD loci and provides a novel explanation for the maintenance and high prevalence of CAD in modern humans. Lastly, we found that positive selection more often targeted CAD gene regulatory variants using HapMap3 lymphoblastoid cell lines, which further highlights the unique biological significance of candidate adaptive loci underlying CAD. Our study provides a novel approach for detecting selection on polygenic traits and evidence that modern human genomes have evolved in response to CAD-induced selection pressures and other early-life traits sharing pleiotropic links with CAD.

Contrasting patterns of nucleotide polymorphism suggest different selective regimes within different parts of the PgiC1 gene in Festuca ovina L

Background

Phosphoglucose isomerase (PGI, EC 5.3.1.9) is an essential metabolic enzyme in all eukaryotes. An earlier study of the PgiC1 gene, which encodes cytosolic PGI in the grass Festuca ovina L., revealed a marked difference in the levels of nucleotide polymorphism between the 5’ and 3’ portions of the gene.

Methods

In the present study, we characterized the sequence polymorphism in F. ovina PgiC1 in more detail and examined possible explanations for the non-uniform pattern of nucleotide polymorphism across the gene.

Results

Our study confirms that the two portions of the PgiC1 gene show substantially different levels of DNA polymorphism and also suggests that the peptide encoded by the 3’ portion of PgiC1 is functionally and structurally more important than that encoded by the 5’ portion. Although there was some evidence of purifying selection (d_N/d_S test) on the 5’ portion of the gene, the signature of purifying selection was considerably stronger on the 3’ portion of the gene (d_N/d_S and McDonald–Kreitman tests). Several tests support the action of balancing selection within the 5’ portion of the gene. Wall’s B and Q tests were significant only for the 5’ portion of the gene. There were also marked peaks of nucleotide diversity, Tajima’s D and the d_N/d_S ratio at or around a PgiC1 codon site (within the 5’ portion of the gene) that a previous study had suggested was subject to positive diversifying selection.

Conclusions

Our results suggest that the two portions of the gene have been subject to different selective regimes. Purifying selection appears to have been the main force contributing to the relatively low level of polymorphism within the 3’ portion of the sequence. In contrast, it is possible that balancing selection has contributed to the maintenance of the polymorphism within the 5’ portion of the gene.

Electronic supplementary material

The online version of this article (doi:10.1186/s41065-017-0032-6) contains supplementary material, which is available to authorized users.

Molecular Population Genetics

Molecular population genetics aims to explain genetic variation and molecular evolution from population genetics principles. The field was born 50 years ago with the first measures of genetic variation in allozyme loci, continued with the nucleotide sequencing era, and is currently in the era of population genomics. During this period, molecular population genetics has been revolutionized by progress in data acquisition and theoretical developments. The conceptual elegance of the neutral theory of molecular evolution or the footprint carved by natural selection on the patterns of genetic variation are two examples of the vast number of inspiring findings of population genetics research. Since the inception of the field, Drosophila has been the prominent model species: molecular variation in populations was first described in Drosophila and most of the population genetics hypotheses were tested in Drosophila species. In this review, we describe the main concepts, methods, and landmarks of molecular population genetics, using the Drosophila model as a reference. We describe the different genetic data sets made available by advances in molecular technologies, and the theoretical developments fostered by these data. Finally, we review the results and new insights provided by the population genomics approach, and conclude by enumerating challenges and new lines of inquiry posed by increasingly large population scale sequence data.

Genomic patterns of diversity and divergence of two introduced salmonid species in Patagonia, South America

Invasive species have become widespread in aquatic environments throughout the world, yet there are few studies that have examined genomic variation of multiple introduced species in newly colonized environments. In this study, we contrast genomic variation in two salmonid species (anadromous Chinook Salmon, Oncorhynchus tshawytscha, 11,579 SNPs and resident Brook Charr Salvelinus fontinalis, 13,522 SNPs) with differing invasion success after introduction to new environments in South America relative to populations from their native range in North America. Estimates of genetic diversity were not significantly different between introduced and source populations for either species, indicative of propagule pressure that has been shown to maintain diversity in founding populations relative to their native range. Introduced populations also demonstrated higher connectivity and gene flow than those in their native range. Evidence for candidate loci under divergent selection was observed, but was limited to specific introduced populations and was not widely evident. Patterns of genomic variation were consistent with general dispersal potential of each species and therefore also the notion that life history variation may contribute to both invasion success and subsequent genetic structure of these two salmonids in Patagonia.

The genome landscape of indigenous African cattle

BACKGROUND

The history of African indigenous cattle and their adaptation to environmental and human selection pressure is at the root of their remarkable diversity. Characterization of this diversity is an essential step towards understanding the genomic basis of productivity and adaptation to survival under African farming systems.

RESULTS

We analyze patterns of African cattle genetic variation by sequencing 48 genomes from five indigenous populations and comparing them to the genomes of 53 commercial taurine breeds. We find the highest genetic diversity among African zebu and sanga cattle. Our search for genomic regions under selection reveals signatures of selection for environmental adaptive traits. In particular, we identify signatures of selection including genes and/or pathways controlling anemia and feeding behavior in the trypanotolerant N'Dama, coat color and horn development in Ankole, and heat tolerance and tick resistance across African cattle especially in zebu breeds.

CONCLUSIONS

Our findings unravel at the genome-wide level, the unique adaptive diversity of African cattle while emphasizing the opportunities for sustainable improvement of livestock productivity on the continent.

Archaic Hominin Admixture Facilitated Adaptation to Out-of-Africa Environments

As modern humans dispersed from Africa throughout the world, they encountered and interbred with archaic hominins, including Neanderthals and Denisovans [1, 2]. Although genome-scale maps of introgressed sequences have been constructed [3-6], considerable gaps in knowledge remain about the functional, phenotypic, and evolutionary significance of archaic hominin DNA that persists in present-day individuals. Here, we describe a comprehensive set of analyses that identified 126 high-frequency archaic haplotypes as putative targets of adaptive introgression in geographically diverse populations. These loci are enriched for immune-related genes (such as OAS1/2/3, TLR1/6/10, and TNFAIP3) and also encompass genes (including OCA2 and BNC2) that influence skin pigmentation phenotypes. Furthermore, we leveraged existing and novel large-scale gene expression datasets to show many positively selected archaic haplotypes act as expression quantitative trait loci (eQTLs), suggesting that modulation of transcript abundance was a common mechanism facilitating adaptive introgression. Our results demonstrate that hybridization between modern and archaic hominins provided an important reservoir of advantageous alleles that enabled adaptation to out-of-Africa environments.

Evolutionary History, Selective Sweeps, and Deleterious Variation in the Dog

The dog is our oldest domesticate and has experienced a wide variety of demographic histories, including a bottleneck associated with domestication and individual bottlenecks associated with the formation of modern breeds. Admixture with gray wolves, and among dog breeds and populations, has also occurred throughout its history. Likewise, the intensity and focus of selection have varied, from an initial focus on traits enhancing cohabitation with humans, to more directed selection on specific phenotypic characteristics and behaviors. In this review, we summarize and synthesize genetic findings from genome-wide and complete genome studies that document the genomic consequences of demography and selection, including the effects on adaptive and deleterious variation. Consistent with the evolutionary history of the dog, signals of natural and artificial selection are evident in the dog genome. However, conclusions from studies of positive selection are fraught with the problem of false positives given that demographic history is often not taken into account.

Effects of Linked Selective Sweeps on Demographic Inference and Model Selection

The availability of large-scale population genomic sequence data has resulted in an explosion in efforts to infer the demographic histories of natural populations across a broad range of organisms. As demographic events alter coalescent genealogies, they leave detectable signatures in patterns of genetic variation within and between populations. Accordingly, a variety of approaches have been designed to leverage population genetic data to uncover the footprints of demographic change in the genome. The vast majority of these methods make the simplifying assumption that the measures of genetic variation used as their input are unaffected by natural selection. However, natural selection can dramatically skew patterns of variation not only at selected sites, but at linked, neutral loci as well. Here we assess the impact of recent positive selection on demographic inference by characterizing the performance of three popular methods through extensive simulation of data sets with varying numbers of linked selective sweeps. In particular, we examined three different demographic models relevant to a number of species, finding that positive selection can bias parameter estimates of each of these models-often severely. We find that selection can lead to incorrect inferences of population size changes when none have occurred. Moreover, we show that linked selection can lead to incorrect demographic model selection, when multiple demographic scenarios are compared. We argue that natural populations may experience the amount of recent positive selection required to skew inferences. These results suggest that demographic studies conducted in many species to date may have exaggerated the extent and frequency of population size changes.

Genetic hitchhiking and resistance evolution to transgenic Bt toxins: insights from the African stalk borer Busseola fusca (Noctuidae)

Since transgenic crops expressing Bacillus thuringiensis (Bt) toxins were first released, resistance evolution leading to failure in control of pests populations has been observed in a number of species. Field resistance of the moth Busseola fusca was acknowledged 8 years after Bt maize was introduced in South Africa. Since then, field resistance of this corn borer has been observed at several locations, raising questions about the nature, distribution and dynamics of the resistance trait. Using genetic markers, our study identified four outlier loci clearly associated with resistance. In addition, genetic structure at neutral loci reflected extensive gene flow among populations. A realistically parameterised model suggests that resistance could travel in space at speed of several kilometres a year. Markers at outlier loci delineated a geographic region associated with resistance spread. This was an area of approximately 100 km radius, including the location where resistance was first reported. Controlled crosses corroborated these findings and showed significant differences of progeny survival on Bt plants depending on the origin of the resistant parent. Last, our study suggests diverse resistance mutations, which would explain the widespread occurrence of resistant larvae in Bt fields across the main area of maize production in South Africa.

Genome scans for divergent selection in natural populations of the widespread hardwood species Eucalyptus grandis (Myrtaceae) using microsatellites

Identification of loci or genes under natural selection is important for both understanding the genetic basis of local adaptation and practical applications, and genome scans provide a powerful means for such identification purposes. In this study, genome-wide simple sequence repeats markers (SSRs) were used to scan for molecular footprints of divergent selection in Eucalyptus grandis, a hardwood species occurring widely in costal areas from 32° S to 16° S in Australia. High population diversity levels and weak population structure were detected with putatively neutral genomic SSRs. Using three FST outlier detection methods, a total of 58 outlying SSRs were collectively identified as loci under divergent selection against three non-correlated climatic variables, namely, mean annual temperature, isothermality and annual precipitation. Using a spatial analysis method, nine significant associations were revealed between FST outlier allele frequencies and climatic variables, involving seven alleles from five SSR loci. Of the five significant SSRs, two (EUCeSSR1044 and Embra394) contained alleles of putative genes with known functional importance for response to climatic factors. Our study presents critical information on the population diversity and structure of the important woody species E. grandis and provides insight into the adaptive responses of perennial trees to climatic variations.

Feralisation targets different genomic loci to domestication in the chicken

Feralisation occurs when a domestic population recolonizes the wild, escaping its previous restricted environment, and has been considered as the reverse of domestication. We have previously shown that Kauai Island's feral chickens are a highly variable and admixed population. Here we map selective sweeps in feral Kauai chickens using whole-genome sequencing. The detected sweeps were mostly unique to feralisation and distinct to those selected for during domestication. To ascribe potential phenotypic functions to these genes we utilize a laboratory-controlled equivalent to the Kauai population-an advanced intercross between Red Junglefowl and domestic layer birds that has been used previously for both QTL and expression QTL studies. Certain sweep genes exhibit significant correlations with comb mass, maternal brooding behaviour and fecundity. Our analyses indicate that adaptations to feral and domestic environments involve different genomic regions and feral chickens show some evidence of adaptation at genes associated with sexual selection and reproduction.

Targeted re-sequencing confirms the importance of chemosensory genes in aphid host race differentiation

Host‐associated races of phytophagous insects provide a model for understanding how adaptation to a new environment can lead to reproductive isolation and speciation, ultimately enabling us to connect barriers to gene flow to adaptive causes of divergence. The pea aphid (Acyrthosiphon pisum) comprises host races specializing on legume species and provides a unique system for examining the early stages of diversification along a gradient of genetic and associated adaptive divergence. As host choice produces assortative mating, understanding the underlying mechanisms of choice will contribute directly to understanding of speciation. As host choice in the pea aphid is likely mediated by smell and taste, we use capture sequencing and SNP genotyping to test for the role of chemosensory genes in the divergence between eight host plant species across the continuum of differentiation and sampled at multiple locations across western Europe. We show high differentiation of chemosensory loci relative to control loci in a broad set of pea aphid races and localities, using a model‐free approach based on principal component analysis. Olfactory and gustatory receptors form the majority of highly differentiated genes and include loci that were already identified as outliers in a previous study focusing on the three most closely related host races. Consistent indications that chemosensory genes may be good candidates for local adaptation and barriers to gene flow in the pea aphid open the way to further investigations aiming to understand their impact on gene flow and to determine their precise functions in response to host plant metabolites.

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.

Local Adaptation in European Firs Assessed through Extensive Sampling across Altitudinal Gradients in Southern Europe

BACKGROUND

Local adaptation is a key driver of phenotypic and genetic divergence at loci responsible for adaptive traits variations in forest tree populations. Its experimental assessment requires rigorous sampling strategies such as those involving population pairs replicated across broad spatial scales.

METHODS

A hierarchical Bayesian model of selection (HBM) that explicitly considers both the replication of the environmental contrast and the hierarchical genetic structure among replicated study sites is introduced. Its power was assessed through simulations and compared to classical 'within-site' approaches (FDIST, BAYESCAN) and a simplified, within-site, version of the model introduced here (SBM).

RESULTS

HBM demonstrates that hierarchical approaches are very powerful to detect replicated patterns of adaptive divergence with low false-discovery (FDR) and false-non-discovery (FNR) rates compared to the analysis of different sites separately through within-site approaches. The hypothesis of local adaptation to altitude was further addressed by analyzing replicated Abies alba population pairs (low and high elevations) across the species' southern distribution range, where the effects of climatic selection are expected to be the strongest. For comparison, a single population pair from the closely related species A. cephalonica was also analyzed. The hierarchical model did not detect any pattern of adaptive divergence to altitude replicated in the different study sites. Instead, idiosyncratic patterns of local adaptation among sites were detected by within-site approaches.

CONCLUSION

Hierarchical approaches may miss idiosyncratic patterns of adaptation among sites, and we strongly recommend the use of both hierarchical (multi-site) and classical (within-site) approaches when addressing the question of adaptation across broad spatial scales.

Genome-wide analysis reveals adaptation to high altitudes in Tibetan sheep

Tibetan sheep have lived on the Tibetan Plateau for thousands of years; however, the process and consequences of adaptation to this extreme environment have not been elucidated for important livestock such as sheep. Here, seven sheep breeds, representing both highland and lowland breeds from different areas of China, were genotyped for a genome-wide collection of single-nucleotide polymorphisms (SNPs). The FST and XP-EHH approaches were used to identify regions harbouring local positive selection between these highland and lowland breeds, and 236 genes were identified. We detected selection events spanning genes involved in angiogenesis, energy production and erythropoiesis. In particular, several candidate genes were associated with high-altitude hypoxia, including EPAS1, CRYAA, LONP1, NF1, DPP4, SOD1, PPARG and SOCS2. EPAS1 plays a crucial role in hypoxia adaption; therefore, we investigated the exon sequences of EPAS1 and identified 12 mutations. Analysis of the relationship between blood-related phenotypes and EPAS1 genotypes in additional highland sheep revealed that a homozygous mutation at a relatively conserved site in the EPAS1 3' untranslated region was associated with increased mean corpuscular haemoglobin concentration and mean corpuscular volume. Taken together, our results provide evidence of the genetic diversity of highland sheep and indicate potential high-altitude hypoxia adaptation mechanisms, including the role of EPAS1 in adaptation.

Selection and explosive growth alter genetic architecture and hamper the detection of causal rare variants

The role of rare alleles in complex phenotypes has been hotly debated, but most rare variant association tests (RVATs) do not account for the evolutionary forces that affect genetic architecture. Here, we use simulation and numerical algorithms to show that explosive population growth, as experienced by human populations, can dramatically increase the impact of very rare alleles on trait variance. We then assess the ability of RVATs to detect causal loci using simulations and human RNA-seq data. Surprisingly, we find that statistical performance is worst for phenotypes in which genetic variance is due mainly to rare alleles, and explosive population growth decreases power. Although many studies have attempted to identify causal rare variants, few have reported novel associations. This has sometimes been interpreted to mean that rare variants make negligible contributions to complex trait heritability. Our work shows that RVATs are not robust to realistic human evolutionary forces, so general conclusions about the impact of rare variants on complex traits may be premature.

Evaluating the performance of selection scans to detect selective sweeps in domestic dogs

Selective breeding of dogs has resulted in repeated artificial selection on breed-specific morphological phenotypes. A number of quantitative trait loci associated with these phenotypes have been identified in genetic mapping studies. We analysed the population genomic signatures observed around the causal mutations for 12 of these loci in 25 dog breeds, for which we genotyped 25 individuals in each breed. By measuring the population frequencies of the causal mutations in each breed, we identified those breeds in which specific mutations most likely experienced positive selection. These instances were then used as positive controls for assessing the performance of popular statistics to detect selection from population genomic data. We found that artificial selection during dog domestication has left characteristic signatures in the haplotype and nucleotide polymorphism patterns around selected loci that can be detected in the genotype data from a single population sample. However, the sensitivity and accuracy at which such signatures were detected varied widely between loci, the particular statistic used and the choice of analysis parameters. We observed examples of both hard and soft selective sweeps and detected strong selective events that removed genetic diversity almost entirely over regions >10 Mbp. Our study demonstrates the power and limitations of selection scans in populations with high levels of linkage disequilibrium due to severe founder effects and recent population bottlenecks.

Genomic signatures of the plateless phenotype in the threespine stickleback

Understanding the genetic basis of traits involved in adaptive divergence and speciation is one of the most fundamental objectives in evolutionary biology. Toward that end, we look for signatures of extreme plate loss in the genome of freshwater threespine sticklebacks (Gasterosteus aculeatus). Plateless stickleback have been found in only a few lakes and streams across the world; they represent the far extreme of a phenotypic continuum (plate number) that has been studied for years, although plateless individuals have not yet been the subject of much investigation. We use a dense single nucleotide polymorphism dataset made using RADseq to study fish from three freshwater populations containing plateless and low plated individuals, as well as fish from full plated marine populations. Analyses were performed using FastStructure, sliding windows F_ST, Bayescan and latent factor mixed models to search for genomic differences between the low plated and plateless phenotypes both within and among the three lakes. At least 18 genomic regions which may contribute to within‐morph plate number variation were detected in our low plated stickleback populations. We see no evidence of a selective sweep between low and plateless fish; rather reduction of plate number within the low plated morph seems to be polygenic.

Surprisingly Low Limits of Selection in Plant Domestication

Current debate concerns the pace at which domesticated plants emerged from cultivated wild populations and how many genes were involved. Using an individual-based model, based on the assumptions of Haldane and Maynard Smith, respectively, we estimate that a surprisingly low number of 50–100 loci are the most that could be under selection in a cultivation regime at the selection strengths observed in the archaeological record. This finding is robust to attempts to rescue populations from extinction through selection from high standing genetic variation, gene flow, and the Maynard Smith-based model of threshold selection. Selective sweeps come at a cost, reducing the capacity of plants to adapt to new environments, which may contribute to the explanation of why selective sweeps have not been detected more frequently and why expansion of the agrarian package during the Neolithic was so frequently associated with collapse.

S/HIC: Robust Identification of Soft and Hard Sweeps Using Machine Learning

Detecting the targets of adaptive natural selection from whole genome sequencing data is a central problem for population genetics. However, to date most methods have shown sub-optimal performance under realistic demographic scenarios. Moreover, over the past decade there has been a renewed interest in determining the importance of selection from standing variation in adaptation of natural populations, yet very few methods for inferring this model of adaptation at the genome scale have been introduced. Here we introduce a new method, S/HIC, which uses supervised machine learning to precisely infer the location of both hard and soft selective sweeps. We show that S/HIC has unrivaled accuracy for detecting sweeps under demographic histories that are relevant to human populations, and distinguishing sweeps from linked as well as neutrally evolving regions. Moreover, we show that S/HIC is uniquely robust among its competitors to model misspecification. Thus, even if the true demographic model of a population differs catastrophically from that specified by the user, S/HIC still retains impressive discriminatory power. Finally, we apply S/HIC to the case of resequencing data from human chromosome 18 in a European population sample, and demonstrate that we can reliably recover selective sweeps that have been identified earlier using less specific and sensitive methods.

Demographically-Based Evaluation of Genomic Regions under Selection in Domestic Dogs

Controlling for background demographic effects is important for accurately identifying loci that have recently undergone positive selection. To date, the effects of demography have not yet been explicitly considered when identifying loci under selection during dog domestication. To investigate positive selection on the dog lineage early in the domestication, we examined patterns of polymorphism in six canid genomes that were previously used to infer a demographic model of dog domestication. Using an inferred demographic model, we computed false discovery rates (FDR) and identified 349 outlier regions consistent with positive selection at a low FDR. The signals in the top 100 regions were frequently centered on candidate genes related to brain function and behavior, including LHFPL3, CADM2, GRIK3, SH3GL2, MBP, PDE7B, NTAN1, and GLRA1. These regions contained significant enrichments in behavioral ontology categories. The 3rd top hit, CCRN4L, plays a major role in lipid metabolism, that is supported by additional metabolism related candidates revealed in our scan, including SCP2D1 and PDXC1. Comparing our method to an empirical outlier approach that does not directly account for demography, we found only modest overlaps between the two methods, with 60% of empirical outliers having no overlap with our demography-based outlier detection approach. Demography-aware approaches have lower-rates of false discovery. Our top candidates for selection, in addition to expanding the set of neurobehavioral candidate genes, include genes related to lipid metabolism, suggesting a dietary target of selection that was important during the period when proto-dogs hunted and fed alongside hunter-gatherers.

Whole-genome sequence analyses of Western Central African Pygmy hunter-gatherers reveal a complex demographic history and identify candidate genes under positive natural selection

African Pygmies practicing a mobile hunter-gatherer lifestyle are phenotypically and genetically diverged from other anatomically modern humans, and they likely experienced strong selective pressures due to their unique lifestyle in the Central African rainforest. To identify genomic targets of adaptation, we sequenced the genomes of four Biaka Pygmies from the Central African Republic and jointly analyzed these data with the genome sequences of three Baka Pygmies from Cameroon and nine Yoruba famers. To account for the complex demographic history of these populations that includes both isolation and gene flow, we fit models using the joint allele frequency spectrum and validated them using independent approaches. Our two best-fit models both suggest ancient divergence between the ancestors of the farmers and Pygmies, 90,000 or 150,000 yr ago. We also find that bidirectional asymmetric gene flow is statistically better supported than a single pulse of unidirectional gene flow from farmers to Pygmies, as previously suggested. We then applied complementary statistics to scan the genome for evidence of selective sweeps and polygenic selection. We found that conventional statistical outlier approaches were biased toward identifying candidates in regions of high mutation or low recombination rate. To avoid this bias, we assigned P-values for candidates using whole-genome simulations incorporating demography and variation in both recombination and mutation rates. We found that genes and gene sets involved in muscle development, bone synthesis, immunity, reproduction, cell signaling and development, and energy metabolism are likely to be targets of positive natural selection in Western African Pygmies or their recent ancestors.

Controlling false discoveries in genome scans for selection

Population differentiation (PD) and ecological association (EA) tests have recently emerged as prominent statistical methods to investigate signatures of local adaptation using population genomic data. Based on statistical models, these genomewide testing procedures have attracted considerable attention as tools to identify loci potentially targeted by natural selection. An important issue with PD and EA tests is that incorrect model specification can generate large numbers of false-positive associations. Spurious association may indeed arise when shared demographic history, patterns of isolation by distance, cryptic relatedness or genetic background are ignored. Recent works on PD and EA tests have widely focused on improvements of test corrections for those confounding effects. Despite significant algorithmic improvements, there is still a number of open questions on how to check that false discoveries are under control and implement test corrections, or how to combine statistical tests from multiple genome scan methods. This tutorial study provides a detailed answer to these questions. It clarifies the relationships between traditional methods based on allele frequency differentiation and EA methods and provides a unified framework for their underlying statistical tests. We demonstrate how techniques developed in the area of genomewide association studies, such as inflation factors and linear mixed models, benefit genome scan methods and provide guidelines for good practice while conducting statistical tests in landscape and population genomic applications. Finally, we highlight how the combination of several well-calibrated statistical tests can increase the power to reject neutrality, improving our ability to infer patterns of local adaptation in large population genomic data sets.

Functional Genomic Insights into Regulatory Mechanisms of High-Altitude Adaptation

Recent studies of indigenous human populations at high altitude have provided proof-of-principle that genome scans of DNA polymorphism can be used to identify candidate loci for hypoxia adaptation. When integrated with experimental analyses of physiological phenotypes, genome-wide surveys of DNA polymorphism and tissue-specific transcriptional profiles can provide insights into actual mechanisms of adaptation. It has been suggested that adaptive phenotypic evolution is largely mediated by cis-regulatory changes in genes that are located at integrative control points in regulatory networks. This hypothesis can be tested by conducting transcriptomic analyses of hypoxic signaling pathways in conjunction with experimental measures of vascular oxygen supply and metabolic pathway flux. Such studies may reveal whether the architecture of gene regulatory networks can be used to predict which loci (and which types of loci) are likely to be "hot spots" for adaptive physiological evolution. Functional genomic studies of deer mice (Peromyscus maniculatus) demonstrate how the integrated analysis of variation in tissue-specific transcriptomes, whole-animal physiological performance, and various subordinate traits can yield insights into the mechanistic underpinnings of high-altitude adaptation.

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.

A probabilistic method for testing and estimating selection differences between populations

Human populations around the world encounter various environmental challenges and, consequently, develop genetic adaptations to different selection forces. Identifying the differences in natural selection between populations is critical for understanding the roles of specific genetic variants in evolutionary adaptation. Although numerous methods have been developed to detect genetic loci under recent directional selection, a probabilistic solution for testing and quantifying selection differences between populations is lacking. Here we report the development of a probabilistic method for testing and estimating selection differences between populations. By use of a probabilistic model of genetic drift and selection, we showed that logarithm odds ratios of allele frequencies provide estimates of the differences in selection coefficients between populations. The estimates approximate a normal distribution, and variance can be estimated using genome-wide variants. This allows us to quantify differences in selection coefficients and to determine the confidence intervals of the estimate. Our work also revealed the link between genetic association testing and hypothesis testing of selection differences. It therefore supplies a solution for hypothesis testing of selection differences. This method was applied to a genome-wide data analysis of Han and Tibetan populations. The results confirmed that both the EPAS1 and EGLN1 genes are under statistically different selection in Han and Tibetan populations. We further estimated differences in the selection coefficients for genetic variants involved in melanin formation and determined their confidence intervals between continental population groups. Application of the method to empirical data demonstrated the outstanding capability of this novel approach for testing and quantifying differences in natural selection.

Population Genomics for Understanding Adaptation in Wild Plant Species

Darwin's theory of evolution by natural selection is the foundation of modern biology. However, it has proven remarkably difficult to demonstrate at the genetic, genomic, and population level exactly how wild species adapt to their natural environments. We discuss how one can use large sets of multiple genome sequences from wild populations to understand adaptation, with an emphasis on the small herbaceous plant Arabidopsis thaliana. We present motivation for such studies; summarize progress in describing whole-genome, species-wide sequence variation; and then discuss what insights have emerged from these resources, either based on sequence information alone or in combination with phenotypic data. We conclude with thoughts on opportunities with other plant species and the impact of expected progress in sequencing technology and genome engineering for studying adaptation in nature.

Shared and nonshared genomic divergence in parallel ecotypes of Littorina saxatilis at a local scale

Parallel speciation occurs when selection drives repeated, independent adaptive divergence that reduces gene flow between ecotypes. Classical examples show parallel speciation originating from shared genomic variation, but this does not seem to be the case in the rough periwinkle (Littorina saxatilis) that has evolved considerable phenotypic diversity across Europe, including several distinct ecotypes. Small 'wave' ecotype snails inhabit exposed rocks and experience strong wave action, while thick-shelled, 'crab' ecotype snails are larger and experience crab predation on less exposed shores. Crab and wave ecotypes appear to have arisen in parallel, and recent evidence suggests only marginal sharing of molecular variation linked to evolution of similar ecotypes in different parts of Europe. However, the extent of genomic sharing is expected to increase with gene flow and more recent common ancestry. To test this, we used de novo RAD-sequencing to quantify the extent of shared genomic divergence associated with phenotypic similarities amongst ecotype pairs on three close islands (<10 km distance) connected by weak gene flow (Nm ~ 0.03) and with recent common ancestry (<10 000 years). After accounting for technical issues, including a large proportion of null alleles due to a large effective population size, we found ~8-28% of positive outliers were shared between two islands and ~2-9% were shared amongst all three islands. This low level of sharing suggests that parallel phenotypic divergence in this system is not matched by shared genomic divergence despite a high probability of gene flow and standing genetic variation.

Nucleotide Sequence Diversity and Linkage Disequilibrium of Four Nuclear Loci in Foxtail Millet (Setaria italica)

Foxtail millet (Setaria italica (L.) Beauv) is one of the earliest domesticated grains, which has been cultivated in northern China by 8,700 years before present (YBP) and across Eurasia by 4,000 YBP. Owing to a small genome and diploid nature, foxtail millet is a tractable model crop for studying functional genomics of millets and bioenergy grasses. In this study, we examined nucleotide sequence diversity, geographic structure, and levels of linkage disequilibrium at four nuclear loci (ADH1, G3PDH, IGS1 and TPI1) in representative samples of 311 landrace accessions across its cultivated range. Higher levels of nucleotide sequence and haplotype diversity were observed in samples from China relative to other sampled regions. Genetic assignment analysis classified the accessions into seven clusters based on nucleotide sequence polymorphisms. Intralocus LD decayed rapidly to half the initial value within ~1.2 kb or less.

Hierarchical boosting: a machine-learning framework to detect and classify hard selective sweeps in human populations

MOTIVATION

Detecting positive selection in genomic regions is a recurrent topic in natural population genetic studies. However, there is little consistency among the regions detected in several genome-wide scans using different tests and/or populations. Furthermore, few methods address the challenge of classifying selective events according to specific features such as age, intensity or state (completeness).

RESULTS

We have developed a machine-learning classification framework that exploits the combined ability of some selection tests to uncover different polymorphism features expected under the hard sweep model, while controlling for population-specific demography. As a result, we achieve high sensitivity toward hard selective sweeps while adding insights about their completeness (whether a selected variant is fixed or not) and age of onset. Our method also determines the relevance of the individual methods implemented so far to detect positive selection under specific selective scenarios. We calibrated and applied the method to three reference human populations from The 1000 Genome Project to generate a genome-wide classification map of hard selective sweeps. This study improves detection of selective sweep by overcoming the classical selection versus no-selection classification strategy, and offers an explanation to the lack of consistency observed among selection tests when applied to real data. Very few signals were observed in the African population studied, while our method presents higher sensitivity in this population demography.

AVAILABILITY AND IMPLEMENTATION

The genome-wide results for three human populations from The 1000 Genomes Project and an R-package implementing the 'Hierarchical Boosting' framework are available at http://hsb.upf.edu/.

Evaluating the ability of the pairwise joint site frequency spectrum to co-estimate selection and demography

The ability to infer the parameters of positive selection from genomic data has many important implications, from identifying drug-resistance mutations in viruses to increasing crop yield by genetically integrating favorable alleles. Although it has been well-described that selection and demography may result in similar patterns of diversity, the ability to jointly estimate these two processes has remained elusive. Here, we use simulation to explore the utility of the joint site frequency spectrum to estimate selection and demography simultaneously, including developing an extension of the previously proposed Jaatha program (Mathew et al., 2013). We evaluate both complete and incomplete selective sweeps under an isolation-with-migration model with and without population size change (both population growth and bottlenecks). Results suggest that while it may not be possible to precisely estimate the strength of selection, it is possible to infer the presence of selection while estimating accurate demographic parameters. We further demonstrate that the common assumption of selective neutrality when estimating demographic models may lead to severe biases. Finally, we apply the approach we have developed to better characterize the within-host demographic and selective history of human cytomegalovirus (HCMV) infection using published next generation sequencing data.

Potential Signals of Natural Selection in the Top Risk Loci for Coronary Artery Disease: 9p21 and 10q11

BACKGROUND

Coronary artery disease (CAD) is a complex disease and the leading cause of death in the world. Populations of different ancestry do not always share the same risk markers. Natural selective processes may be the cause of some of the population differences detected for specific risk mutations.

OBJECTIVE

In this study, 384 single nucleotide polymorphisms (SNPs) located in four genomic regions associated with CAD (1p13, 1q41, 9p21 and 10q11) are analysed in a set of 19 populations from Europe, Middle East and North Africa and also in Asian and African samples from the 1000 Genomes Project. The aim of this survey is to explore for the first time whether the genetic variability in these genomic regions is better explained by demography or by natural selection.

RESULTS

The results indicate significant differences in the structure of genetic variation and in the LD patterns among populations that probably explain the population disparities found in markers of susceptibility to CAD.

CONCLUSIONS

The results are consistent with potential signature of positive selection in the 9p21 region and of balancing selection in the 9p21 and 10q11. Specifically, in Europe three CAD risk markers in the 9p21 region (rs9632884, rs1537371 and rs1333042) show consistent signals of positive selection. The results of this study are consistent with a potential selective role of CAD in the configuration of genetic diversity in current human populations.

Genetic approaches in comparative and evolutionary physiology

Whole animal physiological performance is highly polygenic and highly plastic, and the same is generally true for the many subordinate traits that underlie performance capacities. Quantitative genetics, therefore, provides an appropriate framework for the analysis of physiological phenotypes and can be used to infer the microevolutionary processes that have shaped patterns of trait variation within and among species. In cases where specific genes are known to contribute to variation in physiological traits, analyses of intraspecific polymorphism and interspecific divergence can reveal molecular mechanisms of functional evolution and can provide insights into the possible adaptive significance of observed sequence changes. In this review, we explain how the tools and theory of quantitative genetics, population genetics, and molecular evolution can inform our understanding of mechanism and process in physiological evolution. For example, lab-based studies of polygenic inheritance can be integrated with field-based studies of trait variation and survivorship to measure selection in the wild, thereby providing direct insights into the adaptive significance of physiological variation. Analyses of quantitative genetic variation in selection experiments can be used to probe interrelationships among traits and the genetic basis of physiological trade-offs and constraints. We review approaches for characterizing the genetic architecture of physiological traits, including linkage mapping and association mapping, and systems approaches for dissecting intermediary steps in the chain of causation between genotype and phenotype. We also discuss the promise and limitations of population genomic approaches for inferring adaptation at specific loci. We end by highlighting the role of organismal physiology in the functional synthesis of evolutionary biology.

Detecting adaptive evolution based on association with ecological gradients: orientation matters!

Population genetic signatures of local adaptation are frequently investigated by identifying loci with allele frequencies that exhibit high correlation with ecological variables. One difficulty with this approach is that ecological associations might be confounded by geographic variation at selectively neutral loci. Here, we consider populations that underwent spatial expansion from their original range, and for which geographical variation of adaptive allele frequency coincides with habitat gradients. Using range expansion simulations, we asked whether our ability to detect genomic regions involved in adaptation could be impacted by the orientation of the ecological gradients. For three ecological association methods tested, we found, counter-intuitively, fewer false-positive associations when ecological gradients aligned along the main axis of expansion than when they aligned along any other direction. This result has important consequences for the analysis of genomic data under non-equilibrium population genetic models. Alignment of gradients with expansion axes is likely to be common in scenarios in which expanding species track their ecological niche during climate change while adapting to changing environments at their rear edge.

Exploring the genetic signature of body size in Yucatan miniature pig

Since being domesticated about 10,000-12,000 years ago, domestic pigs (Sus scrofa domesticus) have been selected for traits of economic importance, in particular large body size. However, Yucatan miniature pigs have been selected for small body size to withstand high temperature environment and for laboratory use. This renders the Yucatan miniature pig a valuable model for understanding the evolution of body size. We investigate the genetic signature for selection of body size in the Yucatan miniature pig. Phylogenetic distance of Yucatan miniature pig was compared to other large swine breeds (Yorkshire, Landrace, Duroc and wild boar). By estimating the XP-EHH statistic using re-sequencing data derived from 70 pigs, we were able to unravel the signatures of selection of body size. We found that both selections at the level of organism, and at the cellular level have occurred. Selection at the higher levels include feed intake, regulation of body weight and increase in mass while selection at the molecular level includes cell cycle and cell proliferation. Positively selected genes probed by XP-EHH may provide insight into the docile character and innate immunity as well as body size of Yucatan miniature pig.

Enhancing the mathematical properties of new haplotype homozygosity statistics for the detection of selective sweeps

Soft selective sweeps represent an important form of adaptation in which multiple haplotypes bearing adaptive alleles rise to high frequency. Most statistical methods for detecting selective sweeps from genetic polymorphism data, however, have focused on identifying hard selective sweeps in which a favored allele appears on a single haplotypic background; these methods might be underpowered to detect soft sweeps. Among exceptions is the set of haplotype homozygosity statistics introduced for the detection of soft sweeps by Garud et al. (2015). These statistics, examining frequencies of multiple haplotypes in relation to each other, include H12, a statistic designed to identify both hard and soft selective sweeps, and H2/H1, a statistic that conditional on high H12 values seeks to distinguish between hard and soft sweeps. A challenge in the use of H2/H1 is that its range depends on the associated value of H12, so that equal H2/H1 values might provide different levels of support for a soft sweep model at different values of H12. Here, we enhance the H12 and H2/H1 haplotype homozygosity statistics for selective sweep detection by deriving the upper bound on H2/H1 as a function of H12, thereby generating a statistic that normalizes H2/H1 to lie between 0 and 1. Through a reanalysis of resequencing data from inbred lines of Drosophila, we show that the enhanced statistic both strengthens interpretations obtained with the unnormalized statistic and leads to empirical insights that are less readily apparent without the normalization.

Recent positive selection has acted on genes encoding proteins with more interactions within the whole human interactome

Genes vary in their likelihood to undergo adaptive evolution. The genomic factors that determine adaptability, however, remain poorly understood. Genes function in the context of molecular networks, with some occupying more important positions than others and thus being likely to be under stronger selective pressures. However, how positive selection distributes across the different parts of molecular networks is still not fully understood. Here, we inferred positive selection using comparative genomics and population genetics approaches through the comparison of 10 mammalian and 270 human genomes, respectively. In agreement with previous results, we found that genes with lower network centralities are more likely to evolve under positive selection (as inferred from divergence data). Surprisingly, polymorphism data yield results in the opposite direction than divergence data: Genes with higher centralities are more likely to have been targeted by recent positive selection during recent human evolution. Our results indicate that the relationship between centrality and the impact of adaptive evolution highly depends on the mode of positive selection and/or the evolutionary time-scale.

Inferring positive selection in humans from genomic data

Adaptation can be described as an evolutionary process that leads to an adjustment of the phenotypes of a population to their environment. In the classical view, new mutations can introduce novel phenotypic features into a population that leave footprints in the genome after fixation, such as selective sweeps. Alternatively, existing genetic variants may become beneficial after an environmental change and increase in frequency. Although they may not reach fixation, they may cause a shift of the optimum of a phenotypic trait controlled by multiple loci. With the availability of polymorphism data from various organisms, including humans and chimpanzees, it has become possible to detect molecular evidence of adaptation and to estimate the strength and target of positive selection. In this review, we discuss the two competing models of adaptation and suitable approaches for detecting the footprints of positive selection on the molecular level.

The effects of microsatellite selection on linked sequence diversity

The genome-wide scan for selection is an important method for identifying loci involved in adaptive evolution. However, theory that underlies standard scans for selection assumes a simple mutation model. In particular, recurrent mutation of the selective target is not considered. Although this assumption is reasonable for single-nucleotide variants (SNVs), a microsatellite targeted by selection will reliably violate this assumption due to high mutation rate. Moreover, the mutation rate of microsatellites is generally high enough to ensure that recurrent mutation is pervasive rather than occasional. It is therefore unclear if positive selection targeting microsatellites can be detected using standard scanning statistics. Examples of functional variation at microsatellites underscore the significance of understanding the genomic effects of microsatellite selection. Here, we investigate the joint effects of selection and complex mutation on linked sequence diversity, comparing simulations of microsatellite selection and SNV-based selective sweeps. We find that selection on microsatellites is generally difficult to detect using popular summaries of the site frequency spectrum, and, under certain conditions, using popular methods such as the integrated haplotype statistic and SweepFinder. However, comparisons of the number of haplotypes (K) and segregating sites (S) often provide considerable power to detect selection on microsatellites. We apply this knowledge to a scan of autosomes in the human CEU population (CEPH population sampled from Utah). In addition to the most commonly reported targets of selection in European populations, we identify numerous novel genomic regions that bear highly anomalous haplotype configurations. Using one of these regions—intron 1 of MAGI2—as an example, we show that the anomalous configuration is coincident with a perfect CA repeat of length 22. We conclude that standard genome-wide scans will commonly fail to detect mutationally complex targets of selection but that comparisons of K and S will, in many cases, facilitate their identification.

Soft shoulders ahead: spurious signatures of soft and partial selective sweeps result from linked hard sweeps

Characterizing the nature of the adaptive process at the genetic level is a central goal for population genetics. In particular, we know little about the sources of adaptive substitution or about the number of adaptive variants currently segregating in nature. Historically, population geneticists have focused attention on the hard-sweep model of adaptation in which a de novo beneficial mutation arises and rapidly fixes in a population. Recently more attention has been given to soft-sweep models, in which alleles that were previously neutral, or nearly so, drift until such a time as the environment shifts and their selection coefficient changes to become beneficial. It remains an active and difficult problem, however, to tease apart the telltale signatures of hard vs. soft sweeps in genomic polymorphism data. Through extensive simulations of hard- and soft-sweep models, here we show that indeed the two might not be separable through the use of simple summary statistics. In particular, it seems that recombination in regions linked to, but distant from, sites of hard sweeps can create patterns of polymorphism that closely mirror what is expected to be found near soft sweeps. We find that a very similar situation arises when using haplotype-based statistics that are aimed at detecting partial or ongoing selective sweeps, such that it is difficult to distinguish the shoulder of a hard sweep from the center of a partial sweep. While knowing the location of the selected site mitigates this problem slightly, we show that stochasticity in signatures of natural selection will frequently cause the signal to reach its zenith far from this site and that this effect is more severe for soft sweeps; thus inferences of the target as well as the mode of positive selection may be inaccurate. In addition, both the time since a sweep ends and biologically realistic levels of allelic gene conversion lead to errors in the classification and identification of selective sweeps. This general problem of "soft shoulders" underscores the difficulty in differentiating soft and partial sweeps from hard-sweep scenarios in molecular population genomics data. The soft-shoulder effect also implies that the more common hard sweeps have been in recent evolutionary history, the more prevalent spurious signatures of soft or partial sweeps may appear in some genome-wide scans.

Recent selective sweeps in North American Drosophila melanogaster show signatures of soft sweeps

Adaptation from standing genetic variation or recurrent de novo mutation in large populations should commonly generate soft rather than hard selective sweeps. In contrast to a hard selective sweep, in which a single adaptive haplotype rises to high population frequency, in a soft selective sweep multiple adaptive haplotypes sweep through the population simultaneously, producing distinct patterns of genetic variation in the vicinity of the adaptive site. Current statistical methods were expressly designed to detect hard sweeps and most lack power to detect soft sweeps. This is particularly unfortunate for the study of adaptation in species such as Drosophila melanogaster, where all three confirmed cases of recent adaptation resulted in soft selective sweeps and where there is evidence that the effective population size relevant for recent and strong adaptation is large enough to generate soft sweeps even when adaptation requires mutation at a specific single site at a locus. Here, we develop a statistical test based on a measure of haplotype homozygosity (H12) that is capable of detecting both hard and soft sweeps with similar power. We use H12 to identify multiple genomic regions that have undergone recent and strong adaptation in a large population sample of fully sequenced Drosophila melanogaster strains from the Drosophila Genetic Reference Panel (DGRP). Visual inspection of the top 50 candidates reveals that in all cases multiple haplotypes are present at high frequencies, consistent with signatures of soft sweeps. We further develop a second haplotype homozygosity statistic (H2/H1) that, in combination with H12, is capable of differentiating hard from soft sweeps. Surprisingly, we find that the H12 and H2/H1 values for all top 50 peaks are much more easily generated by soft rather than hard sweeps. We discuss the implications of these results for the study of adaptation in Drosophila and in species with large census population sizes.

Evolutionary adaptation is a process in which beneficial mutations increase in frequency in response to selective pressures. If these mutations were previously rare or absent from the population, adaptation should generate a characteristic signature in the genetic diversity around the adaptive locus, known as a selective sweep. Such selective sweeps can be distinguished into hard selective sweeps, where only a single adaptive mutation rises in frequency, or soft selective sweeps, where multiple adaptive mutations at the same locus sweep through the population simultaneously. Here we design a new statistical method that can identify both hard and soft sweeps in population genomic data and apply this method to a Drosophila melanogaster population genomic dataset consisting of 145 sequenced strains collected in North Carolina. We find that selective sweeps were abundant in the recent history of this population. Interestingly, we also find that practically all of the strongest and most recent sweeps show patterns that are more consistent with soft rather than hard sweeps. We discuss the implications of these findings for the discovery and quantification of adaptation from population genomic data in Drosophila and other species with large population sizes.