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The role of balancing selection is a long-standing evolutionary puzzle. Balancing selection is a crucial evolutionary process that maintains genetic variation (polymorphism) over extended periods of time; however, detecting it poses a significant challenge. Building upon the Polymorphism-aware phylogenetic Models (PoMos) framework rooted in the Moran model, we introduce a PoMoBalance model. This novel approach is designed to disentangle the interplay of mutation, genetic drift, and directional selection (GC-biased gene conversion), along with the previously unexplored balancing selection pressures on ultra-long timescales comparable with species divergence times by analyzing multi-individual genomic and phylogenetic divergence data. Implemented in the open-source RevBayes Bayesian framework, PoMoBalance offers a versatile tool for inferring phylogenetic trees as well as quantifying various selective pressures. The novel aspect of our approach in studying balancing selection lies in polymorphism-aware phylogenetic models' ability to account for ancestral polymorphisms and incorporate parameters that measure frequency-dependent selection, allowing us to determine the strength of the effect and exact frequencies under selection. We implemented validation tests and assessed the model on the data simulated with SLiM and a custom Moran model simulator. Real sequence analysis of Drosophila populations reveals insights into the evolutionary dynamics of regions subject to frequency-dependent balancing selection, particularly in the context of sex-limited color dimorphism in Drosophila erecta. [ABSTRACT FROM AUTHOR]

Two important characteristics of metapopulations are extinction–(re)colonization dynamics and gene flow between subpopulations. These processes can cause strong shifts in genome-wide allele frequencies that are generally not observed in "classical" (large, stable, and panmictic) populations. Subpopulations founded by one or a few individuals, the so-called propagule model, are initially expected to show intermediate allele frequencies at polymorphic sites until natural selection and genetic drift drive allele frequencies toward a mutation–selection–drift equilibrium characterized by a negative exponential-like distribution of the site frequency spectrum. We followed changes in site frequency spectrum distribution in a natural metapopulation of the cyclically parthenogenetic pond-dwelling microcrustacean Daphnia magna using biannual pool-seq samples collected over a 5-yr period from 118 ponds occupied by subpopulations of known age. As expected under the propagule model, site frequency spectra in newly founded subpopulations trended toward intermediate allele frequencies and shifted toward right-skewed distributions as the populations aged. Immigration and subsequent hybrid vigor altered this dynamic. We show that the analysis of site frequency spectrum dynamics is a powerful approach to understand evolution in metapopulations. It allowed us to disentangle evolutionary processes occurring in a natural metapopulation, where many subpopulations evolve in parallel. Thereby, stochastic processes like founder and immigration events lead to a pattern of subpopulation divergence, while genetic drift leads to converging site frequency spectrum distributions in the persisting subpopulations. The observed processes are well explained by the propagule model and highlight that metapopulations evolve differently from classical populations. [ABSTRACT FROM AUTHOR]

Modern and ancient genomes are not necessarily drawn from homogeneous populations, as they may have been collected from different places and at different times. This heterogeneous sampling can be an issue for demographic inferences and results in biased demographic parameters and incorrect model choice if not properly considered. When explicitly accounted for, it can result in very complex models and high data dimensionality that are difficult to analyse. In this paper, we formally study the impact of such spatial and temporal sampling heterogeneity on demographic inference, and we introduce a way to circumvent this problem. To deal with structured samples without increasing the dimensionality of the site frequency spectrum (SFS), we introduce a new structured approach to the existing program fastsimcoal2. We assess the efficiency and relevance of this methodological update with simulated and modern human genomic data. We particularly focus on spatial and temporal heterogeneities to evidence the interest of this new SFS‐based approach, which can be especially useful when handling scattered and ancient DNA samples, as in conservation genetics or archaeogenetics. [ABSTRACT FROM AUTHOR]

The Structured Coalescent was introduced to describe the coalescent process in spatially subdivided populations with migration. Here, we re-interpret migration routes of individuals in the original model as "migration routes" of single genes in tandemly arranged gene arrays. A gene copy may change its position within the array via unequal recombination. Hence, in a coalescent framework, two copies sampled from two chromosomes may coalesce only if they are at exactly homologous positions. Otherwise, one or multiple recombination events have to occur before they can coalesce, thereby increasing mean coalescence time and expected genetic diversity among the copies in a gene array. We explicitly calculate the transition probabilities on these routes backward in time. We simulate the structured coalescent with migration and coalescence rates informed by the unequal recombination process of gene copies. With this novel interpretation of population structure models we determine coalescence times and expected genetic diversity in samples of orthologous and paralogous copies from a gene family. As a case study, we discuss the site frequency spectrum of a small gene family in the two scenarios of high and of no gene copy number variation among individuals. These examples underline the significance of our model, since standard test-statistics may lead to misinterpretations when analyzing sequence data of multi-copy genes due to their different expected genetic diversity. [ABSTRACT FROM AUTHOR]

Multiple-merger coalescents, also known as Λ -coalescents, have been used to describe the genealogy of populations that have a skewed offspring distribution or that undergo strong selection. Inferring the characteristic measure Λ , which describes the rates of the multiple-merger events, is key to understand these processes. So far, most inference methods only work for some particular families of Λ -coalescents that are described by only one parameter, but not for more general models. This article is devoted to the construction of a non-parametric estimator of the density of Λ that is based on the observation at a single time of the so-called Site Frequency Spectrum (SFS), which describes the allelic frequencies in a present population sample. First, we produce estimates of the multiple-merger rates by solving a linear system, whose coefficients are obtained by appropriately subsampling the SFS. Then, we use a technique that aggregates the information extracted from the previous step through a kernel type of re-construction to give a non-parametric estimation of the measure Λ. We give a consistency result of this estimator under mild conditions on the behavior of Λ around 0. We also show some numerical examples of how our method performs. [ABSTRACT FROM AUTHOR]

Recognition of the important role of transposable elements (TEs) in eukaryotic genomes quickly led to a burgeoning literature modeling and estimating the effects of selection on TEs. Much of the empirical work on selection has focused on analyzing the site frequency spectrum (SFS) of TEs. But TE evolution differs from standard models in a number of ways that can impact the power and interpretation of the SFS. For example, rather than mutating under a clock-like model, transposition often occurs in bursts which can inflate particular frequency categories compared with expectations under a standard neutral model. If a TE burst has been recent, the excess of low-frequency polymorphisms can mimic the effect of purifying selection. Here, we investigate how transposition bursts affect the frequency distribution of TEs and the correlation between age and allele frequency. Using information on the TE age distribution, we propose an age-adjusted SFS to compare TEs and neutral polymorphisms to more effectively evaluate whether TEs are under selective constraints. We show that our approach can minimize instances of false inference of selective constraint, remains robust to simple demographic changes, and allows for a correct identification of even weak selection affecting TEs which experienced a transposition burst. The results presented here will help researchers working on TEs to more reliably identify the effects of selection on TEs without having to rely on the assumption of a constant transposition rate.

The distribution of fitness effects is a key property in evolutionary genetics as it has implications for several evolutionary phenomena including the evolution of sex and mating systems, the rate of adaptive evolution, and the prevalence of deleterious mutations. Despite the distribution of fitness effects being extensively studied, the effects of strongly deleterious mutations are difficult to infer since such mutations are unlikely to be present in a sample of haplotypes, so genetic data may contain very little information about them. Recent work has attempted to correct for this issue by expanding the classic gamma-distributed model to explicitly account for strongly deleterious mutations. Here, we use simulations to investigate one such method, adding a parameter (plth) to capture the proportion of strongly deleterious mutations. We show that plth can improve the model fit when applied to individual species but underestimates the true proportion of strongly deleterious mutations. The parameter can also artificially maximize the likelihood when used to jointly infer a distribution of fitness effects from multiple species. As plth and related parameters are used in current inference algorithms, our results are relevant with respect to avoiding model artifacts and improving future tools for inferring the distribution of fitness effects. [ABSTRACT FROM AUTHOR]

One of the main necessities for population geneticists is the availability of sensitive statistical tools that enable to accept or reject the standard Wright–Fisher model of neutral evolution. A number of statistical tests have been developed to detect specific deviations from the null frequency spectrum in different directions (e.g., Tajima's D, Fu and Li's F and D tests, Fay and Wu's H). A general framework exists to generate all neutrality tests that are linear functions of the frequency spectrum. In this framework, it is possible to develop a family of optimal tests with almost maximum power against a specific alternative evolutionary scenario. In this paper we provide a thorough discussion of the structure and properties of linear and nonlinear neutrality tests. First, we present the general framework for linear tests and emphasise the importance of the property of scalability with the sample size (that is, the interpretation of the tests should not depend on the sample size), which, if missing, can lead to errors in interpreting the data. After summarising the motivation and structure of linear optimal tests, we present a more general framework for the optimisation of linear tests, leading to a new family of tunable neutrality tests. In a further generalisation, we extend the framework to nonlinear neutrality tests and we derive nonlinear optimal tests for polynomials of any degree in the frequency spectrum. [ABSTRACT FROM AUTHOR]

In plants, mammals and insects, some genes are methylated in the CG dinucleotide context, a phenomenon called gene body methylation (gbM). It has been controversial whether this phenomenon has any functional role. Here, we took advantage of the availability of 876 leaf methylomes in Arabidopsis thaliana to characterize the population frequency of methylation at the gene level and to estimate the site-frequency spectrum of allelic states. Using a population genetics model specifically designed for epigenetic data, we found that genes with ancestral gbM are under significant selection to remain methylated. Conversely, ancestrally unmethylated genes were under selection to remain unmethylated. Repeating the analyses at the level of individual cytosines confirmed these results. Estimated selection coefficients were small, on the order of 4 Nes = 1.4, which is similar to the magnitude of selection acting on codon usage. We also estimated that A. thaliana is losing gbM threefold more rapidly than gaining it, which could be due to a recent reduction in the efficacy of selection after a switch to selfing. Finally, we investigated the potential function of gbM through its link with gene expression. Across genes with polymorphic methylation states, the expression of gene body methylated alleles was consistently and significantly higher than unmethylated alleles. Although it is difficult to disentangle genetic from epigenetic effects, our work suggests that gbM has a small but measurable effect on fitness, perhaps due to its association to a phenotype-like gene expression.

Polyploidy is an important generator of evolutionary novelty across diverse groups in the Tree of Life, including many crops. However, the impact of whole-genome duplication depends on the mode of formation: doubling within a single lineage (autopolyploidy) versus doubling after hybridization between two different lineages (allopolyploidy). Researchers have historically treated these two scenarios as completely separate cases based on patterns of chromosome pairing, but these cases represent ideals on a continuum of chromosomal interactions among duplicated genomes. Understanding the history of polyploid species thus demands quantitative inferences of demographic history and rates of exchange between subgenomes. To meet this need, we developed diffusion models for genetic variation in polyploids with subgenomes that cannot be bioinformatically separated and with potentially variable inheritance patterns, implementing them in the dadi software. We validated our models using forward SLiM simulations and found that our inference approach is able to accurately infer evolutionary parameters (timing, bottleneck size) involved with the formation of auto- and allotetraploids, as well as exchange rates in segmental allotetraploids. We then applied our models to empirical data for allotetraploid shepherd's purse (Capsella bursa-pastoris), finding evidence for allelic exchange between the subgenomes. Taken together, our model provides a foundation for demographic modeling in polyploids using diffusion equations, which will help increase our understanding of the impact of demography and selection in polyploid lineages. [ABSTRACT FROM AUTHOR]

The presence and impact of recessive lethal mutations have been widely documented in diploid outcrossing species. However, precise estimates of the proportion of new mutations that are recessive lethal remain limited. Here, we evaluate the performance of Fit∂a∂i, a commonly used method for inferring the distribution of fitness effects (DFE), in the presence of lethal mutations. Using simulations, we demonstrate that in both additive and recessive cases, inference of the deleterious nonlethal portion of the DFE is minimally affected by a small proportion (<10%) of lethal mutations. Additionally, we demonstrate that while Fit∂a∂i cannot estimate the fraction of recessive lethal mutations, Fit∂a∂i can accurately infer the fraction of additive lethal mutations. Finally, as an alternative approach to estimate the proportion of mutations that are recessive lethal, we employ models of mutation–selection–drift balance using existing genomic parameters and estimates of segregating recessive lethals for humans and Drosophila melanogaster. In both species, the segregating recessive lethal load can be explained by a very small fraction (<1%) of new nonsynonymous mutations being recessive lethal. Our results refute recent assertions of a much higher proportion of mutations being recessive lethal (4%–5%), while highlighting the need for additional information on the joint distribution of selection and dominance coefficients. [ABSTRACT FROM AUTHOR]

The site frequency spectrum is an important summary statistic in population genetics used for inference on demographic history and selection. However, estimation of the site frequency spectrum from called genotypes introduces bias when working with low-coverage sequencing data. Methods exist for addressing this issue but sometimes suffer from 2 problems. First, they can have very high computational demands, to the point that it may not be possible to run estimation for genome-scale data. Second, existing methods are prone to overfitting, especially for multidimensional site frequency spectrum estimation. In this article, we present a stochastic expectation–maximization algorithm for inferring the site frequency spectrum from NGS data that address these challenges. We show that this algorithm greatly reduces runtime and enables estimation with constant, trivial RAM usage. Furthermore, the algorithm reduces overfitting and thereby improves downstream inference. An implementation is available at github.com/malthesr/winsfs. [ABSTRACT FROM AUTHOR]

The aim of this article is to study the impact of resistance acquisition on the distribution of neutral mutations in a cell population under therapeutic pressure. The cell population is modeled by a bi-type branching process. Initially, the cells all carry type 0, associated with a negative growth rate. Mutations towards type 1 are assumed to be rare and random, and lead to the survival of cells under treatment, i.e. type 1 is associated with a positive growth rate, and thus models the acquisition of a resistance. Cells also carry neutral mutations, acquired at birth and accumulated by inheritance, that do not affect their type. We describe the expectation of the "Site Frequency Spectrum" (SFS), which is an index of neutral mutation distribution in a population, under the asymptotic of rare events of resistance acquisition and of large initial population. Precisely, we give asymptotically-equivalent expressions of the expected number of neutral mutations shared by both a small and a large number of cells. To identify the influence of relatives on the SFS, our work also lead us to study in detail subcritical binary Galton–Watson trees, where each leaf is marked with a small probability. As a by-product of this study, we thus provide the law of the generation of a randomly chosen leaf in such a Galton–Watson tree conditioned on the number of marks. [ABSTRACT FROM AUTHOR]

The reduction of genetic diversity due to genetic hitchhiking is widely used to find past selective sweeps from sequencing data, but very little is known about how spatial structure affects hitchhiking. We use mathematical modeling and simulations to find the unfolded site frequency spectrum left by hitchhiking in the genomic region of a sweep in a population occupying a 1D range. For such populations, sweeps spread as Fisher waves, rather than logistically. We find that this leaves a characteristic 3-part site frequency spectrum at loci very close to the swept locus. Very low frequencies are dominated by recent mutations that occurred after the sweep and are unaffected by hitchhiking. At moderately low frequencies, there is a transition zone primarily composed of alleles that briefly "surfed" on the wave of the sweep before falling out of the wavefront, leaving a spectrum close to that expected in well-mixed populations. However, for moderate-to-high frequencies, there is a distinctive scaling regime of the site frequency spectrum produced by alleles that drifted to fixation in the wavefront and then were carried throughout the population. For loci slightly farther away from the swept locus on the genome, recombination is much more effective at restoring diversity in 1D populations than it is in well-mixed ones. We find that these signatures of space can be strong even in apparently well-mixed populations with negligible spatial genetic differentiation, suggesting that spatial structure may frequently distort the signatures of hitchhiking in natural populations. [ABSTRACT FROM AUTHOR]

Due to the emergence of new variants of the SARS-CoV-2 coronavirus, the question of how the viral genomes evolved, leading to the formation of highly infectious strains, becomes particularly important. Three major emergent strains, Alpha, Beta and Delta, characterized by a significant number of missense mutations, provide a natural test field. We accumulated and aligned 4.7 million SARS-CoV-2 genomes from the GISAID database and carried out a comprehensive set of analyses. This collection covers the period until the end of October 2021, i.e., the beginnings of the Omicron variant. First, we explored combinatorial complexity of the genomic variants emerging and their timing, indicating very strong, albeit hidden, selection forces. Our analyses show that the mutations that define variants of concern did not arise gradually but rather co-evolved rapidly, leading to the emergence of the full variant strain. To explore in more detail the evolutionary forces at work, we developed time trajectories of mutations at all 29,903 sites of the SARS-CoV-2 genome, week by week, and stratified them into trends related to (i) point substitutions, (ii) deletions and (iii) non-sequenceable regions. We focused on classifying the genetic forces active at different ranges of the mutational spectrum. We observed the agreement of the lowest-frequency mutation spectrum with the Griffiths–Tavaré theory, under the Infinite Sites Model and neutrality. If we widen the frequency range, we observe the site frequency spectra much more consistently with the Tung–Durrett model assuming clone competition and selection. The coefficients of the fitting model indicate the possibility of selection acting to promote gradual growth slowdown, as observed in the history of the variants of concern. These results add up to a model of genomic evolution, which partly fits into the classical drift barrier ideas. Certain observations, such as mutation "bands" persistent over the epidemic history, suggest contribution of genetic forces different from mutation, drift and selection, including recombination or other genome transformations. In addition, we show that a "toy" mathematical model can qualitatively reproduce how new variants (clones) stem from rare advantageous driver mutations, and then acquire neutral or disadvantageous passenger mutations which gradually reduce their fitness so they can be then outcompeted by new variants due to other driver mutations. [ABSTRACT FROM AUTHOR]

One of the unique features of SARS-CoV-2 is its apparent neutral evolution during the early pandemic (before February 2020). This contrasts with the preceding SARS-CoV epidemics, where viruses evolved adaptively. SARS-CoV-2 may exhibit a unique or adaptive feature which deviates from other coronaviruses. Alternatively, the virus may have been cryptically circulating in humans for a sufficient time to have acquired adaptive changes before the onset of the current pandemic. To test the scenarios above, we analyzed the SARS-CoV-2 sequences from minks (Neovision vision) and parental humans. In the early phase of the mink epidemic (April to May 2020), nonsynonymous to synonymous mutation ratio per site in the spike protein is 2.93, indicating a selection process favoring adaptive amino acid changes. Mutations in the spike protein were concentrated within its receptor-binding domain and receptor-binding motif. An excess of high-frequency derived variants produced by genetic hitchhiking was found during the middle (June to July 2020) and late phase I (August to September 2020) of the mink epidemic. In contrast, the site frequency spectra of early SARS-CoV-2 in humans only show an excess of low-frequency mutations, consistent with the recent outbreak of the virus. Strong positive selection in the mink SARS-CoV-2 implies that the virus may not be preadapted to a wide range of hosts and illustrates how a virus evolves to establish a continuous infection in a new host. Therefore, the lack of positive selection signal during the early pandemic in humans deserves further investigation. [ABSTRACT FROM AUTHOR]

The site frequency spectrum (SFS) is an important statistic that summarizes the molecular variation in a population, and is used to estimate population-genetic parameters and detect natural selection. Here, we study the SFS in a randomly mating, diploid population in which both the population size and selection coefficient vary periodically with time using a diffusion theory approach, and derive simple analytical expressions for the time-averaged SFS in slowly and rapidly changing environments. We show that for strong selection and in slowly changing environments where the population experiences both positive and negative cycles of the selection coefficient, the time-averaged SFS differs significantly from the equilibrium SFS in a constant environment. The deviation is found to depend on the time spent by the population in the deleterious part of the selection cycle and the phase difference between the selection coefficient and population size, and can be captured by an effective population size. [ABSTRACT FROM AUTHOR]

The application of demographic history modelling and inference to the study of divergence between species has become a cornerstone of speciation genomics. Speciation histories are usually reconstructed by analysing single populations from each species, assuming that the inferred population history represents the actual speciation history. However, this assumption may not be met when species diverge with gene flow, for example, when secondary contact may be confined to specific geographic regions. Here, we tested whether divergence histories inferred from heterospecific populations may vary depending on their geographic locations, using the two wood ant species Formica polyctena and F. aquilonia. We performed whole‐genome resequencing of 20 individuals sampled in multiple locations across the European ranges of both species. Then, we reconstructed the histories of distinct heterospecific population pairs using a coalescent‐based approach. Our analyses always supported a scenario of divergence with gene flow, suggesting that divergence started in the Pleistocene (c. 500 kya) and occurred with continuous asymmetrical gene flow from F. aquilonia to F. polyctena until a recent time, when migration became negligible (2–19 kya). However, we found support for contemporary gene flow in a sympatric pair from Finland, where the species hybridise, but no signature of recent bidirectional gene flow elsewhere. Overall, our results suggest that divergence histories reconstructed from a few individuals may be applicable at the species level. Nonetheless, the geographical context of populations chosen to represent their species should be taken into account, as it may affect estimates of migration rates between species when gene flow is spatially heterogeneous. [ABSTRACT FROM AUTHOR]

The distribution of fitness effects (DFE) has considerable importance in population genetics. To date, estimates of the DFE come from studies using a small number of individuals. Thus, estimates of the proportion of moderately to strongly deleterious new mutations may be unreliable because such variants are unlikely to be segregating in the data. Additionally, the true functional form of the DFE is unknown, and estimates of the DFE differ significantly between studies. Here we present a flexible and computationally tractable method, called Fit∂a∂i, to estimate the DFE of new mutations using the site frequency spectrum from a large number of individuals. We apply our approach to the frequency spectrum of 1300 Europeans from the Exome Sequencing Project ESP6400 data set, 1298 Danes from the LuCamp data set, and 432 Europeans from the 1000 Genomes Project to estimate the DFE of deleterious nonsynonymous mutations. We infer significantly fewer (0.38-0.84 fold) strongly deleterious mutations with selection coefficient |s| > 0.01 and more (1.24-1.43 fold) weakly deleterious mutations with selection coefficient |s| < 0.001 compared to previous estimates. Furthermore, a DFE that is a mixture distribution of a point mass at neutrality plus a gamma distribution fits better than a gamma distribution in two of the three data sets. Our results suggest that nearly neutral forces play a larger role in human evolution than previously thought.

The disjunct temperate rainforests of the Pacific Northwest of North America (PNW) are characterized by late‐successional dominant tree species Thuja plicata (western redcedar) and Tsuga heterophylla (western hemlock). The demographic histories of these species, along with the PNW rainforest ecosystem in its entirety, have been heavily impacted by geological and climatic changes the PNW has experienced over the last 5 million years, including mountain orogeny and repeated Pleistocene glaciations. These environmental events have ultimately shaped the history of these species, with inland populations potentially being extirpated during the Pleistocene glaciations. Here, we collect genomic data for both species across their ranges to test multiple demographic models, each reflecting a different phylogeographical hypothesis on how the ecosystem‐dominating species may have responded to dramatic climatic change. Our results indicate that inland and coastal populations in both species diverged ~2.5 million years ago in the early Pleistocene and experienced decreases in population size during glacial cycles, with subsequent population expansion. Importantly, we found evidence for gene flow between coastal and inland populations during the mid‐Holocene. It is likely that intermittent migration in these species during this time has prevented allopatric speciation via genetic drift alone. In conclusion, our results from combining genomic data and demographic inference procedures establish that populations of the ecosystem dominants Thuja plicata and Tsuga heterophylla persisted in refugia located in both the coastal and inland regions of the PNW throughout the Pleistocene, with populations expanding and contracting in response to glacial cycles with occasional gene flow. [ABSTRACT FROM AUTHOR]

Neutrality tests such as Tajima's D and Fay and Wu's H are standard implements in the population genetics toolbox. One of their most common uses is to scan the genome for signals of natural selection. However, it is well understood that D and H are confounded by other evolutionary forces--in particular, population expansion--that may be unrelated to selection. Because they are not model-based, it is not clear how to deconfound these tests in a principled way. In this article, we derive new likelihood-based methods for detecting natural selection, which are robust to fluctuations in effective population size. At the core of our method is a novel probabilistic model of tree imbalance, which generalizes Kingman's coalescent to allow certain aberrant tree topologies to arise more frequently than is expected under neutrality. We derive a frequency spectrum-based estimator that can be used in place of D, and also extend to the case where genealogies are first estimated. We benchmark our methods on real and simulated data, and provide an open source software implementation. [ABSTRACT FROM AUTHOR]

The identification of genes that evolve under recessive natural selection is a long-standing goal of population genetics research that has important applications to the discovery of genes associated with disease. We found that commonly used methods to evaluate selective constraint at the gene level are highly sensitive to genes under heterozygous selection but ubiquitously fail to detect recessively evolving genes. Additionally, more sophisticated likelihood-based methods designed to detect recessivity similarly lack power for a human gene of realistic length from current population sample sizes. However, extensive simulations suggested that recessive genes may be detectable in aggregate. Here, we offer a method informed by population genetics simulations designed to detect recessive purifying selection in gene sets. Applying this to empirical gene sets produced significant enrichments for strong recessive selection in genes previously inferred to be under recessive selection in a consanguineous cohort and in genes involved in autosomal recessive monogenic disorders. [ABSTRACT FROM AUTHOR]

The distribution of fitness effects (DFE) for new mutations is one of the most theoretically important but difficult to estimate properties in population genetics. A crucial challenge to inferring the DFE from natural genetic variation is the sensitivity of the site frequency spectrum to factors like population size change, population substructure, genome structure, and nonrandom mating. Although inference methods aim to control for population size changes, the influence of nonrandom mating remains incompletely understood, despite being a common feature of many species. We report the DFE estimated from 326 genomes of Caenorhabditis elegans, a nematode roundworm with a high rate of self-fertilization. We evaluate the robustness of DFE inferences using simulated data that mimics the genomic structure and reproductive life history of C. elegans. Our observations demonstrate how the combined influence of self-fertilization, genome structure, and natural selection on linked sites can conspire to compromise estimates of the DFE from extant polymorphisms with existing methods. These factors together tend to bias inferences toward weakly deleterious mutations, making it challenging to have full confidence in the inferred DFE of new mutations as deduced from standing genetic variation in species like C. elegans. Improved methods for inferring the DFE are needed to appropriately handle strong linked selection and selfing. These results highlight the importance of understanding the combined effects of processes that can bias our interpretations of evolution in natural populations. [ABSTRACT FROM AUTHOR]

Background The site frequency spectrum summarizes the distribution of allele frequencies throughout the genome, and it is widely used as a summary statistic to infer demographic parameters and to detect signals of natural selection. The use of high-throughput low-coverage DNA sequencing data can lead to biased estimates of the site frequency spectrum due to high levels of uncertainty in genotyping. Results Here we design and implement a method to efficiently and accurately estimate the multidimensional joint site frequency spectrum for large numbers of haploid or diploid individuals across an arbitrary number of populations, using low-coverage sequencing data. The method maximizes a likelihood function that represents the probability of the sequencing data observed given a multidimensional site frequency spectrum using genotype likelihoods. Notably, it uses an advanced binning heuristic paired with an accelerated expectation-maximization algorithm for a fast and memory-efficient computation, and can generate both unfolded and folded spectra and bootstrapped replicates for haploid and diploid genomes. On the basis of extensive simulations, we show that the new method requires remarkably less storage and is faster than previous implementations whilst retaining the same accuracy. When applied to low-coverage sequencing data from the fungal pathogen Neonectria neomacrospora , results recapitulate the patterns of population differentiation generated using the original high-coverage data. Conclusion The new implementation allows for accurate estimation of population genetic parameters from arbitrarily large, low-coverage datasets, thus facilitating cost-effective sequencing experiments in model and non-model organisms. [ABSTRACT FROM AUTHOR]

The site frequency spectrum (SFS) is a popular summary statistic of genomic data. While the SFS of a constant-sized population undergoing neutral mutations has been extensively studied in population genetics, the rapidly growing amount of cancer genomic data has attracted interest in the spectrum of an exponentially growing population. Recent theoretical results have generally dealt with special or limiting cases, such as considering only cells with an infinite line of descent, assuming deterministic tumor growth, or taking large-time or large-population limits. In this work, we derive exact expressions for the expected SFS of a cell population that evolves according to a stochastic branching process, first for cells with an infinite line of descent and then for the total population, evaluated either at a fixed time (fixed-time spectrum) or at the stochastic time at which the population reaches a certain size (fixed-size spectrum). We find that while the rate of mutation scales the SFS of the total population linearly, the rates of cell birth and cell death change the shape of the spectrum at the small-frequency end, inducing a transition between a 1 / j 2 power-law spectrum and a 1 / j spectrum as cell viability decreases. We show that this insight can in principle be used to estimate the ratio between the rate of cell death and cell birth, as well as the mutation rate, using the site frequency spectrum alone. Although the discussion is framed in terms of tumor dynamics, our results apply to any exponentially growing population of individuals undergoing neutral mutations. [ABSTRACT FROM AUTHOR]

Linear functions of the site frequency spectrum (SFS) play a major role for understanding and investigating genetic diversity. Estimators of the mutation rate (e.g. based on the total number of segregating sites or average of the pairwise differences) and tests for neutrality (e.g. Tajima’s D) are perhaps the most well-known examples. The distribution of linear functions of the SFS is important for constructing confidence intervals for the estimators, and to determine significance thresholds for neutrality tests. These distributions are often approximated using simulation procedures. In this paper we use multivariate phase-type theory to specify, characterize and calculate the distribution of linear functions of the site frequency spectrum. In particular, we show that many of the classical estimators of the mutation rate are distributed according to a discrete phase-type distribution. Neutrality tests, however, are generally not discrete phase-type distributed. For neutrality tests we derive the probability generating function using continuous multivariate phase-type theory, and numerically invert the function to obtain the distribution. A main result is an analytically tractable formula for the probability generating function of the SFS. Software implementation of the phase-type methodology is available in the R package PhaseTypeR, and R code for the reproduction of our results is available as an accompanying vignette. [ABSTRACT FROM AUTHOR]

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., Competing Interests: Conflicts of interest None declared.

Natural hybrid zones offer a powerful framework for understanding the genetic basis of speciation in progress because ongoing hybridization continually creates unfavorable gene combinations. Evidence indicates that postzygotic reproductive isolation is often caused by epistatic interactions between mutations in different genes that evolved independently of one another (hybrid incompatibilities). We examined the potential to detect epistatic selection against incompatibilities from genome sequence data using the site frequency spectrum (SFS) of polymorphisms by conducting individual-based simulations in SLiM. We found that the genome-wide SFS in hybrid populations assumes a diagnostic shape, with the continual input of fixed differences between source populations via migration inducing a mass at intermediate allele frequency. Epistatic selection locally distorts the SFS as non-incompatibility alleles rise in frequency in a manner analogous to a selective sweep. Building on these results, we present a statistical method to identify genomic regions containing incompatibility loci that locates departures in the local SFS compared with the genome-wide SFS. Cross-validation studies demonstrate that our method detects recessive and codominant incompatibilities across a range of scenarios varying in the strength of epistatic selection, migration rate, and hybrid zone age. Our approach takes advantage of whole genome sequence data, does not require knowledge of demographic history, and can be applied to any pair of nascent species that forms a hybrid zone. [ABSTRACT FROM AUTHOR]

Due to the emergence of new variants of the SARS-CoV-2 coronavirus, the question of how the viral genomes evolved, leading to the formation of highly infectious strains, becomes particularly important. Three major emergent strains, Alpha, Beta and Delta, characterized by a significant number of missense mutations, provide a natural test field. We accumulated and aligned 4.7 million SARS-CoV-2 genomes from the GISAID database and carried out a comprehensive set of analyses. This collection covers the period until the end of October 2021, i.e., the beginnings of the Omicron variant. First, we explored combinatorial complexity of the genomic variants emerging and their timing, indicating very strong, albeit hidden, selection forces. Our analyses show that the mutations that define variants of concern did not arise gradually but rather co-evolved rapidly, leading to the emergence of the full variant strain. To explore in more detail the evolutionary forces at work, we developed time trajectories of mutations at all 29,903 sites of the SARS-CoV-2 genome, week by week, and stratified them into trends related to (i) point substitutions, (ii) deletions and (iii) non-sequenceable regions. We focused on classifying the genetic forces active at different ranges of the mutational spectrum. We observed the agreement of the lowest-frequency mutation spectrum with the Griffiths–Tavaré theory, under the Infinite Sites Model and neutrality. If we widen the frequency range, we observe the site frequency spectra much more consistently with the Tung–Durrett model assuming clone competition and selection. The coefficients of the fitting model indicate the possibility of selection acting to promote gradual growth slowdown, as observed in the history of the variants of concern. These results add up to a model of genomic evolution, which partly fits into the classical drift barrier ideas. Certain observations, such as mutation “bands” persistent over the epidemic history, suggest contribution of genetic forces different from mutation, drift and selection, including recombination or other genome transformations. In addition, we show that a “toy” mathematical model can qualitatively reproduce how new variants (clones) stem from rare advantageous driver mutations, and then acquire neutral or disadvantageous passenger mutations which gradually reduce their fitness so they can be then outcompeted by new variants due to other driver mutations.

Balancing selection (BLS) is the evolutionary force that maintains high levels of genetic variability in many important genes. To further our understanding of its evolutionary significance, we analyze models with BLS acting on a biallelic locus: an equilibrium model with long-term BLS, a model with long-term BLS and recent changes in population size, and a model of recent BLS. Using phase-type theory, a mathematical tool for analyzing continuous time Markov chains with an absorbing state, we examine how BLS affects polymorphism patterns in linked neutral regions, as summarized by nucleotide diversity, the expected number of segregating sites, the site frequency spectrum, and the level of linkage disequilibrium (LD). Long-term BLS affects polymorphism patterns in a relatively small genomic neighborhood, and such selection targets are easier to detect when the equilibrium frequencies of the selected variants are close to 50%, or when there has been a population size reduction. For a new mutation subject to BLS, its initial increase in frequency in the population causes linked neutral regions to have reduced diversity, an excess of both high and low frequency derived variants, and elevated LD with the selected locus. These patterns are similar to those produced by selective sweeps, but the effects of recent BLS are weaker. Nonetheless, compared to selective sweeps, nonequilibrium polymorphism and LD patterns persist for a much longer period under recent BLS, which may increase the chance of detecting such selection targets. An R package for analyzing these models, among others (e.g., isolation with migration), is available. [ABSTRACT FROM AUTHOR]

The site frequency spectrum (SFS) is of primary interest in population genetic studies, because the SFS compresses variation data into a simple summary from which many population genetic inferences can proceed. However, inferring the SFS from sequencing data is challenging because genotype calls from sequencing data are often inaccurate due to high error rates and if not accounted for, this genotype uncertainty can lead to serious bias in downstream analysis based on the inferred SFS. Here, we compare two approaches to estimate the SFS from sequencing data: one approach infers individual genotypes from aligned sequencing reads and then estimates the SFS based on the inferred genotypes (call-based approach) and the other approach directly estimates the SFS from aligned sequencing reads by maximum likelihood (direct estimation approach). We find that the SFS estimated by the direct estimation approach is unbiased even at low coverage, whereas the SFS by the call-based approach becomes biased as coverage decreases. The direction of the bias in the call-based approach depends on the pipeline to infer genotypes. Estimating genotypes by pooling individuals in a sample (multisample calling) results in underestimation of the number of rare variants, whereas estimating genotypes in each individual and merging them later (single-sample calling) leads to overestimation of rare variants. We characterize the impact of these biases on downstream analyses, such as demographic parameter estimation and genome-wide selection scans. Our work highlights that depending on the pipeline used to infer the SFS, one can reach different conclusions in population genetic inference with the same data set. Thus, careful attention to the analysis pipeline and SFS estimation procedures is vital for population genetic inferences.

Population genomics is a fast‐developing discipline with promising applications in a growing number of life sciences fields. Advances in sequencing technologies and bioinformatics tools allow population genomics to exploit genome‐wide information to identify the molecular variants underlying traits of interest and the evolutionary forces that modulate these variants through space and time. However, the cost of genomic analyses of multiple populations is still too high to address them through individual genome sequencing. Pooling individuals for sequencing can be a more effective strategy in Single Nucleotide Polymorphism (SNP) detection and allele frequency estimation because of a higher total coverage. However, compared to individual sequencing, SNP calling from pools has the additional difficulty of distinguishing rare variants from sequencing errors, which is often avoided by establishing a minimum threshold allele frequency for the analysis. Finding an optimal balance between minimizing information loss and reducing sequencing costs is essential to ensure the success of population genomics studies. Here, we have benchmarked the performance of SNP callers for Pool‐seq data, based on different approaches, under different conditions, and using computer simulations and real data. We found that SNP callers performance varied for allele frequencies up to 0.35. We also found that SNP callers based on Bayesian (SNAPE‐pooled) or maximum likelihood (MAPGD) approaches outperform the two heuristic callers tested (VarScan and PoolSNP), in terms of the balance between sensitivity and FDR both in simulated and sequencing data. Our results will help inform the selection of the most appropriate SNP caller not only for large‐scale population studies but also in cases where the Pool‐seq strategy is the only option, such as in metagenomic or polyploid studies. [ABSTRACT FROM AUTHOR]

Genotyping‐by‐sequencing methods such as RADseq are popular for generating genomic and population‐scale data sets from a diverse range of organisms. These often lack a usable reference genome, restricting users to RADseq specific software for processing. However, these come with limitations compared to generic next generation sequencing (NGS) toolkits. Here, we describe and test a simple pipeline for reference‐free RADseq data processing that blends de novo elements from STACKS with the full suite of state‐of‐the art NGS tools. Specifically, we use the de novo RADseq assembly employed by STACKS to create a catalogue of RAD loci that serves as a reference for read mapping, variant calling and site filters. Using RADseq data from 28 zebra sequenced to ~8x depth‐of‐coverage we evaluate our approach by comparing the site frequency spectra (SFS) to those from alternative pipelines. Most pipelines yielded similar SFS at 8x depth, but only a genotype likelihood based pipeline performed similarly at low sequencing depth (2–4x). We compared the RADseq SFS with medium‐depth (~13x) shotgun sequencing of eight overlapping samples, revealing that the RADseq SFS was persistently slightly skewed towards rare and invariant alleles. Using simulations and human data we confirm that this is expected when there is allelic dropout (AD) in the RADseq data. AD in the RADseq data caused a heterozygosity deficit of ~16%, which dropped to ~5% after filtering AD. Hence, AD was the most important source of bias in our RADseq data. [ABSTRACT FROM AUTHOR]

Genetic adaptation to external stimuli occurs through the combined action of mutation and selection. A central problem in genetics is to identify loci responsive to specific selective constraints. Many tests have been proposed to identify the genomic signatures of natural selection by quantifying the skew in the site frequency spectrum (SFS) under selection relative to neutrality. We build upon recent work that connects many of these tests under a common framework, by describing how selective sweeps affect the scaled SFS. We show that the specific skew depends on many attributes of the sweep, including the selection coefficient and the time under selection. Using supervised learning on extensive simulated data, we characterize the features of the scaled SFS that best separate different types of selective sweeps from neutrality. We develop a test, SFselect, that consistently outperforms many existing tests over a wide range of selective sweeps. We apply SFselect to polymorphism data from a laboratory evolution experiment of Drosophila melanogaster adapted to hypoxia and identify loci that strengthen the role of the Notch pathway in hypoxia tolerance, but were missed by previous approaches. We further apply our test to human data and identify regions that are in agreement with earlier studies, as well as many novel regions.

We derive explicit formulas for the two first moments of he site frequency spectrum (SFSn,b)1≤b≤n-1 of the Bolthausen-Sznitman coalescent along with some precise and efficient approximations, even for small sample sizes n. These results provide new L²-asymptotics for some values of b=o(n). We also study the length of internal branches carrying b>n/2 individuals. In this case we obtain the distribution function and a convergence in law. Our results rely on the random recursive tree construction of the Bolthausen-Sznitman coalescent. [ABSTRACT FROM AUTHOR]

Demographic inference using the site frequency spectrum (SFS) is a common way to understand historical events affecting genetic variation. However, most methods for estimating demography from the SFS assume random mating within populations, precluding these types of analyses in inbred populations. To address this issue, we developed a model for the expected SFS that includes inbreeding by parameterizing individual genotypes using beta-binomial distributions. We then take the convolution of these genotype probabilities to calculate the expected frequency of biallelic variants in the population. Using simulations, we evaluated the model's ability to coestimate demography and inbreeding using one- and two-population models across a range of inbreeding levels. We also applied our method to two empirical examples, American pumas (Puma concolor) and domesticated cabbage (Brassica oleracea var. capitata), inferring models both with and without inbreeding to compare parameter estimates and model fit. Our simulations showed that we are able to accurately coestimate demographic parameters and inbreeding even for highly inbred populations (F  =   0.9). In contrast, failing to include inbreeding generally resulted in inaccurate parameter estimates in simulated data and led to poor model fit in our empirical analyses. These results show that inbreeding can have a strong effect on demographic inference, a pattern that was especially noticeable for parameters involving changes in population size. Given the importance of these estimates for informing practices in conservation, agriculture, and elsewhere, our method provides an important advancement for accurately estimating the demographic histories of these species. [ABSTRACT FROM AUTHOR]

We derive statistical tools to analyze the patterns of genetic variability produced by models related to seed banks; in particular the Kingman coalescent, its time-changed counterpart describing so-called weak seed banks, the strong seed bank coalescent, and the two-island structured coalescent. As (strong) seed banks stratify a population, we expect them to produce a signal comparable to population structure. We present tractable formulas for Wright's F S T and the expected site frequency spectrum for these models, and show that they can distinguish between some models for certain ranges of parameters. We then use pseudo-marginal MCMC to show that the full likelihood can reliably distinguish between all models in the presence of parameter uncertainty under moderate stratification, and point out statistical pitfalls arising from stratification that is either too strong or too weak. We further show that it is possible to infer parameters, and in particular determine whether mutation is taking place in the (strong) seed bank. • The presence of a seed bank can be detected from DNA sequence data. • Seed banks can also be distinguished from population structure. • Sequence data is informative of whether mutation takes place in the seed bank. • Classical summary statistics suffice for inference in some cases. • Computationally intensive Monte Carlo methods give robust conclusions. [ABSTRACT FROM AUTHOR]

Inference of demographic history from genetic data is a primary goal of population genetics of model and nonmodel organisms. Whole genome-based approaches such as the pairwise/multiple sequentially Markovian coalescent methods use genomic data from one to four individuals to infer the demographic history of an entire population, while site frequency spectrum (SFS)-based methods use the distribution of allele frequencies in a sample to reconstruct the same historical events. Although both methods are extensively used in empirical studies and perform well on data simulated under simple models, there have been only limited comparisons of them in more complex and realistic settings. Here we use published demographic models based on data from three human populations (Yoruba, descendants of northwest-Europeans, and Han Chinese) as an empirical test case to study the behavior of both inference procedures. We find that several of the demographic histories inferred by the whole genome-based methods do not predict the genome-wide distribution of heterozygosity, nor do they predict the empirical SFS. However, using simulated data, we also find that the whole genome methods can reconstruct the complex demographic models inferred by SFS-based methods, suggesting that the discordant patterns of genetic variation are not attributable to a lack of statistical power, but may reflect unmodeled complexities in the underlying demography. More generally, our findings indicate that demographic inference from a small number of genomes, routine in genomic studies of nonmodel organisms, should be interpreted cautiously, as these models cannot recapitulate other summaries of the data.

We present a method for inductively determining exact allele frequency spectrum (AFS) probabilities for samples derived from a population comprising two demes under the infinite-allele model of mutation. This method builds on a labeled coalescent argument to extend the Ewens sampling formula (ESF) to structured populations. A key departure from the panmictic case is that the AFS conditioned on the number of alleles in the sample is no longer independent of the scaled mutation rate (θ). In particular, biallelic site frequency spectra, widely-used in explorations of genome-wide patterns of variation, depend on the mutation rate in structured populations. Variation in the rate of substitution across loci and through time may contribute to apparent distortions of site frequency spectra exhibited by samples derived from structured populations. [ABSTRACT FROM AUTHOR]

Aim: Grasslands have been pivotal in the development of herbivore breeding since the Neolithic and still represent the most widespread agricultural land use across Europe. However, it remains unclear whether the current large‐scale genetic variation of plant species found in natural grasslands of Europe is the result of human activities or natural processes. Location: Europe. Taxon: Lolium perenne L. (perennial ryegrass). Methods: We reconstructed the phylogeographic history of L. perenne, a dominant grassland species, using 481 natural populations, including 11 populations of closely related taxa. We combined Genotyping‐by‐Sequencing (GBS) and pool‐Sequencing (pool‐Seq) to obtain high‐quality allele frequency calls of ~500 k SNP loci. We performed genetic structure analyses and demographic reconstructions based on the site frequency spectrum (SFS). We additionally used the same genotyping protocol to assess the genomic diversity of a set of 32 cultivars representative of the L. perenne cultivars widely used for forage purposes. Results: Expansion across Europe took place during the Würm glaciation (12–110 kya), a cooling period that decreased the dominance of trees in favour of grasses. Splits and admixtures in L. perenne fit historical climate changes in the Mediterranean basin. The development of agriculture in Europe (7–3.5 kya), that caused an increase in the abundance of grasslands, did not have an effect on the demographic patterns of L. perenne. We found that most modern cultivars are closely related to natural diversity from north‐western Europe. Thus, modern cultivars do not represent the wide genetic variation found in natural populations. Main conclusions: Demographic events in L. perenne can be explained by the changing climatic conditions during the Pleistocene. Natural populations maintain a wide genomic variability at continental scale that has been minimally exploited by recent breeding activities. This variability constitutes valuable standing genetic variation for future adaptation of grasslands to climate change, safeguarding the agricultural services they provide. [ABSTRACT FROM AUTHOR]

Probability modelling for DNA sequence evolution is well established and provides a rich framework for understanding genetic variation between samples of individuals from one or more populations. We show that both classical and more recent models for coalescence (with or without recombination) can be described in terms of the so-called phase-type theory, where complicated and tedious calculations are circumvented by the use of matrix manipulations. The application of phase-type theory in population genetics consists of describing the biological system as a Markov model by appropriately setting up a state space and calculating the corresponding intensity and reward matrices. Formulae of interest are then expressed in terms of these aforementioned matrices. We illustrate this procedure by a number of examples: (a) Calculating the mean, (co)variance and even higher order moments of the site frequency spectrum in multiple merger coalescent models, (b) Analysing a sample of DNA sequences from the Atlantic Cod using the Beta-coalescent, and (c) Determining the correlation of the number of segregating sites for multiple samples in the two-locus ancestral recombination graph. We believe that phase-type theory has great potential as a tool for analysing probability models in population genetics. The compact matrix notation is useful for clarification of current models, and in particular their formal manipulation and calculations, but also for further development or extensions. [ABSTRACT FROM AUTHOR]

If viruses or other pathogens infect a single host, the outcome of infection often hinges on the fate of the initial invaders. The initial basic reproduction number R 0 , the expected number of cells infected by a single infected cell, helps determine whether the initial viruses can establish a successful beachhead. To determine R 0 , the Kingman coalescent or continuous-time birth-and-death process can be used to infer the rate of exponential growth in an historical population. Given M sequences sampled in the present, the two models can make the inference from the site frequency spectrum (SFS), the count of mutations that appear in exactly k sequences (k = 1 , 2 , ... , M). In the case of viruses, however, if R 0 is large and an infected cell bursts while propagating virus, the two models are suspect, because they are Markovian with only binary branching. Accordingly, this article develops an approximation for the SFS of a discrete-time branching process with synchronous generations (i.e., a Galton–Watson process). When evaluated in simulations with an asynchronous, non-Markovian model (a Bellman–Harris process) with parameters intended to mimic the bursting viral reproduction of HIV, the approximation proved superior to approximations derived from the Kingman coalescent or continuous-time birth-and-death process. This article demonstrates that in analogy to methods in human genetics, the SFS of viral sequences sampled well after latent infection can remain informative about the initial R 0. Thus, it suggests the utility of analyzing the SFS of sequences derived from patient and animal trials of viral therapies, because in some cases, the initial R 0 may be able to indicate subtle therapeutic progress, even in the absence of statistically significant differences in the infection of treatment and control groups. [ABSTRACT FROM AUTHOR]

Inferring the mode and tempo of natural selection helps further our understanding of adaptation to past environmental changes. Here, we introduce McSwan, a method to detect and date past and recent natural selection events in the case of a hard sweep. The method is based on the comparison of site frequency spectra obtained under various demographic models that include selection. McSwan demonstrated high power (high sensitivity and specificity) in capturing hard selective sweep events without requiring haplotype phasing. It performed slightly better than SweeD when the recent effective population size was low and the genomic region was small. We then applied our method to a European (CEU) and an African (LWK) human re‐sequencing data set. Most hard sweeps were detected in the CEU population (96%). Moreover, hard sweeps in the African population were estimated to have occurred further back in time (mode: 43,625 years BP) compared to those of Europeans (mode: 24,850 years BP). Most of the estimated ages of hard sweeps in Europeans were associated with the Last Glacial Maximum and were enriched in immunity‐associated genes. [ABSTRACT FROM AUTHOR]