The Linked Selection Signature of Rapid Adaptation in Temporal Genomic Data

The genome-wide signature of short-term temporal selection

Despite evolutionary biology's obsession with natural selection, few studies have evaluated multigenerational series of patterns of selection on a genome-wide scale in natural populations. Here, we report on a 10-y population-genomic survey of the microcrustacean Daphnia pulex. The genome sequences of [Formula: see text]800 isolates provide insights into patterns of selection that cannot be obtained from long-term molecular-evolution studies, including the following: the pervasiveness of near quasi-neutrality across the genome (mean net selection coefficients near zero, but with significant temporal variance about the mean, and little evidence of positive covariance of selection across time intervals); the preponderance of weak positive selection operating on minor alleles; and a genome-wide distribution of numerous small linkage islands of observable selection influencing levels of nucleotide diversity. These results suggest that interannual fluctuating selection is a major determinant of standing levels of variation in natural populations, challenge the conventional paradigm for interpreting patterns of nucleotide diversity and divergence, and motivate the need for the further development of theoretical expressions for the interpretation of population-genomic data.

Allele frequency dynamics under sex-biased demography and sex-specific inheritance in a pedigreed jay population

Sex-biased demography, including sex-biased survival or migration, can alter allele frequency changes across the genome. In particular, we can expect different patterns of genetic variation on autosomes and sex chromosomes due to sex-specific differences in life histories, as well as differences in effective population size, transmission modes, and the strength and mode of selection. Here, we demonstrate the role that sex differences in life history played in shaping short-term evolutionary dynamics across the genome. We used a 25-year pedigree and genomic dataset from a long-studied population of Florida Scrub-Jays (Aphelocoma coerulescens) to directly characterize the relative roles of sex-biased demography and inheritance in shaping genome-wide allele frequency trajectories. We used gene dropping simulations to estimate individual genetic contributions to future generations and to model drift and immigration on the known pedigree. We quantified differential expected genetic contributions of males and females over time, showing the impact of sex-biased dispersal in a monogamous system. Due to female-biased dispersal, more autosomal variation is introduced by female immigrants. However, due to male-biased transmission, more Z variation is introduced by male immigrants. Finally, we partitioned the proportion of variance in allele frequency change through time due to male and female contributions. Overall, most allele frequency change is due to variance in survival and births. Males and females make similar contributions to autosomal allele frequency change, but males make higher contributions to allele frequency change on the Z chromosome. Our work shows the importance of understanding sex-specific demographic processes in characterizing genome-wide allele frequency change in wild populations.

Temporal genomics help in deciphering neutral and adaptive patterns in the contemporary evolution of kelp populations

The impact of climate change on populations will be contingent upon their contemporary adaptive evolution. In this study, we investigated the contemporary evolution of 4 populations of the cold-water kelp Laminaria digitata by analyzing their spatial and temporal genomic variations using ddRAD-sequencing. These populations were sampled from the center to the southern margin of its north-eastern Atlantic distribution at 2 time points, spanning at least 2 generations. Through genome scans for local adaptation at a single time point, we identified candidate loci that showed clinal variation correlated with changes in sea surface temperature (SST) along latitudinal gradients. This finding suggests that SST may drive the adaptive response of these kelp populations, although factors such as species' demographic history should also be considered. Additionally, we performed a simulation approach to distinguish the effect of selection from genetic drift in allele frequency changes over time. This enabled the detection of loci in the southernmost population that exhibited temporal differentiation beyond what would be expected from genetic drift alone: these are candidate loci which could have evolved under selection over time. In contrast, we did not detect any outlier locus based on temporal differentiation in the population from the North Sea, which also displayed low and decreasing levels of genetic diversity. The diverse evolutionary scenarios observed among populations can be attributed to variations in the prevalence of selection relative to genetic drift across different environments. Therefore, our study highlights the potential of temporal genomics to offer valuable insights into the contemporary evolution of marine foundation species facing climate change.

Approximate Bayesian Computation applied to time series of population genetic data disentangles rapid genetic changes and demographic variations in a pathogen population

Adaptation can occur at remarkably short timescales in natural populations, leading to drastic changes in phenotypes and genotype frequencies over a few generations only. The inference of demographic parameters can allow understanding how evolutionary forces interact and shape the genetic trajectories of populations during rapid adaptation. Here we propose a new Approximate Bayesian Computation (ABC) framework that couples a forward and individual-based model with temporal genetic data to disentangle genetic changes and demographic variations in a case of rapid adaptation. We test the accuracy of our inferential framework and evaluate the benefit of considering a dense versus sparse sampling. Theoretical investigations demonstrate high accuracy in both model and parameter estimations, even if a strong thinning is applied to time series data. Then, we apply our ABC inferential framework to empirical data describing the population genetic changes of the poplar rust pathogen following a major event of resistance overcoming. We successfully estimate key demographic and genetic parameters, including the proportion of resistant hosts deployed in the landscape and the level of standing genetic variation from which selection occurred. Inferred values are in accordance with our empirical knowledge of this biological system. This new inferential framework, which contrasts with coalescent-based ABC analyses, is promising for a better understanding of evolutionary trajectories of populations subjected to rapid adaptation.

Lineage frequency time series reveal elevated levels of genetic drift in SARS-CoV-2 transmission in England

Genetic drift in infectious disease transmission results from randomness of transmission and host recovery or death. The strength of genetic drift for SARS-CoV-2 transmission is expected to be high due to high levels of superspreading, and this is expected to substantially impact disease epidemiology and evolution. However, we don't yet have an understanding of how genetic drift changes over time or across locations. Furthermore, noise that results from data collection can potentially confound estimates of genetic drift. To address this challenge, we develop and validate a method to jointly infer genetic drift and measurement noise from time-series lineage frequency data. Our method is highly scalable to increasingly large genomic datasets, which overcomes a limitation in commonly used phylogenetic methods. We apply this method to over 490,000 SARS-CoV-2 genomic sequences from England collected between March 2020 and December 2021 by the COVID-19 Genomics UK (COG-UK) consortium and separately infer the strength of genetic drift for pre-B.1.177, B.1.177, Alpha, and Delta. We find that even after correcting for measurement noise, the strength of genetic drift is consistently, throughout time, higher than that expected from the observed number of COVID-19 positive individuals in England by 1 to 3 orders of magnitude, which cannot be explained by literature values of superspreading. Our estimates of genetic drift suggest low and time-varying establishment probabilities for new mutations, inform the parametrization of SARS-CoV-2 evolutionary models, and motivate future studies of the potential mechanisms for increased stochasticity in this system.

Genomics for monitoring and understanding species responses to global climate change

All life forms across the globe are experiencing drastic changes in environmental conditions as a result of global climate change. These environmental changes are happening rapidly, incur substantial socioeconomic costs, pose threats to biodiversity and diminish a species' potential to adapt to future environments. Understanding and monitoring how organisms respond to human-driven climate change is therefore a major priority for the conservation of biodiversity in a rapidly changing environment. Recent developments in genomic, transcriptomic and epigenomic technologies are enabling unprecedented insights into the evolutionary processes and molecular bases of adaptation. This Review summarizes methods that apply and integrate omics tools to experimentally investigate, monitor and predict how species and communities in the wild cope with global climate change, which is by genetically adapting to new environmental conditions, through range shifts or through phenotypic plasticity. We identify advantages and limitations of each method and discuss future research avenues that would improve our understanding of species' evolutionary responses to global climate change, highlighting the need for holistic, multi-omics approaches to ecosystem monitoring during global climate change.

The contribution of gene flow, selection, and genetic drift to five thousand years of human allele frequency change

Genomic time series from experimental evolution studies and ancient DNA datasets offer us a chance to directly observe the interplay of various evolutionary forces. We show how the genome-wide variance in allele frequency change between two time points can be decomposed into the contributions of gene flow, genetic drift, and linked selection. In closed populations, the contribution of linked selection is identifiable because it creates covariances between time intervals, and genetic drift does not. However, repeated gene flow between populations can also produce directionality in allele frequency change, creating covariances. We show how to accurately separate the fraction of variance in allele frequency change due to admixture and linked selection in a population receiving gene flow. We use two human ancient DNA datasets, spanning around 5,000 y, as time transects to quantify the contributions to the genome-wide variance in allele frequency change. We find that a large fraction of genome-wide change is due to gene flow. In both cases, after correcting for known major gene flow events, we do not observe a signal of genome-wide linked selection. Thus despite the known role of selection in shaping long-term polymorphism levels, and an increasing number of examples of strong selection on single loci and polygenic scores from ancient DNA, it appears to be gene flow and drift, and not selection, that are the main determinants of recent genome-wide allele frequency change. Our approach should be applicable to the growing number of contemporary and ancient temporal population genomics datasets.

A cosmopolitan inversion facilitates seasonal adaptation in overwintering Drosophila

Fluctuations in the strength and direction of natural selection through time are a ubiquitous feature of life on Earth. One evolutionary outcome of such fluctuations is adaptive tracking, wherein populations rapidly adapt from standing genetic variation. In certain circumstances, adaptive tracking can lead to the long-term maintenance of functional polymorphism despite allele frequency change due to selection. Although adaptive tracking is likely a common process, we still have a limited understanding of aspects of its genetic architecture and its strength relative to other evolutionary forces such as drift. Drosophila melanogaster living in temperate regions evolve to track seasonal fluctuations and are an excellent system to tackle these gaps in knowledge. By sequencing orchard populations collected across multiple years, we characterized the genomic signal of seasonal demography and identified that the cosmopolitan inversion In(2L)t facilitates seasonal adaptive tracking and shows molecular footprints of selection. A meta-analysis of phenotypic studies shows that seasonal loci within In(2L)t are associated with behavior, life history, physiology, and morphological traits. We identify candidate loci and experimentally link them to phenotype. Our work contributes to our general understanding of fluctuating selection and highlights the evolutionary outcome and dynamics of contemporary selection on inversions.

Genome-wide methylome stability and parental effects in the worldwide distributed Lombardy poplar

BACKGROUND

Despite the increasing number of epigenomic studies in plants, little is known about the forces that shape the methylome in long-lived woody perennials. The Lombardy poplar offers an ideal opportunity to investigate the impact of the individual environmental history of trees on the methylome.

RESULTS

We present the results of three interconnected experiments on Lombardy poplar. In the first experiment, we investigated methylome variability during a growing season and across vegetatively reproduced generations. We found that ramets collected over Europe and raised in common conditions have stable methylomes in symmetrical CG-contexts. In contrast, seasonal dynamics occurred in methylation patterns in CHH context. In the second experiment, we investigated whether methylome patterns of plants grown in a non-parental environment correlate with the parental climate. We did not observe a biological relevant pattern that significantly correlates with the parental climate. Finally, we investigated whether the parental environment has persistent carry-over effects on the vegetative offspring's phenotype. We combined new bud set observations of three consecutive growing seasons with former published bud set data. Using a linear mixed effects analysis, we found a statistically significant but weak short-term, parental carry-over effect on the timing of bud set. However, this effect was negligible compared to the direct effects of the offspring environment.

CONCLUSIONS

Genome-wide cytosine methylation patterns in symmetrical CG-context are stable in Lombardy poplar and appear to be mainly the result of random processes. In this widespread poplar clone, methylation patterns in CG-context can be used as biomarkers to infer a common ancestor and thus to investigate the recent environmental history of a specific Lombardy poplar. The Lombardy poplar shows high phenotypic plasticity in a novel environment which enabled this clonal tree to adapt and survive all over the temperate regions of the world.

The contribution of gene flow, selection, and genetic drift to five thousand years of human allele frequency change

Genomic time series from experimental evolution studies and ancient DNA datasets offer us a chance to directly observe the interplay of various evolutionary forces. We show how the genome-wide variance in allele frequency change between two time points can be decomposed into the contributions of gene flow, genetic drift, and linked selection. In closed populations, the contribution of linked selection is identifiable because it creates covariances between time intervals, and genetic drift does not. However, repeated gene flow between populations can also produce directionality in allele frequency change, creating covariances. We show how to accurately separate the fraction of variance in allele frequency change due to admixture and linked selection in a population receiving gene flow. We use two human ancient DNA datasets, spanning around 5,000 years, as time transects to quantify the contributions to the genome-wide variance in allele frequency change. We find that a large fraction of genome-wide change is due to gene flow. In both cases, after correcting for known major gene flow events, we do not observe a signal of genome-wide linked selection. Thus despite the known role of selection in shaping long-term polymorphism levels, and an increasing number of examples of strong selection on single loci and polygenic scores from ancient DNA, it appears to be gene flow and drift, and not selection, that are the main determinants of recent genome-wide allele frequency change. Our approach should be applicable to the growing number of contemporary and ancient temporal population genomics datasets.

How chromosomal inversions reorient the evolutionary process

Inversions are structural mutations that reverse the sequence of a chromosome segment and reduce the effective rate of recombination in the heterozygous state. They play a major role in adaptation, as well as in other evolutionary processes such as speciation. Although inversions have been studied since the 1920s, they remain difficult to investigate because the reduced recombination conferred by them strengthens the effects of drift and hitchhiking, which in turn can obscure signatures of selection. Nonetheless, numerous inversions have been found to be under selection. Given recent advances in population genetic theory and empirical study, here we review how different mechanisms of selection affect the evolution of inversions. A key difference between inversions and other mutations, such as single nucleotide variants, is that the fitness of an inversion may be affected by a larger number of frequently interacting processes. This considerably complicates the analysis of the causes underlying the evolution of inversions. We discuss the extent to which these mechanisms can be disentangled, and by which approach.

Evolutionary Insights from a Large-Scale Survey of Population-Genomic Variation

The field of genomics has ushered in new methods for studying molecular-genetic variation in natural populations. However, most population-genomic studies still rely on small sample sizes (typically, <100 individuals) from single time points, leaving considerable uncertainties with respect to the behavior of relatively young (and rare) alleles and, owing to the large sampling variance of measures of variation, to the specific gene targets of unusually strong selection. Genomic sequences of ∼1,700 haplotypes distributed over a 10-year period from a natural population of the microcrustacean Daphnia pulex reveal evolutionary-genomic features at a refined scale, including previously hidden information on the behavior of rare alleles predicted by recent theory. Background selection, resulting from the recurrent introduction of deleterious alleles, appears to strongly influence the dynamics of neutral alleles, inducing indirect negative selection on rare variants and positive selection on common variants. Temporally fluctuating selection increases the persistence of nonsynonymous alleles with intermediate frequencies, while reducing standing levels of variation at linked silent sites. Combined with the results from an equally large metapopulation survey of the study species, classes of genes that are under strong positive selection can now be confidently identified in this key model organism. Most notable among rapidly evolving Daphnia genes are those associated with ribosomes, mitochondrial functions, sensory systems, and lifespan determination.

Detecting parallel polygenic adaptation to novel evolutionary pressure in wild populations: a case study in Atlantic cod (Gadus morhua)

Populations can adapt to novel selection pressures through dramatic frequency changes in a few genes of large effect or subtle shifts in many genes of small effect. The latter (polygenic adaptation) is expected to be the primary mode of evolution for many life-history traits but tends to be more difficult to detect than changes in genes of large effect. Atlantic cod (Gadus morhua) were subjected to intense fishing pressure over the twentieth century, leading to abundance crashes and a phenotypic shift toward earlier maturation across many populations. Here, we use spatially replicated temporal genomic data to test for a shared polygenic adaptive response to fishing using methods previously applied to evolve-and-resequence experiments. Cod populations on either side of the Atlantic show covariance in allele frequency change across the genome that are characteristic of recent polygenic adaptation. Using simulations, we demonstrate that the degree of covariance in allele frequency change observed in cod is unlikely to be explained by neutral processes or background selection. As human pressures on wild populations continue to increase, understanding and attributing modes of adaptation using methods similar to those demonstrated here will be important in identifying the capacity for adaptive responses and evolutionary rescue. This article is part of the theme issue 'Detecting and attributing the causes of biodiversity change: needs, gaps and solutions'.

Evolutionary Insights from a Large-scale Survey of Population-genomic Variation

Results from data on > 1000 haplotypes distributed over a nine-year period from a natural population of the microcrustacean Daphnia pulex reveal evolutionary-genomic features at a refined scale, including key population-genetic properties that are obscured in studies with smaller sample sizes. Background selection, resulting from the recurrent introduction of deleterious alleles, appears to strongly influence the dynamics of neutral alleles, inducing indirect negative selection on rare variants and positive selection on common variants. Fluctuating selection increases the persistence of nonsynonymous alleles with intermediate frequencies, while reducing standing levels of variation at linked silent sites. Combined with the results from an equally large metapopulation survey of the study species, regions of gene structure that are under strong purifying selection and classes of genes that are under strong positive selection in this key species can be confidently identified. Most notable among rapidly evolving Daphnia genes are those associated with ribosomes, mitochondrial functions, sensory systems, and lifespan determination.

Estimating linkage disequilibrium and selection from allele frequency trajectories

Genetic sequences collected over time provide an exciting opportunity to study natural selection. In such studies, it is important to account for linkage disequilibrium to accurately measure selection and to distinguish between selection and other effects that can cause changes in allele frequencies, such as genetic hitchhiking or clonal interference. However, most high-throughput sequencing methods cannot directly measure linkage due to short-read lengths. Here we develop a simple method to estimate linkage disequilibrium from time-series allele frequencies. This reconstructed linkage information can then be combined with other inference methods to infer the fitness effects of individual mutations. Simulations show that our approach reliably outperforms inference that ignores linkage disequilibrium and, with sufficient sampling, performs similarly to inference using the true linkage information. We also introduce two regularization methods derived from random matrix theory that help to preserve its performance under limited sampling effects. Overall, our method enables the use of linkage-aware inference methods even for data sets where only allele frequency time series are available.

Highly differentiated loci resolve phylogenetic relationships in the Bean Goose complex

BACKGROUND

Reconstructing phylogenetic relationships with genomic data remains a challenging endeavor. Numerous phylogenomic studies have reported incongruent gene trees when analyzing different genomic regions, complicating the search for a 'true' species tree. Some authors have argued that genomic regions of increased divergence (i.e. differentiation islands) reflect the species tree, although other studies have shown that these regions might produce misleading topologies due to species-specific selective sweeps or ancient introgression events. In this study, we tested the extent to which highly differentiated loci can resolve phylogenetic relationships in the Bean Goose complex, a group of goose taxa that includes the Taiga Bean Goose (Anser fabalis), the Tundra Bean Goose (Anser serrirostris) and the Pink-footed Goose (Anser brachyrhynchus).

RESULTS

First, we show that a random selection of genomic loci-which mainly samples the undifferentiated regions of the genome-results in an unresolved species complex with a monophyletic A. brachyrhynchus embedded within a paraphyletic cluster of A. fabalis and A. serrirostris. Next, phylogenetic analyses of differentiation islands converged upon a topology of three monophyletic clades in which A. brachyrhynchus is sister to A. fabalis, and A. serrirostris is sister to the clade uniting these two species. Close inspection of the locus trees within the differentiated regions revealed that this topology was consistently supported over other phylogenetic arrangements. As it seems unlikely that selection or introgression events have impacted all differentiation islands in the same way, we are convinced that this topology reflects the 'true' species tree. Additional analyses, based on D-statistics, revealed extensive introgression between A. fabalis and A. serrirostris, which partly explains the failure to resolve the species complex with a random selection of genomic loci. Recent introgression between these taxa has probably erased the phylogenetic branching pattern across a large section of the genome, whereas differentiation islands were unaffected by the homogenizing gene flow and maintained the phylogenetic patterns that reflect the species tree.

CONCLUSIONS

The evolution of the Bean Goose complex can be depicted as a simple bifurcating tree, but this would ignore the impact of introgressive hybridization. Hence, we advocate that the evolutionary relationships between these taxa are best represented as a phylogenetic network.

Bait-ER: A Bayesian method to detect targets of selection in Evolve-and-Resequence experiments

For over a decade, experimental evolution has been combined with high-throughput sequencing techniques. In so-called Evolve-and-Resequence (E&R) experiments, populations are kept in the laboratory under controlled experimental conditions where their genomes are sampled and allele frequencies monitored. However, identifying signatures of adaptation in E&R datasets is far from trivial, and it is still necessary to develop more efficient and statistically sound methods for detecting selection in genome-wide data. Here, we present Bait-ER - a fully Bayesian approach based on the Moran model of allele evolution to estimate selection coefficients from E&R experiments. The model has overlapping generations, a feature that describes several experimental designs found in the literature. We tested our method under several different demographic and experimental conditions to assess its accuracy and precision, and it performs well in most scenarios. Nevertheless, some care must be taken when analysing trajectories where drift largely dominates and starting frequencies are low. We compare our method with other available software and report that ours has generally high accuracy even for trajectories whose complexity goes beyond a classical sweep model. Furthermore, our approach avoids the computational burden of simulating an empirical null distribution, outperforming available software in terms of computational time and facilitating its use on genome-wide data. We implemented and released our method in a new open-source software package that can be accessed at https://doi.org/10.5281/zenodo.7351736.

Museum Genomics of an Agricultural Super-Pest, the Colorado Potato Beetle, Leptinotarsa decemlineata (Chrysomelidae), Provides Evidence of Adaptation from Standing Variation

Despite extensive research on agricultural pests, our knowledge about their evolutionary history is often limited. A mechanistic understanding of the demographic changes and modes of adaptation remains an important goal, as it improves our understanding of organismal responses to environmental change and our ability to sustainably manage pest populations. Emerging genomic datasets now allow for characterization of demographic and adaptive processes, but face limits when they are drawn from contemporary samples, especially in the context of strong demographic change, repeated selection, or adaptation involving modest shifts in allele frequency at many loci. Temporal sampling, however, can improve our ability to reconstruct evolutionary events. Here, we leverage museum samples to examine whether population genomic diversity and structure has changed over time, and to identify genomic regions that appear to be under selection. We focus on the Colorado potato beetle (CPB), Leptinotarsa decemlineata (Say 1824; Coleoptera: Chrysomelidae), which is widely regarded as a super-pest due to its rapid, and repeated, evolution to insecticides. By combining whole genome resequencing data from 78 museum samples with modern sampling, we demonstrate that CPB expanded rapidly in the 19th century, leading to a reduction in diversity and limited genetic structure from the Midwest to Northeast United States. Temporal genome scans provide extensive evidence for selection acting in resistant field populations in Wisconsin and New York, including numerous known insecticide resistance genes. We also validate these results by showing that known selective sweeps in modern populations are identified by our genome scan. Perhaps most importantly, temporal analysis indicates selection on standing genetic variation, as we find evidence for parallel evolution in the two geographical regions. Parallel evolution involves a range of phenotypic traits not previously identified as under selection in CPB, such as reproductive and morphological functional pathways that might be important for adaptation to agricultural habitats.

On the hidden temporal dynamics of plant adaptation

Adaptation to a wide range of environments is a major driver of plant diversity. It is now possible to catalog millions of potential adaptive genomic differences segregating between environments within a plant species in a single experiment. Understanding which of these changes contributes to adaptive phenotypic divergence between plant populations is a major goal of evolutionary biologists and crop breeders. In this review, we briefly highlight the approaches frequently used to understand the genetic basis of adaptive phenotypes in plants, and we discuss some of the limitations of these methods. We propose that direct observation of the process of adaptation using multigenerational studies and whole genome sequencing is a crucial missing component of recent studies of plant adaptation because it complements several shortcomings of sampling-based techniques.

Experimental evolution reveals the synergistic genomic mechanisms of adaptation to ocean warming and acidification in a marine copepod

Metazoan adaptation to global change relies on selection of standing genetic variation. Determining the extent to which this variation exists in natural populations, particularly for responses to simultaneous stressors, is essential to make accurate predictions for persistence in future conditions. Here, we identified the genetic variation enabling the copepod Acartia tonsa to adapt to experimental ocean warming, acidification, and combined ocean warming and acidification (OWA) over 25 generations of continual selection. Replicate populations showed a consistent polygenic response to each condition, targeting an array of adaptive mechanisms including cellular homeostasis, development, and stress response. We used a genome-wide covariance approach to partition the allelic changes into three categories: selection, drift and replicate-specific selection, and laboratory adaptation responses. The majority of allele frequency change in warming (57%) and OWA (63%) was driven by shared selection pressures across replicates, but this effect was weaker under acidification alone (20%). OWA and warming shared 37% of their response to selection but OWA and acidification shared just 1%, indicating that warming is the dominant driver of selection in OWA. Despite the dominance of warming, the interaction with acidification was still critical as the OWA selection response was highly synergistic with 47% of the allelic selection response unique from either individual treatment. These results disentangle how genomic targets of selection differ between single and multiple stressors and demonstrate the complexity that nonadditive multiple stressors will contribute to predictions of adaptation to complex environmental shifts caused by global change.

On the genetic architecture of rapidly adapting and convergent life history traits in guppies

The genetic basis of traits shapes and constrains how adaptation proceeds in nature; rapid adaptation can proceed using stores of polygenic standing genetic variation or hard selective sweeps, and increasing polygenicity fuels genetic redundancy, reducing gene re-use (genetic convergence). Guppy life history traits evolve rapidly and convergently among natural high- and low-predation environments in northern Trinidad. This system has been studied extensively at the phenotypic level, but little is known about the underlying genetic architecture. Here, we use four independent F2 QTL crosses to examine the genetic basis of seven (five female, two male) guppy life history phenotypes and discuss how these genetic architectures may facilitate or constrain rapid adaptation and convergence. We use RAD-sequencing data (16,539 SNPs) from 370 male and 267 female F2 individuals. We perform linkage mapping, estimates of genome-wide and per-chromosome heritability (multi-locus associations), and QTL mapping (single-locus associations). Our results are consistent with architectures of many loci of small-effect for male age and size at maturity and female interbrood period. Male trait associations are clustered on specific chromosomes, but female interbrood period exhibits a weak genome-wide signal suggesting a potentially highly polygenic component. Offspring weight and female size at maturity are also associated with a single significant QTL each. These results suggest rapid, repeatable phenotypic evolution of guppies may be facilitated by polygenic trait architectures, but subsequent genetic redundancy may limit gene re-use across populations, in agreement with an absence of strong signatures of genetic convergence from recent analyses of wild guppies.

Genome-wide evolutionary response of European oaks during the Anthropocene

The pace of tree microevolution during Anthropocene warming is largely unknown. We used a retrospective approach to monitor genomic changes in oak trees since the Little Ice Age (LIA). Allelic frequency changes were assessed from whole-genome pooled sequences for four age-structured cohorts of sessile oak (Quercus petraea) dating back to 1680, in each of three different oak forests in France. The genetic covariances of allelic frequency changes increased between successive time periods, highlighting genome-wide effects of linked selection. We found imprints of parallel linked selection in the three forests during the late LIA, and a shift of selection during more recent time periods of the Anthropocene. The changes in allelic covariances within and between forests mirrored the documented changes in the occurrence of extreme events (droughts and frosts) over the last 300 years. The genomic regions with the highest covariances were enriched in genes involved in plant responses to pathogens and abiotic stresses (temperature and drought). These responses are consistent with the reported sequence of frost (or drought) and disease damage ultimately leading to the oak dieback after extreme events. They provide support for adaptive evolution of long-lived species during recent climatic changes. Although we acknowledge that other sources (e.g., gene flow, generation overlap) may have contributed to temporal covariances of allelic frequency changes, the consistent and correlated response across the three forests lends support to the existence of a systematic driving force such as natural selection.

Polygenic adaptation after a sudden change in environment

Polygenic adaptation is thought to be ubiquitous, yet remains poorly understood. Here, we model this process analytically, in the plausible setting of a highly polygenic, quantitative trait that experiences a sudden shift in the fitness optimum. We show how the mean phenotype changes over time, depending on the effect sizes of loci that contribute to variance in the trait, and characterize the allele dynamics at these loci. Notably, we describe the two phases of the allele dynamics: The first is a rapid phase, in which directional selection introduces small frequency differences between alleles whose effects are aligned with or opposed to the shift, ultimately leading to small differences in their probability of fixation during a second, longer phase, governed by stabilizing selection. As we discuss, key results should hold in more general settings and have important implications for efforts to identify the genetic basis of adaptation in humans and other species.

A Population Genomic Assessment of Three Decades of Evolution in a Natural Drosophila Population

Population genetics seeks to illuminate the forces shaping genetic variation, often based on a single snapshot of genomic variation. However, utilizing multiple sampling times to study changes in allele frequencies can help clarify the relative roles of neutral and non-neutral forces on short time scales. This study compares whole-genome sequence variation of recently collected natural population samples of Drosophila melanogaster against a collection made approximately 35 years prior from the same locality—encompassing roughly 500 generations of evolution. The allele frequency changes between these time points would suggest a relatively small local effective population size on the order of 10,000, significantly smaller than the global effective population size of the species. Some loci display stronger allele frequency changes than would be expected anywhere in the genome under neutrality—most notably the tandem paralogs Cyp6a17 and Cyp6a23, which are impacted by structural variation associated with resistance to pyrethroid insecticides. We find a genome-wide excess of outliers for high genetic differentiation between old and new samples, but a larger number of adaptation targets may have affected SNP-level differentiation versus window differentiation. We also find evidence for strengthening latitudinal allele frequency clines: northern-associated alleles have increased in frequency by an average of nearly 2.5% at SNPs previously identified as clinal outliers, but no such pattern is observed at random SNPs. This project underscores the scientific potential of using multiple sampling time points to investigate how evolution operates in natural populations, by quantifying how genetic variation has changed over ecologically relevant timescales.

Genome evolution in an agricultural pest following adoption of transgenic crops

Evolution of resistance to management approaches in agricultural landscapes is common and results in economic losses. Early detection of pest resistance prior to significant crop damage would benefit the agricultural community. It has been hypothesized that new genomic approaches could track molecular signals of emerging resistance and trigger efforts to preempt widespread damage. We tested this hypothesis by quantifying genomic changes in the pest Helicoverpa zea over a 15-y period concurrent with commercialization of transgenic Bacillus thuringiensis–expressing crops and their subsequent loss of efficacy. Our results demonstrate the complex nature of evolution in agricultural ecosystems and provide insight into the potential and pitfalls of using genomic approaches for resistance monitoring.

Replacing synthetic insecticides with transgenic crops for pest management has been economically and environmentally beneficial, but these benefits erode as pests evolve resistance. It has been proposed that novel genomic approaches could track molecular signals of emerging resistance to aid in resistance management. To test this, we quantified patterns of genomic change in Helicoverpa zea, a major lepidopteran pest and target of transgenic Bacillus thuringiensis (Bt) crops, between 2002 and 2017 as both Bt crop adoption and resistance increased in North America. Genomic scans of wild H. zea were paired with quantitative trait locus (QTL) analyses and showed the genomic architecture of field-evolved Cry1Ab resistance was polygenic, likely arising from standing genetic variation. Resistance to pyramided Cry1A.105 and Cry2Ab2 toxins was controlled by fewer loci. Of the 11 previously described Bt resistance genes, 9 showed no significant change over time or major effects on resistance. We were unable to rule out a contribution of aminopeptidases (apns), as a cluster of apn genes were found within a Cry-associated QTL. Molecular signals of emerging Bt resistance were detectable as early as 2012 in our samples, and we discuss the potential and pitfalls of whole-genome analysis for resistance monitoring based on our findings. This first study of Bt resistance evolution using whole-genome analysis of field-collected specimens demonstrates the need for a more holistic approach to examining rapid adaptation to novel selection pressures in agricultural ecosystems.

Identification of natural selection in genomic data with deep convolutional neural network

Background

With the increase in the size of genomic datasets describing variability in populations, extracting relevant information becomes increasingly useful as well as complex. Recently, computational methodologies such as Supervised Machine Learning and specifically Convolutional Neural Networks have been proposed to make inferences on demographic and adaptive processes using genomic data. Even though it was already shown to be powerful and efficient in different fields of investigation, Supervised Machine Learning has still to be explored as to unfold its enormous potential in evolutionary genomics.

Results

The paper proposes a method based on Supervised Machine Learning for classifying genomic data, represented as windows of genomic sequences from a sample of individuals belonging to the same population. A Convolutional Neural Network is used to test whether a genomic window shows the signature of natural selection. Training performed on simulated data show that the proposed model can accurately predict neutral and selection processes on portions of genomes taken from real populations with almost 90% accuracy.

Allele frequency divergence reveals ubiquitous influence of positive selection in Drosophila

Resolving the role of natural selection is a basic objective of evolutionary biology. It is generally difficult to detect the influence of selection because ubiquitous non-selective stochastic change in allele frequencies (genetic drift) degrades evidence of selection. As a result, selection scans typically only identify genomic regions that have undergone episodes of intense selection. Yet it seems likely such episodes are the exception; the norm is more likely to involve subtle, concurrent selective changes at a large number of loci. We develop a new theoretical approach that uncovers a previously undocumented genome-wide signature of selection in the collective divergence of allele frequencies over time. Applying our approach to temporally resolved allele frequency measurements from laboratory and wild Drosophila populations, we quantify the selective contribution to allele frequency divergence and find that selection has substantial effects on much of the genome. We further quantify the magnitude of the total selection coefficient (a measure of the combined effects of direct and linked selection) at a typical polymorphic locus, and find this to be large (of order 1%) even though most mutations are not directly under selection. We find that selective allele frequency divergence is substantially elevated at intermediate allele frequencies, which we argue is most parsimoniously explained by positive-not negative-selection. Thus, in these populations most mutations are far from evolving neutrally in the short term (tens of generations), including mutations with neutral fitness effects, and the result cannot be explained simply as an ongoing purging of deleterious mutations.

Quantifying the relationship between genetic diversity and population size suggests natural selection cannot explain Lewontin's Paradox

Neutral theory predicts that genetic diversity increases with population size, yet observed levels of diversity across metazoans vary only two orders of magnitude while population sizes vary over several. This unexpectedly narrow range of diversity is known as Lewontin's Paradox of Variation (1974). While some have suggested selection constrains diversity, tests of this hypothesis seem to fall short. Here, I revisit Lewontin's Paradox to assess whether current models of linked selection are capable of reducing diversity to this extent. To quantify the discrepancy between pairwise diversity and census population sizes across species, I combine previously-published estimates of pairwise diversity from 172 metazoan taxa with newly derived estimates of census sizes. Using phylogenetic comparative methods, I show this relationship is significant accounting for phylogeny, but with high phylogenetic signal and evidence that some lineages experience shifts in the evolutionary rate of diversity deep in the past. Additionally, I find a negative relationship between recombination map length and census size, suggesting abundant species have less recombination and experience greater reductions in diversity due to linked selection. However, I show that even assuming strong and abundant selection, models of linked selection are unlikely to explain the observed relationship between diversity and census sizes across species.

Genomic time-series data show that gene flow maintains high genetic diversity despite substantial genetic drift in a butterfly species

Effective population size affects the efficacy of selection, rate of evolution by drift, and neutral diversity levels. When species are subdivided into multiple populations connected by gene flow, evolutionary processes can depend on global or local effective population sizes. Theory predicts that high levels of diversity might be maintained by gene flow, even very low levels of gene flow, consistent with species long-term effective population size, but tests of this idea are mostly lacking. Here, we show that Lycaeides buttery populations maintain low contemporary (variance) effective population sizes (e.g., ~200 individuals) and thus evolve rapidly by genetic drift. In contrast, populations harbored high levels of genetic diversity consistent with an effective population size several orders of magnitude larger. We hypothesized that the differences in the magnitude and variability of contemporary versus long-term effective population sizes were caused by gene flow of sufficient magnitude to maintain diversity but only subtly affect evolution on generational time scales. Consistent with this hypothesis, we detected low but non-trivial gene flow among populations. Furthermore, using short-term population-genomic time-series data, we documented patterns consistent with predictions from this hypothesis, including a weak but detectable excess of evolutionary change in the direction of the mean (migrant gene pool) allele frequencies across populations, and consistency in the direction of allele frequency change over time. The documented decoupling of diversity levels and short-term change by drift in Lycaeides has implications for our understanding of contemporary evolution and the maintenance of genetic variation in the wild.

Extensive standing genetic variation from a small number of founders enables rapid adaptation in Daphnia

We lack a thorough understanding of the origin and maintenance of standing genetic variation that enables rapid evolutionary responses of natural populations. Whole genome sequencing of a resurrected Daphnia population shows that standing genetic variation in over 500 genes follows an evolutionary trajectory that parallels the pronounced and rapid adaptive evolution of multiple traits in response to predator-driven natural selection and its subsequent relaxation. Genetic variation carried by only five founding individuals from the regional genotype pool is shown to suffice at enabling the observed evolution. Our results provide insight on how natural populations can acquire the genomic variation, through colonization by a few regional genotypes, that fuels rapid evolution in response to strong selection pressures. While these evolutionary responses in our study population involved hundreds of genes, we observed no evidence of genetic erosion.

The Limits to Estimating Population-Genetic Parameters with Temporal Data

The ability to obtain genome-wide sequences of very large numbers of individuals from natural populations raises questions about optimal sampling designs and the limits to extracting information on key population-genetic parameters from temporal-survey data. Methods are introduced for evaluating whether observed temporal fluctuations in allele frequencies are consistent with the hypothesis of random genetic drift, and expressions for the expected sampling variances for the relevant statistics are given in terms of sample sizes and numbers. Estimation methods and aspects of statistical reliability are also presented for the mean and temporal variance of selection coefficients. For nucleotide sites that pass the test of neutrality, the current effective population size can be estimated by a method of moments, and expressions for its sampling variance provide insight into the degree to which such methodology can yield meaningful results under alternative sampling schemes. Finally, some caveats are raised regarding the use of the temporal covariance of allele-frequency change to infer selection. Taken together, these results provide a statistical view of the limits to population-genetic inference in even the simplest case of a closed population.

How is epigenetics predicted to contribute to climate change adaptation? What evidence do we need?

Transgenerational effects that are interpreted in terms of epigenetics have become an important research focus at a time when rapid environmental changes are occurring. These effects are usually interpreted as enhancing fitness extremely rapidly, without depending on the slower process of natural selection changing DNA-encoded (fixed) genetic variants in populations. Supporting evidence comes from a variety of sources, including environmental associations with epialleles, cross-generation responses of clonal material exposed to different environmental conditions, and altered patterns of methylation or frequency changes in epialleles across time. Transgenerational environmental effects have been postulated to be larger than those associated with DNA-encoded genetic changes, based on (for instance) stronger associations between epialleles and environmental conditions. Yet environmental associations for fixed genetic differences may always be weak under polygenic models where multiple combinations of alleles can lead to the same evolutionary outcome. The ultimate currency of adaptation is fitness, and few transgenerational studies have robustly determined fitness effects, particularly when compared to fixed genetic variants. Not all transgenerational modifications triggered by climate change will increase fitness: stressful conditions often trigger negative fitness effects across generations that can eliminate benefits. Epigenetic responses and other transgenerational effects will undoubtedly play a role in climate change adaptation, but further, well-designed, studies are required to test their importance relative to DNA-encoded changes. This article is part of the theme issue 'How does epigenetics influence the course of evolution?'

Contemporary Ne estimation using temporally spaced data with linked loci

The contemporary effective population size Ne is important in many disciplines including population genetics, conservation science and pest management. One of the most popular methods of estimating this quantity uses temporal changes in allele frequency due to genetic drift. A significant assumption of the existing methods is the independence among loci while constructing confidence intervals (CI), which restricts the types of species or genetic data applicable to the methods. Although genetic linkage does not bias point Ne estimates, applying these methods to linked loci can yield unreliable CI that are far too narrow. We extend the current methods to enable the use of many linked loci to produce precise contemporary Ne estimates, while preserving the targeted CI width and coverage. This is achieved by deriving the covariance of changes in allele frequency at linked loci in the face of recombination and sampling errors, such that the extra sampling variance due to between‐locus correlation is properly handled. Extensive simulations are used to verify the new method. We apply the method to two temporally spaced genomic data sets of Anopheles mosquitoes collected from a cluster of villages in Burkina Faso between 2012 and 2014. With over 33,000 linked loci considered, the Ne estimate for Anopheles coluzzii is 9,242 (95% CI 5,702–24,282), and for Anopheles gambiae it is 4,826 (95% CI 3,602–7,353).

A population-genomic approach for estimating selection on polygenic traits in heterogeneous environments

Strong selection can cause rapid evolutionary change, but temporal fluctuations in the form, direction and intensity of selection can limit net evolutionary change over longer time periods. Fluctuating selection could affect molecular diversity levels and the evolution of plasticity and ecological specialization. Nonetheless, this phenomenon remains understudied, in part because of analytical limitations and the general difficulty of detecting selection that does not occur in a consistent manner. Herein, I fill this analytical gap by presenting an approximate Bayesian computation (ABC) method to detect and quantify fluctuating selection on polygenic traits from population genomic time-series data. I propose a model for environment-dependent phenotypic selection. The evolutionary genetic consequences of selection are then modelled based on a genotype-phenotype map. Using simulations, I show that the proposed method generates accurate and precise estimates of selection when the generative model for the data is similar to the model assumed by the method. The performance of the method when applied to an evolve-and-resequence study of host adaptation in the cowpea seed beetle (Callosobruchus maculatus) was more idiosyncratic and depended on specific analytical choices. Despite some limitations, these results suggest the proposed method provides a powerful approach to connect the causes of (variable) selection to traits and genome-wide patterns of evolution. Documentation and open-source computer software (fsabc) implementing this method are available from github (https://github.com/zgompert/fsabc.git).

The Effects of Quantitative Trait Architecture on Detection Power in Short-Term Artificial Selection Experiments

Evolve and resequence (E&R) experiments, in which artificial selection is imposed on organisms in a controlled environment, are becoming an increasingly accessible tool for studying the genetic basis of adaptation. Previous work has assessed how different experimental design parameters affect the power to detect the quantitative trait loci (QTL) that underlie adaptive responses in such experiments, but so far there has been little exploration of how this power varies with the genetic architecture of the evolving traits. In this study, we use forward simulation to build a more realistic model of an E&R experiment in which a quantitative polygenic trait experiences a short, but strong, episode of truncation selection. We study the expected power for QTL detection in such an experiment and how this power is influenced by different aspects of trait architecture, including the number of QTL affecting the trait, their starting frequencies, effect sizes, clustering along a chromosome, dominance, and epistasis patterns. We show that all of these parameters can affect allele frequency dynamics at the QTL and linked loci in complex and often unintuitive ways, and thus influence our power to detect them. One consequence of this is that existing detection methods based on models of independent selective sweeps at individual QTL often have lower detection power than a simple measurement of allele frequency differences before and after selection. Our findings highlight the importance of taking trait architecture into account when designing and interpreting studies of molecular adaptation with temporal data. We provide a customizable modeling framework that will enable researchers to easily simulate E&R experiments with different trait architectures and parameters tuned to their specific study system, allowing for assessment of expected detection power and optimization of experimental design.

Estimating the genome-wide contribution of selection to temporal allele frequency change

Rapid phenotypic adaptation is often observed in natural populations and selection experiments. However, detecting the genome-wide impact of this selection is difficult since adaptation often proceeds from standing variation and selection on polygenic traits, both of which may leave faint genomic signals indistinguishable from a noisy background of genetic drift. One promising signal comes from the genome-wide covariance between allele frequency changes observable from temporal genomic data (e.g., evolve-and-resequence studies). These temporal covariances reflect how heritable fitness variation in the population leads changes in allele frequencies at one time point to be predictive of the changes at later time points, as alleles are indirectly selected due to remaining associations with selected alleles. Since genetic drift does not lead to temporal covariance, we can use these covariances to estimate what fraction of the variation in allele frequency change through time is driven by linked selection. Here, we reanalyze three selection experiments to quantify the effects of linked selection over short timescales using covariance among time points and across replicates. We estimate that at least 17 to 37% of allele frequency change is driven by selection in these experiments. Against this background of positive genome-wide temporal covariances, we also identify signals of negative temporal covariance corresponding to reversals in the direction of selection for a reasonable proportion of loci over the time course of a selection experiment. Overall, we find that in the three studies we analyzed, linked selection has a large impact on short-term allele frequency dynamics that is readily distinguishable from genetic drift.

Recent introgression between Taiga Bean Goose and Tundra Bean Goose results in a largely homogeneous landscape of genetic differentiation

Several studies have uncovered a highly heterogeneous landscape of genetic differentiation across the genomes of closely related species. Specifically, genetic differentiation is often concentrated in particular genomic regions (“islands of differentiation”) that might contain barrier loci contributing to reproductive isolation, whereas the rest of the genome is homogenized by introgression. Alternatively, linked selection can produce differentiation islands in allopatry without introgression. We explored the influence of introgression on the landscape of genetic differentiation in two hybridizing goose taxa: the Taiga Bean Goose (Anser fabalis) and the Tundra Bean Goose (A. serrirostris). We re-sequenced the whole genomes of 18 individuals (9 of each taxon) and, using a combination of population genomic summary statistics and demographic modeling, we reconstructed the evolutionary history of these birds. Next, we quantified the impact of introgression on the build-up and maintenance of genetic differentiation. We found evidence for a scenario of allopatric divergence (about 2.5 million years ago) followed by recent secondary contact (about 60,000 years ago). Subsequent introgression events led to high levels of gene flow, mainly from the Tundra Bean Goose into the Taiga Bean Goose. This scenario resulted in a largely undifferentiated genomic landscape (genome-wide F_ST = 0.033) with a few notable differentiation peaks that were scattered across chromosomes. The summary statistics indicated that some peaks might contain barrier loci while others arose in allopatry through linked selection. Finally, based on the low genetic differentiation, considerable morphological variation and incomplete reproductive isolation, we argue that the Taiga and the Tundra Bean Goose should be treated as subspecies.

Inference of natural selection from ancient DNA

Evolutionary processes, including selection, can be indirectly inferred based on patterns of genomic variation among contemporary populations or species. However, this often requires unrealistic assumptions of ancestral demography and selective regimes. Sequencing ancient DNA from temporally spaced samples can inform about past selection processes, as time series data allow direct quantification of population parameters collected before, during, and after genetic changes driven by selection. In this Comment and Opinion, we advocate for the inclusion of temporal sampling and the generation of paleogenomic datasets in evolutionary biology, and highlight some of the recent advances that have yet to be broadly applied by evolutionary biologists. In doing so, we consider the expected signatures of balancing, purifying, and positive selection in time series data, and detail how this can advance our understanding of the chronology and tempo of genomic change driven by selection. However, we also recognize the limitations of such data, which can suffer from postmortem damage, fragmentation, low coverage, and typically low sample size. We therefore highlight the many assumptions and considerations associated with analyzing paleogenomic data and the assumptions associated with analytical methods.