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

A model for background selection in non-equilibrium populations

The magnitude of negative selection varies across the genome, affecting local levels of diversity by virtue of linked selection. Predicting patterns of diversity under such background selection has received considerable attention, and recent applications were able to explain a large portion of the variation in diversity across the human genome. However, current models have yielded conflicting results, stemming from two key limitations. First, they display inaccuracies in the most critical region of parameter space ( N e s ∼ -1), where the reduction in diversity is sharpest; second, they assume a constant population size over time. Here, we develop predictions for diversity under background selection based on the Hill-Robertson system of two-locus statistics allowing for population size changes. We treat the joint effect of multiple selected loci independently, but we show that interference among them is well captured through local rescaling of mutation, recombination and selection in an iterative procedure that converges quickly. We further accommodate existing background selection theory to population size changes, bridging the gap between weak and strong selection. Simulations show that our predictions are accurate over the entire distribution of selection coefficients. We characterize the temporal dynamics of linked selection under non-equilibrium demography and demonstrate that patterns of diversity can be misinterpreted by other models. Specifically, neglecting population size changes bias predictions of diversity, which then carry over to downstream inferences of the distribution of fitness effects and deleterious mutation rate. Jointly modeling demography and linked selection therefore improves our understanding of the genomic landscape of diversity, which will help refine inferences of linked selection in humans and other species.

Metabolic differentiation of brushtail possum populations resistant and susceptible to plant toxins revealed via differential gene expression

The Australian brushtail possum (Trichosurus vulpecula) is adapted to a wide range of food plants across its range and is exposed to numerous physiological challenges. Populations that are resistant to the plant toxin sodium fluoroacetate are of particular interest as this compound has been used since the 1940s for vertebrate pest management around the world. Candidate gene identification is an important first step in understanding how spatial populations have responded to local selection resulting in local physiological divergence. We employ differential gene expression of liver samples from wild-caught brushtail possums from toxin-resistant and toxin-susceptible populations to identify candidate genes that might be involved in metabolic pathways associated with toxin-resistance. This allowed us to identify genetic pathways involved in resistance to the plant toxin sodium fluoroacetate in Western Australian possums but not those originally from south eastern Australia. We identified differentially expressed genes in the liver that are associated with cell signalling, encapsulating structure, cell mobility, and tricarboxylic acid cycle. The gene expression differences detected indicate which metabolic pathways are most likely to be associated with sodium fluoroacetate resistance in these marsupials and we provide a comprehensive list of candidate genes and pathways to focus on for future studies.

A path integral approach for allele frequency dynamics under polygenic selection

Many phenotypic traits have a polygenic genetic basis, making it challenging to learn their genetic architectures and predict individual phenotypes. One promising avenue to resolve the genetic basis of complex traits is through evolve-and-resequence (E&R) experiments, in which laboratory populations are exposed to some selective pressure and trait-contributing loci are identified by extreme frequency changes over the course of the experiment. However, small laboratory populations will experience substantial random genetic drift, and it is difficult to determine whether selection played a role in a given allele frequency change (AFC). Predicting AFCs under drift and selection, even for alleles contributing to simple, monogenic traits, has remained a challenging problem. Recently, there have been efforts to apply the path integral, a method borrowed from physics, to solve this problem. So far, this approach has been limited to genic selection, and is therefore inadequate to capture the complexity of quantitative, highly polygenic traits that are commonly studied. Here, we extend one of these path integral methods, the perturbation approximation, to selection scenarios that are of interest to quantitative genetics. We derive analytic expressions for the transition probability (i.e. the probability that an allele will change in frequency from x to y in time t) of an allele contributing to a trait subject to stabilizing selection, as well as that of an allele contributing to a trait rapidly adapting to a new phenotypic optimum. We use these expressions to characterize the use of AFC to test for selection, as well as explore optimal design choices for E&R experiments to uncover the genetic architecture of polygenic traits under selection.

Population size rescaling significantly biases outcomes of forward-in-time population genetic simulations

Simulations are an essential tool in all areas of population genetic research, used in tasks such as the validation of theoretical analysis and the study of complex evolutionary models. Forward-in-time simulations are especially flexible, allowing for various types of natural selection, complex genetic architectures, and non-Wright-Fisher dynamics. However, their intense computational requirements can be prohibitive to simulating large populations and genomes. A popular method to alleviate this burden is to scale down the population size by some scaling factor while scaling up the mutation rate, selection coefficients, and recombination rate by the same factor. However, this rescaling approach may in some cases bias simulation results. To investigate the manner and degree to which rescaling impacts simulation outcomes, we carried out simulations with different demographic histories and distributions of fitness effects using several values of the rescaling factor, Q, and compared the deviation of key outcomes (fixation times, allele frequencies, linkage disequilibrium, and the fraction of mutations that fix during the simulation) between the scaled and unscaled simulations. Our results indicate that scaling introduces substantial biases to each of these measured outcomes, even at small values of Q. Moreover, the nature of these effects depends on the evolutionary model and scaling factor being examined. While increasing the scaling factor tends to increase the observed biases, this relationship is not always straightforward; thus, it may be difficult to know the impact of scaling on simulation outcomes a priori. However, it appears that for most models, only a small number of replicates was needed to accurately quantify the bias produced by rescaling for a given Q. In summary, while rescaling forward-in-time simulations may be necessary in many cases, researchers should be aware of the rescaling procedure's impact on simulation outcomes and consider investigating its magnitude in smaller scale simulations of the desired model(s) before selecting an appropriate value of Q.

Natural selection acting on complex traits hampers the predictive accuracy of polygenic scores in ancient samples

The prediction of phenotypes from ancient humans has gained interest due to its potential to investigate the evolution of complex traits. These predictions are commonly performed using polygenic scores computed with DNA information from ancient humans along with genome-wide association studies (GWAS) data from present-day humans. However, numerous evolutionary processes could impact the prediction of phenotypes from ancient humans based on polygenic scores. In this work we investigate how natural selection impacts phenotypic predictions on ancient individuals using polygenic scores. We use simulations of an additive trait to analyze how natural selection impacts phenotypic predictions with polygenic scores. We simulate a trait evolving under neutrality, stabilizing selection and directional selection. We find that stabilizing and directional selection have contrasting effects on ancient phenotypic predictions. Stabilizing selection accelerates the loss of large-effect alleles contributing to trait variation. Conversely, directional selection accelerates the loss of small and large-effect alleles that drive individuals farther away from the optimal phenotypic value. These effects result in specific shared genetic variation patterns between ancient and modern populations which hamper the accuracy of polygenic scores to predict phenotypes. Furthermore, we conducted simulations that include realistic strengths of stabilizing selection and heritability estimates to show how natural selection could impact the predictive accuracy of ancient polygenic scores for two widely studied traits: height and body mass index. We emphasize the importance of considering how natural selection can decrease the reliability of ancient polygenic scores to perform phenotypic predictions on an ancient population.

Population size rescaling significantly biases outcomes of forward-in-time population genetic simulations

Simulations are an essential tool in all areas of population genetic research, used in tasks such as the validation of theoretical analysis and the study of complex evolutionary models. Forward-in-time simulations are especially flexible, allowing for various types of natural selection, complex genetic architectures, and non-Wright-Fisher dynamics. However, their intense computational requirements can be prohibitive to simulating large populations and genomes. A popular method to alleviate this burden is to scale down the population size by some scaling factor while scaling up the mutation rate, selection coefficients, and recombination rate by the same factor. However, this rescaling approach may in some cases bias simulation results. To investigate the manner and degree to which rescaling impacts simulation outcomes, we carried out simulations with different demographic histories and distributions of fitness effects using several values of the rescaling factor, Q , and compared the deviation of key outcomes (fixation times, allele frequencies, linkage disequilibrium, and the fraction of mutations that fix during the simulation) between the scaled and unscaled simulations. Our results indicate that scaling introduces substantial biases to each of these measured outcomes, even at small values of Q . Moreover, the nature of these effects depends on the evolutionary model and scaling factor being examined. While increasing the scaling factor tends to increase the observed biases, this relationship is not always straightforward, thus it may be difficult to know the impact of scaling on simulation outcomes a priori. However, it appears that for most models, only a small number of replicates was needed to accurately quantify the bias produced by rescaling for a given Q . In summary, while rescaling forward-in-time simulations may be necessary in many cases, researchers should be aware of the rescaling procedure's impact on simulation outcomes and consider investigating its magnitude in smaller scale simulations of the desired model(s) before selecting an appropriate value of Q .

Polygenic dynamics underlying the response of quantitative traits to directional selection

We study the response of a quantitative trait to exponential directional selection in a finite haploid population, both at the genetic and the phenotypic level. We assume an infinite sites model, in which the number of new mutations per generation in the population follows a Poisson distribution (with mean Θ) and each mutation occurs at a new, previously monomorphic site. Mutation effects are beneficial and drawn from a distribution. Sites are unlinked and contribute additively to the trait. Assuming that selection is stronger than random genetic drift, we model the initial phase of the dynamics by a supercritical Galton-Watson process. This enables us to obtain time-dependent results. We show that the copy-number distribution of the mutant in generation n, conditioned on non-extinction until n, is described accurately by the deterministic increase from an initial distribution with mean 1. This distribution is related to the absolutely continuous part W+ of the random variable, typically denoted W, that characterizes the stochasticity accumulating during the mutant's sweep. A suitable transformation yields the approximate dynamics of the mutant frequency distribution in a Wright-Fisher population of size N. Our expression provides a very accurate approximation except when mutant frequencies are close to 1. On this basis, we derive explicitly the (approximate) time dependence of the expected mean and variance of the trait and of the expected number of segregating sites. Unexpectedly, we obtain highly accurate approximations for all times, even for the quasi-stationary phase when the expected per-generation response and the trait variance have equilibrated. The latter refine classical results. In addition, we find that Θ is the main determinant of the pattern of adaptation at the genetic level, i.e., whether the initial allele-frequency dynamics are best described by sweep-like patterns at few loci or small allele-frequency shifts at many. The number of segregating sites is an appropriate indicator for these patterns. The selection strength determines primarily the rate of adaptation. The accuracy of our results is tested by comprehensive simulations in a Wright-Fisher framework. We argue that our results apply to more complex forms of directional selection.

Polygenic selection to a changing optimum under self-fertilisation

Many traits are polygenic, affected by multiple genetic variants throughout the genome. Selection acting on these traits involves co-ordinated allele-frequency changes at these underlying variants, and this process has been extensively studied in random-mating populations. Yet many species self-fertilise to some degree, which incurs changes to genetic diversity, recombination and genome segregation. These factors cumulatively influence how polygenic selection is realised in nature. Here, we use analytical modelling and stochastic simulations to investigate to what extent self-fertilisation affects polygenic adaptation to a new environment. Our analytical solutions show that while selfing can increase adaptation to an optimum, it incurs linkage disequilibrium that can slow down the initial spread of favoured mutations due to selection interference, and favours the fixation of alleles with opposing trait effects. Simulations show that while selection interference is present, high levels of selfing (at least 90%) aids adaptation to a new optimum, showing a higher long-term fitness. If mutations are pleiotropic then only a few major-effect variants fix along with many neutral hitchhikers, with a transient increase in linkage disequilibrium. These results show potential advantages to self-fertilisation when adapting to a new environment, and how the mating system affects the genetic composition of polygenic selection.

A Path Integral Approach for Allele Frequency Dynamics Under Polygenic Selection

Many phenotypic traits have a polygenic genetic basis, making it challenging to learn their genetic architectures and predict individual phenotypes. One promising avenue to resolve the genetic basis of complex traits is through evolve-and-resequence experiments, in which laboratory populations are exposed to some selective pressure and trait-contributing loci are identified by extreme frequency changes over the course of the experiment. However, small laboratory populations will experience substantial random genetic drift, and it is difficult to determine whether selection played a roll in a given allele frequency change. Predicting how much allele frequencies change under drift and selection had remained an open problem well into the 21st century, even those contributing to simple, monogenic traits. Recently, there have been efforts to apply the path integral, a method borrowed from physics, to solve this problem. So far, this approach has been limited to genic selection, and is therefore inadequate to capture the complexity of quantitative, highly polygenic traits that are commonly studied. Here we extend one of these path integral methods, the perturbation approximation, to selection scenarios that are of interest to quantitative genetics. In particular, we derive analytic expressions for the transition probability (i.e., the probability that an allele will change in frequency from x , to y in time t ) of an allele contributing to a trait subject to stabilizing selection, as well as that of an allele contributing to a trait rapidly adapting to a new phenotypic optimum. We use these expressions to characterize the use of allele frequency change to test for selection, as well as explore optimal design choices for evolve-and-resequence experiments to uncover the genetic architecture of polygenic traits under selection.

Inherited CARD9 Deficiency Due to a Founder Effect in East Asia

Autosomal recessive CARD9 deficiency can underly deep and superficial fungal diseases. We identified two Japanese patients, suffering from superficial and invasive Candida albicans diseases, carrying biallelic variants of CARD9. Both patients, in addition to another Japanese and two Korean patients who were previously reported, carried the c.820dup CARD9 variant, either in the homozygous (two patients) or heterozygous (three patients) state. The other CARD9 alleles were c.104G > A, c.1534C > T and c.1558del. The c.820dup CARD9 variant has thus been reported, in the homozygous or heterozygous state, in patients originating from China, Japan, or South Korea. The Japanese, Korean, and Chinese patients share a 10 Kb haplotype encompassing the c.820dup CARD9 variant. This variant thus originates from a common ancestor, estimated to have lived less than 4,000 years ago. While phaeohyphomycosis caused by Phialophora spp. was common in the Chinese patients, none of the five patients in our study displayed Phialophora spp.-induced disease. This difference between Chinese and our patients probably results from environmental factors. (161/250).

The distribution of fitness effects during adaptive walks using a simple genetic network

The tempo and mode of adaptation depends on the availability of beneficial alleles. Genetic interactions arising from gene networks can restrict this availability. However, the extent to which networks affect adaptation remains largely unknown. Current models of evolution consider additive genotype-phenotype relationships while often ignoring the contribution of gene interactions to phenotypic variance. In this study, we model a quantitative trait as the product of a simple gene regulatory network, the negative autoregulation motif. Using forward-time genetic simulations, we measure adaptive walks towards a phenotypic optimum in both additive and network models. A key expectation from adaptive walk theory is that the distribution of fitness effects of new beneficial mutations is exponential. We found that both models instead harbored distributions with fewer large-effect beneficial alleles than expected. The network model also had a complex and bimodal distribution of fitness effects among all mutations, with a considerable density at deleterious selection coefficients. This behavior is reminiscent of the cost of complexity, where correlations among traits constrain adaptation. Our results suggest that the interactions emerging from genetic networks can generate complex and multimodal distributions of fitness effects.

Background Selection From Unlinked Sites Causes Nonindependent Evolution of Deleterious Mutations

Background selection describes the reduction in neutral diversity caused by selection against deleterious alleles at other loci. It is typically assumed that the purging of deleterious alleles affects linked neutral variants, and indeed simulations typically only treat a genomic window. However, background selection at unlinked loci also depresses neutral diversity. In agreement with previous analytical approximations, in our simulations of a human-like genome with a realistically high genome-wide deleterious mutation rate, the effects of unlinked background selection exceed those of linked background selection. Background selection reduces neutral genetic diversity by a factor that is independent of census population size. Outside of genic regions, the strength of background selection increases with the mean selection coefficient, contradicting the linked theory but in agreement with the unlinked theory. Neutral diversity within genic regions is fairly independent of the strength of selection. Deleterious genetic load among haploid individuals is underdispersed, indicating nonindependent evolution of deleterious mutations. Empirical evidence for underdispersion was previously interpreted as evidence for global epistasis, but we recover it from a non-epistatic model.

Polygenic response of sex chromosomes to sexual antagonism

Sexual antagonism occurs when males and females differ in their phenotypic fitness optima but are constrained in their evolution to these optima because of their shared genome. The sex chromosomes, which have distinct evolutionary "interests" relative to the autosomes, are theorized to play an important role in sexually antagonistic conflict. However, the evolutionary responses of sex chromosomes and autosomes have usually been considered independently, that is, via contrasting the response of a gene located on either an X chromosome or an autosome. Here, we study the coevolutionary response of the X chromosome and autosomes to sexually antagonistic selection acting on a polygenic phenotype. We model a phenotype initially under stabilizing selection around a single optimum, followed by a sudden divergence of the male and female optima. We find that, in the absence of dosage compensation, the X chromosome promotes evolution toward the female optimum, inducing coevolutionary male-biased responses on the autosomes. Dosage compensation obscures the female-biased interests of the X, causing it to contribute equally to male and female phenotypic change. We further demonstrate that fluctuations in an adaptive landscape can generate prolonged intragenomic conflict and accentuate the differential responses of the X and autosomes to this conflict.

Demographic modeling of admixed Latin American populations from whole genomes

Demographic models of Latin American populations often fail to fully capture their complex evolutionary history, which has been shaped by both recent admixture and deeper-in-time demographic events. To address this gap, we used high-coverage whole-genome data from Indigenous American ancestries in present-day Mexico and existing genomes from across Latin America to infer multiple demographic models that capture the impact of different timescales on genetic diversity. Our approach, which combines analyses of allele frequencies and ancestry tract length distributions, represents a significant improvement over current models in predicting patterns of genetic variation in admixed Latin American populations. We jointly modeled the contribution of European, African, East Asian, and Indigenous American ancestries into present-day Latin American populations. We infer that the ancestors of Indigenous Americans and East Asians diverged ∼30 thousand years ago, and we characterize genetic contributions of recent migrations from East and Southeast Asia to Peru and Mexico. Our inferred demographic histories are consistent across different genomic regions and annotations, suggesting that our inferences are robust to the potential effects of linked selection. In conjunction with published distributions of fitness effects for new nonsynonymous mutations in humans, we show in large-scale simulations that our models recover important features of both neutral and deleterious variation. By providing a more realistic framework for understanding the evolutionary history of Latin American populations, our models can help address the historical under-representation of admixed groups in genomics research and can be a valuable resource for future studies of populations with complex admixture and demographic histories.

A theory of oligogenic adaptation of a quantitative trait

Rapid phenotypic adaptation is widespread in nature, but the underlying genetic dynamics remain controversial. Whereas population genetics envisages sequential beneficial substitutions, quantitative genetics assumes a collective response through subtle shifts in allele frequencies. This dichotomy of a monogenic and a highly polygenic view of adaptation raises the question of a middle ground, as well as the factors controlling the transition. Here, we consider an additive quantitative trait with equal locus effects under Gaussian stabilizing selection that adapts to a new trait optimum after an environmental change. We present an analytical framework based on Yule branching processes to describe how phenotypic adaptation is achieved by collective changes in allele frequencies at the underlying loci. In particular, we derive an approximation for the joint allele-frequency distribution conditioned on the trait mean as a comprehensive descriptor of the adaptive architecture. Depending on the model parameters, this architecture reproduces the well-known patterns of sequential, monogenic sweeps, or of subtle, polygenic frequency shifts. Between these endpoints, we observe oligogenic architecture types that exhibit characteristic patterns of partial sweeps. We find that a single compound parameter, the population-scaled background mutation rate Θbg, is the most important predictor of the type of adaptation, while selection strength, the number of loci in the genetic basis, and linkage only play a minor role.

Human deleterious mutation rate implies high fitness variance, with declining mean fitness compensated by rarer beneficial mutations of larger effect

Each new human has an expected Ud = 2 - 10 new deleterious mutations. This deluge of deleterious mutations cannot all be purged, and therefore accumulate in a declining fitness ratchet. Using a novel simulation framework designed to efficiently handle genome-wide linkage disequilibria across many segregating sites, we find that rarer, beneficial mutations of larger effect are sufficient to compensate fitness declines due to the fixation of many slightly deleterious mutations. Drift barrier theory posits a similar asymmetric pattern of fixations to explain ratcheting genome size and complexity, but in our theory, the cause is Ud > 1 rather than small population size. In our simulations, Ud ~2 - 10 generates high within-population variance in relative fitness; two individuals will typically differ in fitness by 15-40%. Ud ~2 - 10 also slows net adaptation by ~13%-39%. Surprisingly, fixation rates are more sensitive to changes in the beneficial than the deleterious mutation rate, e.g. a 10% increase in overall mutation rate leads to faster adaptation; this puts to rest dysgenic fears about increasing mutation rates due to rising paternal age.

Polygenic adaptation dynamics in large, finite populations

Although many phenotypic traits are determined by a large number of genetic variants, how a polygenic trait adapts in response to a change in the environment is not completely understood. In the framework of diffusion theory, we study the steady state and the adaptation dynamics of a large but finite population evolving under stabilizing selection and symmetric mutations when selection and mutation are moderately large. We find that in the stationary state, the allele frequency distribution at a locus is unimodal if its effect size is below a threshold effect and bimodal otherwise; these results are the stochastic analog of the deterministic ones where the stable allele frequency becomes bistable when the effect size exceeds a threshold. It is known that following a sudden shift in the phenotypic optimum, in an infinitely large population, selective sweeps at a large-effect locus are prevented and adaptation proceeds exclusively via subtle changes in the allele frequency; in contrast, we find that the chance of sweep is substantially enhanced in large, finite populations and the allele frequency at a large-effect locus can reach a high frequency at short times even for small shifts in the phenotypic optimum.

Demes: a standard format for demographic models

Understanding the demographic history of populations is a key goal in population genetics, and with improving methods and data, ever more complex models are being proposed and tested. Demographic models of current interest typically consist of a set of discrete populations, their sizes and growth rates, and continuous and pulse migrations between those populations over a number of epochs, which can require dozens of parameters to fully describe. There is currently no standard format to define such models, significantly hampering progress in the field. In particular, the important task of translating the model descriptions in published work into input suitable for population genetic simulators is labor intensive and error prone. We propose the Demes data model and file format, built on widely used technologies, to alleviate these issues. Demes provide a well-defined and unambiguous model of populations and their properties that is straightforward to implement in software, and a text file format that is designed for simplicity and clarity. We provide thoroughly tested implementations of Demes parsers in multiple languages including Python and C, and showcase initial support in several simulators and inference methods. An introduction to the file format and a detailed specification are available at https://popsim-consortium.github.io/demes-spec-docs/.

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.

How Can We Resolve Lewontin's Paradox?

We discuss the genetic, demographic, and selective forces that are likely to be at play in restricting observed levels of DNA sequence variation in natural populations to a much smaller range of values than would be expected from the distribution of census population sizes alone-Lewontin's Paradox. While several processes that have previously been strongly emphasized must be involved, including the effects of direct selection and genetic hitchhiking, it seems unlikely that they are sufficient to explain this observation without contributions from other factors. We highlight a potentially important role for the less-appreciated contribution of population size change; specifically, the likelihood that many species and populations may be quite far from reaching the relatively high equilibrium diversity values that would be expected given their current census sizes.

Octopaminergic/tyraminergic Tdc2 neurons regulate biased sperm usage in female Drosophila melanogaster

In polyandrous internally fertilizing species, a multiply-mated female can use stored sperm from different males in a biased manner to fertilize her eggs. The female's ability to assess sperm quality and compatibility is essential for her reproductive success, and represents an important aspect of postcopulatory sexual selection. In Drosophila melanogaster, previous studies demonstrated that the female nervous system plays an active role in influencing progeny paternity proportion, and suggested a role for octopaminergic/tyraminergic Tdc2 neurons in this process. Here, we report that inhibiting Tdc2 neuronal activity causes females to produce a higher-than-normal proportion of first-male progeny. This difference is not due to differences in sperm storage or release, but instead is attributable to the suppression of second-male sperm usage bias that normally occurs in control females. We further show that a subset of Tdc2 neurons innervating the female reproductive tract is largely responsible for the progeny proportion phenotype that is observed when Tdc2 neurons are inhibited globally. On the contrary, overactivation of Tdc2 neurons does not further affect sperm storage and release or progeny proportion. These results suggest that octopaminergic/tyraminergic signaling allows a multiply-mated female to bias sperm usage, and identify a new role for the female nervous system in postcopulatory sexual selection.

The evolution of larger size in high-altitude Drosophila melanogaster has a variable genetic architecture

Important uncertainties persist regarding the genetic architecture of adaptive trait evolution in natural populations, including the number of genetic variants involved, whether they are drawn from standing genetic variation, and whether directional selection drives them to complete fixation. Here, we take advantage of a unique natural population of Drosophila melanogaster from the Ethiopian highlands, which has evolved larger body size than any other known population of this species. We apply a bulk segregant quantitative trait locus mapping approach to 4 unique crosses between highland Ethiopian and lowland Zambian populations for both thorax length and wing length. Results indicated a persistently variable genetic basis for these evolved traits (with largely distinct sets of quantitative trait loci for each cross), and at least a moderately polygenic architecture with relatively strong effects present. We complemented these mapping experiments with population genetic analyses of quantitative trait locus regions and gene ontology enrichment analysis, generating strong hypotheses for specific genes and functional processes that may have contributed to these adaptive trait changes. Finally, we find that the genetic architectures indicated by our quantitative trait locus mapping results for size traits mirror those from similar experiments on other recently evolved traits in this species. Collectively, these studies suggest a recurring pattern of polygenic adaptation in this species, in which causative variants do not approach fixation and moderately strong effect loci are present.

A novel cost framework reveals evidence for competitive selection in the evolution of complex traits during plant domestication

Most models of selection incorporate some notion of environmental degradation where the majority of the population becomes less fit concerning a character resulting in pressure to adapt. Such models have been variously associated with an adaptation cost, the substitution load. Conversely, adaptative mutations that represent an improvement in fitness in the absence of environmental change have generally been assumed to be associated with negligible cost. However, such adaptations could represent a competitive advantage that diminishes resource availability for others and so induces a cost. This type of adaptation in the form of seedling competition has been suggested as a mechanism for increases in seed sizes during domestication, a trait associated with the standard stabilizing selection model. We present a novel cost framework for competitive selection that demonstrates significant differences in behaviour to environmental-based selection in intensity, intensity over time and directly contrasts with the expectations of the standard model. Grain metrics of nine archaeological crops fit a mixed model in which episodes of competitive selection often emerge from shifting optimum episodes of stabilizing selection, highlighting the potential prevalence of the mechanism outlined here and providing fundamental insight into the factors driving domestication.

Using singleton densities to detect recent selection in Bos taurus

Many quantitative traits are subject to polygenic selection, where several genomic regions undergo small, simultaneous changes in allele frequency that collectively alter a phenotype. The widespread availability of genome data, along with novel statistical techniques, has made it easier to detect these changes. We apply one such method, the "Singleton Density Score" (SDS), to the Holstein breed of Bos taurus to detect recent selection (arising up to around 740 years ago). We identify several genes as candidates for targets of recent selection, including some relating to cell regulation, catabolic processes, neural-cell adhesion and immunity. We do not find strong evidence that three traits that are important to humans-milk protein content, milk fat content, and stature-have been subject to directional selection. Simulations demonstrate that because B. taurus recently experienced a population bottleneck, singletons are depleted so the power of SDS methods is reduced. These results inform on which genes underlie recent genetic change in B. taurus, while providing information on how polygenic selection can be best investigated in future studies.

Populations, Traits, and Their Spatial Structure in Humans

The spatial distribution of genetic variants is jointly determined by geography, past demographic processes, natural selection, and its interplay with environmental variation. A fraction of these genetic variants are "causal alleles" that affect the manifestation of a complex trait. The effect exerted by these causal alleles on complex traits can be independent or dependent on the environment. Understanding the evolutionary processes that shape the spatial structure of causal alleles is key to comprehend the spatial distribution of complex traits. Natural selection, past population size changes, range expansions, consanguinity, assortative mating, archaic introgression, admixture, and the environment can alter the frequencies, effect sizes, and heterozygosities of causal alleles. This provides a genetic axis along which complex traits can vary. However, complex traits also vary along biogeographical and sociocultural axes which are often correlated with genetic axes in complex ways. The purpose of this review is to consider these genetic and environmental axes in concert and examine the ways they can help us decipher the variation in complex traits that is visible in humans today. This initiative necessarily implies a discussion of populations, traits, the ability to infer and interpret "genetic" components of complex traits, and how these have been impacted by adaptive events. In this review, we provide a history-aware discussion on these topics using both the recent and more distant past of our academic discipline and its relevant contexts.

Emerging evidence of plant domestication as a landscape-level process

The evidence from ancient crops over the past decade challenges some of our most basic assumptions about the process of domestication. The emergence of crops has been viewed as a technologically progressive process in which single or multiple localized populations adapt to human environments in response to cultivation. By contrast, new genetic and archaeological evidence reveals a slow process that involved large populations over wide areas with unexpectedly sustained cultural connections in deep time. We review evidence that calls for a new landscape framework of crop origins. Evolutionary processes operate across vast distances of landscape and time, and the origins of domesticates are complex. The domestication bottleneck is a redundant concept and the progressive nature of domestication is in doubt.

Ethanol resistance in Drosophila melanogaster has increased in parallel cold-adapted populations and shows a variable genetic architecture within and between populations

Understanding the genetic properties of adaptive trait evolution is a fundamental crux of biological inquiry that links molecular processes to biological diversity. Important uncertainties persist regarding the genetic predictability of adaptive trait change, the role of standing variation, and whether adaptation tends to result in the fixation of favored variants. Here, we use the recurrent evolution of enhanced ethanol resistance in Drosophila melanogaster during this species' worldwide expansion as a promising system to add to our understanding of the genetics of adaptation. We find that elevated ethanol resistance has evolved at least three times in different cooler regions of the species' modern range-not only at high latitude but also in two African high-altitude regions. Applying a bulk segregant mapping framework, we find that the genetic architecture of ethanol resistance evolution differs substantially not only between our three resistant populations, but also between two crosses involving the same European population. We then apply population genetic scans for local adaptation within our quantitative trait locus regions, and we find potential contributions of genes with annotated roles in spindle localization, membrane composition, sterol and alcohol metabolism, and other processes. We also apply simulation-based analyses that confirm the variable genetic basis of ethanol resistance and hint at a moderately polygenic architecture. However, these simulations indicate that larger-scale studies will be needed to more clearly quantify the genetic architecture of adaptive evolution and to firmly connect trait evolution to specific causative loci.

Rapid Evolutionary Adaptation in Response to Selection on Quantitative Traits

Evolutionary adaptation after sudden environmental changes can occur very rapidly. The mechanisms facilitating rapid adaptation range from strong positive directional selection leading to large shifts in the allele frequencies at a few loci (selective sweeps) to polygenic selection causing small changes in allele frequencies at many loci. In addition, combinations of these two extreme mechanisms may also result in fast evolution. In recent years, following reports of new case studies of rapid adaptation, population genetic models have been proposed to explain these observations. In these models, the role of the major selective forces (positive directional and stabilizing selection) is highlighted as well as the genetic architecture of quantitative traits. Furthermore, the factors limiting the speed of adaptation are analyzed, in particular, the effects of random genetic drift and demography due to finite population size.

The genetic architecture of temperature adaptation is shaped by population ancestry and not by selection regime

BACKGROUND

Understanding the genetic architecture of temperature adaptation is key for characterizing and predicting the effect of climate change on natural populations. One particularly promising approach is Evolve and Resequence, which combines advantages of experimental evolution such as time series, replicate populations, and controlled environmental conditions, with whole genome sequencing. Recent analysis of replicate populations from two different Drosophila simulans founder populations, which were adapting to the same novel hot environment, uncovered very different architectures-either many selection targets with large heterogeneity among replicates or fewer selection targets with a consistent response among replicates.

RESULTS

Here, we expose the founder population from Portugal to a cold temperature regime. Although almost no selection targets are shared between the hot and cold selection regime, the adaptive architecture was similar. We identify a moderate number of targets under strong selection (19 selection targets, mean selection coefficient = 0.072) and parallel responses in the cold evolved replicates. This similarity across different environments indicates that the adaptive architecture depends more on the ancestry of the founder population than the specific selection regime.

CONCLUSIONS

These observations will have broad implications for the correct interpretation of the genomic responses to a changing climate in natural populations.

Polygenic Adaptation: Integrating Population Genetics and Gene Regulatory Networks

The adaptation of populations to local environments often relies on the selection of optimal values for polygenic traits. Here, we first summarize the results obtained from different quantitative genetics and population genetics models, about the genetic architecture of polygenic traits and their response to directional selection. We then highlight the contribution of systems biology to the understanding of the molecular bases of polygenic traits and the evolution of gene regulatory networks involved in these traits. Finally, we discuss the need for a unifying framework merging the fields of population genetics, quantitative genetics and systems biology to better understand the molecular bases of polygenic traits adaptation.

Disentangling selection on genetically correlated polygenic traits via whole-genome genealogies

We present a full-likelihood method to infer polygenic adaptation from DNA sequence variation and GWAS summary statistics to quantify recent transient directional selection acting on a complex trait. Through simulations of polygenic trait architecture evolution and GWASs, we show the method substantially improves power over current methods. We examine the robustness of the method under stratification, uncertainty and bias in marginal effects, uncertainty in the causal SNPs, allelic heterogeneity, negative selection, and low GWAS sample size. The method can quantify selection acting on correlated traits, controlling for pleiotropy even among traits with strong genetic correlation (|rg|=80%) while retaining high power to attribute selection to the causal trait. When the causal trait is excluded from analysis, selection is attributed to its closest proxy. We discuss limitations of the method, cautioning against strongly causal interpretations of the results, and the possibility of undetectable gene-by-environment (GxE) interactions. We apply the method to 56 human polygenic traits, revealing signals of directional selection on pigmentation, life history, glycated hemoglobin (HbA1c), and other traits. We also conduct joint testing of 137 pairs of genetically correlated traits, revealing widespread correlated response acting on these traits (2.6-fold enrichment, p = 1.5 × 10-7). Signs of selection on some traits previously reported as adaptive (e.g., educational attainment and hair color) are largely attributable to correlated response (p = 2.9 × 10-6 and 1.7 × 10-4, respectively). Lastly, our joint test shows antagonistic selection has increased type 2 diabetes risk and decrease HbA1c (p = 1.5 × 10-5).

The evolution of group differences in changing environments

The selection pressures that have shaped the evolution of complex traits in humans remain largely unknown, and in some contexts highly contentious, perhaps above all where they concern mean trait differences among groups. To date, the discussion has focused on whether such group differences have any genetic basis, and if so, whether they are without fitness consequences and arose via random genetic drift, or whether they were driven by selection for different trait optima in different environments. Here, we highlight a plausible alternative: that many complex traits evolve under stabilizing selection in the face of shifting environmental effects. Under this scenario, there will be rapid evolution at the loci that contribute to trait variation, even when the trait optimum remains the same. These considerations underscore the strong assumptions about environmental effects that are required in ascribing trait differences among groups to genetic differences.

Background Selection Does Not Mimic the Patterns of Genetic Diversity Produced by Selective Sweeps

It is increasingly evident that natural selection plays a prominent role in shaping patterns of diversity across the genome. The most commonly studied modes of natural selection are positive selection and negative selection, which refer to directional selection for and against derived mutations, respectively. Positive selection can result in hitchhiking events, in which a beneficial allele rapidly replaces all others in the population, creating a valley of diversity around the selected site along with characteristic skews in allele frequencies and linkage disequilibrium among linked neutral polymorphisms. Similarly, negative selection reduces variation not only at selected sites but also at linked sites, a phenomenon called background selection (BGS). Thus, discriminating between these two forces may be difficult, and one might expect efforts to detect hitchhiking to produce an excess of false positives in regions affected by BGS. Here, we examine the similarity between BGS and hitchhiking models via simulation. First, we show that BGS may somewhat resemble hitchhiking in simplistic scenarios in which a region constrained by negative selection is flanked by large stretches of unconstrained sites, echoing previous results. However, this scenario does not mirror the actual spatial arrangement of selected sites across the genome. By performing forward simulations under more realistic scenarios of BGS, modeling the locations of protein-coding and conserved noncoding DNA in real genomes, we show that the spatial patterns of variation produced by BGS rarely mimic those of hitchhiking events. Indeed, BGS is not substantially more likely than neutrality to produce false signatures of hitchhiking. This holds for simulations modeled after both humans and Drosophila, and for several different demographic histories. These results demonstrate that appropriately designed scans for hitchhiking need not consider BGS's impact on false-positive rates. However, we do find evidence that BGS increases the false-negative rate for hitchhiking, an observation that demands further investigation.

Low concordance of short-term and long-term selection responses in experimental Drosophila populations

Experimental evolution is becoming a popular approach to study the genomic selection response of evolving populations. Computer simulation studies suggest that the accuracy of the signature increases with the duration of the experiment. Since some assumptions of the computer simulations may be violated, it is important to scrutinize the influence of the experimental duration with real data. Here, we use a highly replicated Evolve and Resequence study in Drosophila simulans to compare the selection targets inferred at different time points. At each time point, approximately the same number of SNPs deviates from neutral expectations, but only 10% of the selected haplotype blocks identified from the full data set can be detected after 20 generations. Those haplotype blocks that emerge already after 20 generations differ from the others by being strongly selected at the beginning of the experiment and display a more parallel selection response. Consistent with previous computer simulations, our results demonstrate that only Evolve and Resequence experiments with a sufficient number of generations can characterize complex adaptive architectures.

Polygenic Adaptation in a Population of Finite Size

Polygenic adaptation in response to selection on quantitative traits has become an important topic in evolutionary biology. Here we review the recent literature on models of polygenic adaptation. In particular, we focus on a model that includes mutation and both directional and stabilizing selection on a highly polygenic trait in a population of finite size (thus experiencing random genetic drift). Assuming that a sudden environmental shift of the fitness optimum occurs while the population is in a stochastic equilibrium, we analyze the adaptation of the trait to the new optimum. When the shift is not too large relative to the equilibrium genetic variance and this variance is determined by loci with mostly small effects, the approach of the mean phenotype to the optimum can be approximated by a rapid exponential process (whose rate is proportional to the genetic variance). During this rapid phase the underlying changes to allele frequencies, however, may depend strongly on genetic drift. While trait-increasing alleles with intermediate equilibrium frequencies are dominated by selection and contribute positively to changes of the trait mean (i.e., are aligned with the direction of the optimum shift), alleles with low or high equilibrium frequencies show more of a random dynamics, which is expected when drift is dominating. A strong effect of drift is also predicted for population size bottlenecks. Our simulations show that the presence of a bottleneck results in a larger deviation of the population mean of the trait from the fitness optimum, which suggests that more loci experience the influence of drift.

Human adaptation over the past 40,000 years

Over the past few years several methodological and data-driven advances have greatly improved our ability to robustly detect genomic signatures of selection in humans. New methods applied to large samples of present-day genomes provide increased power, while ancient DNA allows precise estimation of timing and tempo. However, despite these advances, we are still limited in our ability to translate these signatures into understanding about which traits were actually under selection, and why. Combining information from different populations and timescales may allow interpretation of selective sweeps. Other modes of selection have proved more difficult to detect. In particular, despite strong evidence of the polygenicity of most human traits, evidence for polygenic selection is weak, and its importance in recent human evolution remains unclear. Balancing selection and archaic introgression seem important for the maintenance of potentially adaptive immune diversity, but perhaps less so for other traits.

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

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

The Linked Selection Signature of Rapid Adaptation in Temporal Genomic Data

The majority of empirical population genetic studies have tried to understand the evolutionary processes that have shaped genetic variation in a single sample taken from a present-day population. However, genomic data collected over tens of generations in both natural and laboratory populations are increasingly used to find selected loci underpinning adaptation over these short timescales. Although these studies have been quite successful in detecting selection on large-effect loci, the fitness differences between individuals are often polygenic, such that selection leads to allele frequency changes that are difficult to distinguish from genetic drift. However, one promising signal comes from polygenic selection's effect on neutral sites that become stochastically associated with the genetic backgrounds that lead to fitness differences between individuals. Previous theoretical work has established that the random associations between a neutral allele and heritable fitness backgrounds act to reduce the effective population size experienced by this neutral allele. These associations perturb neutral allele frequency trajectories, creating autocovariance in the allele frequency changes across generations. Here, we show how temporal genomic data allow us to measure the temporal autocovariance in allele frequency changes and characterize the genome-wide impact of polygenic selection. We develop expressions for these temporal autocovariances, showing that their magnitude is determined by the level of additive genetic variation, recombination, and linkage disequilibria in a region. Furthermore, by using analytic expressions for the temporal variances and autocovariances in allele frequency, we demonstrate that one can estimate the additive genetic variation for fitness and the drift-effective population size from temporal genomic data. We also show how the proportion of total variation in allele frequency change due to linked selection can be estimated from temporal data. Overall, we demonstrate that temporal genomic data offer opportunities to identify the role of linked selection on genome-wide diversity over short timescales, and can help bridge population genetic and quantitative genetic studies of adaptation.