Purifying Selection Influences the Comparison of Heterozygosities between Populations

Heterozygosity is a fundamental measure routinely used to compare between populations to infer the level of genetic variation and their relative effective population sizes. However, such comparison is highly influenced by the magnitude of selection pressure on the genomic regions used. Using over 2 million Single Nucleotide Variants (SNVs) from chimpanzee and mouse populations, this study shows that the heterozygosities estimated using neutrally evolving sites of large populations were two times higher than those of small populations. However, this difference was only ~1.6 times for the heterozygosities estimated using nonsynonymous sites. This suggests an excess in the nonsynonymous heterozygosities due to the segregation of deleterious variants in small populations. This excess in the nonsynonymous heterozygosities of the small populations was estimated to be 23-31%. Further analysis revealed that the magnitude of the excess is modulated by effective population size (Ne) and selection intensity (s). Using chimpanzee populations, this investigation found that the excess in nonsynonymous diversity in the small population was little (6%) when the difference between the Ne values of large and small populations was small (2.4 times). Conversely, this was high (23%) when the difference in Ne was large (5.9 times). Analysis using mouse populations showed that the excess in the nonsynonymous diversity of highly constrained genes of the small population was much higher (38%) than that observed for the genes under relaxed selective constraints (21%). Similar results were observed when the expression levels of genes were used as a proxy for selection intensity. These results emphasize the use of neutral regions, less constrained genes, or lowly expressed genes when comparing the heterozygosities between populations.

Biases in ARG-Based Inference of Historical Population Size in Populations Experiencing Selection

Inferring the demographic history of populations provides fundamental insights into species dynamics and is essential for developing a null model to accurately study selective processes. However, background selection and selective sweeps can produce genomic signatures at linked sites that mimic or mask signals associated with historical population size change. While the theoretical biases introduced by the linked effects of selection have been well established, it is unclear whether ancestral recombination graph (ARG)-based approaches to demographic inference in typical empirical analyses are susceptible to misinference due to these effects. To address this, we developed highly realistic forward simulations of human and Drosophila melanogaster populations, including empirically estimated variability of gene density, mutation rates, recombination rates, purifying, and positive selection, across different historical demographic scenarios, to broadly assess the impact of selection on demographic inference using a genealogy-based approach. Our results indicate that the linked effects of selection minimally impact demographic inference for human populations, although it could cause misinference in populations with similar genome architecture and population parameters experiencing more frequent recurrent sweeps. We found that accurate demographic inference of D. melanogaster populations by ARG-based methods is compromised by the presence of pervasive background selection alone, leading to spurious inferences of recent population expansion, which may be further worsened by recurrent sweeps, depending on the proportion and strength of beneficial mutations. Caution and additional testing with species-specific simulations are needed when inferring population history with non-human populations using ARG-based approaches to avoid misinference due to the linked effects of selection.

Temporal challenges in detecting balancing selection from population genomic data

The role of balancing selection in maintaining genetic variation remains an open question in population genetics. Recent years have seen numerous studies identifying candidate loci potentially experiencing balancing selection, most predominantly in human populations. There are however numerous alternative evolutionary processes that may leave similar patterns of variation, thereby potentially confounding inference, and the expected signatures of balancing selection additionally change in a temporal fashion. Here we use forward-in-time simulations to quantify expected statistical power to detect balancing selection using both site frequency spectrum- and linkage disequilibrium-based methods under a variety of evolutionarily realistic null models. We find that whilst site frequency spectrum-based methods have little power immediately after a balanced mutation begins segregating, power increases with time since the introduction of the balanced allele. Conversely, linkage disequilibrium-based methods have considerable power whilst the allele is young, and power dissipates rapidly as the time since introduction increases. Taken together, this suggests that site frequency spectrum-based methods are most effective at detecting long-term balancing selection (>25N generations since the introduction of the balanced allele) whilst linkage disequilibrium-based methods are effective over much shorter timescales (<1N generations), thereby leaving a large time frame over which current methods have little power to detect the action of balancing selection. Finally, we investigate the extent to which alternative evolutionary processes may mimic these patterns, and demonstrate the need for caution in attempting to distinguish the signatures of balancing selection from those of both neutral processes (e.g. population structure and admixture) as well as of alternative selective processes (e.g. partial selective sweeps).

A quantitative genetic model of background selection in humans

Across the human genome, there are large-scale fluctuations in genetic diversity caused by the indirect effects of selection. This "linked selection signal" reflects the impact of selection according to the physical placement of functional regions and recombination rates along chromosomes. Previous work has shown that purifying selection acting against the steady influx of new deleterious mutations at functional portions of the genome shapes patterns of genomic variation. To date, statistical efforts to estimate purifying selection parameters from linked selection models have relied on classic Background Selection theory, which is only applicable when new mutations are so deleterious that they cannot fix in the population. Here, we develop a statistical method based on a quantitative genetics view of linked selection, that models how polygenic additive fitness variance distributed along the genome increases the rate of stochastic allele frequency change. By jointly predicting the equilibrium fitness variance and substitution rate due to both strong and weakly deleterious mutations, we estimate the distribution of fitness effects (DFE) and mutation rate across three geographically distinct human samples. While our model can accommodate weaker selection, we find evidence of strong selection operating similarly across all human samples. Although our quantitative genetic model of linked selection fits better than previous models, substitution rates of the most constrained sites disagree with observed divergence levels. We find that a model incorporating selective interference better predicts observed divergence in conserved regions, but overall our results suggest uncertainty remains about the processes generating fitness variation in humans.

The effects of mutation and recombination rate heterogeneity on the inference of demography and the distribution of fitness effects

Disentangling the effects of demography and selection has remained a focal point of population genetic analysis. Knowledge about mutation and recombination is essential in this endeavour; however, despite clear evidence that both mutation and recombination rates vary across genomes, it is common practice to model both rates as fixed. In this study, we quantify how this unaccounted for rate heterogeneity may impact inference using common approaches for inferring selection (DFE-alpha, Grapes, and polyDFE) and/or demography (fastsimcoal2 and δaδi). We demonstrate that, if not properly modelled, this heterogeneity can increase uncertainty in the estimation of demographic and selective parameters and in some scenarios may result in mis-leading inference. These results highlight the importance of quantifying the fundamental evolutionary parameters of mutation and recombination prior to utilizing population genomic data to quantify the effects of genetic drift (i.e., as modulated by demographic history) and selection; or, at the least, that the effects of uncertainty in these parameters can and should be directly modelled in downstream inference.

The divergence of mean phenotypes under persistent directional selection

Numerous organismal traits, particularly at the cellular level, are likely to be under persistent directional selection across phylogenetic lineages. Unless all mutations affecting such traits have large enough effects to be efficiently selected in all species, gradients in mean phenotypes are expected to arise as a consequence of differences in the power of random genetic drift, which varies by approximately five orders of magnitude across the Tree of Life. Prior theoretical work examining the conditions under which such gradients can arise focused on the simple situation in which all genomic sites affecting the trait have identical and constant mutational effects. Here, we extend this theory to incorporate the more biologically realistic situation in which mutational effects on a trait differ among nucleotide sites. Pursuit of such modifications leads to the development of semi-analytic expressions for the ways in which selective interference arises via linkage effects in single-effects models, which then extend to more complex scenarios. The theory developed clarifies the conditions under which mutations of different selective effects mutually interfere with each others' fixation and shows how variance in effects among sites can substantially modify and extend the expected scaling relationships between mean phenotypes and effective population sizes.

The effect of mutational robustness on the evolvability of multicellular organisms and eukaryotic cells

Canalization involves mutational robustness, the lack of phenotypic change as a result of genetic mutations. Given the large divergence in phenotype across species, understanding the relationship between high robustness and evolvability has been of interest to both theorists and experimentalists. Although canalization was originally proposed in the context of multicellular organisms, the effect of multicellularity and other classes of hierarchical organization on evolvability has not been considered by theoreticians. We address this issue using a Boolean population model with explicit representation of an environment in which individuals with explicit genotype and a hierarchical phenotype representing multicellularity evolve. Robustness is described by a single real number between zero and one which emerges from the genotype-phenotype map. We find that high robustness is favoured in constant environments, and lower robustness is favoured after environmental change. Multicellularity and hierarchical organization severely constrain robustness: peak evolvability occurs at an absolute level of robustness of about 0.99 compared with values of about 0.5 in a classical neutral network model. These constraints result in a sharp peak of evolvability in which the maximum is set by the fact that the fixation of adaptive mutations becomes more improbable as robustness decreases. When robustness is put under genetic control, robustness levels leading to maximum evolvability are selected for, but maximal relative fitness appears to require recombination.

Why does the X chromosome lag behind autosomes in GWAS findings?

The X-chromosome is among the largest human chromosomes. It differs from autosomes by a number of important features including hemizygosity in males, an almost complete inactivation of one copy in females, and unique patterns of recombination. We used data from the Catalog of Published Genome Wide Association Studies to compare densities of the GWAS-detected SNPs on the X-chromosome and autosomes. The density of GWAS-detected SNPs on the X-chromosome is 6-fold lower compared to the density of the GWAS-detected SNPs on autosomes. Differences between the X-chromosome and autosomes cannot be explained by differences in the overall SNP density, lower X-chromosome coverage by genotyping platforms or low call rate of X-chromosomal SNPs. Similar differences in the density of GWAS-detected SNPs were found in female-only GWASs (e.g. ovarian cancer GWASs). We hypothesized that the lower density of GWAS-detected SNPs on the X-chromosome compared to autosomes is not a result of a methodological bias, e.g. differences in coverage or call rates, but has a real underlying biological reason-a lower density of functional SNPs on the X-chromosome versus autosomes. This hypothesis is supported by the observation that (i) the overall SNP density of X-chromosome is lower compared to the SNP density on autosomes and that (ii) the density of genic SNPs on the X-chromosome is lower compared to autosomes while densities of intergenic SNPs are similar.

X chromosomes show relaxed selection and complete somatic dosage compensation across Timema stick insect species

Sex chromosomes have evolved repeatedly across the tree of life. As they are present in different copy numbers in males and females, they are expected to experience different selection pressures than the autosomes, with consequences including a faster rate of evolution, increased accumulation of sexually antagonistic alleles and the evolution of dosage compensation. Whether these consequences are general or linked to idiosyncrasies of specific taxa is not clear as relatively few taxa have been studied thus far. Here, we use whole-genome sequencing to identify and characterize the evolution of the X chromosome in five species of Timema stick insects with XX:X0 sex determination. The X chromosome had a similar size (approximately 12% of the genome) and gene content across all five species, suggesting that the X chromosome originated prior to the diversification of the genus. Genes on the X showed evidence of relaxed selection (elevated dN/dS) and a slower evolutionary rate (dN + dS) than genes on the autosomes, likely due to sex-biased mutation rates. Genes on the X also showed almost complete dosage compensation in somatic tissues (heads and legs), but dosage compensation was absent in the reproductive tracts. Contrary to prediction, sex-biased genes showed little enrichment on the X, suggesting that the advantage X-linkage provides to the accumulation of sexually antagonistic alleles is weak. Overall, we found the consequences of X-linkage on gene sequences and expression to be similar across Timema species, showing the characteristics of the X chromosome are surprisingly consistent over 30 million years of evolution.

Recommendations for improving statistical inference in population genomics

The field of population genomics has grown rapidly in response to the recent advent of affordable, large-scale sequencing technologies. As opposed to the situation during the majority of the 20th century, in which the development of theoretical and statistical population genetic insights outpaced the generation of data to which they could be applied, genomic data are now being produced at a far greater rate than they can be meaningfully analyzed and interpreted. With this wealth of data has come a tendency to focus on fitting specific (and often rather idiosyncratic) models to data, at the expense of a careful exploration of the range of possible underlying evolutionary processes. For example, the approach of directly investigating models of adaptive evolution in each newly sequenced population or species often neglects the fact that a thorough characterization of ubiquitous nonadaptive processes is a prerequisite for accurate inference. We here describe the perils of these tendencies, present our consensus views on current best practices in population genomic data analysis, and highlight areas of statistical inference and theory that are in need of further attention. Thereby, we argue for the importance of defining a biologically relevant baseline model tuned to the details of each new analysis, of skepticism and scrutiny in interpreting model fitting results, and of carefully defining addressable hypotheses and underlying uncertainties.

Strong neutral sweeps occurring during a population contraction

A strong reduction in diversity around a specific locus is often interpreted as a recent rapid fixation of a positively selected allele, a phenomenon called a selective sweep. Rapid fixation of neutral variants can however lead to a similar reduction in local diversity, especially when the population experiences changes in population size, e.g. bottlenecks or range expansions. The fact that demographic processes can lead to signals of nucleotide diversity very similar to signals of selective sweeps is at the core of an ongoing discussion about the roles of demography and natural selection in shaping patterns of neutral variation. Here, we quantitatively investigate the shape of such neutral valleys of diversity under a simple model of a single population size change, and we compare it to signals of a selective sweep. We analytically describe the expected shape of such "neutral sweeps" and show that selective sweep valleys of diversity are, for the same fixation time, wider than neutral valleys. On the other hand, it is always possible to parametrize our model to find a neutral valley that has the same width as a given selected valley. Our findings provide further insight into how simple demographic models can create valleys of genetic diversity similar to those attributed to positive selection.

Inferring the distribution of fitness effects in patient-sampled and experimental virus populations: two case studies

We here propose an analysis pipeline for inferring the distribution of fitness effects (DFE) from either patient-sampled or experimentally-evolved viral populations, that explicitly accounts for non-Wright-Fisher and non-equilibrium population dynamics inherent to pathogens. We examine the performance of this approach via extensive power and performance analyses, and highlight two illustrative applications - one from an experimentally-passaged RNA virus, and the other from a clinically-sampled DNA virus. Finally, we discuss how such DFE inference may shed light on major research questions in virus evolution, ranging from a quantification of the population genetic processes governing genome size, to the role of Hill-Robertson interference in dictating adaptive outcomes, to the potential design of novel therapeutic approaches to eradicate within-patient viral populations via induced mutational meltdown.

Balancing selection for pathogen resistance reveals an intercontinental signature of Red Queen coevolution

The link between long-term host–parasite coevolution and genetic diversity is key to understanding genetic epidemiology and the evolution of resistance. The model of Red Queen host–parasite coevolution posits that high genetic diversity is maintained when rare host resistance variants have a selective advantage, which is believed to be the mechanistic basis for the extraordinarily high levels of diversity at disease-related genes such as the major histocompatibility complex in jawed vertebrates and R-genes in plants. The parasites that drive long-term coevolution are, however, often elusive. Here we present evidence for long-term balancing selection at the phenotypic (variation in resistance) and genomic (resistance locus) level in a particular host–parasite system: the planktonic crustacean Daphnia magna and the bacterium Pasteuria ramosa. The host shows widespread polymorphisms for pathogen resistance regardless of geographic distance, even though there is a clear genome-wide pattern of isolation by distance at other sites. In the genomic region of a previously identified resistance supergene, we observed consistent molecular signals of balancing selection, including higher genetic diversity, older coalescence times, and lower differentiation between populations, which set this region apart from the rest of the genome. We propose that specific long-term coevolution by negative-frequency-dependent selection drives this elevated diversity at the host's resistance loci on an intercontinental scale and provide an example of a direct link between the host’s resistance to a virulent pathogen and the large-scale diversity of its underlying genes.

Inferring genome-wide correlations of mutation fitness effects between populations

The effect of a mutation on fitness may differ between populations depending on environmental and genetic context, but little is known about the factors that underlie such differences. To quantify genome-wide correlations in mutation fitness effects, we developed a novel concept called a joint distribution of fitness effects (DFE) between populations. We then proposed a new statistic w to measure the DFE correlation between populations. Using simulation, we showed that inferring the DFE correlation from the joint allele frequency spectrum is statistically precise and robust. Using population genomic data, we inferred DFE correlations of populations in humans, Drosophila melanogaster, and wild tomatoes. In these species, we found that the overall correlation of the joint DFE was inversely related to genetic differentiation. In humans and D. melanogaster, deleterious mutations had a lower DFE correlation than tolerated mutations, indicating a complex joint DFE. Altogether, the DFE correlation can be reliably inferred, and it offers extensive insight into the genetics of population divergence.

The Impact of Purifying and Background Selection on the Inference of Population History: Problems and Prospects

Current procedures for inferring population history generally assume complete neutrality—that is, they neglect both direct selection and the effects of selection on linked sites. We here examine how the presence of direct purifying selection and background selection may bias demographic inference by evaluating two commonly-used methods (MSMC and fastsimcoal2), specifically studying how the underlying shape of the distribution of fitness effects and the fraction of directly selected sites interact with demographic parameter estimation. The results show that, even after masking functional genomic regions, background selection may cause the mis-inference of population growth under models of both constant population size and decline. This effect is amplified as the strength of purifying selection and the density of directly selected sites increases, as indicated by the distortion of the site frequency spectrum and levels of nucleotide diversity at linked neutral sites. We also show how simulated changes in background selection effects caused by population size changes can be predicted analytically. We propose a potential method for correcting for the mis-inference of population growth caused by selection. By treating the distribution of fitness effect as a nuisance parameter and averaging across all potential realizations, we demonstrate that even directly selected sites can be used to infer demographic histories with reasonable accuracy.

Population Genomics in the Great Apes

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

The impact of purifying and background selection on the inference of population history: problems and prospects

Current procedures for inferring population history generally assume complete neutrality - that is, they neglect both direct selection and the effects of selection on linked sites. We here examine how the presence of direct purifying selection and background selection may bias demographic inference by evaluating two commonly-used methods (MSMC and fastsimcoal2), specifically studying how the underlying shape of the distribution of fitness effects (DFE) and the fraction of directly selected sites interact with demographic parameter estimation. The results show that, even after masking functional genomic regions, background selection may cause the mis-inference of population growth under models of both constant population size and decline. This effect is amplified as the strength of purifying selection and the density of directly selected sites increases, as indicated by the distortion of the site frequency spectrum and levels of nucleotide diversity at linked neutral sites. We also show how simulated changes in background selection effects caused by population size changes can be predicted analytically. We propose a potential method for correcting for the mis-inference of population growth caused by selection. By treating the DFE as a nuisance parameter and averaging across all potential realizations, we demonstrate that even directly selected sites can be used to infer demographic histories with reasonable accuracy.

Imposed mutational meltdown as an antiviral strategy

Following widespread infections of the most recent coronavirus known to infect humans, SARS‐CoV‐2, attention has turned to potential therapeutic options. With no drug or vaccine yet approved, one focal point of research is to evaluate the potential value of repurposing existing antiviral treatments, with the logical strategy being to identify at least a short‐term intervention to prevent within‐patient progression, while long‐term vaccine strategies unfold. Here, we offer an evolutionary/population‐genetic perspective on one approach that may overwhelm the capacity for pathogen defense (i.e., adaptation) – induced mutational meltdown – providing an overview of key concepts, review of previous theoretical and experimental work of relevance, and guidance for future research. Applied with appropriate care, including target specificity, induced mutational meltdown may provide a general, rapidly implemented approach for the within‐patient eradication of a wide range of pathogens or other undesirable microorganisms.

Pairwise common variant meta-analyses of schizophrenia with other psychiatric disorders reveals shared and distinct gene and gene-set associations

The complex aetiology of schizophrenia is postulated to share components with other psychiatric disorders. We investigated pleiotropy amongst the common variant genomics of schizophrenia and seven other psychiatric disorders using a multimarker association test. Transcriptomic imputation was then leveraged to investigate the functional significance of variation mapped to these genes, prioritising several interesting functional candidates. Gene-based analysis of common variation revealed 67 schizophrenia-associated genes shared with other psychiatric phenotypes, including bipolar disorder, major depressive disorder, ADHD and autism-spectrum disorder. In addition, we uncovered 78 genes significantly enriched with common variant associations for schizophrenia that were not linked to any of these seven disorders (P > 0.05). Multivariable gene-set association suggested that common variation enrichment within biologically constrained genes observed for schizophrenia also occurs across several psychiatric phenotypes. Pairwise meta-analysis of schizophrenia and each psychiatric phenotype was implemented and identified 330 significantly associated genes (PMeta < 2.7 × 10-6) that were only nominally associated with each disorder individually (P < 0.05). These analyses consolidate the overlap between the genomic architecture of schizophrenia and other psychiatric disorders, uncovering several candidate pleiotropic genes which warrant further investigation.

Toward an Evolutionarily Appropriate Null Model: Jointly Inferring Demography and Purifying Selection

The question of the relative evolutionary roles of adaptive and nonadaptive processes has been a central debate in population genetics for nearly a century. While advances have been made in the theoretical development of the underlying models, and statistical methods for estimating their parameters from large-scale genomic data, a framework for an appropriate null model remains elusive. A model incorporating evolutionary processes known to be in constant operation, genetic drift (as modulated by the demographic history of the population) and purifying selection, is lacking. Without such a null model, the role of adaptive processes in shaping within- and between-population variation may not be accurately assessed. Here, we investigate how population size changes and the strength of purifying selection affect patterns of variation at "neutral" sites near functional genomic components. We propose a novel statistical framework for jointly inferring the contribution of the relevant selective and demographic parameters. By means of extensive performance analyses, we quantify the utility of the approach, identify the most important statistics for parameter estimation, and compare the results with existing methods. Finally, we reanalyze genome-wide population-level data from a Zambian population of Drosophila melanogaster, and find that it has experienced a much slower rate of population growth than was inferred when the effects of purifying selection were neglected. Our approach represents an appropriate null model, against which the effects of positive selection can be assessed.

Understanding the Factors That Shape Patterns of Nucleotide Diversity in the House Mouse Genome

A major goal of population genetics has been to determine the extent by which selection at linked sites influences patterns of neutral nucleotide diversity in the genome. Multiple lines of evidence suggest that diversity is influenced by both positive and negative selection. For example, in many species there are troughs in diversity surrounding functional genomic elements, consistent with the action of either background selection (BGS) or selective sweeps. In this study, we investigated the causes of the diversity troughs that are observed in the wild house mouse genome. Using the unfolded site frequency spectrum, we estimated the strength and frequencies of deleterious and advantageous mutations occurring in different functional elements in the genome. We then used these estimates to parameterize forward-in-time simulations of chromosomes, using realistic distributions of functional elements and recombination rate variation in order to determine whether selection at linked sites can explain the observed patterns of nucleotide diversity. The simulations suggest that BGS alone cannot explain the dips in diversity around either exons or conserved noncoding elements. A combination of BGS and selective sweeps produces deeper dips in diversity than BGS alone, but the inferred parameters of selection cannot fully explain the patterns observed in the genome. Our results provide evidence of sweeps shaping patterns of nucleotide diversity across the mouse genome and also suggest that infrequent, strongly advantageous mutations play an important role in this. The limitations of using the unfolded site frequency spectrum for inferring the frequency and effects of advantageous mutations are discussed.

What can be learned by scanning the genome for molecular convergence in wild populations?

Convergent evolution, where independent lineages evolve similar phenotypes in response to similar challenges, can provide valuable insight into how selection operates and the limitations it encounters. However, it has only recently become possible to explore how convergent evolution is reflected at the genomic level. The overlapping outlier approach (OOA), where genome scans of multiple independent lineages are used to find outliers that overlap and therefore identify convergently evolving loci, is becoming popular. Here, we present a quantitative analysis of 34 studies that used this approach across many sampling designs, taxa, and sampling intensities. We found that OOA studies with increased biological sampling power within replicates have increased likelihood of finding overlapping, "convergent" signals of adaptation between them. When identifying convergent loci as overlapping outliers, it is tempting to assume that any false-positive outliers derived from individual scans will fail to overlap across replicates, but this cannot be guaranteed. We highlight how population demographics and genomic context can contribute toward both true convergence and false positives in OOA studies. We finish with an exploration of emerging methods that couple genome scans with phenotype and environmental measures, leveraging added information from genome data to more directly test hypotheses of the likelihood of convergent evolution.

Ecological and Evolutionary Processes Shaping Viral Genetic Diversity

The contemporary genomic diversity of viruses is a result of the continuous and dynamic interaction of past ecological and evolutionary processes. Thus, genome sequences of viruses can be a valuable source of information about these processes. In this review, we first describe the relevant processes shaping viral genomic variation, with a focus on the role of host–virus coevolution and its potential to give rise to eco-evolutionary feedback loops. We further give a brief overview of available methodology designed to extract information about these processes from genomic data. Short generation times and small genomes make viruses ideal model systems to study the joint effect of complex coevolutionary and eco-evolutionary interactions on genetic evolution. This complexity, together with the diverse array of lifetime and reproductive strategies in viruses ask for extensions of existing inference methods, for example by integrating multiple information sources. Such integration can broaden the applicability of genetic inference methods and thus further improve our understanding of the role viruses play in biological communities.

Large ancestral effective population size explains the difficult phylogenetic placement of owl monkeys

The phylogenetic position of owl monkeys, grouped in the genus Aotus, has been a controversial issue for understanding Neotropical primate evolution. Explanations of the difficult phylogenetic assignment of owl monkeys have been elusive, frequently relying on insufficient data (stochastic error) or scenarios of rapid speciation (adaptive radiation) events. Using a coalescent-based approach, we explored the population-level mechanisms likely explaining these topological discrepancies. We examined the topological variance of 2,192 orthologous genes shared between representatives of the three major Cebidae lineages and the outgroup. By employing a methodological framework that allows for reticulated tree topologies, our analysis explicitly tested for non-dichotomous evolutionary processes impacting the finding of the position of owl monkeys in the cebid phylogeny. Our findings indicated that Aotus is a sister lineage of the callitrichines. Most gene trees (>50%) failed to recover the species tree topology, although the distribution of gene trees mismatching the true species topology followed the standard expectation of the multispecies coalescent without reticulation. We showed that the large effective population size of the common ancestor of Aotus and callitrichines was the most likely factor responsible for generating phylogenetic uncertainty. On the other hand, fast speciation scenarios or introgression played minor roles. We propose that the difficult phylogenetic placement of Aotus is explained by population-level processes associated with the large ancestral effective size. These results shed light on the biogeography of the early cebid diversification in the Miocene, highlighting the relevance of evaluating phylogenetic relationships employing population-aware approaches.

Local PCA Shows How the Effect of Population Structure Differs Along the Genome

Population structure leads to systematic patterns in measures of mean relatedness between individuals in large genomic data sets, which are often discovered and visualized using dimension reduction techniques such as principal component analysis (PCA). Mean relatedness is an average of the relationships across locus-specific genealogical trees, which can be strongly affected on intermediate genomic scales by linked selection and other factors. We show how to use local PCA to describe this intermediate-scale heterogeneity in patterns of relatedness, and apply the method to genomic data from three species, finding in each that the effect of population structure can vary substantially across only a few megabases. In a global human data set, localized heterogeneity is likely explained by polymorphic chromosomal inversions. In a range-wide data set of Medicago truncatula, factors that produce heterogeneity are shared between chromosomes, correlate with local gene density, and may be caused by linked selection, such as background selection or local adaptation. In a data set of primarily African Drosophila melanogaster, large-scale heterogeneity across each chromosome arm is explained by known chromosomal inversions thought to be under recent selection and, after removing samples carrying inversions, remaining heterogeneity is correlated with recombination rate and gene density, again suggesting a role for linked selection. The visualization method provides a flexible new way to discover biological drivers of genetic variation, and its application to data highlights the strong effects that linked selection and chromosomal inversions can have on observed patterns of genetic variation.

On the importance of time scales when studying adaptive evolution

Long‐term field studies coupled with quantitative genomics offer a powerful means to understand the genetic bases underlying quantitative traits and their evolutionary changes. However, analyzing and interpreting the time scales at which adaptive evolution occurs is challenging. First, while evolution is predictable in the short term, with strikingly rapid phenotypic changes in data series, it remains unpredictable in the long term. Second, while the temporal dynamics of some loci with large effects on phenotypic variation and fitness have been characterized, this task can be complicated in cases of highly polygenic trait architecture implicating numerous small effect size loci, or when statistical tests are sensitive to the heterogeneity of some key characteristics of the genome, like variation in recombination rate along the chromosomes. After introducing these aforementioned challenges, we discuss a recent investigation of the genomic architecture and spatio‐temporal variation in great tit bill length, which was related to the recent use of bird feeders. We discuss how this case study illustrates the importance of considering different temporal scales and evolutionary mechanisms both while analyzing trait temporal trends and when searching for and interpreting the signals of putative genomic footprints of selection. More generally this commentary discusses interesting challenges for unraveling the time scale at which adaptive traits evolve and their genomic bases.

High-throughput inference of pairwise coalescence times identifies signals of selection and enriched disease heritability

Interest in reconstructing demographic histories has motivated the development of methods to estimate locus-specific pairwise coalescence times from whole-genome sequence data. Here we introduce a powerful new method, ASMC, that can estimate coalescence times using only SNP array data, and is orders of magnitude faster than previous approaches. We applied ASMC to detect recent positive selection in 113,851 phased British samples from the UK Biobank, and detected 12 genome-wide significant signals, including 6 novel loci. We also applied ASMC to sequencing data from 498 Dutch individuals to detect background selection at deeper time scales. We detected strong heritability enrichment in regions of high background selection in an analysis of 20 independent diseases and complex traits using stratified LD score regression, conditioned on a broad set of functional annotations (including other background selection annotations). These results underscore the widespread effects of background selection on the genetic architecture of complex traits.

RAiSD detects positive selection based on multiple signatures of a selective sweep and SNP vectors

Selective sweeps leave distinct signatures locally in genomes, enabling the detection of loci that have undergone recent positive selection. Multiple signatures of a selective sweep are known, yet each neutrality test only identifies a single signature. We present RAiSD (Raised Accuracy in Sweep Detection), an open-source software that implements a novel, to our knowledge, and parameter-free detection mechanism that relies on multiple signatures of a selective sweep via the enumeration of SNP vectors. RAiSD achieves higher sensitivity and accuracy than the current state of the art, while the computational complexity is greatly reduced, allowing up to 1000 times faster processing than widely used tools, and negligible memory requirements.

Nikolaos Alachiotis and Pavlos Pavlidis present RAiSD, a computational method for identifying multiple signatures of selective sweeps using single nucleotide polymorphism vectors. They show that RAiSD has higher sensitivity and accuracy with reduced computational complexity than current methods.

Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection

Schizophrenia is a debilitating psychiatric condition often associated with poor quality of life and decreased life expectancy. Lack of progress in improving treatment outcomes has been attributed to limited knowledge of the underlying biology, although large-scale genomic studies have begun to provide insights. We report a new genome-wide association study of schizophrenia (11,260 cases and 24,542 controls), and through meta-analysis with existing data we identify 50 novel associated loci and 145 loci in total. Through integrating genomic fine-mapping with brain expression and chromosome conformation data, we identify candidate causal genes within 33 loci. We also show for the first time that the common variant association signal is highly enriched among genes that are under strong selective pressures. These findings provide new insights into the biology and genetic architecture of schizophrenia, highlight the importance of mutation-intolerant genes and suggest a mechanism by which common risk variants persist in the population.

Correlated patterns of genetic diversity and differentiation across an avian family

Comparative studies of closely related taxa can provide insights into the evolutionary forces that shape genome evolution and the prevalence of convergent molecular evolution. We investigated patterns of genetic diversity and differentiation in stonechats (genus Saxicola), a widely distributed avian species complex with phenotypic variation in plumage, morphology and migratory behaviour, to ask whether similar genomic regions have become differentiated in independent, but closely related, taxa. We used whole-genome pooled sequencing of 262 individuals from five taxa and found that levels of genetic diversity and divergence are strongly correlated among different stonechat taxa. We then asked whether these patterns remain correlated at deeper evolutionary scales and found that homologous genomic regions have become differentiated in stonechats and the closely related Ficedula flycatchers. Such correlation across a range of evolutionary divergence and among phylogenetically independent comparisons suggests that similar processes may be driving the differentiation of these independently evolving lineages, which in turn may be the result of intrinsic properties of particular genomic regions (e.g. areas of low recombination). Consequently, studies employing genome scans to search for areas important for reproductive isolation or adaptation should account for corresponding regions of differentiation, as these regions may not necessarily represent speciation islands or evidence of local adaptation.

Dynamics and Fate of Beneficial Mutations Under Lineage Contamination by Linked Deleterious Mutations

Beneficial mutations drive adaptive evolution, yet their selective advantage does not ensure their fixation. Haldane's application of single-type branching process theory showed that genetic drift alone could cause the extinction of newly arising beneficial mutations with high probability. With linkage, deleterious mutations will affect the dynamics of beneficial mutations and might further increase their extinction probability. Here, we model the lineage dynamics of a newly arising beneficial mutation as a multitype branching process. Our approach accounts for the combined effects of drift and the stochastic accumulation of linked deleterious mutations, which we call lineage contamination We first study the lineage-contamination phenomenon in isolation, deriving dynamics and survival probabilities (the complement of extinction probabilities) of beneficial lineages. We find that survival probability is zero when [Formula: see text] where U is deleterious mutation rate and [Formula: see text] is the selective advantage of the beneficial mutation in question, and is otherwise depressed below classical predictions by a factor bounded from below by [Formula: see text] We then put the lineage contamination phenomenon into the context of an evolving population by incorporating the effects of background selection. We find that, under the combined effects of lineage contamination and background selection, ensemble survival probability is never zero but is depressed below classical predictions by a factor bounded from below by [Formula: see text] where [Formula: see text] is mean selective advantage of beneficial mutations, and [Formula: see text] This factor, and other bounds derived from it, are independent of the fitness effects of deleterious mutations. At high enough mutation rates, lineage contamination can depress fixation probabilities to values that approach zero. This fact suggests that high mutation rates can, perhaps paradoxically, (1) alleviate competition among beneficial mutations, or (2) potentially even shut down the adaptive process. We derive critical mutation rates above which these two events become likely.

Joint Prediction of the Effective Population Size and the Rate of Fixation of Deleterious Mutations

Mutation, genetic drift, and selection are considered the main factors shaping genetic variation in nature. There is a lack, however, of general predictions accounting for the mutual interrelation between these factors. In the context of the background selection model, we provide a set of equations for the joint prediction of the effective population size and the rate of fixation of deleterious mutations, which are applicable both to sexual and asexual species. For a population of N haploid individuals and a model of deleterious mutations with effect s appearing with rate U in a genome L Morgans long, the asymptotic effective population size (Ne) and the average number of generations (T) between consecutive fixations can be approximated by [Formula: see text] and [Formula: see text] The solution is applicable to Muller's ratchet, providing satisfactory approximations to the rate of accumulation of mutations for a wide range of parameters. We also obtain predictions of the effective size accounting for the expected nucleotide diversity. Predictions for sexual populations allow for outlining the general conditions where mutational meltdown occurs. The equations can be extended to any distribution of mutational effects and the consideration of hotspots of recombination, showing that Ne is rather insensitive and not proportional to changes in N for many combinations of parameters. This could contribute to explain the observed small differences in levels of polymorphism between species with very different census sizes.

Prediction and estimation of effective population size

Effective population size (Ne) is a key parameter in population genetics. It has important applications in evolutionary biology, conservation genetics and plant and animal breeding, because it measures the rates of genetic drift and inbreeding and affects the efficacy of systematic evolutionary forces, such as mutation, selection and migration. We review the developments in predictive equations and estimation methodologies of effective size. In the prediction part, we focus on the equations for populations with different modes of reproduction, for populations under selection for unlinked or linked loci and for the specific applications to conservation genetics. In the estimation part, we focus on methods developed for estimating the current or recent effective size from molecular marker or sequence data. We discuss some underdeveloped areas in predicting and estimating Ne for future research.

A Genomic Map of the Effects of Linked Selection in Drosophila

Natural selection at one site shapes patterns of genetic variation at linked sites. Quantifying the effects of "linked selection" on levels of genetic diversity is key to making reliable inference about demography, building a null model in scans for targets of adaptation, and learning about the dynamics of natural selection. Here, we introduce the first method that jointly infers parameters of distinct modes of linked selection, notably background selection and selective sweeps, from genome-wide diversity data, functional annotations and genetic maps. The central idea is to calculate the probability that a neutral site is polymorphic given local annotations, substitution patterns, and recombination rates. Information is then combined across sites and samples using composite likelihood in order to estimate genome-wide parameters of distinct modes of selection. In addition to parameter estimation, this approach yields a map of the expected neutral diversity levels along the genome. To illustrate the utility of our approach, we apply it to genome-wide resequencing data from 125 lines in Drosophila melanogaster and reliably predict diversity levels at the 1Mb scale. Our results corroborate estimates of a high fraction of beneficial substitutions in proteins and untranslated regions (UTR). They allow us to distinguish between the contribution of sweeps and other modes of selection around amino acid substitutions and to uncover evidence for pervasive sweeps in untranslated regions (UTRs). Our inference further suggests a substantial effect of other modes of linked selection and of adaptation in particular. More generally, we demonstrate that linked selection has had a larger effect in reducing diversity levels and increasing their variance in D. melanogaster than previously appreciated.

On the relative roles of background selection and genetic hitchhiking in shaping human cytomegalovirus genetic diversity

A central focus of population genetics has been examining the contribution of selective and neutral processes in shaping patterns of intraspecies diversity. In terms of selection specifically, surveys of higher organisms have shown considerable variation in the relative contributions of background selection and genetic hitchhiking in shaping the distribution of polymorphisms, although these analyses have rarely been extended to bacteria and viruses. Here, we study the evolution of a ubiquitous, viral pathogen, human cytomegalovirus (HCMV), by analysing the relationship among intraspecies diversity, interspecies divergence and rates of recombination. We show that there is a strong correlation between diversity and divergence, consistent with expectations of neutral evolution. However, after correcting for divergence, there remains a significant correlation between intraspecies diversity and recombination rates, with additional analyses suggesting that this correlation is largely due to the effects of background selection. In addition, a small number of loci, centred on long noncoding RNAs, also show evidence of selective sweeps. These data suggest that HCMV evolution is dominated by neutral mechanisms as well as background selection, expanding our understanding of linked selection to a novel class of organisms.

The demography and population genomics of evolutionary transitions to self-fertilization in plants

The evolution of self-fertilization from outcrossing has occurred on numerous occasions in flowering plants. This shift in mating system profoundly influences the morphology, ecology, genetics and evolution of selfing lineages. As a result, there has been sustained interest in understanding the mechanisms driving the evolution of selfing and its environmental context. Recently, patterns of molecular variation have been used to make inferences about the selective mechanisms associated with mating system transitions. However, these inferences can be complicated by the action of linked selection following the transition. Here, using multilocus simulations and comparative molecular data from related selfers and outcrossers, we demonstrate that there is little evidence for strong bottlenecks associated with initial transitions to selfing, and our simulation results cast doubt on whether it is possible to infer the role of bottlenecks associated with reproductive assurance in the evolution of selfing. They indicate that the effects of background selection on the loss of diversity and efficacy of selection occur rapidly following the shift to high selfing. Future comparative studies that integrate explicit ecological and genomic details are necessary for quantifying the independent and joint effects of selection and demography on transitions to selfing and the loss of genetic diversity.

Thinking too positive? Revisiting current methods of population genetic selection inference

In the age of next-generation sequencing, the availability of increasing amounts and improved quality of data at decreasing cost ought to allow for a better understanding of how natural selection is shaping the genome than ever before. However, alternative forces, such as demography and background selection (BGS), obscure the footprints of positive selection that we would like to identify. In this review, we illustrate recent developments in this area, and outline a roadmap for improved selection inference. We argue (i) that the development and obligatory use of advanced simulation tools is necessary for improved identification of selected loci, (ii) that genomic information from multiple time points will enhance the power of inference, and (iii) that results from experimental evolution should be utilized to better inform population genomic studies.

Exome resequencing reveals signatures of demographic and adaptive processes across the genome and range of black cottonwood (Populus trichocarpa)

Extant variation in temperate and boreal plant species has been influenced by both demographic histories associated with Pleistocene glacial cycles and adaptation to local climate. We used sequence capture to investigate the role of these neutral and adaptive processes in shaping diversity in black cottonwood (Populus trichocarpa). Nucleotide diversity and Tajima's D were lowest at replacement sites and highest at intergenic sites, while LD showed the opposite pattern. With samples grouped into three populations arrayed latitudinally, effective population size was highest in the north, followed by south and centre, and LD was highest in the south followed by the north and centre, suggesting a possible northern glacial refuge. FST outlier analysis revealed that promoter, 5'-UTR and intronic sites were enriched for outliers compared with coding regions, while no outliers were found among intergenic sites. Codon usage bias was evident, and genes with synonymous outliers had 30% higher average expression compared with genes containing replacement outliers. These results suggest divergent selection related to regulation of gene expression is important to local adaptation in P. trichocarpa. Finally, within-population selective sweeps were much more pronounced in the central population than in putative northern and southern refugia, which may reflect the different demographic histories of the populations and concomitant effects on signatures of genetic hitchhiking from standing variation.

The evolutionary genetics of the genes underlying phenotypic associations for loblolly pine (Pinus taeda, Pinaceae)

A primary goal of evolutionary genetics is to discover and explain the genetic basis of fitness-related traits and how this genetic basis evolves within natural populations. Unprecedented technological advances have fueled the discovery of genetic variants associated with ecologically relevant phenotypes in many different life forms, as well as the ability to scan genomes for deviations from selectively neutral models of evolution. Theoretically, the degree of overlap between lists of genomic regions identified using each approach is related to the genetic architecture of fitness-related traits and the strength and type of natural selection molding variation at these traits within natural populations. Here we address for the first time in a plant the degree of overlap between these lists, using patterns of nucleotide diversity and divergence for >7000 unique amplicons described from the extensive expressed sequence tag libraries generated for loblolly pine (Pinus taeda L.) in combination with the >1000 published genetic associations. We show that loci associated with phenotypic traits are distinct with regard to neutral expectations. Phenotypes measured at the whole plant level (e.g., disease resistance) exhibit an approximately twofold increase in the proportion of adaptive nonsynonymous substitutions over the genome-wide average. As expected for polygenic traits, these signals were apparent only when loci were considered at the level of functional sets. The ramifications of this result are discussed in light of the continued efforts to dissect the genetic basis of quantitative traits.

Distortions in genealogies due to purifying selection and recombination

Purifying selection at many linked sites alters patterns of molecular evolution, reducing overall diversity and distorting the shapes of genealogies. Recombination attenuates these effects; however, purifying selection can significantly distort genealogies even for substantial recombination rates. Here, we show that when selection and/or recombination are sufficiently strong, the genealogy at any single site can be described by a time-dependent effective population size, Ne(t), which has a simple analytic form. Our results illustrate how recombination reduces distortions in genealogies and allow us to quantitatively describe the shapes of genealogies in the presence of strong purifying selection and recombination. We also analyze the effects of a distribution of selection coefficients across the genome.