Evaluating power to detect recurrent selective sweeps under increasingly realistic evolutionary null models

Inferring demographic and selective histories from population genomic data using a 2-step approach in species with coding-sparse genomes: an application to human data

The demographic history of a population, and the distribution of fitness effects (DFE) of newly arising mutations in functional genomic regions, are fundamental factors dictating both genetic variation and evolutionary trajectories. Although both demographic and DFE inference has been performed extensively in humans, these approaches have generally either been limited to simple demographic models involving a single population, or, where a complex population history has been inferred, without accounting for the potentially confounding effects of selection at linked sites. Taking advantage of the coding-sparse nature of the genome, we propose a 2-step approach in which coalescent simulations are first used to infer a complex multi-population demographic model, utilizing large non-functional regions that are likely free from the effects of background selection. We then use forward-in-time simulations to perform DFE inference in functional regions, conditional on the complex demography inferred and utilizing expected background selection effects in the estimation procedure. Throughout, recombination and mutation rate maps were used to account for the underlying empirical rate heterogeneity across the human genome. Importantly, within this framework it is possible to utilize and fit multiple aspects of the data, and this inference scheme represents a generalized approach for such large-scale inference in species with coding-sparse genomes.

Inferring the Demographic History of Aye-Ayes (Daubentonia madagascariensis) from High-Quality, Whole-Genome, Population-Level Data

The nocturnal aye-aye, Daubentonia madagascariensis, is one of the most elusive lemurs on the island of Madagascar. The timing of its activity and arboreal lifestyle has generally made it difficult to obtain accurate assessments of population size using traditional census methods. Therefore, alternative estimates provided by population genetic inference are essential for yielding much needed information for conservation measures and for enabling ecological and evolutionary studies of this species. Here, we utilize genomic data from 17 individuals-including 5 newly sequenced, high-coverage genomes-to estimate this history. Essential to this estimation are recently published annotations of the aye-aye genome which allow for variation at putatively neutral genomic regions to be included in the estimation procedures, and regions subject to selective constraints, or in linkage to such sites, to be excluded owing to the biasing effects of selection on demographic inference. By comparing a variety of demographic estimation tools to develop a well-supported model of population history, we find strong support for two demes, separating northern Madagascar from the rest of the island. Additionally, we find that the aye-aye has experienced two severe reductions in population size. The first occurred rapidly, ∼3,000 to 5,000 years ago, and likely corresponded with the arrival of humans to Madagascar. The second occurred over the past few decades and is likely related to substantial habitat loss, suggesting that the species is still undergoing population decline and remains at great risk for extinction.

Estimating the distribution of fitness effects in aye-ayes ( Daubentonia madagascariensis ), accounting for population history as well as mutation and recombination rate heterogeneity

The distribution of fitness effects (DFE) characterizes the range of selection coefficients from which new mutations are sampled, and thus holds a fundamentally important role in evolutionary genomics. To date, DFE inference in primates has been largely restricted to haplorrhines, with limited data availability leaving the other suborder of primates, strepsirrhines, largely under-explored. To advance our understanding of the population genetics of this important taxonomic group, we here map exonic divergence in aye-ayes ( Daubentonia madagascariensis ) - the only extant member of the Daubentoniidae family of the Strepsirrhini suborder. We further infer the DFE in this highly-endangered species, utilizing a recently published high-quality annotated reference genome, a well-supported model of demographic history, as well as both direct and indirect estimates of underlying mutation and recombination rates. The inferred distribution is generally characterized by a greater proportion of deleterious mutations relative to humans, providing evidence of a larger long-term effective population size. In addition however, both immune-related and sensory-related genes were found to be amongst the most rapidly evolving in the aye-aye genome.

Inferring fine-scale mutation and recombination rate maps in aye-ayes ( Daubentonia madagascariensis )

The rate of input of new genetic mutations, and the rate at which that variation is reshuffled, are key evolutionary processes shaping genomic diversity. Importantly, these rates vary not just across populations and species, but also across individual genomes. Despite previous studies having demonstrated that failing to account for rate heterogeneity across the genome can bias the inference of both selective and neutral population genetic processes, mutation and recombination rate maps have to date only been generated for a relatively small number of organisms. Here, we infer such fine-scale maps for the aye-aye ( Daubentonia madagascariensis ) - a highly endangered strepsirrhine that represents one of the earliest splits in the primate clade, and thus stands as an important outgroup to the more commonly-studied haplorrhines - utilizing a recently released fully-annotated genome combined with high-quality population sequencing data. We compare our indirectly inferred rates to previous pedigree-based estimates, finding further evidence of relatively low mutation and recombination rates in aye-ayes compared to other primates.

iHDSel software: The price equation and the population stability index to detect genomic patterns compatible with selective sweeps. An example with SARS-CoV-2

A large number of methods have been developed and continue to evolve for detecting the signatures of selective sweeps in genomes. Significant advances have been made, including the combination of different statistical strategies and the incorporation of artificial intelligence (machine learning) methods. Despite these advances, several common problems persist, such as the unknown null distribution of the statistics used, necessitating simulations and resampling to assign significance to the statistics. Additionally, it is not always clear how deviations from the specific assumptions of each method might affect the results. In this work, allelic classes of haplotypes are used along with the informational interpretation of the Price equation to design a statistic with a known distribution that can detect genomic patterns caused by selective sweeps. The statistic consists of Jeffreys divergence, also known as the population stability index, applied to the distribution of allelic classes of haplotypes in two samples. Results with simulated data show optimal performance of the statistic in detecting divergent selection. Analysis of real severe acute respiratory syndrome coronavirus 2 genome data also shows that some of the sites playing key roles in the virus's fitness and immune escape capability are detected by the method. The new statistic, called JHAC , is incorporated into the iHDSel (informed HacDivSel) software available at https://acraaj.webs.uvigo.es/iHDSel.html.

Inferring demographic and selective histories from population genomic data using a two-step approach in species with coding-sparse genomes: an application to human data

The demographic history of a population, and the distribution of fitness effects (DFE) of newly arising mutations in functional genomic regions, are fundamental factors dictating both genetic variation and evolutionary trajectories. Although both demographic and DFE inference has been performed extensively in humans, these approaches have generally either been limited to simple demographic models involving a single population, or, where a complex population history has been inferred, without accounting for the potentially confounding effects of selection at linked sites. Taking advantage of the coding-sparse nature of the genome, we propose a 2-step approach in which coalescent simulations are first used to infer a complex multi-population demographic model, utilizing large non-functional regions that are likely free from the effects of background selection. We then use forward-in-time simulations to perform DFE inference in functional regions, conditional on the complex demography inferred and utilizing expected background selection effects in the estimation procedure. Throughout, recombination and mutation rate maps were used to account for the underlying empirical rate heterogeneity across the human genome. Importantly, within this framework it is possible to utilize and fit multiple aspects of the data, and this inference scheme represents a generalized approach for such large-scale inference in species with coding-sparse genomes.

Inferring the demographic history of aye-ayes (Daubentonia madagascariensis) from high-quality, whole-genome, population-level data

The nocturnal aye-aye, Daubentonia madagascariensis, is one of the most elusive lemurs on the island of Madagascar. The timing of its activity and arboreal lifestyle has generally made it difficult to obtain accurate assessments of population size using traditional census methods. Therefore, alternative estimates provided by population genetic inference are essential for yielding much needed information for conservation measures and for enabling ecological and evolutionary studies of this species. Here, we utilize genomic data from 17 unrelated individuals - including 5 newly sequenced, high-coverage genomes - to estimate this history. Essential to this estimation are recently published annotations of the aye-aye genome which allow for variation at putatively neutral genomic regions to be included in the estimation procedures, and regions subject to selective constraints, or in linkage to such sites, to be excluded owing to the biasing effects of selection on demographic inference. By comparing a variety of demographic estimation tools to develop a well-supported model of population history, we find strong support for the species to consist of two demes, separating northern Madagascar from the rest of the island. Additionally, we find that the aye-aye has experienced two severe reductions in population size. The first occurred rapidly, approximately 3,000 to 5,000 years ago, and likely corresponded with the arrival of humans to Madagascar. The second occurred over the past few decades and is likely related to substantial habitat loss, suggesting that the species is still undergoing population decline and remains at great risk for extinction.

A whole-genome scan for evidence of recent positive and balancing selection in aye-ayes (Daubentonia madagascariensis) utilizing a well-fit evolutionary baseline model

The aye-aye (Daubentonia madagascariensis) is one of the 25 most endangered primate species in the world, maintaining amongst the lowest genetic diversity of any primate measured to date. Characterizing patterns of genetic variation within aye-aye populations, and the relative influences of neutral and selective processes in shaping that variation, is thus important for future conservation efforts. In this study, we performed the first whole-genome scans for recent positive and balancing selection in the species, utilizing high-coverage population genomic data from newly sequenced individuals. We generated null thresholds for our genomic scans by creating an evolutionarily appropriate baseline model that incorporates the demographic history of this aye-aye population, and identified a small number of candidate genes. Most notably, a suite of genes involved in olfaction - a key trait in these nocturnal primates - were identified as experiencing long-term balancing selection. We also conducted analyses to quantify the expected statistical power to detect positive and balancing selection in this population using site frequency spectrum-based inference methods, once accounting for the potentially confounding contributions of population history, recombination and mutation rate variation, and purifying and background selection. This work, presenting the first high-quality, genome-wide polymorphism data across the functional regions of the aye-aye genome, thus provides important insights into the landscape of episodic selective forces in this highly endangered species.

Genomes of Microtus Rodents Highlight the Importance of Olfactory and Immune Systems in Their Fast Radiation

The characterization of genes and biological functions underlying functional diversification and the formation of species is a major goal of evolutionary biology. In this study, we investigated the fast radiation of Microtus voles, one of the most speciose group of mammals, which shows strong genetic divergence despite few readily observable morphological differences. We produced an annotated reference genome for the common vole, Microtus arvalis, and resequenced the genomes of 10 different species and evolutionary lineages spanning the Microtus speciation continuum. Our full-genome sequences illustrate the recent and fast diversification of this group, and we identified genes in highly divergent genomic windows that have likely particular roles in their radiation. We found three biological functions enriched for highly divergent genes in most Microtus species and lineages: olfaction, immunity and metabolism. In particular, olfaction-related genes (mostly olfactory receptors and vomeronasal receptors) are fast evolving in all Microtus species indicating the exceptional importance of the olfactory system in the evolution of these rodents. Of note is e.g. the shared signature among vole species on Olfr1019 which has been associated with fear responses against predator odors in rodents. Our analyses provide a genome-wide basis for the further characterization of the ecological factors and processes of natural and sexual selection that have contributed to the fast radiation of Microtus voles.

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).

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 endeavor; 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 modeled, 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 before 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 modeled in downstream inference.

Population genomics provides insights into the genetic diversity and adaptation of the Pieris rapae in China

The cabbage white butterfly (Pieris rapae), a major agricultural pest, has become one of the most abundant and destructive butterflies in the world. It is widely distributed in a large variety of climates and terrains of China due to its strong adaptability. To gain insight into the population genetic characteristics of P. rapae in China, we resequenced the genome of 51 individuals from 19 areas throughout China. Using population genomics approaches, a dense variant map of P. rapae was observed, indicating a high level of polymorphism that could result in adaptation to a changing environment. The feature of the genetic structure suggested considerable genetic admixture in different geographical groups. Additionally, our analyses suggest that physical barriers may have played a more important role than geographic distance in driving genetic differentiation. Population history showed the effective population size of P. rapae was greatly affected by global temperature changes, with mild periods (i.e., temperatures warmer than those during glaciation but not excessively hot) leading to an increase in population size. Furthermore, by comparing populations from south and north China, we have identified selected genes related to sensing temperature, growth, neuromodulation and immune response, which may reveal the genetic basis of adaptation to different environments. Our study is the first to illustrate the genetic signatures of P. rapae in China at the population genomic level, providing fundamental knowledge of the genetic diversity and adaptation of P. rapae.

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.