Population history and natural selection shape patterns of genetic variation in 132 genes

Identifying regions of the human genome that have been targets of natural selection will provide important insights into human evolutionary history and may facilitate the identification of complex disease genes. Although the signature that natural selection imparts on DNA sequence variation is difficult to disentangle from the effects of neutral processes such as population demographic history, selective and demographic forces can be distinguished by analyzing multiple loci dispersed throughout the genome. We studied the molecular evolution of 132 genes by comprehensively resequencing them in 24 African-Americans and 23 European-Americans. We developed a rigorous computational approach for taking into account multiple hypothesis tests and demographic history and found that while many apparent selective events can instead be explained by demography, there is also strong evidence for positive or balancing selection at eight genes in the European-American population, but none in the African-American population. Our results suggest that the migration of modern humans out of Africa into new environments was accompanied by genetic adaptations to emergent selective forces. In addition, a region containing four contiguous genes on Chromosome 7 showed striking evidence of a recent selective sweep in European-Americans. More generally, our results have important implications for mapping genes underlying complex human diseases.     

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.     

DNA水平上检测正选择方法的研究进展

达尔文的自然选择学说指出, 自然选择作用是物种进化的主要因素。而1968年Kimura提出的中性进化学说认为中性突变和随机漂变才是进化的主要动力。在接下来的30多年时间中, 人们尝试从各种角度来检测自然选择是否存在。随着DNA测序技术的发展, 大量的DNA序列信息为检验自然选择提供了丰富的数据。因为自然选择会影响DNA变异模式, 所以可以通过分析现有的DNA样本来推断过去是否发生了自然选择。另一方面, 种群历史等因素也会影响到DNA变异模式, 因此会对自然选择的检测产生干扰。文章主要介绍了中性检验基本的概念, 全面回顾了一些经典的检验方法, 并着重介绍了近几年新发展出的研究方向。     

Recent progress in polymorphism-based population genetic inference

The recent availability of whole-genome sequencing data affords tremendous power for statistical inference. With this, there has been great interest in the development of polymorphism-based approaches for the estimation of population genetic parameters. These approaches seek to estimate, for example, recently fixed or sweeping beneficial mutations, the rate of recurrent positive selection, the distribution of selection coefficients, and the demographic history of the population. Yet despite estimating similar parameters using similar data sets, results between methodologies are far from consistent. We here summarize the current state of the field, compare existing approaches, and attempt to reconcile emerging discrepancies. We also discuss the biases in selection estimators introduced by ignoring the demographic history of the population, discuss the biases in demographic estimators introduced by assuming neutrality, and highlight the important challenge to the field of achieving a true joint estimation procedure to circumvent these confounding effects.     

A practical introduction to sequentially Markovian coalescent methods for estimating demographic history from genomic data

A common goal of population genomics and molecular ecology is to reconstruct the demographic history of a species of interest. A pair of powerful tools based on the sequentially Markovian coalescent have been developed to infer past population sizes using genome sequences. These methods are most useful when sequences are available for only a limited number of genomes and when the aim is to study ancient demographic events. The results of these analyses can be difficult to interpret accurately, because doing so requires some understanding of their theoretical basis and of their sensitivity to confounding factors. In this practical review, we explain some of the key concepts underpinning the pairwise and multiple sequentially Markovian coalescent methods (PSMC and MSMC, respectively). We relate these concepts to the use and interpretation of these methods, and we explain how the choice of different parameter values by the user can affect the accuracy and precision of the inferences. Based on our survey of 100 PSMC studies and 30 MSMC studies, we describe how the two methods are used in practice. Readers of this article will become familiar with the principles, practice, and interpretation of the sequentially Markovian coalescent for inferring demographic history.     

Effects of recent population bottlenecks on reconstructing the demographic history of prairie-chickens

Current methods of DNA sequence analysis attempt to reconstruct historical patterns of population structure and growth from contemporary samples. However, these techniques may be influenced by recent population bottlenecks, which have the potential to eliminate lineages that reveal past changes in demography. One way to examine the performance of these demographic methods is to compare samples from populations before and after recent bottlenecks. We compared estimates of demographic history from populations of greater prairie-chickens (Tympanuchus cupido) before and after recent bottlenecks using four common methods (nested clade analysis [NCA], Tajima's D, mismatch distribution, and MDIV). We found that NCA did not perform well in the presence of bottleneck events, although it did recover some genetic signals associated with increased isolation and the extinction of intermediate populations. The majority of estimates for Tajima's D, including those from bottlenecked populations, were not significantly different from zero, suggesting our data conformed to neutral expectations. In contrast, mismatch distributions including the raggedness index were more likely to identify recently bottlenecked populations with this data set. Estimates of population mutation rate (theta), population divergence time (t), and time to the most recent common ancestor (TMRCA) from MDIV were similar before and after bottlenecks; however, estimates of gene flow (M) were significantly lower in a few cases following a bottleneck. These results suggest that caution should be used when assessing demographic history from contemporary data sets, as recently fragmented and bottlenecked populations may have lost lineages that affect inferences of their demographic history.     

Skyline-plot methods for estimating demographic history from nucleotide sequences

Estimation of demographic history from nucleotide sequences represents an important component of many studies in molecular ecology. For example, knowledge of a population's history can allow us to test hypotheses about the impact of climatic and anthropogenic factors. In the past, demographic analysis was typically limited to relatively simple population models, such as exponential or logistic growth. More flexible approaches are now available, including skyline-plot methods that are able to reconstruct changes in population sizes through time. This technical review focuses on these skyline-plot methods. We describe some general principles relating to sampling design and data collection. We then provide an outline of the methodological framework, which is based on coalescent theory, before tracing the development of the various skyline-plot methods and describing their key features. The performance and properties of the methods are illustrated using two simulated data sets.     

Population growth of human Y chromosomes: a study of Y chromosome microsatellites

We use variation at a set of eight human Y chromosome microsatellite loci to investigate the demographic history of the Y chromosome. Instead of assuming a population of constant size, as in most of the previous work on the Y chromosome, we consider a model which permits a period of recent population growth. We show that for most of the populations in our sample this model fits the data far better than a model with no growth. We estimate the demographic parameters of this model for each population and also the time to the most recent common ancestor. Since there is some uncertainty about the details of the microsatellite mutation process, we consider several plausible mutation schemes and estimate the variance in mutation size simultaneously with the demographic parameters of interest. Our finding of a recent common ancestor (probably in the last 120,000 years), coupled with a strong signal of demographic expansion in all populations, suggests either a recent human expansion from a small ancestral population, or natural selection acting on the Y chromosome.     

Molecular population genetics and the search for adaptive evolution in plants

The first papers on plant molecular population genetics were published approximately 10 years ago. Since that time, well over 50 additional studies of plant nucleotide polymorphism have been published, and many of these studies focused on detecting the signature of balancing or positive selection at a locus. In this review, we discuss some of the theoretical and statistical issues surrounding the detection of selection, with focus on plant populations, and we also summarize the empirical plant molecular population genetics literature. At face value, the literature suggests that a history of balancing or positive selection in plant genes is rampant. In two well-studied taxa (maize and Arabidopsis) over 20% of studied genes have been interpreted as containing the signature of selection. We argue that this is probably an overstatement of the prevalence of natural selection in plant genomes, for two reasons. First, demographic effects are difficult to incorporate and have generally not been well integrated into the plant population genetics literature. Second, the genes studied to date are not a random sample, so selected genes may be overrepresented. The next generation of studies in plant molecular population genetics requires additional sampling of local populations, explicit comparisons among loci, and improved theoretical methods to control for demography. Eventually, candidate loci should be confirmed by explicit consideration of phenotypic effects.     

Natural selection in the water: freshwater invasion and adaptation by water colour in the Amazonian pufferfish

Natural selection and ecological adaptation are ultimately responsible for much of the origin of biodiversity. Yet, the identification of divergent natural selection has been hindered by the spatial complexity of natural systems, the difficulty in identifying genes under selection and their relationship to environment, and the confounding genomic effects of time. Here, we employed genome scans, population genetics and sequence-based phylogeographic methods to identify divergent natural selection on population boundaries in a freshwater invader, the Amazonian pufferfish, Colomesus asellus. We sampled extensively across markedly different hydrochemical settings in the Amazon Basin and use 'water colour' to test for ecological isolation. We distinguish the relative contribution of natural selection across hydrochemical gradients from biogeographic history in the origin and maintenance of population boundaries within a single species and across a complex ecosystem. We show that spatially distinct population structure generated by multiple forces (i.e. water colour and vicariant biogeographic history) can be identified if the confounding effects of genetic drift have not accumulated between selective populations. Our findings have repercussions for studies aimed at identifying engines of biodiversity and assessing their temporal progression in understudied and ecologically complex tropical ecosystems.     

Interpretation of variation across marker loci as evidence of selection

Population structure and history have similar effects on the genetic diversity at all neutral loci. However, some marker loci may also have been strongly influenced by natural selection. Selection shapes genetic diversity in a locus-specific manner. If we could identify those loci that have responded to selection during the divergence of populations, then we may obtain better estimates of the parameters of population history by excluding these loci. Previous attempts were made to identify outlier loci from the distribution of sample statistics under neutral models of population structure and history. Unfortunately these methods depend on assumptions about population structure and history that usually cannot be verified. In this article, we define new population-specific parameters of population divergence and construct sample statistics that are estimators of these parameters. We then use the joint distribution of these estimators to identify outlier loci that may be subject to selection. We found that outlier loci are easier to recognize when this joint distribution is conditioned on the total number of allelic states represented in the pooled sample at each locus. This is so because the conditional distribution is less sensitive to the values of nuisance parameters.     

Beyond F<Subscript>ST</Subscript>: Analysis of population genetic data for conservation

Both the ability to generate DNA data and the variety of analytical methods for conservation genetics are expanding at an ever-increasing pace. Analytical approaches are now possible that were unthinkable even five years ago due to limitations in computational power or the availability of DNA data, and this has vastly expanded the accuracy and types of information that may be gained from population genetic data. Here we provide a guide to recently developed methods for population genetic analysis, including identification of population structure, quantification of gene flow, and inference of demographic history. We cover both allele-frequency and sequence-based approaches, with a special focus on methods relevant to conservation genetic applications. Although classical population genetic approaches such as F _st (and its derivatives) have carried the field thus far, newer, more powerful, methods can infer much more from the data, rely on fewer assumptions, and are appropriate for conservation genetic management when precise estimates are needed.     

Inferences of Demography and Selection in an African Population of Drosophila melanogaster

It remains a central problem in population genetics to infer the past action of natural selection, and these inferences pose a challenge because demographic events will also substantially affect patterns of polymorphism and divergence. Thus it is imperative to explicitly model the underlying demographic history of the population whenever making inferences about natural selection. In light of the considerable interest in adaptation in African populations of Drosophila melanogaster, which are considered ancestral to the species, we generated a large polymorphism data set representing 2.1 Mb from each of 20 individuals from a Ugandan population of D. melanogaster. In contrast to previous inferences of a simple population expansion in eastern Africa, our demographic modeling of this ancestral population reveals a strong signature of a population bottleneck followed by population expansion, which has significant implications for future demographic modeling of derived populations of this species. Taking this more complex underlying demographic history into account, we also estimate a mean X-linked region-wide rate of adaptation of 6 × 10⁻¹¹/site/generation and a mean selection coefficient of beneficial mutations of 0.0009. These inferences regarding the rate and strength of selection are largely consistent with most other estimates from D. melanogaster and indicate a relatively high rate of adaptation driven by weakly beneficial mutations.     

Inference with selection,varying population size,and evolving population structure: application of ABC to a forward–backward coalescent process with interactions

Genetic data are often used to infer demographic history and changes or detect genes under selection. Inferential methods are commonly based on models making various strong assumptions: demography and population structures are supposed a priori known, the evolution of the genetic composition of a population does not affect demography nor population structure, and there is no selection nor interaction between and within genetic strains. In this paper, we present a stochastic birth-death model with competitive interactions and asexual reproduction. We develop an inferential procedure for ecological, demographic, and genetic parameters. We first show how genetic diversity and genealogies are related to birth and death rates, and to how individuals compete within and between strains. This leads us to propose an original model of phylogenies, with trait structure and interactions, that allows multiple merging. Second, we develop an Approximate Bayesian Computation framework to use our model for analyzing genetic data. We apply our procedure to simulated data from a toy model, and to real data by analyzing the genetic diversity of microsatellites on Y-chromosomes sampled from Central Asia human populations in order to test whether different social organizations show significantly different fertilities.Subject terms: Genetic variation, Phylogenetics, Ecological genetics     

It's about time: divergence, demography, and the evolution of developmental modes in marine invertebrates

Differences in larval developmental mode are predicted to affect ecological and evolutionary processes ranging from gene flow and population bottlenecks to rates of population recovery from anthropogenic disturbance and capacity for local adaptation. The most powerful tests of these predictions use comparisons among species to ask how phylogeographic patterns are correlated with the evolution and loss of prolonged planktonic larval development. An important and largely untested assumption of these studies is that interspecific differences in population genetic structure are mainly caused by differences in dispersal and gene flow (rather than by differences in divergence times among populations or changes in effective population sizes), and that species with similar patterns of spatial genetic variation have similar underlying temporal demographic histories. Teasing apart these temporal and spatial patterns is important for understanding the causes and consequences of evolutionary changes in larval developmental mode. New analytical methods that use the coalescent history of allelic diversity can reveal these temporal patterns, test the strength of traditional population-genetic explanations for variation in spatial structure based on differences in dispersal, and identify strongly supported alternative explanations for spatial structure based on demographic history rather than on gene flow alone. We briefly review some of these recent analytical developments, and show their potential for refining ideas about the correspondence between the evolution of larval developmental mode, population demographic history, and spatial genetic variation.     

The long and winding road of evolutionary demography: preface

Fitness can be calculated using demographic parameters such as survival and fecundity, which are normally used to examine population dynamics in ecology. This concept is at the heart of Darwin's thinking on natural selection. Natural selection optimizes survival and fertility schedules through differential fitness, and these optimal schedules drive changes in population dynamics. Therefore, there must exist an interaction between ecology and evolution. One of the disciplines that focus on the interaction is "Evolutionary demography". It uses age- or stage-specific demographic parameters throughout the whole life history to explore the evolution of life histories. Data throughout the life history of a species is indispensable to study evolutionary demography. To this end, two large-scale databases of plant and animal life history are now available online, the COMPADRE Plant Matrix Database and the COMADRE Animal Matrix Database. We are now in a revolutionary era in the demographic research of plant and animal populations (including human populations). Many skills and approaches are needed to answer questions on evolutionary demography including bodies of theory and analytical toolkits. This special issue covers a wide array of subjects: (1) Demographic analysis of populations (including human populations) from the point of view of evolutionary ecology, (2) Meta-analysis using big databases of populations, (3) Eco-evolutionary studies at the population and/or community level and (4) Theoretical studies and the development of mathematical models of life history evolution. 14 collected papers are published to answer a variety of questions using original ideas, new tools, and big data.     

Deep Learning for Population Genetic Inference

Given genomic variation data from multiple individuals, computing the likelihood of complex population genetic models is often infeasible. To circumvent this problem, we introduce a novel likelihood-free inference framework by applying deep learning, a powerful modern technique in machine learning. Deep learning makes use of multilayer neural networks to learn a feature-based function from the input (e.g., hundreds of correlated summary statistics of data) to the output (e.g., population genetic parameters of interest). We demonstrate that deep learning can be effectively employed for population genetic inference and learning informative features of data. As a concrete application, we focus on the challenging problem of jointly inferring natural selection and demography (in the form of a population size change history). Our method is able to separate the global nature of demography from the local nature of selection, without sequential steps for these two factors. Studying demography and selection jointly is motivated by Drosophila, where pervasive selection confounds demographic analysis. We apply our method to 197 African Drosophila melanogaster genomes from Zambia to infer both their overall demography, and regions of their genome under selection. We find many regions of the genome that have experienced hard sweeps, and fewer under selection on standing variation (soft sweep) or balancing selection. Interestingly, we find that soft sweeps and balancing selection occur more frequently closer to the centromere of each chromosome. In addition, our demographic inference suggests that previously estimated bottlenecks for African Drosophila melanogaster are too extreme.     

A regression-based approach to selection mapping

Selection mapping applies the population genetics theory of hitchhiking to the localization of genomic regions containing genes under selection. This approach predicts that neutral loci linked to genes under positive selection will have reduced diversity due to their shared history with a selected locus, and thus, genome scans of diversity levels can be used to identify regions containing selected loci. Most previous approaches to this problem ignore the spatial genomic pattern of diversity expected under selection. The regression-based approach advocated in this paper takes into account the expected pattern of decreasing genetic diversity with increased proximity to a selected locus. Simulated data are used to examine the patterns of diversity under different scenarios, in order to assess the power of a regression-based approach to the identification of regions under selection. Application of this method to both simulated and empirical data demonstrates its potential to detect selection. In contrast to some other methods, the regression approach described in this paper can be applied to any marker type. Results also suggest that this approach may give more precise estimates of the location of the selected locus than alternative methods, although the power is slightly lower in some cases.     

Life span correlates with population dynamics in perennial herbaceous plants

Survival and fecundity are basic components of demography and therefore have a strong influence on population dynamics. These two key parameters and their relationship are crucial to understand the evolution of life histories. It remains, however, to be empirically established how life span, fecundity, and population dynamics are linked in different organism groups. We conducted a comparative study based on demographic data sets of 55 populations of 23 perennial herbs for which structured demographic models and among-year natural variation in demographic attributes were available. Life span (from 4 to 128 yr old), estimated by using an algorithm, was inversely correlated with the deviance of the population growth rate from equilibrium as well as with among-year population fluctuations. Temporal variability was greater for short-lived species than for the long-lived ones because fecundity was more variable than survival and relatively more important for population dynamics for the short-lived species. The relationship between life span and population stability suggests that selection for longevity may have played an important role in the life history evolution of plants because of its ability to buffer temporal fluctuations in population size.     

Recent and ongoing selection in the human genome

The recent availability of genome-scale genotyping data has led to the identification of regions of the human genome that seem to have been targeted by selection. These findings have increased our understanding of the evolutionary forces that affect the human genome, have augmented our knowledge of gene function and promise to increase our understanding of the genetic basis of disease. However, inferences of selection are challenged by several confounding factors, especially the complex demographic history of human populations, and concordance between studies is variable. Although such studies will always be associated with some uncertainty, steps can be taken to minimize the effects of confounding factors and improve our interpretation of their findings.