Applications of selective neutrality tests to molecular ecology

This paper reviews how statistical tests of neutrality have been used to address questions in molecular ecology are reviewed. The work consists of four major parts: a brief review of the current status of the neutral theory; a review of several particularly interesting examples of how statistical tests of neutrality have led to insight into ecological problems; a brief discussion of the pitfalls of assuming a strictly neutral model if it is false; and a discussion of some of the opportunities and problems that molecular ecologists face when using neutrality tests to study natural selection.     

Neutrality tests using DNA polymorphism from multiple samples

The polymorphism of a gene or a locus is studied with increasing frequency by multiple laboratories or the same group at different times. Such practice results in polymorphism being revealed by different samples at different regions of the locus. Tests of neutrality have been widely conducted for polymorphism data but commonly used statistical tests cannot be applied directly to such data. This article provides a procedure to conduct a neutrality test and details are given for two commonly used tests. Applying the two new tests to the chemokine-receptor gene (CCR5) in humans, we found that the hypothesis that all mutations are selectively neutral cannot explain the observed pattern of DNA polymorphism.     

Multivariate QST-FST comparisons: a neutrality test for the evolution of the g matrix in structured populations

Neutrality tests in quantitative genetics provide a statistical framework for the detection of selection on polygenic traits in wild populations. However, the existing method based on comparisons of divergence at neutral markers and quantitative traits (Q(st)-F(st)) suffers from several limitations that hinder a clear interpretation of the results with typical empirical designs. In this article, we propose a multivariate extension of this neutrality test based on empirical estimates of the among-populations (D) and within-populations (G) covariance matrices by MANOVA. A simple pattern is expected under neutrality: D = 2F(st)/(1 - F(st))G, so that neutrality implies both proportionality of the two matrices and a specific value of the proportionality coefficient. This pattern is tested using Flury's framework for matrix comparison [common principal-component (CPC) analysis], a well-known tool in G matrix evolution studies. We show the importance of using a Bartlett adjustment of the test for the small sample sizes typically found in empirical studies. We propose a dual test: (i) that the proportionality coefficient is not different from its neutral expectation [2F(st)/(1 - F(st))] and (ii) that the MANOVA estimates of mean square matrices between and among populations are proportional. These two tests combined provide a more stringent test for neutrality than the classic Q(st)-F(st) comparison and avoid several statistical problems. Extensive simulations of realistic empirical designs suggest that these tests correctly detect the expected pattern under neutrality and have enough power to efficiently detect mild to strong selection (homogeneous, heterogeneous, or mixed) when it is occurring on a set of traits. This method also provides a rigorous and quantitative framework for disentangling the effects of different selection regimes and of drift on the evolution of the G matrix. We discuss practical requirements for the proper application of our test in empirical studies and potential extensions.     

The genealogy of a sequence subject to purifying selection at multiple sites

We investigate the effect of purifying selection at multiple sites on both the shape of the genealogy and the distribution of mutations on the tree. We find that the primary effect of purifying selection on a genealogy is to shift the distribution of mutations on the tree, whereas the shape of the tree remains largely unchanged. This result is relevant to the large number of coalescent estimation procedures, which generally assume neutrality for segregating polymorphisms--applying these estimators to evolutionarily constrained sequences could lead to a significant degree of bias. We also estimate the statistical power of several neutrality tests in detecting weak to moderate purifying selection and find that the power is quite good for some parameter combinations. This result contrasts with previous studies, which predicted low statistical power because of the minor effect that weak purifying selection has on the shape of a genealogy. Finally, we investigate the effect of Hill-Robertson interference among linked deleterious mutations on patterns of molecular variation. We find that dependence among selected loci can substantially reduce the efficacy of even fairly strong purifying selection.     

Controlling false discoveries in genome scans for selection

Population differentiation (PD) and ecological association (EA) tests have recently emerged as prominent statistical methods to investigate signatures of local adaptation using population genomic data. Based on statistical models, these genomewide testing procedures have attracted considerable attention as tools to identify loci potentially targeted by natural selection. An important issue with PD and EA tests is that incorrect model specification can generate large numbers of false‐positive associations. Spurious association may indeed arise when shared demographic history, patterns of isolation by distance, cryptic relatedness or genetic background are ignored. Recent works on PD and EA tests have widely focused on improvements of test corrections for those confounding effects. Despite significant algorithmic improvements, there is still a number of open questions on how to check that false discoveries are under control and implement test corrections, or how to combine statistical tests from multiple genome scan methods. This tutorial study provides a detailed answer to these questions. It clarifies the relationships between traditional methods based on allele frequency differentiation and EA methods and provides a unified framework for their underlying statistical tests. We demonstrate how techniques developed in the area of genomewide association studies, such as inflation factors and linear mixed models, benefit genome scan methods and provide guidelines for good practice while conducting statistical tests in landscape and population genomic applications. Finally, we highlight how the combination of several well‐calibrated statistical tests can increase the power to reject neutrality, improving our ability to infer patterns of local adaptation in large population genomic data sets.     

DNA水平自然选择作用的检测

上个世纪60年代,Kimura提出的“中性进化”假说使经典的达尔文自然选择学说遭遇了前所未有的挑战。但新近的研究表明：在DNA水平,越来越多的证据支持“自然选择”的进化理论。这些研究成果得益于近年来大量群体和基因组DNA数据的积累,以及理论群体遗传学的发展。在DNA水平检测选择作用是否存在的方法包括两大类：种内多态性检验和种间差异度检验。前者以Tajima(1989)提出的D检验为代表,后者大都基于“中性条件下,种内与种间进化速率一致”的原理。这些方法以中性假说作为零假设,结合统计检验方法分析DNA数据,被称为“中性检验”。这些方法对于解决一些有关进化的基础理论问题和人类遗传学及生物信息学的深入研究都具有重要意义。本文介绍几个应用广泛的检测方法,以使国内的读者了解它们的基本思路和操作方法。     

Directional selection and the site-frequency spectrum.

In this article we explore statistical properties of the maximum-likelihood estimates (MLEs) of the selection and mutation parameters in a Poisson random field population genetics model of directional selection at DNA sites. We derive the asymptotic variances and covariance of the MLEs and explore the power of the likelihood ratio tests (LRT) of neutrality for varying levels of mutation and selection as well as the robustness of the LRT to deviations from the assumption of free recombination among sites. We also discuss the coverage of confidence intervals on the basis of two standard-likelihood methods. We find that the LRT has high power to detect deviations from neutrality and that the maximum-likelihood estimation performs very well when the ancestral states of all mutations in the sample are known. When the ancestral states are not known, the test has high power to detect deviations from neutrality for negative selection but not for positive selection. We also find that the LRT is not robust to deviations from the assumption of independence among sites.     

Statistical analysis of insect preference in two-choice experiments

Many studies of insect behavior in relation to chemicals from different plants require the use of statistical tests for determining whether attraction, repulsion, or neutrality is significant. In this paper, we propose a simple and efficient nonparametric statistical procedure for testing hypotheses about insect preference in two-choice experiments with individual insects. Three criteria are provided in order to detect the following alternatives to neutrality: (i) general attraction (or repulsion) in a sample population, (ii) strong variability of the individual responses, and (iii) slight but similar behavioral effects. Illustrations are given from two-choice experiments concerning the European corn borer (Ostrinia nubilalisHbn.) female's oviposition behavior.     

Testing neutrality in subdivided populations

Statistical tests of the neutrality hypothesis that are based on the sampling theory of Ewens (1972) require the assumption of panmixia. It is proposed that for a population comprising numerous local populations with weak gene flow among them, tests based on Ewens' theory can be applied separately to samples from each local population. At low levels of gene flow, migration acts primarily like mutation, introducing new alleles to each local population. It is shown with simulation results that, at low levels of migration, correlations in allele frequencies among demes are sufficiently small that the results from the application of Ewens' theory to each deme are statistically independent. It is also shown that, by combining the results of the tests in different demes, some statistical power to detect deviations from neutrality is gained. The method is illustrated with the application to data on a salamander species. At low levels of gene flow, population subdivision must be taken account of in testing neutrality and the proposed test provides one way to do so.     

Distinguishing positive selection from neutral evolution: boosting the performance of summary statistics

Summary statistics are widely used in population genetics, but they suffer from the drawback that no simple sufficient summary statistic exists, which captures all information required to distinguish different evolutionary hypotheses. Here, we apply boosting, a recent statistical method that combines simple classification rules to maximize their joint predictive performance. We show that our implementation of boosting has a high power to detect selective sweeps. Demographic events, such as bottlenecks, do not result in a large excess of false positives. A comparison to other neutrality tests shows that our boosting implementation performs well compared to other neutrality tests. Furthermore, we evaluated the relative contribution of different summary statistics to the identification of selection and found that for recent sweeps integrated haplotype homozygosity is very informative whereas older sweeps are better detected by Tajima's π. Overall, Watterson's was found to contribute the most information for distinguishing between bottlenecks and selection.     

Perspective: detecting adaptive molecular polymorphism: lessons from the MHC

Abstract. In the 1960s, when population geneticists first began to collect data on the amount of genetic variation in natural populations, balancing selection was invoked as a possible explanation for how such high levels of molecular variation are maintained. However, the predictions of the neutral theory of molecular evolution have since become the standard by which cases of balancing selection may be inferred. Here we review the evidence for balancing selection acting on the major histocompatibility complex (MHC) of vertebrates, a genetic system that defies many of the predictions of neutrality. We apply many widely used tests of neutrality to MHC data as a benchmark for assessing the power of these tests. These tests can be categorized as detecting selection in the current generation, over the history of populations, or over the histories of species. We find that selection is not detectable in MHC datasets in every generation, population, or every evolutionary lineage. This suggests either that selection on the MHC is heterogeneous or that many of the current neutrality tests lack sufficient power to detect the selection consistently. Additionally, we identify a potential inference problem associated with several tests of neutrality. We demonstrate that the signals of selection may be generated in a relatively short period of microevolutionary time, yet these signals may take exceptionally long periods of time to be erased in the absence of selection. This is especially true for the neutrality test based on the ratio of nonsynonymous to synonymous substitutions. Inference of the nature of the selection events that create such signals should be approached with caution. However, a combination of tests on different time scales may overcome such problems.     

Adjusting Scientific Practices to the Precautionary Principle

In most discussions of the Precautionary Principle, it is implicitly assumed that we are at a point near risk neutrality, so that the principle aims at moving away from risk neutrality in the direction of more risk-averse behavior. In this paper it is argued that actual decision-making in environmental issues is often on the opposite, risk taking, side of risk neutrality. A minimal version of the Precautionary Principle consists in moving from such a position in the direction of risk neutrality. Some methods for achieving this are discussed, such as less consensus-seeking scientific procedures, requirements that scientific committees identify less probable but serious scenarios, interpretative presumptions, and supplementary statistical measures for type II errors.     

An investigation of the statistical power of neutrality tests based on comparative and population genetic data

In this report, we investigate the statistical power of several tests of selective neutrality based on patterns of genetic diversity within and between species. The goal is to compare tests based solely on population genetic data with tests using comparative data or a combination of comparative and population genetic data. We show that in the presence of repeated selective sweeps on relatively neutral background, tests based on the d(N)/d(S) ratios in comparative data almost always have more power to detect selection than tests based on population genetic data, even if the overall level of divergence is low. Tests based solely on the distribution of allele frequencies or the site frequency spectrum, such as the Ewens-Watterson test or Tajima's D, have less power in detecting both positive and negative selection because of the transient nature of positive selection and the weak signal left by negative selection. The Hudson-Kreitman-Aguadé test is the most powerful test for detecting positive selection among the population genetic tests investigated, whereas McDonald-Kreitman test typically has more power to detect negative selection. We discuss our findings in the light of the discordant results obtained in several recently published genomic scans.     

A class of models of selectively neutral alleles

Using exchangeability as a statistical analog of neutrality, we derive a generalized sampling distribution for neutral alleles. The distribution depends upon a parameter that determines the underlying marginal distribution of the number of copies of a neutral allele and that can range from zero to infinity. The sampling model of Ewens (1972) is a special case characterized by an extreme value (0) of this parameter. Two other special cases are considered, one of which seems to be applicable to populations with a structure like that of the Yanomama Indians of South America. We then investigate the expected frequency spectra under these three special cases and discover that all three models yield a broad range of possible spectra with overlap between the special cases. We finally show that Ewens' sampling model cannot be used to construct tests of neutrality versus selection tending to maintain polymorphisms, but it can be used to construct tests of directional selection versus neutrality plus selection tending to yield polymorphic states.     

Statistical power analysis of neutrality tests under demographic expansions, contractions and bottlenecks with recombination

Several tests have been proposed to detect departures of nucleotide variability patterns from neutral expectations. However, very different kinds of evolutionary processes, such as selective events or demographic changes, can produce similar deviations from these tests, thus making interpretation difficult when a significant departure of neutrality is detected. Here we study the effects of demography and recombination upon neutrality tests by analyzing their power under sudden population expansions, sudden contractions, and bottlenecks. We evaluate tests based on the frequency spectrum of mutations and the distribution of haplotypes and explore the consequences of using incorrect estimates of the rates of recombination when testing for neutrality. We show that tests that rely on haplotype frequencies-especially Fs and ZnS, which are based, respectively, on the number of different haplotypes and on the r2 values between all pairs of polymorphic sites-are the most powerful for detecting expansions on nonrecombining genomic regions. Nevertheless, they are strongly affected by misestimations of recombination, so they should not be used when recombination levels are unknown. Instead, class I tests, particularly Tajima's D or R2, are recommended.     

Statistical Tests of Neutrality of Mutations against Population Growth, Hitchhiking and Background Selection

The main purpose of this article is to present several new statistical tests of neutrality of mutations against a class of alternative models, under which DNA polymorphisms tend to exhibit excesses of rare alleles or young mutations. Another purpose is to study the powers of existing and newly developed tests and to examine the detailed pattern of polymorphisms under population growth, genetic hitchhiking and background selection. It is found that the polymorphic patterns in a DNA sample under logistic population growth and genetic hitchhiking are very similar and that one of the newly developed tests, F(s), is considerably more powerful than existing tests for rejecting the hypothesis of neutrality of mutations. Background selection gives rise to quite different polymorphic patterns than does logistic population growth or genetic hitchhiking, although all of them show excesses of rare alleles or young mutations. We show that Fu and Li''s tests are among the most powerful tests against background selection. Implications of these results are discussed.     

Three Signatures of Adaptive Polymorphism Exemplified by Malaria-Associated Genes

Malaria has been one of the strongest selective pressures on our species. Many of the best-characterized cases of adaptive evolution in humans are in genes tied to malaria resistance. However, the complex evolutionary patterns at these genes are poorly captured by standard scans for nonneutral evolution. Here, we present three new statistical tests for selection based on population genetic patterns that are observed more than once among key malaria resistance loci. We assess these tests using forward-time evolutionary simulations and apply them to global whole-genome sequencing data from humans, and thus we show that they are effective at distinguishing selection from neutrality. Each test captures a distinct evolutionary pattern, here called Divergent Haplotypes, Repeated Shifts, and Arrested Sweeps, associated with a particular period of human prehistory. We clarify the selective signatures at known malaria-relevant genes and identify additional genes showing similar adaptive evolutionary patterns. Among our top outliers, we see a particular enrichment for genes involved in erythropoiesis and for genes previously associated with malaria resistance, consistent with a major role for malaria in shaping these patterns of genetic diversity. Polymorphisms at these genes are likely to impact resistance to malaria infection and contribute to ongoing host–parasite coevolutionary dynamics.     

Testing for neutrality in samples with sequencing errors

Many data sets one could use for population genetics contain artifactual sites, i.e., sequencing errors. Here, we first explore the impact of such errors on several common summary statistics, assuming that sequencing errors are mostly singletons. We thus show that in the presence of those errors, estimators of can be strongly biased. We further show that even with a moderate number of sequencing errors, neutrality tests based on the frequency spectrum reject neutrality. This implies that analyses of data sets with such errors will systematically lead to wrong inferences of evolutionary scenarios. To avoid to these errors, we propose two new estimators of theta that ignore singletons as well as two new tests Y and Y* that can be used to test neutrality despite sequencing errors. All in all, we show that even though singletons are ignored, these new tests show some power to detect deviations from a standard neutral model. We therefore advise the use of these new tests to strengthen conclusions in suspicious data sets.     

Impact of population structure on genetic diversity of a potential vaccine target in the canine hookworm (Ancylostoma caninum)

Ancylostoma caninum is a globally distributed canine parasitic nematode. To test whether positive selection, population structure, or both affect genetic variation at the candidate vaccine target Ancylostoma secreted protein 1 (asp-1), we have quantified the genetic variation in A. caninum at asp-1 and a mitochondrial gene, cytochrome oxidase subunit 1 (cox-1), using the statistical population analysis tools found in the SNAP Workbench. The mitochondrial gene cox-1 exhibits moderate diversity within 2 North American samples, comparable to the level of variation observed in other parasitic nematodes. The protein coding portion for the C-terminal half of asp-1 shows similar levels of genetic variation in a Wake County, North Carolina, sample as cox-1. Standard tests of neutrality provide little formal evidence for selection acting on this locus, but haplotype networks for 2 of the exon regions have significantly different topologies, consistent with different evolutionary forces shaping variation at either end of a 1.3-kilobase stretch of sequence. Evidence for gene flow among geographically distinct samples suggests that the mobility of hosts of A. caninum is an important contributing factor to the population structure of the parasite.     

Context dependence, ancestral misidentification, and spurious signatures of natural selection

Population genetic analyses often use polymorphism data from one species, and orthologous genomic sequences from closely related outgroup species. These outgroup sequences are frequently used to identify ancestral alleles at segregating sites and to compare the patterns of polymorphism and divergence. Inherent in such studies is the assumption of parsimony, which posits that the ancestral state of each single nucleotide polymorphism (SNP) is the allele that matches the orthologous site in the outgroup sequence, and that all nucleotide substitutions between species have been observed. This study tests the effect of violating the parsimony assumption when mutation rates vary across sites and over time. Using a context-dependent mutation model that accounts for elevated mutation rates at CpG dinucleotides, increased propensity for transitional versus transversional mutations, as well as other directional and contextual mutation biases estimated along the human lineage, we show (using both simulations and a theoretical model) that enough unobserved substitutions could have occurred since the divergence of human and chimpanzee to cause many statistical tests to spuriously reject neutrality. Moreover, using both the chimpanzee and rhesus macaque genomes to parsimoniously identify ancestral states causes a large fraction of the data to be removed while not completely alleviating problem. By constructing a novel model of the context-dependent mutation process, we can correct polymorphism data for the effect of ancestral misidentification using a single outgroup.