A population genetics-phylogenetics approach to inferring natural selection in coding sequences

Evolutionary analysis identifies an MX2 haplotype associated with natural resistance to HIV-1 infection

The protein product of the myxovirus resistance 2 (MX2) gene restricts HIV-1 and simian retroviruses. We demonstrate that MX2 evolved adaptively in mammals with distinct sites representing selection targets in distinct branches; selection mainly involved residues in loop 4, previously shown to carry antiviral determinants. Modeling data indicated that positively selected sites form a continuous surface on loop 4, which folds into two antiparallel α-helices protruding from the stalk domain. A population genetics-phylogenetics approach indicated that the coding region of MX2 mainly evolved under negative selection in the human lineage. Nonetheless, population genetic analyses demonstrated that natural selection operated on MX2 during the recent history of human populations: distinct selective events drove the frequency increase of two haplotypes in the populations of Asian and European ancestry. The Asian haplotype carries a susceptibility allele for melanoma; the European haplotype is tagged by rs2074560, an intronic variant. Analyses performed on three independent European cohorts of HIV-1-exposed seronegative individuals with different geographic origin and distinct exposure route showed that the ancestral (G) allele of rs2074560 protects from HIV-1 infection with a recessive effect (combined P = 1.55 × 10(-4)). The same allele is associated with lower in vitro HIV-1 replication and increases MX2 expression levels in response to IFN-α. Data herein exploit evolutionary information to identify a novel host determinant of HIV-1 infection susceptibility.

Effects of new mutations on fitness: insights from models and data

The rates and properties of new mutations affecting fitness have implications for a number of outstanding questions in evolutionary biology. Obtaining estimates of mutation rates and effects has historically been challenging, and little theory has been available for predicting the distribution of fitness effects (DFE); however, there have been recent advances on both fronts. Extreme-value theory predicts the DFE of beneficial mutations in well-adapted populations, while phenotypic fitness landscape models make predictions for the DFE of all mutations as a function of the initial level of adaptation and the strength of stabilizing selection on traits underlying fitness. Direct experimental evidence confirms predictions on the DFE of beneficial mutations and favors distributions that are roughly exponential but bounded on the right. A growing number of studies infer the DFE using genomic patterns of polymorphism and divergence, recovering a wide range of DFE. Future work should be aimed at identifying factors driving the observed variation in the DFE. We emphasize the need for further theory explicitly incorporating the effects of partial pleiotropy and heterogeneity in the environment on the expected DFE.

An evolutionary analysis of antigen processing and presentation across different timescales reveals pervasive selection

The antigenic repertoire presented by MHC molecules is generated by the antigen processing and presentation (APP) pathway. We analyzed the evolutionary history of 45 genes involved in APP at the inter- and intra-species level. Results showed that 11 genes evolved adaptively in mammals. Several positively selected sites involve positions of fundamental importance to the protein function (e.g. the TAP1 peptide-binding domains, the sugar binding interface of langerin, and the CD1D trafficking signal region). In CYBB, all selected sites cluster in two loops protruding into the endosomal lumen; analysis of missense mutations responsible for chronic granulomatous disease (CGD) showed the action of different selective forces on the very same gene region, as most CGD substitutions involve aminoacid positions that are conserved in all mammals. As for ERAP2, different computational methods indicated that positive selection has driven the recurrent appearance of protein-destabilizing variants during mammalian evolution. Application of a population-genetics phylogenetics approach showed that purifying selection represented a major force acting on some APP components (e.g. immunoproteasome subunits and chaperones) and allowed identification of positive selection events in the human lineage.

We also investigated the evolutionary history of APP genes in human populations by developing a new approach that uses several different tests to identify the selection target, and that integrates low-coverage whole-genome sequencing data with Sanger sequencing. This analysis revealed that 9 APP genes underwent local adaptation in human populations. Most positive selection targets are located within noncoding regions with regulatory function in myeloid cells or act as expression quantitative trait loci. Conversely, balancing selection targeted nonsynonymous variants in TAP1 and CD207 (langerin). Finally, we suggest that selected variants in PSMB10 and CD207 contribute to human phenotypes. Thus, we used evolutionary information to generate experimentally-testable hypotheses and to provide a list of sites to prioritize in follow-up analyses.

Antigen-presenting cells digest intracellular and extracellular proteins and display the resulting antigenic repertoire on cell surface molecules for recognition by T cells. This process initiates cell-mediated immune responses and is essential to detect infections. The antigenic repertoire is generated by the antigen processing and presentation pathway. Because several pathogens evade immune recognition by hampering this process, genes involved in antigen processing and presentation may represent common natural selection targets. Thus, we analyzed the evolutionary history of these genes during mammalian evolution and in the more recent history of human populations. Evolutionary analyses in mammals indicated that positive selection targeted a very high proportion of genes (24%), and revealed that many selected sites affect positions of fundamental importance to the protein function. In humans, we found different signatures of natural selection acting both on regions that are expected to regulate gene expression levels or timing and on coding variants; two human selected polymorphisms may modulate the susceptibility to Crohn's disease and to HIV-1 infection. Therefore, we provide a comprehensive evolutionary analysis of antigen processing and we show that evolutionary studies can provide useful information concerning the location and nature of functional variants, ultimately helping to clarify phenotypic differences between and within species.

Comparative population genomics: power and principles for the inference of functionality

The availability of sequenced genomes from multiple related organisms allows the detection and localization of functional genomic elements based on the idea that such elements evolve more slowly than neutral sequences. Although such comparative genomics methods have proven useful in discovering functional elements and ascertaining levels of functional constraint in the genome as a whole, here we outline limitations intrinsic to this approach that cannot be overcome by sequencing more species. We argue that it is essential to supplement comparative genomics with ultra-deep sampling of populations from closely related species to enable substantially more powerful genomic scans for functional elements. The convergence of sequencing technology and population genetics theory has made such projects feasible and has exciting implications for functional genomics.

Speciation in fungal and oomycete plant pathogens

The process of speciation, by definition, involves evolution of one or more reproductive isolating mechanisms that split a single species into two that can no longer interbreed. Determination of which processes are responsible for speciation is important yet challenging. Several studies have proposed that speciation in pathogens is heavily influenced by host-pathogen dynamics and that traits that mediate such interactions (e.g., host mobility, reproductive mode of the pathogen, complexity of the life cycle, and host specificity) must lead to reproductive isolation and ultimately affect speciation rates. In this review, we summarize the main evolutionary processes that lead to speciation of fungal and oomycete plant pathogens and provide an outline of how speciation can be studied rigorously, including novel genetic/genomic developments.

Linking great apes genome evolution across time scales using polymorphism-aware phylogenetic models

The genomes of related species contain valuable information on the history of the considered taxa. Great apes in particular exhibit variation of evolutionary patterns along their genomes. However, the great ape data also bring new challenges, such as the presence of incomplete lineage sorting and ancestral shared polymorphisms. Previous methods for genome-scale analysis are restricted to very few individuals or cannot disentangle the contribution of mutation rates and fixation biases. This represents a limitation both for the understanding of these forces as well as for the detection of regions affected by selection. Here, we present a new model designed to estimate mutation rates and fixation biases from genetic variation within and between species. We relax the assumption of instantaneous substitutions, modeling substitutions as mutational events followed by a gradual fixation. Hence, we straightforwardly account for shared ancestral polymorphisms and incomplete lineage sorting. We analyze genome-wide synonymous site alignments of human, chimpanzee, and two orangutan species. From each taxon, we include data from several individuals. We estimate mutation rates and GC-biased gene conversion intensity. We find that both mutation rates and biased gene conversion vary with GC content. We also find lineage-specific differences, with weaker fixation biases in orangutan species, suggesting a reduced historical effective population size. Finally, our results are consistent with directional selection acting on coding sequences in relation to exonic splicing enhancers.

Different selective pressures shape the evolution of Toll-like receptors in human and African great ape populations

The study of the genetic and selective landscape of immunity genes across primates can provide insight into the existing differences in susceptibility to infection observed between human and non-human primates. Here, we explored how selection has driven the evolution of a key family of innate immunity receptors, the Toll-like receptors (TLRs), in African great ape species. We sequenced the 10 TLRs in various populations of chimpanzees and gorillas, and analysed these data jointly with a human data set. We found that purifying selection has been more pervasive in great apes than in humans. Furthermore, in chimpanzees and gorillas, purifying selection has targeted TLRs irrespectively of whether they are endosomal or cell surface, in contrast to humans where strong selective constraints are restricted to endosomal TLRs. These observations suggest important differences in the relative importance of TLR-mediated pathogen sensing, such as that of recognition of flagellated bacteria by TLR5, between humans and great apes. Lastly, we used a population genetics-phylogenetics method that jointly analyses polymorphism and divergence data to detect fine-scale variation in selection pressures at specific codons within TLR genes. We identified different codons at different TLRs as being under positive selection in each species, highlighting that functional variation at these genes has conferred a selective advantage in immunity to infection to specific primate species. Overall, this study showed that the degree of selection driving the evolution of TLRs has largely differed between human and non-human primates, increasing our knowledge on their respective biological contribution to host defence in the natural setting.

Genome-wide inference of natural selection on human transcription factor binding sites

For decades, it has been hypothesized that gene regulation has had a central role in human evolution, yet much remains unknown about the genome-wide impact of regulatory mutations. Here we use whole-genome sequences and genome-wide chromatin immunoprecipitation and sequencing data to demonstrate that natural selection has profoundly influenced human transcription factor binding sites since the divergence of humans from chimpanzees 4-6 million years ago. Our analysis uses a new probabilistic method, called INSIGHT, for measuring the influence of selection on collections of short, interspersed noncoding elements. We find that, on average, transcription factor binding sites have experienced somewhat weaker selection than protein-coding genes. However, the binding sites of several transcription factors show clear evidence of adaptation. Several measures of selection are strongly correlated with predicted binding affinity. Overall, regulatory elements seem to contribute substantially to both adaptive substitutions and deleterious polymorphisms with key implications for human evolution and disease.

Frequent adaptation and the McDonald-Kreitman test

Population genomic studies have shown that genetic draft and background selection can profoundly affect the genome-wide patterns of molecular variation. We performed forward simulations under realistic gene-structure and selection scenarios to investigate whether such linkage effects impinge on the ability of the McDonald-Kreitman (MK) test to infer the rate of positive selection (α) from polymorphism and divergence data. We find that in the presence of slightly deleterious mutations, MK estimates of α severely underestimate the true rate of adaptation even if all polymorphisms with population frequencies under 50% are excluded. Furthermore, already under intermediate rates of adaptation, genetic draft substantially distorts the site frequency spectra at neutral and functional sites from the expectations under mutation-selection-drift balance. MK-type approaches that first infer demography from synonymous sites and then use the inferred demography to correct the estimation of α obtain almost the correct α in our simulations. However, these approaches typically infer a severe past population expansion although there was no such expansion in the simulations, casting doubt on the accuracy of methods that infer demography from synonymous polymorphism data. We propose a simple asymptotic extension of the MK test that yields accurate estimates of α in our simulations and should provide a fruitful direction for future studies.

Inference of natural selection from interspersed genomic elements based on polymorphism and divergence

Complete genome sequences contain valuable information about natural selection, but this information is difficult to access for short, widely scattered noncoding elements such as transcription factor binding sites or small noncoding RNAs. Here, we introduce a new computational method, called Inference of Natural Selection from Interspersed Genomically coHerent elemenTs (INSIGHT), for measuring the influence of natural selection on such elements. INSIGHT uses a generative probabilistic model to contrast patterns of polymorphism and divergence in the elements of interest with those in flanking neutral sites, pooling weak information from many short elements in a manner that accounts for variation among loci in mutation rates and coalescent times. The method is able to disentangle the contributions of weak negative, strong negative, and positive selection based on their distinct effects on patterns of polymorphism and divergence. It obtains information about divergence from multiple outgroup genomes using a general statistical phylogenetic approach. The INSIGHT model is efficiently fitted to genome-wide data using an approximate expectation maximization algorithm. Using simulations, we show that the method can accurately estimate the parameters of interest even in complex demographic scenarios, and that it significantly improves on methods based on summary statistics describing polymorphism and divergence. To demonstrate the usefulness of INSIGHT, we apply it to several classes of human noncoding RNAs and to GATA2-binding sites in the human genome.

A comparison of models to infer the distribution of fitness effects of new mutations

Knowing the distribution of fitness effects (DFE) of new mutations is important for several topics in evolutionary genetics. Existing computational methods with which to infer the DFE based on DNA polymorphism data have frequently assumed that the DFE can be approximated by a unimodal distribution, such as a lognormal or a gamma distribution. However, if the true DFE departs substantially from the assumed distribution (e.g., if the DFE is multimodal), this could lead to misleading inferences about its properties. We conducted simulations to test the performance of parametric and nonparametric discretized distribution models to infer the properties of the DFE for cases in which the true DFE is unimodal, bimodal, or multimodal. We found that lognormal and gamma distribution models can perform poorly in recovering the properties of the distribution if the true DFE is bimodal or multimodal, whereas discretized distribution models perform better. If there is a sufficient amount of data, the discretized models can detect a multimodal DFE and can accurately infer the mean effect and the average fixation probability of a new deleterious mutation. We fitted several models for the DFE of amino acid-changing mutations using whole-genome polymorphism data from Drosophila melanogaster and the house mouse subspecies Mus musculus castaneus. A lognormal DFE best explains the data for D. melanogaster, whereas we find evidence for a bimodal DFE in M. m. castaneus.

Modeling the effect of changing selective pressures on polymorphism and divergence

The most common models of sequence evolution used to make inferences about adaptation rely on the assumption that selective pressures at a site remain constant through time. Instead, one might plausibly imagine that a change in the environment renders an allele beneficial and that when it fixes, the site is now constrained-until another change in the environment occurs that affects the selective pressures at that site. With this view in mind, we introduce a simple dynamic model for the evolution of coding regions, in which non-synonymous sites alternate between being fixed for the favored allele and being neutral with respect to other alleles. We use the pruning algorithm to derive closed forms for observable patterns of polymorphism and divergence in terms of the model parameters. Using our model, estimates of the fraction of beneficial substitutions α would remain similar to those obtained from existing approaches. In this framework, however, it becomes natural to ask how often adaptive substitutions originate from previously constrained or previously neutral sites, i.e., about the source of adaptive substitutions. We show that counts of coding sites that are both polymorphic in a sample from one species and divergent between two others carry information about this parameter. We also extend the basic model to include the effects of weakly deleterious mutations and discuss the importance of assumptions about the distribution of deleterious mutations among constrained non-synonymous sites. Finally, we derive a likelihood function for the parameters and apply it to a toy example, variation data for coding regions from chromosome 2 of the Drosophila melanogaster subgroup. This modeling work underscores how restrictive assumptions about adaptation have been to date, and how further work in this area will help to reveal unexplored and yet basic characteristics of adaptation.

Population genomic scan for candidate signatures of balancing selection to guide antigen characterization in malaria parasites

Acquired immunity in vertebrates maintains polymorphisms in endemic pathogens, leading to identifiable signatures of balancing selection. To comprehensively survey for genes under such selection in the human malaria parasite Plasmodium falciparum, we generated paired-end short-read sequences of parasites in clinical isolates from an endemic Gambian population, which were mapped to the 3D7 strain reference genome to yield high-quality genome-wide coding sequence data for 65 isolates. A minority of genes did not map reliably, including the hypervariable var, rifin, and stevor families, but 5,056 genes (90.9% of all in the genome) had >70% sequence coverage with minimum read depth of 5 for at least 50 isolates, of which 2,853 genes contained 3 or more single nucleotide polymorphisms (SNPs) for analysis of polymorphic site frequency spectra. Against an overall background of negatively skewed frequencies, as expected from historical population expansion combined with purifying selection, the outlying minority of genes with signatures indicating exceptionally intermediate frequencies were identified. Comparing genes with different stage-specificity, such signatures were most common in those with peak expression at the merozoite stage that invades erythrocytes. Members of clag, PfMC-2TM, surfin, and msp3-like gene families were highly represented, the strongest signature being in the msp3-like gene PF10_0355. Analysis of msp3-like transcripts in 45 clinical and 11 laboratory adapted isolates grown to merozoite-containing schizont stages revealed surprisingly low expression of PF10_0355. In diverse clonal parasite lines the protein product was expressed in a minority of mature schizonts (<1% in most lines and ∼10% in clone HB3), and eight sub-clones of HB3 cultured separately had an intermediate spectrum of positive frequencies (0.9 to 7.5%), indicating phase variable expression of this polymorphic antigen. This and other identified targets of balancing selection are now prioritized for functional study.

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.

Weak selection and protein evolution

The "nearly neutral" theory of molecular evolution proposes that many features of genomes arise from the interaction of three weak evolutionary forces: mutation, genetic drift, and natural selection acting at its limit of efficacy. Such forces generally have little impact on allele frequencies within populations from generation to generation but can have substantial effects on long-term evolution. The evolutionary dynamics of weakly selected mutations are highly sensitive to population size, and near neutrality was initially proposed as an adjustment to the neutral theory to account for general patterns in available protein and DNA variation data. Here, we review the motivation for the nearly neutral theory, discuss the structure of the model and its predictions, and evaluate current empirical support for interactions among weak evolutionary forces in protein evolution. Near neutrality may be a prevalent mode of evolution across a range of functional categories of mutations and taxa. However, multiple evolutionary mechanisms (including adaptive evolution, linked selection, changes in fitness-effect distributions, and weak selection) can often explain the same patterns of genome variation. Strong parameter sensitivity remains a limitation of the nearly neutral model, and we discuss concave fitness functions as a plausible underlying basis for weak selection.

A second-generation assembly of the Drosophila simulans genome provides new insights into patterns of lineage-specific divergence

We create a new assembly of the Drosophila simulans genome using 142 million paired short-read sequences and previously published data for strain w⁵⁰¹. Our assembly represents a higher-quality genomic sequence with greater coverage, fewer misassemblies, and, by several indexes, fewer sequence errors. Evolutionary analysis of this genome reference sequence reveals interesting patterns of lineage-specific divergence that are different from those previously reported. Specifically, we find that Drosophila melanogaster evolves faster than D. simulans at all annotated classes of sites, including putatively neutrally evolving sites found in minimal introns. While this may be partly explained by a higher mutation rate in D. melanogaster, we also find significant heterogeneity in rates of evolution across classes of sites, consistent with historical differences in the effective population size for the two species. Also contrary to previous findings, we find that the X chromosome is evolving significantly faster than autosomes for nonsynonymous and most noncoding DNA sites and significantly slower for synonymous sites. The absence of a X/A difference for putatively neutral sites and the robustness of the pattern to Gene Ontology and sex-biased expression suggest that partly recessive beneficial mutations may comprise a substantial fraction of noncoding DNA divergence observed between species. Our results have more general implications for the interpretation of evolutionary analyses of genomes of different quality.

The role of background selection in shaping patterns of molecular evolution and variation: evidence from variability on the Drosophila X chromosome

In the putatively ancestral population of Drosophila melanogaster, the ratio of silent DNA sequence diversity for X-linked loci to that for autosomal loci is approximately one, instead of the expected "null" value of 3/4. One possible explanation is that background selection (the hitchhiking effect of deleterious mutations) is more effective on the autosomes than on the X chromosome, because of the lack of crossing over in male Drosophila. The expected effects of background selection on neutral variability at sites in the middle of an X chromosome or an autosomal arm were calculated for different models of chromosome organization and methods of approximation, using current estimates of the deleterious mutation rate and distributions of the fitness effects of deleterious mutations. The robustness of the results to different distributions of fitness effects, dominance coefficients, mutation rates, mapping functions, and chromosome size was investigated. The predicted ratio of X-linked to autosomal variability is relatively insensitive to these variables, except for the mutation rate and map length. Provided that the deleterious mutation rate per genome is sufficiently large, it seems likely that background selection can account for the observed X to autosome ratio of variability in the ancestral population of D. melanogaster. The fact that this ratio is much less than one in D. pseudoobscura is also consistent with the model's predictions, since this species has a high rate of crossing over. The results suggest that background selection may play a major role in shaping patterns of molecular evolution and variation.

Rate of amino acid substitution is influenced by the degree and conservation of male-biased transcription over 50 myr of Drosophila evolution

Sex-biased gene expression (i.e., the differential expression of genes between males and females) is common among sexually reproducing species. However, genes often differ in their sex-bias classification or degree of sex bias between species. There is also an unequal distribution of sex-biased genes (especially male-biased genes) between the X chromosome and the autosomes. We used whole-genome expression data and evolutionary rate estimates for two different Drosophilid lineages, melanogaster and obscura, spanning an evolutionary time scale of around 50 Myr to investigate the influence of sex-biased gene expression and chromosomal location on the rate of molecular evolution. In both lineages, the rate of protein evolution correlated positively with the male/female expression ratio. Genes with highly male-biased expression, genes expressed specifically in male reproductive tissues, and genes with conserved male-biased expression over long evolutionary time scales showed the fastest rates of evolution. An analysis of sex-biased gene evolution in both lineages revealed evidence for a "fast-X" effect in which the rate of evolution was greater for X-linked than for autosomal genes. This pattern was particularly pronounced for male-biased genes. Genes located on the obscura "neo-X" chromosome, which originated from a recent X-autosome fusion, showed rates of evolution that were intermediate between genes located on the ancestral X-chromosome and the autosomes. This suggests that the shift to X-linkage led to an increase in the rate of molecular evolution.