Clustered mutations in hominid genome evolution are consistent with APOBEC3G enzymatic activity.

The gradual accumulation of mutations by any of a number of mutational processes is a major driving force of divergence and evolution. Here, we investigate a potentially novel mutational process that is based on the activity of members of the AID/APOBEC family of deaminases. This gene family has been recently shown to introduce-in multiple types of cancer-enzyme-induced clusters of co-occurring somatic mutations caused by cytosine deamination. Going beyond somatic mutations, we hypothesized that APOBEC3-following its rapid expansion in primates-can introduce unique germline mutation clusters that can play a role in primate evolution. In this study, we tested this hypothesis by performing a comprehensive comparative genomic screen for APOBEC3-induced mutagenesis patterns across different hominids. We detected thousands of mutation clusters introduced along primate evolution which exhibit features that strongly fit the known patterns of APOBEC3G mutagenesis. These results suggest that APOBEC3G-induced mutations have contributed to the evolution of all genomes we studied. This is the first indication of site-directed, enzyme-induced genome evolution, which played a role in the evolution of both modern and archaic humans. This novel mutational mechanism exhibits several unique features, such as its higher tendency to mutate transcribed regions and regulatory elements and its ability to generate clusters of concurrent point mutations that all occur in a single generation. Our discovery demonstrates the exaptation of an anti-viral mechanism as a new source of genomic variation in hominids with a strong potential for functional consequences.

Indigenous Arabs are descendants of the earliest split from ancient Eurasian populations.

An open question in the history of human migration is the identity of the earliest Eurasian populations that have left contemporary descendants. The Arabian Peninsula was the initial site of the out-of-Africa migrations that occurred between 125,000 and 60,000 yr ago, leading to the hypothesis that the first Eurasian populations were established on the Peninsula and that contemporary indigenous Arabs are direct descendants of these ancient peoples. To assess this hypothesis, we sequenced the entire genomes of 104 unrelated natives of the Arabian Peninsula at high coverage, including 56 of indigenous Arab ancestry. The indigenous Arab genomes defined a cluster distinct from other ancestral groups, and these genomes showed clear hallmarks of an ancient out-of-Africa bottleneck. Similar to other Middle Eastern populations, the indigenous Arabs had higher levels of Neanderthal admixture compared to Africans but had lower levels than Europeans and Asians. These levels of Neanderthal admixture are consistent with an early divergence of Arab ancestors after the out-of-Africa bottleneck but before the major Neanderthal admixture events in Europe and other regions of Eurasia. When compared to worldwide populations sampled in the 1000 Genomes Project, although the indigenous Arabs had a signal of admixture with Europeans, they clustered in a basal, outgroup position to all 1000 Genomes non-Africans when considering pairwise similarity across the entire genome. These results place indigenous Arabs as the most distant relatives of all other contemporary non-Africans and identify these people as direct descendants of the first Eurasian populations established by the out-of-Africa migrations.

Strong Constraint on Human Genes Escaping X-Inactivation Is Modulated by their Expression Level and Breadth in Both Sexes.

In eutherian mammals, X-linked gene expression is normalized between XX females and XY males through the process of X chromosome inactivation (XCI). XCI results in silencing of transcription from one ChrX homolog per female cell. However, approximately 25% of human ChrX genes escape XCI to some extent and exhibit biallelic expression in females. The evolutionary basis of this phenomenon is not entirely clear, but high sequence conservation of XCI escapers suggests that purifying selection may directly or indirectly drive XCI escape at these loci. One hypothesis is that this signal results from contributions to developmental and physiological sex differences, but presently there is limited evidence supporting this model in humans. Another potential driver of this signal is selection for high and/or broad gene expression in both sexes, which are strong predictors of reduced nucleotide substitution rates in mammalian genes. Here, we compared purifying selection and gene expression patterns of human XCI escapers with those of X-inactivated genes in both sexes. When we accounted for the functional status of each ChrX gene's Y-linked homolog (or "gametolog"), we observed that XCI escapers exhibit greater degrees of purifying selection in the human lineage than X-inactivated genes, as well as higher and broader gene expression than X-inactivated genes across tissues in both sexes. These results highlight a significant role for gene expression in both sexes in driving purifying selection on XCI escapers, and emphasize these genes' potential importance in human disease.

Contrasting X-linked and autosomal diversity across 14 human populations.

Contrasting the genetic diversity of the human X chromosome (X) and autosomes has facilitated understanding historical differences between males and females and the influence of natural selection. Previous studies based on smaller data sets have left questions regarding how empirical patterns extend to additional populations and which forces can explain them. Here, we address these questions by analyzing the ratio of X-to-autosomal (X/A) nucleotide diversity with the complete genomes of 569 females from 14 populations. Results show that X/A diversity is similar within each continental group but notably lower in European (EUR) and East Asian (ASN) populations than in African (AFR) populations. X/A diversity increases in all populations with increasing distance from genes, highlighting the stronger impact of diversity-reducing selection on X than on the autosomes. However, relative X/A diversity (between two populations) is invariant with distance from genes, suggesting that selection does not drive the relative reduction in X/A diversity in non-Africans (0.842 ± 0.012 for EUR-to-AFR and 0.820 ± 0.032 for ASN-to-AFR comparisons). Finally, an array of models with varying population bottlenecks, expansions, and migration from the latest studies of human demographic history account for about half of the observed reduction in relative X/A diversity from the expected value of 1. They predict values between 0.91 and 0.94 for EUR-to-AFR comparisons and between 0.91 and 0.92 for ASN-to-AFR comparisons. Further reductions can be predicted by more extreme demographic events in excess of those captured by the latest studies but, in the absence of these, also by historical sex-biased demographic events or other processes.

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.

NRE: a tool for exploring neutral loci in the human genome.

BACKGROUND: Analyzing regions of the genome where genetic variation is free from the confounding effects of natural selection is essential for many population genetic studies. Several recent studies in humans have stressed the large effect of natural selection at linked neutral sites and have shown that the choice of putatively neutral regions can have a marked effect on estimates of demographic history. RESULTS: NRE (Neutral Region Explorer) provides a mechanism for the easy extraction and analysis of nearly neutral regions from the human genome. It can combine many genomic filters, including filters for selection, recombination rate, genetic distance to the nearest gene, percent overlap with annotated regions, and user-provided loci. The program implements a two-step filtering process for greater versatility, allowing users to compile a basic set of neutrality criteria, explore their effect, and use this knowledge to refine filtering. Results can be instantly downloaded in standard formats, along with summary and ranking statistics, or exported to genome browsers such as those from the 1000 Genomes and UCSC. The applicability and value of NRE are demonstrated through an example in the estimation of the ratio of chromosome X-to-autosomal effective population size using different strategies for the selection of neutral regions. CONCLUSIONS: The combined features of NRE make possible the sort of flexible, rigorous mining and analysis of neutral loci increasingly demanded by population genetic studies. NRE is available at http://nre.cb.bscb.cornell.edu.

Natural selection on functional modules, a genome-wide analysis.

Classically, the functional consequences of natural selection over genomes have been analyzed as the compound effects of individual genes. The current paradigm for large-scale analysis of adaptation is based on the observed significant deviations of rates of individual genes from neutral evolutionary expectation. This approach, which assumed independence among genes, has not been able to identify biological functions significantly enriched in positively selected genes in individual species. Alternatively, pooling related species has enhanced the search for signatures of selection. However, grouping signatures does not allow testing for adaptive differences between species. Here we introduce the Gene-Set Selection Analysis (GSSA), a new genome-wide approach to test for evidences of natural selection on functional modules. GSSA is able to detect lineage specific evolutionary rate changes in a notable number of functional modules. For example, in nine mammal and Drosophilae genomes GSSA identifies hundreds of functional modules with significant associations to high and low rates of evolution. Many of the detected functional modules with high evolutionary rates have been previously identified as biological functions under positive selection. Notably, GSSA identifies conserved functional modules with many positively selected genes, which questions whether they are exclusively selected for fitting genomes to environmental changes. Our results agree with previous studies suggesting that adaptation requires positive selection, but not every mutation under positive selection contributes to the adaptive dynamical process of the evolution of species.

Sexual selection drives weak positive selection in protamine genes and high promoter divergence, enhancing sperm competitiveness.

Phenotypic adaptations may be the result of changes in gene structure or gene regulation, but little is known about the evolution of gene expression. In addition, it is unclear whether the same selective forces may operate at both levels simultaneously. Reproductive proteins evolve rapidly, but the underlying selective forces promoting such rapid changes are still a matter of debate. In particular, the role of sexual selection in driving positive selection among reproductive proteins remains controversial, whereas its potential influence on changes in promoter regions has not been explored. Protamines are responsible for maintaining DNA in a compacted form in chromosomes in sperm and the available evidence suggests that they evolve rapidly. Because protamines condense DNA within the sperm nucleus, they influence sperm head shape. Here, we examine the influence of sperm competition upon protamine 1 and protamine 2 genes and their promoters, by comparing closely related species of Mus that differ in relative testes size, a reliable indicator of levels of sperm competition. We find evidence of positive selection in the protamine 2 gene in the species with the highest inferred levels of sperm competition. In addition, sperm competition levels across all species are strongly associated with high divergence in protamine 2 promoters that, in turn, are associated with sperm swimming speed. We suggest that changes in protamine 2 promoters are likely to enhance sperm swimming speed by making sperm heads more hydrodynamic. Such phenotypic changes are adaptive because sperm swimming speed may be a major determinant of fertilization success under sperm competition. Thus, when species have diverged recently, few changes in gene-coding sequences are found, while high divergence in promoters seems to be associated with the intensity of sexual selection.

Phylemon: a suite of web tools for molecular evolution, phylogenetics and phylogenomics.

Phylemon is an online platform for phylogenetic and evolutionary analyses of molecular sequence data. It has been developed as a web server that integrates a suite of different tools selected among the most popular stand-alone programs in phylogenetic and evolutionary analysis. It has been conceived as a natural response to the increasing demand of data analysis of many experimental scientists wishing to add a molecular evolution and phylogenetics insight into their research. Tools included in Phylemon cover a wide yet selected range of programs: from the most basic for multiple sequence alignment to elaborate statistical methods of phylogenetic reconstruction including methods for evolutionary rates analyses and molecular adaptation. Phylemon has several features that differentiates it from other resources: (i) It offers an integrated environment that enables the direct concatenation of evolutionary analyses, the storage of results and handles required data format conversions, (ii) Once an outfile is produced, Phylemon suggests the next possible analyses, thus guiding the user and facilitating the integration of multi-step analyses, and (iii) users can define and save complete pipelines for specific phylogenetic analysis to be automatically used on many genes in subsequent sessions or multiple genes in a single session (phylogenomics). The Phylemon web server is available at http://phylemon.bioinfo.cipf.es.

Use of estimated evolutionary strength at the codon level improves the prediction of disease-related protein mutations in humans.

Predicting the functional impact of protein variation is one of the most challenging problems in bioinformatics. A rapidly growing number of genome-scale studies provide large amounts of experimental data, allowing the application of rigorous statistical approaches for predicting whether a given single point mutation has an impact on human health. Up until now, existing methods have limited their source data to either protein or gene information. Novel in this work, we take advantage of both and focus on protein evolutionary information by using estimated selective pressures at the codon level. Here we introduce a new method (SeqProfCod) to predict the likelihood that a given protein variant is associated with human disease or not. Our method relies on a support vector machine (SVM) classifier trained using three sources of information: protein sequence, multiple protein sequence alignments, and the estimation of selective pressure at the codon level. SeqProfCod has been benchmarked with a large dataset of 8,987 single point mutations from 1,434 human proteins from SWISS-PROT. It achieves 82% overall accuracy and a correlation coefficient of 0.59, indicating that the estimation of the selective pressure helps in predicting the functional impact of single-point mutations. Moreover, this study demonstrates the synergic effect of combining two sources of information for predicting the functional effects of protein variants: protein sequence/profile-based information and the evolutionary estimation of the selective pressures at the codon level. The results of large-scale application of SeqProfCod over all annotated point mutations in SWISS-PROT (available for download at http://sgu.bioinfo.cipf.es/services/Omidios/; last accessed: 24 August 2007), could be used to support clinical studies.

PupaSuite: finding functional single nucleotide polymorphisms for large-scale genotyping purposes.

We have developed a web tool, PupaSuite, for the selection of single nucleotide polymorphisms (SNPs) with potential phenotypic effect, specifically oriented to help in the design of large-scale genotyping projects. PupaSuite uses a collection of data on SNPs from heterogeneous sources and a large number of pre-calculated predictions to offer a flexible and intuitive interface for selecting an optimal set of SNPs. It improves the functionality of PupaSNP and PupasView programs and implements new facilities such as the analysis of user's data to derive haplotypes with functional information. A new estimator of putative effect of polymorphisms has been included that uses evolutionary information. Also SNPeffect database predictions have been included. The PupaSuite web interface is accessible through http://pupasuite.bioinfo.cipf.es and through http://www.pupasnp.org.

From genes to functional classes in the study of biological systems.

BACKGROUND: With the popularization of high-throughput techniques, the need for procedures that help in the biological interpretation of results has increased enormously. Recently, new procedures inspired in systems biology criteria have started to be developed. RESULTS: Here we present FatiScan, a web-based program which implements a threshold-independent test for the functional interpretation of large-scale experiments that does not depend on the pre-selection of genes based on the multiple application of independent tests to each gene. The test implemented aims to directly test the behaviour of blocks of functionally related genes, instead of focusing on single genes. In addition, the test does not depend on the type of the data used for obtaining significance values, and consequently different types of biologically informative terms (gene ontology, pathways, functional motifs, transcription factor binding sites or regulatory sites from CisRed) can be applied to different classes of genome-scale studies. We exemplify its application in microarray gene expression, evolution and interactomics. CONCLUSION: Methods for gene set enrichment which, in addition, are independent from the original data and experimental design constitute a promising alternative for the functional profiling of genome-scale experiments. A web server that performs the test described and other similar ones can be found at: http://www.babelomics.org.

Selective pressures at a codon-level predict deleterious mutations in human disease genes.

Deleterious mutations affecting biological function of proteins are constantly being rejected by purifying selection from the gene pool. The non-synonymous/synonymous substitution rate ratio (omega) is a measure of selective pressure on amino acid replacement mutations for protein-coding genes. Different methods have been developed in order to predict non-synonymous changes affecting gene function. However, none has considered the estimation of selective constraints acting on protein residues. Here, we have used codon-based maximum likelihood models in order to estimate the selective pressures on the individual amino acid residues of a well-known model protein: p53. We demonstrate that the number of residues under strong purifying selection in p53 is much higher than those that are strictly conserved during the evolution of the species. In agreement with theoretical expectations, residues that have been noted to be of structural relevance, or in direct association with DNA, were among those showing the highest signals of purifying selection. Conversely, those changing according to a neutral, or nearly neutral mode of evolution, were observed to be irrelevant for protein function. Finally, using more than 40 human disease genes, we demonstrate that residues evolving under strong selective pressures (omega<0.1) are significantly associated (p<0.01) with human disease. We hypothesize that non-synonymous change on amino acids showing omega<0.1 will most likely affect protein function. The application of this evolutionary prediction at a genomic scale will provide an a priori hypothesis of the phenotypic effect of non-synonymous coding single nucleotide polymorphisms (SNPs) in the human genome.

Positive selection, relaxation, and acceleration in the evolution of the human and chimp genome.

For years evolutionary biologists have been interested in searching for the genetic bases underlying humanness. Recent efforts at a large or a complete genomic scale have been conducted to search for positively selected genes in human and in chimp. However, recently developed methods allowing for a more sensitive and controlled approach in the detection of positive selection can be employed. Here, using 13,198 genes, we have deduced the sets of genes involved in rate acceleration, positive selection, and relaxation of selective constraints in human, in chimp, and in their ancestral lineage since the divergence from murids. Significant deviations from the strict molecular clock were observed in 469 human and in 651 chimp genes. The more stringent branch-site test of positive selection detected 108 human and 577 chimp positively selected genes. An important proportion of the positively selected genes did not show a significant acceleration in rates, and similarly, many of the accelerated genes did not show significant signals of positive selection. Functional differentiation of genes under rate acceleration, positive selection, and relaxation was not statistically significant between human and chimp with the exception of terms related to G-protein coupled receptors and sensory perception. Both of these were over-represented under relaxation in human in relation to chimp. Comparing differences between derived and ancestral lineages, a more conspicuous change in trends seems to have favored positive selection in the human lineage. Since most of the positively selected genes are different under the same functional categories between these species, we suggest that the individual roles of the alternative positively selected genes may be an important factor underlying biological differences between these species.

Population genomic analysis of 962 whole genome sequences of humans reveals natural selection in non-coding regions.

Whole genome analysis in large samples from a single population is needed to provide adequate power to assess relative strengths of natural selection across different functional components of the genome. In this study, we analyzed next-generation sequencing data from 962 European Americans, and found that as expected approximately 60% of the top 1% of positive selection signals lie in intergenic regions, 33% in intronic regions, and slightly over 1% in coding regions. Several detailed functional annotation categories in intergenic regions showed statistically significant enrichment in positively selected loci when compared to the null distribution of the genomic span of ENCODE categories. There was a significant enrichment of purifying selection signals detected in enhancers, transcription factor binding sites, microRNAs and target sites, but not on lincRNA or piRNAs, suggesting different evolutionary constraints for these domains. Loci in "repressed or low activity regions" and loci near or overlapping the transcription start site were the most significantly over-represented annotations among the top 1% of signals for positive selection.

Cis-regulatory elements and human evolution.

Modification of gene regulation has long been considered an important force in human evolution, particularly through changes to cis-regulatory elements (CREs) that function in transcriptional regulation. For decades, however, the study of cis-regulatory evolution was severely limited by the available data. New data sets describing the locations of CREs and genetic variation within and between species have now made it possible to study CRE evolution much more directly on a genome-wide scale. Here, we review recent research on the evolution of CREs in humans based on large-scale genomic data sets. We consider inferences based on primate divergence, human polymorphism, and combinations of divergence and polymorphism. We then consider 'new frontiers' in this field stemming from recent research on transcriptional regulation.

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.

Evolutionary Genomics of Genes Involved in Olfactory Behavior in the Drosophila melanogaster Species Group.

Previous comparative genomic studies of genes involved in olfactory behavior in Drosophila focused only on particular gene families such as odorant receptor and/or odorant binding proteins. However, olfactory behavior has a complex genetic architecture that is orchestrated by many interacting genes. In this paper, we present a comparative genomic study of olfactory behavior in Drosophila including an extended set of genes known to affect olfactory behavior. We took advantage of the recent burst of whole genome sequences and the development of powerful statistical tools to analyze genomic data and test evolutionary and functional hypotheses of olfactory genes in the six species of the Drosophila melanogaster species group for which whole genome sequences are available. Our study reveals widespread purifying selection and limited incidence of positive selection on olfactory genes. We show that the pace of evolution of olfactory genes is mostly independent of the life cycle stage, and of the number of life cycle stages, in which they participate in olfaction. However, we detected a relationship between evolutionary rates and the position that the gene products occupy in the olfactory system, genes occupying central positions tend to be more constrained than peripheral genes. Finally, we demonstrate that specialization to one host does not seem to be associated with bursts of adaptive evolution in olfactory genes in D. sechellia and D. erecta, the two specialists species analyzed, but rather different lineages have idiosyncratic evolutionary histories in which both historical and ecological factors have been involved.

Genome-wide heterogeneity of nucleotide substitution model fit.

At a genomic scale, the patterns that have shaped molecular evolution are believed to be largely heterogeneous. Consequently, comparative analyses should use appropriate probabilistic substitution models that capture the main features under which different genomic regions have evolved. While efforts have concentrated in the development and understanding of model selection techniques, no descriptions of overall relative substitution model fit at the genome level have been reported. Here, we provide a characterization of best-fit substitution models across three genomic data sets including coding regions from mammals, vertebrates, and Drosophila (24,000 alignments). According to the Akaike Information Criterion (AIC), 82 of 88 models considered were selected as best-fit models at least in one occasion, although with very different frequencies. Most parameter estimates also varied broadly among genes. Patterns found for vertebrates and Drosophila were quite similar and often more complex than those found in mammals. Phylogenetic trees derived from models in the 95% confidence interval set showed much less variance and were significantly closer to the tree estimated under the best-fit model than trees derived from models outside this interval. Although alternative criteria selected simpler models than the AIC, they suggested similar patterns. All together our results show that at a genomic scale, different gene alignments for the same set of taxa are best explained by a large variety of different substitution models and that model choice has implications on different parameter estimates including the inferred phylogenetic trees. After taking into account the differences related to sample size, our results suggest a noticeable diversity in the underlying evolutionary process. All together, we conclude that the use of model selection techniques is important to obtain consistent phylogenetic estimates from real data at a genomic scale.

Analyses of X-linked and autosomal genetic variation in population-scale whole genome sequencing.

The ratio of genetic diversity on chromosome X to that on the autosomes is sensitive to both natural selection and demography. On the basis of whole-genome sequences of 69 females, we report that whereas this ratio increases with genetic distance from genes across populations, it is lower in Europeans than in West Africans independent of proximity to genes. This relative reduction is most parsimoniously explained by differences in demographic history without the need to invoke natural selection.