Among primates, genome-wide analysis of recent positive selection is currently limited to the human species because it requires extensive sampling of genotypic data from many individuals. The extent to which genes positively selected in human also present adaptive changes in other primates therefore remains unknown. This question is important because a gene that has been positively selected independently in the human and in other primate lineages may be less likely to be involved in human specific phenotypic changes such as dietary habits or cognitive abilities. To answer this question, we analysed heterozygous Single Nucleotide Polymorphisms (SNPs) in the genomes of single human, chimpanzee, orangutan, and macaque individuals using a new method aiming to identify selective sweeps genome-wide. We found an unexpectedly high number of orthologous genes exhibiting signatures of a selective sweep simultaneously in several primate species, suggesting the presence of hotspots of positive selection. A similar significant excess is evident when comparing genes positively selected during recent human evolution with genes subjected to positive selection in their coding sequence in other primate lineages and identified using a different test. These findings are further supported by comparing several published human genome scans for positive selection with our findings in non-human primate genomes. We thus provide extensive evidence that the co-occurrence of positive selection in humans and in other primates at the same genetic loci can be measured with only four species, an indication that it may be a widespread phenomenon. The identification of positive selection in humans alongside other primates is a powerful tool to outline those genes that were selected uniquely during recent human evolution.
An advantageous mutation spreads from generation to generation in a population until individuals that carry it, because of their higher reproductive success, completely replace those that do not. This process, commonly known as positive Darwinian selection, requires the selected mutation to induce a new non-neutral heritable phenotypic trait, and this has been shown to occur unexpectedly frequently during recent human evolution. Although the exact advantageous mutation is difficult to identify, it leaves a wider footprint on neighbouring linked neutral variation called a selective sweep. We have developed an empirical method that uses whole-genome shotgun sequences of single individuals to detect selective sweeps. By doing so, we were able to extend to chimpanzee, orangutan, and macaque individuals analyses of recent positive selection that until now were only available for human. Comparisons of genes candidates for positive selection between human and non-human primates then revealed an unexpectedly high number of cases where a selective sweep at a gene in humans is mirrored by independent positive selection at the same gene in multiple other primates. This result has future implications for understanding the nature of biological changes that underlie selective sweeps in humans.
Citation: Enard D, Depaulis F, Roest Crollius H (2010) Human and Non-Human Primate Genomes Share Hotspots of Positive Selection. PLoS Genet 6(2): e1000840. https://doi.org/10.1371/journal.pgen.1000840
Editor: Molly Przeworski, University of Chicago, United States of America
Received: July 27, 2009; Accepted: January 6, 2010; Published: February 5, 2010
Copyright: © 2010 Enard et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work is funded in part by the ACI-IMPBIO program, French Ministère de l'Enseignement Supérieur et de la Recherche ACI no 045223. DE is funded by a grant from the French Ministère de l'Enseignement Supérieur et de la Recherche. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
The respective contribution of neutral and advantageous mutations to genetic differences between species has been a pivotal question in molecular evolution for more than half a century . Until recently, results were based on typically small genetic samples leading to controversial conclusions. Only during the past decade did large genetic variation datasets make it possible to estimate reliable distributions of fitness effects  for a series of species such as drosophila  and human ,. Although estimating this distribution is not trivial under complex demographic histories and although differences remain between studies on details, different approaches now converge to conclude that a substantial proportion of non-deleterious mutations are indeed weakly to strongly advantageous –. In drosophila, it was found that between approximately 25% and 50% of amino-acid substitutions , and 20% of intergenic substitutions  may be adaptive. In human where effective population size is smaller, estimated proportions vary from 10% to 20% ,.
Such substantial proportions agree well with several scans for selective sweeps in the human genome concluding that selective sweeps are common and affect human genetic diversity , –. This may however seem contradictory with results from methods based on non-synonymous versus synonymous divergence analyses in coding sequences, such as PAML site and branch-site likelihood ratio tests for positive selection. Indeed, the PAML branch-site test 2 infers positive selection in the human lineage following divergence with chimpanzee for far fewer genes than scans for selective sweeps –, despite the fact that such scans examine a comparatively much narrower evolutionary period. However, site and branch-site tests for positive selection are generally conservative and coding sequences represent only a small part of mammalian genomes, thus explaining much of the differences between the two approaches. Despite their conservativeness, site tests for positive selection were recently able to show that hundreds of coding sequences experienced multiple rounds of positive selection during mammalian evolution . Scans for selective sweeps nevertheless capture many more adaptive events and, together with an increasing number of striking cases of parallel/convergent adaptive evolution –, suggest that the current view of the quantitative importance of positive selection acting at the same locus independently in distinct species is still underestimated.
This question is of particular interest in the context of recent human evolution. Here and in the rest of this manuscript “recent” means detectable at the genetic intraspecific variation level, as opposed to positive selection detectable at the divergence level. It is currently unknown (i) which proportion of genes were positively selected recently in human but also experienced positive selection in other primate lineages, either recently or within a more extensive evolutionary time and (ii) which genes were in contrast positively selected only in modern human. The distinction is important to unravel the plausible nature and “uniqueness” of adaptive changes that underlie selective sweeps. For example if a selective sweep is found for a gene in human, it is often tempting to first examine if this gene governs a specifically human phenotype, and if so to interpret the sweep in terms of a strictly human-specific adaptation. But knowing that orthologs of this gene are also associated with positive selection in other primates, although not excluding the possibility of a human-specific adaptation (same gene, human-specific nature of phenotypic change), might more accurately redirect interpretations on the nature of adaptation towards scenarios that are not restricted to human-specific phenotypes (same gene, similar nature of phenotypic change in human and other primates).
Here, we estimated the quantitative importance of positive selection acting on the same genes independently in human evolution and three other primate lineages, either recently or across more extensive evolutionary times (chimpanzee, orangutan and macaque). Because genome wide genotyping datasets such as those provided by the HapMap  and Perlegen  projects for human populations are not available for non-human primates, we have developed an empirical method that detects candidate selective sweeps using complete genomes of single individuals from natural populations (see Methods). We exploited the fact that alleles linked to a positively selected mutation also increase in frequency through genetic hitchhiking , which results in a loss of variants unlinked to the selected haplotypes and thus in a reduction of the surrounding genetic diversity that defines a selective sweep. Our results show that positive selection affecting the same genes independently in human and other primates is (i) a common phenomenon and (ii) is not restricted to specific functions such as defence against pathogens or reproduction.
Human selective sweeps from the point of view of two individuals
Our method is inspired by the HKA test , and contrasts the heterozygosity measured in a local genomic window with the value measured within its surrounding genomic context, while using inter species divergence to control for variable neutral mutation rate (including selective constraint). The corrected level of heterozygosity is indicated by the value of a statistic K computed for each window (0≤K≤1). The method thus exploits the localized nature of the hitchhiking effect of an adaptive mutation, and controls for natural and experimental factors known to influence the observed genetic diversity in primate genome sequences obtained by shotgun sequencing (see Methods; Text S1 and Table S1). We first validated the method using extensive forward population simulations – (Text S2, Figures S1, S2, S3), and applied the method independently to two individual human genome sequences , (respectively J. Craig Venter (CV) and James Watson (JW)). First, we find extensive overlap (24%; co-occurrence test P<10−5; see Methods) between the 2,244 and 2,193 genes identified in the respective genomes of CV and JW with K≤0.05. This is expected if the method correctly identifies genes with reduced heterozygosity due either to selective sweeps or shared demographic history. Second, genes detected with K≤0.05 when averaging both individuals include well known examples of recent positive selection in Europeans, such as the FOXP2 , OCA2-HERC2 , and SLC24A5 , loci (Figure 1, Table S2). Interestingly, we identified a sweep across the lactase locus LCT ,, in CV but not in JW, in line with the fact that the latter is heterozygous for the European lactase advantageous mutation (Figure 1), while the former is homozygous. Third, we found that candidate genes detected empirically by our method (K averaged over the two individuals scanned independently) significantly overlap with those identified by alternative approaches –, which include methods aimed at detecting partial or complete sweeps (Table 1; see Methods). Visual inspection of our data in comparison with previous scans (e.g. Williamson et al., Carlson et al. and Pickrell et al.; see Methods) in the UCSC Genome Browser provides additional examples of convergent detection of positively selected loci by different methods (Table S3). Fourth, genes located in candidate sweeps tend to be more strongly expressed in cerebellum, spleen and testes comparatively to their expression in other tissues  (Figure S4), and generally K is significantly lower for several Gene Ontology biological processes , already highlighted in previous scans for selective sweeps such as defence response or transcription ,, (Table S4). Therefore, despite the lower specificity and sensitivity expected from using data from individual genomes, the set of candidate sweeps detected by this method are highly enriched in positively selected loci (Text S2).
From left to right, each graph presents the variation of K at four known examples of selective sweeps in the European human population, namely the lactase gene LCT, FOXP2, OCA2-HERC2 and SLC24A5. The upper, middle and lower panels show genome scans (200 kb windows in steps of 10 kb) for CV, JW and the average between both individuals, respectively. The X axis indicates chromosome coordinates in megabases, the Y axis indicates the log2 of K (+0.001 to avoid null values). Dotted horizontal lines delineate the K = 0.05 threshold. Genes are highlighted in grey above the X axis, with the gene known to be affected by positive selection highlighted in black.
Co-occurring sweeps between primates reveal recent positive selection hotspots
Our method is applicable to individual genomes and was used to scan the genomes of chimpanzee, orangutan and macaque that were all sequenced from single outbred individuals ,. We first used ssahaSNP2  to identify 875,182, 1,364,646 and 2,294,239 heterozygous SNPs in chimpanzee, orangutan and macaque respectively (see Methods). In order to estimate how frequently recent positive selection has independently targeted the same gene in human and these other primate lineages, we computed the number of orthologous genes candidates for selection in human and at least one of the three other tested primates, and compared the results with random expectations. We first selected the 9,972 four-way protein coding orthologous genes tested in all primates, and identified candidate genes with K lower than threshold values ranging from 0 to 0.1 in human, chimpanzee, orangutan and macaque (see Methods; Figure 2 and Table S5). For each K threshold the number of candidates in non-human primates is higher than in human, because the use of two human individuals substantially increases specificity in this species and because non-human primate candidate genes are the sum of several scans with different window sizes. We devised a co-occurrence test that compares the observed numbers of genes found in candidate selective sweeps simultaneously in human and in one, two or three non-human primate species with the numbers expected if all genes are equally likely to experience positive selection (see Methods and Figure S5). The difference between observed and expected values thus reflects the excess or deficit of positive selection co-occurring at orthologous genes in multiple primates relative to the rate of positive selection in each primate. The ability of the test to detect an excess of co-occurrence, i.e. hotspots of positive selection, depends on three factors. The first is the “usage” frequency of a given hotspot by positive selection during evolution, which will affect the magnitude of the excess of co-occurrence. Indeed, hotspots might be used rarely enough that four species may not be sufficient to observe a significant excess. The second factor is the rate of false positive candidates in each tested species, which tend to occur randomly across genomes. False positives thus lead to underestimating the relative difference between real and random co-occurrences. However, we show that detecting hotspots of positive selection is possible even with a high rate of false positive candidates within each species (Texts S2, S3; Figure S6). The third factor is the power of the test to correctly identify, within each species, candidates for positive selection. Obviously, the lower the power the higher the risk of missing hotspots. For instance a hotspot active in three species where the power to detect positive selection events is only 30% will be identified with a power of only 2.7% (0.33). Because the second and third factors act in opposite directions, the optimal K threshold to identify the footprint of hotspots through a statistically significant excess of co-occurring candidates of positive selection is therefore not the most stringent (few false positives but low power), but the one with the best compromise between power and the rate of false positives.
The relative co-occurrence excess (observed score/expected score –1) between human and other primate candidates was measured for several upper thresholds of K ranging from 0 to 0.1. *: P≤5.10−2. **: P≤10−2. ***: P≤10−3.
Using different K thresholds and controlling for several potentially confounding factors (see below), we find that although genes in candidate sweeps are mostly specific of a given primate, genes found in candidate sweeps in human and two or more other primates are systematically in significant excess (Figure 2 and Table S6). Overall, the relative co-occurrence excess increases with lower, more stringent K thresholds, as expected if false positives partly dissipate the signal of positive selection hotspots. Although we cannot precisely estimate their rate for different K values due to the approximate nature of population simulations, we expect the false positive rate to be high when using one or two individuals to detect sweeps, and most likely always above 50% of genes identified in selective sweeps (Text S2, Figures S1, S2, S3). Notably, the excess observed at the most stringent K = 0 is slightly lower than at the second most stringent K≤0.005, which likely reflects a loss of power to detect hotspots. Such a loss of power is also observed for the most stringent realisations of our test presented throughout this analysis (see below). Finally, we also analysed a set of 70 genes identified in common in at least three out of four previous human genome scans for selective sweeps – and thus likely to have a very low rate of false positives. Strikingly, 22 of the 70 genes (31%), nearly three times more than expected (11% expected, co-occurrence test P = 2.10−3; Table S7), are also in candidate sweeps in at least two non-human primates. These results are obtained by examining a short period of recent primate evolution and by comparing only four species with few individuals. We are therefore likely to underestimate the true frequency of positive selection hotspots active in human and other primates, which could plausibly be common and thus significantly impact the biological interpretations of human selective sweeps.
The level of co-occurrence could also be in principle the consequence of the presence of coldspots of positive selection instead of hotspots, where a fraction of genes are constantly under low rates of positive selection in all the lineages studied, leading to selective sweeps concentrating on the remaining genes. However, using a simple analytical model, we show that the level of co-occurrence observed here is most likely explained by hotspots of positive selection (Text S3 and Figure S6).
Controlling for potentially confounding genomic factors
Although false positives lead to underestimating the relative excess of co-occurring candidate genes, several genomic factors known to correlate with diversity could in contrast lead to overestimate the observed excess of co-occurrences. Such factors include sequencing depth, local divergence used in the estimation of K, base composition, gene density and recombination. As expected if our method correctly controls for sequencing heterogeneity, neutral mutation rate and composition (Text S1, Table S1), these factors explain a very small fraction of the variance of K (n = 18,605; human-chimpanzee divergence: Spearman's ρ = −0.015, P = 0.04; human-orangutan divergence: ρ = 0.026, P = 3.10−4; human-macaque divergence: ρ = 0.006, P = 0.43; human base composition: ρ = −0.014, P = 0.055; sequencing depth: ρ = −0.009, P = 0.19). In contrast, correlation of K is higher with gene density and recombination (n = 18,605; recombination rate: ρ = 0.18, P<10−15; gene density: ρ = −0.07, P<10−15). In order to quantify separately the effect of these factors on the relative excess of observed co-occurring candidate genes, we divided genes into ten classes of equal sizes according to the 10-quantiles of one specific factor, and ran separate randomizations in each class during the co-occurrence test (see Methods). Only gene density and recombination have a notable yet moderate impact on the expected level of co-occurrence, in agreement with correlations observed with K (Figure 3). The effect of recombination on co-occurrence supports findings that recombination rates tend to be conserved at a scale of 100 to 1,000 kb between closely related primates ,. We may however be exaggerating the effect of recombination in these controls, because the measures of recombination rates used here may be locally underestimated in the presence of selective sweeps . As a consequence, low recombination classes tend to concentrate more candidate sweeps, thus leading to an overall inflated expected number of co-occurrences in the control. We nevertheless tested the impact of recombination and gene density simultaneously by further defining 100 different gene density/recombination combinations classes (see Methods). Although reduced, the relative excess of co-occurring candidates remains significant (Figure 2 and Figure 3).
There are 61 triplets and 11 quartets of orthologous genes that occur in putative selective sweeps simultaneously yet independently in human and two or three other primate genomes respectively (C3+C4 score = 227, black arrow). This is significantly more than expected by chance as shown by a co-occurrence test (black distribution) with 100,000 iterations, even after controlling for gene density alone (blue distribution), recombination alone (red distribution), or gene density and recombination simultaneously (green distribution) (see Methods). The dotted black distribution represents the hypothetical distribution where the observed score would still be significant at the 5% threshold.
We also tested the effect of using human-orangutan and chimpanzee-orangutan divergence instead of human-chimpanzee and chimpanzee-human divergence to compute K in human and chimpanzee, respectively. Measures of K with the two approaches show a 95% correlation coefficient, and more than 80% of genes are systematically below the same K threshold. None of the co-occurrence tests we conducted are affected, including the most compelling cases discussed below.
Selective sweeps versus positive selection in coding sequences
If a fraction of genes with a low K in multiple primates represent positive selection hotspots, then those genes may have been positively selected not only during recent primate evolution, but also for longer evolutionary times. We therefore used the PAML branch-site likelihood ratio test 2 , to analyse positive selection in orthologous coding sequences along five distinct branches of a phylogenetic tree including the four primates studied here, and using mouse as an outgroup  (see Methods). Using the co-occurrence test (Figure S5), we find a significant excess of co-occurrence between positive selection in coding sequences and thresholds of K ranging from 0 to 0.1 in human alone or human and at least one additional primate (Figure 4), thus extending our analysis to a much wider evolutionary time scale. Notably, the excess of co-occurrence increases substantially when using both more stringent K and more stringent inference of positive selection in coding sequences (with the exception of the most stringent conditions yielding slightly lower excess, again reflecting a plausible loss of power). Potentially biasing factors including recombination and gene density have no effect on this result. This therefore confirms positive selection hotspots independently from comparisons based only on recent selection, and shows that genes positively selected recently in human evolved similarly during more ancient primate evolution (Table S8).
The co-occurrence test was used between coding sequence positive selection detected with the test 2 of PAML separately in five primate phylogenetic branches one the one hand, and recent positive selection in human and at least one additional primate one the other hand (see Methods; Figure S5). Genes were considered as positively selected in specific branches if twice the log-likelihood ratio (2ΔL) obtained with test 2 was greater than an arbitrary threshold comprised between 20 and 100. *: P≤5.10−2. **: P≤10−2. ***: P≤10−3.
Non-human primates versus worldwide human populations
In order to further validate the evidence for positive selection hotspots, we first compared the results obtained with our statistic K in chimpanzee, orangutan and macaque with recently published scans for selective sweeps in seven worldwide human populations using the XP-EHH test . XP-EHH based scans of a representative set of human populations have several advantages over our own scan based only on two European individuals. First, XP-EHH shows a good power when identifying close to complete or recently completed selective sweeps even at low fixed false discovery rates ,. In line with this, XP-EHH has an excellent overlap with other scans in our comparison (Table 1). This should make the comparison of human with other primates more powerful despite a smaller absolute number of high confidence human candidates. Second, using seven human populations instead of one makes the comparison more representative. Using different K thresholds in non-human primates and increasingly high XP-EHH thresholds at genomic centres of human genes to isolate candidates, we confirm the previously observed excess of co-occurring selective sweeps candidates after controlling for recombination and gene density (Figure 5).
The co-occurrence test was applied using K in chimpanzee, orangutan and macaque to assign candidate genes on the one hand (Figure S5, group 1) and increasing XP-EHH values at genomic centers of genes tested in seven human populations on the other hand  (Figure S5, group 2). The histogram represents the relative co-occurrence excess obtained using 9,873 orthologous genes (observed score/expected score – 1) for XP-EHH values increasing from 2 to 3.5 with recombination and gene density being accounted for (see Methods). The excess of co-occurrence observed is lower for K≤0.01 than for K≤0.05, likely reflecting a loss of power to detect hotspots at the most stringent thresholds. *: co-occurrence test P≤0.05. **: P≤0.01. ***: P≤0.001.
In line with our previous observation that a comparison between recent (our test) and ancient (PAML's branch-site test 2) candidates for positive selection show an excess of co-occurring cases, we also find significant co-occurrence between worldwide human population XP-EHH candidates and PAML branch-site test 2 positive selection candidates in non-human branches of the primate tree (Figure 6). This last comparison further confirms the existence of “primate” positive selection hotspots recently active in human evolution. In particular, it does so independently of our own statistic K, and neither gene density nor recombination had an effect in this configuration of the co-occurrence test.
The co-occurrence test was applied using increasing 2ΔL values (twice the log-likelihood ratio) to assign positive selection candidate genes in chimpanzee, orangutan, macaque and human-chimpanzee branches of a primate phylogenetic tree on the one hand (see Methods; Figure S5, group 1), and increasing XP-EHH values at genomic centres of genes tested in seven human populations  (Figure S5, group 2) on the other hand. The histogram represents the relative co-occurrence excess (observed/expected – 1) for combinations of XP-EHH threshold values increasing from 2 to 3.5 and branch-site test 2 2ΔL thresholds from 20 to 100 (Methods). *: co-occurrence test P≤0.05. **: P≤0.01. ***: P≤0.001.
Functional analysis of hotspot genes
The existence of positive selection hotspots is also supported by functional gene annotations. Several typical candidates of positive selection are over-represented among the functions of the 72 genes with K≤0.05 in human and at least two other primates, including defence response, gametogenesis and forebrain development (Table 2). Importantly however, these functions are represented alongside a wide variety of other biological processes and molecular functions, which cannot be due exclusively to false positives. Indeed overrepresented functions encompass only 2.1% of all GO terms included in hotspots, making it highly unlikely that these would concentrate all true positives. Recent positive selection hotspots are thus not limited to a few specific functions, but instead cover a diverse functional repertoire .
A finer inspection of candidate hotspots within over-represented functions reveals particularly interesting cases. Among defence response candidates, we infer independent selective sweeps in human, chimpanzee and orangutan for the cluster of Toll-like receptors (TLR) 1, 6 and 10 (Figure 7 and Figure S7). These receptors are involved in the non-specific recognition of a wide variety of bacteria during the first steps of the innate immune response. Interestingly, TLR 1, 6 and 10 are three of the nine strongest candidates with K = 0 in human, chimpanzee and orangutan in our analysis (Table S6), and were recently found as a strong case of local adaptation in the northern European population ,, to which the two individuals used in the present study also belong. In addition, the role of TLR 1, 6 and 10 in the response to a broad spectrum of bacteria in multiple species is consistent with these receptors being hotspots of positive selection. Within gametogenesis SPIN1 (Figure 7 and Figure S7) codes for spindlin, a protein involved in oocyte maturation . SPIN1 is one of the top ten candidates of the European population using the XP-EHH test , and is the strongest of our 13 strongest hotspot candidates (Table S6) with K = 0 in human, chimpanzee, orangutan and macaque, and is therefore a very strong positive selection hotspot candidate. The fact that several of the best candidates from the present analysis can be found within over-represented GO functions and, most importantly, are also found as top candidates in human using other methods argues strongly in favour of positive selection hotspots. Gametogenesis candidates also include the ACVR2A and SPA17 (Figure 7) genes which both play a role during spermatogenesis ,. These examples show how multi-species comparisons may order priorities for deeper functional and evolutionary analyses among genes with positive selection during recent human evolution. More surprisingly, we found the FOXP2 gene in candidate selective sweeps in human (K = 0.049), chimpanzee (K = 0) and orangutan (K = 0.049) (Figure 7 and Figure S7). FOXP2 is an archetype of positively selected genes , interpreted in the context of human-specific phenotypes, in this case linguistic processing. Yet our data suggests that positive selection on FOXP2 recently occurred in other primates. Thus, recent positive selection on FOXP2 may need to be also considered in the context of other FOXP2 functions that could be shared among primates. This example further illustrates how interpretation of selective sweeps in human evolution can be guided by a broader and comparative view of positive selection among primates.
Each graph shows the variation of the log2 of K (+0.001 to avoid null values) at two candidate hotspots of recent adaptive evolution in human (average of the two individuals, red), chimpanzee (blue) and orangutan (green). To facilitate comparisons between genomes, values of K for chimpanzee and orangutan were projected on their human orthologous coordinates and gene symbols are those for human in all three species. Other legends are identical to Figure 1.
Hotspots of positive or background selection?
Could co-occurring candidate genes be due to conserved background selection hotspots instead of recent positive selection hotspots ,? Although we cannot completely exclude an effect of background selection, our results are better explained by positive selection instead of recurrent deleterious mutations hotspots. First, functional analyses suggest that regions with low K in the human genome are dominated by positive selection. Indeed, several of the Gene Ontology biological processes found here with downwardly biased values of K were previously found over-represented for positively, not negatively selected genes . Second, we find an excess of co-occurrence when comparing genes with K≤0.05 in non-human primates with positive selection candidates found in at least two of three human genome scans for selective sweeps that are insensitive to background selection ,, (relative co-occurrence score C3+C4 excess = 91%, co-occurrence test P = 7.10−3; Figure S5). Third, the co-occurrence between recent or coding sequence positive selection can be explained by positive, not background selection hotspots. Although background selection might have an influence, the presence of positive selection hotspots in primates active during recent human evolution remain the only reasonable explanation for our results.
We have performed a comparative analysis of positive selection in human and non-human primate genomes. Because non-human primate genomes do not benefit from genotyping data, we developed a new test to identify selective sweeps in single individual genomes. As shown by population simulations, this test is only moderately sensitive to demographic changes, and is thus widely applicable to genomes from single outbred individuals. The systematic comparison of genes positively selected during recent human evolution with candidates for positive selection in chimpanzee, orangutang and macaque shows a clear excess of genes that were positively selected independently in multiple primate lineages. This is independently confirmed by comparing recently published human positively selected genes – either to candidates identified by our test in non-human primates or to genes positively selected at the level of their coding sequence during more extensive evolutionary times. All these independent lines of evidence converge towards the same conclusion: primate genomes share hotspots of positive selection, including during recent human evolution.
Positive selection hotspots in primates raise several questions. First, do they mainly represent cases of parallel/convergent evolution, or cases of adaptations in different phenotypic directions involving the same locus? In the first scenario, hotspots could be seen as recurrent targets of positive selection for species under similar selective pressures. In the second scenario, hotspots would rather be considered as a toolbox to fine tune shared molecular functions according to different selective pressures. Second, we do not know how many genes can be described as positive selection hotspots in a primate genome. Each primate genome scan produced a high number of false positives, thus preventing unbiased attempts at estimating the minimal number of positive selection hotspots needed to explain our results. Third, the phylogenetic depth at which hotspots can be detected remains to be investigated. Our analysis in primates does not preclude that a subset of primate hotspots may be found under positive selection also in other mammals, or in other vertebrates. Indeed, isolated cases of parallel evolution have been observed between birds and rodents and human and fish ,, raising the possibility that some hotspots may be shared among distant vertebrates.
A deeper knowledge of positive selection hotspots has additional practical and conceptual implications. Most scans for positively selected genes deliver false positive results ,, thus making it difficult to interpret the evolution of any given gene in a biological context. Yet genes inferred to be positively selected independently in multiple scans or alternatively in multiple species within the same evolutionary period reduce this uncertainty because they are more likely to be true positives. More importantly, hotspots provide a means to identify positive selection events more specific to one species, and in particular positive selection events specific to human. For instance, 19 of the 70 human genes identified in common between at least three previous scans for selective sweeps – are not candidates in any of the three other primates, and are therefore more likely to be linked to specific human changes. For instance several of the 19 candidates belong to the same sweep as the lactase gene, an adaptive event associated with the emergence of cattle breeding and thus expected to be human-specific (Table S9). However, because our test and most of the published scans used for comparisons (except the scan of Voight et al.) aim at detecting complete sweeps, we cannot exclude that the most recent events of positive selection, those with still ongoing selective sweeps, reflect more specific human adaptation events.
We show here through independent comparisons based on a diverse array of methods that positive selection hotspots have been frequent during primate evolution, and in particular that genes positively selected during recent human evolution were also positively selected in other primate lineages, either recently or not. Yet importantly, our sampling of species and individuals is necessarily limited to sequenced genomes, and it is likely that the identification of hotspots of positive selection will increase in power as the genomes and variation information of more species become available. We predict that this will greatly facilitate the interpretation of biological changes underlying human selective sweeps. In particular, the identification of genes positively selected in human but never or infrequently in other primates will help to better outline truly specific aspects of recent human evolution. Our results provide a glimpse of the benefits of a hypothetical “1,000 Primate Genomes” project for the understanding of human adaptive evolution.
Estimation of K
We developed a new method inspired by the HKA test  to estimate the probability that a given locus has recently been subject to positive selection, using genome wide heterozygous sites and divergence data from single individuals. K values are computed for a given region L of size q (here 100 kb≤q≤300 kb) by first computing the ratio rl between the number of heterozygous sites and the number of divergent sites observed with a closely related species (e.g. human-chimpanzee when scanning the human and chimpanzee genomes, human-orangutan when scanning the orangutan genome and human-macaque when scanning the macaque genome). The same ratio rg is then computed for a region G extending 10 times q on both sides of L. A weighing scheme is applied to ratio rg to control for repeats, shotgun coverage and nucleotide composition (see next paragraph). The ratio Robs = rl/rg then expresses the local reduction (Robs<1) or increase (Robs>1) in heterozygosity given its level in the surrounding genomic region and the local divergence. Next, the ratio R is computed for 5,000 additional windows of size q randomly sampled within G but at a distance at least five times q from L (this is done with replacement, meaning that the same position in the background window can be represented in several random q sized windows). This generates an empirical distribution of R across the region G, thus providing an empirical means to estimate the probability of observing R lower than Robs in this region: K is the proportion of random windows with R lower than Robs. Because randomly sampled windows have globally comparable demographic histories, increases or decreases of the local diversity variance due to demographic expansions or contractions should be partially accounted for in the estimation of K (Text S2).
We introduced a weighing scheme to account for varying base composition and repeat density, two major factors that are known to affect genetic diversity in primate genomes. This scheme also controls for the random nature of genome shotgun sequencing, where variable numbers of reads covering a given position affect the probability of detecting the two alleles. To do so, we first compute nij the number of positions of the tested window L occupied by a specific base i (A,T,C or G) identified or not in a repeat by RepeatMasker and covered by a number of reads j. For instance if a given window includes 4,500 positions occupied by nucleotide G outside of a repeat and covered by four reads then nG4 = 4,500. The same procedure is applied for DNA inside repeats. Next, we compute rg for the genomic background window weighted by nij in the tested window:where pHij and pDij are respectively the proportions of heterozygous and divergent sites for all sites of class nucleotide i and coverage j in the genomic background window. We show, using simulations, that this weighing scheme removes the effect of a diverse range of factors that may bias measures of genetic diversity (Text S1). In this study, K was measured for local window sizes of 200 kb for human, 100, 150 and 200 kb for orangutan, 200 kb and 300 kb for chimpanzee, 70 kb, 100 kb and 140 kb for macaque. Larger windows were used for chimpanzee to account for its lower level of heterozygosity, while smaller windows were used for macaque due to its higher heterozygosity. The power to detect sweeps depends on the average initial level of heterozygosity (a lower initial level of heterozygosity means that there will be less contrast between a selective sweep and the neutral background), which can be compensated by adjusting window size so that the average number of heterozygous sites per window remains similar between species. Windows were excluded if they did not meet specific criterions: at least 60% of sites must be sequenced in the two species and be covered by less than 20 shotgun reads. For each measure of K, the genomic background window was adjusted to span 20 times the size of local windows, 10 times downstream and 10 times upstream. K was measured using 5,000 random resamplings for analyses with windows centred on genes (method validation and co-occurrence test), and 1,000 random samplings for windows sliding along chromosomes (Figure 1, Figure 7, Figure S7, Table S3).
The four primate species possess 14,480 mutual orthologs, of which 9,972 were tested in all four species. The remaining genes either reside on sexual chromosomes (human and chimpanzee genomes are sequenced from males, and thus provide no heterozygosity data for the X chromosome) or are located in windows that did not meet the required criterions to measure K. To test for co-occurrence, the following datasets were used for each species. In human, the average K between CV and JW was used to select candidate genes with K lower than a fixed threshold in 200 kb windows centred on the genomic centres of genes. These criterions showed the best overlap with published scans for selective sweeps –. In other primates, a candidate gene was selected if one of the three window sizes centred on this gene had K lower than the same threshold used for human. We then computed the sum of pairs (C2), triplets (C3) and quartets (C4) of genes seen in a putative sweep respectively in human and one, two or three species simultaneously (Figure S5). The human genome was then shuffled randomly and C2, C3 and C4 computed for 100,000 iterations. More specifically, for each iteration, the human genome was randomly divided into 20 intervals, with the gene order preserved within a given interval. Intervals were then rearranged in a random order and the sum C2, C3 and C4 computed across the randomized genome. The preservation of gene order within the intervals accounts for the clustered organisation of candidate sweep-associated orthologous genes. Clustering reflects the fact that a selective sweep in primate genomes often spans several neighbouring genes. This increases the variance, while leaving the average sum of co-occurrences unaffected. Increasing the number of intervals used for shuffling genes at each iteration did not change the results.
Several genomic factors such as recombination or gene density are correlated with K and have to be accounted for in our co-occurrence test. Such factors are indeed likely to increase the expected co-occurrence if they are conserved across species. We controlled for these factors separately by dividing the genes into n classes delimited by the n−1 quantiles of the factor to account for, and then running permutations within each class separately. We found that dividing the genes into 10 classes is sufficient in each case, since no gain of co-occurrence was observed when using more classes. Introducing classes however requires two corrections. First, dividing genes into classes can destroy clusters of contiguous candidate genes, thus reducing the variance of co-occurrence obtained after 100,000 permutations. Since the distribution of random co-occurrences is normal and since clustering does not affect the mean of this distribution but only its variance, we can address this issue by allocating the variance measured on the distribution without any class, to the distribution with 10 classes (Figure 3). Second, the simple fact of dividing genes into classes inflates the expected average level of co-occurrence in the presence of hotspots. This is due to the fact that each hotspot once “trapped” into a specific class will be randomized across a much smaller number of genomic locations and thus reconstructed randomly more frequently than when no class is defined. We accounted for this effect as follows: measures of K were first randomly permuted across genes, thus effectively removing the specific effect of any putative correlated genomic factors. This step was followed by 100 iterations of the co-occurrence test, and the two successive operations were repeated 1,000 times. The difference between the resulting average co-occurrence score and the average score when no classes are used finally represents the effect of using classes, independently of any genomic factor. This difference was therefore substracted from the average co-occurrence score every time classes were defined to account for genomic factors.
Finally, two factors could be tested simultaneously based on the information that a specific gene may for instance be in class 3 (out of 10) for factor 1 and in class 6 for factor 2, thus belonging to the class (3,6) used together with 99 other combinations in the co-occurrence test. This was done in particular to test the effect on co-occurrence of recombination and gene density considered simultaneously.
Genome assemblies and alignments
Genome assemblies with softmasked repeated sequences identified by RepeatMasker, for human (HG18), chimpanzee (PanTro2), orangutan (PonAbe2) and macaque (RheMac2) were downloaded from the UCSC genome browser (http://hgdownload.cse.ucsc.edu/). Human-chimpanzee, chimpanzee-human, orangutan-human, human-orangutan, macaque-human and human-macaque Blastz alignments  in axt.net format were also downloaded from the UCSC genome browser.
Levels of shotgun sequencing coverage were measured using different strategies depending on the availability of read location information. For chimpanzee, orangutan and macaque, coverage was directly deduced from read positions downloaded from the Washington University Genome Sequencing Center WUGSC website (http://genome.wustl.edu/pub/organism/Primates/) in reads.placed files. For the two human individuals, reads were first downloaded from the NCBI Trace archive site (ftp://ftp.ncbi.nih.gov/pub/TraceDB/) and mapped on the NCBI36 human genome assembly using Blat  with the -fastMap and minimal 95% identity options activated. Only those reads mapped on more than 80% of their length were retained to measure coverage.
Heterozygous SNP detection
Heterozygous sites for the two human individuals were retrieved from the J. Craig Venter Institute web site (http://www.jcvi.org/) and from the Jim Watson Sequence website at CSHL (ftp://jimwatsonsequence.cshl.edu/jimwatsonsequence/), respectively. For chimpanzee, orangutan and macaque reads were first downloaded from the NCBI Trace archive (ftp://ftp.ncbi.nih.gov/pub/TraceDB/). Reads were then mapped on genome assemblies using ssahaSNP2  (parsing parameters -identity = 92 -match = 80 -copy = 20 -cover = 20) to detect heterozygous SNPs.
Gene annotations and orthology relationships
All analyses were conducted using Ensembl v48 annotations for protein coding genes and their homology relationships, except for PAML analysis where Ensembl v52 were used  (http://www.ensembl.org/). A total of 14,480 human-chimpanzee-orangutan-macaque four-way orthologs were found.
(((human #, chimp #) #, orangutan #), macaque #, mouse)
We first retrieved protein and coding sequences of all Ensembl v52 human-chimp-orangutan-macaque-mouse five-way one-to-one orthologs. When a gene had multiple protein and coding sequences, only the longest were considered for further analysis. Nucleotides in chimpanzee, orangutan and macaque coding sequences with a Phred quality lower than 20 were excluded together with their 10 downstream and 10 upstream nucleotides neighbours. Downstream and upstream positions were also excluded because we noticed that nucleotides with quality lower than 20 were often found close to each other, thus raising doubts about the quality of interspaced nucleotides. This procedure indeed reduced the rate of false positives due to sequence inconsistencies (data not shown). Protein sequences were aligned with MAFFT  with high accuracy options activated. Coding sequence alignments were then obtained by projection on protein alignments. Only those 11,293 alignments containing at least 50 codons with no excluded nucleotide, starting with a start codon in one of the species and with at least one synonymous substitution between each pair of species were finally tested for positive selection.
Analysis of microarray expression and Gene Ontology annotations
Affymetrix Human Exon microarray expression data for eleven tissues (breast, cerebellum, heart, kidney, liver, muscle, pancreas, prostate, spleen, testis and thyroid) was downloaded from the UCSC genome browser database. Values of expression for each Ensembl gene correspond to the average deduced from all probesets mapping the exons of a gene. Expressions of human genes candidates for positive selection were compared with expressions of the remaining genes using the log of Relative Abundance . Gene Ontology annotations  of biological processes were analysed using the FatiGO and FatiScan , software available at http://babelomics.bioinfo.cipf.es/.
Measures of recombination rates, gene densities, and other genomic factors
Recombination and gene densities were controlled for in our test of co-occurrence. Recombination rates from the HapMap release 22 build 36 and estimated by LDHat , were downloaded at http://www.hapmap.org/. The average recombination rate (cM/Mb) was calculated for 200 kb windows centred on genes. Gene densities were measured as the number of genes present within 100 kb downstream and 100 kb upstream of every gene. Other sizes from 200 kb to 1 Mb and from 100 kb to 2 Mb were investigated for gene density and recombination, respectively, but 100 kb and 200 kb are the ones showing the strongest effects when testing co-occurrence, respectively. Average sequencing depth, proportion of divergent sites for every possible pair of species, and average GC content were measured for windows ranging from 100 to 1000 kb, none of which had an impact on co-occurrence. Correlations shown in Results are for 200 kb windows.
Comparison with other published human genome-wide scans
We compared our set of candidate positively selected genes (K≤0.05) in human with those found in four published scans for selective sweeps in the European population –. To compare several sets of genes and measure the level of observed overlap versus the level of expected overlap, the numbers of genes involved in the comparison must be of the same order of magnitude and large enough to avoid exceedingly high variance in estimated overlap. For these reasons and when needed, we use relaxed criteria to include larger numbers of genes in a given set than provided in highly specific shortlists in the original publications. By doing so, the frequency of potential false positive might increase, but this makes our conclusions conservative since the objective is only to compare the sets of genes relative to each other. In the study by Voight et al.  the 460 selected genes overlap regions where at least 20 out of 50 SNPs show an |iHS|≥2 in the HapMap phase II data. In the analysis by Williamson et al.  444 genes with an associated p-value lower than 10−4 were selected. The 1,030 genes from the Tang et al. study  are those found within the candidate genomic intervals provided as supporting material of this publication. The 986 genes selected from the Carlson et al. scan  are those found within the 200 largest areas of negative Tajima's D in the genome. XP-EHH values were downloaded from the UCSC Genome Browser . 516 genes with XP-EHH ≥2 at their genomic centre were used for comparison with other scans.
Performance of the test under panmictic and European populations demographic models using one individual. Power (Y axis) versus false positive rate (X axis) of the test to detect selective sweeps using 200 kb windows and 20 fold greater genomic background windows sliding every 10 kb are represented for a panmictic population (blue dots and curves) and for a demographic model of the European population (red dots and curves). Power and false positive rates were measured for a range of K thresholds for adaptive mutations fixed between 0 and 2,500 generations before testing (line 1), 2,500 and 5,000 (line 2), 5,000 and 7,500 (line 3) and 7,500 and 10,000 generations after fixation (line 4). Left column: selection coefficient s = 0.01. Right column: s = 0.1. Curves are second order polynomials fitted to the data.
(0.13 MB TIF)
Performance of the test under panmictic and European populations demographic models using two individuals. Same as Figure S1.
(0.15 MB TIF)
Performance of the test under panmictic and European populations demographic models using 20 individuals. Same as Figure S1. No curves could be fitted to the data.
(0.12 MB TIF)
Expression patterns of human selective sweeps candidate genes. The distributions of the log of Relative Abundance (RA)  measured for eleven human tissues with Affymetrix Human Exon microarrays (see Methods) were compared between human selective sweeps candidate genes (left distribution; K≤0.05, 563 candidates with complete expression information) and all other genes that were tested (right distribution; K>0.05, 12,652 genes with complete expression information). Using the Relative Abundance instead of absolute intensities allows us to identify tissues where candidate genes are up-regulated when compared to their expression in other tissues. *: Mann-Whitney U test, one sided P≤0.05. **: P≤0.01. ***: P≤0.001.
(0.24 MB TIF)
Randomization strategy for testing co-occurrence between two groups. Orange circles: genes with K> fixed threshold in group 1. Red circles: genes with K≤ fixed threshold in group 1. Light blue circles: genes with K> fixed threshold in group 2. Blue circles: genes with K≤ fixed threshold in group 2. In our case, group 1 represents non-human primates and group 2 represents human.
(1.71 MB TIF)
Combinations of α and fn that are compatible with the observed excess of co-occurrence of positive selection. The plot presents combinations of α, the proportion of genes with a low rate of positive selection in our model, and fn, the rate of false positives in non-human primates in our model that are compatible with the observed excess of co-occurrence between candidate genes for positive selection (Text S3).
(0.10 MB TIF)
Human-chimpanzee-orangutan candidate hotspots of recent positive selection at five loci. Each graph of lines 1, 2, and 3 shows the variation of the log2 of K (+0.001 to avoid null values) at candidate hotspots of recent positive selection in human (average of the two individuals, red), chimpanzee (blue) and orangutan (green). All five candidates belong to sets of genes with over-represented functions in the Gene Ontology. To facilitate comparisons between genomes, values of K for chimpanzee and orangutan were projected on their human orthologous coordinates and gene symbols are those for human in all three species. Other legends for graphs of lines 1, 2 and 3 are identical to Figure 1. From graphs on line 4 to graphs on line 12 all values were measured within 200 kb windows sliding every 10 kb. Line 4: human Venter + Watson heterozygosity. Line 5: chimpanzee heterozygosity. Line 6: orangutan heterozygosity. Line 7: Human-chimpanzee divergence. Line 8: Human-orangutan divergence. Line 9: Human GC content. Line 10: human sequencing depth (Venter+Watson). Line 11: chimpanzee sequencing depth. Line 12: orangutan sequencing depth. Importantly, this figure clearly shows that the five hotspot candidates presented are due to drops of heterozygosity and not to local anomalies in the other data that were used to calculate K.
(0.79 MB TIF)
Evaluation of the weighing scheme to correct for K biasing factors. The table shows the Spearman's rank correlations of K with diverse simulated biasing factors whether or not the weighing scheme is applied (Methods; Text S1). F(n) (n being null or a positive integer) gives the probability that a heterozygous site is detected depending on the function F and the value n taken by the factor. Factor shape gives the shape of the gamma distribution used to model factor values and scale gives the scale parameter. Factor a and b give the values so that the final values n of the biasing factor were obtained by multiplying values from the gamma distributions with a and adding b. Any negative resulting value was set to 0. Region shape gives the shape of the gamma distribution used to model region sizes, Region scale the scale parameter. Region a and Region b give the values so that the final region size values were obtained by multiplying sizes from the gamma distribution with a and finally adding b. Any size lower than 1 kb was set to 1 kb.
(0.04 MB DOC)
Average K for 19,331 Ensembl human genes. Column 1: Ensembl gene ID. Column 2: chromosome. Columns 3 and 4: Gene start and end coordinates on NCBI36/HG18. Column 5: Average K for CV and JW for 200 kb windows centred on the genomic centre of each genes.
(0.85 MB TXT)
Custom tracks for visualizing scan results in the UCSC Genome Browser. The wiggle file contains three custom tracks that can be visualized via upload in the UCSC Genome Browser. The first track represents the average K obtained from 1,000 random samplings for 200 kb windows sliding every 10 kb. Other tracks highlight regions with CLR P≤0.05, and CLR P≤10−4 remapped on the NCBI36/HG18 assembly in the Williamson et al. study . Users can also activate the Tajima's D, XP-EHH and iHS tracks already available in the UCSC Genome Browser for further comparisons.
(4.08 MB TXT)
Gene Ontology biological processes with significantly lower distributions of K in the human genome. Gene Ontology biological processes with lower values of K than the rest of the genome were identified using the FatiScan , tool available at http://babelomics.bioinfo.cipf.es/. Several parent and daughter significant processes of those indicated were removed. FDR is for False Discovery Rate.
(0.03 MB DOC)
Values of K for human, chimpanzee, orangutan and macaque used in the co-occurrence test. Column 1: Human Ensembl gene ID. Column 2: human chromosome. Columns 3 and 4: gene start and end in human. Columns 5, 6, 7, 8: K in human, chimpanzee, orangutan and macaque, respectively.
(0.62 MB TXT)
Best candidate hotspots of recent positive selection. Column 1: HGNC symbol. Other columns are the same as for Table S5.
(0.05 MB DOC)
(0.07 MB DOC)
Top candidates identified in both the PAML test on coding sequences and our test for selective sweeps (recent positive selection).
(0.05 MB DOC)
Ensembl genes observed in a selective sweep only in human. Note that all candidates on chromosome 2 except the first correspond to the lactase locus.
(0.05 MB DOC)
Forward population simulations.
(0.03 MB DOC)
We thank Pierre Vincens and Alexandra Louis for help with computing resources. We thank Lluis Quintana-Murci for comments on the manuscript and the Genome Center at Washington University, Washington University School of Medicine, the Human Genome Sequencing Center, Baylor College of Medicine, and the Orangutan Genome Analysis Consortium for making available unpublished Orangutan genome data.
Conceived and designed the experiments: DE FD HRC. Performed the experiments: DE. Analyzed the data: DE FD. Contributed reagents/materials/analysis tools: DE HRC. Wrote the paper: DE HRC.
- 1. Kimura M (1979) The neutral theory of molecular evolution. Sci Am 241: 98–100, 102, 108 passim.M. Kimura1979The neutral theory of molecular evolution.Sci Am24198100, 102, 108 passim
- 2. Eyre-Walker A, Keightley PD (2007) The distribution of fitness effects of new mutations. Nat Rev Genet 8: 610–618.A. Eyre-WalkerPD Keightley2007The distribution of fitness effects of new mutations.Nat Rev Genet8610618
- 3. Keightley PD, Eyre-Walker A (2007) Joint inference of the distribution of fitness effects of deleterious mutations and population demography based on nucleotide polymorphism frequencies. Genetics 177: 2251–2261.PD KeightleyA. Eyre-Walker2007Joint inference of the distribution of fitness effects of deleterious mutations and population demography based on nucleotide polymorphism frequencies.Genetics17722512261
- 4. Boyko AR, Williamson SH, Indap AR, Degenhardt JD, Hernandez RD, et al. (2008) Assessing the evolutionary impact of amino acid mutations in the human genome. PLoS Genet 4: e1000083.AR BoykoSH WilliamsonAR IndapJD DegenhardtRD Hernandez2008Assessing the evolutionary impact of amino acid mutations in the human genome.PLoS Genet4e1000083
- 5. Charlesworth J, Eyre-Walker A (2006) The rate of adaptive evolution in enteric bacteria. Mol Biol Evol 23: 1348–1356.J. CharlesworthA. Eyre-Walker2006The rate of adaptive evolution in enteric bacteria.Mol Biol Evol2313481356
- 6. Bierne N, Eyre-Walker A (2004) The genomic rate of adaptive amino acid substitution in Drosophila. Mol Biol Evol 21: 1350–1360.N. BierneA. Eyre-Walker2004The genomic rate of adaptive amino acid substitution in Drosophila.Mol Biol Evol2113501360
- 7. Welch JJ (2006) Estimating the genomewide rate of adaptive protein evolution in Drosophila. Genetics 173: 821–837.JJ Welch2006Estimating the genomewide rate of adaptive protein evolution in Drosophila.Genetics173821837
- 8. Andolfatto P (2005) Adaptive evolution of non-coding DNA in Drosophila. Nature 437: 1149–1152.P. Andolfatto2005Adaptive evolution of non-coding DNA in Drosophila.Nature43711491152
- 9. Cai JJ, Macpherson JM, Sella G, Petrov DA (2009) Pervasive hitchhiking at coding and regulatory sites in humans. PLoS Genet 5: e1000336.JJ CaiJM MacphersonG. SellaDA Petrov2009Pervasive hitchhiking at coding and regulatory sites in humans.PLoS Genet5e1000336
- 10. Gojobori J, Tang H, Akey JM, Wu CI (2007) Adaptive evolution in humans revealed by the negative correlation between the polymorphism and fixation phases of evolution. Proc Natl Acad Sci U S A 104: 3907–3912.J. GojoboriH. TangJM AkeyCI Wu2007Adaptive evolution in humans revealed by the negative correlation between the polymorphism and fixation phases of evolution.Proc Natl Acad Sci U S A10439073912
- 11. Williamson SH, Hubisz MJ, Clark AG, Payseur BA, Bustamante CD, et al. (2007) Localizing recent adaptive evolution in the human genome. PLoS Genet 3: e90.SH WilliamsonMJ HubiszAG ClarkBA PayseurCD Bustamante2007Localizing recent adaptive evolution in the human genome.PLoS Genet3e90
- 12. Voight BF, Kudaravalli S, Wen X, Pritchard JK (2006) A map of recent positive selection in the human genome. PLoS Biol 4: e72.BF VoightS. KudaravalliX. WenJK Pritchard2006A map of recent positive selection in the human genome.PLoS Biol4e72
- 13. Carlson CS, Thomas DJ, Eberle MA, Swanson JE, Livingston RJ, et al. (2005) Genomic regions exhibiting positive selection identified from dense genotype data. Genome Res 15: 1553–1565.CS CarlsonDJ ThomasMA EberleJE SwansonRJ Livingston2005Genomic regions exhibiting positive selection identified from dense genotype data.Genome Res1515531565
- 14. Tang K, Thornton KR, Stoneking M (2007) A New Approach for Using Genome Scans to Detect Recent Positive Selection in the Human Genome. PLoS Biol 5: e171.K. TangKR ThorntonM. Stoneking2007A New Approach for Using Genome Scans to Detect Recent Positive Selection in the Human Genome.PLoS Biol5e171
- 15. Pickrell JK, Coop G, Novembre J, Kudaravalli S, Li JZ, et al. (2009) Signals of recent positive selection in a worldwide sample of human populations. Genome Res 19: 826–837.JK PickrellG. CoopJ. NovembreS. KudaravalliJZ Li2009Signals of recent positive selection in a worldwide sample of human populations.Genome Res19826837
- 16. Bustamante CD, Fledel-Alon A, Williamson S, Nielsen R, Hubisz MT, et al. (2005) Natural selection on protein-coding genes in the human genome. Nature 437: 1153–1157.CD BustamanteA. Fledel-AlonS. WilliamsonR. NielsenMT Hubisz2005Natural selection on protein-coding genes in the human genome.Nature43711531157
- 17. Sabeti PC, Varilly P, Fry B, Lohmueller J, Hostetter E, et al. (2007) Genome-wide detection and characterization of positive selection in human populations. Nature 449: 913–918.PC SabetiP. VarillyB. FryJ. LohmuellerE. Hostetter2007Genome-wide detection and characterization of positive selection in human populations.Nature449913918
- 18. Barreiro LB, Laval G, Quach H, Patin E, Quintana-Murci L (2008) Natural selection has driven population differentiation in modern humans. Nat Genet 40: 340–345.LB BarreiroG. LavalH. QuachE. PatinL. Quintana-Murci2008Natural selection has driven population differentiation in modern humans.Nat Genet40340345
- 19. Hellmann I, Mang Y, Gu Z, Li P, de la Vega FM, et al. (2008) Population genetic analysis of shotgun assemblies of genomic sequences from multiple individuals. Genome Res 18: 1020–1029.I. HellmannY. MangZ. GuP. LiFM de la Vega2008Population genetic analysis of shotgun assemblies of genomic sequences from multiple individuals.Genome Res1810201029
- 20. Yang Z (1997) PAML: a program package for phylogenetic analysis by maximum likelihood. Comput Appl Biosci 13: 555–556.Z. Yang1997PAML: a program package for phylogenetic analysis by maximum likelihood.Comput Appl Biosci13555556
- 21. Zhang J, Nielsen R, Yang Z (2005) Evaluation of an improved branch-site likelihood method for detecting positive selection at the molecular level. Mol Biol Evol 22: 2472–2479.J. ZhangR. NielsenZ. Yang2005Evaluation of an improved branch-site likelihood method for detecting positive selection at the molecular level.Mol Biol Evol2224722479
- 22. Kosiol C, Vinar T, da Fonseca RR, Hubisz MJ, Bustamante CD, et al. (2008) Patterns of positive selection in six Mammalian genomes. PLoS Genet 4: e1000144.C. KosiolT. VinarRR da FonsecaMJ HubiszCD Bustamante2008Patterns of positive selection in six Mammalian genomes.PLoS Genet4e1000144
- 23. Bakewell MA, Shi P, Zhang J (2007) More genes underwent positive selection in chimpanzee evolution than in human evolution. Proc Natl Acad Sci U S A 104: 7489–7494.MA BakewellP. ShiJ. Zhang2007More genes underwent positive selection in chimpanzee evolution than in human evolution.Proc Natl Acad Sci U S A10474897494
- 24. Mallick S, Gnerre S, Muller P, Reich D (2009) The difficulty of avoiding false positives in genome scans for natural selection. Genome Res 19: 922–933.S. MallickS. GnerreP. MullerD. Reich2009The difficulty of avoiding false positives in genome scans for natural selection.Genome Res19922933
- 25. Zhang J (2003) Parallel functional changes in the digestive RNases of ruminants and colobines by divergent amino acid substitutions. Mol Biol Evol 20: 1310–1317.J. Zhang2003Parallel functional changes in the digestive RNases of ruminants and colobines by divergent amino acid substitutions.Mol Biol Evol2013101317
- 26. Colosimo PF, Peichel CL, Nereng K, Blackman BK, Shapiro MD, et al. (2004) The genetic architecture of parallel armor plate reduction in threespine sticklebacks. PLoS Biol 2: e109.PF ColosimoCL PeichelK. NerengBK BlackmanMD Shapiro2004The genetic architecture of parallel armor plate reduction in threespine sticklebacks.PLoS Biol2e109
- 27. Shapiro MD, Marks ME, Peichel CL, Blackman BK, Nereng KS, et al. (2004) Genetic and developmental basis of evolutionary pelvic reduction in threespine sticklebacks. Nature 428: 717–723.MD ShapiroME MarksCL PeichelBK BlackmanKS Nereng2004Genetic and developmental basis of evolutionary pelvic reduction in threespine sticklebacks.Nature428717723
- 28. Colosimo PF, Hosemann KE, Balabhadra S, Villarreal G Jr, Dickson M, et al. (2005) Widespread parallel evolution in sticklebacks by repeated fixation of Ectodysplasin alleles. Science 307: 1928–1933.PF ColosimoKE HosemannS. BalabhadraG. Villarreal JrM. Dickson2005Widespread parallel evolution in sticklebacks by repeated fixation of Ectodysplasin alleles.Science30719281933
- 29. Hoekstra HE (2006) Genetics, development and evolution of adaptive pigmentation in vertebrates. Heredity 97: 222–234.HE Hoekstra2006Genetics, development and evolution of adaptive pigmentation in vertebrates.Heredity97222234
- 30. Zhang J (2006) Parallel adaptive origins of digestive RNases in Asian and African leaf monkeys. Nat Genet 38: 819–823.J. Zhang2006Parallel adaptive origins of digestive RNases in Asian and African leaf monkeys.Nat Genet38819823
- 31. Miller CT, Beleza S, Pollen AA, Schluter D, Kittles RA, et al. (2007) cis-Regulatory changes in Kit ligand expression and parallel evolution of pigmentation in sticklebacks and humans. Cell 131: 1179–1189.CT MillerS. BelezaAA PollenD. SchluterRA Kittles2007cis-Regulatory changes in Kit ligand expression and parallel evolution of pigmentation in sticklebacks and humans.Cell13111791189
- 32. Tishkoff SA, Reed FA, Ranciaro A, Voight BF, Babbitt CC, et al. (2007) Convergent adaptation of human lactase persistence in Africa and Europe. Nat Genet 39: 31–40.SA TishkoffFA ReedA. RanciaroBF VoightCC Babbitt2007Convergent adaptation of human lactase persistence in Africa and Europe.Nat Genet393140
- 33. Frazer KA, Ballinger DG, Cox DR, Hinds DA, Stuve LL, et al. (2007) A second generation human haplotype map of over 3.1 million SNPs. Nature 449: 851–861.KA FrazerDG BallingerDR CoxDA HindsLL Stuve2007A second generation human haplotype map of over 3.1 million SNPs.Nature449851861
- 34. Hinds DA, Stuve LL, Nilsen GB, Halperin E, Eskin E, et al. (2005) Whole-genome patterns of common DNA variation in three human populations. Science 307: 1072–1079.DA HindsLL StuveGB NilsenE. HalperinE. Eskin2005Whole-genome patterns of common DNA variation in three human populations.Science30710721079
- 35. Smith JM, Haigh J (1974) The hitch-hiking effect of a favourable gene. Genet Res 23: 23–35.JM SmithJ. Haigh1974The hitch-hiking effect of a favourable gene.Genet Res232335
- 36. Hudson RR, Kreitman M, Aguade M (1987) A test of neutral molecular evolution based on nucleotide data. Genetics 116: 153–159.RR HudsonM. KreitmanM. Aguade1987A test of neutral molecular evolution based on nucleotide data.Genetics116153159
- 37. Hoggart CJ, Chadeau-Hyam M, Clark TG, Lampariello R, Whittaker JC, et al. (2007) Sequence-level population simulations over large genomic regions. Genetics 177: 1725–1731.CJ HoggartM. Chadeau-HyamTG ClarkR. LamparielloJC Whittaker2007Sequence-level population simulations over large genomic regions.Genetics17717251731
- 38. Chadeau-Hyam M, Hoggart CJ, O'Reilly PF, Whittaker JC, De Iorio M, et al. (2008) Fregene: simulation of realistic sequence-level data in populations and ascertained samples. BMC Bioinformatics 9: 364.M. Chadeau-HyamCJ HoggartPF O'ReillyJC WhittakerM. De Iorio2008Fregene: simulation of realistic sequence-level data in populations and ascertained samples.BMC Bioinformatics9364
- 39. Schaffner SF, Foo C, Gabriel S, Reich D, Daly MJ, et al. (2005) Calibrating a coalescent simulation of human genome sequence variation. Genome Res 15: 1576–1583.SF SchaffnerC. FooS. GabrielD. ReichMJ Daly2005Calibrating a coalescent simulation of human genome sequence variation.Genome Res1515761583
- 40. Zhai W, Nielsen R, Slatkin M (2009) An investigation of the statistical power of neutrality tests based on comparative and population genetic data. Mol Biol Evol 26: 273–283.W. ZhaiR. NielsenM. Slatkin2009An investigation of the statistical power of neutrality tests based on comparative and population genetic data.Mol Biol Evol26273283
- 41. Levy S, Sutton G, Ng PC, Feuk L, Halpern AL, et al. (2007) The diploid genome sequence of an individual human. PLoS Biol 5: e254.S. LevyG. SuttonPC NgL. FeukAL Halpern2007The diploid genome sequence of an individual human.PLoS Biol5e254
- 42. Wheeler DA, Srinivasan M, Egholm M, Shen Y, Chen L, et al. (2008) The complete genome of an individual by massively parallel DNA sequencing. Nature 452: 872–876.DA WheelerM. SrinivasanM. EgholmY. ShenL. Chen2008The complete genome of an individual by massively parallel DNA sequencing.Nature452872876
- 43. Enard W, Przeworski M, Fisher SE, Lai CS, Wiebe V, et al. (2002) Molecular evolution of FOXP2, a gene involved in speech and language. Nature 418: 869–872.W. EnardM. PrzeworskiSE FisherCS LaiV. Wiebe2002Molecular evolution of FOXP2, a gene involved in speech and language.Nature418869872
- 44. Sturm RA, Duffy DL, Zhao ZZ, Leite FP, Stark MS, et al. (2008) A single SNP in an evolutionary conserved region within intron 86 of the HERC2 gene determines human blue-brown eye color. Am J Hum Genet 82: 424–431.RA SturmDL DuffyZZ ZhaoFP LeiteMS Stark2008A single SNP in an evolutionary conserved region within intron 86 of the HERC2 gene determines human blue-brown eye color.Am J Hum Genet82424431
- 45. Izagirre N, Garcia I, Junquera C, de la Rua C, Alonso S (2006) A scan for signatures of positive selection in candidate loci for skin pigmentation in humans. Mol Biol Evol 23: 1697–1706.N. IzagirreI. GarciaC. JunqueraC. de la RuaS. Alonso2006A scan for signatures of positive selection in candidate loci for skin pigmentation in humans.Mol Biol Evol2316971706
- 46. Bersaglieri T, Sabeti PC, Patterson N, Vanderploeg T, Schaffner SF, et al. (2004) Genetic signatures of strong recent positive selection at the lactase gene. Am J Hum Genet 74: 1111–1120.T. BersaglieriPC SabetiN. PattersonT. VanderploegSF Schaffner2004Genetic signatures of strong recent positive selection at the lactase gene.Am J Hum Genet7411111120
- 47. Liao BY, Zhang J (2006) Evolutionary conservation of expression profiles between human and mouse orthologous genes. Mol Biol Evol 23: 530–540.BY LiaoJ. Zhang2006Evolutionary conservation of expression profiles between human and mouse orthologous genes.Mol Biol Evol23530540
- 48. Al-Shahrour F, Diaz-Uriarte R, Dopazo J (2004) FatiGO: a web tool for finding significant associations of Gene Ontology terms with groups of genes. Bioinformatics 20: 578–580.F. Al-ShahrourR. Diaz-UriarteJ. Dopazo2004FatiGO: a web tool for finding significant associations of Gene Ontology terms with groups of genes.Bioinformatics20578580
- 49. Al-Shahrour F, Minguez P, Vaquerizas JM, Conde L, Dopazo J (2005) BABELOMICS: a suite of web tools for functional annotation and analysis of groups of genes in high-throughput experiments. Nucleic Acids Res 33: W460–464.F. Al-ShahrourP. MinguezJM VaquerizasL. CondeJ. Dopazo2005BABELOMICS: a suite of web tools for functional annotation and analysis of groups of genes in high-throughput experiments.Nucleic Acids Res33W460464
- 50. Nielsen R, Bustamante C, Clark AG, Glanowski S, Sackton TB, et al. (2005) A scan for positively selected genes in the genomes of humans and chimpanzees. PLoS Biol 3: e170.R. NielsenC. BustamanteAG ClarkS. GlanowskiTB Sackton2005A scan for positively selected genes in the genomes of humans and chimpanzees.PLoS Biol3e170
- 51. (2005) Initial sequence of the chimpanzee genome and comparison with the human genome. Nature 437: 69–87.2005Initial sequence of the chimpanzee genome and comparison with the human genome.Nature4376987
- 52. Gibbs RA, Rogers J, Katze MG, Bumgarner R, Weinstock GM, et al. (2007) Evolutionary and biomedical insights from the rhesus macaque genome. Science 316: 222–234.RA GibbsJ. RogersMG KatzeR. BumgarnerGM Weinstock2007Evolutionary and biomedical insights from the rhesus macaque genome.Science316222234
- 53. Ning Z, Cox AJ, Mullikin JC (2001) SSAHA: a fast search method for large DNA databases. Genome Res 11: 1725–1729.Z. NingAJ CoxJC Mullikin2001SSAHA: a fast search method for large DNA databases.Genome Res1117251729
- 54. Myers S, Bottolo L, Freeman C, McVean G, Donnelly P (2005) A fine-scale map of recombination rates and hotspots across the human genome. Science 310: 321–324.S. MyersL. BottoloC. FreemanG. McVeanP. Donnelly2005A fine-scale map of recombination rates and hotspots across the human genome.Science310321324
- 55. Duret L, Arndt PF (2008) The impact of recombination on nucleotide substitutions in the human genome. PLoS Genet 4: e1000071.L. DuretPF Arndt2008The impact of recombination on nucleotide substitutions in the human genome.PLoS Genet4e1000071
- 56. O'Reilly PF, Birney E, Balding DJ (2008) Confounding between recombination and selection, and the Ped/Pop method for detecting selection. Genome Res 18: 1304–1313.PF O'ReillyE. BirneyDJ Balding2008Confounding between recombination and selection, and the Ped/Pop method for detecting selection.Genome Res1813041313
- 57. Waterston RH, Lindblad-Toh K, Birney E, Rogers J, Abril JF, et al. (2002) Initial sequencing and comparative analysis of the mouse genome. Nature 420: 520–562.RH WaterstonK. Lindblad-TohE. BirneyJ. RogersJF Abril2002Initial sequencing and comparative analysis of the mouse genome.Nature420520562
- 58. Akey JM (2009) Constructing genomic maps of positive selection in humans: where do we go from here? Genome Res 19: 711–722.JM Akey2009Constructing genomic maps of positive selection in humans: where do we go from here?Genome Res19711722
- 59. Barreiro LB, Ben-Ali M, Quach H, Laval G, Patin E, et al. (2009) Evolutionary dynamics of human Toll-like receptors and their different contributions to host defense. PLoS Genet 5: e1000562.LB BarreiroM. Ben-AliH. QuachG. LavalE. Patin2009Evolutionary dynamics of human Toll-like receptors and their different contributions to host defense.PLoS Genet5e1000562
- 60. Oh B, Hwang SY, Solter D, Knowles BB (1997) Spindlin, a major maternal transcript expressed in the mouse during the transition from oocyte to embryo. Development 124: 493–503.B. OhSY HwangD. SolterBB Knowles1997Spindlin, a major maternal transcript expressed in the mouse during the transition from oocyte to embryo.Development124493503
- 61. Anderson RA, Cambray N, Hartley PS, McNeilly AS (2002) Expression and localization of inhibin alpha, inhibin/activin betaA and betaB and the activin type II and inhibin beta-glycan receptors in the developing human testis. Reproduction 123: 779–788.RA AndersonN. CambrayPS HartleyAS McNeilly2002Expression and localization of inhibin alpha, inhibin/activin betaA and betaB and the activin type II and inhibin beta-glycan receptors in the developing human testis.Reproduction123779788
- 62. Chiriva-Internati M, Gagliano N, Donetti E, Costa F, Grizzi F, et al. (2009) Sperm protein 17 is expressed in the sperm fibrous sheath. J Transl Med 7: 61.M. Chiriva-InternatiN. GaglianoE. DonettiF. CostaF. Grizzi2009Sperm protein 17 is expressed in the sperm fibrous sheath.J Transl Med761
- 63. Zhang J, Webb DM, Podlaha O (2002) Accelerated protein evolution and origins of human-specific features: Foxp2 as an example. Genetics 162: 1825–1835.J. ZhangDM WebbO. Podlaha2002Accelerated protein evolution and origins of human-specific features: Foxp2 as an example.Genetics16218251835
- 64. Charlesworth B, Morgan MT, Charlesworth D (1993) The effect of deleterious mutations on neutral molecular variation. Genetics 134: 1289–1303.B. CharlesworthMT MorganD. Charlesworth1993The effect of deleterious mutations on neutral molecular variation.Genetics13412891303
- 65. Charlesworth D, Charlesworth B, Morgan MT (1995) The pattern of neutral molecular variation under the background selection model. Genetics 141: 1619–1632.D. CharlesworthB. CharlesworthMT Morgan1995The pattern of neutral molecular variation under the background selection model.Genetics14116191632
- 66. Teshima KM, Coop G, Przeworski M (2006) How reliable are empirical genomic scans for selective sweeps? Genome Res 16: 702–712.KM TeshimaG. CoopM. Przeworski2006How reliable are empirical genomic scans for selective sweeps?Genome Res16702712
- 67. Schwartz S, Kent WJ, Smit A, Zhang Z, Baertsch R, et al. (2003) Human-mouse alignments with BLASTZ. Genome Res 13: 103–107.S. SchwartzWJ KentA. SmitZ. ZhangR. Baertsch2003Human-mouse alignments with BLASTZ.Genome Res13103107
- 68. Kent WJ (2002) BLAT–the BLAST-like alignment tool. Genome Res 12: 656–664.WJ Kent2002BLAT–the BLAST-like alignment tool.Genome Res12656664
- 69. Flicek P, Aken BL, Beal K, Ballester B, Caccamo M, et al. (2008) Ensembl 2008. Nucleic Acids Res 36: D707–714.P. FlicekBL AkenK. BealB. BallesterM. Caccamo2008Ensembl 2008.Nucleic Acids Res36D707714
- 70. Katoh K, Misawa K, Kuma K, Miyata T (2002) MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res 30: 3059–3066.K. KatohK. MisawaK. KumaT. Miyata2002MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform.Nucleic Acids Res3030593066
- 71. (2008) The Gene Ontology project in 2008. Nucleic Acids Res 36: D440–444.2008The Gene Ontology project in 2008.Nucleic Acids Res36D440444