Different populations suffer from different rates of obesity and type-2 diabetes (T2D). Little is known about the genetic or adaptive component, if any, that underlies these differences. Given the cultural, geographic, and dietary variation that accumulated among humans over the last 60,000 years, we examined whether loci identified by genome-wide association studies for these traits have been subject to recent selection pressures. Using genome-wide SNP data on 938 individuals in 53 populations from the Human Genome Diversity Panel, we compare population differentiation and haplotype patterns at these loci to the rest of the genome. Using an “expanding window” approach (100 to 1,600 kb) for the individual loci as well as the loci as ensembles, we find a high degree of differentiation for the ensemble of T2D loci. This differentiation is most pronounced for East Asians and sub-Saharan Africans, suggesting that these groups experienced natural selection at loci associated with T2D. Haplotype analysis suggests an excess of obesity loci with evidence of recent positive selection among South Asians and Europeans, compared to sub-Saharan Africans and Native Americans. We also identify individual loci that may have been subjected to natural selection, such as the T2D locus, HHEX, which displays both elevated differentiation and extended haplotype homozygosity in comparisons of East Asians with other groups. Our findings suggest that there is an evolutionary genetic basis for population differences in these traits, and we have identified potential group-specific genetic risk factors.
obesity; type-2 diabetes; genetics; natural selection; population differentiation
We used a high-density single-nucleotide polymorphism array to genotype 75 Plasmodium falciparum isolates recently collected from Senegal and The Gambia to search for signals of selection in this malaria endemic region. We found little geographic or temporal stratification of the genetic diversity among the sampled parasites. Through application of the iHS and REHH haplotype-based tests for positive selection, we found evidence of recent selective sweeps at a known drug resistance locus, at several known antigenic loci, and at several genomic regions not previously identified as sites of recent selection. We discuss the value of deep population-specific genomic analyses for identifying selection signals within sampled endemic populations of parasites, which may correspond to local selection pressures such as distinctive therapeutic regimes or mosquito vectors.
Plasmodium falciparum; natural selection; SNP; genome; West Africa
There is great interindividual variability in HIV-1 viral setpoint after seroconversion, some of which is known to be due to genetic differences among infected individuals. Here, our focus is on determining, genome-wide, the contribution of variable gene expression to viral control, and to relate it to genomic DNA polymorphism. RNA was extracted from purified CD4+ T-cells from 137 HIV-1 seroconverters, 16 elite controllers, and 3 healthy blood donors. Expression levels of more than 48,000 mRNA transcripts were assessed by the Human-6 v3 Expression BeadChips (Illumina). Genome-wide SNP data was generated from genomic DNA using the HumanHap550 Genotyping BeadChip (Illumina). We observed two distinct profiles with 260 genes differentially expressed depending on HIV-1 viral load. There was significant upregulation of expression of interferon stimulated genes with increasing viral load, including genes of the intrinsic antiretroviral defense. Upon successful antiretroviral treatment, the transcriptome profile of previously viremic individuals reverted to a pattern comparable to that of elite controllers and of uninfected individuals. Genome-wide evaluation of cis-acting SNPs identified genetic variants modulating expression of 190 genes. Those were compared to the genes whose expression was found associated with viral load: expression of one interferon stimulated gene, OAS1, was found to be regulated by a SNP (rs3177979, p = 4.9E-12); however, we could not detect an independent association of the SNP with viral setpoint. Thus, this study represents an attempt to integrate genome-wide SNP signals with genome-wide expression profiles in the search for biological correlates of HIV-1 control. It underscores the paradox of the association between increasing levels of viral load and greater expression of antiviral defense pathways. It also shows that elite controllers do not have a fully distinctive mRNA expression pattern in CD4+ T cells. Overall, changes in global RNA expression reflect responses to viral replication rather than a mechanism that might explain viral control.
There has been recent progress in understanding the genetic factors that modulate susceptibility to HIV-1 infection. Genetic variation explains to a certain extent differences in disease progression among individuals. Less is known regarding the contribution of differences in gene expression to viral control. The present study evaluated, genome-wide, gene expression levels in CD4+ T cell, the main target of HIV-1. Thereafter, it searched for genetic variants that would modify gene expression. Specific expression profiles associated with high levels of viremia—in particular, the upregulation of genes of the antiviral defense. In contrast, no expression profile associated with effective viral control. Multiple genetic variants modulated gene expression in CD4+ T cells; however, none had a strong influence on viral control. This integrated genome-wide assessment suggests that viral replication drives gene expression rather than expression pointing to mechanisms of viral control.
Analyzing genetic variation of human populations for detecting loci that have been affected by positive natural selection is important for understanding adaptive history and phenotypic variation in humans. In this study, we analyzed recent positive selection in Northern Europe from genome-wide data sets of 250 000 and 500 000 single-nucleotide polymorphisms (SNPs) in a total of 999 individuals from Great Britain, Northern Germany, Eastern and Western Finland, and Sweden. Coalescent simulations were used for demonstrating that the integrated haplotype score (iHS) and long-range haplotype (LRH) statistics have sufficient power in genome-wide data sets of different sample sizes and SNP densities. Furthermore, the behavior of the FST statistic in closely related populations was characterized by allele frequency simulations. In the analysis of the North European data set, 60 regions in the genome showed strong signs of recent positive selection. Out of these, 21 regions have not been discovered in previous scans, and many contain genes with interesting functions (eg, RAB38, INFG, NOS1AP, and APOE). In the putatively selected regions, we observed a statistically significant overrepresentation of genetic association with complex disease, which emphasizes the importance of the analysis of positive selection in understanding the evolution of human disease. Altogether, this study demonstrates the potential of genome-wide data sets to discover loci that lie behind evolutionary adaptation in different human populations.
natural selection; genetic variation; population; Europe
Genetic differences both between individuals and populations are studied for their evolutionary relevance and for their potential medical applications. Most of the genetic differentiation among populations are caused by random drift that should affect all loci across the genome in a similar manner. When a locus shows extraordinary high or low levels of population differentiation, this may be interpreted as evidence for natural selection. The most used measure of population differentiation was devised by Wright and is known as fixation index, or FST. We performed a genome-wide estimation of FST on about 4 millions of SNPs from HapMap project data. We demonstrated a heterogeneous distribution of FST values between autosomes and heterochromosomes. When we compared the FST values obtained in this study with another evolutionary measure obtained by comparative interspecific approach, we found that genes under positive selection appeared to show low levels of population differentiation. We applied a gene set approach, widely used for microarray data analysis, to detect functional pathways under selection. We found that one pathway related to antigen processing and presentation showed low levels of FST, while several pathways related to cell signalling, growth and morphogenesis showed high FST values. Finally, we detected a signature of selection within genes associated with human complex diseases. These results can help to identify which process occurred during human evolution and adaptation to different environments. They also support the hypothesis that common diseases could have a genetic background shaped by human evolution.
Phenotypic divergences between modern human populations have developed as a result of genetic adaptation to local environments over the past 100,000 years. To identify genes involved in population-specific phenotypes, it is necessary to detect signatures of recent positive selection in the human genome. Although detection of elongated linkage disequilibrium (LD) has been a powerful tool in the field of evolutionary genetics, current LD-based approaches are not applicable to already fixed loci. Here, we report a method of scanning for population-specific strong selective sweeps that have reached fixation. In this method, genome-wide SNP data is used to analyze differences in the haplotype frequency, nucleotide diversity, and LD between populations, using the ratio of haplotype homozygosity between populations. To estimate the detection power of the statistics used in this study, we performed computer simulations and found that these tests are relatively robust against the density of typed SNPs and demographic parameters if the advantageous allele has reached fixation. Therefore, we could determine the threshold for maintaining high detection power, regardless of SNP density and demographic history. When this method was applied to the HapMap data, it was able to identify the candidates of population-specific strong selective sweeps more efficiently than the outlier approach that depends on the empirical distribution. This study, confirming strong positive selection on genes previously reported to be associated with specific phenotypes, also identifies other candidates that are likely to contribute to phenotypic differences between human populations.
The Maasai are a pastoral people in Kenya and Tanzania, whose traditional diet of milk, blood and meat is rich in lactose, fat and cholesterol. In spite of this, they have low levels of blood cholesterol, and seldom suffer from gallstones or cardiac diseases. Field studies in the 1970s suggested that the Maasai have a genetic adaptation for cholesterol homeostasis. Analysis of HapMap 3 data using Fixation Index (Fst) and two metrics of haplotype diversity: the integrated Haplotype Score (iHS) and the Cross Population Extended Haplotype Homozygosity (XP-EHH), identified genomic regions and single nucleotide polymorphisms (SNPs) as strong candidates for recent selection for lactase persistence and cholesterol regulation in 143–156 founder individuals from the Maasai population in Kinyawa, Kenya (MKK). The non-synonmous SNP with the highest genome-wide Fst was the TC polymorphism at rs2241883 in Fatty Acid Binding Protein 1(FABP1), known to reduce low density lipoprotein and tri-glyceride levels in Europeans. The strongest signal identified by all three metrics was a 1.7 Mb region on Chr2q21. This region contains the genes LCT (Lactase) and MCM6 (Minichromosome Maintenance Complex Component) involved in lactase persistence, and the gene Rab3GAP1 (Rab3 GTPase-activating Protein Catalytic Subunit), which contains polymorphisms associated with total cholesterol levels in a genome-wide association study of >100,000 individuals of European ancestry. Sanger sequencing of DNA from six MKK samples showed that the GC-14010 polymorphism in the MCM6 gene, known to be associated with lactase persistence in Africans, is segregating in MKK at high frequency (∼58%). The Cytochrome P450 Family 3 Subfamily A (CYP3A) cluster of genes, involved in cholesterol metabolism, was identified by Fst and iHS as candidate loci under selection. Overall, our study identified several specific genomic regions under selection in the Maasai which contain polymorphisms in genes associated with lactase persistence and cholesterol regulation.
The distribution of genetic variation among populations is conveniently measured by Wright’s FST, which is a scaled variance taking on values in [0,1]. For certain types of genetic markers, and for single-nucleotide polymorphisms (SNPs) in particular, it is reasonable to presume that allelic differences at most loci are selectively neutral. For such loci, the distribution of genetic variation among populations is determined by the size of local populations, the pattern and rate of migration among those populations, and the rate of mutation. Because the demographic parameters (population sizes and migration rates) are common across all autosomal loci, locus-specific estimates of FST will depart from a common distribution only for loci with unusually high or low rates of mutation or for loci that are closely associated with genomic regions having a relationship with fitness. Thus, loci that are statistical outliers showing significantly more among-population differentiation than others may mark genomic regions subject to diversifying selection among the sample populations. Similarly, statistical outliers showing significantly less differentiation among populations than others may mark genomic regions subject to stabilizing selection across the sample populations. We propose several Bayesian hierarchical models to estimate locus-specific effects on FST, and we apply these models to single nucleotide polymorphism data from the HapMap project. Because loci that are physically associated with one another are likely to show similar patterns of variation, we introduce conditional autoregressive models to incorporate the local correlation among loci for high-resolution genomic data. We estimate the posterior distributions of model parameters using Markov chain Monte Carlo (MCMC) simulations. Model comparison using several criteria, including DIC and LPML, reveals that a model with locus- and population-specific effects is superior to other models for the data used in the analysis. To detect statistical outliers we propose an approach that measures divergence between the posterior distributions of locus-specific effects and the common FST with the Kullback-Leibler divergence measure. We calibrate this measure by comparing values with those produced from the divergence between a biased and a fair coin. We conduct a simulation study to illustrate the performance of our approach for detecting loci subject to stabilizing/divergent selection, and we apply the proposed models to low- and high-resolution SNP data from the HapMap project. Model comparison using DIC and LPML reveals that CAR models are superior to alternative models for the high resolution data. For both low and high resolution data, we identify statistical outliers that are associated with known genes.
Bayesian approach; Hierarchical model; SNP; Wright’s Fst; MCMC
Allele frequency differences across populations can provide valuable information both for studying population structure and for identifying loci that have been targets of natural selection. Here, we examine the relationship between recombination rate and population differentiation in humans by analyzing two uniformly-ascertained, whole-genome data sets. We find that population differentiation as assessed by inter-continental FST shows negative correlation with recombination rate, with FST reduced by 10% in the tenth of the genome with the highest recombination rate compared with the tenth of the genome with the lowest recombination rate (P≪10−12). This pattern cannot be explained by the mutagenic properties of recombination and instead must reflect the impact of selection in the last 100,000 years since human continental populations split. The correlation between recombination rate and FST has a qualitatively different relationship for FST between African and non-African populations and for FST between European and East Asian populations, suggesting varying levels or types of selection in different epochs of human history.
A common assumption when analyzing patterns of human genetic variation is that most of the genome can be treated as “nearly neutral,” in the sense that the effects of natural selection on allele frequencies are very small compared with the influence of population demographic history. To test the validity of this assumption, we analyzed data from more than a million human polymorphisms and summarized allele frequency differences across populations. We find that, compared with the genome-wide average, allele frequency differences are 7% reduced on average in the tenth of the genome with the highest recombination rate and are 3% increased in the tenth with the lowest rate. Such a correlation cannot be explained by demography. Instead, the pattern reflects the fact that forces of natural selection have had a profound impact on patterns of variation throughout the genome in the last 100,000 years.
Genetic variants in the human androgen receptor gene (AR) are associated with male pattern baldness (androgenetic alopecia, AGA) in Europeans. Previous observations of long-range linkage disequilibrium at the AR locus are consistent with the hypothesis of recent positive selection. Here, we further investigate this signature and its relationship to the AGA risk haplotype. The haplotype homozygosity suggests that the AGA risk haplotype was driven to high frequency by positive selection in Europeans although a low meiotic recombination rate contributed to the high haplotype homozygosity. Further, we find high levels of population differentiation as measured by FST and a series of fixed derived alleles along an extended region centromeric to AR in the Asian HapMap sample. The predominant AGA risk haplotype also carries the putatively functional variant 57K in the flanking ectodysplasin A2 receptor gene (EDA2R). It is therefore probable that the AGA risk haplotype rose to high frequency in combination with this EDA2R variant, possibly by hitchhiking on a positively selected 57K haplotype.
Electronic supplementary material
The online version of this article (doi:10.1007/s00439-009-0668-z) contains supplementary material, which is available to authorized users.
The creation of a coherent genomic map of recent selection is one of the greatest challenges towards a better understanding of human evolution and the identification of functional genetic variants. Several methods have been proposed to detect linkage disequilibrium (LD), which is indicative of natural selection, from genome-wide profiles of common genetic variations but are designed for large regions.
To find population-specific LD within small regions, we have devised an entropy-based method that utilizes differences in haplotype frequency between populations. The method has the advantages of incorporating multilocus association, conciliation with low allele frequencies, and independence from allele polarity, which are ideal for short haplotype analysis. The comparison of HapMap SNPs data from African and Caucasian populations with a median resolution size of ~23 kb gave us novel candidates as well as known selection targets. Enrichment analysis for the yielded genes showed associations with diverse diseases such as cardiovascular, immunological, neurological, and skeletal and muscular diseases. A possible scenario for a selective force is discussed. In addition, we have developed a web interface (ENIGMA, available at http://gibk21.bse.kyutech.ac.jp/ENIGMA/index.html), which allows researchers to query their regions of interest for population-specific LD.
The haplotype entropy method is powerful for detecting population-specific LD embedded in short regions and should contribute to further studies aiming to decipher the evolutionary histories of modern humans.
Large-scale genome-wide association studies are promising for unraveling the genetic basis of complex diseases. Population structure is a potential problem, the effects of which on genetic association studies are controversial. The first step to systematically quantify the effects of population structure is to choose an appropriate measure of population structure for human data. The commonly used measure is Wright's FST. For a set of subpopulations it is generally assumed to be one value of FST. However, the estimates could be different for distinct loci. Since population structure is a concept at the population level, a measure of population structure that utilized the information across loci would be desirable.
In this study we propose an adjusted C parameter according to the sample size from each sub-population. The new measure C is based on the c parameter proposed for SNP data, which was assumed to be subpopulation-specific and common for all loci. In this study, we performed extensive simulations of samples with varying levels of population structure to investigate the properties and relationships of both measures. It is found that the two measures generally agree well.
The new measure simultaneously uses the marker information across the genome. It has the advantage of easy interpretation as one measure of population structure and yet can also assess population differentiation.
'Selection signatures' delimit regions of the genome that are, or have been, functionally important and have therefore been under either natural or artificial selection. In this study, two different and complementary methods--integrated Haplotype Homozygosity Score (|iHS|) and population differentiation index (FST)--were applied to identify traces of decades of intensive artificial selection for traits of economic importance in modern cattle.
We scanned the genome of a diverse set of dairy and beef breeds from Germany, Canada and Australia genotyped with a 50 K SNP panel. Across breeds, a total of 109 extreme |iHS| values exceeded the empirical threshold level of 5% with 19, 27, 9, 10 and 17 outliers in Holstein, Brown Swiss, Australian Angus, Hereford and Simmental, respectively. Annotating the regions harboring clustered |iHS| signals revealed a panel of interesting candidate genes like SPATA17, MGAT1, PGRMC2 and ACTC1, COL23A1, MATN2, respectively, in the context of reproduction and muscle formation. In a further step, a new Bayesian FST-based approach was applied with a set of geographically separated populations including Holstein, Brown Swiss, Simmental, North American Angus and Piedmontese for detecting differentiated loci. In total, 127 regions exceeding the 2.5 per cent threshold of the empirical posterior distribution were identified as extremely differentiated. In a substantial number (56 out of 127 cases) the extreme FST values were found to be positioned in poor gene content regions which deviated significantly (p < 0.05) from the expectation assuming a random distribution. However, significant FST values were found in regions of some relevant genes such as SMCP and FGF1.
Overall, 236 regions putatively subject to recent positive selection in the cattle genome were detected. Both |iHS| and FST suggested selection in the vicinity of the Sialic acid binding Ig-like lectin 5 gene on BTA18. This region was recently reported to be a major QTL with strong effects on productive life and fertility traits in Holstein cattle. We conclude that high-resolution genome scans of selection signatures can be used to identify genomic regions contributing to within- and inter-breed phenotypic variation.
The population structure of an organism reflects its evolutionary history and influences its evolutionary trajectory. It constrains the combination of genetic diversity and reveals patterns of past gene flow. Understanding it is a prerequisite for detecting genomic regions under selection, predicting the effect of population disturbances, or modeling gene flow. This paper examines the detailed global population structure of Arabidopsis thaliana. Using a set of 5,707 plants collected from around the globe and genotyped at 149 SNPs, we show that while A. thaliana as a species self-fertilizes 97% of the time, there is considerable variation among local groups. This level of outcrossing greatly limits observed heterozygosity but is sufficient to generate considerable local haplotypic diversity. We also find that in its native Eurasian range A. thaliana exhibits continuous isolation by distance at every geographic scale without natural breaks corresponding to classical notions of populations. By contrast, in North America, where it exists as an exotic species, A. thaliana exhibits little or no population structure at a continental scale but local isolation by distance that extends hundreds of km. This suggests a pattern for the development of isolation by distance that can establish itself shortly after an organism fills a new habitat range. It also raises questions about the general applicability of many standard population genetics models. Any model based on discrete clusters of interchangeable individuals will be an uneasy fit to organisms like A. thaliana which exhibit continuous isolation by distance on many scales.
Much of the modern field of population genetics is premised on particular models of what an organism's population structure is and how it behaves. The classic models generally start with the idea of a single randomly mating population that has reached an evolutionary equilibrium. Many models relax some of these assumptions, allowing for phenomena such as assortative mating, discrete sub-populations with migration, self-fertilization, and sex-ratio distortion. Virtually all models, however, have as their core premise the notion that there exist classes of exchangeable individuals each of which represents an identical, independent sample from that class' distribution. For certain organisms, such as Drosophila melanogaster, these models do an excellent job of describing how populations work. For other organisms, such as humans, these models can be reasonable approximations but require a great deal of care in assembling samples and can begin to break down as sampling becomes locally dense. For the vast majority of organisms the applicability of these models has never been investigated.
Understanding the patterns of genetic variation within and among populations is a central problem in population and evolutionary genetics. We examine this question in the acorn barnacle, Semibalanus balanoides, in which the allozyme loci Mpi and Gpi have been implicated in balancing selection due to varying selective pressures at different spatial scales. We review the patterns of genetic variation at the Mpi locus, compare this to levels of population differentiation at mtDNA and microsatellites, and place these data in the context of genome-wide variation from high-throughput sequencing of population samples spanning the North Atlantic. Despite considerable geographic variation in the patterns of selection at the Mpi allozyme, this locus shows rather low levels of population differentiation at ecological and trans-oceanic scales (FST ∼ 5%). Pooled population sequencing was performed on samples from Rhode Island (RI), Maine (ME), and Southwold, England (UK). Analysis of more than 650 million reads identified approximately 335,000 high-quality SNPs in 19 million base pairs of the S. balanoides genome. Much variation is shared across the Atlantic, but there are significant examples of strong population differentiation among samples from RI, ME, and UK. An FST outlier screen of more than 22,000 contigs provided a genome-wide context for interpretation of earlier studies on allozymes, mtDNA, and microsatellites. FST values for allozymes, mtDNA and microsatellites are close to the genome-wide average for random SNPs, with the exception of the trans-Atlantic FST for mtDNA. The majority of FST outliers were unique between individual pairs of populations, but some genes show shared patterns of excess differentiation. These data indicate that gene flow is high, that selection is strong on a subset of genes, and that a variety of genes are experiencing diversifying selection at large spatial scales. This survey of polymorphism in S. balanoides provides a number of genomic tools that promise to make this a powerful model for ecological genomics of the rocky intertidal.
Genome-wide association studies (GWAS) simultaneously investigating hundreds of thousands of single nucleotide polymorphisms (SNP) have become a powerful tool in the investigation of new disease susceptibility loci. Haplotypes are sometimes thought to be superior to SNPs and are promising in genetic association analyses. The application of genome-wide haplotype analysis, however, is hindered by the complexity of haplotypes themselves and sophistication in computation. We systematically analyzed the haplotype effects for breast cancer risk among 5,761 African American women (3,016 cases and 2,745 controls) using a sliding window approach on the genome-wide scale. Three regions on chromosomes 1, 4 and 18 exhibited moderate haplotype effects. Furthermore, among 21 breast cancer susceptibility loci previously established in European populations, 10p15 and 14q24 are likely to harbor novel haplotype effects. We also proposed a heuristic of determining the significance level and the effective number of independent tests by the permutation analysis on chromosome 22 data. It suggests that the effective number was approximately half of the total (7,794 out of 15,645), thus the half number could serve as a quick reference to evaluating genome-wide significance if a similar sliding window approach of haplotype analysis is adopted in similar populations using similar genotype density.
Humans show tremendous phenotypic diversity across geographically distributed populations, and much of this diversity undoubtedly results from genetic adaptations to different environmental pressures. The availability of genome-wide genetic variation data from densely sampled populations offers unprecedented opportunities for identifying the loci responsible for these adaptations and for elucidating the genetic architecture of human adaptive traits. Several approaches have been used to detect signals of selection in human populations, and these approaches differ in the assumptions they make about the underlying mode of selection. We contrast the results of approaches based on haplotype structure and differentiation of allele frequencies to those from a method for identifying single nucleotide polymorphisms strongly correlated with environmental variables. Although the first group of approaches tends to detect new beneficial alleles that were driven to high frequencies by selection, the environmental correlation approach has power to identify alleles that experienced small shifts in frequency owing to selection. We suggest that the first group of approaches tends to identify only variants with relatively strong phenotypic effects, whereas the environmental correlation methods can detect variants that make smaller contributions to an adaptive trait.
climate; subsistence; genome-wide association studies; pigmentation; energy metabolism; soft sweeps
Thoroughbred horses have been selected for exceptional racing performance resulting in system-wide structural and functional adaptations contributing to elite athletic phenotypes. Because selection has been recent and intense in a closed population that stems from a small number of founder animals Thoroughbreds represent a unique population within which to identify genomic contributions to exercise-related traits. Employing a population genetics-based hitchhiking mapping approach we performed a genome scan using 394 autosomal and X chromosome microsatellite loci and identified positively selected loci in the extreme tail-ends of the empirical distributions for (1) deviations from expected heterozygosity (Ewens-Watterson test) in Thoroughbred (n = 112) and (2) global differentiation among four geographically diverse horse populations (FST). We found positively selected genomic regions in Thoroughbred enriched for phosphoinositide-mediated signalling (3.2-fold enrichment; P<0.01), insulin receptor signalling (5.0-fold enrichment; P<0.01) and lipid transport (2.2-fold enrichment; P<0.05) genes. We found a significant overrepresentation of sarcoglycan complex (11.1-fold enrichment; P<0.05) and focal adhesion pathway (1.9-fold enrichment; P<0.01) genes highlighting the role for muscle strength and integrity in the Thoroughbred athletic phenotype. We report for the first time candidate athletic-performance genes within regions targeted by selection in Thoroughbred horses that are principally responsible for fatty acid oxidation, increased insulin sensitivity and muscle strength: ACSS1 (acyl-CoA synthetase short-chain family member 1), ACTA1 (actin, alpha 1, skeletal muscle), ACTN2 (actinin, alpha 2), ADHFE1 (alcohol dehydrogenase, iron containing, 1), MTFR1 (mitochondrial fission regulator 1), PDK4 (pyruvate dehydrogenase kinase, isozyme 4) and TNC (tenascin C). Understanding the genetic basis for exercise adaptation will be crucial for the identification of genes within the complex molecular networks underlying obesity and its consequential pathologies, such as type 2 diabetes. Therefore, we propose Thoroughbred as a novel in vivo large animal model for understanding molecular protection against metabolic disease.
Various observations argue for a role of adaptation in recent human evolution, including results from genome-wide studies and analyses of selection signals at candidate genes. Here, we use genome-wide SNP data from the HapMap and CEPH-Human Genome Diversity Panel samples to study the geographic distributions of putatively selected alleles at a range of geographic scales. We find that the average allele frequency divergence is highly predictive of the most extreme FST values across the whole genome. On a broad scale, the geographic distribution of putatively selected alleles almost invariably conforms to population clusters identified using randomly chosen genetic markers. Given this structure, there are surprisingly few fixed or nearly fixed differences between human populations. Among the nearly fixed differences that do exist, nearly all are due to fixation events that occurred outside of Africa, and most appear in East Asia. These patterns suggest that selection is often weak enough that neutral processes—especially population history, migration, and drift—exert powerful influences over the fate and geographic distribution of selected alleles.
Since the beginning of the study of evolution, people have been fascinated by recent human evolution and adaptation. Despite great progress in our understanding of human history, we still know relatively little about the selection pressures and historical factors that have been important over the past 100,000 years. In that time human populations have spread around the world and adapted in a wide variety of ways to the new environments they have encountered. Here, we investigate the genomic signal of these adaptations using a large set of geographically diverse human populations typed at thousands of genetic markers across the genome. We find that patterns at selected loci are predictable from the patterns found at all markers genome-wide. On the basis of this, we argue that selection has been strongly constrained by the historical relationships and gene flow between populations.
It is likely that evolutionary differences among species are driven by sequence changes in regulatory regions. Likewise, polymorphisms in the promoter regions may be responsible for interindividual differences at the level of populations. We present an unbiased survey of genetic variation in 2-kb segments upstream of the transcription start sites of 28 protein-coding genes, characterized in five population groups of different geographic origin. On average, we found 9.1 polymorphisms and 8.8 haplotypes per segment with corresponding nucleotide and haplotype diversities of 0.082% and 58%, respectively. We characterized these segments through different summary statistics, Hardy-Weinberg equilibria fixation index (Fst) estimates, and neutrality tests, as well as by analyzing the distributions of haplotype allelic classes, introduced here to assess the departure from neutrality and examined by coalescent simulations under a simple population model, assuming recombinations or different demography. Our results suggest that genetic diversity in some of these regions could have been shaped by purifying selection and driven by adaptive changes in the other, thus explaining the relatively large variance in the corresponding genetic diversity indices loci. However, some of these effects could be also due to linkage with surrounding sequences, and the neutralists' explanations cannot be ruled out given uncertainty in the underlying demographic histories and the possibility of random effects due to the small size of the studied segments. Hum Mutat 28(5), 441–450, 2007. © 2007 Wiley-Liss, Inc.
DNA diversity; promoter regions; haplotypes; selective sweeps; human populations
Divergent natural selection caused by differences in solar exposure has resulted in distinctive variations in skin color between human populations. The derived light skin color allele of the SLC24A5 gene, A111T, predominates in populations of Western Eurasian ancestry. To gain insight into when and where this mutation arose, we defined common haplotypes in the genomic region around SLC24A5 across diverse human populations and deduced phylogenetic relationships between them. Virtually all chromosomes carrying the A111T allele share a single 78-kb haplotype that we call C11, indicating that all instances of this mutation in human populations share a common origin. The C11 haplotype was most likely created by a crossover between two haplotypes, followed by the A111T mutation. The two parental precursor haplotypes are found from East Asia to the Americas but are nearly absent in Africa. The distributions of C11 and its parental haplotypes make it most likely that these two last steps occurred between the Middle East and the Indian subcontinent, with the A111T mutation occurring after the split between the ancestors of Europeans and East Asians.
natural selection; skin color; SLC24A5; haplotype; recombination
During the past decades, neutral DNA markers have been extensively employed to study demography, population genetics and structure in livestock, but less interest has been devoted to the evaluation of livestock adaptive potential through the identification of genomic regions likely to be under natural selection.
Landscape genomics can greatly benefit the entire livestock system through the identification of genotypes better adapted to specific or extreme environmental conditions. Therefore we analyzed 101 AFLP markers in 43 European and Western Asian goat breeds both with Matsam software, based on a correlative approach (SAM), and with Mcheza and Bayescan, two FST based software able to detect markers carrying signatures of natural selection.
Matsam identified four loci possibly under natural selection – also confirmed by FST-outlier methods – and significantly associated with environmental variables such as diurnal temperature range, frequency of precipitation, relative humidity and solar radiation.
These results show that landscape genomics can provide useful information on the environmental factors affecting the adaptive potential of livestock living in specific climatic conditions. Besides adding conservation value to livestock genetic resources, this knowledge may lead to the development of novel molecular tools useful to preserve the adaptive potential of local breeds during genetic improvement programs, and to increase the adaptability of industrial breeds to changing environments.
The wide use of genome wide association studies (GWAS) has led to the successful identification of multiple genetic susceptibility variants to several complex human diseases. Given the limited amount of data on genetic variation at these loci in populations of non-European origin, we investigated population variation among 11 population groups for loci showing strong and consistent association from GWAS with several complex human diseases.
Data from the International HapMap Project Phase 3, comprising 11 population groups, were used to estimate allele frequencies at loci showing strong and consistent association from GWAS with any of 26 complex human diseases and traits. Allele frequency summary statistics and FST at each locus were used to estimate population differentiation.
There is wide variation in allele frequencies and FST across the 11 population groups for susceptibility loci to these complex human diseases and traits. Allele frequencies varied widely across populations, often by as much as 20- to 40-fold. FST, as a measure of population differentiation, also varied widely across the loci studied (for example, 0.019 to 0.201 for type 2 diabetes, 0.022 to 0.520 for prostate cancer loci, and 0.006 to 0.520 for serum lipid levels).
The public health risk posed by any of these risk alleles is likely to show wide variation across populations simply as a function of its frequency, and this risk difference may be amplified by gene-gene and gene-environment interactions. These analyses offer compelling reasons for including multiple human populations from different parts of the world in the international effort to use genomic tools to understand disease etiology and differential distribution of diseases across ethnic groups.
Complex disease; Genome wide association studies; Population genetics
Surveying deleterious variation in human populations is crucial for our understanding, diagnosis and potential treatment of human genetic pathologies. A number of recent genome-wide analyses focused on the prevalence of segregating deleterious alleles in the nuclear genome. However, such studies have not been conducted for the mitochondrial genome.
We present a systematic survey of polymorphisms in the human mitochondrial genome, including those predicted to be deleterious and those that correspond to known pathogenic mutations. Analyzing 4458 completely sequenced mitochondrial genomes we characterize the genetic diversity of different types of single nucleotide polymorphisms (SNPs) in African (L haplotypes) and non-African (M and N haplotypes) populations. We find that the overall level of polymorphism is higher in the mitochondrial compared to the nuclear genome, although the mitochondrial genome appears to be under stronger selection as indicated by proportionally fewer nonsynonymous than synonymous substitutions. The African mitochondrial genomes show higher heterozygosity, a greater number of polymorphic sites and higher frequencies of polymorphisms for synonymous, benign and damaging polymorphism than non-African genomes. However, African genomes carry significantly fewer SNPs that have been previously characterized as pathogenic compared to non-African genomes.
Finding SNPs classified as pathogenic to be the only category of polymorphisms that are more abundant in non-African genomes is best explained by a systematic ascertainment bias that favours the discovery of pathogenic polymorphisms segregating in non-African populations. This further suggests that, contrary to the common disease-common variant hypothesis, pathogenic mutations are largely population-specific and different SNPs may be associated with the same disease in different populations. Therefore, to obtain a comprehensive picture of the deleterious variability in the human population, as well as to improve the diagnostics of individuals carrying African mitochondrial haplotypes, it is necessary to survey different populations independently.
This article was reviewed by Dr Mikhail Gelfand, Dr Vasily Ramensky (nominated by Dr Eugene Koonin) and Dr David Rand (nominated by Dr Laurence Hurst).
The goal of genome wide analyses of polymorphisms is to achieve a better understanding of the link between genotype and phenotype. Part of that goal is to understand the selective forces that have operated on a population.
In this study we compared the signals of selection, identified through population divergence in the Bovine HapMap project, to those found in an independent sample of cattle from Australia. Evidence for population differentiation across the genome, as measured by FST, was highly correlated in the two data sets. Nevertheless, 40% of the variance in FST between the two studies was attributed to the differences in breed composition. Seventy six percent of the variance in FST was attributed to differences in SNP composition and density when the same breeds were compared. The difference between FST of adjacent loci increased rapidly with the increase in distance between SNP, reaching an asymptote after 20 kb. Using 129 SNP that have highly divergent FST values in both data sets, we identified 12 regions that had additive effects on the traits residual feed intake, beef yield or intramuscular fatness measured in the Australian sample. Four of these regions had effects on more than one trait. One of these regions includes the R3HDM1 gene, which is under selection in European humans.
Firstly, many different populations will be necessary for a full description of selective signatures across the genome, not just a small set of highly divergent populations. Secondly, it is necessary to use the same SNP when comparing the signatures of selection from one study to another. Thirdly, useful signatures of selection can be obtained where many of the groups have only minor genetic differences and may not be clearly separated in a principal component analysis. Fourthly, combining analyses of genome wide selection signatures and genome wide associations to traits helps to define the trait under selection or the population group in which the QTL is likely to be segregating. Finally, the FST difference between adjacent loci suggests that 150,000 evenly spaced SNP will be required to study selective signatures in all parts of the bovine genome.