Recent and ongoing selection in the human genome

Human brain evolution: harnessing the genomics (r)evolution to link genes, cognition, and behavior

The evolution of the human brain has resulted in numerous specialized features including higher cognitive processes such as language. Knowledge of whole-genome sequence and structural variation via high-throughput sequencing technology provides an unprecedented opportunity to view human evolution at high resolution. However, phenotype discovery is a critical component of these endeavors and the use of nontraditional model organisms will also be critical for piecing together a complete picture. Ultimately, the union of developmental studies of the brain with studies of unique phenotypes in a myriad of species will result in a more thorough model of the groundwork the human brain was built upon. Furthermore, these integrative approaches should provide important insights into human diseases.

Genetic variations in Tibetan populations and high-altitude adaptation at the Himalayas

Modern humans have occupied almost all possible environments globally since exiting Africa about 100,000 years ago. Both behavioral and biological adaptations have contributed to their success in surviving the rigors of climatic extremes, including cold, strong ultraviolet radiation, and high altitude. Among these environmental stresses, high-altitude hypoxia is the only condition in which traditional technology is incapable of mediating its effects. Inhabiting at >3,000-m high plateau, the Tibetan population provides a widely studied example of high-altitude adaptation. Yet, the genetic mechanisms underpinning long-term survival in this environmental extreme remain unknown. We performed an analysis of genome-wide sequence variations in Tibetans. In combination with the reported data, we identified strong signals of selective sweep in two hypoxia-related genes, EPAS1 and EGLN1. For these two genes, Tibetans show unusually high divergence from the non-Tibetan lowlanders (Han Chinese and Japanese) and possess high frequencies of many linked sequence variations as reflected by the Tibetan-specific haplotypes. Further analysis in seven Tibetan populations (1,334 individuals) indicates the prevalence of selective sweep across the Himalayan region. The observed indicators of natural selection on EPAS1 and EGLN1 suggest that during the long-term occupation of high-altitude areas, the functional sequence variations for acquiring biological adaptation to high-altitude hypoxia have been enriched in Tibetan populations.

Allele-specific down-regulation of RPTOR expression induced by retinoids contributes to climate adaptations

The mechanistic target of rapamycin (MTOR) pathway regulates cell growth, energy homeostasis, apoptosis, and immune response. The regulatory associated protein of MTOR encoded by the RPTOR gene is a key component of this pathway. A previous survey of candidate genes found that RPTOR contains multiple SNPs with strong correlations between allele frequencies and climate variables, consistent with the action of selective pressures that vary across environments. Using data from a recent genome scan for selection signals, we honed in on a SNP (rs11868112) 26 kb upstream to the transcription start site of RPTOR that exhibits the strongest association with temperature variables. Transcription factor motif scanning and mining of recently mapped transcription factor binding sites identified a binding site for POU class 2 homeobox 1 (POU2F1) spanning the SNP and an adjacent retinoid acid receptor (RAR) binding site. Using expression quantification, chromatin immunoprecipitation (ChIP), and reporter gene assays, we demonstrate that POU2F1 and RARA do bind upstream of the RPTOR gene to regulate its expression in response to retinoids; this regulation is affected by the allele status at rs11868112 with the derived allele resulting in lower expression levels. We propose a model in which the derived allele influences thermogenesis or immune response by altering MTOR pathway activity and thereby increasing fitness in colder climates. Our results show that signatures of genetic adaptations can identify variants with functional effects, consistent with the idea that selection signals may be used for SNP annotation.

Climate has exerted strong selective pressures in human populations during their dispersal, and signatures of these adaptations are still detectable in the geographic distribution of polymorphisms. RPTOR is a key component of the mechanistic target of rapamycin pathway, which regulates cell growth, metabolism, and immune response; and its deregulation is associated with human diseases, including cancer and diabetes. Previous studies showed that variation in RPTOR carry strong signatures of adaptations to different climates. Here, we used evolutionary genetics approaches coupled with transcription factor motif data mining to refine the location of the selection target. We then used functional assays to show that the selected polymorphism resides in a sequence element that regulates gene expression levels in response to retinoids. The derived allele at this SNP, which results in lower expression levels, increases in frequency with decreasing temperatures, consistent with the notion that it confers a selective advantage in colder climates possibly through its effects on energy metabolism or immune response. These results suggest a novel regulatory role for retinoids in MTOR signaling. Moreover, they support the proposal that evolutionary approaches can be informative for SNP functional annotation.

Adaptive selection of an incretin gene in Eurasian populations

Diversities in human physiology have been partially shaped by adaptation to natural environments and changing cultures. Recent genomic analyses have revealed single nucleotide polymorphisms (SNPs) that are associated with adaptations in immune responses, obvious changes in human body forms, or adaptations to extreme climates in select human populations. Here, we report that the human GIP locus was differentially selected among human populations based on the analysis of a nonsynonymous SNP (rs2291725). Comparative and functional analyses showed that the human GIP gene encodes a cryptic glucose-dependent insulinotropic polypeptide (GIP) isoform (GIP55S or GIP55G) that encompasses the SNP and is resistant to serum degradation relative to the known mature GIP peptide. Importantly, we found that GIP55G, which is encoded by the derived allele, exhibits a higher bioactivity compared with GIP55S, which is derived from the ancestral allele. Haplotype structure analysis suggests that the derived allele at rs2291725 arose to dominance in East Asians ∼8100 yr ago due to positive selection. The combined results suggested that rs2291725 represents a functional mutation and may contribute to the population genetics observation. Given that GIP signaling plays a critical role in homeostasis regulation at both the enteroinsular and enteroadipocyte axes, our study highlights the importance of understanding adaptations in energy-balance regulation in the face of the emerging diabetes and obesity epidemics.

Recent and recurrent selective sweeps of the antiviral RNAi gene Argonaute-2 in three species of Drosophila

Antagonistic host–parasite interactions can drive rapid adaptive evolution in genes of the immune system, and such arms races may be an important force shaping polymorphism in the genome. The RNA interference pathway gene Argonaute-2 (AGO2) is a key component of antiviral defense in Drosophila, and we have previously shown that genes in this pathway experience unusually high rates of adaptive substitution. Here we study patterns of genetic variation in a 100-kbp region around AGO2 in three different species of Drosophila. Our data suggest that recent independent selective sweeps in AGO2 have reduced genetic variation across a region of more than 50 kbp in Drosophila melanogaster, D. simulans, and D. yakuba, and we estimate that selection has fixed adaptive substitutions in this gene every 30–100 thousand years. The strongest signal of recent selection is evident in D. simulans, where we estimate that the most recent selective sweep involved an allele with a selective advantage of the order of 0.5–1% and occurred roughly 13–60 Kya. To evaluate the potential consequences of the recent substitutions on the structure and function of AGO2, we used fold-recognition and homology-based modeling to derive a structural model for the Drosophila protein, and this suggests that recent substitutions in D. simulans are overrepresented at the protein surface. In summary, our results show that selection by parasites can consistently target the same genes in multiple species, resulting in areas of the genome that have markedly reduced genetic diversity.

The origins and evolution of genetic disease risk in modern humans

Patterns and risks of human disease have evolved. In this article, I review evidence regarding the importance of recent adaptive evolution, positive selection, and genomic conflicts in shaping the genetic and phenotypic architectures of polygenic human diseases. Strong recent selection in human populations can create and maintain genetically based disease risk primarily through three processes: increased scope for dysregulation from recent human adaptations, divergent optima generated by intraspecific genomic conflicts, and transient or stable deleterious by-products of positive selection caused by antagonistic pleiotropy, ultimately due to trade-offs at the levels of molecular genetics, development, and physiology. Human disease due to these processes appears to be concentrated in three sets of phenotypes: cognition and emotion, reproductive traits, and life-history traits related to long life-span. Diverse, convergent lines of evidence suggest that a small set of tissues whose pleiotropic patterns of gene function and expression are under especially strong selection-brain, placenta, testis, prostate, breast, and ovary-has mediated a considerable proportion of disease risk in modern humans.

A novel adaptive method for the analysis of next-generation sequencing data to detect complex trait associations with rare variants due to gene main effects and interactions

There is solid evidence that rare variants contribute to complex disease etiology. Next-generation sequencing technologies make it possible to uncover rare variants within candidate genes, exomes, and genomes. Working in a novel framework, the kernel-based adaptive cluster (KBAC) was developed to perform powerful gene/locus based rare variant association testing. The KBAC combines variant classification and association testing in a coherent framework. Covariates can also be incorporated in the analysis to control for potential confounders including age, sex, and population substructure. To evaluate the power of KBAC: 1) variant data was simulated using rigorous population genetic models for both Europeans and Africans, with parameters estimated from sequence data, and 2) phenotypes were generated using models motivated by complex diseases including breast cancer and Hirschsprung's disease. It is demonstrated that the KBAC has superior power compared to other rare variant analysis methods, such as the combined multivariate and collapsing and weight sum statistic. In the presence of variant misclassification and gene interaction, association testing using KBAC is particularly advantageous. The KBAC method was also applied to test for associations, using sequence data from the Dallas Heart Study, between energy metabolism traits and rare variants in ANGPTL 3,4,5 and 6 genes. A number of novel associations were identified, including the associations of high density lipoprotein and very low density lipoprotein with ANGPTL4. The KBAC method is implemented in a user-friendly R package.

It has been demonstrated that both rare and common variants are involved in complex disease etiology. Until recently it was only possible to perform large scale analysis of common variants. With the development of next-generation sequencing technologies, detection and mapping of rare variants have been made possible. However, methods used to analyze common variants are not powerful for the analysis of rare variants. To address the problems of rare variant analysis working in a novel framework, the kernel-based adaptive cluster (KBAC) method was developed to perform gene/locus based analysis. The KBAC combines variant classification and association testing in a coherent framework. Through simulations motivated by population genetic and disease data, it is demonstrated that the KBAC has superior power to other rare variant analysis methods, especially in the presence of variant misclassification and gene interaction. Using data from the Dallas Heart Study, the KBAC method was applied to test for associations between energy metabolism traits and rare variants in ANGPTL 3,4,5 and 6 genes. A number of novel associations were identified. The KBAC method is implemented in a user-friendly R package.

The neutral theory of molecular evolution in the genomic era

The neutral theory of molecular evolution has been widely accepted and is the guiding principle for studying evolutionary genomics and the molecular basis of phenotypic evolution. Recent data on genomic evolution are generally consistent with the neutral theory. However, many recently published papers claim the detection of positive Darwinian selection via the use of new statistical methods. Examination of these methods has shown that their theoretical bases are not well established and often result in high rates of false-positive and false-negative results. When the deficiencies of these statistical methods are rectified, the results become largely consistent with the neutral theory. At present, genome-wide analyses of natural selection consist of collections of single-locus analyses. However, because phenotypic evolution is controlled by the interaction of many genes, the study of natural selection ought to take such interactions into account. Experimental studies of evolution will also be crucial.

Efficient population assignment and outlier detection in human populations using biallelic markers chosen by principal component-based rankings

Whole-genome studies of genetic variation are now performed routinely and have accelerated the identification of disease-associated allelic variants, positive selection, recombination, and structural variation. However, these studies are sensitive to the presence of outlier data from individuals of different ancestry than the rest of the sample. Currently, the most common method of excluding outlier individuals is to collect a population sample and exclude outliers after genome-wide data have been collected. Here we show that a small collection of 20-27 polymorphic Alu insertions, selected using a principal component-based method with genetic ancestry estimates, may be used to easily assign Africans, East Asians, and Europeans to their population of origin. In addition, we show that samples from a geographically and genetically intermediate population (in our study, samples from India) can be identified within the original sample of Africans, East Asians, and Europeans. Finally, we show that outlier individuals from neighboring geographic regions (in our study, Yemen and sub-Saharan Africa) can be identified. These results will be of value in preselection of samples for more in-depth analysis as well as customized identification of maximally informative polymorphic markers for regional studies.

Adaptation - not by sweeps alone

There has been recent progress in identifying selective sweeps underlying a range of adaptations. Jonathan Pritchard and Anna Di Rienzo argue that many adaptive events in natural populations may occur by polygenic adaptation, which would largely go undetected by conventional methods for detecting selection.

Evolutionary evidence of the effect of rare variants on disease etiology

The common disease/common variant hypothesis has been popular for describing the genetic architecture of common human diseases for several years. According to the originally stated hypothesis, one or a few common genetic variants with a large effect size control the risk of common diseases. A growing body of evidence, however, suggests that rare single-nucleotide polymorphisms (SNPs), i.e. those with a minor allele frequency of less than 5%, are also an important component of the genetic architecture of common human diseases. In this study, we analyzed the relevance of rare SNPs to the risk of common diseases from an evolutionary perspective and found that rare SNPs are more likely than common SNPs to be functional and tend to have a stronger effect size than do common SNPs. This observation, and the fact that most of the SNPs in the human genome are rare, suggests that rare SNPs are a crucial element of the genetic architecture of common human diseases. We propose that the next generation of genomic studies should focus on analyzing rare SNPs. Further, targeting patients with a family history of the disease, an extreme phenotype, or early disease onset may facilitate the detection of risk-associated rare SNPs.

Population genetic analysis of the uncoupling proteins supports a role for UCP3 in human cold resistance

Production of heat via nonshivering thermogenesis (NST) is critical for temperature homeostasis in mammals. Uncoupling protein UCP1 plays a central role in NST by uncoupling the proton gradients produced in the inner membranes of mitochondria to produce heat; however, the extent to which UCP1 homologues, UCP2 and UCP3, are involved in NST is the subject of an ongoing debate. We used an evolutionary approach to test the hypotheses that variants that are associated with increased expression of these genes (UCP1 -3826A, UCP2 -866A, and UCP3 -55T) show evidence of adaptation with winter climate. To that end, we calculated correlations between allele frequencies and winter climate variables for these single-nucleotide polymorphisms (SNPs), which we genotyped in a panel of 52 worldwide populations. We found significant correlations with winter climate for UCP1 -3826G/A and UCP3 -55C/T. Further, by analyzing previously published genotype data for these SNPs, we found that the peak of the correlation for the UCP1 region occurred at the disease-associated -3826A/G variant and that the UCP3 region has a striking signal overall, with several individual SNPs showing interesting patterns, including the -55C/T variant. Resequencing of the regions in a set of three diverse population samples helped to clarify the signals that we found with the genotype data. At UCP1, the resequencing data revealed modest evidence that the haplotype carrying the -3826A variant was driven to high frequency by selection. In the UCP3 region, combining results from the climate analysis and resequencing survey suggest a more complex model in which variants on multiple haplotypes may independently be correlated with temperature. This is further supported by an excess of intermediate frequency variants in the UCP3 region in the Han Chinese population. Taken together, our results suggest that adaptation to climate influenced the global distribution of allele frequencies in UCP1 and UCP3 and provide an independent source of evidence for a role in cold resistance for UCP3.

Evolutionary adaptations to dietary changes

Through cultural innovation and changes in habitat and ecology, there have been a number of major dietary shifts in human evolution, including meat eating, cooking, and those associated with plant and animal domestication. The identification of signatures of adaptations to such dietary changes in the genome of extant primates (including humans) may shed light not only on the evolutionary history of our species, but also on the mechanisms that underlie common metabolic diseases in modern human populations. In this review, we provide a brief overview of the major dietary shifts that occurred during hominin evolution, and we discuss the methods and approaches used to identify signals of natural selection in patterns of sequence variation. We then review the results of studies aimed at detecting the genetic loci that played a major role in dietary adaptations and conclude by outlining the potential of future studies in this area.

Association screening of common and rare genetic variants by penalized regression

MOTIVATION

This article extends our recent research on penalized estimation methods in genome-wide association studies to the realm of rare variants.

RESULTS

The new strategy is tested on both simulated and real data. Our findings on breast cancer data replicate previous results and shed light on variant effects within genes.

AVAILABILITY

Rare variant discovery by group penalized regression is now implemented in the free program Mendel at http://www.genetics.ucla.edu/software/.

Natural selection shapes nucleotide polymorphism across the genome of the nematode Caenorhabditis briggsae

The combined actions of natural selection, mutation, and recombination forge the landscape of genetic variation across genomes. One frequently observed manifestation of these processes is a positive association between neutral genetic variation and local recombination rates. Two selective mechanisms and/or recombination-associated mutation (RAM) could generate this pattern, and the relative importance of these alternative possibilities remains unresolved generally. Here we quantify nucleotide differences within populations, between populations, and between species to test for genome-wide effects of selection and RAM in the partially selfing nematode Caenorhabditis briggsae. We find that nearly half of genome-wide variation in nucleotide polymorphism is explained by differences in local recombination rates. By quantifying divergence between several reproductively isolated lineages, we demonstrate that ancestral polymorphism generates a spurious signal of RAM for closely related lineages, with implications for analyses of humans and primates; RAM is, at most, a minor factor in C. briggsae. We conclude that the positive relation between nucleotide polymorphism and the rate of crossover represents the footprint of natural selection across the C. briggsae genome and demonstrate that background selection against deleterious mutations is sufficient to explain this pattern. Hill-Robertson interference also leaves a signature of more effective purifying selection in high-recombination regions of the genome. Finally, we identify an emerging contrast between widespread adaptive hitchhiking effects in species with large outcrossing populations (e.g., Drosophila) versus pervasive background selection effects on the genomes of organisms with self-fertilizing lifestyles and/or small population sizes (e.g., Caenorhabditis elegans, C. briggsae, Arabidopsis thaliana, Lycopersicon, human). These results illustrate how recombination, mutation, selection, and population history interact in important ways to shape molecular heterogeneity within and between genomes.

Individual histories and selection in heterogeneous populations

The strength of selection in populations has traditionally been inferred by measuring changes in bulk population parameters, such as mean reproductive rates. Untangling the effect of selection from other factors, such as specific responses to environmental fluctuations, poses a significant problem both in microbiology and in other fields, including cancer biology and immunology, where selection occurs within phenotypically heterogeneous populations of cells. Using "individual histories"--temporal sequences of all reproduction events and phenotypic changes of individuals and their ancestors--we present an alternative approach to quantifying selection in diverse experimental settings. Selection is viewed as a process that acts on histories, and a measure of selection that employs the distribution of histories is introduced. We apply this measure to phenotypically structured populations in fluctuating environments across different evolutionary regimes. Additionally, we show that reproduction events alone, recorded in the population's tree of cell divisions, may be sufficient to accurately measure selection. The measure is thus applicable in a wide range of biological systems, from microorganisms--including species for which genetic tools do not yet exist--to cellular populations, such as tumors and stem cells, where detailed temporal data are becoming available.

The genetics of human adaptation: hard sweeps, soft sweeps, and polygenic adaptation

There has long been interest in understanding the genetic basis of human adaptation. To what extent are phenotypic differences among human populations driven by natural selection? With the recent arrival of large genome-wide data sets on human variation, there is now unprecedented opportunity for progress on this type of question. Several lines of evidence argue for an important role of positive selection in shaping human variation and differences among populations. These include studies of comparative morphology and physiology, as well as population genetic studies of candidate loci and genome-wide data. However, the data also suggest that it is unusual for strong selection to drive new mutations rapidly to fixation in particular populations (the 'hard sweep' model). We argue, instead, for alternatives to the hard sweep model: in particular, polygenic adaptation could allow rapid adaptation while not producing classical signatures of selective sweeps. We close by discussing some of the likely opportunities for progress in the field.

Rapid evolution and selection inferred from the transcriptomes of sympatric crater lake cichlid fishes

Crater lakes provide a natural laboratory to study speciation of cichlid fishes by ecological divergence. Up to now, there has been a dearth of transcriptomic and genomic information that would aid in understanding the molecular basis of the phenotypic differentiation between young species. We used next-generation sequencing (Roche 454 massively parallel pyrosequencing) to characterize the diversity of expressed sequence tags between ecologically divergent, endemic and sympatric species of cichlid fishes from crater lake Apoyo, Nicaragua: benthic Amphilophus astorquii and limnetic Amphilophus zaliosus. We obtained 24 174 A. astorquii and 21 382 A. zaliosus high-quality expressed sequence tag contigs, of which 13 106 pairs are orthologous between species. Based on the ratio of nonsynonymous to synonymous substitutions, we identified six sequences exhibiting signals of strong diversifying selection (K(a)/K(s) > 1). These included genes involved in biosynthesis, metabolic processes and development. This transcriptome sequence variation may be reflective of natural selection acting on the genomes of these young, sympatric sister species. Based on Ks ratios and p-distances between 3'-untranslated regions (UTRs) calibrated to previously published species divergence times, we estimated a neutral transcriptome-wide substitutional mutation rate of approximately 1.25 x 10(-6) per site per year. We conclude that next-generation sequencing technologies allow us to infer natural selection acting to diversify the genomes of young species, such as crater lake cichlids, with much greater scope than previously possible.

Formulating a historical and demographic model of recent human evolution based on resequencing data from noncoding regions

BACKGROUND

Estimating the historical and demographic parameters that characterize modern human populations is a fundamental part of reconstructing the recent history of our species. In addition, the development of a model of human evolution that can best explain neutral genetic diversity is required to identify confidently regions of the human genome that have been targeted by natural selection.

METHODOLOGY/PRINCIPAL FINDINGS

We have resequenced 20 independent noncoding autosomal regions dispersed throughout the genome in 213 individuals from different continental populations, corresponding to a total of approximately 6 Mb of diploid resequencing data. We used these data to explore and co-estimate an extensive range of historical and demographic parameters with a statistical framework that combines the evaluation of multiple models of human evolution via a best-fit approach, followed by an Approximate Bayesian Computation (ABC) analysis. From a methodological standpoint, evaluating the accuracy of the parameter co-estimation allowed us to identify the most accurate set of statistics to be used for the estimation of each of the different historical and demographic parameters characterizing recent human evolution.

CONCLUSIONS/SIGNIFICANCE

Our results support a model in which modern humans left Africa through a single major dispersal event occurring approximately 60,000 years ago, corresponding to a drastic reduction of approximately 5 times the effective population size of the ancestral African population of approximately 13,800 individuals. Subsequently, the ancestors of modern Europeans and East Asians diverged much later, approximately 22,500 years ago, from the population of ancestral migrants. This late diversification of Eurasians after the African exodus points to the occurrence of a long maturation phase in which the ancestral Eurasian population was not yet diversified.

Restriction Site Tiling Analysis: accurate discovery and quantitative genotyping of genome-wide polymorphisms using nucleotide arrays

A method for the simultaneous identification of polymorphic loci and the quantitative genotyping of thousands of loci in individuals is presented.

High-throughput genotype data can be used to identify genes important for local adaptation in wild populations, phenotypes in lab stocks, or disease-related traits in human medicine. Here we advance microarray-based genotyping for population genomics with Restriction Site Tiling Analysis. The approach simultaneously discovers polymorphisms and provides quantitative genotype data at 10,000s of loci. It is highly accurate and free from ascertainment bias. We apply the approach to uncover genomic differentiation in the purple sea urchin.

Fine mapping of AHI1 as a schizophrenia susceptibility gene: from association to evolutionary evidence

In previous studies, we identified a locus for schizophrenia on 6q23.3 and proposed the Abelson helper integration site 1 (AHI1) as the candidate gene. AHI1 is expressed in the brain and plays a key role in neurodevelopment, is involved in Joubert syndrome, and has been recently associated with autism. The neurodevelopmental role of AHI1 fits with etiological hypotheses of schizophrenia. To definitively confirm our hypothesis, we searched for associations using a dense map of the region. Our strongest findings lay within the AHI1 gene: single-nucleotide polymorphisms rs11154801 and rs7759971 showed significant associations (P=6.23E-06; P=0.84E-06) and haplotypes gave P values in the 10E-8 to 10E-10 range. The second highest significant region maps close to AHI1 and includes the intergenic region between BC040979 and PDE7B (rs2038549 at P=9.70E-06 and rs1475069 at P=6.97E-06), and PDE7B and MAP7. Using a sample of Palestinian Arab families to confirm these findings, we found isolated signals. While these results did not retain their significance after correction for multiple testing, the joint analysis across the 2 samples supports the role of AHI1, despite the presence of heterogeneity. Given the hypothesis of positive selection of schizophrenia genes, we resequenced a 11 kb region within AHI1 in ethnically defined populations and found evidence for a selective sweep. Network analysis indicates 2 haplotype clades, with schizophrenia-susceptibility haplotypes clustering within the major clade. In conclusion, our data support the role of AHI1 as a susceptibility gene for schizophrenia and confirm it has been subjected to positive selection, also shedding light on new possible candidate genes, MAP7 and PDE7B.

Adaptive genetic variation and heart disease risk

PURPOSE OF REVIEW

Obesity, dyslipidemia and cardiovascular disease are complex and determined by both genetic and environmental factors and their inter-relationships. Many associations from genome-wide association studies and candidate gene approaches have described a multitude of polymorphisms associating with lipid and obesity phenotypes but identified genetic variants account for only a small fraction of phenotypic variation.

RECENT FINDINGS

That many genotype-phenotype associations involve variants under positive selection and that those variants respond to environmental cues together suggest prominent roles for both genetic adaptation and their interactions with the environment. Adaptive genetic variations interacting with environment modulate disease susceptibility but the level to which those variants contribute to dyslipidemia and obesity and how environmental factors, especially diet, alter the genetic association is not yet completely known.

SUMMARY

It is evident that genetic variants under positive selection make important contributions to obesity and heart disease risk. Advances in resequencing the entire human genome will enable accurate identification of adaptive variants. Considering interactions between environmental factors and genotypes will empower both genome-wide association studies and characterization of the relationship between positive selection and the obese and dyslipidemic conditions.

Human population differentiation is strongly correlated with local recombination rate

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 F(ST) shows negative correlation with recombination rate, with F(ST) 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 F(ST) has a qualitatively different relationship for F(ST) between African and non-African populations and for F(ST) between European and East Asian populations, suggesting varying levels or types of selection in different epochs of human history.

Diversity in the glucose transporter-4 gene (SLC2A4) in humans reflects the action of natural selection along the old-world primates evolution

BACKGROUND

Glucose is an important source of energy for living organisms. In vertebrates it is ingested with the diet and transported into the cells by conserved mechanisms and molecules, such as the trans-membrane Glucose Transporters (GLUTs). Members of this family have tissue specific expression, biochemical properties and physiologic functions that together regulate glucose levels and distribution. GLUT4 -coded by SLC2A4 (17p13) is an insulin-sensitive transporter with a critical role in glucose homeostasis and diabetes pathogenesis, preferentially expressed in the adipose tissue, heart muscle and skeletal muscle. We tested the hypothesis that natural selection acted on SLC2A4.

METHODOLOGY/PRINCIPAL FINDINGS

We re-sequenced SLC2A4 and genotyped 104 SNPs along a approximately 1 Mb region flanking this gene in 102 ethnically diverse individuals. Across the studied populations (African, European, Asian and Latin-American), all the eight common SNPs are concentrated in the N-terminal region upstream of exon 7 ( approximately 3700 bp), while the C-terminal region downstream of intron 6 ( approximately 2600 bp) harbors only 6 singletons, a pattern that is not compatible with neutrality for this part of the gene. Tests of neutrality based on comparative genomics suggest that: (1) episodes of natural selection (likely a selective sweep) predating the coalescent of human lineages, within the last 25 million years, account for the observed reduced diversity downstream of intron 6 and, (2) the target of natural selection may not be in the SLC2A4 coding sequence.

CONCLUSIONS

We propose that the contrast in the pattern of genetic variation between the N-terminal and C-terminal regions are signatures of the action of natural selection and thus follow-up studies should investigate the functional importance of different regions of the SLC2A4 gene.

Genome-wide scans for footprints of natural selection

Detecting recent selected ‘genomic footprints’ applies directly to the discovery of disease genes and in the imputation of the formative events that molded modern population genetic structure. The imprints of historic selection/adaptation episodes left in human and animal genomes allow one to interpret modern and ancestral gene origins and modifications. Current approaches to reveal selected regions applied in genome-wide selection scans (GWSSs) fall into eight principal categories: (I) phylogenetic footprinting, (II) detecting increased rates of functional mutations, (III) evaluating divergence versus polymorphism, (IV) detecting extended segments of linkage disequilibrium, (V) evaluating local reduction in genetic variation, (VI) detecting changes in the shape of the frequency distribution (spectrum) of genetic variation, (VII) assessing differentiating between populations (F_ST), and (VIII) detecting excess or decrease in admixture contribution from one population. Here, we review and compare these approaches using available human genome-wide datasets to provide independent verification (or not) of regions found by different methods and using different populations. The lessons learned from GWSSs will be applied to identify genome signatures of historic selective pressures on genes and gene regions in other species with emerging genome sequences. This would offer considerable potential for genome annotation in functional, developmental and evolutionary contexts.

How culture shaped the human genome: bringing genetics and the human sciences together

Researchers from diverse backgrounds are converging on the view that human evolution has been shaped by gene-culture interactions. Theoretical biologists have used population genetic models to demonstrate that cultural processes can have a profound effect on human evolution, and anthropologists are investigating cultural practices that modify current selection. These findings are supported by recent analyses of human genetic variation, which reveal that hundreds of genes have been subject to recent positive selection, often in response to human activities. Here, we collate these data, highlighting the considerable potential for cross-disciplinary exchange to provide novel insights into how culture has shaped the human genome.

Population genomics of parallel adaptation in threespine stickleback using sequenced RAD tags

Next-generation sequencing technology provides novel opportunities for gathering genome-scale sequence data in natural populations, laying the empirical foundation for the evolving field of population genomics. Here we conducted a genome scan of nucleotide diversity and differentiation in natural populations of threespine stickleback (Gasterosteus aculeatus). We used Illumina-sequenced RAD tags to identify and type over 45,000 single nucleotide polymorphisms (SNPs) in each of 100 individuals from two oceanic and three freshwater populations. Overall estimates of genetic diversity and differentiation among populations confirm the biogeographic hypothesis that large panmictic oceanic populations have repeatedly given rise to phenotypically divergent freshwater populations. Genomic regions exhibiting signatures of both balancing and divergent selection were remarkably consistent across multiple, independently derived populations, indicating that replicate parallel phenotypic evolution in stickleback may be occurring through extensive, parallel genetic evolution at a genome-wide scale. Some of these genomic regions co-localize with previously identified QTL for stickleback phenotypic variation identified using laboratory mapping crosses. In addition, we have identified several novel regions showing parallel differentiation across independent populations. Annotation of these regions revealed numerous genes that are candidates for stickleback phenotypic evolution and will form the basis of future genetic analyses in this and other organisms. This study represents the first high-density SNP-based genome scan of genetic diversity and differentiation for populations of threespine stickleback in the wild. These data illustrate the complementary nature of laboratory crosses and population genomic scans by confirming the adaptive significance of previously identified genomic regions, elucidating the particular evolutionary and demographic history of such regions in natural populations, and identifying new genomic regions and candidate genes of evolutionary significance.

Population genomics of parallel adaptation in threespine stickleback using sequenced RAD tags

Next-generation sequencing technology provides novel opportunities for gathering genome-scale sequence data in natural populations, laying the empirical foundation for the evolving field of population genomics. Here we conducted a genome scan of nucleotide diversity and differentiation in natural populations of threespine stickleback (Gasterosteus aculeatus). We used Illumina-sequenced RAD tags to identify and type over 45,000 single nucleotide polymorphisms (SNPs) in each of 100 individuals from two oceanic and three freshwater populations. Overall estimates of genetic diversity and differentiation among populations confirm the biogeographic hypothesis that large panmictic oceanic populations have repeatedly given rise to phenotypically divergent freshwater populations. Genomic regions exhibiting signatures of both balancing and divergent selection were remarkably consistent across multiple, independently derived populations, indicating that replicate parallel phenotypic evolution in stickleback may be occurring through extensive, parallel genetic evolution at a genome-wide scale. Some of these genomic regions co-localize with previously identified QTL for stickleback phenotypic variation identified using laboratory mapping crosses. In addition, we have identified several novel regions showing parallel differentiation across independent populations. Annotation of these regions revealed numerous genes that are candidates for stickleback phenotypic evolution and will form the basis of future genetic analyses in this and other organisms. This study represents the first high-density SNP-based genome scan of genetic diversity and differentiation for populations of threespine stickleback in the wild. These data illustrate the complementary nature of laboratory crosses and population genomic scans by confirming the adaptive significance of previously identified genomic regions, elucidating the particular evolutionary and demographic history of such regions in natural populations, and identifying new genomic regions and candidate genes of evolutionary significance.

A population genetic hidden Markov model for detecting genomic regions under selection

Recently, hidden Markov models have been applied to numerous problems in genomics. Here, we introduce an explicit population genetics hidden Markov model (popGenHMM) that uses single nucleotide polymorphism (SNP) frequency data to identify genomic regions that have experienced recent selection. Our popGenHMM assumes that SNP frequencies are emitted independently following diffusion approximation expectations but that neighboring SNP frequencies are partially correlated by selective state. We give results from the training and application of our popGenHMM to a set of early release data from the Drosophila Population Genomics Project (dpgp.org) that consists of approximately 7.8 Mb of resequencing from 32 North American Drosophila melanogaster lines. These results demonstrate the potential utility of our model, making predictions based on the site frequency spectrum (SFS) for regions of the genome that represent selected elements.

Haplotype allelic classes for detecting ongoing positive selection

Background

Natural selection eliminates detrimental and favors advantageous phenotypes. This process leaves characteristic signatures in underlying genomic segments that can be recognized through deviations in allelic or haplotypic frequency spectra. To provide an identifiable signature of recent positive selection that can be detected by comparison with the background distribution, we introduced a new way of looking at genomic polymorphisms: haplotype allelic classes.

Results

The model combines segregating sites and haplotypic information in order to reveal useful data characteristics. We developed a summary statistic, Svd, to compare the distribution of the haplotypes carrying the selected allele with the distribution of the remaining ones. Coalescence simulations are used to study the distributions under standard population models assuming neutrality, demographic scenarios and selection models. To test, in practice, haplotype allelic class performance and the derived statistic in capturing deviation from neutrality due to positive selection, we analyzed haplotypic variation in detail in the locus of lactase persistence in the three HapMap Phase II populations.

Conclusions

We showed that the Svd statistic is less sensitive than other tests to confounding factors such as demography or recombination. Our approach succeeds in identifying candidate loci, such as the lactase-persistence locus, as targets of strong positive selection and provides a new tool complementary to other tests to study natural selection in genomic data.

Population genetic inference from genomic sequence variation

Population genetics has evolved from a theory-driven field with little empirical data into a data-driven discipline in which genome-scale data sets test the limits of available models and computational analysis methods. In humans and a few model organisms, analyses of whole-genome sequence polymorphism data are currently under way. And in light of the falling costs of next-generation sequencing technologies, such studies will soon become common in many other organisms as well. Here, we assess the challenges to analyzing whole-genome sequence polymorphism data, and we discuss the potential of these data to yield new insights concerning population history and the genomic prevalence of natural selection.

Detecting natural selection by empirical comparison to random regions of the genome

Historical episodes of natural selection can skew the frequencies of genetic variants, leaving a signature that can persist for many tens or even hundreds of thousands of years. However, formal tests for selection based on allele frequency skew require strong assumptions about demographic history and mutation, which are rarely well understood. Here, we develop an empirical approach to test for signals of selection that compares patterns of genetic variation at a candidate locus with matched random regions of the genome collected in the same way. We apply this approach to four genes that have been implicated in syndromes of impaired neurological development, comparing the pattern of variation in our re-sequencing data with a large-scale, genomic data set that provides an empirical null distribution. We confirm a previously reported signal at FOXP2, and find a novel signal of selection centered at AHI1, a gene that is involved in motor and behavior abnormalities. The locus is marked by many high frequency derived alleles in non-Africans that are of low frequency in Africans, suggesting that selection at this or a closely neighboring gene occurred in the ancestral population of non-Africans. Our study also provides a prototype for how empirical scans for ancient selection can be carried out once many genomes are sequenced.

Measuring and detecting molecular adaptation in codon usage against nonsense errors during protein translation

Codon usage bias (CUB) has been documented across a wide range of taxa and is the subject of numerous studies. While most explanations of CUB invoke some type of natural selection, most measures of CUB adaptation are heuristically defined. In contrast, we present a novel and mechanistic method for defining and contextualizing CUB adaptation to reduce the cost of nonsense errors during protein translation. Using a model of protein translation, we develop a general approach for measuring the protein production cost in the face of nonsense errors of a given allele as well as the mean and variance of these costs across its coding synonyms. We then use these results to define the nonsense error adaptation index (NAI) of the allele or a contiguous subset thereof. Conceptually, the NAI value of an allele is a relative measure of its elevation on a specific and well-defined adaptive landscape. To illustrate its utility, we calculate NAI values for the entire coding sequence and across a set of nonoverlapping windows for each gene in the Saccharomyces cerevisiae S288c genome. Our results provide clear evidence of adaptation to reduce the cost of nonsense errors and increasing adaptation with codon position and expression. The magnitude and nature of this adaptation are also largely consistent with simulation results in which nonsense errors are the only selective force driving CUB evolution. Because NAI is derived from mechanistic models, it is both easier to interpret and more amenable to future refinement than other commonly used measures of codon bias. Further, our approach can also be used as a starting point for developing other mechanistically derived measures of adaptation such as for translational accuracy.

From evolutionary genetics to human immunology: how selection shapes host defence genes

Pathogens have always been a major cause of human mortality, so they impose strong selective pressure on the human genome. Data from population genetic studies, including genome-wide scans for selection, are providing important insights into how natural selection has shaped immunity and host defence genes in specific human populations and in the human species as a whole. These findings are helping to delineate genes that are important for host defence and to increase our understanding of how past selection has had an impact on disease susceptibility in modern populations. A tighter integration between population genetic studies and immunological phenotype studies is now necessary to reveal the mechanisms that have been crucial for our past and present survival against infection.

Coalescent simulation of intracodon recombination

The coalescent with recombination is a very useful tool in molecular population genetics. Under this framework, genealogies often represent the evolution of the substitution unit, and because of this, the few coalescent algorithms implemented for the simulation of coding sequences force recombination to occur only between codons. However, it is clear that recombination is expected to occur most often within codons. Here we have developed an algorithm that can evolve coding sequences under an ancestral recombination graph that represents the genealogies at each nucleotide site, thereby allowing for intracodon recombination. The algorithm is a modification of Hudson's coalescent in which, in addition to keeping track of events occurring in the ancestral material that reaches the sample, we need to keep track of events occurring in ancestral material that does not reach the sample but that is produced by intracodon recombination. We are able to show that at typical substitution rates the number of nonsynonymous changes induced by intracodon recombination is small and that intracodon recombination does not generally result in inflated estimates of the overall nonsynonymous/synonymous substitution ratio (omega). On the other hand, recombination can bias the estimation of omega at particular codons, resulting in apparent rate variation among sites and in the spurious identification of positively selected sites. Importantly, in this case, allowing for variable synonymous rates across sites greatly reduces the false-positive rate and recovers statistical power. Finally, coalescent simulations with intracodon recombination could be used to better represent the evolution of nuclear coding genes or fast-evolving pathogens such as HIV-1.We have implemented this algorithm in a computer program called NetRecodon, freely available at http://darwin.uvigo.es.

Genome-wide scan for signatures of human population differentiation and their relationship with natural selection, functional pathways and diseases

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 F_ST. We performed a genome-wide estimation of F_ST on about 4 millions of SNPs from HapMap project data. We demonstrated a heterogeneous distribution of F_ST values between autosomes and heterochromosomes. When we compared the F_ST 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 F_ST, while several pathways related to cell signalling, growth and morphogenesis showed high F_ST 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.

Genetic variation and recent positive selection in worldwide human populations: evidence from nearly 1 million SNPs

Background

Genome-wide scans of hundreds of thousands of single-nucleotide polymorphisms (SNPs) have resulted in the identification of new susceptibility variants to common diseases and are providing new insights into the genetic structure and relationships of human populations. Moreover, genome-wide data can be used to search for signals of recent positive selection, thereby providing new insights into the genetic adaptations that occurred as modern humans spread out of Africa and around the world.

Methodology

We genotyped approximately 500,000 SNPs in 255 individuals (5 individuals from each of 51 worldwide populations) from the Human Genome Diversity Panel (HGDP-CEPH). When merged with non-overlapping SNPs typed previously in 250 of these same individuals, the resulting data consist of over 950,000 SNPs. We then analyzed the genetic relationships and ancestry of individuals without assigning them to populations, and we also identified candidate regions of recent positive selection at both the population and regional (continental) level.

Conclusions

Our analyses both confirm and extend previous studies; in particular, we highlight the impact of various dispersals, and the role of substructure in Africa, on human genetic diversity. We also identified several novel candidate regions for recent positive selection, and a gene ontology (GO) analysis identified several GO groups that were significantly enriched for such candidate genes, including immunity and defense related genes, sensory perception genes, membrane proteins, signal receptors, lipid binding/metabolism genes, and genes involved in the nervous system. Among the novel candidate genes identified are two genes involved in the thyroid hormone pathway that show signals of selection in African Pygmies that may be related to their short stature.

Correlation of microsynteny conservation and disease gene distribution in mammalian genomes

Background

With the completion of the whole genome sequence for many organisms, investigations into genomic structure have revealed that gene distribution is variable, and that genes with similar function or expression are located within clusters. This clustering suggests that there are evolutionary constraints that determine genome architecture. However, as most of the evidence for constraints on genome evolution comes from studies on yeast, it is unclear how much of this prior work can be extrapolated to mammalian genomes. Therefore, in this work we wished to examine the constraints on regions of the mammalian genome containing conserved gene clusters.

Results

We first identified regions of the mouse genome with microsynteny conservation by comparing gene arrangement in the mouse genome to the human, rat, and dog genomes. We then asked if any particular gene types were found preferentially in conserved regions. We found a significant correlation between conserved microsynteny and the density of mouse orthologs of human disease genes, suggesting that disease genes are clustered in genomic regions of increased microsynteny conservation.

Conclusion

The correlation between microsynteny conservation and disease gene locations indicates that regions of the mouse genome with microsynteny conservation may contain undiscovered human disease genes. This study not only demonstrates that gene function constrains mammalian genome organization, but also identifies regions of the mouse genome that can be experimentally examined to produce mouse models of human disease.

Genomic and geographic distribution of private SNPs and pathways in human populations

AIMS: Geography-based genetic differentials operating on entire biochemical pathways may reflect different adaptive evolutionary processes that separated populations may have undergone. They may also influence treatment outcome for a variety of drugs - an emerging and important area of study. This research article leverages the International HapMap Consortium data to identify pathway components that differ in genotype frequency for four populations: individuals of Northern European descent from the USA (CEU), individuals from West Africa (YRI), Japan (JPT) and China (CHB). MATERIALS #ENTITYSTARTX00026; METHODS: By identifying loci with fixed or large frequency differences (δ = 1) between paired population samples (CEU vs YRI, CEU vs CHB, CEU vs JPT, YRI vs CHB, YRI vs JPT and CHB vs JPT), and reconstructing the physiological functions of genes at these loci, we report a list of pathways affected by natural selection during human evolution. RESULTS: Of the 3.7 million HapMap SNPs, 463 loci (which mapped to 38 genes) were fixed (δ = 1) in at least one population pair. These private loci included four nonsynonymous coding SNPs: rs4536103 (NEUROG3), rs1385699 (EDA2R), rs11946338 (ARHGAP24) and rs4422842 (CACNA1B). A total of four additional genes demonstrated evidence of recent positive selection: three genes in European subjects (IER5L, NPNT and SESTD1) and a single gene in Asian subjects (EXOC6B). DISCUSSION: Gene ontology and pathway analyses suggest that cellular differentiation, apoptosis and activation of the NF-κB transcription factor vary between populations in genomic regions of fixed (private) SNPs identified in this study. Variability in these pathways may provide important clues into the mechanisms of human adaptation to different environments. An improved understanding of their variability may also help to explain race-specific differences in the treatment outcomes observed for a variety of modern drugs.

Enhanced Edar signalling has pleiotropic effects on craniofacial and cutaneous glands

The skin carries a number of appendages, including hair follicles and a range of glands, which develop under the influence of EDAR signalling. A gain of function allele of EDAR is found at high frequency in human populations of East Asia, with genetic evidence suggesting recent positive selection at this locus. The derived EDAR allele, estimated to have reached fixation more than 10,000 years ago, causes thickening of hair fibres, but the full spectrum of phenotypic changes induced by this allele is unknown. We have examined the changes in glandular structure caused by elevation of Edar signalling in a transgenic mouse model. We find that sebaceous and Meibomian glands are enlarged and that salivary and mammary glands are more elaborately branched with increased Edar activity, while the morphology of eccrine sweat and tracheal submucosal glands appears to be unaffected. Similar changes to gland sizes and structures may occur in human populations carrying the derived East Asian EDAR allele. As this allele attained high frequency in an environment that was notably cold and dry, increased glandular secretions could represent a trait that was positively selected to achieve increased lubrication and reduced evaporation from exposed facial structures and upper airways.

Inferring the joint demographic history of multiple populations from multidimensional SNP frequency data

Demographic models built from genetic data play important roles in illuminating prehistorical events and serving as null models in genome scans for selection. We introduce an inference method based on the joint frequency spectrum of genetic variants within and between populations. For candidate models we numerically compute the expected spectrum using a diffusion approximation to the one-locus, two-allele Wright-Fisher process, involving up to three simultaneous populations. Our approach is a composite likelihood scheme, since linkage between neutral loci alters the variance but not the expectation of the frequency spectrum. We thus use bootstraps incorporating linkage to estimate uncertainties for parameters and significance values for hypothesis tests. Our method can also incorporate selection on single sites, predicting the joint distribution of selected alleles among populations experiencing a bevy of evolutionary forces, including expansions, contractions, migrations, and admixture. We model human expansion out of Africa and the settlement of the New World, using 5 Mb of noncoding DNA resequenced in 68 individuals from 4 populations (YRI, CHB, CEU, and MXL) by the Environmental Genome Project. We infer divergence between West African and Eurasian populations 140 thousand years ago (95% confidence interval: 40–270 kya). This is earlier than other genetic studies, in part because we incorporate migration. We estimate the European (CEU) and East Asian (CHB) divergence time to be 23 kya (95% c.i.: 17–43 kya), long after archeological evidence places modern humans in Europe. Finally, we estimate divergence between East Asians (CHB) and Mexican-Americans (MXL) of 22 kya (95% c.i.: 16.3–26.9 kya), and our analysis yields no evidence for subsequent migration. Furthermore, combining our demographic model with a previously estimated distribution of selective effects among newly arising amino acid mutations accurately predicts the frequency spectrum of nonsynonymous variants across three continental populations (YRI, CHB, CEU).

The demographic history of our species is reflected in patterns of genetic variation within and among populations. We developed an efficient method for calculating the expected distribution of genetic variation, given a demographic model including such events as population size changes, population splits and joins, and migration. We applied our approach to publicly available human sequencing data, searching for models that best reproduce the observed patterns. Our joint analysis of data from African, European, and Asian populations yielded new dates for when these populations diverged. In particular, we found that African and Eurasian populations diverged around 100,000 years ago. This is earlier than other genetic studies suggest, because our model includes the effects of migration, which we found to be important for reproducing observed patterns of variation in the data. We also analyzed data from European, Asian, and Mexican populations to model the peopling of the Americas. Here, we find no evidence for recurrent migration after East Asian and Native American populations diverged. Our methods are not limited to studying humans, and we hope that future sequencing projects will offer more insights into the history of both our own species and others.

Genomic landscape of positive natural selection in Northern European populations

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 F(ST) 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.

Constructing genomic maps of positive selection in humans: where do we go from here?

Identifying targets of positive selection in humans has, until recently, been frustratingly slow, relying on the analysis of individual candidate genes. Genomics, however, has provided the necessary resources to systematically interrogate the entire genome for signatures of natural selection. To date, 21 genome-wide scans for recent or ongoing positive selection have been performed in humans. A key challenge is to begin synthesizing these newly constructed maps of positive selection into a coherent narrative of human evolutionary history and derive a deeper mechanistic understanding of how natural populations evolve. Here, I chronicle the recent history of the burgeoning field of human population genomics, critically assess genome-wide scans for positive selection in humans, identify important gaps in knowledge, and discuss both short- and long-term strategies for traversing the path from the low-resolution, incomplete, and error-prone maps of selection today to the ultimate goal of a detailed molecular, mechanistic, phenotypic, and population genetics characterization of adaptive alleles.