Positive Selection in East Asians for an EDAR Allele that Enhances NF-κB Activation

Genome-wide scans for positive selection in humans provide a promising approach to establish links between genetic variants and adaptive phenotypes. From this approach, lists of hundreds of candidate genomic regions for positive selection have been assembled. These candidate regions are expected to contain variants that contribute to adaptive phenotypes, but few of these regions have been associated with phenotypic effects. Here we present evidence that a derived nonsynonymous substitution (370A) in EDAR, a gene involved in ectodermal development, was driven to high frequency in East Asia by positive selection prior to 10,000 years ago. With an in vitro transfection assay, we demonstrate that 370A enhances NF-κB activity. Our results suggest that 370A is a positively selected functional genetic variant that underlies an adaptive human phenotype.     

Adaptation in isolated populations: when does it happen and when can we tell?

Isolated populations with novel phenotypes present an exciting opportunity to uncover the genetic basis of ecologically significant adaptation, and genomic scans have often, but not always, led to candidate genes directly related to an adaptive phenotype. However, in many cases these populations were established by a severe bottleneck, which can make identifying targets of selection problematic. Here, we simulate severe bottlenecks and subsequent selection on standing variation, mimicking adaptation after establishment of a new small population, such as an island or an artificial selection experiment. Using simulations of single loci under positive selection and population genetics theory, we examine how population size and age of the population isolate affect the ability of outlier scans for selection to identify adaptive alleles using both single‐site measures and haplotype structure. We find and explain an optimal combination of selection strength, starting frequency, and age of the adaptive allele, which we refer to as a Goldilocks zone, where adaptation is likely to occur and yet the adaptive variants are most likely to derive from a single ancestor (a ‘hard’ selective sweep); in this zone, four commonly used statistics detect selection with high power. Real‐world examples of both island colonization and experimental evolution studies are discussed. Our study provides concrete considerations to be made before embarking on whole‐genome sequencing of differentiated populations.     

Proceedings of the SMBE Tri-National Young Investigators' Workshop 2005. Molecular genetics of natural populations

Significant progress in evolutionary genetics has been made by studying, on the one hand, patterns of DNA sequence polymorphism and, on the other, genetic architecture of complex adaptive traits. However, connections between nucleotide variants under selection and adaptively relevant phenotypes are missing. Such connections can be established using precise gene replacement. We review the recent successful introduction of this technique to the analysis of two evolutionarily interesting loci--Odysseus and desaturase2. Both genes have subtle phenotypes that nevertheless could be identified using gene replacement, demonstrating that effects of naturally occurring alleles can be measured in the laboratory. This is an important first step in connecting statistical signatures of selection with adaptation in nature. More candidate genes involved in adaptation, for example, through cloning of genes responsible for reproductive isolation, now need to be identified. Molecular genetic manipulation, DNA polymorphism analysis, and field studies then have to be integrated to provide fresh insights into the mechanisms of evolutionary change.     

Selection and evaluation of tagging SNPs in the neuronal-sodium-channel gene SCN1A: implications for linkage-disequilibrium gene mapping

Association studies are widely seen as the most promising approach for finding polymorphisms that influence genetically complex traits, such as common diseases and responses to their treatment. Considerable interest has therefore recently focused on the development of methods that efficiently screen genomic regions or whole genomes for gene variants associated with complex phenotypes. One key element in this search is the use of linkage disequilibrium to gain maximal information from typing a selected subset of highly informative single-nucleotide polymorphism (SNP) markers, now often called "tagging SNPs" (tSNPs). Probably the most common approach to linkage-disequilibrium gene mapping involves a three-step program: (1) characterization of the haplotype structure in candidate genes or genomic regions of interest, (2) identification of tSNPs sufficient to represent the most common haplotypes, and (3) typing of tSNPs in clinical material. Early definitions of tSNPs focused on the amount of haplotype diversity that they explained. To select tSNPs that would have maximal power in a genetic association study, however, we have developed optimization criteria based on the r2 measure of association and have compared these with other criteria based on the haplotype diversity. To evaluate the full program and to assess how well the selected tags are likely to perform, we have determined the haplotype structure and have assessed tSNPs in the SCN1A gene, an important candidate gene for sporadic epilepsy. We find that as few as four tSNPs are predicted to maintain a consistently high r2 value with all other common SNPs in the gene, indicating that the tags could be used in an association study with only a modest reduction in power relative to direct assays of all common SNPs. This implies that very large case-control studies can be screened for variation in hundreds of candidate genes with manageable experimental effort, once tSNPs are identified. However, our results also show that tSNPs identified in one population may not necessarily perform well in another, indicating that the preliminary study to identify tSNPs and the later case-control study should be performed in the same population. Our results also indicate that tSNPs will not easily identify discrepant SNPs, which lie on importantly discriminating but apparently short genealogical branches. This could significantly complicate tagging approaches for phenotypes influenced by variants that have experienced positive selection.     

Multiple regression methods show great potential for rare variant association tests

The investigation of associations between rare genetic variants and diseases or phenotypes has two goals. Firstly, the identification of which genes or genomic regions are associated, and secondly, discrimination of associated variants from background noise within each region. Over the last few years, many new methods have been developed which associate genomic regions with phenotypes. However, classical methods for high-dimensional data have received little attention. Here we investigate whether several classical statistical methods for high-dimensional data: ridge regression (RR), principal components regression (PCR), partial least squares regression (PLS), a sparse version of PLS (SPLS), and the LASSO are able to detect associations with rare genetic variants. These approaches have been extensively used in statistics to identify the true associations in data sets containing many predictor variables. Using genetic variants identified in three genes that were Sanger sequenced in 1998 individuals, we simulated continuous phenotypes under several different models, and we show that these feature selection and feature extraction methods can substantially outperform several popular methods for rare variant analysis. Furthermore, these approaches can identify which variants are contributing most to the model fit, and therefore both goals of rare variant analysis can be achieved simultaneously with the use of regression regularization methods. These methods are briefly illustrated with an analysis of adiponectin levels and variants in the ADIPOQ gene.     

Detection of selection signatures in Limousin cattle using whole-genome resequencing

Limousin, a renowned beef breed originating from central France, has been selectively bred over the last 100 years to improve economically important traits. We used whole-genome sequencing data from 10 unrelated Limousin bull calves to detect polymorphisms and identify regions under selection. A total of 13 943 766 variants were identified. Moreover, 311 852 bi-allelic SNPs and 92 229 indels located on autosomes were fixed for the alternative allele in all sequenced animals, including the previously reported missense deleterious F94L mutation in MSTN. We performed a whole-genome screen to discover genomic regions with excess homozygosity, using the pooled heterozygosity score and identified 171 different candidate selective sweeps. In total, 68 candidate genes were found in only 57 of these regions, indicating that a large fraction of the genome under selection might lie in non-coding regions and suggesting that a majority of adaptive mutations might be regulatory in nature. Many QTL were found within candidate selective sweep regions, including QTL associated with shear force or carcass weight. Among the putative selective sweeps, we located genes (MSTN, NCKAP5, RUNX2) that potentially contribute to important phenotypes in Limousin. Several candidate regions and genes under selection were also found in previous genome-wide selection scans performed in Limousin. In addition, we were able to pinpoint candidate causative regulatory polymorphisms in GRIK3 and RUNX2 that might have been under selection. Our results will contribute to improved understanding of the mechanisms and targets of artificial selection and will facilitate the interpretation of GWASs performed in Limousin.     

Genome-wide scans provide evidence for positive selection of genes implicated in Lassa fever

Rapidly evolving viruses and other pathogens can have an immense impact on human evolution as natural selection acts to increase the prevalence of genetic variants providing resistance to disease. With the emergence of large datasets of human genetic variation, we can search for signatures of natural selection in the human genome driven by such disease-causing microorganisms. Based on this approach, we have previously hypothesized that Lassa virus (LASV) may have been a driver of natural selection in West African populations where Lassa haemorrhagic fever is endemic. In this study, we provide further evidence for this notion. By applying tests for selection to genome-wide data from the International Haplotype Map Consortium and the 1000 Genomes Consortium, we demonstrate evidence for positive selection in LARGE and interleukin 21 (IL21), two genes implicated in LASV infectivity and immunity. We further localized the signals of selection, using the recently developed composite of multiple signals method, to introns and putative regulatory regions of those genes. Our results suggest that natural selection may have targeted variants giving rise to alternative splicing or differential gene expression of LARGE and IL21. Overall, our study supports the hypothesis that selective pressures imposed by LASV may have led to the emergence of particular alleles conferring resistance to Lassa fever, and opens up new avenues of research pursuit.     

A critical analysis of production-associated DNA polymorphisms in the genes of cattle, goat, sheep, and pig

Increasing productivity is one of the main objectives in animal production. Traditional breeding methods have led to increased gains in some traits but gains are not easily attainable in traits with low heritabilities. Exploiting the genetic variations underlying desired phenotypes is the goal of today's animal producers. Such positive genetic variants must, however, be known before possible application. Consequently, candidate genes of traits of interest have been searched for possible relationships with such traits or to explain reported quantitative trait loci (QTL) for such traits. DNA variants or polymorphisms have been identified in many such genes and their relationships with production traits determined. However, only a few genes have been evaluated, given the wealth of information on reported QTL for production traits, and in most cases genes are only partially investigated. This review presents available information on DNA variants for production traits and discusses steps that are required for effective utilization of this information for successful marker-assisted selection programs.     

Editor's choice: Functional classification of 15 million SNPs detected from diverse chicken populations

Next-generation sequencing has prompted a surge of discovery of millions of genetic variants from vertebrate genomes. Besides applications in genetic association and linkage studies, a fraction of these variants will have functional consequences. This study describes detection and characterization of 15 million SNPs from chicken genome with the goal to predict variants with potential functional implications (pfVars) from both coding and non-coding regions. The study reports: 183K amino acid-altering SNPs of which 48% predicted as evolutionary intolerant, 13K splicing variants, 51K likely to alter RNA secondary structures, 500K within most conserved elements and 3K from non-coding RNAs. Regions of local fixation within commercial broiler and layer lines were investigated as potential selective sweeps using genome-wide SNP data. Relationships with phenotypes, if any, of the pfVars were explored by overlaying the sweep regions with known QTLs. Based on this, the candidate genes and/or causal mutations for a number of important traits are discussed. Although the fixed variants within sweep regions were enriched with non-coding SNPs, some non-synonymous-intolerant mutations reached fixation, suggesting their possible adaptive advantage. The results presented in this study are expected to have important implications for future genomic research to identify candidate causal mutations and in poultry breeding.     

Strategies for performing genotype–phenotype association studies in nonhuman primates

Anthropoid primate models offer opportunities to study genetic influence on alcohol consumption and alcohol-related intermediate phenotypes in socially and behaviorally complex animal models that are closely related to humans, and in which functionally equivalent or orthologous genetic variants exist. This review will discuss the methods commonly used for performing candidate gene-based studies in rhesus macaques in order to model how functional genetic variation moderates risk for human psychiatric disorders. Various in silico and in vitro approaches to identifying functional genetic variants for performance of these studies will be discussed. Next, I will provide examples of how this approach can be used for performing candidate gene-based studies and for examining gene by environment interactions. Finally, these approaches will then be placed in the context of how function-guided studies can inform us of genetic variants that may be under selection across species, demonstrating how functional genetic variants that may have conferred selective advantage at some point in the evolutionary history of humans could increase risk for addictive disorders in modern society.     

The use of mouse models to unravel genetic architecture of physical activity: a review

The discovery of genetic variants that underlie a complex phenotype is challenging. One possible approach to facilitate this endeavor is to identify quantitative trait loci (QTL) that contribute to the phenotype and consequently unravel the candidate genes within these loci. Each proposed candidate locus contains multiple genes and, therefore, further analysis is required to choose plausible candidate genes. One of such methods is to use comparative genomics in order to narrow down the QTL to a region containing only a few genes. We illustrate this strategy by applying it to genetic findings regarding physical activity (PA) in mice and human. Here, we show that PA is a complex phenotype with a strong biological basis and complex genetic architecture. Furthermore, we provide considerations for the translatability of this phenotype between species. Finally, we review studies which point to candidate genetic regions for PA in humans (genetic association and linkage studies) or use mouse models of PA (QTL studies) and we identify candidate genetic regions that overlap between species. On the basis of a large variety of studies in mice and human, statistical analysis reveals that the number of overlapping regions is not higher than expected on a chance level. We conclude that the discovery of new candidate genes for complex phenotypes, such as PA levels, is hampered by various factors, including genetic background differences, phenotype definition and a wide variety of methodological differences between studies .     

Identification of contemporary selection signatures using composite log likelihood and their associations with marbling score in Korean cattle

Positive selection not only increases beneficial allele frequency but also causes augmentation of allele frequencies of sequence variants in close proximity. Signals for positive selection were detected by the statistical differences in subsequent allele frequencies. To identify selection signatures in Korean cattle, we applied a composite log‐likelihood (CLL)‐based method, which calculates a composite likelihood of the allelic frequencies observed across sliding windows of five adjacent loci and compares the value with the critical statistic estimated by 50 000 permutations. Data for a total of 11 799 nucleotide polymorphisms were used with 71 Korean cattle and 209 foreign beef cattle. As a result, 147 signals were identified for Korean cattle based on CLL estimates (P < 0.01). The signals might be candidate genetic factors for meat quality by which the Korean cattle have been selected. Further genetic association analysis with 41 intragenic variants in the selection signatures with the greatest CLL for each chromosome revealed that marbling score was associated with five variants. Intensive association studies with all the selection signatures identified in this study are required to exclude signals associated with other phenotypes or signals falsely detected and thus to identify genetic markers for meat quality.     

Linked genetic variants on chromosome 10 control ear morphology and body mass among dog breeds

Background

The domestic dog is a rich resource for mapping the genetic components of phenotypic variation due to its unique population history involving strong artificial selection. Genome-wide association studies have revealed a number of chromosomal regions where genetic variation associates with morphological characters that typify dog breeds. A region on chromosome 10 is among those with the highest levels of genetic differentiation between dog breeds and is associated with body mass and ear morphology, a common motif of animal domestication. We characterised variation in this region to uncover haplotype structure and identify candidate functional variants.

Results

We first identified SNPs that strongly associate with body mass and ear type by comparing sequence variation in a 3 Mb region between 19 breeds with a variety of phenotypes. We next genotyped a subset of 123 candidate SNPs in 288 samples from 46 breeds to identify the variants most highly associated with phenotype and infer haplotype structure. A cluster of SNPs that associate strongly with the drop ear phenotype is located within a narrow interval downstream of the gene MSRB3, which is involved in human hearing. These SNPs are in strong genetic linkage with another set of variants that correlate with body mass within the gene HMGA2, which affects human height. In addition we find evidence that this region has been under selection during dog domestication, and identify a cluster of SNPs within MSRB3 that are highly differentiated between dogs and wolves.

Conclusions

We characterise genetically linked variants that potentially influence ear type and body mass in dog breeds, both key traits that have been modified by selective breeding that may also be important for domestication. The finding that variants on long haplotypes have effects on more than one trait suggests that genetic linkage can be an important determinant of the phenotypic response to selection in domestic animals.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1702-2) contains supplementary material, which is available to authorized users.     

A genome scan to detect candidate regions influenced by local natural selection in human populations

As human populations dispersed throughout the world, they were subjected to new selective forces, which must have led to local adaptation via natural selection and hence altered patterns of genetic variation. Yet, there are very few examples known in which such local selection has clearly influenced human genetic variation. A potential approach for detecting local selection is to screen random loci across the genome; those loci that exhibit unusually large genetic distances between human populations are then potential markers of genomic regions under local selection. We investigated this approach by genotyping 332 short tandem repeat (STR) loci in Africans and Europeans and calculating the genetic differentiation for each locus. Patterns of genetic diversity at these loci were consistent with greater variation in Africa and with local selection operating on populations as they moved out of Africa. For 11 loci exhibiting the largest genetic differences, we genotyped an additional STR locus located nearby; the genetic distances for these nearby loci were significantly larger than average. These genomic regions therefore reproducibly exhibit larger genetic distances between populations than the "average" genomic region, consistent with local selection. Our results demonstrate that genome scans are a promising means of identifying candidate regions that have been subjected to local selection.     

EP300 contributes to high-altitude adaptation in Tibetans by regulating nitric oxide production

The genetic adaptation of Tibetans to high altitude hypoxia likely involves a group of genes in the hypoxic pathway,as suggested by earlier studies.To test the adaptive role of the previously reported candidate gene EP300 (histone acetyltransferase p300),we conducted resequencing of a 108.9 kb gene region of EP300 in 80 unrelated Tibetans.The allele-frequency and haplotype-based neutrality tests detected signals of positive Darwinian selection on EP300 in Tibetans,with a group of variants showing allelic divergence between Tibetans and lowland reference populations,including Han Chinese,Europeans,and Africans.Functional prediction suggested the involvement of multiple EP300 variants in gene expression regulation.More importantly,genetic association tests in 226 Tibetans indicated significant correlation of the adaptive EP300 variants with blood nitric oxide (NO) concentration.Collectively,we propose that EP300 harbors adaptive variants in Tibetans,which might contribute to high-altitude adaptation through regulating NO production.     

Identifying Signatures of Natural Selection in Tibetan and Andean Populations Using Dense Genome Scan Data

High-altitude hypoxia (reduced inspired oxygen tension due to decreased barometric pressure) exerts severe physiological stress on the human body. Two high-altitude regions where humans have lived for millennia are the Andean Altiplano and the Tibetan Plateau. Populations living in these regions exhibit unique circulatory, respiratory, and hematological adaptations to life at high altitude. Although these responses have been well characterized physiologically, their underlying genetic basis remains unknown. We performed a genome scan to identify genes showing evidence of adaptation to hypoxia. We looked across each chromosome to identify genomic regions with previously unknown function with respect to altitude phenotypes. In addition, groups of genes functioning in oxygen metabolism and sensing were examined to test the hypothesis that particular pathways have been involved in genetic adaptation to altitude. Applying four population genetic statistics commonly used for detecting signatures of natural selection, we identified selection-nominated candidate genes and gene regions in these two populations (Andeans and Tibetans) separately. The Tibetan and Andean patterns of genetic adaptation are largely distinct from one another, with both populations showing evidence of positive natural selection in different genes or gene regions. Interestingly, one gene previously known to be important in cellular oxygen sensing, EGLN1 (also known as PHD2), shows evidence of positive selection in both Tibetans and Andeans. However, the pattern of variation for this gene differs between the two populations. Our results indicate that several key HIF-regulatory and targeted genes are responsible for adaptation to high altitude in Andeans and Tibetans, and several different chromosomal regions are implicated in the putative response to selection. These data suggest a genetic role in high-altitude adaption and provide a basis for future genotype/phenotype association studies necessary to confirm the role of selection-nominated candidate genes and gene regions in adaptation to altitude.     

Adaptive evolution of loci covarying with the human African Pygmy phenotype

African Pygmies are hunter-gatherer populations from the equatorial rainforest that present the lowest height averages among humans. The biological basis and the putative adaptive role of the short stature of Pygmy populations has been one of the most intriguing topics for human biologists in the last century, which still remains elusive. Worldwide convergent evolution of the Pygmy size suggests the presence of strong selective pressures on the phenotype. We developed a novel approach to survey the genetic architecture of phenotypes and applied it to study the genomic covariation between allele frequencies and height measurements among Pygmy and non-Pygmy populations. Among the regions that were most associated with the phenotype, we identified a significant excess of genes with pivotal roles in bone homeostasis, such as PPPT3B and the height associated SUPT3H-RUNX2. We hypothesize that skeletal remodeling could be a key biological process underlying the Pygmy phenotype. In addition, we showed that these regions have most likely evolved under positive selection. These results constitute the first genetic hint of adaptive evolution in the African Pygmy phenotype, which is consistent with the independent emergence of the Pygmy height in other continents with similar environments.     

Genetic susceptibility to sporadic ovarian cancer: A systematic review

Ovarian cancer is a highly lethal disease. Many researchers have, therefore, attempted to identify high risk populations. In this perspective, numerous genetic association studies have been performed to discover common ovarian cancer susceptibility variants. Accordingly, there is an increasing need to synthesize the evidence in order to identify true associations. A comprehensive and systematic assessment of all available data on genetic susceptibility to sporadic ovarian cancer was carried out. The evidence of statistically significant findings was evaluated based on the number of positive replications, the ratio of positive and negative studies, and the false-positive report probability (FPRP). The authors reviewed three genome-wide association studies (GWAS) and 147 candidate gene studies, published from 1990 to October 2010, including around 1100 genetic variants in more than 200 candidate genes and 20 intergenic regions. Genetic variants with the strongest evidence for an association with ovarian cancer include the rs2854344 in the RB1 gene and SNPs on chromosomes 9p22.2, 8q24, 2q31, and 19p13. Promising genetic pathways for ovarian cancer include the cell cycle, DNA repair, sex steroid hormone and oncogenic pathway. Concluding, this review shows that many genetic association studies have been performed, but only a few genetic variants show strong evidence for an association with ovarian cancer. More research is needed to elucidate causal genetic variants, taking into consideration gene-gene and gene-environment interactions, combined effects of common and rare variants, and differences between histological subtypes of this cancer.     

Detecting patches of protein sites of influenza A viruses under positive selection

Influenza A viruses are single-stranded RNA viruses capable of evolving rapidly to adapt to environmental conditions. Examples include the establishment of a virus in a novel host or an adaptation to increasing immunity within the host population due to prior infection or vaccination against a circulating strain. Knowledge of the viral protein regions under positive selection is therefore crucial for surveillance. We have developed a method for detecting positively selected patches of sites on the surface of viral proteins, which we assume to be relevant for adaptive evolution. We measure positive selection based on dN/dS ratios of genetic changes inferred by considering the phylogenetic structure of the data and suggest a graph-cut algorithm to identify such regions. Our algorithm searches for dense and spatially distinct clusters of sites under positive selection on the protein surface. For the hemagglutinin protein of human influenza A viruses of the subtypes H3N2 and H1N1, our predicted sites significantly overlap with known antigenic and receptor-binding sites. From the structure and sequence data of the 2009 swine-origin influenza A/H1N1 hemagglutinin and PB2 protein, we identified regions that provide evidence of evolution under positive selection since introduction of the virus into the human population. The changes in PB2 overlap with sites reported to be associated with mammalian adaptation of the influenza A virus. Application of our technique to the protein structures of viruses of yet unknown adaptive behavior could identify further candidate regions that are important for host-virus interaction.     

A hierarchical Bayesian model for next-generation population genomics

The demography of populations and natural selection shape genetic variation across the genome and understanding the genomic consequences of these evolutionary processes is a fundamental aim of population genetics. We have developed a hierarchical Bayesian model to quantify genome-wide population structure and identify candidate genetic regions affected by selection. This model improves on existing methods by accounting for stochastic sampling of sequences inherent in next-generation sequencing (with pooled or indexed individual samples) and by incorporating genetic distances among haplotypes in measures of genetic differentiation. Using simulations we demonstrate that this model has a low false-positive rate for classifying neutral genetic regions as selected genes (i.e., Φ(ST) outliers), but can detect recent selective sweeps, particularly when genetic regions in multiple populations are affected by selection. Nonetheless, selection affecting just a single population was difficult to detect and resulted in a high false-negative rate under certain conditions. We applied the Bayesian model to two large sets of human population genetic data. We found evidence of widespread positive and balancing selection among worldwide human populations, including many genetic regions previously thought to be under selection. Additionally, we identified novel candidate genes for selection, several of which have been linked to human diseases. This model will facilitate the population genetic analysis of a wide range of organisms on the basis of next-generation sequence data.