A high resolution genome-wide scan for significant selective sweeps: an application to pooled sequence data in laying chickens

In most studies aimed at localizing footprints of past selection, outliers at tails of the empirical distribution of a given test statistic are assumed to reflect locus-specific selective forces. Significance cutoffs are subjectively determined, rather than being related to a clear set of hypotheses. Here, we define an empirical p-value for the summary statistic by means of a permutation method that uses the observed SNP structure in the real data. To illustrate the methodology, we applied our approach to a panel of 2.9 million autosomal SNPs identified from re-sequencing a pool of 15 individuals from a brown egg layer line. We scanned the genome for local reductions in heterozygosity, suggestive of selective sweeps. We also employed a modified sliding window approach that accounts for gaps in the sequence and increases scanning resolution by moving the overlapping windows by steps of one SNP only, and suggest to call this a "creeping window" strategy. The approach confirmed selective sweeps in the region of previously described candidate genes, i.e. TSHR, PRL, PRLHR, INSR, LEPR, IGF1, and NRAMP1 when used as positive controls. The genome scan revealed 82 distinct regions with strong evidence of selection (genome-wide p-value<0.001), including genes known to be associated with eggshell structure and immune system such as CALB1 and GAL cluster, respectively. A substantial proportion of signals was found in poor gene content regions including the most extreme signal on chromosome 1. The observation of multiple signals in a highly selected layer line of chicken is consistent with the hypothesis that egg production is a complex trait controlled by many genes

ENGINES: exploring single nucleotide variation in entire human genomes

<p>Abstract</p> <p>Background</p> <p>Next generation ultra-sequencing technologies are starting to produce extensive quantities of data from entire human genome or exome sequences, and therefore new software is needed to present and analyse this vast amount of information. The 1000 Genomes project has recently released raw data for 629 complete genomes representing several human populations through their Phase I interim analysis and, although there are certain public tools available that allow exploration of these genomes, to date there is no tool that permits comprehensive population analysis of the variation catalogued by such data.</p> <p>Description</p> <p>We have developed a genetic variant site explorer able to retrieve data for Single Nucleotide Variation (SNVs), population by population, from entire genomes without compromising future scalability and agility. ENGINES (ENtire Genome INterface for Exploring SNVs) uses data from the 1000 Genomes Phase I to demonstrate its capacity to handle large amounts of genetic variation (>7.3 billion genotypes and 28 million SNVs), as well as deriving summary statistics of interest for medical and population genetics applications. The whole dataset is pre-processed and summarized into a data mart accessible through a web interface. The query system allows the combination and comparison of each available population sample, while searching by rs-number list, chromosome region, or genes of interest. Frequency and F_STfilters are available to further refine queries, while results can be visually compared with other large-scale Single Nucleotide Polymorphism (SNP) repositories such as HapMap or Perlegen.</p> <p>Conclusions</p> <p>ENGINES is capable of accessing large-scale variation data repositories in a fast and comprehensive manner. It allows quick browsing of whole genome variation, while providing statistical information for each variant site such as allele frequency, heterozygosity or F_STvalues for genetic differentiation. Access to the data mart generating scripts and to the web interface is granted from <url>http://spsmart.cesga.es/engines.php</url></p

Local Genealogies in a Linear Mixed Model for Genome-Wide Association Mapping in Complex Pedigreed Populations

INTRODUCTION: The state-of-the-art for dealing with multiple levels of relationship among the samples in genome-wide association studies (GWAS) is unified mixed model analysis (MMA). This approach is very flexible, can be applied to both family-based and population-based samples, and can be extended to incorporate other effects in a straightforward and rigorous fashion. Here, we present a complementary approach, called 'GENMIX (genealogy based mixed model)' which combines advantages from two powerful GWAS methods: genealogy-based haplotype grouping and MMA. SUBJECTS AND METHODS: We validated GENMIX using genotyping data of Danish Jersey cattle and simulated phenotype and compared to the MMA. We simulated scenarios for three levels of heritability (0.21, 0.34, and 0.64), seven levels of MAF (0.05, 0.10, 0.15, 0.20, 0.25, 0.35, and 0.45) and five levels of QTL effect (0.1, 0.2, 0.5, 0.7 and 1.0 in phenotypic standard deviation unit). Each of these 105 possible combinations (3 h(2) x 7 MAF x 5 effects) of scenarios was replicated 25 times. RESULTS: GENMIX provides a better ranking of markers close to the causative locus' location. GENMIX outperformed MMA when the QTL effect was small and the MAF at the QTL was low. In scenarios where MAF was high or the QTL affecting the trait had a large effect both GENMIX and MMA performed similarly. CONCLUSION: In discovery studies, where high-ranking markers are identified and later examined in validation studies, we therefore expect GENMIX to enrich candidates brought to follow-up studies with true positives over false positives more than the MMA would

A high-resolution integrated map of copy number polymorphisms within and between breeds of the modern domesticated dog

<p>Abstract</p> <p>Background</p> <p>Structural variation contributes to the rich genetic and phenotypic diversity of the modern domestic dog, <it>Canis lupus familiaris</it>, although compared to other organisms, catalogs of canine copy number variants (CNVs) are poorly defined. To this end, we developed a customized high-density tiling array across the canine genome and used it to discover CNVs in nine genetically diverse dogs and a gray wolf.</p> <p>Results</p> <p>In total, we identified 403 CNVs that overlap 401 genes, which are enriched for defense/immunity, oxidoreductase, protease, receptor, signaling molecule and transporter genes. Furthermore, we performed detailed comparisons between CNVs located within versus outside of segmental duplications (SDs) and find that CNVs in SDs are enriched for gene content and complexity. Finally, we compiled all known dog CNV regions and genotyped them with a custom aCGH chip in 61 dogs from 12 diverse breeds. These data allowed us to perform the first population genetics analysis of canine structural variation and identify CNVs that potentially contribute to breed specific traits.</p> <p>Conclusions</p> <p>Our comprehensive analysis of canine CNVs will be an important resource in genetically dissecting canine phenotypic and behavioral variation.</p

Evidence for Pervasive Adaptive Protein Evolution in Wild Mice

The relative contributions of neutral and adaptive substitutions to molecular evolution has been one of the most controversial issues in evolutionary biology for more than 40 years. The analysis of within-species nucleotide polymorphism and between-species divergence data supports a widespread role for adaptive protein evolution in certain taxa. For example, estimates of the proportion of adaptive amino acid substitutions (alpha) are 50% or more in enteric bacteria and Drosophila. In contrast, recent estimates of alpha for hominids have been at most 13%. Here, we estimate alpha for protein sequences of murid rodents based on nucleotide polymorphism data from multiple genes in a population of the house mouse subspecies Mus musculus castaneus, which inhabits the ancestral range of the Mus species complex and nucleotide divergence between M. m. castaneus and M. famulus or the rat. We estimate that 57% of amino acid substitutions in murids have been driven by positive selection. Hominids, therefore, are exceptional in having low apparent levels of adaptive protein evolution. The high frequency of adaptive amino acid substitutions in wild mice is consistent with their large effective population size, leading to effective natural selection at the molecular level. Effective natural selection also manifests itself as a paucity of effectively neutral nonsynonymous mutations in M. m. castaneus compared to humans

Intriguing Balancing Selection on the Intron 5 Region of LMBR1 in Human Population

Background: The intron 5 of gene LMBR1 is the cis-acting regulatory module for the sonic hedgehog (SHH) gene. Mutation in this non-coding region is associated with preaxial polydactyly, and may play crucial roles in the evolution of limb and skeletal system. Methodology/Principal Findings: We sequenced a region of the LMBR1 gene intron 5 in East Asian human population, and found a significant deviation of Tajima’s D statistics from neutrality taking human population growth into account. Data from HapMap also demonstrated extended linkage disequilibrium in the region in East Asian and European population, and significantly low degree of genetic differentiation among human populations. Conclusion/Significance: We proposed that the intron 5 of LMBR1 was presumably subject to balancing selection during the evolution of modern human

Empirical Distributions of F-ST from Large-Scale Human Polymorphism Data

Studies of the apportionment of human genetic variation have long established that most human variation is within population groups and that the additional variation between population groups is small but greatest when comparing different continental populations. These studies often used Wright’s FST that apportions the standardized variance in allele frequencies within and between population groups. Because local adaptations increase population differentiation, high-FST may be found at closely linked loci under selection and used to identify genes undergoing directional or heterotic selection. We re-examined these processes using HapMap data. We analyzed 3 million SNPs on 602 samples from eight worldwide populations and a consensus subset of 1 million SNPs found in all populations. We identified four major features of the data: First, a hierarchically FST analysis showed that only a paucity (12%) of the total genetic variation is distributed between continental populations and even a lesser genetic variation (1%) is found between intra-continental populations. Second, the global FST distribution closely follows an exponential distribution. Third, although the overall FST distribution is similarly shaped (inverse J), FST distributions varies markedly by allele frequency when divided into non-overlapping groups by allele frequency range. Because the mean allele frequency is a crude indicator of allele age, these distributions mark the time-dependent change in genetic differentiation. Finally, the change in mean-FST of these groups is linear in allele frequency. These results suggest that investigating the extremes of the FST distribution for each allele frequency group is more efficient for detecting selection. Consequently, we demonstrate that such extreme SNPs are more clustered along the chromosomes than expected from linkage disequilibrium for each allele frequency group. These genomic regions are therefore likely candidates for natural selection

Natural Selection Affects Multiple Aspects of Genetic Variation at Putatively Neutral Sites across the Human Genome

A major question in evolutionary biology is how natural selection has shaped patterns of genetic variation across the human genome. Previous work has documented a reduction in genetic diversity in regions of the genome with low recombination rates. However, it is unclear whether other summaries of genetic variation, like allele frequencies, are also correlated with recombination rate and whether these correlations can be explained solely by negative selection against deleterious mutations or whether positive selection acting on favorable alleles is also required. Here we attempt to address these questions by analyzing three different genome-wide resequencing datasets from European individuals. We document several significant correlations between different genomic features. In particular, we find that average minor allele frequency and diversity are reduced in regions of low recombination and that human diversity, human-chimp divergence, and average minor allele frequency are reduced near genes. Population genetic simulations show that either positive natural selection acting on favorable mutations or negative natural selection acting against deleterious mutations can explain these correlations. However, models with strong positive selection on nonsynonymous mutations and little negative selection predict a stronger negative correlation between neutral diversity and nonsynonymous divergence than observed in the actual data, supporting the importance of negative, rather than positive, selection throughout the genome. Further, we show that the widespread presence of weakly deleterious alleles, rather than a small number of strongly positively selected mutations, is responsible for the correlation between neutral genetic diversity and recombination rate. This work suggests that natural selection has affected multiple aspects of linked neutral variation throughout the human genome and that positive selection is not required to explain these observations

Parallel Adaptive Divergence among Geographically Diverse Human Populations

Few genetic differences between human populations conform to the classic model of positive selection, in which a newly arisen mutation rapidly approaches fixation in one lineage, suggesting that adaptation more commonly occurs via moderate changes in standing variation at many loci. Detecting and characterizing this type of complex selection requires integrating individually ambiguous signatures across genomically and geographically extensive data. Here, we develop a novel approach to test the hypothesis that selection has favored modest divergence at particular loci multiple times in independent human populations. We find an excess of SNPs showing non-neutral parallel divergence, enriched for genic and nonsynonymous polymorphisms in genes encompassing diverse and often disease related functions. Repeated parallel evolution in the same direction suggests common selective pressures in disparate habitats. We test our method with extensive coalescent simulations and show that it is robust to a wide range of demographic events. Our results demonstrate phylogenetically orthogonal patterns of local adaptation caused by subtle shifts at many widespread polymorphisms that likely underlie substantial phenotypic diversity