A map of recent positive selection in the human genome

The identification of signals of very recent positive selection provides information about the adaptation of modern humans to local conditions. We report here on a genome-wide scan for signals of very recent positive selection in favor of variants that have not yet reached fixation. We describe a new analytical method for scanning single nucleotide polymorphism (SNP) data for signals of recent selection, and apply this to data from the International HapMap Project. In all three continental groups we find widespread signals of recent positive selection. Most signals are region-specific, though a significant excess are shared across groups. Contrary to some earlier low resolution studies that suggested a paucity of recent selection in sub-Saharan Africans, we find that by some measures our strongest signals of selection are from the Yoruba population. Finally, since these signals indicate the existence of genetic variants that have substantially different fitnesses, they must indicate loci that are the source of significant phenotypic variation. Though the relevant phenotypes are generally not known, such loci should be of particular interest in mapping studies of complex traits. For this purpose we have developed a set of SNPs that can be used to tag the strongest ∼250 signals of recent selection in each population.     

A new approach for using genome scans to detect recent positive selection in the human genome

Genome-wide scanning for signals of recent positive selection is essential for a comprehensive and systematic understanding of human adaptation. Here, we present a genomic survey of recent local selective sweeps, especially aimed at those nearly or recently completed. A novel approach was developed for such signals, based on contrasting the extended haplotype homozygosity (EHH) profiles between populations. We applied this method to the genome single nucleotide polymorphism (SNP) data of both the International HapMap Project and Perlegen Sciences, and detected widespread signals of recent local selection across the genome, consisting of both complete and partial sweeps. A challenging problem of genomic scans of recent positive selection is to clearly distinguish selection from neutral effects, given the high sensitivity of the test statistics to departures from neutral demographic assumptions and the lack of a single, accurate neutral model of human history. We therefore developed a new procedure that is robust across a wide range of demographic and ascertainment models, one that indicates that certain portions of the genome clearly depart from neutrality. Simulations of positive selection showed that our tests have high power towards strong selection sweeps that have undergone fixation. Gene ontology analysis of the candidate regions revealed several new functional groups that might help explain some important interpopulation differences in phenotypic traits.     

A Bayesian method for jointly estimating allele age and selection intensity.

The problem of jointly estimating the intensity of past selection affecting an allele and the allele's age is formulated in a Bayesian framework. The prior distribution of allele age given its frequency is obtained from existing population genetics theory. The prior distribution of selection intensity is assumed to reflect the fact that positive selection on a new mutant is more likely to be weak than strong. The general approach is illustrated by the development of an importance sampling method applicable to low-frequency alleles. This method can be used either when the haplotypes of closely linked marker loci are known or when the lengths of linked ancestral chromosomal segments can be inferred. The method is illustrated with an application to the A-allele of G6PD in Africa. Because changes in allele frequency and recombination are both intrinsically stochastic, there are limits to the accuracy achievable with any method.     

Maximum-likelihood methods for detecting recent positive selection and localizing the selected site in the genome

Two maximum-likelihood methods are proposed for detecting recent, strongly positive selection and for localizing the target of selection along a recombining chromosome. The methods utilize the compact mutation frequency spectrum at multiple neutral loci that are partially linked to the selected site. Using simulated data, we show that the power of the tests lies between 80 and 98% in most cases, and the false positive rate could be as low as approximately 10% when the number of sampled marker loci is sufficiently large (> or = 20). The confidence interval around the estimated position of selection is reasonably narrow. The methods are applied to X chromosome data of Drosophila melanogaster from a European and an African population. Evidence of selection was found for both populations (including a selective sweep that was shared between both populations).     

DNA水平上检测正选择方法的研究进展

达尔文的自然选择学说指出, 自然选择作用是物种进化的主要因素。而1968年Kimura提出的中性进化学说认为中性突变和随机漂变才是进化的主要动力。在接下来的30多年时间中, 人们尝试从各种角度来检测自然选择是否存在。随着DNA测序技术的发展, 大量的DNA序列信息为检验自然选择提供了丰富的数据。因为自然选择会影响DNA变异模式, 所以可以通过分析现有的DNA样本来推断过去是否发生了自然选择。另一方面, 种群历史等因素也会影响到DNA变异模式, 因此会对自然选择的检测产生干扰。文章主要介绍了中性检验基本的概念, 全面回顾了一些经典的检验方法, 并着重介绍了近几年新发展出的研究方向。     

A method for estimating the intensity of overdominant selection from the distribution of allele frequencies

A method is proposed for estimating the intensity of overdominant selection scaled by the effective population size, S = 2Ns, from allele frequencies. The method is based on the assumption that, with strong overdominant selection, allele frequencies are nearly at their deterministic equilibrium values and that, to a first approximation, deviations depend only on S. Simulations verify that reasonably accurate estimates of S can be obtained for realistic sample sizes. The method is applied to data from several loci in the major histocompatibility complex (Mhc) in numerous human populations. For alleles distinguished by both serological typing and the sequence of the peptide-binding region, our estimates of S are comparable to those obtained by analysis of DNA sequences in showing that selection is strongest on HLA-B and weaker on HLA-A, HLA-DRB1, and HLA-DQA1. The intensity of selection on HLA-B varied considerably among populations. Two populations, Native American and Inuit, showed an excess rather than a deficiency in homozygosity. Comparable estimates of S were obtained for alleles at Mhc class II loci distinguished by serological reactions (serotyping) and by differences in the amino acid sequences of the peptide-binding region (molecular typing). A comparison of two types of data for DQA1 and DRB1 showed that serotyping led to generally lower estimates of S.     

A correction for allele frequency estimates derived from isofemale lines

Isofemale lines are commonly used inDrosophila and other genera for the purpose of assaying genetic variation. Isofemale lines can be kept in the laboratory for many generations before genetic work is carried out, and permit the confirmation of newly discovered alleles. A problem not realized by many workers is that the commonly used estimate of allele frequency from these lines is biased. This estimation bias occurs at all times after the first laboratory generation, regardless of whether single individuals or pooled samples are used in each well of an electrophoretic gel. This bias can potentially affect the estimation of population genetic parameters, and in the case of rare allele analysis it can cause gross overestimates of gene flow. This paper provides a correction for allele frequency estimates derived from isofemale lines for any time after the lines are established in the laboratory. When pooled samples are used, this estimator performs better than the standard estimator at all times after the first generation. The estimator is also insensitive to multiple inseminations. After the lines have drifted oneN _e generations, multiple inseminations actually make the new estimator perform better than it does in singly inseminated females. Simulations show that estimates made using either estimator after the lines have drifted to fixation have a much greater error associated with their use than do those estimates made earlier in time using the correction. In general it is better to use corrected estimates of gene frequency soon after lines are established than to use uncorrected estimates made after the first laboratory generation. This work was supported by an NSERC fellowship to A.D.L.     

A whole genome long-range haplotype (WGLRH) test for detecting imprints of positive selection in human populations

MOTIVATION: The identification of signatures of positive selection can provide important insights into recent evolutionary history in human populations. Current methods mostly rely on allele frequency determination or focus on one or a small number of candidate chromosomal regions per study. With the availability of large-scale genotype data, efficient approaches for an unbiased whole genome scan are becoming necessary. METHODS: We have developed a new method, the whole genome long-range haplotype test (WGLRH), which uses genome-wide distributions to test for recent positive selection. Adapted from the long-range haplotype (LRH) test, the WGLRH test uses patterns of linkage disequilibrium (LD) to identify regions with extremely low historic recombination. Common haplotypes with significantly longer than expected ranges of LD given their frequencies are identified as putative signatures of recent positive selection. In addition, we have also determined the ancestral alleles of SNPs by genotyping chimpanzee and gorilla DNA, and have identified SNPs where the non-ancestral alleles have risen to extremely high frequencies in human populations, termed 'flipped SNPs'. Combining the haplotype test and the flipped SNPs determination, the WGLRH test serves as an unbiased genome-wide screen for regions under putative selection, and is potentially applicable to the study of other human populations. RESULTS: Using WGLRH and high-density oligonucleotide arrays interrogating 116 204 SNPs, we rapidly identified putative regions of positive selection in three populations (Asian, Caucasian, African-American), and extended these observations to a fourth population, Yoruba, with data obtained from the International HapMap consortium. We mapped significant regions to annotated genes. While some regions overlap with genes previously suggested to be under positive selection, many of the genes have not been previously implicated in natural selection and offer intriguing possibilities for further study. AVAILABILITY: the programs for the WGLRH algorithm are freely available and can be downloaded at http://www.affymetrix.com/support/supplement/WGLRH_program.zip.     

Progress and prospects in mapping recent selection in the genome

One of the central goals of evolutionary biology is to understand the genetic basis of adaptive evolution. The availability of nearly complete genome sequences from a variety of organisms has facilitated the collection of data on naturally occurring genetic variation on the scale of hundreds of loci to whole genomes. Such data have changed the focus of molecular population genetics from making inferences about adaptive evolution at single loci to identifying which loci, out of hundreds to thousands, have been recent targets of natural selection. A major challenge in this effort is distinguishing the effects of selection from those of the demographic history of populations. Here we review some current progress and remaining challenges in the field.     

Revisiting the false positive rate in detecting recent positive selection

There is increasing interest in studying the molecular mechanisms of recent adaptations caused by positive selection in the genomics era. Such endeavors to detect recent positive selection, however, have been severely handicapped by false positives due to the confounding impact of demography and the population structure. To reduce false positives, it is critical to conduct a functional analysis to identify the true candidate genes/mutations from those that are filtered through neutrality tests. However, the extremely high cost of such functional analysis may restrict studies within a small number of model species. In particular, when the false positive rate of neutrality tests is high, the efficiency of the functional analysis will also be very low. Therefore, although the recent improvements have been made in the (joint) inference of demography and selection, our ultimate goal, which is to understand the mechanism of adaptation generally in a wide variety of natural populations, may not be achieved using the currently available approaches. More attention should thus be spent on the development of more reliable tests that could not only free themselves from the confounding impact of demography and the population structure but also have reasonable power to detect selection.     

Scanning the human genome for signals of selection

The search for adaptive evolution in the human genome has reached a new era with the advent of genome-wide surveys of genetic variation. However, making sense, let alone use, of such experiments is far from straightforward. Key problems include the way in which the data have been collected, the need to control for factors such as population history and variable recombination rates, which influence the discovery rates for both true and false positives, and the inherent difficulty of falsification. Nevertheless, recent work has shown that genome scans can be used to identify both functional polymorphisms underlying selected traits and entire classes of genes enriched for signals of adaptation.     

Localizing recent adaptive evolution in the human genome

Identifying genomic locations that have experienced selective sweeps is an important first step toward understanding the molecular basis of adaptive evolution. Using statistical methods that account for the confounding effects of population demography, recombination rate variation, and single-nucleotide polymorphism ascertainment, while also providing fine-scale estimates of the position of the selected site, we analyzed a genomic dataset of 1.2 million human single-nucleotide polymorphisms genotyped in African-American, European-American, and Chinese samples. We identify 101 regions of the human genome with very strong evidence (p < 10⁻⁵) of a recent selective sweep and where our estimate of the position of the selective sweep falls within 100 kb of a known gene. Within these regions, genes of biological interest include genes in pigmentation pathways, components of the dystrophin protein complex, clusters of olfactory receptors, genes involved in nervous system development and function, immune system genes, and heat shock genes. We also observe consistent evidence of selective sweeps in centromeric regions. In general, we find that recent adaptation is strikingly pervasive in the human genome, with as much as 10% of the genome affected by linkage to a selective sweep.     

A method for detecting positive selection at single amino acid sites

A method was developed for detecting the selective force at single amino acid sites given a multiple alignment of protein-coding sequences. The phylogenetic tree was reconstructed using the number of synonymous substitutions. Then, the neutrality was tested for each codon site using the numbers of synonymous and nonsynonymous changes throughout the phylogenetic tree. Computer simulation showed that this method accurately estimated the numbers of synonymous and nonsynonymous substitutions per site, as long as the substitution number on each branch was relatively small. The false-positive rate for detecting the selective force was generally low. On the other hand, the true-positive rate for detecting the selective force depended on the parameter values. Within the range of parameter values used in the simulation, the true-positive rate increased as the strength of the selective force and the total branch length (namely the total number of synonymous substitutions per site) in the phylogenetic tree increased. In particular, with the relative rate of nonsynonymous substitutions to synonymous substitutions being 5.0, most of the positively selected codon sites were correctly detected when the total branch length in the phylogenetic tree was > or = 2.5. When this method was applied to the human leukocyte antigen (HLA) gene, which included antigen recognition sites (ARSs), positive selection was detected mainly on ARSs. This finding confirmed the effectiveness of the present method with actual data. Moreover, two amino acid sites were newly identified as positively selected in non-ARSs. The three-dimensional structure of the HLA molecule indicated that these sites might be involved in antigen recognition. Positively selected amino acid sites were also identified in the envelope protein of human immunodeficiency virus and the influenza virus hemagglutinin protein. This method may be helpful for predicting functions of amino acid sites in proteins, especially in the present situation, in which sequence data are accumulating at an enormous speed.     

Meta-basic estimates the size of druggable human genome

We present here the estimation of the upper limit of the number of molecular targets in the human genome that represent an opportunity for further therapeutic treatment. We select around ∼6300 human proteins that are similar to sequences of known protein targets collected from DrugBank database. Our bioinformatics study estimates the size of ‘druggable’ human genome to be around 20% of human proteome, i.e. the number of the possible protein targets for small-molecule drug design in medicinal chemistry. We do not take into account any toxicity prediction, the three-dimensional characteristics of the active site in the predicted ‘druggable’ protein families, or detailed chemical analysis of known inhibitors/drugs. Instead we rely on remote homology detection method Meta-BASIC, which is based on sequence and structural similarity. The prepared dataset of all predicted protein targets from human genome presents the unique opportunity for developing and benchmarking various in silico chemo/bio-informatics methods in the context of the virtual high throughput screening.     

Low frequency of the Mx allele for viral resistance predates recent intensive selection in domestic chickens

Avian influenza is a serious threat to the poultry industry and, as the potential source of a human pandemic virus, to public health. Different Mx alleles have been reported to confer resistance or susceptibility to influenza virus replication, and so knowledge of their frequencies is important when considering the potential for improvement of modern commercial flocks. We analysed a range of chicken lines and ancestral breeds for the relevant Mx codon that confers resistance or susceptibility to influenza virus replication. We confirmed the high frequency of the susceptibility allele in contemporary meat-type (broiler) birds compared to egg-laying strains and found this difference is present already in ancestral breeds. We sequenced full-length complementary DNA (cDNA) and noted additional substitutions, which may be associated with the resistance haplotypes. High frequencies of the susceptibility allele could be readily reduced by modern breeding techniques.     

Recent and ongoing selection in the human genome

The recent availability of genome-scale genotyping data has led to the identification of regions of the human genome that seem to have been targeted by selection. These findings have increased our understanding of the evolutionary forces that affect the human genome, have augmented our knowledge of gene function and promise to increase our understanding of the genetic basis of disease. However, inferences of selection are challenged by several confounding factors, especially the complex demographic history of human populations, and concordance between studies is variable. Although such studies will always be associated with some uncertainty, steps can be taken to minimize the effects of confounding factors and improve our interpretation of their findings.     

新一代测序的拷贝数变异检测算法研究与设计

基于不同的测序技术,基因拷贝数变异的检测方法有多种,但时间复杂度较高,而新一代测序技术的发展为基因拷贝数变异检测的研究开辟了新领域。通过仿真实验、置换检验设计出一种新的基于新一代测序的拷贝数变异检测算法。不同于其它算法,本算法无需参考样本,通过直接研究比对后的序列以及reads与拷贝数的关系,来研究检测拷贝数变异,实验结果表明在时间复杂度上能提高50%以上的运算速度,这对今后拷贝数与疾病的研究具有重要意义。