Controlling the false-positive rate in multilocus genome scans for selection

Signatures of recent directional selection under different models of population expansion during colonization of new selective environments

A major problem in population genetics is understanding how the genomic pattern of polymorphism is shaped by natural selection and the demographic history of populations. Complex population dynamics confounds patterns of variation and poses serious challenges for identifying genomic imprints of selection. We examine patterns of polymorphism using computer simulations and provide analytical predictions for hitchhiking effects under two models of adaptive niche expansion. The population split (PS) model assumes the separation of a founding population followed by directional selection in the new environment. Here, the new population undergoes a bottleneck and later expands in size. This model has been used in previous studies to account for demographic effects when testing for signatures of selection under colonization or domestication. The genotype-dependent colonization and introgression (GDCI) model is proposed in this study and assumes that a small number of migrants carrying adaptive genotype found a new population, which then grows logistically. The GDCI model also allows for constant migration between the parental and the new population. Both models predict reduction in variation and excess of high frequency of derived alleles relative to neutral expectations, with and without hitchhiking. Under comparable conditions, the GDCI model results in greater reduction in expected heterozygosity and more skew of the site frequency spectrum than the PS model. We also find that soft selective sweeps (fixation of multiple copies of a beneficial mutation) occurs less often in the GDCI model than in the PS model. This result demonstrates the importance of correctly modeling the ecological process in inferring adaptive evolution using DNA sequence polymorphism.

Selection on grain shattering genes and rates of rice domestication

Molecular cloning of major quantitative trait loci (QTLs) responsible for the reduction of rice grain shattering, a hallmark of cereal domestication, provided opportunities for in-depth investigation of domestication processes. Here, we studied nucleotide variation at the shattering loci, sh4 and qSH1, for cultivated rice, Oryza sativa ssp. indica and Oryza sativa ssp. japonica, and the wild progenitors, Oryza nivara andOryza rufipogon. The nonshattering sh4 allele was fixed in all rice cultivars, with levels of sequence polymorphism significantly reduced in both indica and japonica cultivars relative to the wild progenitors. The sh4 phylogeny together with the neutrality tests and coalescent simulations suggested that sh4 had a single origin and was fixed by artificial selection during the domestication of rice. Selection on qSH1 was not detected in indica and remained unclear in japonica. Selection on sh4 could be strong enough to have driven its fixation in a population of cultivated rice within a period of c. 100 yr. The slow fixation of the nonshattering phenotype observed at the archeological sites might be a result of relatively weak selection on mutations other than sh4 in early rice cultivation. The fixation of sh4 could have been achieved later through strong selection for the optimal phenotype.

Integration of evolutionary features for the identification of functionally important residues in major facilitator superfamily transporters

The identification of functionally important residues is an important challenge for understanding the molecular mechanisms of proteins. Membrane protein transporters operate two-state allosteric conformational changes using functionally important cooperative residues that mediate long-range communication from the substrate binding site to the translocation pathway. In this study, we identified functionally important cooperative residues of membrane protein transporters by integrating sequence conservation and co-evolutionary information. A newly derived evolutionary feature, the co-evolutionary coupling number, was introduced to measure the connectivity of co-evolving residue pairs and was integrated with the sequence conservation score. We tested this method on three Major Facilitator Superfamily (MFS) transporters, LacY, GlpT, and EmrD. MFS transporters are an important family of membrane protein transporters, which utilize diverse substrates, catalyze different modes of transport using unique combinations of functional residues, and have enough characterized functional residues to validate the performance of our method. We found that the conserved cores of evolutionarily coupled residues are involved in specific substrate recognition and translocation of MFS transporters. Furthermore, a subset of the residues forms an interaction network connecting functional sites in the protein structure. We also confirmed that our method is effective on other membrane protein transporters. Our results provide insight into the location of functional residues important for the molecular mechanisms of membrane protein transporters.

Major Facilitator Superfamily (MFS) transporters are one of the largest families of membrane protein transporters and are ubiquitous to all three kingdoms of life. Structural studies of MFS transporters have revealed that the members of this superfamily share structural homology; however, due to weak sequence similarity, their structural similarity has only been found after structural determination. Even after the structures were solved, painstaking efforts were needed to detect functionally important residues. The identification of functionally important cooperative residues from sequences may provide an alternative way to understanding the function of this important class of proteins. Here, we show that it is possible to identify functionally important residues of MFS transporters by integrating two different evolutionary features, sequence conservation and co-evolutionary information. Our results suggest that the conserved cores of evolutionarily coupled residues are involved in specific substrate recognition and translocation of membrane protein transporters. Also, a subset of the identified residues comprises an interaction network connecting functional sites in the protein structure. The ability to identify functional residues from protein sequences may be helpful for locating potential mutagenesis targets in mechanistic studies of membrane protein transporters.

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.

On reconciling single and recurrent hitchhiking models

A major focus of modern population genetics involves using polymorphism data in order to identify regions impacted by recent positive selection (so-called genomic scans). Recently, methodology has been proposed not to identify individual loci, but rather to quantify genomic recurrent hitchhiking (RHH) parameters using this same type of polymorphism data. I here examine to what extent genomic scans for adaptively important loci may be informed by recently estimated RHH parameters (and vice versa). I find that published results are largely incompatible with one another, with approximately an order of magnitude more sweeps being empirically identified than would be predicted under RHH estimates. Results demonstrate that making this connection between SHH and RHH models is crucial for a more complete and accurate characterization of adaptive evolution.

[Advances in detecting positive selection on genome]

After Darwin's natural selection theory emerged, Kimura proposed the theory of neutral evolution in 1968, which considered neutral mutations and random genetic drift as the major evolutionary forces. In the following years, various kinds of methods were developed to test whether natural selection has ever happened. With the improvement of DNA sequencing technologies, large amount of DNA sequence polymorphism data is available, providing a mass of materials for testing the natural selection theory. Since natural selection would leave its footprint on the genome, we are able to refer the adaptive evolutionary history of a population. On the other hand, population demographic history and other evolutionary forces may affect DNA polymorphic pattern in similar way, which may interfere with tests. In this paper, we summarized some basic concepts of neutral test, and briefly introduced some classical methods. Focus was given to several recently developed methods.

Signature of diversifying selection on members of the pentatricopeptide repeat protein family in Arabidopsis lyrata

Pentatricopeptide repeat (PPR) proteins compose a family of nuclear-encoded transcriptional regulators of cytoplasmic genes. They have shown dramatic expansion in copy number in plants, and although the functional importance of many remains unclear, a subset has been repeatedly implicated as nuclear restorers for cytoplasmic male sterility. Here we investigate the molecular population genetics and molecular evolution of seven single-copy PPR genes in the outcrossing model plant Arabidopsis lyrata. In comparison with neutral reference loci, we find, on average, elevated levels of polymorphism and an excess of high-frequency variants at these PPR genes, suggesting that natural selection is maintaining polymorphism at some of these loci. This elevation in diversity persists when we control for divergence and generally decreases in the flanking regions, suggesting that these genes are themselves the targets of selection. Some of the PPR genes also demonstrate elevated population differentiation, which is consistent with spatially varying selection. In contrast, no comparable patterns are observed at these loci in A. thaliana, providing no evidence for the action of balancing selection in this selfing species. Taken together, these results suggest that a subset of PPR genes may be subject to balancing selection associated with ongoing cytonuclear coevolution in the outcrossing A. lyrata, which is possibly mediated either by intergenomic conflict or by compensatory evolution.

Detecting loci under selection in a hierarchically structured population

Patterns of genetic diversity between populations are often used to detect loci under selection in genome scans. Indeed, loci involved in local adaptations should show high F(ST) values, whereas loci under balancing selection should rather show low F(ST) values. Most tests of selection based on F(ST) use a null distribution generated under a simple island model of population differentiation. Although this model has been shown to be robust, many species have a more complex genetic structure, with some populations sharing a recent ancestry or due to the presence of barriers to gene flow between different parts of a species range. In this paper, we propose the use of a hierarchical island model, in which demes exchange more migrants within groups than between groups, to generate the joint distribution of genetic diversity within and between populations. We show that tests not accounting for a hierarchical structure, when it exists, do generate a large excess of false positive loci, whereas the hierarchical island model is robust to uncertainties about the exact number of groups and demes per group in the system. Our approach also explicitly takes into account the mutational process, and does not just rely on allele frequencies, which is important for short tandem repeat (STR) data. An application to human and stickleback STR data sets reveals a much lower number of significant loci than previously obtained under a non-hierarchical model. The elimination of false positive loci from genome scans should allow us to better determine on which specific class of genes selection is operating.

Automating approximate Bayesian computation by local linear regression

Background

In several biological contexts, parameter inference often relies on computationally-intensive techniques. "Approximate Bayesian Computation", or ABC, methods based on summary statistics have become increasingly popular. A particular flavor of ABC based on using a linear regression to approximate the posterior distribution of the parameters, conditional on the summary statistics, is computationally appealing, yet no standalone tool exists to automate the procedure. Here, I describe a program to implement the method.

Results

The software package ABCreg implements the local linear-regression approach to ABC. The advantages are: 1. The code is standalone, and fully-documented. 2. The program will automatically process multiple data sets, and create unique output files for each (which may be processed immediately in R), facilitating the testing of inference procedures on simulated data, or the analysis of multiple data sets. 3. The program implements two different transformation methods for the regression step. 4. Analysis options are controlled on the command line by the user, and the program is designed to output warnings for cases where the regression fails. 5. The program does not depend on any particular simulation machinery (coalescent, forward-time, etc.), and therefore is a general tool for processing the results from any simulation. 6. The code is open-source, and modular.

Examples of applying the software to empirical data from Drosophila melanogaster, and testing the procedure on simulated data, are shown.

Conclusion

In practice, the ABCreg simplifies implementing ABC based on local-linear regression.

Population genomic analysis of ALMS1 in humans reveals a surprisingly complex evolutionary history

Mutations in the human gene ALMS1 result in Alström Syndrome, which presents with early childhood obesity and insulin resistance leading to Type 2 diabetes. Previous genomewide scans for selection in the HapMap data based on linkage disequilibrium and population structure suggest that ALMS1 was subject to recent positive selection. Through a detailed population genomic analysis of existing genomewide data sets and new resequencing data obtained in geographically diverse populations, we find that the signature of selection at ALMS1 is considerably more complex than what would be expected for an idealized model of a selective sweep acting on a newly arisen advantageous mutation. Specifically, we observed three highly divergent and globally dispersed haplogroups, two of which carry a set of seven derived nonsynonymous single nucleotide polymorphisms that are nearly fixed in Asian populations. Our data suggest that the interaction of human demographic history and positive selection on standing variation in Eurasian populations approximately 15 thousand years ago parsimoniously explains the spectrum of extant ALMS1 variation. These results provide new insights into the evolutionary history of ALMS1 in humans and suggest that selective events identified in genomewide scans may be more complex than currently appreciated.

SNP deserts of Asian cultivated rice: genomic regions under domestication

When performing a genome-wide comparison between indica (93-11) and japonica (Nipponbare), we find 8% of the genome, which have an extremely low SNP rate (< 1 SNP/kb). Inside these 'SNP deserts', experimentally confirmed genes show increased K(a)/K(s) that indicate adaptive selection. To further elucidate this connection, we survey the level and pattern of genetic variation in both cultivated and wild rice groups, using 155 noncoding regions located within SNP deserts. The results suggest that cultivated rice has greatly reduced genetic variation within SNP deserts as compared to either the nondesert or corresponding genomic regions in wild rice. Consistent with this reduction in genetic variation, we find a biased distribution of derived allele frequency in the cultivated group, indicative of positive selection. Furthermore, over half of the confirmed, domestication-related genes are found within SNP deserts, also suggesting that SNP deserts are strongly related to domestication, and might be the key sites in the process of domestication.

Detecting selective sweeps: a new approach based on hidden markov models

Detecting and localizing selective sweeps on the basis of SNP data has recently received considerable attention. Here we introduce the use of hidden Markov models (HMMs) for the detection of selective sweeps in DNA sequences. Like previously published methods, our HMMs use the site frequency spectrum, and the spatial pattern of diversity along the sequence, to identify selection. In contrast to earlier approaches, our HMMs explicitly model the correlation structure between linked sites. The detection power of our methods, and their accuracy for estimating the selected site location, is similar to that of competing methods for constant size populations. In the case of population bottlenecks, however, our methods frequently showed fewer false positives.

The promise and challenges of incorporating genetic data into longitudinal social science surveys and research

In this paper, I argue that social science and genomics can be integrated; however, the way this marriage is currently occurring rests on spurious methods and assumptions and, as a result, will yield few lasting insights. However, recent advances in both econometrics and in developmental genomics provide scientists with a novel opportunity to understand how genes and environment interact to produce social outcomes. Key to any causal inference about the interplay between genes and social environment is that either genotype be exogenously manipulated (i.e. through sibling fixed effects) while environmental conditions are held constant, and/or that environmental variation is exogenous in nature, i.e. experimental or arising from a natural experiment of sorts. Further, initial allele selection should be motivated by findings from genetic experiments in model animal studies linked to orthologous human genes. Likewise, genetic associations found in human population studies should then be tested through knock-out and over-expression studies in model organisms.

Inferring the strength of selection in Drosophila under complex demographic models

Transposable elements (TEs) constitute a substantial fraction of the genomes of many species, and it is thus important to understand their population dynamics. The strength of natural selection against TEs is a key parameter in understanding these dynamics. In principle, the strength of selection can be inferred from the frequencies of a sample of TEs. However, complicated demographic histories, such as found in Drosophila melanogaster, could lead to a substantial distortion of the TE frequency distribution compared with that expected for a panmictic, constant-sized population. The current methodology for the estimation of selection intensity acting against TEs does not take into account demographic history and might generate erroneous estimates especially for TE families under weak selection. Here, we develop a flexible maximum likelihood methodology that explicitly accounts both for demographic history and for the ascertainment biases of identifying TEs. We apply this method to the newly generated frequency data of the BS family of non-long terminal repeat retrotransposons in D. melanogaster in concert with two recent models of the demographic history of the species to infer the intensity of selection against this family. We find the estimate to differ substantially compared with a prior estimate that was made assuming a model of constant population size. Further, we find there to be relatively little information about selection intensity present in the derived non-African frequency data and that the ancestral African subpopulation is much more informative in this respect. These findings highlight the importance of accounting for demographic history and bear on study design for the inference of selection coefficients generally.

A population genomic approach to map recent positive selection in model species

Based on nearly complete genome sequences from a variety of organisms data on naturally occurring genetic variation on the scale of hundreds of loci to entire genomes have been collected in recent years. In parallel, new statistical tests have been developed to infer evidence of recent positive selection from these data and to localize the target regions of selection in the genome. These methods have now been successfully applied to Drosophila melanogaster, humans, mice and a few plant species. In genomic regions of normal recombination rates, the targets of positive selection have been mapped down to the level of individual genes.

Hitchhiking both ways: effect of two interfering selective sweeps on linked neutral variation

The neutral polymorphism pattern in the vicinity of a selective sweep can be altered by both stochastic and deterministic factors. Here, we focus on the impact of another selective sweep in the region of influence of a first one. We study the signature left on neutral polymorphism by positive selection at two closely linked loci, when both beneficial mutations reach fixation. We show that, depending on the timing of selective sweeps and on their selection coefficients, the two hitchhiking effects can interfere with each other, leading to less reduction in heterozygosity than a single selective sweep of the same magnitude and more importantly to an excess of intermediate-frequency variants relative to neutrality under some parameter values. This pattern can be sustained and potentially alter the detection of positive selection, including by provoking spurious detection of balancing selection. In situations where positive selection is suspected a priori at several closely linked loci, the polymorphism pattern in the region may also be informative about their selective histories.

Worldwide population differentiation at disease-associated SNPs

BACKGROUND

Recent genome-wide association (GWA) studies have provided compelling evidence of association between genetic variants and common complex diseases. These studies have made use of cases and controls almost exclusively from populations of European ancestry and little is known about the frequency of risk alleles in other populations. The present study addresses the transferability of disease associations across human populations by examining levels of population differentiation at disease-associated single nucleotide polymorphisms (SNPs).

METHODS

We genotyped ~1000 individuals from 53 populations worldwide at 25 SNPs which show robust association with 6 complex human diseases (Crohn's disease, type 1 diabetes, type 2 diabetes, rheumatoid arthritis, coronary artery disease and obesity). Allele frequency differences between populations for these SNPs were measured using Fst. The Fst values for the disease-associated SNPs were compared to Fst values from 2750 random SNPs typed in the same set of individuals.

RESULTS

On average, disease SNPs are not significantly more differentiated between populations than random SNPs in the genome. Risk allele frequencies, however, do show substantial variation across human populations and may contribute to differences in disease prevalence between populations. We demonstrate that, in some cases, risk allele frequency differences are unusually high compared to random SNPs and may be due to the action of local (i.e. geographically-restricted) positive natural selection. Moreover, some risk alleles were absent or fixed in a population, which implies that risk alleles identified in one population do not necessarily account for disease prevalence in all human populations.

CONCLUSION

Although differences in risk allele frequencies between human populations are not unusually large and are thus likely not due to positive local selection, there is substantial variation in risk allele frequencies between populations which may account for differences in disease prevalence between human populations.

Positive selection in East Asians for an EDAR allele that enhances NF-kappaB 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-kappaB activity. Our results suggest that 370A is a positively selected functional genetic variant that underlies an adaptive human phenotype.

Hitchhiking mapping reveals a candidate genomic region for natural selection in three-spined stickleback chromosome VIII

Identification of genes and genomic regions under directional natural selection has become one of the major goals in evolutionary genetics, but relatively little work to this end has been done by applying hitchhiking mapping to wild populations. Hitchhiking mapping starts from a genome scan using a randomly spaced set of molecular markers followed by a fine-scale analysis in the flanking regions of the candidate regions under selection. We used the hitchhiking mapping approach to narrow down a selective sweep in the genomic region flanking a candidate locus (Stn90) in chromosome VIII in the three-spined stickleback (Gasterosteus aculeatus). Twenty-four microsatellite markers were screened in an approximately 800-kb region around the candidate locus in three marine and four freshwater populations. The patterns of genetic diversity and differentiation in the candidate region were compared to those of a putatively neutral set of markers. The Bayesian FST-test indicated an elevated genetic differentiation, deviating significantly from neutral expectations, at a continuous region of approximately 20 kb upstream from the candidate locus. Furthermore, a method developed for an array of microsatellite markers rejected neutrality in a region of approximately 90 kb flanking the candidate locus supporting the selective sweep hypothesis. Likewise, the genomewide pattern of genetic diversity differed from the candidate region in a bottleneck analysis suggesting that selection, rather than demography, explains the reduced genetic diversity at the candidate interval. The neutrality tests suggest that the selective sweep had occurred mainly in the Lake Pulmanki population, but the results from bottleneck analyses indicate that selection might have operated in other populations as well. These results suggest that the narrow interval around locus Stn90 has likely been under directional selection, but the region contains several predicted genes, each of which can be the actual targets of selection. Understanding of the functional significance of this genomic region in an ecological context will require a more detailed sequence analysis.

Identification and analysis of genomic regions with large between-population differentiation in humans

The primary aim of genetic association and linkage studies is to identify genetic variants that contribute to phenotypic variation within human populations. Since the overwhelming majority of human genetic variation is found within populations, these methods are expected to be effective and can likely be extrapolated from one human population to another. However, they may lack power in detecting the genetic variants that contribute to phenotypes that differ greatly between human populations. Phenotypes that show large differences between populations are expected to be associated with genomic regions exhibiting large allele frequency differences between populations. Thus, from genome-wide polymorphism data genomic regions with large allele frequency differences between populations can be identified, and evaluated as candidates for large between-population phenotypic differences. Here we use allele frequency data from approximately 1.5 million SNPs from three human populations, and present an algorithm that identifies genomic regions containing SNPs with extreme Fst. We demonstrate that our candidate regions have reduced heterozygosity in Europeans and Chinese relative to African-Americans, and are likely enriched with genes that have experienced positive natural selection. We identify genes that are likely responsible for phenotypes known to differ dramatically between human populations and present several candidates worthy of future investigation. Our list of high Fst genomic regions is a first step in identifying the genetic variants that contribute to large phenotypic differences between populations, many of which have likely experienced positive natural selection. Our approach based on between population differences can compliment traditional within population linkage and association studies to uncover novel genotype-phenotype relationships.

The great opportunity: Evolutionary applications to medicine and public health

Evolutionary biology is an essential basic science for medicine, but few doctors and medical researchers are familiar with its most relevant principles. Most medical schools have geneticists who understand evolution, but few have even one evolutionary biologist to suggest other possible applications. The canyon between evolutionary biology and medicine is wide. The question is whether they offer each other enough to make bridge building worthwhile. What benefits could be expected if evolution were brought fully to bear on the problems of medicine? How would studying medical problems advance evolutionary research? Do doctors need to learn evolution, or is it valuable mainly for researchers? What practical steps will promote the application of evolutionary biology in the areas of medicine where it offers the most? To address these questions, we review current and potential applications of evolutionary biology to medicine and public health. Some evolutionary technologies, such as population genetics, serial transfer production of live vaccines, and phylogenetic analysis, have been widely applied. Other areas, such as infectious disease and aging research, illustrate the dramatic recent progress made possible by evolutionary insights. In still other areas, such as epidemiology, psychiatry, and understanding the regulation of bodily defenses, applying evolutionary principles remains an open opportunity. In addition to the utility of specific applications, an evolutionary perspective fundamentally challenges the prevalent but fundamentally incorrect metaphor of the body as a machine designed by an engineer. Bodies are vulnerable to disease - and remarkably resilient - precisely because they are not machines built from a plan. They are, instead, bundles of compromises shaped by natural selection in small increments to maximize reproduction, not health. Understanding the body as a product of natural selection, not design, offers new research questions and a framework for making medical education more coherent. We conclude with recommendations for actions that would better connect evolutionary biology and medicine in ways that will benefit public health. It is our hope that faculty and students will send this article to their undergraduate and medical school Deans, and that this will initiate discussions about the gap, the great opportunity, and action plans to bring the full power of evolutionary biology to bear on human health problems.

Inferring selection in partially sequenced regions

A common approach for identifying loci influenced by positive selection involves scanning large portions of the genome for regions that are inconsistent with the neutral equilibrium model or represent outliers relative to the empirical distribution of some aspect of the data. Once identified, partial sequence is generated spanning this more localized region in order to quantify the site-frequency spectrum and evaluate the data with tests of neutrality and selection. This method is widely used as partial sequencing is less expensive with regard to both time and money. Here, we demonstrate that this approach can lead to biased maximum likelihood estimates of selection parameters and reduced rejection rates, with some parameter combinations resulting in clearly misleading results. Most significantly, for a commonly used sample size in Drosophila population genetics (i.e., n = 12), the estimate of the target of selection has a large mean square error and the strength of selection is severely under estimated when the true selected site has not been sampled. We propose sequencing approaches that are much more likely to accurately localize the target and estimate the strength of selection. Additionally, we examine the performance of a commonly used test of selection under a variety of recurrent and single sweep models.

Genomewide spatial correspondence between nonsynonymous divergence and neutral polymorphism reveals extensive adaptation in Drosophila

The effect of recurrent selective sweeps is a spatially heterogeneous reduction in neutral polymorphism throughout the genome. The pattern of reduction depends on the selective advantage and recurrence rate of the sweeps. Because many adaptive substitutions responsible for these sweeps also contribute to nonsynonymous divergence, the spatial distribution of nonsynonymous divergence also reflects the distribution of adaptive substitutions. Thus, the spatial correspondence between neutral polymorphism and nonsynonymous divergence may be especially informative about the process of adaptation. Here we study this correspondence using genomewide polymorphism data from Drosophila simulans and the divergence between D. simulans and D. melanogaster. Focusing on highly recombining portions of the autosomes, at a spatial scale appropriate to the study of selective sweeps, we find that neutral polymorphism is both lower and, as measured by a new statistic Q(S), less homogeneous where nonsynonymous divergence is higher and that the spatial structure of this correlation is best explained by the action of strong recurrent selective sweeps. We introduce a method to infer, from the spatial correspondence between polymorphism and divergence, the rate and selective strength of adaptation. Our results independently confirm a high rate of adaptive substitution (approximately 1/3000 generations) and newly suggest that many adaptations are of surprisingly great selective effect (approximately 1%), reducing the effective population size by approximately 15% even in highly recombining regions of the genome.

Genome scans detect consistent divergent selection among subtidal vs. intertidal populations of the marine angiosperm Zostera marina

Genome scans are a powerful tool to detect natural selection in natural populations among a larger sample of marker loci. We used replicated habitat comparisons to search for consistent signals of selection among contrasting populations of the seagrass Zostera marina, a marine flowering plant with important ecological functions. We compared two different habitat types in the North Frisian Wadden Sea, either permanently submerged (subtidal) or subjected to aerial exposure (intertidal). In three independent population pairs, each consisting of one tidal creek and one tidal flat population each, we carried out a genome scan with 14 expressed sequence tag (EST)-derived microsatellites situated in 5'- or 3'-untranslated regions of putative genes, in addition to 11 anonymous genomic microsatellites. By using two approaches for outlier identification, one anonymous and two EST-derived microsatellites showed population differentiation patterns not consistent with neutrality. These microsatellites were detected in several parallel population comparisons, suggesting that they are under diverging selection. One of these loci is linked to a putative nodulin gene, which is responsible for water channelling across cellular membranes, suggesting a functional link of the observed genetic divergence with habitat characteristics.

Similar levels of X-linked and autosomal nucleotide variation in African and non-African populations of Drosophila melanogaster

Background

Levels of molecular diversity in Drosophila have repeatedly been shown to be higher in ancestral, African populations than in derived, non-African populations. This pattern holds for both coding and noncoding regions for a variety of molecular markers including single nucleotide polymorphisms and microsatellites. Comparisons of X-linked and autosomal diversity have yielded results largely dependent on population of origin.

Results

In an attempt to further elucidate patterns of sequence diversity in Drosophila melanogaster, we studied nucleotide variation at putatively nonfunctional X-linked and autosomal loci in sub-Saharan African and North American strains of D. melanogaster. We combine our experimental results with data from previous studies of molecular polymorphism in this species. We confirm that levels of diversity are consistently higher in African versus North American strains. The relative reduction of diversity for X-linked and autosomal loci in the derived, North American strains depends heavily on the studied loci. While the compiled dataset, comprised primarily of regions within or in close proximity to genes, shows a much more severe reduction of diversity on the X chromosome compared to autosomes in derived strains, the dataset consisting of intergenic loci located far from genes shows very similar reductions of diversities for X-linked and autosomal loci in derived strains. In addition, levels of diversity at X-linked and autosomal loci in the presumably ancestral African population are more similar than expected under an assumption of neutrality and equal numbers of breeding males and females.

Conclusion

We show that simple demographic scenarios under assumptions of neutral theory cannot explain all of the observed patterns of molecular diversity. We suggest that the simplest model is a population bottleneck that retains an ancestral female-biased sex ratio, coupled with higher rates of positive selection at X-linked loci in close proximity to genes specifically in derived, non-African populations.

Approaches for identifying targets of positive selection

Despite significant advancements in both empirical and theoretical population genetics throughout the past century, fundamental questions about the evolutionary forces that shape genomic diversity remain unresolved. Perhaps foremost among these are the strength and frequency of adaptive evolution. To quantify these parameters, statistical tools are needed that are capable of effectively identifying targets of positive selection throughout the genome in an unbiased manner, and functional approaches are needed that are capable of connecting these identified genotypes with the resulting adaptively significant phenotypes. Here we review recent advancements in both statistical and empirical methodology, and discuss important challenges and opportunities that remain as researchers continue to uncouple the relative importance of stochastic and deterministic factors in the evolution of natural populations.

Patterns of sequence variability and divergence at the diminutive gene region of Drosophila melanogaster: complex patterns suggest an ancestral selective sweep

To identify putatively swept regions of the Drosophila melanogaster genome, we performed a microsatellite screen spanning a 260-kb region of the X chromosome in populations from Zimbabwe, Ecuador, the United States, and China. Among the regions identified by this screen as showing a complex pattern of reduced heterozygosity and a skewed frequency spectrum was the gene diminutive (dm). To investigate the microsatellite findings, nucleotide sequence polymorphism data were generated in populations from both China and Zimbabwe spanning a 25-kb region and encompassing dm. Analysis of the sequence data reveals strongly reduced nucleotide variation across the entire gene region in both the non-African and the African populations, an extended haplotype pattern, and structured linkage disequilibrium, as well as a rejection of neutrality in favor of selection using a composite likelihood-ratio test. Additionally, unusual patterns of synonymous site evolution were observed at the second exon of this locus. On the basis of simulation studies as well as recently proposed methods for distinguishing between selection and nonequilibrium demography, we find that this "footprint" is best explained by a selective sweep in the ancestral population, the signal of which has been somewhat blurred via founder effects in the non-African samples.

Annotating ebony on the fly

The distinctive black phenotype of ebony mutants has made it one of the most widely used phenotypic markers in Drosophila genetics. Without doubt, ebony showcases the fruits of the fly community's labours to annotate gene function. As of this writing, FlyBase lists 142 references, 1277 fly stocks, 15 phenotypes and 44 alleles. In addition to its namesake pigmentation phenotype, ebony mutants affect other traits, including phototaxis and courtship. With phenotypic consequences of ebony variants readily apparent in the laboratory, does natural selection also see them in the wild? In this issue of Molecular Ecology, Pool & Aquadro investigate this question and found signs of natural selection on the ebony gene that appear to have resulted from selection for darker pigmentation at higher elevations in sub-Saharan populations of Drosophila melanogaster. Such findings from population genomic analysis of wild-derived strains should be included in gene annotations to provide a more holistic view of a gene's function. The evolutionary annotation of ebony added by Pool & Aquadro substantiates that pigmentation can be adaptive and implicates elevation as an important selective factor. This is important progress because the selective factors seem to differ between populations and species. In addition, the study raises issues to consider when extrapolating from selection at the molecular level to selection at the phenotypic level.

Genetics and evolution of weedy Helianthus annuus populations: adaptation of an agricultural weed

Agricultural weeds are a major cost to economies throughout the world, and have evolved from numerous plant species in many different plant families. Despite their ubiquity, we do not yet know how easily or often weeds evolve from their wild ancestors or the kinds of genes underlying their evolution. Here we report on the evolution of weedy populations of the common sunflower Helianthus annuus. We analysed 106 microsatellites in 48 individuals from each of six wild and four weed populations of the species. The statistical tests lnRV and lnRH were used to test for significant reductions in genetic variability at each locus in weedy populations compared to nearby wild populations. Between 1% and 6% of genes were significant outliers with reduced variation in weedy populations, implying that a small but not insignificant fraction of the genome may be under selection and involved in adaptation of weedy sunflowers. However, there did not appear to be a substantial reduction in variation across the genome, suggesting that effective population sizes have remained very large during the recent evolution of these weedy populations. Additional analyses showed that weedy populations are more closely related to nearby wild populations than to each other, implying that weediness likely evolved multiple times within the species, although a single origin followed by gene flow with local populations cannot be ruled out. Together, our results point to the relative ease with which weedy forms of this species can evolve and persist despite the potentially high levels of geneflow with nearby wild populations.

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.

The evolution of new functions and adaptation to new environments occurs by positive selection, whereby beneficial mutations increase in frequency and eventually become fixed in a population. Detecting such selection in humans is crucial for understanding the importance of past genetic adaptations and their role in contemporary common diseases. Methods have already been developed for detecting the signature of positive selection in large, genome-scale datasets (such as the “HapMap”). Positive selection is expected to more rapidly increase the frequency of an allele, and hence, the length of the haplotype (extent of DNA segment) associated with the selected allele, relative to those that are not under selection. Such methods compare haplotype lengths within a single population. Here, we introduce a new method that compares the lengths of haplotypes associated with the same allele in different populations. We demonstrate that our method has greater power to detect selective sweeps that are fixed or nearly so, and we construct a statistical framework that shows that our method reliably detects positive selection. We applied our method to the HapMap data and identified approximately 500 candidate regions in the human genome that show a signature of recent positive selection. Further targeted studies of these regions should reveal important genetic adaptations in our past.

The authors present a novel genome-scan approach for detecting local positive selection. The approach is designed to detect selective events that have resulted in complete or near-complete fixation of a beneficial allele.

On the utility of linkage disequilibrium as a statistic for identifying targets of positive selection in nonequilibrium populations

A critically important challenge in empirical population genetics is distinguishing neutral nonequilibrium processes from selective forces that produce similar patterns of variation. We here examine the extent to which linkage disequilibrium (i.e., nonrandom associations between markers) improves this discrimination. We show that patterns of linkage disequilibrium recently proposed to be unique to hitchhiking models are replicated under nonequilibrium neutral models. We also demonstrate that jointly considering spatial patterns of association among variants alongside the site-frequency spectrum is nonetheless of value. Through a comparison of models of equilibrium neutrality, nonequilibrium neutrality, equilibrium hitchhiking, nonequilibrium hitchhiking, and recurrent hitchhiking, we evaluate a linkage disequilibrium (LD) statistic (omega(max)) that appears to have power to identify regions recently shaped by positive selection. Most notably, for demographic parameters relevant to non-African populations of Drosophila melanogaster, we demonstrate that selected loci are distinguishable from neutral loci using this statistic.

Stepping-stone spatial structure causes slow decay of linkage disequilibrium and shifts the site frequency spectrum

The symmetric island model with D demes and equal migration rates is often chosen for the investigation of the consequences of population subdivision. Here we show that a stepping-stone model has a more pronounced effect on the genealogy of a sample. For samples from a small geographical region commonly used in genetic studies of humans and Drosophila, there is a shift of the frequency spectrum that decreases the number of low-frequency-derived alleles and skews the distribution of statistics of Tajima, Fu and Li, and Fay and Wu. Stepping-stone spatial structure also changes the two-locus sampling distribution and increases both linkage disequilibrium and the probability that two sites are perfectly correlated. This may cause a false prediction of cold spots of recombination and may confuse haplotype tests that compute probabilities on the basis of a homogeneously mixing population.

Plant domestication, a unique opportunity to identify the genetic basis of adaptation

Despite the fundamental role of plant domestication in human history and the critical importance of a relatively small number of crop plants to modern societies, we still know little about adaptation under domestication. Here we focus on efforts to identify the genes responsible for adaptation to domestication. We start from a historical perspective, arguing that Darwin's conceptualization of domestication and unconscious selection provides valuable insight into the evolutionary history of crops and also provides a framework to evaluate modern methods used to decipher the genetic mechanisms underlying phenotypic change. We then review these methods, framing the discussion in terms of the phenotype-genotype hierarchy. Top-down approaches, such as quantitative trait locus and linkage disequilibrium mapping, start with a phenotype of interest and use genetic analysis to identify candidate genes. Bottom-up approaches, alternatively, use population genetic analyses to identify potentially adaptive genes and then rely on standard bioinformatics and reverse genetic tools to connect selected genes to a phenotype. We discuss the successes, advantages, and challenges of each, but we conclude that bottom-up approaches to understanding domestication as an adaptive process hold greater promise both for the study of adaptation and as a means to identify genes that contribute to agronomically important traits.

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.