Adaptation in response to selection on polygenic phenotypes may occur via subtle allele frequencies shifts at many loci. Current population genomic techniques are not well posed to identify such signals. In the past decade, detailed knowledge about the specific loci underlying polygenic traits has begun to emerge from genome-wide association studies (GWAS). Here we combine this knowledge from GWAS with robust population genetic modeling to identify traits that may have been influenced by local adaptation. We exploit the fact that GWAS provide an estimate of the additive effect size of many loci to estimate the mean additive genetic value for a given phenotype across many populations as simple weighted sums of allele frequencies. We use a general model of neutral genetic value drift for an arbitrary number of populations with an arbitrary relatedness structure. Based on this model, we develop methods for detecting unusually strong correlations between genetic values and specific environmental variables, as well as a generalization of  The process of adaptation is of fundamental importance in evolutionary biology. Within the last few decades, genotyping technologies and new statistical methods have given evolutionary biologists the ability to identify individual regions of the genome that are likely to have been important in this process. When adaptation occurs in traits that are underwritten by many genes, however, the genetic signals left behind are more diffuse, and no individual region of the genome is likely to show strong signatures of selection. Identifying this signature therefore requires a detailed annotation of sites associated with a particular phenotype. Here we develop and implement a suite of statistical methods to integrate this sort of annotation from genome wide association studies with allele frequency data from many populations, providing a powerful way to identify the signal of adaptation in polygenic traits. We apply our methods to test for the impact of selection on human height, skin pigmentation, body mass index, type 2 diabetes risk, and inflammatory bowel disease risk. We find relatively strong signals for height and skin pigmentation, moderate signals for inflammatory bowel disease, and comparatively little evidence for body mass index and type 2 diabetes risk. Population and quantitative genetics were in large part seeded by Fisher's insight Recent advances in population genetics have also allowed for the genome-wide identification of individual recent selective events either by identifying unusually large allele frequency differences among populations and environments or by detecting the effects of these events on linked diversity The advent of well-powered genome wide association studies with large sample sizes Although the field of human medical genetics has been the largest and most rapid to puruse such approaches, evolutionary geneticists studying non-human model organisms have also carried out GWAS for a wide array of fitness-associated traits, and the development of further resources is ongoing Many investigators have begun to test whether the loci uncovered by these studies tend to be enriched for signals of selection, in the hopes of learning more about how adaptation has shaped phenotypic diversity and disease risk Here we develop and implement a general method based on simple quantitative and population genetic principals, using allele frequency data at GWAS loci to test for a signal of selection on the phenotypes they underwrite while accounting for the hierarchical structure among populations induced by shared history and genetic drift. Our work is most closely related to the recent work of Turchin et al Using the set of GWAS effect size estimates and genome wide allele frequency data, we estimate the mean genetic value https://github.com/jjberg2/PolygenicAdaptationCode.We develop a framework to describe how genetic values covary across populations based on a flexible model of genetic drift and population history. In Consider a trait of interest where We are chiefly interested in developing a framework for testing the hypothesis that the joint distribution of A simple approximation to a model of genetic drift is that the current frequency of an allele in a population is normally distributed around some ancestral frequency  may be problematic if there is substantial drift. However, even if that is the case, the estimated genetic values may still be assumed to follow a multivariate normal distribution by appealing to the central limit theorem, as each estimated genetic value is a sum over many loci. We show in the Results that this assumption often holds in practice.It is useful here to note that the relationship between the model for drift at the individual locus level, and at the genetic value level, gives an insight into where most of the information and statistical power for our methods will come from. Each locus adds a contribution The tests described below will give positive results when these observations are violated. The effect of selection is to induce a non-independence of the allele frequency deviation . We can write As such, we can proceed with simple, straightforward and familiar statistical approaches to test for the impact of spatially varying selection on the estimated genetic values. Below we describe three simple methods for identifying the signature of polygenic adaptation, which arise naturally from this observation.We first test if the genetic values are unusually correlated with an environmental variable across populations compared to our null model. A significant correlation is consistent with the hypothesis that the populations are locally adapted, via the phenotype, to local conditions that are correlated with the environmental variable. However, the link from correlation to causation must be supported by alternate forms of evidence, and in the lack of such evidence, a positive result from our environmental correlation tests may be consistent with many explanations.Assume we have a vector There are many possible models to describe the relationship between a trait of interest and a particular environmental variable that may act as a selective agent. We first consider a simple linear model, where we model the distribution of transformed genetic values . Values in the lower tail correspond a paucity of variance, and thus potentially to widespread stabilizing selection, with many populations selected for the same optimum. In this paper we report upper tail p-values from the empirical null distribution of Our If both quantities are well estimated, and we also assume that there is no hierarchical structure among the populations, then To see the close correspondence between Given that our estimated genetic values are simple linear sums of allele frequencies, it is natural to ask how The second term in (13) is less familiar; the numerator is the weighted sum of the covariance of allele frequencies between all pairs of GWAS loci, and the denominator is again the contribution of those loci to the additive genetic variance in the total population. This term is thus a measure of the correlation among loci in the deviation they take from the ancestral value, or the across population component of linkage disequilibrium. For a more in depth discussion of this relationship in the context of As noted above (8), when We refer to the first term in this decomposition as the standardized Having detected a putative signal of selection for a given trait, one may wish to identify individual regions and populations which contribute to the signal. Here we rely on our multivariate normal model of relatedness among populations, along with well understood methods for generating conditional multivariate normal distributions, in order to investigate specific hypotheses about individual populations or groups of populations. Using standard results from multivariate normal theory, we can generate the expected joint conditional distribution of genetic values for an arbitrary set of populations given the observed genetic values in some other set of populations. These conditional distributions allow for a convenient way to ask whether the estimated genetic values observed in certain populations or groups of populations differ significantly from the values we would expect them to take under the neutral model given the values observed in related populations.Specifically, we exclude a population or set of populations, and then calculate the expected mean and variance of genetic values in these excluded populations given the values observed in the remaining populations, and the covariance matrix relating them. Using this conditional mean and variance, we calculate a Z-score to describe how well fit the estimated genetic values of the excluded populations are by our model of drift, conditional on the values in the remaining populations. In simple terms, the observation of an extreme Z-score for a particular population or group of populations may be seen as evidence that that group has experienced directional selection on the trait of interest (or a correlated one) that was not experienced by the related populations on which we condition the analyses. The approach cannot uniquely determine the target of selection, however. For example, conditioning on populations that have themselves been influenced by directional selection may lead to large Z-scores for the population being tested, even if that population has been evolving neutrally. We refer the reader to the We conducted power simulations and an empirical application of our methods based on the Human Genome Diversity Panel (HGDP) population genomic dataset Various components of our procedure involve sampling random sets of SNPs from across the genome. While we control for biases in our test statistics introduced by population structure through our To assess the power of our methods in comparison to other possible approaches, we conducted a series of power simulations. There are two possible approaches to simulate the effect of selection on large scale allele frequency data of the type for which our methods are designed. The first is to simulate under some approximate model of the evolutionary history (e.g. full forward simulation under the Wright-Fisher model with selection). The second is to perturb real data in such a way that approximates the effect of selection. We choose to pursue the latter, both because it is more computationally tractable, and because it allows us to compare the power of our different approaches for populations with evolutionary histories of the same complexity as the real data we analyze. Each of our simulations will thus consist of sampling 1000 sets of SNPs matched to the height dataset (in much the same way we sample SNPs to construct the null distributions of our test statistics), and then adding slight shifts in frequency in various ways to mimic the effect of selection.Below we first describe the set of alternative statistics to which we compare our methods. We then describe the manner in which we add perturbations to mimic selection, and lastly describe a number of variations on this theme which we pursued in order to better demonstrate how the power of our statistics changes as we vary parameters of the trait of interest, evolutionary process, or the ascertainment.naivenaiveenrichment test that better account for sampling noise are available For our first set of simulation experiments we compared two of our statistics, , but give each locus an effect on fitness  and the genetic architecture of the trait. To illustrate the impact these factors have on the power of our methods, we performed two experiments in which we again held The second experiment is nearly identical to the first, except that before adding an effect of selection to the subsampled loci, we linearly rescale the effect sizes such that enrichment) all lose power as the the variance explained is partitioned over more loci, as the effect of selection at each locus is weaker. Somewhat surprisingly, the versions of our tests that fail to adequately control for population structure . Lastly, note that the horizontal axes in These simulations demonstrate that the conditional Z test can detect subtler frequency shifts than the global We estimated genetic values for each of six traits from the subset of GWAS SNPs that were present in the HGDP dataset, as described above. We discuss the analysis of each dataset in detail below, and address general points first. For each dataset, we constructed the covariance matrix from a sample of approximately In an effort to be descriptive and unbiased in our decisions about which environmental variables to test, we tested each trait for an effect of the major climate variables considered by Hancock et al (2008) We also applied our For each GWAS SNP set we also separate our Lastly, we pursue a number or regionally restricted analyses. For each trait and for each of the seven geographic/genetic clusters described by Rosenberg et al (2002) We first analyzed the set of 180 height associated loci identified by Lango Allen and colleagues Of the 180 loci identified by Lango Allen and colleagues, 161 were present in our HGDP dataset. We used these 161 loci in conjunction with the allele frequency data from the HGDP dataset to estimate genetic values for height in the 52 HGDP populations. Although the genetic values are correlated with the observed heights in these populations, they are unsurprisingly imperfect predictions see .We find a signal of excessive correlation with winter PC2 , but fin history . These rWe followed up on these results by conducting regional level analyses, which indicate that our signal of excess variance arises primarily from extreme differentiation among populations within Europe . AnalyseWe next analyzed data from a recent GWAS for skin pigmentation in an African-European admixed population of Cape Verdeans We used these four loci to calculate a genetic skin pigmentation score in each of the HGDP populations. As expected, we identified a strong signal of excess variance among populations, as well as a strong correlation with latitude , again cThis poor fit to observed skin pigmentation is due to the fact that we have failed to capture all of the loci that contribute to variation in skin pigmentation across the range of populations sampled, likely due to the partial convergent evolution of light skin pigmentation in Western and Eastern Eurasian populations Within Africa, the San population has a decidedly lower genetic skin pigmentation score than any other HGDP African population. This is potentially in accordance with the observation that the San are more lightly pigmented than other African populations represented by the HGDP We next investigate two traits related to metabolic phenotypes (BMI and Type 2 diabetes), as there is a long history of adaptive hypotheses put forward to explain phenotypic variation among populations, with little conclusive evidence emerging thus far. We first focus on the set of 32 BMI associated SNPs identified by Speliotes and colleagues Our regional level analysis indicated that the mean genetic BMI score is significantly lower that expected in East Asia  and Ulcerative Colitis (UC) We used these sets of SNPs to calculate genetic risk scores for CD and UC across the 52 HGDP populations. Both CD and UC showed strong negative correlations with summer PC2 , while CWe did not observe any significant In this paper we have developed a powerful framework for identifying the influence of local adaptation on the genetic loci underlying variation in polygenic phenotypes. Below we discuss two major issues related to the application of such methods, namely the effect of the GWAS ascertainment scheme on our inference, and the interpretation of positive results.Among the most significant potential pitfalls of our analysis  is the fact that the loci used to test for the effect of selection on a given phenotype have been obtained through a GWAS ascertainment procedure, which can introduce false signals of selection if potential confounds are not properly controlled. We condition on simple features of the ascertainment process via our allele matching procedure, but deeper issues may arise from artifactual associations that result from the effects of population structure in the GWAS ascertainment panel. Given the importance of addressing this issue to the broader GWAS community, a range of well developed methods exist for doing GWAS in structured populations, and we refer the reader to the existing literature for a full discussion The problem of population structure arises generally when there is a correlation in the ascertainment panel between phenotype and ancestry such that SNPs that are ancestry informative will appear to be associated with the trait, even when no causal relationship exists The primary takeaway from this observation is that the more diverse the array of individuals sampled for a given GWAS are with respect to ancestry, the greater the possibility that failing to control for population structure will generate false associations (or bias effect sizes) and hence false positives for our method.What bearing do these complications have on our empirical results? The GWAS datasets we used can be divided into those conducted within populations of European descent and the skin pigmentation dataset (which used an admixed population). We will first discuss our analysis of the former.The European GWAS loci we used were found in relatively homogeneous populations, in studies with rigorous standards for replication and control for population structure. Therefore, we are reasonably confident that these loci are true positives. Couple this with the fact that they were ascertained in populations that are fairly homogenous relative to the global scale of our analyses, and it is unlikely that population structure in the ascertainment panels is driving our positive signals. One might worry that we could still generate false signals by including European populations in our analysis, however many of the signals we see are driven by patterns outside of Europe (where the influence of structure within Europe should be much lessened). For height, where we do see a strong signal from within Europe, we use a set of loci that have been independently verified using a family based design that is immune to the effects of population structure We further note that for a number of GWAS datasets, including some of those analyzed here, studies of non-European populations have replicated many of the loci identified in European populations Arabidopsis thaliana for 107 phenotypes across an array of 183 inbred lines of diverse geographical and ecological origin. Atwell and colleagues used the genome-wide mixed model program EMMA The issue of population structure may be more profound for our style of approach when GWAS are conducted using individuals from more strongly structured populations. In some cases it is desirable to conduct GWAS in such populations as locally adaptive alleles will be present at intermediate frequencies in these broader samples, whereas they may be nearly fixed in more homogeneous samples. A range of methods have been developed to adjust for population structure in these setting The Cape Verde skin pigmentation data we used may qualify as this second type of study. The Cape Verde population is an admixed population of African/European descent, and has substantial inter-individual variation in admixture proportion. Due to its admixed nature, the population segregates alleles which would not be at intermediate frequency in either parental population, making it an ideal mapping population.Despite the considerable population structure, the fact that recombination continues to mix genotypes in this population means that much of the LD due to the African/European population structure has been broken up . Population structure seems to have been well controlled for in this study, and a number of the loci have been replicated in independent admixed populations. While we think it unlikely that the four loci we use are false associations, they could in principle suffer from the structured ascertainment issues described above, so it is unclear that the null distributions we use are strictly appropriate. That said, provided that Beleza and colleagues have appropriately controlled for population structure, under neutrality there would be no reason to expect that the correlation among the loci should be strongly positive with respect to the sign of their effect on the phenotype, and thus the pattern observed is at least consistent with a history of selection, especially in light of the multiple alternative lines of evidence for adaptation on the basis of skin pigmentation Further work is needed to determine how best to modify the tests proposed herein to deal with GWAS performed in structured populations.Our understanding of the genetic basis of variation in complex traits remains very incomplete, and as such the results of these analyses must be interpreted with caution. That said, because our methods are based simply on the rejection of a robust, neutral null model, an incomplete knowledge of the genetic basis of a given trait should only lead to a loss of statistical power, and not to a high false positive rate.For all traits analyzed here except for skin pigmentation, the within population variance for genetic value is considerably larger than the variance between populations. This suggests that much of what we find is relatively subtle adaptation even on the level of the phenotype, and emphasizes the fact that for most genetic and phenotypic variation in humans, the majority of the variance is within populations rather than between populations see –S19. In The larger conceptual issues relate to the interpretation of our positive findings, which we detail below. A number of these issues are inherent to the conceptual interpretation of evidence for local adaptation In all of our analyses, we have simply extrapolated GWAS effect sizes measured in one population and one environment to the entire panel of HGDP populations. It is therefore prudent to consider the validity of this assumption, as well as the implications for our analyses when it is violated. Aside from simple measurement error, there are two possible reasons that estimated effect sizes from GWAS may not reflect the true effect sizes.The first is that most GWAS hits likely identify tag SNPs that are in strong LD with causal sites that are physically nearby on the chromosome, rather than actual causal sites themselves The second is that the actual value of the additive effect at a causal site may change across environments and genetic backgrounds due to genotype-by-genotype  and genotype-by-environment interactions. Although the response at a given locus due to selection depends only the additive effect of the allele in that generation, the additive effect itself is a function of the environment and the frequencies of all interacting loci. As all of these can change considerably during the course of evolution, the effects estimated in one population may not apply in other populations, either in the present day, or over history of the populations It will be very difficult to know how reasonable it is to extrapolate effect sizes among populations without repeating measurements in different populations and different environments. Perhaps surprisingly, the existing evidence suggests that for a variety of highly polygenic traits, effects sizes and directions may be relatively consistent across human populations As the majority of GWAS studies are performed in a single population they will often miss variants contributing to phenotypic variation. This can occur due to GxG or GxE interactions as outlined above, but also simply because those variants are absent (or at low frequency) due to drift or selection among the populations. Such cases will not create a false signal of selection if only drift is involved, however, they do complicate the interpretation of positive signals. A particularly dramatic example of this is offered by our analysis of skin pigmentation associated loci, whose frequencies are clearly shaped by adaptation. The alleles found by a GWAS in the Cape Verde population completely fail to predict the skin pigmentation of East Asians and Native Americans. This reflects the fact that a number of the alleles responsible for light skin pigmentation in those populations are not variable in Cape Verde due to the partially convergent adaptive evolution of light skin pigmentation One further complication in the interpretation of our results is in how loss of constraint may play a role in driving apparent signals of local adaptation. Traits evolving under uniform stabilizing selection across all populations should be less variable than predicted by our covariance model of drift, due to negative covariances among loci, and so should be underrepresented in the extreme tails of our environmental correlation statistics and the upper tail of An issue may arise, however, when loss of constraint is paired with biased mutational input , resulting in a covariance matrix describing the relationship of the remaining As we do not get to observe the population allele frequencies, the entries of Given a vector of environmental variable measurements for each population, we apply both the This provides us with a set of As described in the Results, we can use our multivariate normal model of relatedness to obtain the expected distribution of genetic values for an arbitrary set of populations, conditional on the observed values in some other arbitrary set.We first partition our populations into two groups, those for which we want to obtain the expected distribution of genetic values (group 1), and those on which we condition in order to obtain this distribution (group 2). We then re–estimate the covariance matrix such that it is centered on the mean of group 2. This step is necessary because the amount of divergence between the populations in group 1 and the mean of group 2 will always be greater than the amount of divergence from the global mean, even under the neutral model, and our covariance matrix needs to reflect this fact in order to make accurate predictions. We can obtain this re-parameterized Once we have calculated this re–centered covariance matrix, we can use well known results from multivariate normal theory to obtain the expected joint distribution of the genetic values for group one, conditional on the values observed in group two.We partition our vector of genetic values and the re–centered covariance matrix such that andandThis distribution is itself multivariate normal, and as such this framework is extremely flexible, as it allows us to obtain the expected joint distribution for arbitrary sets of populations (e.g. geographic regions or continents), or for each individual population. Further, andAs with our excess variance test, explored in the main text, it is natural to ask how our environmental correlation tests can be written in terms of allele frequencies at individual loci.As noted in (8), we can obtain for each underlying locus a set of transformed allele frequencies, which have passed through the same transformation as the estimated genetic values. We assume that each locus enrichment statistic we compute for the power simulations, in which we assume that the correlations of individual loci with the environmental variable are independent of one another, and then perform a test for whether more loci individually show strong correlations with the environmental variable than we would expect by chance.This is in contrast to the We used imputed allele frequency data in the HGDP, where the imputation was performed as part of the phasing procedure of Phasing and imputation were done using fastPHASE Various components of our procedure involve sampling random sets of SNPs from across the genome. While we control for biases in our test statistics introduced by population structure through our We partitioned SNPs into a three way contingency table, with 25 bins for MAF (i.e. a bin size of 0.02), 2 bins for imputation (either imputed or not), and 10 bins for B value . For each set of null genetic values, we sampled one null SNP from the same cell in the contingency table as each of the GWAS SNPs, and assigned this null SNP the effect size associated with the GWAS SNP it was sampled to match. While we do not assign effect sizes to sampled SNPs used to estimate the covariance matrix For the skin pigmentation GWAS Figure S1Power of tests described in the main text to detect a signal of selection on the mapped genetic basis of skin pigmentation (TIFF)Click here for additional data file.Figure S2Power of tests described in the main text to detect a signal of selection on the mapped genetic basis of BMI (TIFF)Click here for additional data file.Figure S3Power of tests described in the main text to detect a signal of selection on the mapped genetic basis of T2D (TIFF)Click here for additional data file.Figure S4Power of tests described in the main text to detect a signal of selection on the mapped genetic basis of CD (TIFF)Click here for additional data file.Figure S5Power of tests described in the main text to detect a signal of selection on the mapped genetic basis of UC (TIFF)Click here for additional data file.Figure S6The two components of (TIFF)Click here for additional data file.Figure S7The two components of (TIFF)Click here for additional data file.Figure S8The two components of (TIFF)Click here for additional data file.Figure S9The two components of (TIFF)Click here for additional data file.Figure S10The two components of (TIFF)Click here for additional data file.Figure S11The genetic values for height in each HGDP population plotted against the measured sex averaged height taken from (TIFF)Click here for additional data file.Figure S12The genetic skin pigmentation score for a each HGDP population plotted against the HGDP populations values on the skin pigmentation index map of Biasutti 1959. Data obtained from Supplementary table of (TIFF)Click here for additional data file.Figure S13The genetic skin pigmentation score for a each HGDP population plotted against the HGDP populations values from the (TIFF)Click here for additional data file.Figure S14The distribution of genetic height score across all 52 HGDP populations. Grey bars represent the (TIFF)Click here for additional data file.Figure S15The distribution of genetic skin pigmentation score across all 52 HGDP populations. Grey bars represent the (TIFF)Click here for additional data file.Figure S16The distribution of genetic BMI score across all 52 HGDP populations. Grey bars represent the (TIFF)Click here for additional data file.Figure S17The distribution of genetic T2D risk score across all 52 HGDP populations. Grey bars represent the (TIFF)Click here for additional data file.Figure S18The distribution of genetic CD risk score across all 52 HGDP populations. Grey bars represent the (TIFF)Click here for additional data file.Figure S19The distribution of genetic UC risk score across all 52 HGDP populations. Grey bars represent the (TIFF)Click here for additional data file.Table S1Genetic height scores as compared to true heights for populations with a suitably close match in the dataset of (PDF)Click here for additional data file.Table S2Genetic skin pigmentation score as compared to values from Biasutti (PDF)Click here for additional data file.Table S3Conditional analysis at the regional level for the height dataset.(PDF)Click here for additional data file.Table S4Conditional analysis at the individual population level for the height dataset.(PDF)Click here for additional data file.Table S5Conditional analysis at the regional level for the skin pigmentation dataset.(PDF)Click here for additional data file.Table S6Conditional analysis at the individual population level for the skin pigmentation dataset.(PDF)Click here for additional data file.Table S7Condtional analysis at the regional level for the BMI dataset.(PDF)Click here for additional data file.Table S8Conditional analysis at the individual population level for the BMI dataset.(PDF)Click here for additional data file.Table S9Conditional analysis at the regional level for the T2D dataset.(PDF)Click here for additional data file.Table S10Conditional analysis at the individual population level for the T2D dataset.(PDF)Click here for additional data file.Table S11Conditional analysis at the regional level for the CD dataset.(PDF)Click here for additional data file.Table S12Conditional analysis at the individual population level for the CD dataset.(PDF)Click here for additional data file.Table S13Conditional analysis at the regional level for the UC dataset.(PDF)Click here for additional data file.Table S14Conditional analysis at the individual population level for the UC dataset.(PDF)Click here for additional data file.Table S15Corresponding (PDF)Click here for additional data file.Table S16Corresponding (PDF)Click here for additional data file.


The human genome contains several active families of transposable elements (TE): Alu, L1 and SVA. Germline transposition of these elements can lead to polymorphic TE (polyTE) loci that differ between individuals with respect to the presence/absence of TE insertions. Limited sets of such polyTE loci have proven to be useful as markers of ancestry in human population genetic studies, but until this time it has not been possible to analyze the full genomic complement of TE polymorphisms in this way.ST values, is provided as a resource for investigators who wish to develop panels of TE-based ancestry markers.For the first time here, we have performed a human population genetic analysis based on a genome-wide polyTE data set consisting of 16,192 loci genotyped in 2,504 individuals across 26 human populations. PolyTEs are found at very low frequencies, > 93 % of loci show < 5 % allele frequency, consistent with the deleteriousness of TE insertions. Nevertheless, polyTEs do show substantial geographic differentiation, with numerous group-specific polymorphic insertions. African populations have the highest numbers of polyTEs and show the highest levels of polyTE genetic diversity; Alu is the most numerous and the most diverse polyTE family. PolyTE genotypes were used to compute allele sharing distances between individuals and to relate them within and between human populations. Populations and continental groups show high coherence based on individuals’ polyTE genotypes, and human evolutionary relationships revealed by these genotypes are consistent with those seen for SNP-based genetic distances. The patterns of genetic diversity encoded by TE polymorphisms recapitulate broad patterns of human evolution and migration over the last 60–100,000 years. The utility of polyTEs as ancestry informative markers is further underscored by their ability to accurately predict both ancestry and admixture at the continental level. A genome-wide list of polyTE loci, along with their population group-specific allele frequencies and FThe genetic diversity represented by TE polymorphisms reflects known patterns of human evolution, and ensembles of polyTE loci are suitable for both ancestry and admixture analyses. The patterns of polyTE allelic diversity suggest the possibility that there may be a connection between TE-based genetic divergence and population-specific phenotypic differences.The online version of this article (doi:10.1186/s13100-015-0052-6) contains supplementary material, which is available to authorized users. Such polymorphic TE (polyTE) insertion sites have been shown to be valuable genetic markers for studies of human ancestry and evolution. PolyTEs provide a number of advantages for such population genetic studies , 19. Firad hoc and laborious, have in fact proven to be useful for the identification of polyTE loci that serve as ancestry informative markers (AIMs).A number of previous studies have leveraged TE polymorphisms for the analysis of human ancestry and evolution , 21–27. The most recent data release from the 1000 Genome Project (Phase3 November 2014) includes, for the first time, a comprehensive genome-wide data set of polyTE sites. There are a total of 16,192 such polyTE loci reported for 2,504 individuals across 26 human populations. These newly available data provide an unprecedented level of depth and resolution for polyTE-based studies of human ancestry and evolution. With these data, it is now possible to evaluate the relationship between TE polymorphism and human evolution in a systematic and unbiased way. In addition, individual polyTE loci genome-wide can be evaluated with respect to their utility as AIMs as well as their applicability to ancestry studies for specific population groups. Such an analysis could provide a useful resource for investigators interested in conducting their own targeted studies on specific populations. With such a comprehensive, genome-wide polyTE data set, it is also possible to evaluate the marker utility of previously under-utilized L1 and SVA sequences. For this study, we have conducted a genome-wide population genetic analysis of human TE polymorphisms in order to address precisely these kinds of issues. This work represents the most comprehensive study of human polyTEs to date.There are three families of polymorphic transposable elements (polyTEs) that show variation in presence/absence patterns at individual insertion sites across human genome sequences; these are Alu (SINE), L1 (LINE) and chimeric SVA elements. The Phase3 data release (November 2014) of the 1000 Genomes Project provides the most complete catalog of human transposable element insertion site polymorphisms available to date. Presence/absence genotypes for these human polyTEs are available for 2,504 individuals from 26 human populations across 16,192 genomic sites.We characterized the frequencies and distributions of human polyTEs for the 26 populations organized into 5 continental groups: African, Asian, European, Indian and American Table . The vasP ≤ 8.5 x 10−56 Wilcoxon ranked sum test). African populations also have the highest levels of variation in polyTE genetic diversity for any of the non-admixed groups, consistent with human origins in Africa and the bottleneck experienced by other population groups during their migrations out of Africa [PolyTE genotypes were analyzed in order to evaluate the polyTE genetic diversity levels for different continental groups and for different TE families. To do this, presence/absence patterns at all polyTE loci were used to genotype individual human genomes and pairwise allele sharing distances between individuals were computed based on these polyTE genotypes see . Africanf Africa , 30. TheThe distributions of polyTE genotypes among individuals were analyzed in an effort to reconstruct the evolutionary relationships among human individuals and populations. To do this, PolyTE genotype allele sharing distances were used to generate multi-dimensional scaling (MDS) plots showing the genetic relationships among all individuals Fig.  and the One of the advantages of using TE polymorphisms for ancestry inference is that the ancestral state for any polyTE loci can be confidently taken to be the absence of an insertion , 22. ThiFor the most part, there is high coherence of polyTE genotypes within both individual populations and for continental groups. The only exception seen is for the admixed American continental group, which has two distinct subgroups, a Latino subgroup  with primarily European and Asian admixture and an African-American subgroup (ACB and ASW) with primarily African and European admixture Fig. . The relResults of the same analyses are shown for individual polyTEs families in Additional file ST and the absolute allele frequency differences δ  and for five-way comparisons between individual populations within the same non-admixed continental group  for the between compared to the within continental group comparisons. This is consistent with the overall ability of polyTE genotypes to better distinguish between continental groups  [ST value. This can be attributed to the overall low frequency of polymorphic TE insertions seen here  , 37. Theere Fig.  since lohe years : 0.3, 0.ST levels and prediction accuracy was computed for sets of polyTE loci of sequentially decreasing size, going from 500 to 10 in steps of 10  and for individual populations within the African continental group. To do this for each comparison, the top 500 ancestry informative polyTE loci were ranked according to their F 10 Fig. . Two meaST values .These results have important implications for the study of human evolution, ancestry and admixture by smaller labs that may not have access to the same level of resources as larger consortia or genome centers since analysis of a small set of polyTE loci (10–50 depending on the application) can prove to be quite informative. Given the size range of TEs insertions, in particular for Alus which are the most numerous family of polyTEs, element presence/absence patterns can be accurately characterized in a cost-effective way using (multiplex) PCR-based techniques. Protocols for PCR-based analysis of polyTEs are well established in a number of labs. The results of this study can be used to help investigators choose the specific TE loci of interest for their own evolutionary studies  [Despite the overall utility of polyTEs as ancestry markers, results from this study suggest that they are not likely to be good markers for mapping by admixture linkage disequilibrium (MALD or admixture mapping) studies , 39. Themetrics) . Thus, SOur initial analysis of human polyTEs within and between populations revealed that TE insertion polymorphisms are found at very low frequencies Fig. . This isad hoc, based on a combination of literature and database surveys together with empirical evaluation on the suitability of individual loci as markers that can discriminate between populations. With the recent release of a genome-wide set of 16,192 TE polymorphisms by the 1000 Genomes Project [Polymorphic TE loci have long been used as markers in human population genetic studies, and they are known to provide a number of advantages for such studies. The selection of which polyTE loci to use for population genetic studies has been largely  Project , 35, genftp://ftp-trace.ncbi.nih.gov/1000genomes/ftp/release/20130502/). These genotypes consist of phased presence/absence patterns of polyTE insertions at specific human genome sites for individual genomes, and they are characterized from human genome reference sequence mapped next-generation sequence data via 1) discordant read mapping for short paired-end reads and/or 2) split read mapping for longer reads as previously described [TEi) normalized by the total number of sites in the population (2n): TEi/2n. The extent to which individual polyTE loci differentiate populations was computed using the fixation index FST with the Weir Cockerham method [Human polymorphic transposable element (polyTE) genotypes were taken from the Phase3 data release (November 2014) of the 1000 Genomes Project , 35  implemented in R. This was done for pairwise distances computed between individual genomes and for average allele sharing distances among populations. Population average allele sharing distances were used to reconstruct a neighbor-joining  phylogenThe program ADMIXTURE was used to infer the proportion of ancestry contributions from ancestral populations to modern admixed populations from the Americas  based on polyTE genotypes. The program was first run in supervised mode with three ancestral clusters: African, Asian and European. Asian ancestry is taken here as a rough surrogate for Native American admixture in American populations given the relatively close evolutionary relationship between East Asian and Native American populations and the lack of Native American samples in the 1000 Genomes Project. PolyTE genotypes were then imputed for Native American genomes from the Human Genome Diversity Project , 45, usiRMSD) between the predicted and actual ancestry inference made with the complete data. RMSD values are reported as the average prediction error for all individuals. This process was done repeatedly across individual polyTE genotypes based on decreasing numbers of polyTE sites, from 500 to 10 in steps of 10. For each polyTE set, this 80/20 prediction process was repeated 100 times.The program ADMIXTURE was used together with a cross-validation approach in order to predict the ancestry of individuals based on their polyTE genotypes. The cross-validation method relied on an 80 %/20 % split of the data, whereby 80 % of individual polyTE genotypes were used to build a three-cluster ancestry model with ADMIXTURE. The remaining 20 % of individual polyTE genotypes were then tested against this model to predict their ancestry membership in one of the three groups. Group-specific ancestry was only assigned if the probability of group membership was calculated as ≥ 90 %. Accuracy is then defined as the number of correct ancestry predictions normalized by the total number of predictions made. Error is defined as the root-mean-square difference (RMSD) for the African and European fractional ancestry contributions.An analogous prediction approach was used to infer the continental ancestry contributions to an admixed American population (ASW) using ADMIXTURE. In this case, the training was done using individual polyTE genotypes from ancestral populations (African and European) and the testing was done using polyTE genotypes from admixed ASW individuals. This was done first using all 16,192 polyTE loci and then for individual polyTE genotypes based on decreasing numbers of polyTE sites, from 500 to 10 in steps of 10. The predicted ancestry contributions to admixed individuals were compared for results based on all polyTE loci and results based on reduced sets of polyTE loci using the root-mean-square difference (


Cycas simplicipinna (T. Smitinand) K. Hill. (Cycadaceae) is an endangered species in China. There were seven populations and 118 individuals that we could collect were genotyped in this study. Here, we assessed the genetic diversity, genetic structure and demographic history of this species.C. simplicipinna. A demographic history inferred from DNA sequencing data indicates that C. simplicipinna experienced a recent population contraction without retreating to a common refugium during the last glacial period. The results derived from SSR data also showed that C. simplicipinna underwent past effective population contraction, likely during the Pleistocene.Analyses of data of DNA sequences  and sixteen microsatellite loci (SSR) were conducted in the species. Of the 118 samples, 86 individuals from the seven populations were used for DNA sequencing and 115 individuals from six populations were used for the microsatellite study. We found high genetic diversity at the species level, low genetic diversity within each of the seven populations and high genetic differentiation among the populations. There was a clear genetic structure within populations of C. simplicipinna such as having high genetic differentiation among the populations, a clear genetic structure and a recent population contraction could provide guidelines for protecting this endangered species from extinction. Furthermore, the genetic features with population dynamics of the species in our study would help provide insights and guidelines for protecting other endangered species effectively.Some genetic features of  Quercus lobata Nee. The results showed that Quercus lobata maintained a stable distribution with local migration from the last interglacial period (~120 ka) through the Last Glacial Maximum  to the present. This contrasts with large-scale range shifts in Quercus alba L [Historical processes leave imprints on the genetic structure of existing populations, especially those of long-lived and sessile organisms. The present genetic structure of many species has therefore been used to estimate the relationship between historical vicariance and geological change , disperss alba L . More res alba L ,9. CycadCycas, in China, and it is considered to be the oldest cycads genus [Cycads are the most primitive living seed plants. Fossil evidence shows that cycads originated approximately 275–300 million years ago ,11. Moleds genus . All cycds genus .Cycas simplicipinna (T. Smitinand) K. Hill was formally described in 1995. It is distinguishable by having the morphological characteristics of a shrub, an unremarkable trunk, and lanceolate cataphylls and is distributed in the Yunnan Province of China, Laos, Northern Thailand, and Vietnam. The species is dioecious and allogamous. Their seeds are mainly distributed by weight and usually distribute around the mother plant. So the phenomenon of severe inbreeding is common in the species, resulting in the expected high genetic differentiation and structure by using maternally inherited DNA. Despite being a national key protected plant, the genetic diversity and genetic structure of C. simplicipinna has not been studied in detail. The reasons for its endangerment are unclear. This study was undertaken to provide better understanding of the species’ genetic diversity and genetic structure and the reasons for its endangerment. Field surveys showed that there are two populations with fewer than 20 individuals. It is urgent to develop effective protection measures that are based on a comprehensive study of its genetic diversity and population structure.psbA-trnH and trnL-trnF), nrDNA (ITS4-ITS5) and SSR markers. The main aim of the study was to evaluate the genetic diversity, genetic structure and demographic history of C. simplicipinna and to provide basic guidelines for its conservation.The organelle DNA of cycads is maternally inherited and is dispersed only in seeds . Their nC. simplicipinna . Of the 118 samples, 86 individuals from the seven populations were used for chloroplast and nuclear DNA sequencing. The population known as BOL was eliminated from SSR analysis because there were only 3 individuals. A total of 115 individuals from six populations were used for the microsatellite study. Information on each sampling location and the number of individuals from each population that were used in DNA sequences and SSR analyses are presented in Table A total of 118 individual samples were collected from seven populations of psbA-trnH [trnL-trnF [2 (25 mM), 2.0 μL dNTPs (10 mM), 4.0 μL 10 × PCR buffer, 0.6 μL of each primer, 0.6 μL Taq DNA polymerase (5 U/μL)  and 26 μL double-distilled water. For nrDNA, the PCR reactions contained 40 ng DNA, 2.4 μL MgCl2 (25 mM), 2.0 μL dNTPs (10 mM), 2.0 DMSO, 4.0 μL 10 × PCR buffer, 0.7 μL of each primer, 0.7 μL Taq DNA polymerase (5 U/μL)  and 24.6 μL double-distilled water. PCR amplifications were performed in a thermocycler under the following conditions: an initial 5 min denaturation at 80°C, followed by 29 cycles of 1 min at 95°C, 1 min annealing at 50°C, and a 1.5 min extension at 65°C, and a final extension for 5 min at 65°C for cpDNA intergenic spacers. For nrDNA sequences we used an initial 4 min denaturation at 94°C, which was followed by 29 cycles of 45 s at 94°C, 1 min annealing at 50°C, and a 1.5 min extension at 72°C, and a final extension for 9 min at 72°C. All PCR products were sequenced in both directions with the same primers for the amplification reactions, using an ABI 3770 automated sequencer at Shanghai Sangon Biological Engineering Technology & Services Company Ltd. For nrDNA, we cloned individuals which had one or more heterozygous sites in the first sequencing round. Six to ten clones were randomly selected and sequenced until the heterozygous site split into two alleles.Young and healthy leaves were collected and dried immediately in silica gel for DNA extraction. Genomic DNA was extracted from dried leaves using the modified CTAB method . After psbA-trnH  and trnLrnL-trnF , and onernL-trnF , for comCycas[2 (25 mM), 1 μL dNTPs (10 mM), 1.5 μL 10 × PCR buffer, 0.6 μL of each primer, 0.16 μL Taq DNA polymerase (5 U/μL)  and 12.14 μL double-distilled water. PCR amplifications were performed in a thermocycler under the following conditions: an initial 4 min denaturation at 94°C, which was followed by 29 cycles of 40 s each at 94°C, 25 s annealing at 48–60°C, and a 30 s extension at 72°C, and a final extension for 10 min at 72°C. PCR products were checked with 8% non-denaturing polyacrylamide gel electrophoresis. Then, we made preliminary screening microsatellite loci for C. simplicipinna. The selected microsatellite loci were stained with a fluorescent dye at the 5' end, their PCR products were separated and visualized using an ABI 3770 automated sequencer, and their profiles were read with the GeneMapper software. An individual was declared null (nonamplifying) at a locus and was treated as missing data after two or more amplification failures. Finally, we chose polymorphic microsatellite loci for C. simplicipinna after calculating polymorphism indices.Microsatellite markers were selected from recently developed nuclear microsatellites in Cycas-33. PCR Sequences were edited and assembled using SeqMan. Multiple alignments of the DNA sequences were performed manually with Clustal X, version 1.83 , with suNei’s nucleotide diversity (Pi) and haplotype diversity (Hd) indices using DnaSP, version 5.0 [HS), gene diversity in total populations . We used the program Arlequin, version 3.11 [Haplotypes were calculated from aligned DNA sequences by DnaSP, version 5.0 . Within-sion 5.0 . We calculations ), and tw & Petit  using thion 3.11  to conduion 3.11  and to eC. simplicipinna were inferred using maximum parsimony (MP) in PAUP* 4.0b10 [Cycas diannanensis was used as the outgroup. We used Mega, version 5 [Phylogenetic relationships among cpDNA and nrDNA haplotypes of * 4.0b10  and Baye* 4.0b10 . Cycas dersion 5 , to consersion 5 . In netw-9 and 5.1-7.1 × 10-9[C. simplicipinna. Posterior estimates of the mutation rate and time of divergence were obtained by Markov Chain Monte Carlo (MCMC) analysis. The analysis was run for 107 iterations with a burn-in of 106 under the HKY model and a strict clock. Genealogies and model parameters were sampled every 1,000 iterations. Convergence of parameters and mixing of chains were followed by visual inspection of parameter trend lines and checking of effective sampling size (ESS) values in three pre-runs. The ESS parameter was found to exceed 200, which suggests acceptable mixing and sufficient sampling. Adequate sampling and convergence to the stationary distribution were checked using TRACER, version 1.5 [P-values were calculated as the proportion of simulations producing a larger SSD than the observed SSD. Arlequin, version 3.11 [FS[A well-documented evolutionary rate is needed to estimate coalescent time between lineages within populations. We used the evolutionary rates that had previously been estimated for seed plants to be 1.01 × 10.1 × 10-9 mutation.1 × 10-9, to estision 1.5 . We usedsion 1.5 , to furtion 3.11 , was als 3.11 [FS to detec 3.11 [FS.Cycas. We also used the Fst-outlier approach to test for signs of positive and balancing selection on those loci [Fst compared with HE[C. simplicipinna in the six populations. The results of positive and balancing selection on the twenty microsatellites detected balancing selection on locus A16 and positive selection on four other loci . However, locus A13 did not reach the significant level of an Fst-outlier , private alleles (AP), effective number of alleles (NE), expected heterozygosity , observed heterozygosity (HO = No. of Hets/N), information index (I), and fixation index (F = 1-(HO/HE)) were calculated using GenAlEx, version 6.3 [AR) was estimated with FSTAT, version 2.9.3 [PPB) was calculated with GenAlEx, version 6.3 [FST and tested with GenAlEx, version 6.3 [ST/(1-FST) with geographic distance for all pairs of populations. FST/(1-FST) was caculated with Genepop, version 4.1.4 [Nm = (1-FST)/4FST[The number of alleles /4FST. Hardy-WFST)/4FST.5 iterations and 1 × 105 subsequent MCMC steps. The combination of an admixture and a correlated-allele frequencies model was used for the analysis. The second-order rate of change of the log probability of the data with respect to the number of clusters (ΔK) was used as an additional estimator of the most likely number of genetic clusters [ΔK by STRUCTURE HARVESTER, version 0.6.8 [The genetic structures of sampled populations and individuals were estimated by unweighted pair group mean analysis (UPGMA) using TEPGA, version 1.3 , with 5,clusters . The beson 0.6.8 . Finallyon 0.6.8 , based oM-ratio [Mc value of 0.68, a value obtained by simulations based on the empirical data in bottlenecked populations, suggesting a reduction in population size [We calculated the effective population sizes of each population to establish the degree of endangerment of the species. We used the program LDNe at three levels of the lowest allele frequency  at a 95% confidence interval . We testM-ratio , the ration size ,68. The ion size . The indion size .psbA-trnH and trnL-trnF) varied from 1,408 to 1,438 bp and aligned with a 1,452 bp consensus length that contained 14 polymorphic sites and 16 indels  and haplotype (Hd) diversity in all populations were, respectively, 0.00259 and 0.864 as inferred from cpDNA and 0.008 and 0.723 as infered from nrDNA  was higher than the average intrapopulation diversity , resulting in high levels of genetic differentiation   and Bayesian methods, using s Figure . Eight cs Figure , a. Fives Figure , b. The s Figure . The haps Figure . Most has Figure , b.C. simplicipinna with the Bayesian method, using BEAST, version 1.6.1 [C. simplicipinna haplotypes were diverged during the Pleistocene (2.6 Ma to 11 ka).We derived the estimated time of divergence of on 1.6.1 . The estC. simplicipinna was stable until approximately 50,000 years ago, at which time a contraction event occurred  and highest in population LUA . The number of alleles (NA) ranged from 2.875 to 6.063, the number of private alleles (AP) ranged from 1 to 14, the effective number of alleles (NE) ranged from 1.925 to 3.521, the information index (I) ranged from 0.635 to 1.268, observed heterozygosity (HO) ranged from 0.306 to 0.473, and expected heterozygosity (HE) ranged from 0.353 to 0.603. These indices all showed a similar trend, with the lowest values in MM and the highest values in LUA. Fixation indices (F) were positive for all six populations, with a mean value F = 0.170, which suggests a high level of inbreeding within each population. The percentage of polymorphic loci (PPB) was high, ranging from 75% to 100%. Population MM had the lowest genetic diversity, and LUA had the highest. The genetic differentiation coefficient FST varied from 0.036 to 0.467, with a mean value 0.261. No significant effect of isolation by distance (IBD) was detected , suggesting that MM deviated from mutation-drift equilibrium. When SMM was used, only ML showed a significant excess of heterozygosity (Wilcoxon text testing). Mode shift models showed that all populations had normal L-shaped distributions, which suggests that C. simplicipinna has not experienced a recent severe bottleneck. While all the Garza-Williamson indices  as shown by the LDNe analysis are listed in Table es Table  of the sC. simplicipinna has high genetic diversity  that was inferred from SSR [FST value greater than 0.25 (C. simplicipinna: FST = 0.26 > 0.25) would indicate that there was significant genetic differentiation among populations. Additionally, according to the results of deviation from Hardy-Weinberg equilibrium test (Table F) were greater than zero. We therefore conclude that there is a notable deficit in heterozygosity and severe inbreeding in C. simplicipinna populations, resulting in a high among-population genetic differentiation as a whole.The genetic diversity of from SSR . Wright C. simplicipinna based on SSR markers showed that the six study populations were divided into three clades . BARRIER analysis  and nuclear markers . Comparisons of genetic with paleoecological data of temperate woody species are known to reveal unique genetic lineages and⁄ or endemic haplotypes in separate refuge populations [C. simplicipinna is a tropical or subtropical woody species, the species similarly possesses unique genetic lineages and endemic cpDNA haplotypes in its separate refuge populations. Duminil [C. simplicipinna displays the opposite results. This outcome may be due to its having a limited geographic distribution and severe population inbreeding or to its being a tropical or subtropical woody species, which differs from temperate trees.The genetic structure of s Figure  showed tulations ,78. Alth Duminil  had propCycas was 2–7 km. However, the distances between extant populations of C. simplicipinna are all greater than 7 km ). Because effective gene flow higher than 1 is often regarded as high enough to prevent population differentiation due to genetic drift, while gene flow less than 1 may be a major reason caused the genetic differentiation among populations[Cycas simplicipinna is dioecious and pollenated by insect  [C. simplicipinna is due mainly to its limited gene flow and severe inbreeding. Our analysis therefore suggests that C. simplicipinna has high genetic diversity at the species level, low genetic diversity within populations, high genetic differentiation among populations and a clear genetic structure. This conclusion is also supported by the AMOVA analysis  . Unlike is Table , which snosperms .Phylogenetic trees constructed on the basis of DNA sequences with different criteria in four different software systems all suggested a consistent systematic relationship of haplotypes Figure  and 4. Te.g., Taxus wallichiana[C. revoluta and C. taitungensis[e.g., C. debaoensis[C. simplicipinna appears to have exhibited population demographic contraction during the LGM and no later expansion. We infer this from the Bayesian skyline plot  of our seven study populations were distributed in protection zones in which there has been no overexploitation and habitat destruction. However, the other four populations  occur outside of protected areas and should be protected. Our study, which revealed a clear population structure of C. simplicipinna that show low genetic variation within populations and high genetic differentiation among populations, has significant implications for conservation of this species. The population structure and demographic history of C. simplicipinna imply that conservation efforts cannot focus on only one part of the species’ range. We suggest that habitat protection be strengthened immediately by establishing protection zones or plots in the distribution areas of C. simplicipinna to improve the conservation awareness of local farmers and to prohibit deforestation in Cycas distribution areas. Protection of populations with the highest genetic diversity, such as populations NZD and LUA, should be of highest priority. Priority should also be given to populations with unique haplotypes. Based on the analysis of the two cpDNA sequences in our study, six of the seven sampled populations of C. simplicipinna each had one unique cpDNA haplotype. Therefore, every C. simplicipinna population should be given maximum protection to prevent losing any haplotypes. The reduction of effective population size in the Ice Age has led to a small effective population size for the species as a whole, which jeopardizes the species’ current genetic diversity. Different populations and/or individuals should be moved from current areas of rich genetic diversity to secure remote areas for ex situ conservation. These measures taken together could protect and enrich the genetic diversity of C. simplicipinna.One goal for the conservation of threatened plants is to maintain the genetic diversity of native plant species . Ex situcycas species underwent a past population contraction during Pleistocene with high genetic differentiation among populations and a clear genetic structure. In addition, unique haplotype was detected in all populations in this study. These populations need to be protected for sustaining high genetic diversity in C. simplicipinna. Furthermore, the reconstruction of population demographic history in C. simplicipinna provides insights and guidelines for protecting C. simplicipinna and other endangered cycas species effectively.This study shows that this The data set of the DNA sequencing data in our study are deposited in GenBank under accession numbers KM065478-KM065496.The authors declare that they have no competing interests.XG participated in the design of the study as well as collected study materials. XF carried out the molecular genetic studies, participated in the data analysis and drafted the manuscript. YW participated in the study’s design and coordination. All authors read and approved the final manuscript.C. simplicipinna.Information of sixteen SSR makers used to study the population genetics of Click here for fileC. simplicipinna.Variable sites from the two combined cpDNA in Click here for fileC. simplicipinna.Variable sites from the nrDNA in Click here for fileP-value of Hardy-Weinberg equilibrium test for six populations of C. simplicipinna.Click here for fileC. simplicipinna based on SSR markers. Numbers on branches indicated bootstrap values from 5000 replicates.An unweighted pair-group method with arithmetic averages (UPGMA) phenogram of six populations of Click here for file


Tscherskia triton) from Raoyang, Guan and Shunyi counties of the North China Plain. The results showed that (i) high genetic diversity and differentiation exist in three geographic populations with patchy distributions; (ii) gene flow occurs among these three populations with physical barriers of Beijing city and Hutuo River, which potentially restricted the dispersal of the animal; (iii) the gene flow is negatively correlated with the geographic distance, while the genetic distance shows the positive correlation. Our results suggest that the effect of the physical barriers is conditional-dependent, including barrier capacity or individual potentially dispersal ability. Geographic distance also acts as an important factor affecting dispersal for the patchy distributed geographic populations. So, gene flow is effective, even at relatively long distances, in balancing the effect of geographic barrier in this study.Dispersal is a fundamental process in ecology influencing the genetic structure and the viability of populations. Understanding how variable factors influence the dispersal of the population is becoming an important question in animal ecology. To date, geographic distance and geographic barriers are often considered as main factors impacting dispersal, but their effects are variable depending on different conditions. In general, geographic barriers affect more significantly than geographic distance on dispersal. In rapidly expanding populations, however, geographic barriers have less effect on dispersal than geographic distance. The effects of both geographic distance and geographic barriers in low-density populations with patchy distributions are poorly understood. By using a panel of 10 microsatellite loci we investigated the genetic structure of three patchy-distributed populations of the Greater long-tailed hamster ( Population persistence strongly depends on its own evolutionary capacity which in turn relies on the genetic variability. The capacity of genetic variability within and among populations results from many processes involving mutation, dispersal, genetic drift and selection. Dispersal refers to the movement of an organism from one place to another, which plays a fundamental role in population biology and conservation because it influences the genetic structure as well as the persistence of populations It is well known that dispersal is conditional-dependent. There are a number of potential driving forces identified for dispersal including kin competition, inbreeding, resource competition and environmental stochasticity Some previous studies have focused on the effects of several main factors on dispersal, such as population density Maniola jurtina uses a non-random, systematic dispersal strategy and can detect and orient towards habitat from distances of 100–150 m Geographic distance is also an important factor affecting dispersal. As the costs of movement increase with distance, a successful dispersal is often considered to occur when the distance between patches decreases In addition to distance, geographic barriers are also thought to be the important factors affecting dispersal Tscherskia triton) is widely distributed in croplands of North China The Greater long-tailed hamster . All researchers had received appropriate training and affirmed before conducting animal studies.2 asphyxiation immediately. These operations were approved by the Institute of Zoology, Chinese Academy of Sciences. Captured hamsters were numbered, sexed, weighed, dissected, and measured to provide estimates of body size, age, and reproductive condition The Greater long-tailed hamsters were captured using wooden-iron traps in Raoyang, Guan and Shunyi Counties of the North China Plain in autumn, 2000. The wooden-iron traps are box-shaped, with a wooden-side at the bottom and iron sheets with holes at other sides. One small side of the traps could be open or closed, and the other sides are fixed. The wooden-iron trap works by the leverage principle. The trap is 20 cm in length, 12 cm in width and 14 cm in height. The wooden side as bottom is baited with peanuts and equipped with spring device. When the Greater long-tailed hamsters step on the wood plate to eat and the spring set up release, the hamsters will be closed in the trap and then euthanized by COTissues were fixed in 90% ethanol, preserved in formalin solution and kept in the animal ecological Laboratory, Institute of Zoology, Chinese Academy of Sciences for more than 10 yrs. The genomic DNA was extracted from liver tissues using an improved phenol-chloroform extraction method 2, 0.2 mM each dNTP, 1 U of Taq DNA polymerase (Promega), 10 pM forward and reverse primers, and approximately 2 ng of template DNA in 25 µL. Amplifications began with a 5-min denaturing step at 94°C, followed by 30–35 cycles of the following thermal reaction: denaturing at 94°C for 45 s, annealing at 47∼55°C for 45 s, and extending at 72°C for 1 min, with a final extension for 5 min at 72°C. All products were analyzed in an ABI 377 instrument  and the gel analysis was performed by using GENESCAN3.1 (Perkin-Elmer Applied Biosystem).Ten microsatellite loci of the Greater long-tailed hamster  were useR, number of alleles independent of sample size), Shannon's Information index (I), observed (HO) and expected (HE) heterozygosity. FSTAT version 2.9.2 FST value Genetic variation within populations were assessed by using the measures including allelic richness  with Arlequin version 2.000 Geographic distances between sampling sites were calculated based on the approximate center of the sampling areas. Correlations between geographic distances and Nei's standard genetic distance Among 10 microsatellite loci, there are 3 to 11 alleles with a mean of 6.1 alleles per locus . Within Some alleles were more restricted, while others showed wide range of geographic distribution as shown in RA), Shannon's Information index (I), observed (HO) and expected (HE) heterozygosity for the microsatellites in each population are shown in RA, I, Ho, and He were 3.57, 1.93, 0.56 and 0.60 for Raoyang population, 3.31, 1.86, 0.63 and 0.62 for Guan population and 3.47, 1.97, 0.68 and 0.65 for Shunyi population, respectively. The parameters of RA, I, Ho, and He show that genetic diversity exists in Raoyang, Guan and Shunyi populations. No significant difference on the genetic diversity level was detected from the used parameters among the three populations.Estimates of allelic richness  was observed between the populations of Raoyang and Shunyi. The lowest STF value  was observed between the populations of Guan and Shunyi. The STF value  between the populations of Raoyang and Guan was moderate. The STF values of populations separated by the Hutuo River were higher than the ones separated by Beijing city,which indicates that the Hutuo River has higher barrier capacity than Beijing city for the Greater long-tailed hamsters. This leads to the higher genetic difference between the two sides populations of the Hutuo River than those of Beijing city.Three pairwise estimates of F-statistics revealed the significance for ISF = 0.0281 (P = 0.0032), ITF = 0.0437 (P = 0.0053) and STF = 0.0382 (P = 0.0039). Wright (1978) identified the problem of interpreting STF values as an absolute value based on highly polymorphic loci and proposed that a STF<0.05 could indicate a considerable population differentiation STF value suggests that the genetic differentiation exists among three tested Greater long-tailed hamster populations.The AMOVA showed that 71.6% of the variance is explained by within-individual variation, 8.9% by variation between individuals within population, and 19.5% by variation between populations. Although the total genetic variation is mainly from the variation of within-individuals, there was more than twice as much variation between populations as the one between individuals within populations. The overall r = 0.98, P = 0.008), as shown in Genetic and geographic distances among the three examined patchy distributed geographic populations were summarized in r = −0.99, P = 0.017) as shown in Gene flows between the different examined geographic populations were also summarized in Generally, the populations that are isolated from one another by geographic barrier can evolve variable characters in order to adapt to local environments. Genetic variation of a population may lead to the character differentiation resulting from natural selection. To some extent analyzing the genetic structure of populations is very important to understand the population dynamics From our study, we examined the genetic structure of three populations of Greater long-tailed hamster and found the significant genetic differentiation exist among these three patchy-distributed populations, suggesting the geographic distance plays an critical role in the genetic differentiation of these three patchy populations despite geographic barriers act on them to a certain degree. We have identified the correlation among genetic distance, gene flow and geographic distance and found that geographic distance is positively correlated with the genetic distance and is negatively correlated with the gene flow.RA) are 3.566, 3.314, and 3.474, and expected heterozygosities (He) are 0.601, 0.615, and 0.647, respectively for Raoyang, Guan and Shunyi populations. The values of the expected heterozygosity (He) for individual geese ranged from 0.38 to 0.51He) detected in three populations are higher than those of the geese By using 10 microsatellites, we were able to detect comparable values of expected heterozygosity and allelic richness in three patchy distributed geographic populations. Mean allelic richnesses  exist among the three geographic populations. Population differentiation can result from two genetic processes. 1) Populations may be isolated from one another by geographic barriers and therefore gene flow will be reduced among local populations. 2) Population differentiation level may increase with the increased geographic distance because the magnitude of gene flow declines with extended length of the geographic distance.In this study, significant genetic differences were found among three geographic populations. High values of differentiation  indicating no gene flow exists between the populations from two sides of the barriers Ursus arctos) was found to be reduced or sometimes absent between four insular populations when separated by stretches of sea water of a few kilometres, but continuous gene flow between populations was detected when the stretches of sea water were much narrower at approximately 600 m Populations separated by the Hutuo river showed high genetic differentiation than populations on opposite sides of the Beijing city, suggesting that Hutuo river has higher barrier capacity than Beijing city for the Greater long-tailed hamster populations. In research of small mammal species, such as voles and ground beetles, barrier effect of roads to gene flow has clearly been shown Genetic diversity and genetic differentiation exist in the three examined hamster populations. The barriers in this study acting on populations are less restricted than expected. Furthermore, the genetic differentiation is positively correlated with the geographic distance, while the gene flow shows a negative correlation. Geographic distance may act as one of main causes for the genetic differentiation of the patchy distributed hamster populations.Table S1Tscherskia triton).Characterization of the microsatellite loci in Greater long-tailed hamster ((DOC)Click here for additional data file.Table S2Tscherskia triton populations.Alleles and their frequencies for the ten microsatellite markers in the three (DOC)Click here for additional data file.Table S3Tscherskia triton populations at ten microsatellite markers.Genetic diversity indices within three (DOC)Click here for additional data file.Table S4Genotypes for the Greater long-tailed hamsters examined in this study at 10 microsatellite loci.(DOC)Click here for additional data file.


Osteomeles schwerinae, a dominant riparian plant species of the hot/warm-dry river valleys of the Hengduan Mountains (HDM), Qinling Mountains (QLM) and Yunnan-Guizhou Plateau (YGP). Three chloroplast DNA (cpDNA) regions , one single copy nuclear gene , and climatic data during the Last Interglacial , Last Glacial Maximum , and Current (c. 1950–2000) periods were used in this study. Six cpDNA haplotypes and 15 nuclear DNA (nDNA) haplotypes were identified in the 40 populations of O. schwerinae. Spatial Analysis of Molecular Variance, median-joining networks, and Bayesian phylogenetic trees based on the cpDNA and nDNA datasets, all suggested population divergence between the QLM and HDM-YGP regions. Our climatic analysis identified significant heterogeneity of the climatic factors in the QLM and HDM-YGP regions during the aforementioned three periods. The divergence times based on cpDNA and nDNA haplotypes were estimated to be 466.4–159.4 ka and 315.8–160.3 ka, respectively, which coincide with the time of the weakening of the Asian monsoons in these regions. In addition, unimodal pairwise mismatch distribution curves, expansion times, and Ecological Niche Modeling suggested a history of population expansion (rather than contraction) during the last glaciation. Interestingly, the expansion times were found being well consistent with the intensification of the Asian monsoons during this period. We inferred that the divergence between the two main lineages is probably caused by disruption of more continuous distribution because of weakening of monsoons/less precipitation, whilst subsequent intensification of the Asian monsoons during the last glaciation facilitated the expansion of O. schwerinae populations.Quaternary climatic factors have played a significant role in population divergence and demography. Here we investigated the phylogeography of  Exploring factors associated with population processes (e.g. population divergence and demography) is an important issue in ecology and evolution –4. DiverTerminalia franchetii (Combretaceae) and Buddleja crispa (Buddlejaceae), both of which indicated that the discontinuous distribution patterns of these plants are strongly linked to historical drainage re-organization events [Hot/warm-dry river valleys are one of the most prominent natural landscape features in the HDM and adjacent regions . In compn events , 17. MorRiver valleys in different regions probably differ in climate because of different monsoon systems. The QLM, which is located on the north-eastern margin of the HDM , is domiOsteomeles schwerinae Schneid. (Rosaceae) is an iconic riparian plant species endemic to the dry river valleys of the HDM, YGP, and QLM [Flora of China [O. schwerinae var. schwerinae and O. schwerinae var. microphylla Rehder & E. H. Wilson). The former is mainly confined to the river valleys of the HDM-YGP and has larger leaflet blades (5–10 mm), while the latter is mostly present in the river valleys of the QLM and has smaller leaflet blades (3–5 mm) [O. schwerinae based on molecular approaches and paleo-and current climatic data. We had the following objectives: (1) to quantify whether there is genetic divergence between the populations of O. schwerinae; (2) to explore whether the genetic divergence coincide with the regional morphological differentiation of O. schwerinae in the QLM and HDM-YGP regions; (3) to date divergences and determine whether these dates relate to geological events  or climatic changes ; and (4) to unravel the demographic history of O. schwerinae. and QLM –31  . Althoug(3–5 mm) , 11, 20,Osteomeles schwerinae is not protected nor endangered in the sampled areas, and all samples were collected by researchers following current Chinese regulations. None of the sampled locations are privately owned or protected by law.O. schwerinae covering its geographical range; out of these, eight populations originated from the QLM region, and 32 populations from the HDM-YGP region . Universal primers and methods described in previous studies [trnD-trnT, psbD-trnT, petL-psbE). Nuclear DNA (nDNA), namely the single copy nuclear gene , was amplified following the PCR procedure in Olsen and Schaal (1999) [Total genomic DNA was extracted from approximately 15 mg dried leaf using an Axygen Plant Mini Kit  , using oG3pdh heterozygotes. In this method, sequences of heterozygotes were compared with those of homozygotes until we could account for the observed combination of double-banded sites. ArcMap v9.3  was used to plot the distribution of haplotypes on a relief map coving the range. Original map in the figures was downloaded from http://www.diva-gis.org/Data. The elevation dataset in the figures was derived from NASA’s SRTM data (http://www2.jpl.nasa.gov/srtm).Haplotypes of the combined three cpDNA sequences were produced in DnaSP v5.0 . Method O. schwerinae using SAMOVA v1.0 [K groups of populations that are geographically homogenous and maximally differentiated from each other. In this analysis, we assumed that K varied from 2 to 13, and computed a corresponding FCT index, which measures the proportion of genetic differentiation among the K groups [FCT value that achieves a plateau first is usually considered the optimal grouping of populations. An analysis of molecular variance (AMOVA) was conducted within ARLEQUIN v3.5 [Spatial Analysis of Molecular Variance (SAMOVA) was conducted to analyze the spatial genetic structure of OVA v1.0 . This prK groups . The conUIN v3.5  to partihttp://www.fluxus-engineering.com) using a median-joining method. Indels (gaps) were treated separately as binary states, namely single events, following Caicedo and Schaal (2004) [Genealogical relationships among haplotypes were inferred from an unrooted statistical parsimony network within Network v4.6.1.2  (availabl (2004) . Mononucl (2004) . Observel (2004) .Phylogenetic relationships among haplotypes were reconstructed by Bayesian inference. This phylogenetic analysis was conducted in MrBayes v3.1.2  using su−9 substitutions per site per year (s/s/y) [−8 s/s/y [The divergence time of haplotypes was estimated via a molecular dating method, and estimated under a strict molecular clock in BEAST v1.7.5 , using t (s/s/y) , 48; nDN−8 s/s/y .O. schwerinae. In this analysis, an expected distribution was generated from 1000 parametric bootstrap replicates to test ‘demographic expansion’ and ‘spatial expansion’ model, respectively [O. schwerinae in cultivation at Kunming Botanical Garden.Pairwise mismatch distribution was carried out using ARLEQUIN to infer the demographic history of ectively . Generalectively . In thishttp://www.worldclim.org/), and were used to identify climatic factors potentially associated with the genetic structure and demographic history identified in this study. DIVA-GIS v7.5 [Climatic data  that sumGIS v7.5  was usedGIS v7.5 , and werO. schwerinae during the LIG, LGM, and Current periods. Species occurrence data for O. schwerinae were obtained from collection records from the three main herbaria in China . We set up 10 replicate runs in each analysis in order to ensure more reliable results. Meanwhile, in order to test the performance of each model, 25% of the data in each run was randomly chosen by MAXENT and compared with the model output created with the remaining data. To reduce the effects of spatial autocorrelation, duplicate records from the same locality were also removed. The area under the receiver operating characteristic curves (AUC) was used to compare model performance. An AUC value of 0.5 indicates that the performance of the model is no better than random, while values closer to 1.0 indicate better model performance [Ecological Niche Modeling (ENM) can simulate past species distributions, and further elucidate demographic history through time . Therefoformance .trnT-trnD; 658–697 bp, psbD-trnT), while the petL-psbE was uniformly 449 bp long in our samples. Removal of mononucleotide repeats and duplicated indels for the alignment of three cpDNA regions from the 304 samples resulted in an overall alignment length of 2039 bp, and produced six cpDNA haplotypes  based on 18 nucleotide substitutions  shared by populations along all rivers surveyed in QLM, HDM, and YGP regions . Apart fThe Bayesian phylogenetic tree clustered five of the cpDNA haplotypes  into two lineages, respectively from the QLM and HDM-YGP regions . In the The mismatch distribution curves for the total, QLM and HDM-YGP populations were clearly unimodal . TherefoOut of the 19 bioclimatic factors, all but five  during the LGM and the current periods showed highly significant differences in mean values between populations of the QLM and HDM-YGP regions . When coO. schwerinae. Paleo-distribution modeling for the total, QLM and HDM-YGP populations, all indicated a much more restricted distribution during the LIG than the Current, with subsequent expansion during the LGM. This predicted subsequent expansion covered a slightly greater range than that predicted based on the current climatic factors  are smaller than the one (5–10 mm) growing in the HDM-YGP. Since the sizes and shapes of leaves are quite sensitive to moisture, with leaves in dry climates generally smaller than in wet climates [O. schwerinae between the two regions is the result of long-term adaptation to the significant climatic heterogeneity between the two regions.In this study, the SAMOVA, median-joining networks, and Bayesian phylogenetic trees, all suggested population genetic divergence between the QLM and HDM-YGP regions Figs , 3 and 4n system  Fig 1)..O. schwen system  .climates , it is qO. schwerinae. When using the same substitution rate range for cpDNA (1.01–3.0×10−9 s/s/y) as previous studies on riparian plants [Terminalia franchetii [Buddleja crispa [O. schwerinae populations in both regions  and thea crispa –58. Howea crispa . Coincida crispa , 27, 60.ons e.g. , and resons e.g. . FinallyO. schwerinae populations: the opposite pattern to most demographic scenarios in the HDM and adjacent regions. Before the period “342-118/200-130 ka” , the O. schwerinae populations probably had a more continuous or wider distribution linking the two geographic regions because of stronger monsoons/more precipitation in both regions. However, during the LIG, the O. schwerinae populations probably had a less continuous or more narrow distribution because of weaker monsoons/less precipitation. The ENM analysis further confirmed this, showing more narrow distribution during the LIG, but larger distribution ranges during the LGM period , regional haplotypes both from the QLM  and HDM-YGP , as well as its own exclusive haplotypes   .O. schwerinae, based on molecular phylogeography and climatic data, reveal a major population divergence between the QLM and HDM-YGP regions, and its correlation to the climatic heterogeneity and changes within both regions since the late Pleistocene. We inferred that weakening of Asian monsoons since 342-118/200-130 ka increased the climatic differences between the two regions, caused the loss/reduction of gene flow, and triggered the genetic divergence between them. Subsequent intensification of the monsoons during the last glaciation facilitated the expansion of O. schwerinae populations, contributed to the second contact between the populations from the QLM and HDM-YGP, and finally led to the development of the current distribution pattern of O. schwerinae populations. This study provides a new example in which riparian plant species in the HDM and adjacent regions are shown to be affected by climatic heterogeneity and changes since the late Pleistocene. However, more studies of riparian plants in these regions are needed to explore this hypothesis.Our analyses of S1 Fig(TIF)Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.