Elymus glaucus, which is now rare in temperate grasslands as a result of biological invasion and land conversion. Our study contrasts data for mainland and Channel Island locations, and makes recommendations for seed provenance selection in ecological restoration using genetic marker data and considering prior field studies of adaptive divergenceKnowledge of species-level patterns of genetic diversity can inform and improve protocols when population reintroduction is a restoration objective. We describe the population genetic structure of a geographically widespread species,  Elymus glaucus, a perennial bunchgrass species native to western North American grasslands that is targeted for grassland restoration. Island populations of E. glaucus represent sensitive sites and potentially distinctive seed sources for reintroduction, and little is known of their genetic composition. Genetic diversity and structure were estimated using amplified fragment length polymorphism markers for 21 populations and 416 individuals distributed across two coastal California mainland locations and three California Channel Islands. Eight primer combinations resulted in 166 markers, of which 165 (99.4 %) were polymorphic. The number of polymorphic bands was significantly greater among mainland populations relative to island sites, and locally common alleles were present for each sampled island and mainland location. Population structure was high (62.9 %), with most variation (55.8 %) distributed among populations, 7.1 % between mainland and island locations, and the remainder (37.1 %) within populations. Isolation by distance was only apparent among islands. Using marker data to recommend appropriate seed sources for restoration, E. glaucus seeds are best derived within islands with collections representing a large number of individuals from matching environments. Given the limited gene flow and prior evidence of adaptive divergence among populations of this species, regional collections are recommended in all cases to maintain diversity and to avoid long-distance introductions of highly differentiated plant material.Genetic marker studies can assist restoration practice through selection of seed sources that conserve historical levels of gene diversity and population genetic differentiation. We examined genetic variation and structure within and among mainland and island populations of  Widespread anthropogenic disturbance and introductions of invasive species have resulted in the fragmentation and conversion of grassland ecosystems worldwide . TemperaFragmented populations of plant species are susceptible to environmental, demographic and genetic stochasticity . RestoraPrimary genetic concerns for reintroduction include the maintenance of patterns of diversity within and among populations, and the introduction of genotypes adapted to environmental conditions at the restoration site e.g. . Data thElymus glaucus (blue wildrye) is a native bunchgrass species once common in California mainland and island grasslands  How much genetic variation is present in island populations, and how does this compare to mainland populations? (ii) Is genetic differentiation between island populations greater relative to nearby mainland locations? (iii) Is genetic distance correlated with geographic distance within and among islands and the mainland? Lower levels of diversity and strong genetic differentiation at island sites may indicate the reduced ability of island plants to adapt to altered environments and a greater risk of local extinction of Channel Island populations. At the same time, the geographic scale of genetic differentiation serves as an indicator of the historical rates of gene flow. These data can assist with seed provenance selection for the restoration of E. glaucus growing in California coastal and island grasslands by means of maintaining population genetic diversity and lowering the risk of introducing maladapted genotypes.In this study, we used amplified fragment length polymorphisms (AFLPs) to characterize genetic diversity and structure among mainland and island populations of Elymus includes 150 species distributed in temperate regions worldwide , but extant populations are highly fragmented as a result of widespread land development and biological invasion  derived from Hordeum and Pseudoroegneria ancestors . A total of 416 plants representing 21 Molecular markers were amplified following the protocol of We analysed AFLP marker data using methods previously employed for allotetraploid species, including band-based and allele-frequency metrics e.g. .He) were estimated for comparison to band-based metrics using a fragment-frequency approach in AFLP-SURV 1.0  within and among island and mainland sites by means of FAMD 1.2 software . We alsoSURV 1.0 . This me AFLPdat  for R so AFLPdat .PT, which is considered to be an analogue of FST  was used to determine if there was an association between genetic distance measured as a matrix of linearized ΦPT values, and log10 transformed geographic distances (Nm) was estimated following PT) values were calculated and assayed for significance with 9999 permutations.We conducted a hierarchical analysis of molecular variance (AMOVA) in GenAlEx to partition genetic variation within and among populations nested in two regions: island and mainland. AMOVA components of variance include Φe of FST . The sigistances . The Manθ(II) (comparable to FST and ΦPT) using dominant markers, and does not assume Hardy–Weinberg equilibrium. We computed θ(II) for three alternative models  and selected a suitable model with the lowest deviance information criterion (DIC). Markov chain Monte Carlo (MCMC) parameters were set to default values . To test for significant differences in population genetic structure among island and mainland locations, we ran posterior comparisons of θ(II) values. If 95 % confidence intervals for the difference in paired samples included zero, we concluded that θ(II) values were not significantly different.We compared AMOVA results for population genetic structure with allele-frequency estimates calculated in HICKORY software 1.1 . HICKORYE. glaucus sites represented distinct populations, we used Bayesian clustering methods implemented in STRUCTURE 2.3.3 software to assign individuals to populations by employing the recessive alleles option for dominant markers  were polymorphic for the full dataset. The proportion of missing data was calculated at 1.91 %. The average number of scorable bands generated by each AFLP primer combination was 20.75  were strongly correlated , indicating concordance among band-based and allele-frequency metrics. The average expected heterozygosity (He) within populations was low (0.1093) and varied among populations and locations (Table P = 0.007). The mean number of fixed bands among populations did not differ significantly between mainland and island regions at the P = 0.05 level . Two private bands separated pooled island and mainland sites, and locally common bands were present among populations at each of the three island and two mainland locations  and ΦPT summed to 0.629. The average pairwise ΦPT for all sites was 0.614 with a range from 0.159  to 0.91 (Sedgwick and Santa Cruz populations). All pairwise comparisons for ΦPT were significant [seeSupporting Information]. Population genetic structure declined within regions (ΦPT = 0.422 among mainland and 0.400 among island populations) relative to structure calculated for all sites. The results of the Mantel test of linearized ΦPT values, and log10 transformed geographic distances indicated no support for isolation by distance among all island and mainland sites (P = 0.175). This result was repeated when mainland sites were considered alone (P = 0.293). However, isolation by distance was detected among populations sampled on the three Channel Islands . This result was limited to the groups of populations among islands and isolation by distance was not observed for populations within each island [seeSupporting Information]. The value of Nm, or the indirect rate of gene flow, was calculated as 0.147, indicating that the number of migrants each generation was significantly <1 and that genetic drift plays a role in population differentiation  (full and f = 0) resulted in low DIC values, and we selected the full model with the expectation that the inbreeding coefficient (f) is >0 in this autogamous species  for all 21 sites was 0.563, while values for the subsets of mainland or island sites were 0.557 and 0.556, respectively. Comparisons of posterior distributions detected no significant difference in population structure between island and mainland regions (difference in θ(II) values: 0.001; confidence interval: −0.0379, 0.03932). Estimates of θ(II) were not greater for populations located on islands relative to sampled populations on the mainland. Sedgwick and VAFB also did not differ significantly for estimates of θ(II). However, Santa Catalina and Santa Rosa populations were significantly less differentiated than populations from Santa Cruz (difference in θ(II) values: 0.189; confidence interval: 1349, 0.2445). Genetic structure was greater among sampled sites located on Santa Cruz Island.Using the Bayesian approach, two models of K = 16  and population level (22–31 %)  for Channel Island and coastal California populations was low and corresponded to values recorded for selfing species with passively dispersed seeds  = 0.563) and similar to all prior studies of this species  were not significant for populations on the mainland and IBD was only apparent between islands. Two previous mainland studies of tect IBD . OverallE. glaucus as the result of gene flow, although this evidence was limited to few individuals and populations . Inference of genetic structure detected 16 genetically distinct populations among 21 collections, and confirmed strong population differentiation in E. glaucus among islands and the mainland. The average pairwise geographic distance among sites was ∼100 km. However, many sites within locations were only 2–3 km apart. We noted that the two closest collection sites  were strongly genetically differentiated. High levels of differentiation at small distances are common among self-pollinating grass species and represent challenges for their restoration in degraded ecosystems , and this scale corresponds to the scale of adaptive differentiation detected in field studies of this species. We consequently recommend the use of multiple seed collections specific to each Channel Island (for use at that island) to maintain diversity while avoiding long-distance introductions of non-local genotypes. Given low levels of pollen flow, these introductions are not likely to affect the viability of existing populations, and in rare cases where gene flow does occur, hybrid vigour among progeny would likely result in the formation of new selfing lines  values among the 21 sampled E. glaucus populations..Figure S1 Relationship between linearized ΦPT values and log10 values for geographic distance among populations of E. glaucus distributed on Santa Rosa, Santa Cruz and Catalina Islands off the coast of mainland California. Axes are oriented to highlight clusters of points representing each island.


KIR cluster in a large and well-characterized sample of worldwide human populations in the Human Genome Diversity Project—Centre d'Etude du Polymorphisme Humain (HGDP-CEPH) panel in order to better understand the patterns of diversity in the region. Comparison of KIR data with that from other genomic regions allows control for strictly demographic factors; over 500,000 additional genomic markers have been typed in this panel by other investigators and the data made publicly available. Presence/absence frequencies and haplotypic associations for the KIR region are analyzed in the 52 populations comprising the panel and in accordance with major world regions . These data represent the first overview of KIR population genetics in the well-documented HGDP-CEPH panel and suggest different evolutionary histories and recent selection in the KIR gene cluster.In the present study, we investigate patterns of variation in the The online version of this article (doi:10.1007/s00251-012-0629-x) contains supplementary material, which is available to authorized users. KIR)  class I molecules as their ligand, the ligands for most of the stimulatory KIR have not been identified definitively. The balance between inhibitory and activating KIR and their specific ligands results in a finely tuned innate-adaptive immune response. The inhibitory KIR, partnered with alternative NK receptor complexes (CD94:NKG2A), ensure that NK cells are tolerant of healthy autologous cells and responsive to cells with compromised HLA class I expression, as what occurs frequently in virus-infected and tumor cells. The stimulatory KIR contribute to the activation of NK cells in response to infection and malignancy. Our recent work and that of others has shown that KIR and their HLA ligands are associated in a variety of autoimmune diseases , variability in gene content haplotypes is responsible for significant diversity both within and between populations. While many KIR haplotypes have been identified in human populations, they are generally categorized into two groups, A and B  exists between the centromeric and telomeric sections of the o et al.  consistsKIR complex via repeated recombination and duplication events . However, direct comparisons between populations are hindered by a lack of equivalence across data sets due to variations in typing methodology and the number of loci typed, as well as clarity regarding population collection and limited data regarding other genomic markers. In the present study, we investigate patterns of variation in the KIR cluster in a large and well-characterized sample of worldwide human populations in the Human Genome Diversity Project—Centre d'Etude du Polymorphisme Humain (HGDP-CEPH) panel .The 52 populations comprising the HGDP-CEPH represent seven major world regions. KIR loci in the HGDP-CEPH, we utilized our high throughput single nucleotide polymorphism (SNP)-based KIR genotyping assay developed using the SEQUENOM™ MassARRAY  on the matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometer platform . Briefly, the two-tiered analysis includes 38 “capture” primer pairs to amplify specific KIR genes in the region surrounding the SNPs to be queried; this is followed by 39 homogenous mass extend primer reactions to differentiate individual SNP patterns for the 16 KIR genes on the MALDI-TOF platform. These assays were run using the KIR sequence alignment in the Immuno Polymorphism Database  using version 1.4.0. Our typing system is regularly assessed against all known alleles in order to insure that all loci will be detected.To genotype the A treemap of HGDP-CEPH sample size and number of unique KIR genotypes was constructed using the “tmPlot” function in the “Treemap” package Tennekes  for the KIR loci were obtained by direct counting. Gene frequencies were estimated according to Lynch and Milligan (KIR2DL2L3 (KIR2DL2/KIR2DL3), gene frequencies were obtained by direct counting. A direct counting approach was also utilized to obtain an estimate for gene frequencies for the KIR3DL1S1 locus. There is evidence that in some populations there are haplotypes that lack this locus .Carrier frequencies for the Milligan  using thMilligan . For theKIR carrier frequencies was constructed using the “heatmap” function in the base “stats” package for the R language for statistical computing  algorithm for haplotype frequency estimation that accommodates KIR-specific constraints. Our constrained EM algorithm uses an a priori list of known/possible haplotypes to restrict the space of possible haplotype patterns due to known constraints on gene content variation and allelic variation. The constrained approach differs from traditional EM implementations, in which the set of all possible haplotypic combinations is generated from the observed genotypic data with no restrictions on possible haplotype patterns. The list of user-designated a priori haplotypes is said to “span” the set of observed genotypes in a study if all observed genotypes can be generated from at least one pair of haplotypes in the a priori list. If pairs of haplotypes from the a priori list do not account for all of the observed genotypes in the sample , care must be taken in the interpretation of the resulting estimates. Our algorithm uses sharp constraints, meaning that if the a priori haplotypes do not span the observed genotypes a warning/error message is produced and the a priori set must be increased. We began with an a priori list of haplotypes taken from published data based on sequence analysis of seven KIR haplotype frequency data were plotted using an adaptation of a shell script written by Owen Solberg for analysis of HLA allele data (www.pypop.org/popdata).Global heatmaps for haplotype frequency data were generated using Generic Mapping tools  across all study populations. Only individuals for whom KIR genotyping was performed were included in the analysis of the SNP data. Empirical p values (pemp) represent the proportion of the distribution of the correlations for the additional markers that were greater in absolute value than the true correlation.Distance from Africa was calculated using Addis Abbaba, Ethiopia as the central point  and the latitude and longitude for each population sample Table , compute Wickham . Data wepemp represents the proportion of the (two-tailed) distribution above or below the FST values obtained for the KIR loci.A similar approach was employed to obtain an empirical distribution for FST values from the same markers. Here, FST was calculated according to Nei  using anKIR gene content profile (genotype) detected and the number of observations in the HGDP-CEPH population are shown in Fig. n = 25) is seen in only one individual, and five genotypes have not been previously reported in 108 populations listed on AFND. The six most frequent KIR genotypes in the HGDP-CEPH each have a frequency of ≥5 % worldwide, and together account for over two thirds of all genotypes observed in the panel  in the diversity panel are observed in the NAN Melanesian sample with one of these found in three individuals. In addition, two of the NAN Melanesian private genotypes have not been previously reported in any population tested to date. Three other previously unreported genotypes were detected in the HGDP-CEPH: one each in the Druze (Middle East), Orcadian (Europe), and Mbuti Pygmies (Africa) populations.Each KIR, with only two or three different genotypes; however, the sample sizes for these populations are among the smallest in the panel. In contrast, the Palestinian population is the most diverse, with 18 unique genotypes, but the sample size is one of the largest in the panel. In order to visualize the relationship between sample size and KIR genotypic diversity in the HGDP populations, we constructed a treemap, which displays a graphical representation of hierarchical data via variation in color and area  are observed in each population; however, there is a wide range among populations. Several Asian populations in the sample are extremely homogenous for the rea Fig. . While tKIR loci typed in the HGDP-CEPH populations are given in Table x-axis, KIR loci) of the strong linkage disequilibrium between the loci associated with the A and B haplotypes that define the two major clades. The heatmap clearly illustrates the much higher frequencies for all A haplotype loci, as well as subnodes within both major clusters along affiliation with the centromeric and telomeric intervals of the KIR region. Clustering in the second dimension  reveals two major clades according to the relative carrier frequency of the B haplotype loci. Within the larger cluster defined by higher overall carrier frequencies of B-haplotype-associated loci, specific patterns of genotypic variation are clearly seen in the independent clustering of the three African hunter-gather populations (the San and Biaka and Mbuti Pygmies); these three populations are characterized by high overall B haplotype frequencies, with the notable exception of KIR2DS1, KIR2DS3, and KIR3DS1. A separate cluster with the two Oceanic populations is defined by high frequencies for most B haplotype loci. A third cluster is composed of three Amerindian populations , where KIR2DS3 is virtually absent. The large cluster associated with generally lower B haplotype frequencies is populated primarily by the remaining African and East Asian populations. The African populations are further distinguished by exceptionally low frequencies of the telomeric B haplotype loci.Gene and carrier frequencies for all of the KIR genotypes is relatively robust , a wide range in the A haplotype frequency is observed between populations, from 8 to 80 %  with which it is generally associated are extremely low. Additionally, the less common Cen-B3 motif, which appears to be a fusion between Cen-A and Cen-B1, and is characterized by the presence of KIR2DL3, KIR2DL5, KIR2DS3S5, and KIR2DP1, is observed at very high frequencies in the two pygmy populations  between KIR3DS1 carrier frequencies with distance from Africa .In contrast to the centromeric l-B Fig. , correspica Fig. , compariica Fig.  which reKIR2DL2L3 locus, and the telomeric haplotypes are marked by the KIR3DL1S1 locus. As gene frequencies give a better view of genetic differentiation than carrier frequencies, we focused on these loci, assuming that the gene frequencies we obtain via direct counting will in general have less missing data regarding gene content on the second chromosome. While there are clear limitations to this assumption (due to genotypes in some populations with deletion of the KIR3DL1S1 locus) as noted in the “Materials and methods” section, we determined that genotypes with this deletion are not common in most populations in our sample. Analysis of KIR3DL1S1 carrier status in our sample set reveals that only one individual out of 976 tested in the panel has a negative carrier status for both KIR3DL1 and KIR3DS1, i.e., is homozygous for the deletion in a previously unreported genotype  is an order of magnitude lower than that which is observed for autosomal markers genome-wide; comparison with FST value SNPs from the HGDP-CEPH database suggests that this FST value is lower than expectations due to chance alone (pemp < 0.05), suggesting a history of balancing selection on the locus.Genetic distances calculated from these gene frequencies for on Table  and compon Table  reveal tKIR diversity within populations is maintained by variation in gene content, and KIR differentiation between populations can be attributed in large part to frequency variation in common, shared haplotypes; this is evidenced by the fact that in most world regions, six genotypes account for the vast majority of observed variation. Nevertheless, numerous other genotypes are observed to varying degrees, highlighting the great degree of plasticity and potential for variation in gene content within the KIR cluster. Moreover, the distribution of genotypes in several populations is characterized by the virtual absence of the more common genotypes, suggesting that they are not requisite at the individual or population level.KIR genotype worldwide, which corresponds to homozygosity for the canonical A haplotype. The extreme examples of the Oceanic populations and the San are representative of a general trend towards region-specific gene content diversity, coupled with higher overall carrier frequencies of some or all stimulatory KIR; these populations occupying the more extreme ends of the spectrum of genetic divergence, specifically the (earliest diverged) African populations and the (most recently diverged) Amerindian and Oceanic populations, generally mirror patterns observed genome-wide , we did not find any individuals bearing the most common KIR genotypic variation in the HGDP-CEPH closely aligns with that reported in other population studies, analysis of KIR frequencies within the panel reveals novel observations and permits a more detailed understanding of particular region-specific and worldwide patterns. As previously noted, the stimulatory KIR3DS1 and KIR2DS1 are present at much lower frequencies in African populations , suggests negative selection on KIR2DS3 in Amerindians.Outside of Africa, this pattern of linkage disequilibrium placing KIR reveals that in most populations there is a very even balance between the inhibitory A haplotypic structure and the more stimulatory B haplotype, except within East Asia, where Cen-A predominates. These data are suggestive of a selective sweep within the centromeric KIR in East Asian populations, resulting in near fixation of the Cen-A motif. Previous work in the HGDP-CEPH and HapMap populations has suggested that recent sweeps of this nature have been relatively common in East Asia, compared to other world populations  events, contributing substantially to gene content variation. In the HGDP-CEPH populations outside of Africa, most notably the Oceanic and Amerindian populations, we observed extensive variation in gene content in the telomeric KIR, further supporting the notion of balancing, or diversifying selection on this section of the KIR.While present only at very low frequencies in African populations, the telomeric B haplotype loci are seen at increasing frequencies in populations outside of Africa, with a positive correlation of KIR in the HGDP-CEPH reveals significant evidence for balancing, diversifying selection within the telomeric region, and purifying selection and/or a history of population bottlenecks within the centromeric region, in general accordance with recent work showing different evolutionary histories for the centromeric and telomeric KIR Below is the link to the electronic supplementary material.


The 1000 Genomes Project (1KG) aims to provide a comprehensive resource on human genetic variations. With an effort of sequencing 2,500 individuals, 1KG is expected to cover the majority of the human genetic diversities worldwide. In this study, using analysis of population structure based on genome-wide single nucleotide polymorphisms (SNPs) data, we examined and evaluated the coverage of genetic diversity of 1KG samples with the available genome-wide SNP data of 3,831 individuals representing 140 population samples worldwide. We developed a method to quantitatively measure and evaluate the genetic diversity revealed by population structure analysis. Our results showed that the 1KG does not have sufficient coverage of the human genetic diversity in Asia, especially in Southeast Asia. We suggested a good coverage of Southeast Asian populations be considered in 1KG or a regional effort be initialized to provide a more comprehensive characterization of the human genetic diversity in Asia, which is important for both evolutionary and medical studies in the future. Due to recent burgeoning in next generation sequencing (NGS) technologies, whole-genome sequencing of a large number of individuals in multiple populations is now feasible. This advanced technology has facilitated genome-wide investigation of human genetic variations and inference of population demographic history and natural selection without ascertainment bias . Pioneer1 was used for the coordinates of HGDP dataset to facilitate the combination. For PASNP, we used the Affymetrix GeneChip Human Mapping 50K Xba array annotation file with netaffx-build 32 (GRCh37) from Affymetrix website to call the SNPs from raw CEL files, followed by quality control using the same criteria as used in the original paper  of the 1KG and fetched around 36.5M autosomal SNPs of 1,092 individuals representing 14 populations worldwide . The genal paper . ApproxiAs the variants of individuals from two Asian populations, CDX and KHV, from 1KG had not been released when we initiated this study, we also incorporated the BAM (binary version of Sequence Alignment/Map format) files of 95 individuals from CDX and 49 individuals from KHV downloaded from 1KG website.The well-curated BAM files from CDX (95) and KHV (49) were processed using the GATK  of the dAs the datasets were from sequencing or genotyping on different platforms, we identified a set of intersected SNPs among these three datasets and used only data from autosomal chromosomes. Any SNP with discordant annotation among any pair of populations is not included in the combined genotype data, for example, strand bias  and annotation misleading . Next, the SNPs with missing genotypes larger than 20% in a population were discarded. Finally, we identified a set of 7,775 clean autosomal SNPs from 3,832 individuals in these three datasets. We noted that the number of SNPs was smaller than the maximum set of overlapping SNPs shared among these datasets, as some SNPs were filtered out during the quality control procedures.Figure 1). Considering the great influence of population admixture and complex human migration history in Southeast Asia as well as the substantial linguistic diversity, we used the linguistic affiliations mentioned in the original publication  denotes one point  in PC plot representing individual i from 1KG, while P represents individual j from HGDP and PASNP. The PC plot can be divided into S rectangles by a grid with side-length d1 and d2. δ P∈Rs  was counted once if there is any individual located in the rectangle defined by R s , otherwise 0 if there is no individual in it. The number of rectangles in the grid overlapping with the points representing the individuals from 1KG, HGDP, and PASNP can be an estimation of the coverage for 1KG, and HGDP and PASNP, respectively. Next we defined the logarithm of the ratio to the base e as D d . The difficult part of D d is how to assign d1 and d2 to an optimal estimation of the different coverage for the population pairs, which depends on the average distance m and defined m = 1, 2, 3, … where Figure 2A. As expected, individuals from Asia, Europe, and Africa can be well distinguished. The over-representing of East Asian and Southeast Asian samples from HGDP and PASNP than those from 1KG was due to the larger sample size in HGDP and PASNP, so we randomly selected individuals from each geographical area to match sample size so that our results are not biased by the fluctuation of sample size in different regions. As there were no West Asian, Central and South Asian, and Oceanian individuals in 1KG phase I, we removed individuals from those regions in HGDP and PASNP and did PCA again . The result showed that 1KG has lower coverage in East Asia and Southeast Asia compared to HGDP and PASNP, but reasonable coverage in America. For example, 1KG does not have any representation for some Asian populations such as Yakut (empty triangles in sea green) and Pacific islanders (green crosses) from Southeast Asia.We performed PCA with EIGENSOFT version 3.0  for all Figure 2C), we examined the diversity representation of 1KG in different parts, i.e., West, Southeast, Central, and South Asia. Since there is no sample or data available for Central and South Asian populations in the latest version of 1KG (phase I), we merged Central Asian and South Asian samples in our analysis. The result also showed substructures in Southeast Asians, for example, Southeast Asian individuals could be roughly classified as those closely related to Han Chinese (CHB) and those showing apparent differentiation from Han Chinese. To examine whether CDX and KHV are genetically closer to Southeast Asian populations than to East Asian populations, we performed PCA of CDX, KHV together with samples from East Asians (CHB and CHS) and Southeast Asians (populations from Thailand). As a result, CDX and KHV were clustered with populations from Southeast Asia instead of those from East Asia. This result confirmed that CDX and KHV should be classified as Southeast Asian . However, as shown below, the coverage of individuals from CDX and KHV indicated insufficient genetic diversity of Southeast Asia in 1KG.Since Asia is the most populous continent with the largest population size, and PCA also showed considerable population substructure in this area  displaying population genetic diversity of different geographic areas in a context of worldwide population, it is clear that the area occupied by the individuals representing Southeast Asian and East Asian populations from HGDP and PASNP is larger than that by the individuals from 1KG. When we compared such difference in individuals from Native American populations, we found that the area occupied by the individuals from HGDP and PASNP is smaller than that of the individuals from 1KG. Such results indicated that 1KG had higher genetic diversity coverage in Native American populations but lower genetic diversity coverage in Southeast Asian and East Asian populations. To evaluate such difference of extended genetic diversity measured by the occupied area in a geographic region in an unbiased way, we pooled all 140 populations from eight main geographic areas, and randomly sampled equal number of individuals for each geographic area to eliminate the bias introduced by different sample sizes in 1KG, HGDP and PASNP. In this analysis, we also included samples from Central and South Asia, Oceania, and West Asia. The sample size of populations representing Africa, Native America, Europe, West Asia, Central and South Asia, East Asia, Southeast Asia, and Oceania in 1KG (and in HGDP and PASNP) was 130 (130), 89 (89), 157 (157), 0 (134), 0 (438), 286 (286), 103 (103), and 0 (32), respectively. After this sampling approach based on worldwide population context, we ran PCA again and observed lower genetic diversity coverage of populations representing Southeast Asians and East Asians from 1KG . We were also interested in evaluating whether 1KG has sufficient diversity coverage in different scales besides worldwide samples comparison. We used similar sampling approach as described above and pooled population samples from East Asia, Southeast Asia, and Oceania . We examined distribution of 1KG samples in the PC plots in the context including both Southeast Asian and East Asian populations , and also in the context including only Southeast Asian populations . The 1KG lacks sufficient representation of samples from Southeast Asian populations compared with HGDP and PASNP data in all the four contexts . Therefore, the above results indicated that 1KG lacks sufficient coverage of Southeast Asian populations.As shown in the PC plot based on all 140 populations , Eurasians and Oceanians (EAO), Asians and Oceanians (AO), non-Western Asians and Oceanians (NWAO), East Asians and Pacific islanders (EPI), East Asians and Southeast Asians (ESEA), Southeast Asians and Oceanians (SEAO), and Southeast Asians (SEA). Again, we applied similar sampling procedure to reduce the effect of unbalanced sample sizes between 1KG and the control groups.To quantitatively evaluate the genetic diversity coverage of 1KG, we developed a statistic m, which can be treated as a coefficient of D d as a function of m and displayed the relationship of m and D d in Figure 4. In the context of worldwide populations, D d for Southeast Asians went down steeply while m increased from 1 to 20  and showed considerable fluctuations subsequently beyond 20. To illustrate the performance of D d in the situation of good representation of population samples, we evaluated coverage of 1KG in European populations using D d based on HGDP and PASNP data. The results showed that D d did not deviate significantly from 0 in the context of European populations, even with different values of m . Interestingly, the D d values have quite different behavior in the context of Native Americans, which were larger than 0 and positively correlated with m . This pattern indicated that Native American samples are over-represented in 1KG compared with those in HGDP and PASNP data, which were also supported by the result illustrated by Figure 2B. Since our method was based on PCA results and the individuals with similar coordinates in PC plots are likely to be genetically closely related, we chose only one individual as proxy of diversity in the area defined by m to reduce redundancy. We suggested that the value of m should be neither too small nor too large. Especially, m = 1 does not make any sense since in this case any two individuals would not be likely to be in the same rectangle defined by Rs . When m was given a very large value, the probability of two distantly related individuals from different populations to be located in the same rectangle would be very high. In both cases, it would lead to unreasonable estimations.To investigate the influence of D d with the sampling procedure repeated for 100 times using m = 10. The results of D d for Native American populations based on worldwide population context (WW) and NA context were all significantly greater than 0 , which indicated high genetic diversity coverage of 1KG in this area. The values of D d for European populations under WW context and NA context did not significantly deviate from 0 (p = 0.66 and 0.13), but less than 0 based on EAO context (p = 5.2 × 10-32). However, the values of D d for East Asian populations were all significantly less than 0 (p < 7 × 10-64), even if we set m = 1 in the calculation . The results for Southeast Asian populations based on AO, NWAO, EPI still had lower values of D d than those for East Asian populations, which indicated that the genetic diversity coverage of 1KG in Southeast Asia was much less sufficient. As we looked closer, the only two population samples (KHV and CDX) collected from Southeast Asia in 1KG could represent a small proportion of genetic diversity of this region, as can also be visualized from PCA result . Therefore, the coverage of population genetic diversity in 1KG is particularly insufficient in Southeast Asia.To evaluate genetic diversity coverage of 1KG in a specific geographic area, we calculated In this study, we developed a method to quantitatively evaluate the genetic diversity coverage of 1KG based on PCA of genome-wide SNP data in 140 populations worldwide. Our results showed insufficient representation of genetic diversity of some geographic regions in 1KG, especially of Southeast Asia. Although our analysis showed that 1KG seemingly has reasonable coverage in many regions such as Africa, Europe and East Asia, we would like to point out that our results as well as conclusions might be conservative for the following reasons.Firstly, our analysis relied on comparison of 1KG data and other available reference data such as HGDP and PASNP, which means that we could not give a good evaluation of 1KG data for their representation of genetic diversity if there were not sufficient data available in either 1KG or reference data. For example, there is no Central Asian data available in 1KG while there are data available in reference data from some Central Asian populations such as the Uyghur in both HGDP and PASNP. However, we did not draw a conclusion that 1KG lacks coverage of genetic diversity in Central Asia. Because previous studies reported that Central Asian populations such as the Uyghur are admixed populations with both Caucasian and East Asian ancestries , 2009, iD d statistic can be easily extended to handle three-dimension or even higher dimensions PCs. Therefore, our analysis only evaluated whether 1KG data cover the majority of genetic diversity in a particular geographical region. From this aspect, our results may be conservative, i.e., genetic diversity coverage of 1KG data in those regions showing good representation could be overestimated.Secondly, our method was developed based on PCA results. On the one hand, population structure revealed by PCA is sensitive to rare variants but not common variants. We compared the minor allele frequency (MAF) spectrum of our data set and the MAF spectra of the data sets with similar number of random SNPs we created from 1KG data. As a result, our data set did not have excess of rare SNPs compared with the other randomly sampled data sets. On the other hand, PCA can only capture the majority of information in genome-wide data and populations, and fine-scale structures could not be fully revealed with limited dimensions of principal components, although the Finally, we noted in our analysis that the sequence error using NGS existed in 1KG, which could affect the estimation of population genetic diversity. Importantly, sequence errors tend to result in a higher estimation of genetic diversity. The consequence, as we discussed above, is overestimating the diversity coverage of 1KG data in those regions with genotyping data being compared. Therefore, regarding this issue, our results as well as conclusion could be conservative.In summary, we concluded that the current version of 1KG data does not have sufficient genetic diversity coverage in Asia, especially in Southeast Asia that harbors significant cultural and linguistic diversity as well as the majority of genetic diversity in Asia. We thus suggest a good coverage of Asian populations, especially of Southeast Asian populations, be considered in 1KG or a regional effort be initialized to provide a more comprehensive characterization of the human genetic diversity in Asia, which is important for both evolutionary and medical studies in the future.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Multivariate statistical techniques such as principal components analysis (PCA) and multidimensional scaling (MDS) have been widely used to summarize the structure of human genetic variation, often in easily visualized two-dimensional maps. Many recent studies have reported similarity between geographic maps of population locations and MDS or PCA maps of genetic variation inferred from single-nucleotide polymorphisms (SNPs). However, this similarity has been evident primarily in a qualitative sense; and, because different multivariate techniques and marker sets have been used in different studies, it has not been possible to formally compare genetic variation datasets in terms of their levels of similarity with geography. In this study, using genome-wide SNP data from 128 populations worldwide, we perform a systematic analysis to quantitatively evaluate the similarity of genes and geography in different geographic regions. For each of a series of regions, we apply a Procrustes analysis approach to find an optimal transformation that maximizes the similarity between PCA maps of genetic variation and geographic maps of population locations. We consider examples in Europe, Sub-Saharan Africa, Asia, East Asia, and Central/South Asia, as well as in a worldwide sample, finding that significant similarity between genes and geography exists in general at different geographic levels. The similarity is highest in our examples for Asia and, once highly distinctive populations have been removed, Sub-Saharan Africa. Our results provide a quantitative assessment of the geographic structure of human genetic variation worldwide, supporting the view that geography plays a strong role in giving rise to human population structure. The spatial pattern of human genetic variation provides a basis for investigating the history of human migrations. Statistical techniques such as principal components analysis (PCA) and multidimensional scaling (MDS) have been used to summarize spatial patterns of genetic variation, typically by placing individuals on a two-dimensional map in such a way that pairwise Euclidean distances between individuals on the map approximately reflect corresponding genetic relationships. Although similarity between these statistical maps of genetic variation and the geographic maps of sampling locations is often observed, it has not been assessed systematically across different parts of the world. In this study, we combine genome-wide SNP data from more than 100 populations worldwide to perform a formal comparison between genes and geography in different regions. By examining a worldwide sample and samples from Europe, Sub-Saharan Africa, Asia, East Asia, and Central/South Asia, we find that significant similarity between genes and geography exists in general in different geographic regions and at different geographic levels. Surprisingly, the highest similarity is found in Asia, even though the geographic barrier of the Himalaya Mountains has created a discontinuity on the PCA map of genetic variation. The geographic structure of human genetic variation has long been of interest for its implications for studying human evolutionary history Several recent studies have reported detectable similarity between statistical maps of genetic variation and geographic maps of population locations. Such observations are particularly prominent within Europe, where striking similarity between genes and geography is observed both at a continental level A formal comparison of genes and geography in different regions using a single technique and a common marker set can provide a systematic basis for evaluating the role of geography in explaining the genetic similarity of individuals or populations in different locations. We have previously developed a Procrustes analysis approach to quantify the similarity between statistical maps of genetic variation and geographic maps In this study, we apply the Procrustes approach together with PCA to systematically study the geographic structure of human genetic variation across different geographic regions. By compiling data from a variety of published sources We integrated published genome-wide SNP data on 4,257 individuals from 149 worldwide populations, taking data from the Human Genome Diversity Project (HGDP) Our analyses were based on 32,991 autosomal SNP markers that were shared among datasets obtained from different genotyping platforms. We applied PCA on datasets after quality control and removal of PCA outliers  geographic coordinates that involved a rotation of the PCA plot by 31.91eography . This quon 0.003 . Some poet al.Visually striking similarity betweeen PCA plots of genetic variation and a geographic map of Europe has been reported by several studies et al.et al.et al.Materials and Methods). Our PC1 and PC2 explain 0.30% and 0.16% of the total genetic variation respectively, in close agreement with the values of 0.30% and 0.15% reported by Novembre et al.Our PCA plot is very similar to the plot of Novembre We used Procrustes analysis to superimpose the PCA plot on the geographic map, rotating the PCA coordinates 72.66lockwise . The rotof 0.007 . Populatncreases .et al. recently studied 12 populations in West Africa, and revealed a high similarity between a SNP-based PCA map and the corresponding geographic map, when Mbororo Fulani, a nomadic pastoralist population, was excluded from the analysis et al.Sub-Saharan Africa is the location of the origin of modern humans and has the highest genetic variation among all continents n Africa .Applying PCA on this combined Sub-Saharan African dataset, we found that PC1 accounts for 1.34% of the total genetic variation, largely separating populations from west to east. PC2 accounts for 0.69% of the total genetic variation and largely separates populations from north to south . GeneralProcrustes analysis identifies a rotation angle of 16.11rdinates , and theanalysis ; as showsample , . This reWhen hunter-gatherer populations  and Mbororo Fulani were included in the analysis, they appeared as isolated clusters on the PCA plots and greatly reduced the similarity between PCA maps and geographic maps . The simet al.et al.Our Asian example included 760 individuals from 44 populations distributed widely across Asia . Previouet al.In our PCA plot , PC1 larOne interesting result concerns the Uygur and Kyrgyzstani populations, both of which lie along ancient trade routes between Europe and East Asia. Compared to the Uygur population, which lies farther to the east, the Kyrgyzstani population clusters closer to East Asian populations, especially to the Yakut and Buryat populations, supporting a view that the Kyrgyzstani group has a proportion of its ancestry in Siberia The PCA map of genetic variation in Asia is rotated 5.05phic map . Despiteexcluded .To further examine populations on either side of the Himalaya Moutains, we performed additional analyses of East Asia and Central/South Asia. We first considered the East Asian populations in our Asian example. This dataset consists of 341 individuals from 23 populations. After excluding seven PCA outliers, our analyses were based on 334 individuals from 23 East Asian populations .Individuals in this East Asian dataset generally align along a curve on the PCA plot. PC1 explains 1.58% of the total genetic variation and largely accounts for a north-south genetic gradient; PC2 explains 0.98% of the genetic variation and mainly separates two Siberian populations (Buryat and Yakut) and three Southeast Asian populations  from the other East Asian populations . The TibThe imperfect match between PCA coordinates and geography is reflected by a relatively low Procrustes similarity score of phic map . Interesphic map . Such abinal PCA .Our last example focused on Central/South Asia, using an initial sample of 372 individuals from 18 populations. Ten individuals were excluded as PCA outliers, leaving 362 individuals from 18 populations for the final analysis .The first two components of the PCA anlaysis account for 1.59% and 1.31% of the total genetic variation, respectively. Overall, the PCA pattern for the separate anlaysis of Central/South Asian populations is similar to the pattern for the same set of populations in our analysis of all of Asia . After reography . Most poxclusion . Excludision  in . A closeWe have found that significant similarity between genes and geography exists in general at different geographic levels . The higty score , howeverutations . By contutations , indicatet al.To examine the robustness of our results to the number of SNPs analyzed, we repeated our analyses with subsets of randomly selected loci. We found that our Procrustes similarity scores between genes and geography are quite robust as long as enough SNPs  to the We performed PCA by applying the function dization .We note that some studies have used the eigenvectors et al.We applied Procrustes analysis To test the statistical significance of We investigated the effect of each population on our PCA and Procrustes analysis using a leave-one-out approach. For each dataset, we excluded one population at a time and repeated PCA to obtain a new set of genetic coordinates . We then performed two Procrustes analyses. In the first one, we compared the new PCA coordinates and the original PCA coordinates obtained before removing any population. This comparison was based on the common set of individuals included in both analyses, and its similarity score was denoted To investigate the effect of the number of markers on our results, we created a series of marker lists by randomly selecting We calculated Figure S1Procrustes analysis of genetic and geographic coordinates of European populations, when reducing the maximal sample size to 50. That is, for each population that has sample size (PDF)Click here for additional data file.Figure S2Procrustes analysis of genetic and geographic coordinates of Sub-Saharan African populations, excluding Maasai (MKK) as well as Mbororo Fulani and four hunter-gatherer populations. (A) Geographic coordinates of 22 populations. (B) Procrustes-transformed PCA plot of genetic variation. The Procrustes analysis is based on the unprojected latitude-longitude coordinates and PC1-PC2 coordinates of 318 individuals. PC1 and PC2 are indicated by dotted lines, crossing over the centroid of all individuals. PC1 and PC2 account for 0.89% and 0.75% of the total variance, respectively. The Procrustes similarity statistic is (PDF)Click here for additional data file.Figure S3Procrustes analysis of genetic and geographic coordinates of Sub-Saharan African populations, including 23 populations in (PDF)Click here for additional data file.Figure S4Histograms of the Procrustes similarity o Fulani , , . (C)ka Pygmy , , . (D)ti Pygmy , , . (E)nd !Kung , , . (F) and San , , .(PDF)Click here for additional data file.Figure S5Procrustes analysis of genetic and geographic coordinates of Asian populations, excluding Irula. (A) Geographic coordinates of 43 populations. (B) Procrustes-transformed PCA plot of genetic variation. The Procrustes analysis is based on the unprojected latitude-longitude coordinates and PC1-PC2 coordinates of 725 individuals. PC1 and PC2 are indicated by dotted lines, crossing over the centroid of all individuals. PC1 and PC2 account for 5.55% and 0.74% of the total variance, respectively. The Procrustes similarity statistic is (PDF)Click here for additional data file.Figure S6Procrustes analysis of genetic and geographic coordinates of East Asian populations, excluding Tibetans. (A) Geographic coordinates of 22 populations. (B) Procrustes-transformed PCA plot of genetic variation. The Procrustes analysis is based on the unprojected latitude-longitude coordinates and PC1-PC2 coordinates of 303 individuals. PC1 and PC2 are indicated by dotted lines, crossing over the centroid of all individuals. PC1 and PC2 account for 1.72% and 1.02% of the total variance, respectively. The Procrustes similarity statistic is (PDF)Click here for additional data file.Figure S7Data preparation procedure for creating datasets for different geographic regions.(PDF)Click here for additional data file.Figure S8Data-processing procedures for datasets from different geographic regions. (A) The worldwide dataset in (PDF)Click here for additional data file.Figure S9Data-processing procedure for the supplementary example of Sub-Saharan Africa when including Mbororo Fulani and four hunter-gatherer populations . Similar procedures (not shown) were also used to prepare datasets for the analyses in (PDF)Click here for additional data file.Table S1Populations included in this study (Part I).(PDF)Click here for additional data file.Table S2Populations included in this study (Part II).(PDF)Click here for additional data file.Table S3Populations included in this study (Part III).(PDF)Click here for additional data file.Table S4Change of the Procrustes similarity when excluding one population from the worldwide example.(PDF)Click here for additional data file.Table S5Change of the Procrustes similarity when excluding one population from the European example.(PDF)Click here for additional data file.Table S6Change of the Procrustes similarity when excluding one population from the Sub-Saharan African example.(PDF)Click here for additional data file.Table S7Summary of the results for Sub-Saharan Africa when all or one of five additional African populations are included (corresponding to (PDF)Click here for additional data file.Table S8Change of the Procrustes similarity when excluding one population from the Asian example.(PDF)Click here for additional data file.Table S9Change of the Procrustes similarity when excluding one population from the East Asian example.(PDF)Click here for additional data file.Table S10Change of the Procrustes similarity when excluding one population from the Central/South Asian example.(PDF)Click here for additional data file.Table S11Samples identified as PCA outliers in the analyses for different geographic regions.(PDF)Click here for additional data file.


Oligosoma otagense), endemic to the region, is one of the most critically endangered vertebrates in New Zealand. We use mitochondrial DNA sequence data to investigate the evolutionary history of the Otago skink, examine its population genetic structure, and assess the level of genetic diversity in the individuals in the captive breeding program. Our data indicate that the Otago skink diverged from its closest relatives in the Miocene, consistent with the commencement of tectonic uplift of the Southern Alps. However, there is evidence for past introgression with the scree skink (O. waimatense) in the northern Otago-southern Canterbury region. The remnant populations in eastern Otago and western Otago are estimated to have diverged in the mid-Pliocene, with no haplotypes shared between these two regions. This divergence accounts for 95% of the genetic diversity in the species. Within both regions there is strong genetic structure among populations, although shared haplotypes are generally evident between adjacent localities. Although substantial genetic diversity is present in the captive population, all individuals originate from the eastern region and the majority had haplotypes that were not evident in the intensively managed populations at Macraes Flat. Our data indicate that eastern and western populations should continue to be regarded as separate management units. Knowledge of the genetic diversity of the breeding stock will act to inform the captive management of the Otago skink and contribute to a key recovery action for the species.Climatic cooling and substantial tectonic activity since the late Miocene have had a pronounced influence on the evolutionary history of the fauna of New Zealand's South Island. However, many species have recently experienced dramatic range reductions due to habitat fragmentation and the introduction of mammalian predators and competitors. These anthropogenic impacts have been particularly severe in the tussock grasslands of the Otago region. The Otago skink ( Geological and climatic processes have acted to dramatically alter the landscape of the South Island of New Zealand Anthropogenic impacts, particularly since European settlement ∼200 years ago, have resulted in the decline or extinction of many native species in the South Island Oligosoma otagense), listed as Nationally Critical, is one of the most endangered vertebrates in New Zealand The Otago skink  were only formally recognised as distinct species in 1997, based on body shape and colour pattern The conservation management of the Otago skink is focused on ∼2400 ha of land (managed by the Department of Conservation) near the Macraes Flat township ND2, ND4, Cytochrome b) and phylogeography (control region) of the Otago skink. In particular, we investigate the population structure within and between the two regions , determine the level of genetic diversity that is present within the captive breeding stock, and examine whether there is evidence for hybridisation between the Otago skink and scree skink.Here we use mitochondrial sequence data to examine the phylogenetic history  and western populations  . The WilO. waimatense, O. acrinasum, O. infrapunctatum, O. taumakae and O. pikitanga , ND4 (∼700 bp), and Cytochrome b (∼700 bp). These regions were targeted because our previous work across several taxonomic levels in New Zealand skinks has indicated useful levels of variability ND2, ND4 and Cytochrome b). PCR products were purified using ExoSAP-IT . The purified product was sequenced directly using a BigDye Terminator v3.1 Cycle Sequencing Kit (Applied Biosystems) and then analysed on an ABI310 or ABI 3730XL capillary sequencer.Total genomic DNA was extracted from liver, muscle, toe or tail-tip samples using a modified phenol-chloroform protocol ontigExpress in Vector NTI Advance v9.1.0 (Invitrogen), and aligned using the default parameters of Clustal X v1.83 Sequence data were edited using CND2, ND4 and cytochrome b sequence data was used for the phylogenetic analyses. Maximum Likelihood (ML) and Bayesian tree building methods were used. We used Modeltest 3.7 odeltest, conducted in PAUP* 4.0b10 The concatenated rBayes 3.1.2 odeltest to determine the most appropriate model of sequence evolution for our dataset. We ran the Bayesian analysis for five million generations, sampling every 100 generations . We ran the analysis twice, using four heated chains per run. We discarded the first 25% of samples as burn-in and the last 37,500 trees were used to estimate the Bayesian posterior probabilities. The program Tracer 1.5 MBootstrap values (500 ML bootstraps) and Bayesian posterior probabilities were used to assess branch support. We considered branches supported by bootstrap values of 70% or greater ogCombiner v1.6.1). The output was viewed in Tracer to check that stationarity had been reached, and ensure that the effective sample size (ESS) exceeded 200 We estimated the divergence time of the eastern and western Otago skink populations using an evolutionary rate of 1.3–1.63% sequence divergence per million years, based on mitochondrial DNA calibrations from other squamate reptile groups 1.3%, . A strich; haplotypic diversity, Hd; number of polymorphic sites, S; nucleotide diversity, π) were calculated in DnaSP v4.50 rlequin v3.5 ST values  were calculated to estimate among population differentiation. We conducted hierarchical Analysis of Molecular Variance  and mismatch distributions to test for signatures of population expansion . Significant and negative Tajima's D and Fu's Fs statistic values are indicative of possible population expansion. Mismatch frequency histograms were plotted in DnaSP to determine whether the populations exhibited evidence of spatial range expansion or a stationary population history in siturlequin. The spatial expansion hypothesis (both RI and SSD) was tested using a parametric bootstrap approach (200 replicates).We used Tajima's ND2, 671 bp ND4, 613 bp Cytochrome b), of which 588 (32%) were variable and 438 (24%) were parsimony-informative. For the ingroup only, the alignment contained 487 (27%) variable characters, of which 397 (22%) were parsimony-informative. Base frequencies were unequal , but a χ2 test confirmed the homogeneity of base frequencies among sequences .The edited alignment comprised 1834 characters , proportion of invariable sites (0.6022), and gamma distribution shape parameter (4.132). The topologies of the ML and Bayesian trees were identical, therefore we present the optimal ML tree (−ln L = 7317.86919) with ML bootstrap (BS) values and Bayesian posterior probabilities (PP) indicating branch support  and western populations , several O. waimatense individuals from the southern limit of its distribution grouped with the eastern  or western  lineages of O. otagense  formed a well-supported lineage , which was genetically divergent (11.5% sequence divergence) from both O. otagense lineages .There is extremely strong support for the monophyly of otagense . In contlineages . The mealineages . The divThe edited control region alignment comprised 419 characters, with 46 variable sites within the six haplotypes identified within the Otago skink . No haplST = 0.958), with 36 variable sites separating the eastern and western populations  which appeared, based on our phylogenetic analyses, to derive from lineages that had experienced past introgression with the Otago skink  and 3c. O. acrinasum, O. infrapunctatum, O. pikitanga, O. taumakae, O. waimatense). The diversification of this lineage has previously been estimated to have occurred during the late Miocene Our study indicates that the Otago skink exhibits substantial genetic divergence (9.7–13.4%) from its closest relatives in the South Island  between the Otago skink and scree skink in the northern Otago-southern Canterbury region. Incomplete lineage sorting is unlikely to explain our results given the deep divergence between the two species, the restriction of the pattern to the southern end of the scree skinks range, and the ability for the two species to produce viable hybrids in captivity. Hardy O. grande, 3.8 mya; O. maccanni, 3.6 mya; O. chloronoton, 5.3 mya; O. inconspicuum, 2.7 mya; Two main lineages were evident within the Otago skink, with the divergence between the populations in eastern Otago and western Otago occurring during the Pliocene . Phylogeographic breaks of an equivalent age have been reported in this region for the sympatric grand skink  and western  populations of the Otago skink. Despite this, the presence of shared haplotypes within each region indicates that there has been recent geneflow among adjacent populations. This pattern of population structuring is consistent with that observed in the grand skink Considerable genetic and haplotypic diversity was evident in the Otago skinks in the captive breeding program. All captive animals had haplotypes that originated from the eastern region. The initial founders of the captive breeding program were sourced from near Sutton in the 1980s–1990s , and this accounts for most captive animals having haplotypes  that originate from outside of the intensively managed populations at Macraes Flat. Given the recent population declines of the Otago skink in the Sutton region The Department of Conservation is in the process of establishing a separate captive breeding program for the western populations of the Otago skink , and continues to implement in-situ conservation management actions at the Glenfoyle station reserve The Otago skink has experienced a drastic reduction in its distribution over the last ∼200 years and now persists in two remnant regions in the periphery of its original distribution


Danio rerio) populations occurring in Nepal, India, and Bangladesh, and we genetically compared wild populations to several commonly used lab strains. We examined genetic variation at 1832 polymorphic EST-based single nucleotide polymorphisms (SNPs) and the cytb mitochondrial gene in 13 wild populations and three lab strains. Natural populations were subdivided into three major mitochondrial DNA clades with an average among-clade sequence divergence of 5.8%. SNPs revealed five major evolutionarily and genetically distinct groups with an overall FST of 0.170 (95% CI 0.105–0.254). These genetic groups corresponded to discrete geographic regions and appear to reflect isolation in refugia during past climate cycles. We detected 71 significantly divergent outlier loci (3.4%) and nine loci (0.5%) with significantly low FST values. Valleys of reduced heterozygosity, consistent with selective sweeps, surrounded six of the 71 outliers (8.5%). The lab strains formed two additional groups that were genetically distinct from all wild populations. An additional subset of outlier loci was consistent with domestication selection within lab strains. Substantial genetic variation that exists in zebrafish as a whole is missing from lab strains that we analysed. A combination of laboratory and field studies that incorporates genetic variation from divergent wild populations along with the wealth of molecular information available for this model organism provides an opportunity to advance our understanding of genetic influences on phenotypic variation for a vertebrate species.Understanding a wider range of genotype–phenotype associations can be achieved through ecological and evolutionary studies of traditional laboratory models. Here, we conducted the first large-scale geographic analysis of genetic variation within and among wild zebrafish ( Drosophila, the mouse or Arabidopsis has made it possible to address fundamental questions in ecology and evolution such as unravelling complex gene networks underlying adaptive evolution  is a prominent model organism in developmental genetics, neurophysiology and biomedical research  gene with primers modified from m MgCl2, 0.2 mm of each dNTP, 1 μm of each primer, 1.25 U Taq polymerase and approximately 100 ng DNA. The PCR profile was as follows: 94 °C for 2 min, 33 cycles of 94 °C for 30 s, 54 °C for 30 s, 72 °C for 2.5 min, 72 °C for an additional 5 min and 4 °C until manually terminated. Standard Sanger sequencing was performed on both strands of DNA for each individual. Sequences were aligned manually with CODONCODE ALIGNER ver. 3.0.2 (CodonCode Corporation). All sequences have been deposited at Genbank (Accession numbers JN234180–JN234356).We amplified a 1122 bp region of the cytochrome b (h), nucleotide diversity (π)(Da) , based on the uncorrected p-distances used by samova. Additional analyses for the structure indicted by the chosen K-value were performed with ARLEQUIN. For this amova, we used the TrN substitution model with 10 000 permutations.Haplotype diversity (rsity (π) and net  (π)(Da)  were est (π)(Da) . We perf (π)(Da)  and MODE (π)(Da) . The sel (π)(Da) , which wulations . We testDanio kyathit , (2) confirmed SNPs from N = 66), (3) high-quality predicted SNPs from N = 1245) and (4) high-quality predicted SNP from N = 6837). We used all of subsets 1, 2 and 3 and sampled through subset 4 seeking to fill gaps and achieve an even distribution up to a total of 3212 SNPs. Based on the sequences we supplied, Affymetrix  developed a Custom Affymetrix Targeted Genotyping assay. This assay is based on the Molecular Inversion Probes (MIPs) approach (10.5061/dryad.505dp), and locus-specific SNP information is available at Genbank.Single nucleotide polymorphism genotypes were collected with a custom zebrafish Affymetrix SNP array following the manufacturer’s protocol at the Génome Québec Innovation Center, Montréal, Canada. This array contained a combination of confirmed and predicted SNPs in gene transcripts based on θ analogues  <1%. This resulted in 1832 variable SNPs among all populations and lab strains. We tested for Hardy–Weinberg proportions in each population or lab strain with GENEPOP ver. 4 . GENETIXnalogues  of FST. r. 0.8-3  to estim-3 (Dest . One thoK) with the highest log likelihood. For STRUCTURE analyses, we did not incorporate prior population information. We used 100 000 replicates and 20 000 burn-in cycles under an admixture model. We inferred a separate α for each population (α is the Dirichlet parameter for degree of admixture). We used the correlated allele frequencies model with an initial λ of 1, where λ parameterizes the allele frequency prior and is based on the Dirichlet distribution of allele frequencies. We allowed F to assume a different value for each population, which allows for different rates of drift among populations. We performed 10 runs for each of K = 1–15, the total number of population samples examined with SNPs. We performed two rounds of analysis with STRUCTURE. For the first round, in addition to the initial filter for fixed loci and MAF <1%, we also filtered the data set for linkage disequilibrium (LD). One locus in a pair was randomly removed from the data set if estimated r2 was >0.5. The LD filter resulted in a set of 522 SNPs distributed throughout the genome . After the first round of STRUCTURE analysis, we performed a hierarchical outlier locus analysis . We therefore performed analyses with and without the lab strains.P < 0.01. To reduce the number of potential false positives, we reported only loci with scaled heterozygosities [http://www.geneontology.org), the KEGG PATHWAY database (http://www.genome.jp/kegg/pathway.html) and the UniProt database (http://www.uniprot.org/), along with corresponding literature searches to assign significant outlier SNPs to putative functional groups.We conducted an outlier analysis with the hierarchical FDIST model  or BAYESK = 7 STRUCTURE model (see SNP divergence among populations in Results section below) to account for substructure within the hierarchical FDIST model. The five groups of natural populations were: PAR, KHA and CHIT each formed a separate group; BER, DHO, JOR, PGM and RCH formed a group; and PNS and UTR formed the final group. We chose to split the latter two populations from the other sites in this genetic group because over-splitting is likely to have less of an effect on false positive rates for this analysis than under-splitting . We did not apply the LD filter for this analysis so that all potential candidate loci and regions of chromosomes had the opportunity to be detected. We excluded lab strains from the analyses initially to estimate outliers among natural populations . We used the population structure consistent with the K = 7 STRUCTURE model. Groups were defined as follows: PAR, KHA and CHIT each formed a separate group; BER, DHO, JOR, PGM and RCH formed a group; PNS, UTR and TM1 formed a group; and AB and SJA were grouped together. The inclusion of AB and SJA as one or as two groups did not influence results (data not shown).We conducted a second hierarchical outlier locus analysis with individuals from all wild populations (again excluding SRN and WYD from southern India) and including the three lab strains. We used the population structure consistent with the FST outliers along chromosomes would be consistent with ‘hotspots’ or genomic regions with multiple loci that have been influenced by selection. To test for nonrandom outlier aggregations, we divided the genome into 20 Mb bins. This resulted in a total of 76 bins among the 25 zebrafish chromosomes. We assumed a Poisson distribution to calculate the probability of observing a given number of significant high or low FST outliers within a bin, following P < 0.01, and we performed calculations separately for high and low outliers. For the analysis that excluded lab strains , the probability of observing four significant high outliers in a given window was 0.008. The probability of observing three significant high outliers was 0.04. The probability of observing two significant low outliers was 0.006. For the analysis that included wild populations and lab strains, the probability of observing four significant high outliers in a given 20 Mb bin was 0.015, and the probability of observing five high outliers was 0.003. The probability of observing two significant low outliers was 0.036.Nonrandom aggregations of significant FST values of outlier loci, linked variation should be ‘swept’ through the population along with the advantageous locus . These populations had very low levels of genetic differentiation  and belonged to the same genetic cluster in STRUCTURE models (see Results below). We used the AB strain as the representative lab strain. The sample size for AB was larger than TM1  gene examined in 177 zebrafish revealed a total of 67 haplotypes defined by 174 segregating sites of which 156 were parsimony informative. There were no gaps in the alignment. Transitions were observed 22 times more often than transversions, as estimated with MrBayes. Base composition was biased towards A and T nucleotides (61% AT content). A low estimate of the shape parameter of the gamma distribution (α = 0.097) indicated pronounced heterogeneity of substitution rate over sites.Sequence data for the 1122 bp region of the h) was 0.64 (range 0–0.92), and mean nucleotide diversity (π) was 0.45% (range 0–1.3%). RCH (in Bangladesh) had an extreme number of segregating sites (S = 67). Haplotypic diversity was not the greatest in this population, but nucleotide diversity was  ranged from −0.03% to 7.06% . KHA, RCH, CHT, SRN and WYD each formed its own group. PAR, BER, SHK, JOR, PGM, PNS, UTR, AB, SJA and TM1 all fell within an additional group. Additional calculation of an amova with 10 000 permutations and genetic distances based on the TrN model yielded a corrected ΦCT of 88.8 (P = 0.0005). Overall ΦST was 0.91 (P < 0.0001), and variation within groups (ΦSC) was 0.20 (P < 0.0001). That latter variance component reflects variation among populations within the group that contained 10 populations, as all other groups each only contained one population.Bayesian phylogenetic analyses of the 67 haplotypes revealed three major genetic groups corresponding to Northern India and western and eastern Nepal  compared with wild populations . However, SJA clearly showed highly reduced diversity relative to the other two lab strains (He = 0.142) relative to wild populations collected near the Ganges and Brahmaputra Rivers in northern India and Nepal (range of mean He: 0.223–0.253).With the 1832 SNP data set, a total of 11 593 tests for H-W proportions were possible. We observed 639 significant results for tests of deviation from H-W proportions, close to that expected because of chance alone (expected 580 significant tests at α = 0.05). Mean expected heterozygosity for SNPs was not significantly lower for the three lab strains together  and overall Dest was 0.104 (95% CI 0.103–0.106). With lab strains removed, overall FST was 0.170 (95% CI 0.105–0.254) and overall Dest was 0.085 (95% CI 0.084–0.086). All pairwise FST and Dest estimates were significant after controlling the FDR (α = 0.05), except for the estimates between PGM and JOR for both measures . A data set of 479 SNPs remained following filtration for fixed loci, MAF <1%, LD and outliers. Overall K = 1 to K = 7, after which estimated log-likelihoods reached an asymptote and variance among the 10 runs increased markedly , a group of northeastern India sites near the Ganges and Brahmaputra Rivers , the southern Bangladesh site (CHT) and the lab strains AB and SJA. The TM1 lab strain fell within the Ganges/Brahmaputra group. The southern India populations appeared to have split ancestry between KHA and CHT  from the samples north or east of the Ganges River  with extreme genetic differentiation consistent with either directional (N = 62) or balancing (N = 9) selection (FST outliers occurred on 22 chromosomes (P = 0.008) occurred within a 20 Mb region on chromosome 14 and three high outliers (P = 0.04) occurred within 20 Mb regions on chromosomes 1, 2, 5 and 19. The nine significant low FST outliers were distributed on seven chromosomes  revealed 71 loci (election  and 6a. omosomes . Clusteromosomes . Seven oP ≤ 0.01) with extreme genetic differentiation consistent with either directional (N = 75) or balancing (N = 24) selection (FST outliers were distributed throughout the genome on 23 linkage groups. Four high outliers (P = 0.015) occurred within 20 Mb regions on chromosomes 11 and 13 (P = 0.003) within a 20 Mb window occurred on chromosome 19 (FST outliers were distributed on 14 chromosomes. Two occurred within a 20 Mb region (P = 0.036) on chromosomes 6, 13 and 22. Sixteen of the 75 (21%) high outliers and four of the 22 (18%) low outliers were also identified when lab strains were excluded from the analysis. The rank correlation between P-values for significant outliers with and without the lab strains was not significant .The hierarchical outlier analysis that included lab strains revealed 99 loci  were significant only when lab strains were included in the analysis and are therefore candidates for the influence of domestication selection. Seven of these 59 high outliers (12%) were nonsynonymous substitutions. These genes were putatively associated with oxidoreductase activity, metabolic processes, chromatin assembly/disassembly and an intermediate filament associated protein of unknown function  and TATA-box-binding protein  compared with genome-wide average heterozygosity in 10 Mb windows (r2 was 0.016 (SD 0.029). The LD decay curve was generally flat  or an r2 of 0.2 was <1 kb. The pattern and extent of LD were markedly different in the AB lab strain. Mean r2 was 0.135 . LD decreased with distance in the AB strain , an uncharacterized protein putatively involved with cell signalling (chromosome 20), and three unknown genes , DBH-like monooxygenase protein 1 homologue  and an unknown gene  and the Ganges Brahmaputra group based on SNPs. These results suggest that several historical or contemporary migration events have occurred between these sites. The two sites from southern India (SRN and WYD) belonged to the same mtDNA clade as CHT but exhibited mixed ancestry from central Nepal (KHA) and Bangladesh (CHT) based on SNPs. The SNP results are consistent with both the so-called Satpura hypothesis, which contends that fish moved from northern India through central India to reach the Western Ghats in southwestern India  and western (PAR) and central Nepal (KHA) likely occurred in separate Quaternary refugia. PAR was not as highly divergent from the Ganges/Brahmaputra group as the KHA population in terms of rn India  and an arn India . SeparatThe three lab strains we examined were derived from U.S. pet stores, and the mtDNA results suggest that they originally were collected from the Ganges/Brahmaputra region, likely near the major city Kolkata . Fixation for separate mtDNA haplotypes between TM1 and AB/SJA is consistent with pet store lines that were obtained separately from the wild. Alternatively, a single sample from the wild could have contained several haplotypes that have subsequently become partitioned among pet store lines. The SNP STRUCTURE results were consistent with collection of the TM1 strain from the Kolkata region. If the AB and SJA lab strains were collected from the same region, as the mtDNA results suggest, the SNP results reveal that nuclear genomes have diverged since being brought into captivity. Ascertainment bias did not influence our interpretations for the loss of genetic diversity in lab strains based on mtDNA. The relative comparison of genetic divergence among wild populations and lab strains with this set of SNPs is likely robust to ascertainment bias, but further comparison of absolute values of divergence and diversity between lab strains and wild populations based on the SNP data would likely be biased. The observed pattern of reduced SNP diversity in both SJA and AB relative to Ganges/Brahmaputra wild populations is likely to be more pronounced with genomic markers not ascertained from lab strains.Ne) of zebrafish lab strains has led to the predicted effects of reduced variation within and divergence among strains. Our scope of inference is limited to these three lab strains, but ‘wild-type’ lab strains for this species are generally derived from pet stores  followed by intentional inbreeding to remove lethal genes and to reduce genetic variation  of the loci in our analysis of natural populations of zebrafish (lab strains excluded), which is a lower proportion than observed in several other EST-based SNPs studies to date  and therefore represents an example of both of these lines of evidence. Third, a selective sweep is expected to cause a valley of reduced genetic variation and elevated LD around the target of selection  also showed this pattern, which suggests that depressed genetic variation surrounding a locus under balancing selection can persist long enough for LD to decay.Of the 71 outlier loci from natural populations, multiple lines of evidence can be used to identify those that are most likely to have been influenced by selection. First, outliers associated with nonsynonymous substitutions may be the direct targets of selection. For example, the nonsynonymous divergent outlier SNP associated with glucose-fructose oxidoreductase activity may be associated with locally adapted metabolic differences among wild populations. The nonsynonymous low outlier SNP associated with the arginase 2 gene have been differentially selected in the lab and wild. Outlier analyses are susceptible to false positives because of bottlenecks and enhanced drift  that face different swimming demands  and therefore offer opportunities for detailed analyses of adaptive evolution under a variety of ecological contexts. For example, zebrafish occur in a gradient of moving and still bodies of water (


Tylonycteris pachypus) is a nonmigratory Asian bat species that roosts in small groups within the internodes of bamboo stems and the habitats are fragmented. Our previous parentage analyses revealed considerable extra-group mating in this species. To assess the spatial limits and sex-biased nature of gene flow in the same population, we used 20 microsatellite loci and mtDNA sequencing of the ND2 gene to quantify genetic structure among 54 groups of adult flat-headed bats, at nine localities in South China. AMOVA and FST estimates revealed significant genetic differentiation among localities. Alternatively, the pairwise FST values among roosting groups appeared to be related to the incidence of associated extra-group breeding, suggesting the impact of mating events on fine-scale genetic structure. Global spatial autocorrelation analyses showed positive genetic correlation for up to 3 km, indicating the role of fragmented habitat and the specialized social organization as a barrier in the movement of individuals among bamboo forests. The male-biased dispersal pattern resulted in weaker spatial genetic structure between localities among males than among females, and fine-scale analyses supported that relatedness levels within internodes were higher among females than among males. Finally, only females were more related to their same sex roost mates than to individuals from neighbouring roosts, suggestive of natal philopatry in females.Population genetic structure has important consequences in evolutionary processes and conservation genetics in animals. Fine-scale population genetic structure depends on the pattern of landscape, the permanent movement of individuals, and the dispersal of their genes during temporary mating events. The lesser flat-headed bat ( Numerous studies of wild animal populations have revealed the importance of behavioral and ecological traits in shaping population genetic structure Bats are a highly speciose (more than 1100 species) and ecologically diverse group in which contrasting behaviours show good correspondence to different patterns of dispersal and population genetic structure Tylonycteris pachypus (Chiroptera: Vespertilionidae) is one of the smallest mammals in the world Gigantochloa scortecluni) and normally forms small groups, where females typically produce twins P>0.05), although males appeared to travel over longer distance  The lesser flat-headed bat, T. pachypus revealed a similar pattern of individual movements T. pachypus in South China, in which offspring belonging to full sibships were often found to be sired by males not present in the group at the time of sampling. More generally, approximately half (43.5%) of sets of twins were of mixed paternity while the other half shared the same fathers Mark-recapture data from adult T. pachypus resembles the commonly seen mammalian system of female natal philopatry combined with male-biased natal dispersal, then (i) mtDNA haplotypes distribution would show a different pattern between the sexes T. pachypus.To further characterize this system of breeding and social structure, and assess its consequences for wider gene flow, we sequenced adults from the same study populations at mtDNA ND2 gene, and combined our results with existing multi-locus microsatellite genotype data. We hypothesized that if This study was carried out in strict accordance with the guidelines of Regulations for the Administration of Laboratory Animals . All bats were released immediately the wing membrane tissue biopsies (diameter: 3 mm) were taken. We obtained approval for this study from the Guangdong Entomological Institute Administrative Panel on Laboratory Animal Care. Permission from the landowners was also obtained.Tylonycteris pachypus were sampled from Chongzuo district, Guangxi Province in South China . Details of amplification and sequencing were also provided in Hua et al. FIS) and among (FST) possible subpopulations were evaluated in T. pachypus. FIS, allelic richness corrected for unequal sample size, the observed and expected heterozygosity were calculated with microsatellite data in FSTAT v2.9.3.2 P values were adjusted with Bonferroni correction for multiple comparisons FST among localities were estimated and the significance was tested using 1000 permutations in GENETIX with both microsatellite and mtDNA data . Overall population differentiation was tested by exact G-test FST between roosting groups with extra-group paternity  and those with no extra-group paternity with Mann-Whitney test. In this analysis, only roosting groups comprising five or more individuals were analyzed and our published data on paternal sibships results was used F-statistics designed to quantify the partitioning of genetic diversity within (FST/(1−FST) and Log10 (geographic distance) between localities was tested with Mantel test in IBDWS v3.23 Analyses of molecular variance (AMOVA) from both microsatellite and mtDNA data were used to estimate the genetic variation among localities, among roosting groups within each locality and within localities as implemented in ARLEQUIN v3.01 FIS, FST, and HS, each based on pooled samples from all localities FST due to similar allele frequencies, yet a higher FIS due to a deficit of heterozygosity resulting from the combined sampling of residents and immigrants (Wahlund effect). We also calculated the probability of corrected mean population assignment (mAIc) and its variance (vAIc) vAIc, the ratio), 10000 randomizations were used. All analyses were performed in FSTAT program.Several specific tests of male-biased dispersal were also carried out with microsatellite data. We compared sex-specific estimates of T. pachypus, we estimated pairwise relatedness (R) within roosting groups with microsatellite data To offer new insights into sex-biased dispersal in r provides a measure of the genetic similarity between pairs of individuals whose geographic separation falls within the specified distance class. A correlogram is produced that shows the autocorrelation coefficient r as a function of distance class. To assess the genetic similarity and spatial distance, the observed r values are compared to expected values (rp) based on 1000 permutations. When r is significantly larger than rp, positive spatial genetic structure is accepted for specified geographic distance class.Spatial autocorrelation analysis to measure the genetic structure across the entire sampling site with microsatellite data was carried out in GENALEX v6 r values In this study, global spatial autocorrelation was measured for all 54 roosting groups of bats within the nine localities. Pairwise linear geographic and squared genetic distance matrices were calculated in GENALEX. Linear Euclidean distances were measured from latitude/longitude coordinates of samples. Genetic distances for codominant data were calculated under the assumption of statistical independence across loci according to the method proposed by Peakall et al. Tylonycteris pachypus, we analyzed genetic data from nine localities comprising 69 males and 227 females. We detected a total of 279 alleles, ranging from three to 21 alleles per locus at the 20 microsatellite loci scored. Tests for Hardy-Weinberg equilibrium within localities across all loci indicated six out of 180 tests were significant for heterozygote deficit after Bonferroni correction (lower than expected by chance when α = 0.05). In total, 146 out of 1710 loci pairs deviated from linkage equilibrium (P<0.05), but only six were detected after Bonferroni correction (P<2.9×10−5). High genetic variation within localities was detected in terms of both observed heterozygosity (HO: 0.668 to 0.770) and expected heterozygosity  and pairwise FST ranged from 0.009  to 0.064 . When the population differentiations were restricted between pairs of populations within the same region, all pairwise FST were less than 0.03, suggesting higher gene flow among localities within regions. All pair of localities indicated significant divergence .Analyses of microsatellite data revealed significant genetic differentiation across all localities . The variance among regions contributed to 2.25% variation . AMOVA with ND2 sequences demonstrated similar results  in allele frequencies was within localities . 2.33% w results .FST/(1−FST) and Log10 (geographic distance) showed strong signal of isolation by distance pattern when all localities were included . Given the large geographic isolation and discontinuity between Longzhou region  and Ningmin region .Mantel test with pairwise T. pachypus, FST values among roosting groups comprising five or more adults within localities were also evaluated. We found the FST values with extra-group paternity were significantly lower than FST with no extra-group paternity . Furthermore, the pairwise relatedness values within internodes were greater than zero for both females and males . In spite of this, we cannot detect the difference of relatedness levels between males within internodes and among internodes . Distributions of relatedness values for different sexes demonstrated females were more related to each other than to males within roosting groups , with the highest relatedness was in TL locality  within internodes was significantly greater than the null expectation of zero  and mAIc values . The tests of FIS, HS and vAIc were not statistically significant .Five one-tailed tests for male-biased dispersal were run separately. Females showed significantly greater r  of T. pachypus makes its flying performance a lot more limited compared to many other bat species T. pachypus is relatively limited and the dispersal distance is considered short Usually, in the roosting internodes of T. pachypus has revealed that the number of migration events between localities was very low  Mark-recapture data collected in natal localities of FST is lower than other animals when assessed with nuclear genetic markers FST value of T. pachypus was 0.024 among all localities, suggesting high gene flow, but still dispersal barriers among localities. On the other hand, all pairwise FST were significant. The results indicated that although the shortest geographic distance was less than 1 km among localities, there still existed strong genetic substructure in most cases. Significant genetic differentiation on a fine scale support our expectation that fragmented habitat combined with the specialized social organization of T. pachypus results in the limited movement of individuals among localities in this species.Usually, in flying, migratory or ocean-dwelling species , the population differentiation estimated by Myotis septentrionalis, Rhinolophus monoceros and Nyctalus noctulaMyotis bechsteiniiSaccopteryx bilineata, also a nonmigratory bat, showed female-biased dispersal T. pachypus, strong male-biased dispersal was detected from FST and mAIc statistics. According to Goubet et al. FIS, HS and vAIc values between sexes.In most mammals and birds, dispersal is biased towards one sex . Male-biased dispersal has been found in some migratory bat species, such as T. pachypus is also expected to limit the gene flow among localities (bamboo forests). Most bamboo forests were small and separated from each other by agricultural land or urban development, etc. T. pachypus. In addition, because of the economy value, many bamboo forests were felled. All of these suggest that T. pachypus is highly reliable on their habitats (bamboo forests), the populations are patchy, and in some regions, likely to be suffering from habitat loss In the common frog populations, Johansson et al. found more pronounced genetic differentiation in the fragmented than in the continuous habitat of this species. On the other hand, the mean values of the fitness related traits and the amount of microsatellite variation was positive related. The results suggest the potential importance of habitat fragmentation in limiting gene flow and impairing future adaptive potential of natural populations In bats, fine-scale genetic structure was unexpected, given many bats are highly vagile and/or migratory, but the multivariate spatial autocorrelation methods such as the multilocus, multi-allele method have proved highly sensitive for detecting unexpected fine-scale genetic structure r values among intra-locality females were a little weaker than that for females, but not significantly different . However, a significant difference between females and males was detected. The extent of genetic structure in females (4.9 km) was greater than that in males (864 m). The more biased dispersal in males reduced the genetic correlation with spatial distance, even within localities. Our results further confirmed the influence of male bias in dispersal behaviours on the local pattern of genetic structure in T. pachypus.Spatial correlation analysis of females revealed similar results to that of all individuals combined. The Plecotus auritus, Myotis bechsteinii, M. nattereri, have been reported to mate at swarming sites during autumn Many nonmigratory bats species, such as T. pachypus does not follow the swarming site mating pattern, their mating events may occur mostly in bamboo internodes T. pachypus have demonstrated that related females preferred to mate with the specific individual males FST among roosting groups, our results revealed that the genetic differentiation among groups was lower significantly when extra-group mating occurred Click here for additional data file.