In many areas, ungulate populations are now largely confined within a network of loosely connected protected areas. These metapopulations face gene flow restriction and run the risk of genetic diversity erosion. In this context, we assessed the “genetic health” of free ranging southern African Cape buffalo populations (S.c. caffer) and investigated the origins of their current genetic structure. The analyses were based on 264 samples from 6 southern African countries that were genotyped for 14 autosomal and 3 Y-chromosomal microsatellites.African wildlife experienced a reduction in population size and geographical distribution over the last millennium, particularly since the 19Ar) of 7.217, average genetic diversity over loci of 0.594, mean private alleles (Pa) of 11), low differentiation, and an absence of an inbreeding depression signal (mean FIS = 0.037). The third (S) cluster, a tiny population enclosed within a small isolated protected area, likely originated from a more recent isolation and experienced genetic drift . This study also highlighted the impact of translocations between clusters on the genetic structure of several African buffalo populations. Lower differentiation estimates were observed between C and N sampling localities that experienced translocation over the last century.The analyses differentiated three significant genetic clusters, hereafter referred to as Northern (N), Central (C) and Southern (S) clusters. The results suggest that splitting of the N and C clusters occurred around 6000 to 8400 years ago. Both N and C clusters displayed high genetic diversity . Management practices of African buffalo populations should consider the micro-evolutionary changes highlighted in the present study.The online version of this article (doi:10.1186/s12862-014-0203-2) contains supplementary material, which is available to authorized users. In the context of recent global changes, the combined effects of human-induced factors (human population growth and subsequent habitat degradation/land conversion and cattle-borne diseases) and climatic fluctuations have had a marked impact on the population size and geographical distribution of African wildlife ,2. ConseSyncerus caffer caffer - Sparrman 1779) was chosen as a model to study the impact of population fragmentation on the genetic diversity of large mammals in southern Africa. The African Cape buffalo is a key species in African savanna ecosystems due to its high contribution to the herbivore biomass. It is also a major attraction for the wildlife-viewing and hunting industries [The southern African Cape buffalo  and sanitary measures (e.g. culling) adopted to control animal diseases tended to increase the fragmentation of buffalo populations [In the early 20epidemic . Howeverulations ,28. Receulations . The redulations . In addiulations . For exaulations . At thisulations ,29,30 haulations .Despite those findings, few studies have investigated the impact of African Cape buffalo population size reductions and gene flow disruption in southern African sub-regions and at a large geographical scale. Available data are mainly related to eastern African populations and/or a limited number of protected areas (PAs) ,19,22,31Syncerus caffer caffer) and investigated the causal factors of their current genetic structure. We thus used 14 autosomal and 3 Y-chromosomal microsatellite markers to analyse 264 buffalo samples from 19 different locations in southern Africa.In this study, we assessed the “genetic health” of free ranging southern African Cape buffalo populations  obtained authorization from the Department of Conservation of the Gorongosa National Park (GNP- Mozambique); CIRAD  obtained the relevant permits from the parks and wildlife management authorities in Bostwana, Mozambique, South Africa and Zimbabwe; Mario Melletti obtained the relevant permits to export samples from the wildlife management authorities of Zimbabwe and the University of Pretoria (South Africa) from the Hluhluwe-iMfolozi National Park. All partners obtained the ethical approval from their institution for the sampling procedure. The animal sampling protocols did not induce pain or distress according to the Animal Care Resource Guide (USDA category C). To facilitate the procedure, sampling of peripheral tissue (i.e. ear) or hair required the capture of buffalo through chemical immobilisation. The animals were released under veterinary supervision in favourable conditions. A total of 264 TGLA227, TGLA263, ETH225, ABS010, BM1824, ETH010, SPS115, INRA006, BM4028, INRA128, CSSM19, AGLA293, ILSTS026, DIK020- described by [UMN1113, INRA189, UMN0304- described by [NMales = 73). All those microsatellites were selected because they displayed good quality and high polymorphic amplification. The forward primer of each locus was 5’-end labeled with a fluorescent dye. Four multiplex sets were elaborated based on size limitations and amplification specificity: set 1 , set 2 , set 3 , set 4 . PCRs were carried out in 10 μl volumes containing between 0.1 and 0.2 μl of each 10 μM diluted primer (forward and reverse), 5 μl Multiplex PCR Master Mix (QIAGEN) and 2.5 μl DNA. Amplifications were performed in thermal VWR Unocycler using an activation step (94°C/15 min) followed by 30 cycles  and final extension step at 60°C for 30 min. PCR products were genotyped on a Applied Biosystems 3130XL Genetic Analyzer using 2 μl of amplified DNA, 12 μl of Hi-Di formamide and 0.4 μl of GeneScan-500 (LIZ) size standard (Applied Biosystem). Length variation determination was performed using GENEMAPPER 4.0 (Applied Biosystems).The 264 samples used in this study were genotyped at 14 variable autosomal microsatellite loci . In ribed by ) (Additihttp://genepop.curtin.edu.au/). For both analyses, the Markov chain method was used with 1000 dememorizations, 1000 batches and 1000 iterations per batch. Fisher’s method for combining independent test results across SL and loci was used to determine the statistical significance of the test results.MICRO-CHECKER 2.2.3  was usedHE) and observed (HO) heterozygosities for each SL using both ARLEQUIN version 3.1 [DEST estimator [FST statistics [http://www.ngcrawford.com/django/jost/) with 1000 bootstrap replicates [DEST appeared to be more accurate than GST and its derivatives [FST was estimated for comparison with DEST, but also because it has been suggested to be more appropriate when both the sample size and number of applied loci are relatively low [F-statistics, was assessed using an analysis of molecular variance (AMOVA) performed with ARLEQUIN 3.1. The populations for the AMOVA analysis were defined according to the clustering results obtained with STRUCTURE 2.3. Allelic richness  was calss  and different genetic indices of differentiation (mean pairwise DEST and mean pairwise FST) and diversity  for each SL were also performed to assess the effect of confinement within small enclosed protected areas (details for each SL are displayed in Table A linear regression between patch areas (expressed as log kmK number of groups so that each group is homogeneous. The number of clusters (K) was inferred by running 10 iterations for each K value from 1 to 20 using an admixture model with a burn-in of 1 × 105 and MCMC repetition values of 1 × 106. As the STRUCTURE software does not provide a statistical indication of the most likely K, results of the 10 iterations for each K value were summarized and averaged using CLUMPP 1.1.2 [K value that best fits the structure of the data set was revealed by the increasing likelihood of the data. It is chosen as the smallest value of K capturing the major structure in the data. The optimal number of clusters was then assessed based on correction as defined by Evanno [Bayesian clustering of microsatellite genotypes was performed using STRUCTURE 2.3, pooling individuals together independently of the spatial sampling, as described in the manual ,53. ThisPP 1.1.2 . The K vy Evanno .Visual oy Evanno . ClusterUMN0304, UMN1113 and INRA189) located on the Y-chromosome were selected for complementary analyses. Haplogroups were defined as a combination of the haplotypes defined for each of the three Y-chromosomal microsatellites, as described in the study of van Hooft et al. [1, n2, n3} into account. Haplotypes for each microsatellite had to be defined because they could appear as multicopies on the Y-chromosome [Moreover, three male-specific non-recombining microsatellites , representing the proximity between each individual genotype in a 2D plot was performed based on the microsatellite allele frequencies using R software version 2.15.2 (R Development Core Team 2008) and the ade-4 package  using thFor all subsequent analyses, software were run on two distinct microsatellite matrices: (i) a first one that included all individuals pooled together in each cluster determined by STRUCTURE 2.3, and (ii) a second matrix that only included individuals displaying a probability of belonging to each of the clusters over 0.9 (STRUCTURE 2.3). This measure was necessary to avoid bias associated with the high number of translocations that have taken place over the last decades in southern Africa see .S. c. caffer in southern Africa was inferred using coalescent-based DIYABC 1.0.4.45 beta software [The evolutionary history of software . This prsoftware . Only thAlternative biogeographic divergence scenarios were inferred and compared using the DIYABC software package. In-depth information about scenario building procedure and alternative competitive scenario representations are available as additional file  to estimate the signal strength. The value obtained (Dσ2) was inversely correlated with the IBD strength. The geographic distance was calculated using the logarithm of the Euclidean distance on GPS coordinates. âr statistics were used to represent the genetic distance between pairs of individuals [Similarly, an isolation by distance (IBD) analysis among and within the three clusters defined with STRUCTURE 2.3  was perffined by ), were cividuals . We testividuals .M)), and the confidence intervals. This analysis was developed by measuring similarities among our three clusters based on optimized FST-based measures. This software employs a Metropolis-Hastings Markov chain Monte Carlo algorithm (MH) to search through genealogies, and a likelihood ratio test to obtain estimates of theta (Θ) and M. It assumes a constant Θ for each population but a variable Θ between them (pairwise migration rate estimates). MIGRATE 3.4.4 was used with default parameters, with the first five runs including FST- based statistics of Θ and M involving 10 short chains with 10,000 sampled genealogies and three long chains with 100,000 sampled genealogies. A second analysis with five additional runs was performed with the parameter estimates of Θ and M from the previous run as starting values. Run convergence was checked by computing the MCMC autocorrelation, effective sample size and by visual comparison of the consistency of the results of each of the independent analyses. The headcount of immigrants per generation was calculated according to xNem = M*Θ, with x being the inheritance scalar, set at 4 for diploid species, Ne the effective population size, and m the mutation rate per generation and per locus. The effective population size was assessed assuming a mean mutation rate within a range of 4.5*10−5 to 15*10−5 per generation.Furthermore, MIGRATE 3.4.4 -66 was uΔK method, as described by Evanno [ΔK was for K = 3 . These clusters were considered as different “populations” in the subsequent analyses. The proportion of each cluster within every sampled locality is represented in Figure The STRUCTURE 2.3 software output was interpreted using the y Evanno . The higNote that most clusters appeared to be admixed Figure . This coet al. [UMN1113 haplotype, n UMN0304 haplotype, n INRA189 haplotype}  was found to show significant linkage disequilibrium in two populations . No single microsatellite marker exhibited an especially high number of private alleles  were low to moderate but significant for all three clusters  for Mana Pools  were all within same order of magnitude. The median (95% CI) of the estimated time since divergence (T1) between the N and C clusters was evaluated at about 1200 generations earlier . The effective population size estimates for the C and N clusters reached a mean of 4700 and 6400 individuals, respectively /4, with ΘS = 0.38, ΘN = 4.40, ΘC = 1.81 and MC→S = 4.17, MS→N = 0.84, MC→N = 2.56, MS→C = 1.06, MN→C = 5.01, MN→S = 1.67. The effective number of immigrants per generation between clusters was low, from less than one migrant per generation (each 5–7 years) coming from the Southern towards the Central/Northern clusters, and vice versa , to approximately two migrants between the Central and Northern clusters (NmN→C = 2.27 and NmC→N = 2.81). The results between the two runs on the separate microsatellite matrices were similar.Migration rate could not be assessed with the IM software . The analysis was not performed on the third cluster because all samples in this cluster originated from a single protected area, i.e. Hluhluwe-iMfolozi. The signal in the Central cluster was not strong, as indicated by the relatively high Dσ2 value and by the slight slope of the regression of pairwise genetic statistics against the log distance. The slight regression slope indicated that the genetic distance between pairs of individuals was weakly correlated with the geographic distance between them. The signal was stronger in the Northern cluster. The absence of significant signal between the three clusters showed that the cluster generation was not driven by the geographic distance. Concerning the linear regression performed between the log habitat area and the different genetic differentiation indices and diversities for each SL, the R2 (coefficients of determination) were close to 0. This indicated an absence of any linear relationship between the log habitat areas and the different genetic indices . Graphic representations are available as supplementary information , during which forests and woodlands expanded -71, towaet al. [In addition to the climate hypothesis, human populations likely had an impact on the buffalo population fragmentation process. Indeed, according to Heller et al. , during et al. . Around et al. -78. By 1et al. . In thisth century due to human and cattle population growth. A good example is central Zimbabwe, a plateau that offers excellent grazing between the Zambezi and Limpopo rivers. In this area, wild herbivores, including buffalo, are generally considered as disease reservoirs and were thus controlled or even eradicated over the last century to protect cattle on commercial farms.The divergence between the N and C buffalo clusters could be explained by this break of continuity in the landscape matrix due to both aridification and progressive human/cattle population growth, and/or by potential overhunting. At the regional level, the combined effects of rapidly expanding human activities and sudden climatic changes may have been primary forces that fragmented a previously panmictic population and shaped the current genetic structure of African buffalo. In addition, N and C clusters are hypothetised to share a common ancestral population. The Y-chromosomal minimum spanning reconstruction identified a haplogroup, namely {5, 5, 7}, that was present in the three clusters and occurred in a central position, while displaying the highest frequencies of appearance (0.123). Its presence in all clusters could support our assumption of a common ancestral panmictic buffalo population that recently experienced fragmentation. The isolation process persisted and amplified during the 20A third potential explanatory factor of the isolation of N and C clusters concerns the spatial arrangement of the water system. Indeed, the African buffalo is a highly water dependent species and the large-scale distribution and regime of rivers may well explain the observed genetic structure. This seems possible since the ranges of N and C clusters roughly span the Zambezi and the Limpopo river basins, as well as Rovuma, Pungoe, Save and other river basins. Colonisation of southern Africa by buffalo from an eastern core (Uganda ) may havWithin the three identified clusters, discrepancies between the genetic affiliation of some individuals and their geographical origins were observed Figures  and 5. NDEST estimates obtained between Malilangwe (C cluster) and Hwange/Victoria Falls/Chobe/Mana Pools complex (N cluster). In addition, translocation would also explain the very low DEST values between Hwange (N cluster) and all C sampled localities, except for Gorongosa and Limpopo. Buffalo from Hwange, Lusulu (south of Chizarira) and Matusadona were selected to form herds free of foot-and-mouth disease, which is transmissible to cattle. These buffalo were then bred and subsequently transferred to many regions of Zimbabwe, including commercial wildlife properties adjacent to PAs (protected areas) within this country  [http://www.iucnredlist.org/). The ecological and evolutionary consequences of resource management decisions are further discussed in the review of Ashley et al. [Translocations over the last century seem to best explain the discrepancies observed in the identified genetic pattern highlighted in this study. In fact, records indicate that buffaloes from Malilangwe (C cluster) were primarly, but not exclusively, stocked with buffaloes from Hwange (N cluster) . Moreover, individuals from three different localities of the N cluster  were moved to Gonarezhou (C cluster) . Save Valley, Bubye Valley and Nuanetsi buffalo populations, all located near Gonarezhou and Malilangwe (C cluster), were restocked from Hwange  Figure . Consequ. comm.) . The pre. comm.) . Neverth. comm.) . This coy et al. , advocatN = ± 4000 [FST and DEST indices between the Southern cluster and the two other clusters had high values (more than twofold higher than between the N and C clusters), suggesting substantial isolation of the Southern cluster.Hluhluwe-iMfolozi is a unique protected area as it has been completely isolated for about 100 years. Only 75 individuals were reported in this region in 1929, and this population may have been reduced partly as a result of the Nagana campaigns against the disease carrying tsetse fly (1919–1950) . The cur= ± 4000 ) directlFIS values two- and fourfold higher than for the C and the N clusters, respectively. The gene flow disruption due to a recent isolation (±17 generations) was likely responsible for the observed genetic erosion, with signs of inbreeding depression (FIS). In this setting, future increases in genetic drift within the S population will probably lead to a more pronounced loss of genetic diversity as compared to the current situation, although faintly detectable by now. O’Ryan [Pa = 2), while eight were noted in the C cluster. The high genetic variation that is still being recorded may be explained by a historically large population size, with current buffalo population sizes still above the critical threshold (as discussed hereafter).Nevertheless, and surprisingly, the Hluhluwe-iMfolozi buffalo population still harbours high estimated allelic richness, heterozygosity and genetic diversity. Note, however, that all of those estimates were lower as compared to those of the two other clusters, with . O’Ryan  similarlPa = 13), heterozygosity and genetic diversity estimates, while displaying the lowest inbreeding coefficient. This may have been the result of a wider geographical distribution and higher population size estimates compared to the two other clusters (see next section). Moreover, in contrast with the two other clusters, the N cluster did not display any recent bottleneck signal. The C cluster (which includes the Kruger and surrounding protected areas) is characterised by a significant signal of recent bottleneck. Buffaloes in the Kruger area were highly affected by rinderpest during the last decade of the 19th century, with a high mortality rate and a small number of survivors reported in 1902 [et al. [th century, all other studies conducted on that topic demonstrated that the outbreak did not seem to impact the genetic variability of this population [et al. [FST and DEST values for the pairwise sampling localities of the C cluster . This suggests the possibility of high gene flow within each of these clusters, as proposed hereafter. Nevertheless, in the N cluster, the mean DEST were sixfold higher than in the C cluster, which may be partly explained by the higher DEST between Niassa and all the other sampling sites (mean 0.050). This may be attributed to the geographical distance separating Niassa from all other sampling localities, as indicated by the significant IBD.Within each of the N and C clusters, the SL showed a low level of genetic differentiation and high heterozygosity, comparable to the values obtained in previous studies on the African buffalo. This extent of genetic diversity in African buffalo is particularly high as compared to estimates in other large African savanna ungulates ,15,22. ADEST values of the Marromeu complex, i.e. a network of protected areas  included within the N cluster, as compared to the other SLs of the N cluster (mean DEST = 0.076) but also to SLs of the two other clusters (mean DEST = 0.100). The Marromeu complex hosts a high number of buffaloes  that haividuals . These aividuals , indicatNe) estimates are generally much smaller than the population census size (Nc) [Ne ranges between 10 and 30% of the Nc [Ne of the N cluster was estimated to range between 7000 and 25,000, those of the S cluster between 600 and 2000, and those of the C cluster between 3000 and 10,000. The corresponding expected Nc is therefore estimated to range from a few tens of thousands to several hundreds of thousands according to the studied regions . Aerial counts, although potentially subject to major bias [Nc estimated ranges of each of the clusters. This suggests that the southern African buffalo populations are relatively healthly, as also supported by the high level of genetic diversity observed within each of the genetic clusters studied. Moreover, the total estimated population census sizes (Nc) reported hereafter represent underestimations of the real current population sizes because all protected areas in southern Africa were not sampled in this study, and also because buffaloes roam outside of protected areas  ,87. Consf the Nc ,9. The Njor bias ,89, werejor bias ,34-38,90In order to maintain historical levels of genetic variability at microsatellite loci for the purpose of long-term conservation, movements between confined protected areas should be facilitated. Hence, the recent development of transfrontier conservation areas (TFCAs) in southern Africa aims at establishing vast ecosystems encompassing protected areas of various statuses  and communal land so as to fulfil development and conservation objectives. These entities are designed to promote ecological connectivity between protected areas (e.g. plan to create the Sengwe Corridor between Kruger and Gonarezhou PAs in the Great Limpopo TFCA) to ease wildlife dispersal and achieve critical population sizes . RegularThe present study supports assumptions that both ancient (over few thousand years ago) and recent (over 100 years ago) population fragmentations had an impact on the genetic structure of the African buffalo, leading to the identification of three distinct clusters in southern Africa. Our results highlight low levels of genetic differentiation within and between each cluster/sampling locality, each with high genetic diversity and low inbreeding coefficient estimates. Even though differentiation was low, migration between clusters was shown to be relatively limited. Connectivity between protected areas is therefore necessary to ensure the conservation of African mammal diversity, as supported by the transfrontier conservation area (TFCA) initiative. Moreover, prior to any translocation operation, it is essential to consider the environmental, behavioural, genetic and conservation specificities in order to mitigate further genetic erosion.http://datadryad.org/).The data set supporting the results of this article is available in the Dryad repository, doi:10.5061/dryad.8f409 (


Larimichthys polyactis) were investigated by genotyping 15 highly polymorphic microsatellites in 337 individuals of 15 geographic populations collected from both spawning and overwintering grounds. Outlier analyses indicated that the locus Lpol03 might be under directional selection, which showed a strong homology with Grid2 gene encoding the glutamate receptor δ2 protein (GluRδ2). Based on Lpol03, two distinct clusters were identified by both STRUCTURE and PCoA analyses, suggesting that there were two overwintering aggregations of L. polyactis. A novel migration pattern was suggested for L. polyactis, which was inconsistent with results of previous studies based on historical fishing yield statistics. These results provided new perspectives on the population genetic structure and migratory routes of L. polyactis, which could have significant implications for sustainable management and utilization of this important fishery resource.The spatial distribution of genetic diversity has been long considered as a key component of policy development for management and conservation of marine fishes. However, unraveling the population genetic structure of migratory fish species is challenging due to high potential for gene flow. Despite the shallow population differentiation revealed by putatively neutral loci, the higher genetic differentiation with panels of putatively adaptive loci could provide greater resolution for stock identification. Here, patterns of population differentiation of small yellow croaker ( Larmuseau et al. [Pomatoschistus minutus), suggesting local adaption to water turbidity between northern and southern North Sea populations. Various environmental transitions, such as salinity and temperature gradients, have been currently demonstrated to be associated with local adaptation of populations to their native environment [Marine fishes are generally expected to show much lower geographical differentiation than their freshwater counterparts, which is understandable in light of lacking geographical barriers within marine habitats as compared to freshwater habitats , 2. In met al.  investigu et al.  detectedironment , 14. In ironment  as well ironment , 17 may ironment , 19.Larimichthys polyactis , is an important fishery species endemic to the Northwest Pacific, inhabiting coastal waters across the Bohai Sea, the Yellow Sea and the East China Sea [L. polyactis is an asynchronous and multiple spawner with relatively long-distance seasonal migration [L. polyactis are always located in estuaries or mixed areas of both low and high salinity near the shore area, while their overwintering grounds are located in the off shore area [L. polyactis migrate to the spawning grounds in early April and start spawning from late April to June with a gradual spatial and temporal variation among different geographic populations [L. polyactis were presumed to migrate towards natal sites to spawn and two geographic populations existed, one in the Northern Yellow Sea and Bohai Sea, and the other in Southern Yellow Sea and East China Sea, respectively [L. polyactis had severely declined since the 1970s in China due to anthropogenic factors such as overfishing, seawater pollution, as well as ocean current and water masses changes [L. polyactis, such as miniaturization, domination of low age groups, increases in growth rate but decreases in body length and weight, etc. [L. polyactis, which have important consequences for conservation, effective management and sustainable utilization of this important fishery resource.The small yellow croaker, hina Sea . L. polyigration . Spawninore area . Overwinulations . After sulations , 23. Preectively –24. As o changes –28. Compht, etc. . TherefoL. polyactis has been the subject of many studies regarding population differentiation, studies on population dynamics, morphology, anatomy, and population genetics obtained discrepant or even conflicting results [L. polyactis, genetic evidence for correlations between sea currents and migration or distribution of L. polyactis has not yet been reported in literature. Recent genetic studies based on RAPD and AFLP [L. polyactis, while studies based on mitochondrial (mtDNA) [L. polyactis. In contrast to most previous studies, Wang et al. (2013) [L. polyactis could add to the understanding of the population structure and thus serves as important information for establishing conservation strategies. In addition,it is emphasized that the accuracy of genetic mixed-stock analysis (MSA) using microsatellite markers for marine fish species is mainly limited by the relatively low level of genetic divergence [L. polyactis is still needed to assess patterns of population genetic divergence throughout its distribution and to resolve discrepant results.Although  results –34. Prev results , 24. Mea results , 24. It  results , 17, 35.and AFLP –32 demon (mtDNA) , 34 and  (mtDNA)  showed nvergence . TherefoL. polyactis populations. Our sampling scheme covered nearly the entire distribution range of L. polyactis including both nearshore spawning and offshore overwintering grounds, representing an open system experiencing multidirectional migration between relatively isolated populations across several environmental gradients. Accordingly, this sampling scheme enhanced our capability to infer processes shaping population structure and migration routes of L. polyactis. Overall, results of the present study could provide useful insights into delineating fine population genetic structure and uncovering possible processes leading to adaptive divergence in L. polyactis, which should have important implications for the management and conservation of this fishery species.In the present study, a total of 15 novel polymorphic microsatellite markers were used to assess the levels of genetic diversity and patterns of population genetic divergence of L. polyactis is not protected by Chinese law or by any of the countries where and when sampling was performed. It is a commercially harvested species in China and the other Northeast Asian countries. The samples were collected by trawling and fishes were already dead when collected.The field studies did not involve any endangered or protected species. L. polyactis adult individuals were collected from 15 geographical locations  originally developed by Liu et al. . The 15 et al. . Fluoresion file .PIC), observed heterozygosity (HO) and expected heterozygosity (HE) were calculated using the Excel Microsatellite Toolkit [FIS, a measure of the extent of nonrandom mating), and allelic richness  were computed in FSTAT 2.9 [For each population, genetic variation indices including polymorphism information content  of 0.05, and assuming a stepwise mutation model. Moreover, it has been demonstrated that the results based on LOSITAN may be biased by the presence of strong hierarchical population structure leading to increased level of false positives [et al. [FIDST2  implemenositives . Therefoositives  as descr [et al. , which w [et al. .FST values were estimated based on neutral microsatellite dataset and locus Lpol03 and significance was adjusted using the Benjamini–Yekutieli multiple testing correction [FST was calculated by GENEPOP 4.3. In order to compare the patterns of genetic structure between neutral and possible selected loci, principal coordinate analysis (PCoA) based on gene frequency data was performed using GenAlEx 6.5 [Pairwise rrection .The globAlEx 6.5 . The sofAlEx 6.5 , 52 was AlEx 6.5  implemenAlEx 6.5 . In ordeBottleneck software ver. 1.2.02  was usedAR per sample was highest in NEA (11.824) and lowest in DY (10.085). The average of expected heterozygosity ranged from 0.862 (DY) to 0.900 (NEA) and the average of observed heterozygosity varied from 0.870 (DY) to 0.924 (CJK), respectively. The average of PIC values ranged from 0.814 to 0.869  which encodes the glutamate receptor δ2 protein (GluRδ2), an excitatory receptor for glutamate. The Lpol03 with a fragment of 454 bp was found to be located in the intron of Grid2 gene in a genomic scaffold of Larimichthys crocea.Two loci (Lpol03 and Lpol04) were identified as outliers at the 99.5% confidence interval after FDR correction, with ectively . Neverthectively . The conFST among all populations based on neutral loci and the outlier locus Lpol03 were 0.0036 and 0.0915, respectively. Pairwise FST values among samples based on neutral loci ranged from -0.0084 (between NEA and NEB) to 0.0184 (between SYC and CJK). Moreover, most FST values between SYC and other sampling populations were statistically significant after Benjamini–Yekutieli correction . Pairwise FST results based on the outlier locus Lpol03 exhibited an overall higher value than those based on neutral loci, suggesting the existence of genetically differentiated populations of L. polyactis under directional selection. PCoA plotting based on neutral loci indicated no clear population structure among populations and a genetic distinctiveness of sample SYC  respectively. Conversely, with populations grouped according to geographic origins, the AMOVA showed nonsignificant genetic differentiation among groups (P = 0.112 and 0.131) respectively , potentially indicating the persistence of high-level gene flow.Genetic bottleneck analysis showed no significant heterozygotes excess in all of the 15 populations under the two-phase model (TPM) and the stepwise mutation model (SMM), only the test based on the infinite alleles model (IAM) identified several excess of heterozygosity . Given tL. polyactis might be attributed to high mutation rate and large population size [L. polyactis [L. polyactis. However, it should be noted that differentiation between groups clustered by sampling time was supported by the AMOVA . No isolation by distance was detected among populations of L. polyactis, which was also in accordance with previous results using both neutral and outlier microsatellite loci in Wang et al. [It has been suggested that genomic regions showing high divergence may represent signatures of local adaptation or genomic islands of divergence recalcitrant to gene flow , 67. In analysis , 36. Cong et al. . The patet al. found that the expression of zebrafish GluRδ2 is selective for cerebellum-like neural wiring [L. polyactis, providing support to the validation of the outlier loci detected. Therefore, these results led us to conclude that a cryptic adaptive population structure might exist in populations of L. polyactis, hence illustrating that local selection pressures might override homogenizing effects of high level gene flow [The genomic sequence containing the outlier Lpol03 showed a strong homology with Grid2 gene encoding the glutamate receptor δ2 protein (GluRδ2), an excitatory receptor for glutamate, which is critically important for dendrite development and synapse formation during neuronal growth such as motor learning and neural wiring , 69. Mikl wiring . As mostl wiring . It has l wiring , 73. Neiene flow , 74.L. polyactis aggregate in the overwintering grounds, a strong thermohaline front forms in the southeastern Yellow Sea (YSWC), which represents an area with a sharp cline in oceanographical conditions such as temperature and salinity and thus acts as physical barriers to exchange in the marine environment [et al. [Engraulis japonicus) could be driven by spatially varying environmental factors such as water temperature and salinity, and both Yellow Sea Cold Water Mass (YSCWM) and YSWC were demonstrated to have a remarkable influence on the migration and distribution of Japanese anchovy. Moreover, genetic composition of individuals from the overwintering population of SYC were more affiliated with those from the East China Sea overwintering populations . Xu et al. (2009) [L. polyactis, which is consistent with findings in the present study. Possible causes for this process may be attributed to the fact that the YSWC weakens in late spring when the spawning migration of L. polyactis begins [In the present study, all the analyses based on the outlier locus Lpol03 indicated that two distinct genetically distinct groups existed among the 15 samples. Moreover, clear genetic differentiation were detected between the overwintering samples from the Yellow Sea  and those from the East China Sea , indicating that there were two genetically distinct overwintering aggregations, which was consistent with the previous findings based on capture fishery data . During ironment . Likewisironment . For exa [et al.  found th. (2009)  reporteds begins , 78, whiet al. [L. polyactis inhabiting in the two overwintering grounds were locally adapted to multiple migration routes. For the Yellow Sea overwintering aggregation: one spawning group migrated northward to the northern part of Bohai Sea and Yellow Sea; the other one migrated southward to the coastal waters of the Central East China Sea and the Yangtze River estuary. Furthermore, overwintering aggregations from the North East China Sea mainly migrated to coastal areas off Shandong Peninsula. Interestingly, individuals of both overwintering groups spawn in separate areas of the Bohai Sea (L. polyactis in the Bohai Sea could be the existence of Northern Yellow Sea Cold Water Mass (NYSCWM), which is a unique hydrographic phenomenon in the Yellow Sea [L. polyactis between the two overwintering grounds in the Yellow Sea and the East China Sea and the coastal spawning areas [Population structure analyses based on the outlier locus Lpol03 indicated that spawning individuals from WL and XP showed more similarity to the Yellow Sea overwintering population (SYA and SYB) rather than the East China Sea overwintering group (NEA and NEB). The result was consistent with previous results detected by diversifying microsatellite markers, which indicated that populations in the CECS  were significantly differentiated from those in the NECS (North East China Sea) . In addiet al. . Therefoohai Sea . One impllow Sea . The cenng areas .L. polyactis were highlighted by the outlier locus potentially under selection, one mainly in the East China Sea and the other in the Yellow Sea. Accordingly, a novel migration pattern of L. polyactis was also suggested, which was inconsistent with results of previous studies inferred by traditional approaches [L. polyactis, there were also strong evidence that gene flow between the two migratory groups might be limited by local adaptation. Population structure based on the outlier locus Lpol03 reported in our study exhibited a high level of genetic differentiation, which was in concordance with previous results based on genetic markers as well as capture data. In total, the consistent results suggested that the outlier locus Lpol03 was a candidate loci with stronger discriminating power leading to increased accuracy for future MSA studies. Future research should aim to generate a comprehensive link between the spawning groups and overwintering aggregations of L. polyactis, and to use the gene-associated marker in combination with seascape variables, which would provide a powerful means for uncovering the processes leading to adaptive divergence. Overall, the present study provided new insights into the population genetic structure and migratory routine of L. polyactis, which would have significant implications for the sustainable management and utilization of this important marine fishery resource.Although high gene flow was indicated by the neutral microsatellites, two genetically differentiated overwintering populations of proaches . It shouS1 Figpale gray), positive selection (red) and balancing selection (yellow). Loci outliers are tagged with labels.The graphical output shows the simulated confidence area for neutral loci ((TIF)Click here for additional data file.S2 Figfilled blue circles, while loci significant at the 1% level are shown as red filled circles. Loci below or above the red lines are marked as ﬁlled red circles and correspond to markers potentially under balancing or directional selection, respectively.Loci significant at the 5% level are shown as (TIF)Click here for additional data file.S3 Fig(TIF)Click here for additional data file.S4 Fig(TIF)Click here for additional data file.S1 File(TXT)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.


Philomachus pugnax) by comparing genetic variation of nuclear microsatellites  versus mitochondrial D-loop sequences , and conversion to comparable nuclear (Ne) and female (Nef) effective population size using published ranges of mutation rates for each marker (μ). We provide a Bayesian method to calculate Ne (θn = 4Neμn) and Nef (θm = 2Nefμm) using 95% credible intervals (CI) of θn and θm as informative priors, and accounting for uncertainty in μ. In 96 male ruffs from one population, Ne was 97% (79–100%) lower than expected under random mating in an ideal population, where Ne:Nef = 2. This substantially lower autosomal variation represents the first genomic support of strong male reproductive skew in a lekking species.Sex differences in skews of vertebrate lifetime reproductive success are difficult to measure directly. Evolutionary histories of differential skew should be detectable in the genome. For example, male-biased skew should reduce variation in the biparentally inherited genome relative to the maternally inherited genome. We tested this approach in lek-breeding ruff (Class Aves,  Chlamydotis undulata undulate) , the simulated reduction in effective population size due to male mating skew was at least 23%, while accounting for the variance in seasonal reproductive success in females, which was almost as high as in males due to a high rate of nest failure , no sex differences in effective population size were found; in this species, a negative effect of the mating system was obscured by the much stronger effect of delayed maturity in both sexes , which has a well-described lek mating system. The species has (1) highly ornamented males that are much larger than the nonornamented females, (2) three genetically based alternative male morphs, and (3) strong annual reproductive skews in males  have to be used to convert genetic variation to comparable units, and estimates of μ often have large uncertainties. Our method explores the use of these markers while accounting for the uncertainty in μ.For this study, highly variable markers were used that are generally applied in population genetic studies: microsatellites or simple sequence repeats (SSRs), and sequences of the mitochondrial D-loop or control region. These mutate fast and are therefore adequate archives of recent genetic evolution Avise . ImportaThe biparentally transmitted genes used to genotype the 96 males were seven polymorphic autosomal microsatellites loci (nDNA): ruff1, ruff6, ruff10, ruff12, and ruff50 are published loci cloned from ruffs  and sequenced on an ABI 3100 automated sequencer. Sequences were edited and aligned in MEGA 3.1  of the mitochondrial genome (mtDNA). A 512 nt segment of domain I and II of the CR was amplified and sequenced with forward primer L141 (5′-TCCATTAATCTACAACCGGGCT) and reverse primer PropR (5′-AATACCAGCTTTGGGAGTTGG). Ruffs have a duplicated CR with high sequence similarity between copies , Ne and Nef are biparental and female-transmitted only effective populations sizes, and μn and μm the marker-scaled mutation rates wherewhere(1)θn and θm were obtained by coalescent analysis .Estimates of the variance parameters R = 33 . After Ne:Nef is provided by equations The quantity θ and μ can substantially affect the estimation of Ne and Nef from genetic data. To estimate Ne:Nef, we used OpenBUGS, a Bayesian analyses dedicated software that makes the MCMC method easily accessible  and the genetic variance parameters (θn and θm), estimated from the data. Instead of using single fixed values, we defined two uniform prior distributions, one for θn and one for θm. The uniform distributions were defined over a range of values that included 95% of their respective posterior distribution obtained by coalescent analysis of the genetic data .However, the uncertainty in the parameters  Colwell . In the μn and μm, we defined, a priori, the range of possible mutation rates according to the literature . This means that the high variability of mutation rates, as reported in the literature, is the starting point of the Bayesian analysis. To determine the ranges of mutation rates, we used the following information. In vertebrates, the typical mutation rate for microsatellites is 1 × 10−4 substitutions/locus/generation . This range includes observed values for shorebirds for domain I of the control region  was 0.695 (0.419–0.845).Ninety-six individuals were successfully genotyped at seven microsatellite loci. None of the loci had null-alleles, significant linkage disequilibrium, or significant deviations from Hardy–Weinberg equilibrium  was 0.954 ± 0.039, and the average nucleotide diversity was 0.007 ± 0.004.Eighteen individuals were successfully sequenced for the 512-bp segment of the mitochondrial control region. The segment had sixteen polymorphic sites (15 transitions and 1 transversion), and across the samples 14 different haplotypes were detected (GenBank under Accession Nos HQ171099-HQ171161). The gene diversity (θn of 13.23 (95% CI 10.2–16.6). For mtDNA, the effective MCMC sample size was 5.5 million, with resulted in a unimodal posterior distribution, and a most probable estimate of θm of 0.0238 (95% CI 0.012–0.048).The coalescent run to estimate theta from microsatellites had an effective MCMC sample size of 609 million and returned an unimodal posterior distribution with a global estimate of θn and θm, the ratio Ne:Nef was 0.068 , assuming prior uniform distributions for μm and μn as given in the Methods. This means that in ruffs, nuclear genetic variation is 97% (range: 79–100%) lower than expected under random mating in an ideal population, where Ne:Nef = 2.Including the 95% CIs of the estimates of The 97% reduction in nuclear genetic variation is empirical support for the assumed stronger interindividual variance in lifetime reproductive success (LRS) of male ruffs relative to females. This result confirms the intuition of previous field researchers who observed the large investments by some males in dominating the lek (Hogan-Warburg Ne) can be applied more generally. The degree of difference in Ne between the two genomes will summarize longer-term mating skews between the sexes in any species. However, knowledge of the ecology of the species is a prerequisite, because sex differences in dispersal, recruitment, survival, and selection may obscure the effect of the mating system .Adult sex ratio can also influence Ne:Nef can also be influenced by differential selection regimes in the two genomes and/or the Hill-Robertson interference; mtDNA does generally not recombine and is therefore susceptible to genetic sweeps reducing variation , microsatellites would have higher diversity than mitochondrial genome because mutation rate rather than Ne would control the speed of recovery. As we found relatively high variation in mtDNA, in ruffs neither process can be likely responsible for the observed low Ne:Nef ratio.In principle, n Marais . This woNe of 97% (range: 79–100%) is stronger than can be accounted for by ecological differences between males and females, and primarily results from the larger variance in LRS in males than in females, which is congruent with the predicted Ne:Nef under the lek model of Chesser and Baker (We conclude that in ruffs, the observed reduction in nd Baker . This isnd Baker , but it nd Baker , but had


Rhodiola sachalinensis is an endangered species with important medicinal value. We used inter-simple sequence repeat (ISSR) and methylation-sensitive amplified polymorphism (MSAP) markers to analyze genetic and epigenetic differentiation in different populations of R. sachalinensis, including three natural populations and an ex situ population. Chromatographic fingerprint was used to reveal HPLC fingerprint differentiation. According to our results, the ex situ population of R. sachalinensis has higher level genetic diversity and greater HPLC fingerprint variation than natural populations, but shows lower epigenetic diversity. Most genetic variation (54.88%) was found to be distributed within populations, and epigenetic variation was primarily distributed among populations (63.87%). UPGMA cluster analysis of ISSR and MSAP data showed identical results, with individuals from each given population grouping together. The results of UPGMA cluster analysis of HPLC fingerprint patterns was significantly different from results obtained from ISSR and MSAP data. Correlation analysis revealed close relationships among altitude, genetic structure, epigenetic structure, and HPLC fingerprint patterns . Taken together, our results indicate that ex situ population of R. sachalinensis show significantly different genetic and epigenetic population structures and HPLC fingerprint patterns. Along with other potential explanations, these findings suggest that the ex situ environmental factors caused by different altitude play an important role in keeping hereditary characteristic of R. sachalinensis. Plant is one of the most important resources that human being depend on, because of the direct benefits to humans that arise from its exploitation in new agricultural and horticultural crops. The development of medical drugs and the pivotal role played by plants are the functions of all natural ecosystems In recent years, some studies of population genetic structure in ex situ conservation species indicated that they may exist in a potential genetic risk, such as the loss of genetic diversity, inbreeding depression, outbreeding depression and genetic adaptation Rhodiola sachalinensis A. Boriss. is a perennial herbaceous plant in the family Crassulaceae. In China, this species is mainly distributed in alpine zones of Changbai Mountain and the southeastern Zhangguangcai Mountain, where plants are found at altitudes ranging from 1,700 to 2,500 m R. sachalinensis is a medicinal plant, containing the effective component of salidroside in roots and rhizomes, and has a reputation for stimulating the nervous system, decreasing depression, relieving fatigue, resisting anoxia and microwave radiation, preventing high altitude sickness, and improving sleep R. sachalinensis. More than 0.2 kg seeds and 60 seedings were transplanted into the ex situ conservation area in CBMAS. The ex situ conservation site is only decades of kilometers away from the main range of distribution of R. sachalinensis, and its main climate conditions are similar in a certain degree , Changbai Mountain Natural Reserve, began to carry out ex situ conservation of n degree . Up to 2ulations .R. sachalinensis populations i.e., three natural populations  and one ex situ population (ESP), and detected the genetic, epigenetic, and HPLC fingerprint differentiation between them by using ISSR, MSAP and HPLC, with an intent to check whether ex situ population conserved representative natural populations, and to shed light on the strategy for ex situ conservation of R. sachalinensis.To understand these changes, we selected as study subjects We confirm that the permits were obtained from Changbai Mountain Natural Reserve where collecting took place. We confirm that the location accessed is not privately owned and all Sample collection complied with “Regulations of the People's Republic of China on Natural Reserves”.R. sachalinensis were collected in June, 2010 from three natural populations  and one ex situ population (ESP) cultivated in CBMAS . DNA concentrations were quantified using a 752 UV-Vis spectrophotometer . DNA quality was verified by electrophoresis on a 1.2% agarose gel. The DNA samples were diluted to 20 ng µlR. sachalinensis individuals in this study , and double-distilled water. The amplifications were carried out in a Mastercycler Gradient thermal cycler  under the following conditions: pre-denaturation for 5 min at 94°C, followed by 40 cycles of denaturation for 30 s at 94°C, annealing for 60 s at various temperatures  designed by the University of British Columbia were screened initially using 12 individuals from the four populations. Twelve primers which produced clear and reproducible banding patterns were selected for amplification of all is study . The optures see , and elo−1). Additionally, 100-bp ladders  were loaded onto each gel. Images were identified using a gel-imaging system .ISSR-PCR products were separated via electrophoresis on a 1.8% agarose gel at 100 V for 150 min in 1×TBE  buffer, and stained with ethidium bromide (0.5 µg mlHpa II and Msp I) to cytosine methylation. Hpa II and Msp I recognize and excise DNA sequences at CCGG restriction sites, but with different digestion sensitivities to cytosine methylation. Msp I cleaves DNA when no cytosines are methylated or when the internal C is methylated (5′-CmCGG-3′) on one or both DNA strands. Hpa II cleaves DNA in the case of no cytosine methylation or in the case of hemimethylation of external (5′-mCCGG-3′) or internal (5′-CmCGG-3′) cytosines The methylation-sensitive amplification polymorphism (MSAP) technique, a modification of the amplified fragment length polymorphism (AFLP) method, exploits the differential sensitivity of a pair of isoschizomers . Digestion and ligation were performed simultaneously in 20-µl volumes containing 200 ng DNA template, 5 U of T4 DNA ligase , 5 U of EcoR I, 5 U of either Msp I or Hpa II, 5 pM of EcoR I adaptor  for 2O and selective amplification using six different primer combinations  with ddHinations  The optiAfter selective amplification, the products were mixed 1∶1 with a loading buffer , denatured at 95°C for 5 min, and separated on 6% denaturing polyacrylamide gels (6% polyacrylamide and 7 M urea) in 1× TBE buffer at 70 W for 4.5 h. Gels were stained according to the silver staining method HPLC fingerprint, a comprehensive and quantifiable identification method, is able to reveal chemical information about herbal medicines using chromatograms, spectrograms, and other graphs derived by analytical and chemical techniques Salidroside used as standards, were provided by Jilin Food and Drug Administration. Methanol used as solvents, were of HPLC grade and were purchased from Fisher Scientific (USA). After intensive drying salidroside was weighed and dissolved in 1 mL of methanol to achieve serial concentrations, and three injections were performed for each dilution. The standard curve was calibrated using the linear least-squares regression equation derived from the peak area. The concentration of salidroside in different samples was calculated according to the regression parameters derived from the standard curves.To extract chemical composition, root samples from the four populations were pulverized, and the powder was then screened through 180-µm sieves. Fine powder (200 mg) was accurately weighed; 10 ml of 50% methanol was then added and the mixture was weighed again. The mixture was extracted by ultrasonication for 1 h at 60°C. After cooling, 50% methanol was added to restore the initial weight. The supernatant fluid was filtered through a 0.45-µm membrane filter, and the filtrate was analyzed by HPLC.18 reversed-phase column  was used with the column temperature set at 25°C. The mobile phase consisted of 15∶85 (v/v) methanol-ddH2O, which was maintained for 40 min. The flow rate was 1.0 ml min−1 and the injection volume was 20 µl. The detection wavelength was set to 277 nm.Chromatographic separations were performed on an Agilent 1100 HPLC system  equipped with a quaternary pump, autosampler, degasser, automatic thermostatic column compartment, DAD detector, and a computer with Chemstation software for analysis of the HPLC data. An Agilent CI) PPL), and genetic distance. Total genetic diversity (Ht) and mean genetic diversity within populations (Hs) were tested using Nei’s STG, according to the equation STG = (Ht−Hs)/Ht. Gene flow was estimated using the formula: Nm = 0.5×[(ST1−G)ST/G] STF) within and among the four different altitudinal populations using AMOVAPREP and AMOVA 1.55 STF was tested with 1,000 random permutations. A UPGMA tree was constructed based on genetic distance ISSR-amplified DNA fragments (bands) were scored as present (1) or absent (0), and a data matrix of the ISSR banding patterns of all populations was assembled for further analysis. The software program POPGENE v1.31 Hpa II and Msp I have different sensitivities to methylation patterns: Hpa II is only sensitive to external hemimethylated cytosine (single-strand methylation), whereas Msp I only recognizes internal fully methylated cytosine (methylation of both strands). We therefore scored the differential methylation pattern based on band presence or absence EcoR I/Hpa II only), Type 0/1 bands indicated full methylation of the internal cytosine of both strands (bands present in EcoR I/Msp I only), and Type 0/0 bands represented non-informative loci (bands absent in both profiles). Non-informative loci were removed from our dataset because they could not discriminate between hyper-methylation and nucleotide mutation, and the number of hemi-methylated and internal fully methylated loci was then counted in each sample. Ratios of hemi-methylation, internal full methylation, and total methylation (Type 1/0+Type 0/1) were calculated against the total number of detected bands (Type 1/1+ Type 1/0+ Type 0/1). To estimate population epigenetic structure in the four different habitats, MSAP data were also converted into a methylation-susceptible matrix according to Herrera and Bazaga EcoR I/Msp I or EcoR I/Hpa II profiles was scored as “1” and band presence in both EcoR I/Msp I and EcoR I/Hpa II profiles was scored as “0”; bands absent in both EcoR I/Msp I and EcoR I/Hpa II profiles were treated as “missing”, as they could have been produced by either genetic or epigenetic factors. The resulting data were analyzed with POPGENE v1.31 STG), Nei’s gene diversity, gene flow (Nm), and genetic distance, to search for epigenetic differentiation. AMOVAPREP and AMOVA 1.55 Only clear fragment patterns were considered. R. sachalinensis root extracts were analyzed using the professional software package Similarity Evaluation System for Chromatographic Fingerprint of Traditional Chinese Medicine (2004 version), recommended by the SFDA of China for evaluating similarities of traditional Chinese medicine chromatographic profiles HPLC fingerprints of R. sachalinensis.We used the DPS 7.05 data processing system  to analyze relationships among environment, heredity, and HPLC fingerprint in different populations of R. sachalinensis, we surveyed 12 ISSR primers in 60 individuals from the different populations sampled in 2010. A total of 349 bands (To assess the genetic diversity of 49 bands  were pro49 bands  were polPPL) and Shannon’s information index (I) were 97.71% and 0.4299, respectively. At the population level, genetic diversity of R. sachalinensis, the ESP, was relatively high, while TZ1, TZ2, and TZ3 genetic diversities were more or less similar to one another. PPL ranged from 63.90% to 50.14%, with an average of 55.37%. I ranged from 0.3195 to 0.2351, with an average of 0.2743  suggested that genetic differentiation of R. sachalinensis populations was relatively high (0.3490), indicating that about 34.90% of the genetic variation was among populations. The calculated gene flow was 0.9327 migrants (Nm) per generation .The calculated coefficient of genetic differentiation  analysis based on genetic distances provided a spatial representation of the 60 individuals from the four sampled populations . The twoR. sachalinensis populations were evaluated by MSAP fingerprint. Based on band absence or presence, cytosine methylation patterns were classified into four types: Type 1/1, Type 1/0, Type 0/1, and Type 0/0  indicated that a certain degree of epigenetic differentiation existed among the four R. sachalinensis populations, corresponding to about 58.92% of the observed epigenetic variation. The calculated gene flow was 0.3486 migrants (Nm) per generation .The coefficient of genetic differentiation . DNA methylation level and altitude were also significantly correlated with one another (2R = 0.90), and negatively correlated with HPLC fingerprint similarity (2R = −0.88). Chemical fingerprint similarity and altitude were apparently negatively correlated (2R = −0.95) as well. Genetic and epigenetic distances were somewhat correlated with HPLC fingerprint similarity, but the correlation coefficients were not significant (2R = 0.35 and 2R = 0.48).Correlation analysis of altitude, genetic distance, epigenetic distance, DNA methylation level, and HPLC fingerprint similarities generated interesting results . GeneticR. sachalinensis, with average of 2.53% percentage of polymorphic loci (PPL) and 0.2593 Shannon’s information index (I) in three natural populations. An allozyme analysis of R. sachalinensis natural population genetic structure by Yan et al. R. sachalinensis is a perennial alpine herb with predominant outcrossing, characteristics associated with high genetic diversity. In addition, R. sachalinensis reproduces both sexually and clonally via sprouting rhizomes. Although “pollen limitation” was invoked by Yan et al. R. sachalinensis populations, recent studies have demonstrated that bumblebees play a positive role in pollen dispersal of alpine plants ISSR revealed a high of genetic diversity in endangered PPB = 63.9% and I = 0.3195) than the other three natural populations. Most researches show that the populations of ex situ populations have a low level of genetic diversity, but we found a different result. On the one hand, rich genotypes of species and cultivation conditions may be the major reason. More than 0.2 kg seeds and 60 seedings from natural populations were transplanted into the ex situ conservation area in CBMAS. The quantity of R. sachalinensis ensured variation of genotypes and genetic information, and cultivation conditions are good at establishing artificial population of R. sachalinensis; On the other hand, according to archived cultivation information, the artificial population of R. sachalinensis is derived from natural populations growing at 2,000 m, and has experienced reproduction by seed  for several generations. All these factors are better for holding a higher level of genetic diversity.Ex situ population (ESP) had higher genetic diversity  found similar results. Partitioning of genetic divergence by AMOVA revealed that most of the genetic variation, 54.88%, was within-population variation. The observed level and pattern of genetic differentiation is consistent with the reproductive strategy of STG = 0.3490) was observed between natural R. sachalinensis populations and ex situ population. An effective gene flow of more than four migrants per generation is generally sufficient to counteract genetic drift and to prevent genetic differentiation between populations R. sachalinensis populations. However, this relatively low gene flow was not the only contributor to the high genetic differentiation observed for R. sachalinensisHigh genetic differentiation , it may not be the major factor responsible for the high epigenetic differentiation of R. sachalinensis. During the past decade, many studies have demonstrated that epigenetic variation is usually associated with phenotypic novelties that can also contribute to the ability of plants to respond to diverse environments R. sachalinensis distributed at different altitudes differ greatly in morphology, environmental conditions at different altitudes may be a major factor responsible for the observed high epigenetic variation. Additionally, R. sachalinensis DNA methylation levels, including total methylation, internal full methylation, and hemi-methylation levels, gradually decreased along an altitudinal gradient. TZ1, at the highest altitude, was the most methylated, whereas ESP as an ex situ population, at the lowest altitude, was the least. Previous studies of epigenetic variation in Pisum sativum L., Nicotiana tabacum L., and Zea mays L. illustrate that species can adapt to adverse environmental conditions by altering DNA methylation levels R. sachalinensis was a response to environmental stress. Although these epigenetic variation are favor for survival of R. sachalinensis in ex situ conservation, a long-term view there is adversely for maintaining the original property of R. sachalinensis.At the species level, high levels of epigenetic variation were uncovered in = 0.5892 . Most of= 0.5892 . BecauseR. sachalinensis is a kind of medicinal plant, so HPLC fingerprint is self-evident importance of maintaining medicinal properties. In this study, we surveyed chromatographic fingerprints of R. sachalinensis individuals from different populations using HPLC fingerprint , consistent with the similar dendrograms generated by cluster analysis of genetic and epigenetic marker data. A correlation between genetic and epigenetic changes has been previously suggested, but an equivalent relationship as revealed in this study has seldom been experimentally demonstrated R. sachalinensis from different altitudes may be correlated with genomic sequence variation (genetic variation), and that epigenetic structure may be influenced by genetic structure.According to our results, genetic structure was significantly correlated with epigenetic structure  and that HPLC fingerprint similarity and altitude were negatively correlated (2R = −0.95). Altitudinal gradients are associated with substantial changes in various environmental conditions over a short distance, while epigenetic variation and HPLC fingerprints are affected by environmental factors Another characteristic of the epigenetic variation observed in 2R = −0.88). It is well known that plant metabolism and DNA methylation have strong organ and tissue specificity. So the correlate methylation patterns of leaves with HPLC fingerprint variation of roots may be unreasonable. However, this correlation may exist, because as a very important tissue of photosynthesis, DNA methylation of leaves may be an indirect influence on the formation and accumulation of secondary metabolites of roots. It may be a point of interest in the future.There is an interesting result that epigenetic variation and HPLC fingerprints were negatively correlated . Those mean that it is a certain degree of difficult to representative natural populations from the ex situ conservation population.The results of this study demonstrate that environmental conditions in different habitats are major factor influenced on hereditary characteristic of species. The ex situ population of R. sachalinensis is an important, endangered medicinal plant species. Combined knowledge of HPLC fingerprint in exploited medicinal plants should provide a useful guide for their efficient utilization, breeding, and management.In addition, Figure S1Morphological variation displayed by R. sachalinensis plants from different altitudes on Changbai Mountain. (a) Clone number; (b) Plant height; (c) Leaf length; (d) Leaf width; (e) Leaf thickness; (f) Stem diameter.(TIF)Click here for additional data file.Table S1R. sachalinensis populations.Morphological summary of Changbai Mountain (DOC)Click here for additional data file.


Whole-genome sequence (WGS) data give access to more complete structural genetic information of individuals, including rare variants, not fully covered by single nucleotide polymorphism chips. We used WGS to investigate the amount of genetic diversity remaining after selection using optimal contribution (OC), considering different methods to estimate the relationships used in OC. OC was applied to minimise average relatedness of the selection candidates and thus miminise the loss of genetic diversity in a conservation strategy, e.g. for establishment of gene bank collections. Furthermore, OC was used to maximise average genetic merit of the selection candidates at a given level of relatedness, similar to a genetic improvement strategy. In this study, we used data from 277 bulls from the 1000 bull genomes project. We measured genetic diversity as the number of variants still segregating after selection using WGS data, and compared strategies that targeted conservation of rare  versus common variants.When OC without restriction on the number of selected individuals was applied, loss of variants was minimal and most individuals were selected, which is often unfeasible in practice. When 20 individuals were selected, the number of segregating rare variants was reduced by 29 % for the conservation strategy, and by 34 % for the genetic improvement strategy. The overall number of segregating variants was reduced by 30 % when OC was restricted to selecting five individuals, for both conservation and genetic improvement strategies. For common variants, this loss was about 15 %, while it was much higher, 72 %, for rare variants. Fewer rare variants were conserved with the genetic improvement strategy compared to the conservation strategy.The use of WGS for genetic diversity quantification revealed that selection results in considerable losses of genetic diversity for rare variants. Using WGS instead of SNP chip data to estimate relationships slightly reduced the loss of rare variants, while using 50 K SNP chip data was sufficient to conserve common variants. The loss of rare variants could be mitigated by a few percent (up to 8 %) depending on which method is chosen to estimate relationships from WGS data.The online version of this article (doi:10.1186/s12711-016-0210-4) contains supplementary material, which is available to authorized users. The increased availability of whole-genome sequence (WGS) data allows access to more complete structural genetic information on individuals than that obtained with commonly used single nucleotide polymorphism (SNP) chips. Most SNP chips target SNPs that have approximately uniformly distributed allele frequencies . In contConservation of livestock species aims at maximising genetic diversity on the long-term. Genetic material is conserved, for example in gene bank collections, in order to allow future use or recovery of genetic variation. However, breeding programs focus mainly on genetic improvement in the next generation. Optimum contribution (OC) selection strategies have been designed to simultaneously target genetic improvement and conservation of genetic diversity. In terms of genetic diversity conservation, OC aims at minimising or restricting average relatedness of the potential parents in order to minimise the rate of inbreeding and maximise genetic diversity in the long-term , 8. PrevOur objective was to investigate the amount of genetic diversity conserved across the whole genome, including common and rare variants, by using OC within the context of conservation of genetic diversity and genetic improvement. Genetic diversity was measured as the number of genetic variants that still segregate in a population after selection. Relationships were estimated with different methods, using pedigree, SNP chip, or WGS data.This study was performed on data from 277 Holstein bulls from Run 4 of the 1000 bulls genome project. These 277 individuals originated from Europe, North-America, Australia and New-Zealand (based on their Interbull ID) and were born between 1965 and 2010. Their full pedigree contained 12,949 individuals of which 4535 were sires and 8414 were dams, and was recorded from the 1900s onward. Base individuals in the pedigree, i.e. 3093 individuals with both parents unknown, had birth years ranging from 1883 to 2002. The average date of birth of the base individuals was 1931, while it was 1948 for the non-base individuals.Within the group of 277 sequenced bulls, we observed 106 parent-offspring relationships, three full-sib pairs and 200 half-sib pairs. All individuals were related to some extent. Generation equivalents were computed as the sum over all ancestors of ncestors , and ransoftware  followinsoftware . \documeRequired estimated breeding values (EBV) were defined as the NVI, which is the Dutch Flemish total merit index estimated by the genetic evaluation of sires for bull ranking in the Netherlands and Flanders . This inWhole-genome sequence data of the 277 bulls contained a total of 35,726,017 variants across the 29 autosomes, of which 20,177,956 segregated in this set of animals. WGS were obtained using sequencing outputs from Illumina HiSeq Systems  that were edited in five steps: sequence alignment, variant calling, phasing, quality controls and imputation. Of the called variants, 94.52 % were SNPs and 5.48 % were insertion-deletions. The overall sequence coverage per individual ranged from 3 to 38, with an average of 12. SNP-type variants that are included in the Illumina BovineSNP50 BeadChip v2  were extracted to be used as 50 K SNP chip. This SNP subset contained 48,652 SNPs of which 46,050 were segregating in the population of 277 bulls.−10 for WGS and <10−6 for 50 K SNP chip data). Moreover, variants that had a minor allele frequency (MAF) lower than 1 % were also excluded since they are more likely to represent genotyping errors rather than true variants. This threshold was equivalent to removing variants for which the rare allele was present less than 6 times in our data set. This step removed 4,000,558 variants from the WGS and 1615 from the SNP chip data. After all editing, a set of 15,864,157 variants for WGS data and 44,367 variants for the 50 K SNP chip remained for our analyses.For both the 50 K SNP chip and WGS data, we used an F-exact test of departure from Hardy–Weinberg equilibrium to estimate P-values for each of the segregating variants. In the case of low allele frequencies, i.e. when only a small number of individuals are allocated to one of the genotype classes, the F-exact test has been shown to be the most suitable method  to assescons), or combined genetic improvement and conservation (impcons). In both selection strategies, estimated relationships between selection candidates were computed using pedigree, 50 K SNP chip or WGS data. OC jointly maximises conservation of genetic diversity and genetic gain, by optimising the contribution of the selection candidates while minimising the rate of inbreeding in the next generation The average coancestry between selected individuals, since it represents the change in inbreeding between the current and next generation, The average genetic merit of the next generation, Selection based on optimal contribution (OC) was performed, using the program Gencont , for conThe algorithm behind the determination of the OC etail in .cons since it targets conservation only), or (2) maximising genetic gain and setting the rate of inbreeding impcons since it targets genetic improvement and conservation). In all cases, we estimated In our study, there were nine individuals with missing EBV, which were marked as unavailable for selection. We optimised genetic contribution of the remaining 268 individuals by: (1) minimising the average relatedness and thereby minimising the rate of inbreeding in the long-term while genetic gain was not constrained , although the differences with other combinations of method and data type were small. For rare variants, similarity-based estimated relationships using WGS data performed best to maintain them in the population  , while similarity-based estimated relationships calculated from WGS data performed best to conserve rare variants   because, applying such a restriction, greatly reduced the number of segregating rare variants from the beginning. Rate of inbreeding followed a similar trend for both cons and impcons strategies and increased as the restriction on the number of selected individuals became more stringent. Selecting for genetic improvement and conservation caused a slightly larger loss of genetic diversity but a major genetic gain compared to selecting for conservation only.No major differences were observed between the cons), and selection for genetic improvement while restricting loss of genetic diversity, in breeding programs (impcons). For both strategies, we observed a dramatic loss of genetic diversity at rare variants due to selection.In this study, we assessed which type of data: pedigree, SNP chip or WGS, and which method should be used to reach optimal conservation of genetic diversity, measured as the number of WGS variants still segregating after selection. We were interested in two strategies that both used OC: selection for conservation only, e.g. to enrich gene bank collection . Such losses of rare variants are not observed when using SNP chip data to measure genetic diversity, because the construction of SNP chips usually focuses on variants with common rather than rare alleles. In general, the overall level of genetic diversity was slightly higher when using estimated genomic relationships compared to pedigree relationships in OC. Among the methods considered to estimate genomic relationships, the similarity-based relationships resulted in the largest amount of genetic diversity conserved in both strategies that target genetic improvement and conservation, or conservation alone. In the


Cyclura cychlura cychlura. This iguana is endemic to Andros, a highly fragmented landmass of large islands and smaller cays. Field observations suggest that geographically isolated demes were panmictic due to high, inferred rates of gene flow. We expand on these observations using 16 polymorphic microsatellites to investigate the genetic structure and rates of gene flow from 188 Andros Iguanas collected across 23 island sites. Bayesian clustering of specimens assigned individuals to three distinct genotypic clusters. An analysis of molecular variance (AMOVA) indicates that allele frequency differences are responsible for a significant portion of the genetic variance across the three defined clusters (Fst =  0.117, pEcological data, the primary source of information on patterns and rates of migration, can be integrated with genetic data to more accurately describe the realized connectivity between geographically isolated demes. In this paper we implement this approach and discuss its implications for managing populations of the endangered Andros Island Rock Iguana,  Natural or anthropogenic habitat fragmentation may hinder or prevent animal dispersal. Inherently linked to dispersal, gene flow is also largely determined by geographic features such as fragmentation Ambystoma) revealed unexpected high connectivity between geographically isolated demes Zosterops borbonicus, exhibited extremely reduced rates of gene flow. Indeed, perceived migration rates fell short of realized gene flow rates determined by population genetic analyses. This discrepancy could be exacerbated in highly fragmented landscapes, and in species of conservation concern, could lead to misguided management. In this paper we use molecular tools to investigate the conformity between inferred and realized patterns of gene flow in the endangered Andros Island Rock Iguana, Cyclura cychlura cychlura.Though life history data and field observations provide important insights regarding patterns of species dispersal, it is imperative that potential differences in perceived and realized gene flow be recognized if we are to implement effective conservation measures. For instance, adopting translocation as a strategy to reinforce declining populations can be harmful if the source population is genetically distant from the recipient one due to historical lack of gene flow C. c. cychlura hatchlings have been documented to readily disperse from nesting areas across a variety of different habitat types The Andros Rock Iguana is endemic to Andros Island in the Bahamas C. c. cychlura on Andros Island. We then examined the population structure in the context of the species ecology and the associated landscape to infer patterns of gene flow. We hypothesized that the high dispersal rates inferred from anecdotal accounts and telemetry studies C. c. cychlura on Andros Island despite their patchy distribution across geographically isolated sites. If true, we anticipate limited evidence for genetic structure across the island.Current conservation plans for the Andros Island Rock Iguana One hundred ninety two individuals were sampled from 23 sites. Identifiers for each sampled location and numbers of individuals sampled per site are listed in Cyclura species and analyzed, 18 were successfully amplified, and 13 were polymorphic in C. c. cychlura  while Z13 and CycCyc9 were the least variable . Individuals from the middle patch showed a slight but non-significant excess of heterozygotes . An overall analysis of deviation from HWE using GENEPOP on the Web 4.2 (see We did not find any evidence for Linkage Disequilibrium (LD) between microsatellite markers. Of the 2760 pairwise comparisons between loci we found significant gametic disequilibrium in only 12 instances (∼0.5%) a value that does not deviate significantly from random expectation at  4.2 see  revealede = 0.616, s.d. = 0.177), though the Central cluster was the only group polymorphic at all 16 loci and with the highest score of Allelic Richness  among the genetic clusters resulted in a Fferences . Resultsferences  and the Cyclura cychlura cychlura does not represent a single, large panmictic population as previously inferred. That is, gene flow is much lower than estimates based on field observations would suggest.We investigated genetic structure of the endangered Andros Iguana to test the hypothesis that rates of gene flow across the island are high. The results of our study indicate that Cyclura tend to experience genetic structuring, presumably because these large-bodied, terrestrial lizards have difficulties dispersing across certain types of physical barriers and establishing viable populations, or because they are tightly associated with local selective pressures such as the availability of forage. Examples include C. carinata in the Turks and Caicos Islands C. cychlura figginsi and C. cychlura inornata in the Exuma islands chain, Bahamas Managing isolated local populations as a single entity, without considering metapopulation dynamics and patterns of genetic diversity, can be dangerous Our results reject the initial hypothesis of a panmictic iguana population on Andros and are indicative of historically limited patterns of gene flow. We foresee two possible explanations that could account for high dispersal yet interrupted gene flow. First, high overland dispersal rate may attenuate over narrow waterways and not extend over broader channels with stronger water currents. This scenario is in line with evidence indicating geography as an important component in lizard isolation and differentiation Amblyrhynchus cristatusFine-scale genetic structuring in reptiles and particularly iguanine lizards is not uncommon Cyclura iguanas makes them less adapted to swim or withstand strong water currents that flow across the bights of Andros. Still, iguanas are particular good at rafting, and are known to be highly salt tolerant Overwater dispersal has been documented to play an important role for colonization, particularly in lizards st = 0.117; pAmblyrhynchus cristatus Fst = 0.002–0.011, Cyclura carinata Fst = 0.18–0.43, st = 0.072, pst = 0.273, pConsistent with evidence suggesting historically low inferred rates of gene flow, private alleles were restricted to each of the three geographic clusters further supporting that Middle and South Bights serve as major barriers to successful iguana dispersal. Differences in genetic makeup that generate genetic structure build up over time in the form of variance in allele frequencies across subpopulations and the emergence of private alleles due to random genetic drift or mutational events n Andros . The proC. c. cychlura, despite its dispersal potential, shows significant genetic structuring and that natural landscape features (i.e. water channels) influence successful migration and thus genetic structure across the island. That is not to say that anthropogenic activities are not important in further compromising the extant metapopulation dynamics on the island, and the extremely low density of individuals on North Andros  Tissue samples were collected from animals captured during fieldwork from 1999 to 2013 using fish-landing nets or nooses. A total blood volume of 1–2 ml was drawn from each individual via the ventral coccygeal vein using a heparinized syringe. Blood samples were stored in SDS lysis buffer  at ambient temperature prior to long term storage at −80°C µl of blood lysate was digested for five hours in a 65°C water bath with Proteinase-K (20 mg/ml) in digestion buffer  Approximately 20 µL volumes with ∼10 ng DNA, 2 mM MgCl2, 30 mM tricine (pH 8.4)-KOH, 50 mM KCl, 100 µM of each dNTP, 200 nM of reverse primer and M-13 forward primer (CACGACGTTGTAAAACGAC) labeled with fluorescent dye , between 40 and 150 nM forward primer with the M-13 extension, and 0.4 U of Taq DNA polymerase. Touchdown-PCR A suite of 23 microsatellites developed for congeneric species were screened for positive amplification and variability C. c. cychlura. Tandem repeat regions were identified using the subtractive hybridization method of Glenn and Schable 15, (AG)15, (AAC)10 and (AGG)10. Enriched PCR products were cloned using the pGEM-T cloning kit  with color screening. Ninety-three color positive (i.e. white) colonies were suspended in 50 µL T.E. buffer . A PCR was used to screen for inserts of suitable size for sequencing. Reactions were 10 µL in volume and contained 0.5 µL template DNA, 0.3 µM each of primers pUC-M13F and pUC-M13R (Integrated DNA Technologies), 1X GoTaq Flexi Buffer , 2mM MgCl2 160 µM of dNTPs and 0.5 units of GoTaq Flexi DNA polymerase . PCR profile consisted of 3-min at 95°C, followed by 35 cycles of 95°C for 30 s, 50°C for 30 s, 72°C for 1.5 min and lastly a single extension period at 72°C for 7 min. Amplicons were electrophoresed in 1.5% agarose TBE gels and visualized by ethidium bromide and UV light. Clones that exhibited a single amplified band of 500–1000 base pairs were cleaned with 16 U of Exonuclease I and 3 U of Antarctic Phosphatase  followed by ethanol precipitation. Cleaned PCR products were sequenced using the pUC-M13F primer, Big Dye Terminator v. 3.1 Cycle Sequencing Kit  in 10 µL reactions including 0.5 µL Big Dye, 0.3 µ primer, 0.87X sequencing buffer , and 2 µL of cleaned PCR product. Sequence reactions were cleaned using columns packed with Sephadex and then electrophoresed at the Arizona State University DNA Lab. Microsatellites were identified using Tandem Repeats Finder Additional microsatellite markers were developed specifically for o and He) were calculated according to Nei We first test for evidence of linkage disequilibrium between pairs of loci using an exact probability test in GENEPOP on the Web 4.2 6 MCMC iterations were calculated, and the first 100,000 replicates were discarded as burn-in. We assumed K putative populations ranking in number from one to 10 and performed 10 iterations of the MCMC sampling procedure for each K value. The most likely number of clusters was estimated using the Evanno method, based on the second order of difference in likelihood function of K  were calculated using the GENALEX 6.5 plug-in for Excel o and He). Due to the differences in sample sizes across different clusters, and given that allelic richness should be a function of sample size, we used an allelic richness index (Ar) that rarefies the number of alleles according to the number of genes examined in the smallest population r was calculated using HIERFSTAT, a module designed for use in the R statistical software Overall genetic variability across putative genetically isolated regions was also quantified. For each population, identified by Bayesian clustering, the percentage of polymorphic loci and private alleles (Pst) was quantified and tested for statistical significance. We also performed a pairwise comparison (pairwise-Fst) in order to estimate migration rates between pairs of clusters.To test the significance of any genetic isolates and to infer the rate of gene flow among hypothesized clusters, hierarchical AMOVAs were performed using ARLEQUIN 3.5 em = 1/Fst-1; 7 MCMCs and discarded 106 chains as burn-in. Chains were sampled every 2,000 generations. To ensure sufficient mixing of the MCMCs and to improve the coverage of the probability space we adjusted the acceptance rate for estimated allele frequencies and inbreeding coefficients. We hence increased the mixing parameters for both allele frequencies Click here for additional data file.Figure S2BAYESASS 3.0 Trace-plot and analysis parameters for the run with the lowest Bayesian deviance. The X-axis is log probability. The Y-axis is number of Bayesian iterations. The gray shaded trace represents the burn-in. Random seed  = 445; MCMC iterations  = 10,000,000; burn-in  = 1,000,000; Sampling interval  = 2,000; Mixing parameters: ((TIFF)Click here for additional data file.Table S1Molecular marker information for loci characterized in congeners of C. c. cychlura. Name, reference, size range number of alleles (Na) observed heterozygosity (Ho(s.e.)) and expected heterozygosity (He(s.e.)) in C. c. cychlura. Summary statistics are based on the total sample.(PDF)Click here for additional data file.Table S2Molecular marker information for newly characterized loci. Name, GenBank Accession number, primer sequences, repeat motifs, annealing temperatures, size ranges, number of alleles (Na), observed heterozygosity (Ho(s.e.)) and expected heterozygosity (He(s.e.)). Summary statistics are based on the total sample.(PDF)Click here for additional data file.Table S3Genotyped individuals. Excel spreadsheet containing the complete list of genotyped individuals used in this study.(XLSX)Click here for additional data file.


Psychiatry is steadily moving toward a new conceptualization of brain disorders that blurs long-held diagnostic distinctions among neurodevelopmental and psychiatric conditions, including autism. Genomic discoveries are driving these changing perceptions, yet there has so far been minimal impact on traditional genetic counseling practices that continue to view autism through the lens of a dichotomous, all-or-none risk model.High rates of comorbidity exist across autism spectrum disorder, schizophrenia, intellectual disability, and other brain-based disorders. Recent epidemiological studies have shown that co-occurrence of neurodevelopmental and psychiatric disorders is the rule, rather than the exception, in affected individuals and within families. Moreover, studies of chromosomal microarray analysis and whole exome sequencing have now detected many of the same pathogenic copy number and sequence-level variants across cohorts with different clinical presentations.Going forward, the genetic counseling field will need to significantly adapt its approaches to pedigree interpretation, variant analysis, and patient education to more precisely describe both the chance and the nature of autism recurrence in terms of a continuum of brain dysfunction. These efforts will have implications for multiple practice areas and require philosophical changes for experienced practitioners and for the training of new genetic counselors. Resetting entrenched dichotomous notions about autism and other brain-based manifestations of genetic conditions will require a strategic educational effort on the part of the genetic counseling profession. When first described as a childhood-onset psychiatric condition over 70 years ago, autism was considered a rare disorder of unknown etiology. Leo Kanner originally reported on 11 children with communication disturbances, a relative lack of motivation for social and emotional interaction, and preference for an excessive degree of routine and “sameness” in their environments . Over thASD is a complex, chronic neurodevelopmental disorder that is clinically and etiologically heterogeneous, yet highly heritable, with a recent meta-analysis of twin studies suggesting heritability estimates from 64 to 91 % –9. By a The much publicized increase in ASD prevalence over the past three decades has been well-documented in public health surveillance studies, with the most recent large-scale report showing a steady rate of ASD of 1 in 68 children in the US between 2010 and 2012 . The extDeliberations about clinical diagnostic criteria for ASD have largely played out within the fields of pediatric psychiatry and psychology, with relatively little cross-fertilization from research efforts to uncover its biological basis. Recent genomic evidence has shed new light on the interconnectedness of many brain-based pediatric and adult-onset psychiatric conditions, including ASD, ID, and SCZ, at the same time revealing biological inconsistencies with clinical diagnoses defined by psychiatry’s Diagnostic and Statistical Manual of Mental Disorders (DSM) . The disRecently, the long-standing practice of defining ASD and other psychiatric diagnoses based on dichotomous, all-or-none symptom constellations  has been called into question , 24•, 26DBD represents a group of developmental, neurological, and psychiatric conditions characterized by cognitive, behavioral, language, motor, and other brain-based functional impairments •. RatherChildren with autism and other neurodevelopmental concerns represent a significant percentage of referrals for clinical genetics evaluation, with etiological investigation being the primary indication. As in other areas of medical practice, genetic testing for individuals with ASD has moved beyond the purview of clinical genetics and is being ordered by developmental pediatricians, neurologists, psychiatrists, and other specialists, as well as by primary care providers. Pediatric genetic counselors therefore see children with ASD referred through a variety of portals and having undergone various degrees of genetic diagnostic work-up. Prenatal genetic counselors routinely field inquiries about ASD recurrence from expectant couples with a previous affected child, or more commonly, a family history of ASD. Within their growing specialty area, psychiatric genetic counselors increasingly encounter adults with mental illness who report autism symptoms in themselves and/or other family members. Additionally, a growing army of research and laboratory-based genetic counselors is employed on the front lines of variant interpretation for genes implicated in ASD. Thus, the impact of the theoretical “sea change” about ASD’s connections to other neurodevelopmental disorders, and to adult-onset psychiatric conditions, has direct relevance across several different areas of genetic counseling practice.Prior to the advent of array and whole genome/exome sequencing technologies, genetic contributors to ASD were largely unknown, and genetic counseling focused on empiric recurrence risks for ASD, occasionally informed by a positive pedigree. Consensus guidelines from national professional organizations, including the American College of Medical Genetics, the National Society of Genetic Counselors, and the American Academy of Pediatrics, recommend consideration of fragile X and CMA for children diagnosed with ASD –41, and NRXN1 that can cause ASD but also confer risk for a wide range of other neurodevelopmental and psychiatric diagnoses, from ID to SCZ, and are found as well in seemingly unaffected individuals [When a specific cause can be identified for ASD, genetic counseling has traditionally followed familiar processes with regard to risk assessment, explanations of inheritance, recommended family testing, anticipatory medical guidance, and discussion of psychosocial aspects of genetic disorders. For well-known genetic causes of ASD, such as fragile X syndrome, there may be an abundance of resources and established support organizations for the family. More commonly, however, little is known about the genetic diagnosis, as is the case for the large number of newly identified rare, pathogenic CNVs and SNVs implicated in autism. These include pathogenic microdeletions of 15q11.2 and loss of function variants in ividuals , 51, 54.ividuals •.A hallmark of genetic counseling practice is the ability to construct a detailed family pedigree in order to inform genetic risk assessment. When a consistent physical trait is present, as in families with multigenerational cystic kidneys, for example, a pedigree serves as a visual shorthand that allows the genetic counselor to quickly deduce an inheritance pattern. For neurodevelopmental and psychiatric phenotypes, these patterns are not quite so straightforward and often obscured by artificial diagnostic distinctions, masking the true magnitude of DBD recurrence. In medical genetics, ASD, EP, BPD, cerebral palsy, and other DBD are still widely and incorrectly viewed as unrelated conditions, each one designated with its own distinct symbol or colored quadrant in a pedigree to emphasize their presumed lack of connection to each another •. Likewiany type of DBD in families with one ASD proband is far higher than previously imagined [Studies published in the last decade suggest that the empiric risk for recurrence of idiopathic ASD hovers around 10 % (range ~7 to 14 %) for couples with one affected child –59, 60•,imagined , 62. It Families who seek genetic counseling about autism are generally unaware of its newly discovered cross-connections with other DBD. Traditionally, genetic counselors list ASD, along with a condition’s other known physical and behavioral traits, and cite its relative chance of occurring as part of the disorder •. While Psychiatry is steadily moving toward a new conceptualization of brain disorders that blurs long-held diagnostic distinctions among neurodevelopmental and psychiatric conditions, including autism. Genomic discoveries lie at the heart of these changing perceptions, yet there has so far been minimal impact on traditional genetic counseling practices that continue to view ASD through the lens of a categorical, all-or-none risk model. Going forward, the genetic counseling field will need to significantly adapt its approaches to pedigree interpretation, variant analysis, and patient education to more precisely describe both the chance and the nature of autism recurrence in terms of a broader DBD continuum. These efforts will have implications for multiple practice areas and require philosophical changes for experienced practitioners and for the training of new genetic counselors. Resetting entrenched dichotomous notions about autism, ID, and other brain-based manifestations of genetic conditions will require a strategic educational effort on the part of the genetic counseling profession. Once accomplished, families seeking genetic counseling will benefit from a more accurate and contextual understanding of these disorders on which to base informed decisions.