Cotinus coggygria in China’s warm-temperate zone.The adaptive evolution of species response to environment are the key issues in molecular ecology and evolutionary biology. The direction of adaptive differentiation of species in regions lacking strong selection pressure is usually diverse. However, the driving mechanism of the diverse adaptive differentiation for regional species is still undetermined to date. In this study, we used landscape genomics modelling to infer the adaptive evolution of C. coggygria, which is due to the divergent selection driven by six environmental factors. Environmental association analyses revealed the environmental variables related to precipitation were associated with high numbers of environment-associated loci.Using fifteen natural populations and nine start codon targeted (SCoT) markers, a total of 1131 unambiguous loci were yielded. Our results showed two genetic groups existed in the fifteen natural populations of C. coggygria, i.e. avoiding wetness and tolerating drought, determine its adaptive evolution. This study provides a reference that ecological character determines the adaptive evolution of species in regions lacking strong selection pressure.Our results indicated that the ecological characters of The online version of this article (10.1186/s12862-017-1055-3) contains supplementary material, which is available to authorized users. The adaptive evolution of species response to environment are the fundamental issues in molecular ecology and evolutionary biology. The global climate is changing rapidly and affecting the global ecosystem and biodiversity. Under the pressure of climate change, species either adapt or become extinct . SpeciesTo date, two strategies can be used to identify the adaptive genes. One is the “top-down” strategy, which measures the adaptive phenotype and phenological data using common garden experiment or reciprocal transplant experiment; then, this strategy links these data to genetic variation via genome-wide association studies  or quantIn recent years, landscape genomics studies have provided information on the interactions between environmental variations and adaptive genetic variations in natural populations . The uniCotinus coggygria Scop. (smoke tree) in China’s warm-temperate zone to infer the relationship between environmental variables and adaptive genetic variations in the plant’s genome. This deciduous tree species is widely distributed in China’s warm-temperate zone. This species prefers light; tolerates semi-shade, cold, and drought; and avoids wetness. Although previous population genetics study on C. coggygria had been conducted [In this study, we sampled onducted , the useonducted . SCoT maonducted . TherefoC. coggygria. The present study aimed (i) to reveal the population genetic structure of C. coggygria, (ii) identify the EAL in the genome of C. coggygria, and (iii) detect the key environmental factors that drive the adaptive differentiation of C. coggygria.We employed SCoT markers to detect environment-associated loci (EAL) in response to environmental variations in natural populations of C. coggygria. A total of 1131 unambiguous loci were identified with sizes ranging from 60 bp to 1000 bp. The numbers of loci of the nine primers ranged from 93 (SCoT31) to 163 (SCoT2). The number of polymorphic alleles (NA) of each population ranged from 134 (P9) to 325 (P14). The percentage of polymorphic alleles (PPA) of each population ranged from 11.8 (P9) to 28.7 (P14). The level of genetic diversity (HE) of each population ranged from 0.038 (P9) to 0.098 (P4). Summary statistics for the genetic diversity analyses for each population are shown in Table Nine SCoT primers were selected in investigating the population genetic structure in ΔK value . Although two groups were divided, only 5.30% genetic variation occurred among groups  and most genetic variation occurred within populations . In addition, significant patterns of isolation by distance were detected by comparing FST values with geographical distances at the species-range scale .The Bayesian analysis with all loci of population structure Fig.  clearly lue Fig.  was obtalue Fig. . The nonlue Fig.  revealedC. coggygria and environmental variables were used as subjects and explanatory variables to perform redundancy analysis (RDA). To avoid overestimation of the contribution of environmental variables to population structure, the strong correlated environmental variables were excluded from 19 environmental variables  in FDIST and 6.54% in BayeScan. The detection rates are consistent with previously reported rates on landscape genomics studies (2.85% to 10%), such as 2.85% in Alnus glutinosa [Picea mariana [Arabis alpina [Cephalotaxus oliveri [This study analyzed the adaptive evolution of lutinosa  and 4.5% mariana  from SNPs alpina  and 10% s alpina  from AFL oliveri  from ISS. The two genetic groups, the East group (P1 to P5) and the West group (P6 to P15) are geographically separated. The spatial pattern is quite different from previous findings in Wang et al. [C. coggygria indicated restricted gene flow. However, the restricted gene flow can only increase the degree of genetic differentiation among populations and cannot explain the spatial separation of the two genetic groups. Three hypotheses can be used to explain this large-scale intraspecific genetic disjunction. First, C. coggygria was compressed to two refugia  during climate fluctuations in the past, and the current distribution pattern resulted from the redistribution of the two gene pools. Second, a geographical barrier existed between the two groups, and long-term blocking of gene flow by geographical barrier led to the genetic divergence of the two groups. Third and last, significant environmental differences occurred between the two regions, and these heterogeneous environmental conditions resulted in divergent selection and eventually led to the genetic divergence of the two groups. Previous phylogeographical study on C. coggygria suggested that this species survives in situ and occupies multiple localized glacial refugia rather than compressing to two refugia during the Pleistocene glaciations [C. coggygria. Assuming that C. coggygria agreed with the second hypothesis, significant genetic differentiation could be expected between the two groups with sufficiently strong geographic barrier. However, our AMOVA analysis based on all loci and STRUCTURE analysis with all loci except EAL  both showed weak genetic differentiation between the two groups. Thus the second hypothesis was also not appropriate for C. coggygria. Considering that the third hypothesis was appropriate for C. coggygria, a shallow genetic divergence might occur between the two groups because of the interaction between natural selection and gene flow. This expectation is consistent with our detection results (FCT = 0.053). To verify that the differences between the groups were caused by environmental factors, we compared the STRUCTURE analyses with all loci and all loci except EAL. When the EAL driven by environmental variables were excluded, the two genetic groups were also dissolved  and mean temperature of coldest quarter (Bio11) were associated with the ecological character of tolerating cold; precipitation of driest month (Bio14) was associated with tolerating drought, precipitation of warmest quarter (Bio18) were associated with avoiding wetness. In China’s warm-temperate zone, rainy and hot seasons overlap most of the time. Thus, precipitation of warmest quarter (Bio18) is tantamount to precipitation of wettest quarter (Bio16). The results of auto correlation analysis of environmental variables also confirmed this climate characteristic in this region. Therefore, we expected that the large numbers of EAL were associated with the environmental variables associated with the ecological characters, i.e. Bio6, Bio11, Bio14 and Bio18. As expected, the results of the Pearson’s correlation analysis and LFMM both showed that Bio14 and Bio18 were associated with the highest number of EAL. However, the environmental variables related to tolerating cold, Bio6 and Bio11, were not associated with high number of EAL. Our results suggested the environmental variables associated with the ecological characters of tolerating drought and avoiding wetness played more important roles in adaptive evolution in C. coggygria. Thus, most aspects of the characterized EAL of C. coggygria agreed with the hypothesis that ecological characters determine adaptation.In this study, we selected Picea mariana are related to temperature and correspond to the nature of cold resistance of budset. De Kort et al. [Alnus glutinosa is related to its resistance to drought. Wang et al. [Cephalotaxus oliveri in response to temperature and precipitation sensitivity determine its adaptive evolution. Roschanski et al. [Abies alba are associated with winter and drought and correspond to the ecological characters related to drought and cold tolerance. On the basis of this considerable evidence, we suggested that the ecological characters of species might be related to species adaptive evolution. Under high intensity of selection pressure, species usually loses ecological character and evolves toward extreme environments for survival. Without the strong selection pressure, species ecological character appears and evolution develops toward variety to ensure a better survival of species. This study provides a reference that ecological character, i.e. species sensitivity, adaptability and resistance to environmental factors, determines the adaptive evolution of species in regions lacking strong selection pressure.Whether the deduction that ecological character determines adaptation is universal must also be discussed. To date, landscape genomics studies in China’s warm-temperate zone are rare. Therefore, comparison with other species cannot help in confirming the universality of our results in this region. Thus, we reviewed some published works on landscape genomics in other regions in recent years. Prunier et al.  argued tt et al.  mentioneg et al.  stated ti et al.  reportedC. coggygria from China’s warm-temperate zone, a region lacking strong selection pressure. Nine SCoT markers were used to investigate the adaptive genetic variation in C. coggygria. Our results showed that significant hierarchical population genetic structure of C. coggygria is due to the divergent selection driven by environmental factors. The ecological characters of C. coggygria, tolerating drought and avoiding wetness, determine its adaptive evolution. Therefore, species ecological character determines the adaptive evolution of species in regions lacking strong selection pressure.The differences in the intensity of selection pressure affect the direction of species evolution. In this study, we sampled C. coggygria were collected from the entire distribution range in China  following the protocols of the manufacturer. DNA concentration was measured using Microcolume Spectrophotometer ND5000 . After preliminary screening, nine SCoT primers  from Collard and Mackill [Electropherograms were viewed with GeneMarker 2.2.0 . To minimize scoring false alleles, peaks between 60 and 1000 bp and heights above 300 relative fluorescent units were scored as a presence (1) or absence (0) matrix for each sample. Subsequent statistical analyses were performed on the basis of this matrix.NA), percentage of polymorphic alleles (PPA), gene diversity of Nei (HE) [Fst between populations, and gene frequencies per allele.AFLPSURV 1.0  was usedei (HE) , pairwisK values were tested from 1 to 10, and 10 replicates were performed for each K. Burn-in periods of 1 × 105 and 2 × 104 Monte Carlo and Markov chains were specified. The choice of the optimal value of K was based on the method introduced by Evanno et al. [FST). RDA was conducted using CANOCO 4.5 [http://www.diva-gis.org/climate). Additional data for each population were extracted using the DIVA-GIS 7.5.0 [r > 0.95) are excluded. Auto correlation analysis of environmental variables was performed using Pearson’s regression in SPSS 19 .The analysis of hierarchical population structure was implemented using the Bayesian-based program STRUCTURE 2.3.4 . For theo et al. , and thio et al. . Hierarco et al.  in infero et al.  in deterNOCO 4.5  in disenIS 7.5.0 . In ChinTwo approaches were used to identify the outlier loci. The first approach was based on the FDIST2 approach proposed by Beaumont and Nichols  and was P < 0.05 were regarded as EAL. The second method is LFMM, which was implemented in LFMM 1.2 [P < 0.001 were regarded as EAL.In further detecting the EAL potentially driven by environmental variations, two methods of environmental association analyses were performed. The first method is Pearson’s correlation analysis, which was implemented using SPSS 19 . This regression analysis ignored the population structure, which might produce a relaxed result of EAL. Similar to RDA, allele frequencies per population were used as the response variable and environmental variables were utilized as explanatory variables. The loci with |r| > 0.50 and LFMM 1.2 . This BaAdditional file 1:The outlier loci identified by FDIST2 and BayeScan. (DOCX 27 kb)Additional file 2;Gene frequencies per allele of 1131 alleles for each population. (DOCX 143 kb)Additional file 3:Environmental variables for each location from the WorldClim database. (DOCX 15 kb)


Tetramorium alpestre sampled in Tyrol, Austria. The social polymorphism is based on genetic and behavioural evidence and contrasts with the supercolonial organisation known from another location in Austria (Carinthia), 150 km away. Microsatellite genotyping using eight polymorphic loci revealed monogyny-monandry and high intranest pairwise relatedness. Interestingly, various experimental one-on-one worker encounters revealed only occasional aggressive behaviour between monogynous colonies, and thus a behavioural polymorphism. Mantel tests revealed a significant negative correlation between spatial distance and relatedness, while worker behaviour was not correlated with relatedness or spatial distance. These results indicate that behaviour might be influenced by other factors – for example, the experience of workers, ecological, chemical, and/or genetic factors not characterised in this study. However, workers distinguished nestmates from non-nestmates also when aggression was lacking. We hypothesise an adaptive value of reduced aggression. We speculate that the non-aggressive and partly aggressive encounters observed represent different options in the social structure of T. alpestre, the non-aggressiveness possibly also promoting supercolony development. The social and behavioural polymorphisms observed offer opportunities to identify the factors triggering these changes and thus further explore the behavioural and social polymorphism of this ant species.Social structure influences animal societies on various levels . In ants, both the number of matings per queen and the number of queens per colony can vary strongly. While workers from both monogynous and polygynous colonies often fight fiercely, in supercolonies , non-nestmates interact peacefully. Studies on social and behavioural polymorphism within ant species can help elucidate their influence on genetic diversity and behaviour and the factors triggering variation in social structure and behaviour. Here, we reveal a behavioural and social polymorphism comprising monogyny with and without internest aggression in  Social structure varies greatly among animal societies. Within each society, the number of reproductive individuals shapes its genetic diversity  Do T. alpestre nests differ in their social structure? (2) Do nests differ in their behaviour in terms of aggression between nests? (3) Are worker behaviour, internest averages of pairwise relatedness, and geographic distance correlated? (4) Do workers that are non-aggressive towards workers from other nests discriminate nestmates from non-nestmates?In this pilot study, we investigate the Alpine ant In 2011, workers were collected from 11 nests in Tyrol, Austria (Supplementary Table 1), with distances among nests ranging from 9 m to 9 km (Supplementary Table 2). Workers were killed in 96% ethanol for genetic analyses or collected alive for aggression tests. See Supplementary online material for all details on fieldwork and worker maintenance.DNA was extracted from 12 workers per nest, that is, a total of 132 workers using the GenElute™ Mammalian Genomic DNA Extraction kit  following the manufacturer’s protocol. The DNA extracts were amplified using primers for nine microsatellites  and expected (HE) heterozygosities. The distribution of genetic differentiation was analysed among all nests. The fixation index (FST) and the inbreeding coefficient (FIS) were calculated as an average over all loci with 10,000 permutations. The FST matrix for pairwise nest-comparisons was analysed for significant genetic differentiation between nests. GenAlEx v6.502  for each nest was calculated following Boomsma and Ratnieks . The NDEMe,p) was calculated following Nielsen et al. (rww) were calculated in GenAlEx using the algorithms of Queller and Goodnight (f) was calculated following Pamilo  with power set to 0.8, a standard threshold for power analyses. A Spearman rank correlation was performed to check for an association between the intranest average of pairwise relatedness and the mean aggression level of each nest . A two-sided t-test was calculated in R using the total antennation time from intranest and non-aggressive internest encounters to detect differences regarding worker discrimination.Mantel tests were calculated to check for correlations between the geographic distance and the behaviour indices (A) AI and, separately, (B) MMAI, internest average of pairwise relatedness and (C) AI and, separately, (D) MMAI, and (E) internest average of pairwise relatedness and geographic distance. Partial Mantel tests (‘Pearson method’) were calculated to check for correlations between geographic distance, internest average of pairwise relatedness, and the behaviour indices AI and, separately, MMAI while controlling for the effect of each of the variables. The Mantel and partial Mantel tests were performed with 9999 permutations using the ‘biotools’ and the ‘ecodist’-package in R, respectively. To determine the power of the Mantel tests for different effect sizes, sensitivity power analyses were performed using the ‘mantelPower’-function in the ‘biotools’-package in R. For these analyses, the effect sizes were set to range from 0.10 to 1.00 incrementing by 0.01, and the respective power was calculated for each effect size (Supplementary Table 3). To assist the interpretation of our results, effect sizes calculated from our data were then compared with the effect sizes that would have yielded significant results . Among all nests, the global FST value for internest genetic differentiation was 0.37 (P < 0.001); the global FIS value for genetic inbreeding was not significant . After Bonferroni–Holm correction, all pairwise-nest comparisons were significant regarding their FST values (internest genetic differentiation), which ranged from 0.20 to 0.52 (data not shown).For both subsets (four workers and one worker from each nest), MICROCHECKER revealed the possible presence of null alleles in locus 55a, which thus was excluded from further analyses. After correction for multiple comparisons, no locus deviated from HWE or departed significantly from LE for both subsets. The eight loci yielded a total of 68 alleles in the 132 workers genotyped. The mean HQest) and 11 males (Mest). A Me,p value of 1.00 for all nests confirmed that queens mated only once. The NDE ranged from 2.02 × 10–8 to 9.97 × 10–37 for all nests. The NSE was 1.62 × 10–3 for all nests. The averages of pairwise intranest and internest relatedness values ranged from 0.62 to 0.82 and from – 0.39 to 0.31, respectively  and the average pairwise intranest relatedness values, each nest was inferred to have one single queen.COLONY assigned 11 monogynous-monandrous colonies to the 11 nests sampled and calculated a number of 11 queens , while the mean frequency of behaviour levels 0 and 3 (ignoring and food exchange) were significantly lower in internest than in intranest encounters .In the 44 intranest encounters, AI and MMAI ranged from 0.00 to 0.11 and from 0.00 to 1.50, respectively. In the 220 internest encounters, AI and MMAI ranged from 0.00 to 2.18 and from 0.00 to 5.75, respectively  or between the geographic distance and AI and, separately MMAI, when controlling for internest averages of pairwise relatedness . The effect sizes of the sensitivity power analyses ranged between ≥ 0.30 and ≥ 0.47 (Supplementary Table 3). There was no significant correlation between the intranest averages of pairwise relatedness and any of the two aggression indices, AI and MMAI . Workers distinguished nestmates from non-nestmates, in that total antennation times were significantly shorter in intranest than non-aggressive internest encounters .Geographic distance had no influence on AI  further indicates strong genetic differentiation among nests , limited or no gene flow can be inferred in our data, which in turn might increase genetic differentiation among nests. The FST value obtained here is comparable with those in various other studies applying a similar spatial sampling design ranging from several metres to kilometres, for example on Anoplolepis gracilipes  of approximately one in all nests. The low internest averages of pairwise relatedness indicated that most workers were unrelated , the results revealed monogynous-monandrous colonies in Tyrol, thus differing from the supercolony detected in Carinthia  in all nests, and the effective number of queens  The colonies could stay monogynous on the long run, but workers might avoid aggression as it is time and energy consuming and linked to injury and mortality  The colonies might represent an intermediate state where aggression is being reduced, but polygyny and, eventually, supercoloniality have not yet been established similarity using gas chromatography-mass spectrometry, and on comparative genomics and transcriptomics to identify and characterise potential variation in coding regions.We detected that neither the internest averages of pairwise relatedness nor the geographic distance correlated with worker behaviour (Mantel test). Only geographic distance and the internest averages of pairwise relatedness were significantly negatively correlated. Also the partial Mantel tests, controlling for an effect of another variable, revealed no significant correlation. Addressing Question (3), the results reveal that, at least in this pilot study in Tyrol, no significant correlation involving the behaviour was detectable, while with increasing geographic distance the internest averages of pairwise relatedness decreased. This decrease of pairwise relatedness is not unexpected as the distance among the most distant colonies was approximately 9 km. The sensitivity power analyses revealed that, with our sample size, alpha level, and power, correlation coefficients ranging between 0.30 and 0.47 would have yielded significant results (Supplementary Table 3), suggesting that moderate to strong correlations should have been detected despite the moderate sample size of this pilot study. At least for this study, the variation in behaviour seems not to be substantially spatially or genetically determined . Rather, the behavioural variation observed might depend on (i) the context as suggested, for example, for T. alpestre, workers distinguished nestmates from non-nestmates, which was also observed in Formica paralugubris : As mentioned above, a reduction in aggression probably increases time spent on tasks beneficial for the colony, such as nest maintenance and foraging. Lacking aggression is often associated with reduced recognition ability, for example in invasive species (Giraud et al.


SNP60 BeadChip in order to identify selection signatures related to the monthly variation of Brazilian environmental variables. Using BayeScan software, 71 SNP markers were identified as STF outliers and 60 genotypes (58 markers) were found by Samβada software in 371 logistic models correlated with 112 environmental variables. Five markers were identified in both methods, with a Kappa value of 0.073 (95% CI: 0.011–0.134). The frequency of these markers indicated a clear north–south country division that reflects Brazilian environmental differences in temperature, solar radiation, and precipitation. Global spatial territory correlation for environmental variables corroborates this finding . The distribution of alleles over the territory was not strongly correlated with the breed/genetic groups. These results are congruent with previous mtDNA studies and should be used to direct germplasm collection for the National gene bank.Samples of 191 animals from 18 different Brazilian locally adapted swine genetic groups were genotyped using Illumina Porcine  Through equilibrium between evolutionary  and local environment forces have originated several locally adapted swine breeds that one which have had sufficient time in the county for to be genetically adapted to the environment  located at Embrapa Genetic Resources and Biotechnology Center, Brasilia, DF. The samples from locally adapted Brazilian swine genetic groups were classified in accordance with a phenotypic description suggested by Viana ((The Brazilian territory is divided into five regions (each further divided into states) based on natural, cultural, social, and economic features. Despite the high mobility of swine, free movement of animals between states and regions is restricted by legal and sanity factors . So, to capture high spatial representation of the environment and genetic territorial dispersion of the swine breeds over the Brazilian territory, the sample selection Table  and S1 wby Viana , Germano Viana  was performed with SNP & Variation Suite v8.x , GTOPO30 (https://lta.cr.usgs.gov/GTOPO30), and Harvest Choice (http://harvestchoice.org/) and 30‐s geographical information system (GIS) layer using Qgis v2.6  and observed heterozygosity (Ho) were computed by the SNP & Variation Suite v8.x , to identify sources of genetic variance by means of analysis of molecular variance (AMOVA) between and within the geopolitical groups (samples within region), as well as between native and commercial breeds (Tables K)  was calculated in PASSaGE . To explore the linkage disequilibrium (LD) of selection signatures detected with other FST outliers and with nearby genes, we calculated the LD from these markers using Plink software  was calculated. Moran's I describes the autocorrelation between the values of a variable in a certain location with the values of this same variable in a neighboring location , which indicates low genetic differentiation between individuals within the herds of this breed. Within regions, the populations from the State of Mato Grosso do Sul showed higher inbreeding levels (μ = 0.296), while the animals from the State of Paraiba revealed a tendency toward excess of heterozygotes (μ = −0.001).The IS Table  varied fK equal to the number of sampled breeds (K = 18). But, using the second‐order rate of change of the likelihood (ΔK), the number of groups was reduced to seven (k = 7).The theoretical number of actual populations using the genetic frequencies of the loci to infer the influence of the genetic groups on the composition and number of populations Figure  was deteST outliers, while Samβada software identified 60 genotypes (from 58 SNP markers) in 371 univariate logistic models, using 112 environmental variables. No multivariate model was significant at α=0.05. The markers MARC0021990; ASGA0033717; MARC0007678 were responsible for 42% of all models generated by Samβada  neighbor windows , and reaching close to zero with 15 (fifteen) neighbor windows. The highest value for Moran′s I was associated with solar radiation in the summer months. The selection signal markers have had a high global spatial correlation between 5 and 10 neighbors and present a rapid decrease to zero with 35 neighbors  > 1e−05, with 99,999 replications).The Mantel test between individual pairwise genetic distances Fst/(1‐Fst) and individual geographical distance had correlation coefficient of 4IS values presented by the Monteiro breed: a breed raised only in the states of Mato Grosso do Sul and Mato Grosso.The evolution and adaptation of pigs are subject to environmental influences, as has been observed in humans Storz , humans ST outliers. The use of georeferenced environmental data associated with FST outliers helps in the understanding of the evolutionary process and the influence of the environment on this process.The small number of groups suggested by STRUCTURE, similar to that found by Sollero et al.  working FST. According to Pérez‐Figueroa, García‐Pereira, Saura, Rolán‐Alvarez, and Caballero  SNP markers, associated with temperature, solar radiation, and BIO18 linked with intracellular activity and circulatory system and were considered important for species adaptation.The distribution of SNP alleles over the Brazilian territory demonstrates a clear north–south orientation, dividing the country into two distinct regions according to climatic conditions, drier and sunnier in the North and wetter and colder in the South. This information on selection signature distribution across Brazilian territory could be included in programs of assisted selectin using genetic markers, helping farmer through easier management of animals selected for adaptive characteristics. In the same way, the markers could be used to direct animals for more suitable regions according to their genotype in both traditional husbandry situations as well as genetic resource conservation programs.None declared.http://nrrc.ars.usda.gov/A-GRIN/genomic_account_page?record_source=US.Genetic data deposited at Animal GRIN/Alelo Animal. It is available in All authors enrolled have had equal contribution to the work presented in this paper, since the acquisition of data, until final approval of the version to be published. Click here for additional data file. Click here for additional data file.


Histone deacetylase 3 (HDAC3) plays pivotal roles in cell cycle regulation and is often aberrantly expressed in various cancers including hepatocellular carcinoma (HCC), but little is known about its role in liver regeneration and liver cancer cells proliferation. Using an inducible hepatocyte-selective HDAC3 knockout mouse, we find that lack of HDAC3 dramatically impaired liver regeneration and blocked hepatocyte proliferation in the G1 phase entry. HDAC3 inactivation robustly disrupted the signal transducer and activator of transcription 3 (STAT3) cascade. HDAC3 silencing impaired the ac-STAT3-to-p-STAT3 transition in the cytoplasm, leading to the subsequent breakdown of STAT3 signaling. Furthermore, overexpressed HDAC3 was further associated with increased tumor growth and a poor prognosis in HCC patients. Inhibition of HDAC3 expression reduced liver cancer cells growth and inhibited xenograft tumor growth. Our results suggest that HDAC3 is an important regulator of STAT3-dependent cell proliferation in liver regeneration and cancer. These findings provide novel insights into the HDAC3–STAT3 pathway in liver pathophysiological processes. Quiescent maturity hepatocytes in the G0 phase of the cell cycle can re-enter into the cell cycle rapidly to produce new hepatocytes following partial hepatectomy (PH) or chemical injury2. In humans, liver regeneration proceeds within weeks of a major liver resection, whereas in rodents, liver recovery can occur within 10 days of PH1. Aberrant liver regeneration is closely related to the pathogenesis of liver failure or hepatocellular carcinoma (HCC)3. A better understanding of the basic mechanisms that regulate liver regeneration will have important implications for potential therapies for human liver disease.The mammalian liver has an enormous regenerative ability to restore its original mass and function via compensatory hyperplasia in response to diverse injuries4. HDAC3 regulates gene transcription by catalyzing the deacetylation of core histones and is involved in various biological processes in the liver5. Inhibition of HDAC3 and other HDACs specifically interferes with liver development in zebrafish6. In mammals, hepatic-specific ablation of HDAC3 dramatically disrupts liver function and causes severe dysregulation of lipid and glucose metabolism8. Conversely, high HDAC3 expression in the liver was found to contribute to high-fat-diet-induced metabolic syndrome9. HDAC3 is also essential for the maintenance of chromatin structure and genomic stability, and hepatic loss of HDAC3 in mice results in an accumulation of DNA damage and the early onset of spontaneous liver cancer11. Still, the role of HDAC3 in HCC development remains unclear because the mRNA and protein levels of HDAC3 can either increase or decrease in human HCC tissues12. Furthermore, HDAC3 is a considered biomarker of tumor recurrence following liver transplantation in hepatitis B virus-associated HCC13. HDAC3 also participates in the self-renewal process of liver cancer stem cells through histone modifications14, making HDAC3 suppression a potential clinical-therapeutic approach for HCC.Histone deacetylase 3 (HDAC3), a member of the highly conserved Class I HDAC enzymes, is ubiquitously expressed in eukaryotic cellsin vitro studies have revealed that HDAC3 plays diverse roles in the regulation of cell cycle progression. HDAC3 is required to generate a hypoacetylated H3 tail as the preferred template for the phosphorylation of H3S10 by Aurora B, which is a crucial step for progression through the G2/M phase and mitosis15. HDAC3 is also required for H3K4 deacetylation at the centromere and sister chromatin cohesion17. HDAC3 inhibition induces G1 phase arrest by increasing p21WAF1/cip1 expression in Hep3B liver cancer cells18. HDAC3 deletion in mouse embryonic fibroblasts induces S-phase arrest through DNA damage accumulation, whereas inactivation of HDAC3 affects replication fork progression in hematopoietic progenitor cells during the S phase19. HDAC3 also controls G2/M progression in adult neural stem/progenitor cells by regulating the CDK1 levels20. In addition, HDAC3 regulates cell cycle progression by modulating cyclin A acetylation to control cyclin A level21. The data regarding the role and molecular mechanism of HDAC3 in regulating cell proliferation are highly multiple and contradictory. However, the biological function of HDAC3 in liver regeneration and liver cancer cells proliferation remains unknown.Multiple In this study, we determined that HDAC3 promotes liver regeneration and liver cancer cells proliferation through the signal transducer and activator of transcription 3 (STAT3) signaling pathway. The role of HDAC3 in the regulation of cell proliferation through the STAT3 pathway provides a potential drug target for the treatment of HCC.10, in the Alb-Cre;HDAC3loxP/loxP mouse, where HDAC3 is constitutively inactivated, dramatic dysregulation of hepatic metabolism and notable damage to the DNA are evident at 2 months of age , in which HDAC3 in the liver tissues was over 80% ablated after 5 days of tamoxifen treatment (hereafter referred to as the HDAC3△ mouse)  to explore the specific role of HDAC3 during liver regeneration. Surprisingly, the post-hepatectomy mutant mice displayed a severely impaired liver mass reconstitution, which was calculated using the liver weight/body weight ratio  injection, the absence of HDAC3 did not increase sensitivity to the toxin but robustly delayed liver repair  term analysis of the differentially regulated genes showed that the interleukin 6 (IL6)/STAT3 signaling pathway, which regulates approximately 36% of the immediate-early genes during the acute hepatic responsebed Fig. . As expeers Fig. . AdditioPCR Fig. . Collect24. When IL6 combines with its membrane receptor glycoprotein 130 (gp130), STAT3 becomes phosphorylated at tyrosine705 (Y705) by receptor-associated Janus kinases (JAKs)24. Compared with the control littermates, in which a large number of p-STAT3(Y705) nuclear-positive hepatocytes were observed at 3 h after PH, nuclear p-STAT3(Y705) was abolished in HDAC3-deficient mice  and c-myc remained notably depressed in the HDAC3△ livers, regardless of the upregulation of p-JAK2 , while the control liver showed the opposite dynamic  is critical for enhancing the transcription of cell growth-related genesers Fig. . As STATmic Fig. . The absmic Fig. . Conversmic Fig. .△;mT/mG mice, respectively 26Sortm4 mice (hereafter referred to as mT/mG mice) and HDAC324, we tested whether ac-STAT3 prevented the nuclear translocation of STAT3. Indeed, the immunofluorescence analysis showed that the nuclear localization of total STAT3 was significantly suppressed in the HDAC3-deficient hepatocytes after IL6 treatment  with significantly increased cytoplasmic ac-STAT3; additionally, IL6 stimulation failed to upregulate p-STAT3(Y705) in the HDAC3-deficient cells Fig. . Becauseent Fig. . Moreoveent Fig. . Meanwhient Fig. , which i29, but the individual roles of the Class I HDAC members in the deacetylation of ac-STAT3 in vivo remain undefined. We previously reported that loss of HDAC1 and/or HDAC2 did not disturb cell cycle progression before the M phase31, which highlighted the negative effects of HDAC1 and HDAC2 on the IL6/STAT3 signaling cascade. Consistently, deletions of HDAC1, HDAC2, or both did not increase the level of ac-STAT3 compared to that of the wild-type (WT) liver during the early phase of liver regeneration  which facilitates the nucleoplasm transport of HDAC3asm Fig. . Moreoveasm Fig. , suggest35. The IL6/STAT3 signaling pathway plays crucial roles in the tumorigenesis and development of HCC36. Because HDAC3 is a critical regulator of STAT3 signaling during liver regeneration, we investigated whether elevated HDAC3 in HCC was correlated with a higher tumor cell proliferation rate. We performed immunohistochemistry to assess HDAC3 expression in tissues from 90 HCC cases, and observed that 27 cases exhibited strong nuclear and cytoplasmic HDAC3 positivity, and that HDAC3 was either negatively or weakly expressed in the corresponding adjacent non-tumor tissues   Fig. . The Kaply) Fig. .Fig. 6HD12, to reduce HDAC3 expression in SMMC-7721 cells. HDAC3 inhibition significantly reduced tumor growth and decreased the expression of Ki67 in HCC xenograft tumors , which activates the cAMP-dependent pathway, leads to a breakdown in growth factor signaling and dramatically arrests the regenerating hepatocytes in the G1 phase extension38. Lack of HDAC3 results in a similar proliferative defect at the cell cycle entrance, which indicates a breakdown in the cytokine-dependent or growth factor-dependent signaling cascade. Using a microarray assay analysis and LPS/PH model, we showed that HDAC3 deficiency dramatically impairs the STAT3 signaling pathway, which is a critical signaling cascade that promotes the transcription of numerous immediate-early genes in response to acute liver injury.Two types of transmembrane enzyme-linked receptor-mediated signaling cascades, namely, the cytokine-dependent pathway and the growth factor-dependent pathway, which transduce extracellular growth stimuli into the nucleus, play crucial roles in priming liver regeneration39; however, the details regarding deacetylation of STAT3 remain unclear. Our data show that HDAC3 deficiency remarkably increases the p300-dependent STAT3 acetylation in the cytoplasm of hepatocytes. It has been suggested that ac-STAT3 is likely to be deacetylated by class I HDAC members in the nucleus28. Unlike HDAC1 or HDAC2, which predominantly function in the nucleus, HDAC3 is a unique Class I HDAC subfamily member that regulates acetylation status of non-histone proteins in the cytoplasm34. Our results revealed that HDAC1 and/or HDAC2 deficiency does not change the acetylation status of STAT3 in the liver, while loss of HDAC3 results in a substantial cytoplasmic accumulation of ac-STAT3, indicating that the deacetylation of ac-STAT3 might be selectively dependent on HDAC3 in the cytoplasm rather than in the nucleus.As a nucleocytoplasmic shuttling transcription factor, STAT3 is acetylated by p300 in the nucleus24. Our data show that HDAC3 deficiency significantly increases ac-STAT3 level and decreases p-STAT3(Y705) level together with a decrease in STAT3 nucleus entry, suggesting that HDAC3-mediated deacetylation of ac-STAT3 is essential for STAT3(Y705) phosphorylation and STAT3 reactivation cycle. It has been shown that the conserved lysine residues for acetylation in STAT3 are all near the Y705 residual of STAT329, supporting the possibility that STAT3 acetylation induced by HDAC3 deficiency could prevent STAT3(Y705) phosphorylation and block STAT3 signaling activation. However, the detail mechanisms of HDAC3-mediated modifications of STAT3 deacetylation and phosphorylation in hepatocytes remain further studies. Regardless of other mechanisms that might be regulated by HDAC3 during the whole process of hepatocyte proliferation, HDAC3 is undoubtedly critical to the initiation of the cell cycle progression through the proper transition of ac-STAT3 to p-STAT3. Therefore, HDAC3 may act as a molecular switch in the STAT3 signaling cascade that transmits growth stimuli from the cytoplasm to the nucleus.After IL6 stimulation, STAT3 that resides in the cytoplasm rapidly becomes phosphorylated and translocates into the nucleus to induce target genes expression37, making HDAC3 inhibition a potential strategy for cancer therapy. The HDAC3 mRNA level is lacked in one-sixth of human HCC tissues11. The inactivation of HDAC3 may contribute to HCC development by increasing global histone acetylation, defective DNA damage repair, and mutation accumulation11. HDAC3 is also upregulated in a specific subset of HCCs13; however, the role of HDAC3 in the pathogenesis of HCC remains unknown. The upregulated expression of HDAC3 in human HCC prompts us to determine whether the HDAC3–STAT3 pathway enhances HCC tumor growth. Notably, IL6, a cytokine that is essential for activating STAT3 signaling in liver inflammation, enhances HCC development36. We found that overexpression of HDAC3 is significantly associated with an increased p-STAT3(Y705) level and Ki67 index in HCC. Moreover, our xenograft assay shows that a low dose of panobinostat could significantly decrease HCC xenografts growth along with a significant reduction in p-STAT3(Y705) level. Our data suggest that the drug panobinostat approved by FDA might also prevent liver cancer cells proliferation through HDAC3–STAT3 signaling pathway in HDAC3-overexpressed HCC, while the detail effects and mechanisms of panobinostat for the clinical treatment of HCC remain further studies.Multiple studies have shown that HDAC3 is aberrantly expressed in various human cancers and that the aberrant expression is often associated with a poor prognosisIn summary, we have demonstrated that HDAC3 acts an essential role in the enhancement of the STAT3 signaling pathway by catalyzing the deacetylation of ac-STAT3 in the cytoplasm, thereby, promoting liver regeneration and tumor growth, as illustrated in Fig. loxP/loxP frozen embryos were purchased from the European Mouse Mutant Archive (EMMA), University of Veterinary Medicine, Vienna. Alb-Cre and Alb-CreERT2 transgenic mice were purchased from Shanghai Biomodel Organism Science & Technology Development Co., Ltd, China. The genotypes of Alb-Cre;HDAC3loxP/loxP and Alb-CreERT2;HDAC3loxP/loxP mice were determined using PCR amplification of tail DNA. The sequences of genotyping primers are presented in Supplementary Table All mouse experimental procedures were approved by the Animal Care and Use Committee of Sichuan University. HDAC320. Six-week-old male Alb-CreERT2;HDAC3loxP/loxP mice were injected intraperitoneally with tamoxifen (2 mg per mouse) for five consecutive days. Experiments on mice were performed 5 days after the last injection. Then, these mice were treated with either classical 2/3 PH surgery or intraperitoneal injection of CCl4 (10 ml/kg body weight)31. BrdU (1 mg/kg body weight) was intraperitoneally injected 60 min before sacrifice.For inducible deletion of HDAC3, tamoxifen was dissolved in corn oil containing 10% (vol/vol) ethanol to 20 mg/ml and shaken overnight at 37 °Ct-test with Welch correction. A p-value < 0.05 was considered significant.Data were expressed as the mean ± standard deviation (SD). Statistical comparisons were assessed with an unpaired, one-tailed Student’s Supplemental Information(DOCX 39 kb)Supplementary Figure1(JPG 2635 kb)Supplementary Figure2(JPG 2619 kb)Supplementary Figure3(JPG 2374 kb)Supplementary Figure4(JPG 5649 kb)Supplementary Figure5(JPG 2727 kb)Supplementary Figure6(JPG 1569 kb)Supplementary Figure7(JPG 3396 kb)Supplementary Figure8(JPG 4006 kb)Supplementary Figure9(JPG 2420 kb)Supplementary Figure10(JPG 2481 kb)


Ophionotus victoriae to examine genetic structure and determine whether barriers to gene flow have existed around the Antarctic continent. Ophionotus victoriae possesses feeding planktotrophic larvae with presumed high dispersal capability, but a previous study revealed genetic structure along the Antarctic Peninsula. To test the extent of genetic differentiation within O. victoriae, we sampled from the Ross Sea through the eastern Weddell Sea. Whereas two mitochondrial DNA markers (16S rDNA and COI) were employed to allow comparison to earlier work, a 2b‐RAD single‐nucleotide polymorphism (SNP) approach allowed sampling of loci across the genome. Mitochondrial data from 414 individuals suggested three major lineages, but 2b‐RAD data generated 1,999 biallelic loci that identified four geographically distinct groups from 89 samples. Given the greater resolution by SNP data, O. victoriae can be divided into geographically distinct populations likely representing multiple species. Specific historical scenarios that explain current population structure were examined with approximate Bayesian computation (ABC) analyses. Although the Bransfield Strait region shows high diversity possibly due to mixing, our results suggest that within the recent past, dispersal processes due to strong currents such as the Antarctic Circumpolar Current have not overcome genetic subdivision presumably due to historical isolation, questioning the idea of large open circumpolar populations in the Southern Ocean.Marine systems have traditionally been thought of as “open” with few barriers to gene flow. In particular, many marine organisms in the Southern Ocean purportedly possess circumpolar distributions that have rarely been well verified. Here, we use the highly abundant and endemic Southern Ocean brittle star  Ophionotus victoriae Bell, 1902 is the most common and is a highly abundant ‐sponsored research expeditions , three British Antarctic Survey (BAS)‐sponsored expeditions , and from an Alfred Wegener Institute (AWI) campaign (RV Polarstern PS77). Upon collection, samples were morphologically examined  to verify species designations as described , and finished sequences were aligned with MEGA 6  for COI (414 samples), 16S (251 samples), and a concatenation of both mtDNA fragments (251 samples).Genomic DNA was extracted using Qiagen's DNeasyO. victoriae (Table S8), Ophionotus hexactis (GenBank Accession Number KU895454.1), and Ophiacantha spectabilis (EU869959.1‐EU869961.1). The latter taxa were employed to reveal genetic distance to other related ophiuroid taxa.To aid with species delineation based on CO1 sequence data, a histogram of uncorrected pairwise distances (p)   on an Illumina Hi‐Seq 2000 using v4 chemistry and generating 50‐bp single‐end reads.For RAD‐tag analyses, a subset of 96 samples spanning 15 sampling locations from the Ross through the western Weddell Seas were examined. Due to logistical issues, only samples from the et al.'s  2b‐RAD phttps://github.com/Eli-Meyer). Specifically, data were first filtered by loci with a minimum coverage of 25X. For all SNPs, loci scored as homozygotic were defined to have a maximum variance of 1%, whereas those that were consider heterozygotic had a minimum of 25% variance. Loci deviating from these definitions were excluded from further analyses. Remaining SNP loci were then further filtered to only include loci that were present in ≥80% of individuals. To ensure individuals with large amounts of missing data did not skew analyses, those with ≤80% of the remaining SNP loci were also removed from the dataset. Raw data were also processed and analyzed using the software Stacks , STRUCTURE 2.3.4 ; (2) an admixture model with correlated allele frequencies; (3) a 50,000 repetition burn‐in period; and (4) 100,000 additional Markov chain Monte Carlo (MCMC) repetitions. Because SNP datasets can vary in λ  compared to mtDNA  was likely an intermixing site based on results of STRUCTURE pairwise FST and PCA. Thus, analyses were performed considering Op877 as belonging to both possible source populations. This did not affect interpretation of results, and thus, samples from Op877 were pooled with the population for which it is the most similar, the Weddell‐A population. Furthermore, BayeScan  accession numbers SAMN05944630‐SAMN5944718. Data matrices and alignments are deposited to Dryad under accession numbers doi:10.5061/dryad.0k1r0.All sequences collected herein are reported under GenBank accession numbers 33.1O. victoriae. COI data analyzed from 414 individual yielded an increased nucleotide diversity from an extended geographic range in comparison with 16S or concatenated COI and 16S data for 252 individuals , for both mitochondrial markers  represents comparisons between O. victoriae and O. hexactis. Finally, comparisons within O. victoriae samples yielded two distinct modes. The mode closest to the origin (~1.8%–4.0% mean = 2.8%) represents comparisons between individuals restricted to subnetwork lineages I, II, and III illustrated in Figure The histogram of uncorrected pairwise distances Figure b yielded3.2K from STRUCTURE HARVESTER for this filtered and reduced dataset, K of 8 had the highest average support for Delta K after seven runs, although individual runs of K at 2 and 4 had the highest maximum‐likelihood scores . Thus, to assess which K was the most appropriate, STRUCTURE analyses were conducted with K set to 8, 4, and 2, then subjected to pairwise FST tests. At K = 2 and 4, all populations were significantly different .Following quality filtering and SNP calling, 16,588 loci were recovered Table . To furtK = 4. All pairwise comparisons of the four STRUCTURE populations were significantly different as judged by a chi‐square test with a p < .01 cutoff (Table S3). To understand whether major geographic regions coincided with inferred STRUCTURE and SMARTPCA populations, we also pooled samples by geographic regions identified in methods . SMARTPCA results reveal significant differences between the Ross Sea/western Antarctic Peninsula, the Bellingshausen Sea, and the Weddell Sea. However, the Bransfield Strait appears to be an intermixing zone.Population structure was further investigated with SMARTPCA analyses that revealed geographic structuring in concordance with STRUCTURE. Figure 2 = 91.12, df = 108 and p = .879).BayeScan analysis of filtered SNP loci reported only one locus under possible selection. When the sequence containing this SNP was searched using BLAST on the NCBI webserver, a 100% match came back to two different genes, specifically leucoanthocyanidin dioxygenase and fam206a. Due to the nature of the short 36‐base pair fragment, we cannot be positive as to the true identity of the SNP‐containing fragment. Furthermore, Hardy–Weinberg equilibrium was not violated for any loci  among populations.The combination of mtDNA COI sequence and nuclear SNP data provided strong evidence for regional genetic structure of 4O. victoriae, questioning its current status as a single species. As mentioned, specimens were examined and morphological differences were not discernable with current taxonomy. However, both mtDNA and 2b‐RAD data suggest distinct genetic lineages within what is currently recognized as O. victoriae. Although the Bransfield Strait appears to be more diverse then other populations indicating a possible mixing zone, the degree of genetic structuring appears ordered by major geographic regions.Both mtDNA and 2b‐RAD data reveal considerable genetic structure across the Western Antarctic in the brittle star 4.1O. victoriae or at least large‐scale geographic dispersal capabilities through the Ross Gyre and out into the ACC.Based on analyses of mtDNA COI, the western Weddell Sea has a recent shared history, or is currently connected with, the oceanic islands, and interestingly, the eastern Weddell Sea samples share a discrete haplotype subnetwork with the Ross Sea and Western Peninsula  to be a single genetic population. One likely reason for this observation is transportation of planktotrophic larvae by the ACC from the Ross Sea to the Western Peninsula. The ACC contacts the northern tip of the Western Peninsula. Depending on the depth planktotrophic larvae reside, the Circumpolar Deep Water and Upper Circumpolar Deep Water could have a significant impact on their distribution as well (Tynan, Given that echinoderm larvae can remain in the water column for several months in the SO (Pearse et al., l Tynan, .O. victoriae populations in both STRUCTURE and SMARTPCA analyses reveals distinct geographic structure. COI data further corroborate 2b‐RAD data in that O. victoriae in the Bellingshausen Sea and Amundsen Sea appear to represent a singular, largely disconnected clade (Figure The level of genetic differentiation recovered from 2b‐RAD between e Figure .4.3Promachocrinus kerguelensis, Wilson et al., Doris kerguelenensis Wilson, Maschek, & Baker, ; Odontaster validus, Janosik et al., Nymphon australe, Mahon et al., O. victoriae populations.The high‐resolution 2b‐RAD approach was consistent with findings from COI while providing greater genetic resolution. Although 2b‐RAD data were more geographically and numerically limited relative to COI, both recovered strong connections between the Ross Sea and Western Peninsula, a distance of over 5,000 km, bypassing the Bellingshausen and Amundsen Seas. This connection is likely the result of transport from the Ross Gyre into the ACC, which does not contact the Antarctic shelf again until the western portion of the Antarctic Peninsula Tynan, . As seenO. victoriae. Whereas some lineages have very broad distributions and may be circumpolar, others are more restricted.Probable causes for this diversity may be mixing of distinct water masses, including water from the ACC, and thus populations in the region Gill, , repeate4.4Our study afforded the opportunity to compare traditional mtDNA markers to a whole‐genome SNP‐based approach such as 2b‐RAD. Other studies have recognized the ability of RAD data to recover structure that traditional markers have overlooked (Reitzel et al., None declared.KY048203‐KY048268. Raw reads for 2b‐RAD SNP data are deposited to SRA SAMN05944630‐SAMN5944718. Data matrices and alignments are deposited in Dryad under accession doi:10.5061/dryad.0k1r0.All sequences collected herein are reported under GenBank accession numbers  Click here for additional data file. Click here for additional data file.


AFLP genetic study implied that the naturally fragmented arid zone tree, Acacia carneorum, is almost entirely dependent on asexual reproduction through suckering, and stands may have lacked genetic diversity and interconnection even prior to the onset of European pastoralism. Here we surveyed microsatellite genetic variation in 20 stands to test for variation in life histories and further assessed the conservation status of the species by comparing genetic diversity within protected stands in National Parks and disturbed range lands. Using herbarium records, we estimate that 219 stands are extant, all of which occur in the arid zone, west of the Darling River in southeastern Australia. With two exceptions, all surveyed stands comprised only one multilocus genet and at least eight were putatively polyploid. Although some stands comprise thousands of stems, our findings imply that the species as a whole may represent ~240 distinct genetic individuals, many of which are polyploid, and most are separated by >10 km of unsuitable habitat. With only 34% of stands (and therefore genets) occurring within conservation reserves, A. carneorum may be at much greater risk of extinction than inferred from on‐ground census data. Land managers should prioritize on‐ground preservation of the genotypes within existing reserves, protecting both vegetative suckers and seedlings from herbivory. Importantly, three stands are known to set viable seed and should be used to generate genetically diverse germ‐plasm for ex situ conservation, population augmentation, or translocation.Long‐lived, widespread plant species are expected to be genetically diverse, reflecting the interaction between large population sizes, overlapping generations, and gene flow. Such species are thought to be resilient to disturbance, but may carry an extinction debt due to reproductive failure. Genetic studies of Australian arid zone plant species suggest an unusually high frequency of asexuality, polyploidy, or both. A preliminary  Furthermore, these seeds are likely to fail to germinate or grow under persistent drought conditions and in the face of extraordinarily high levels of herbivory. Second, surveys of flower visitation and inflorescence development within a small number of stands have shown that the hermaphroditic flowers receive a diverse suite of insect visitors . Moreover, visits per inflorescence are infrequent, and the insects carry very little pollen and hence are probably ineffective pollinators  within each of 10 stands using dominant AFLP markers. These markers cannot be used to detect polyploidy and their characteristically high genotyping error rate and efficiency in detecting somatic mutation may have caused an overestimation of genotypic variability.Several lines of evidence suggest that despite the annual production of flowers, A. carneorum stands. This allowed us to further test for geographic variation in life histories and to compare standing levels of genetic and genotypic diversity. In particular, we set out to characterize the genotypic composition of A. carneorum stands arrayed along the western boundary of the Darling River floodplain, the species' current eastern range limit, including stands under protection within Kinchega National Park, western New South Wales, with others found within the arid zone of eastern South Australia . However, there has been no attempt to consolidate this information. Here, we combined existing records of occurrence with on‐ground survey data to estimate the number of extant stands of A. carneorum across its range. We had the additional aim of determining the number of stands that are located within existing conservation reserves. Stands vary enormously in numbers of stems, density, and area occupied. Several stands comprised single stems and others >10,000 stems , we treated the allelic composition at each locus, and summed over all loci, as a single‐ and multi‐locus phenotype (MLPs), respectively. Indeed, when a genotype cannot be determined unambiguously, then it should be treated as a phenotype rather than a genotype. Based on the MLPs for each stand, and for the entire sample of A. carneorum, we calculated the total number of different alleles summed across all eight loci (A), the mean (±SE) number of different alleles per locus (A'), the mean (±SE) proportion of individuals that were apparently heterozygous (Ho), and the mean (±SE) observed number of alleles per individual (A'MAX). The number of apparently different MLPs was used as an estimate of the number of distinct genetic individuals within each stand, and overall, for A. carneorum, as a species. MLPs were found using the “matches” function implemented in genalex 6.41 , each stand appears to comprise a single ancestral genetic identity (refer to Results). We acknowledge that the 20 stands for which we had collected microsatellite data may all have a polyploid genetic system, with the clear lack of sexual recombination meaning that, in some stands, a third or fourth allele is masked, thus the presence of stands which appear apparently diploid. Indeed, in scoring the electropherograms some individuals within apparently diploid stands appeared to display dosage variation, for one of the alternative alleles. However, it was not possible to optimize the assay conditions to unambiguously score this variation. Alternatively, there may be stands of N'MLP). Such variation is a predictable consequence of somatic mutation  or genotyping errors. Repeated extraction and re‐analysis minimized the possibility of genotyping errors. Bruvo genetic distance estimates for pairs of sexually produced genets  should be much higher than for neighboring stems within the same stand, that is, ramets with a likely asexual origin. Indeed, a frequency distribution plot of Bruvo genetic distances matched this expectation, as it was clearly bimodal, with a minor peak at the lower end of the range of genetic distances , and probability of identity for siblings (pIDsib), to assess the statistical power of our marker set of eight loci to differentiate among sexually generated lineages. Using a reduced data set, that is, just 12 distinct apparently diploid genets , we used Genalex 6.41 to calculate the average pID that, two unrelated individuals, drawn from the same randomly mating population, will by chance have the same multilocus genotype, and the related measure pIDsib, which takes into account the genetic similarity among siblings which could clearly be relevant in small isolated stands.33.1A. carneorum from which we estimate there are 222 distinct stands, of which three are extinct.We obtained a total of 523 records of occurrence of At the time of analysis, the overall set of records represented 206 and 317 records from the New South Wales Wildlife Atlas and the Atlas of Living Australia, respectively. However, many of these records were duplicates generated by submissions from different herbaria or through repeated entry of sightings. In most cases, with the aid of collector and location descriptions, we were able to distinguish the duplicates from novel collections. Recent on‐ground survey work allowed improved precision for many previously known locations, particularly in South Australia . For example, three stands that are present within the records have no live plants remaining . Of the 219 putatively extant stands, 101 occur in New South Wales, including 47 from within Kinchega National Park, the only New South Wales reserve where this species has been recorded. The remaining stands are located in South Australia, where 27 are within reserves including Bimbowrie Conservation Reserve  and Boolcoomatta Bush Heritage Reserve (a private conservation reserve) and one each within the Flinders Ranges National Park and Lake Eyre National Park.3.2Acacia carneorum revealed the overall collection of stems was genetically diverse, with the mean number of alleles per locus = 5.5 ± 0.4 and the overall proportion of observed heterozygotes per locus = 0.54 ± 0.06. However, further analysis revealed only 22 clearly independent genets, typically indicating the presence of one highly replicated genet per stand. Indeed, with only two exceptions (stand 5 and stand 10), it is probable that each stand is derived from a single ancestral genet , and each stand was genetically distinct. The estimated pID was 3.4E‐06, and pIDsib was 0.0043. Our survey of genetic diversity in 484 stems spread across twenty stands of With these two exceptions, the most frequently occurring MLP in each stand was much more genetically distinct than the extremely rare minor variants detected at low frequency (<0.05) within stands, including both dominant and rare MLPs found in closely neighboring stands. Pairwise distribution of Bruvo genetic distances revealed Bruvo distance values were always >0.15 for pairs of MLPs between different stands, whereas values within stands were always <0.1. The exceptions were stand 5 and stand 10, within which Bruvo genetic distances in each case were 0.26, implying that two clonal lineages were present  means that in effect our data set comprise only 22 individuals, thus precluding most standard population genetic analyses.The extremely clonal nature of the 20 widely distributed stands of 4Acacia carneorum supports other accounts of clonal population structures in plant and animal taxa from arid environments, and the occurrence of polyploidy  and thus have been dependent on asexual reproduction prior to the expansion of European agriculture. Radiocarbon dating of stems to accurately determine their ages may provide novel insights, including whether genet ages predate the onset of pastoralism , and thus, whether the almost complete reliance on asexual reproduction is an ancient or recent phenomenon.Our study on A. carneorum stand that we surveyed, each stand appeared to contain either just one or, in two cases, two genets, with eight stands showing electropherograms indicative of polyploidy. While the use of dominant AFLP markers by O'Brien et al.  and such variation is unlikely to signify substantial variation in fitness among ramets. Indeed, somatic mutations would be unlikely to be expressed as they would typically be recessive with mutant alleles present within heterozygous genotypes , which indicates that “a taxon is endangered when the best available information indicates that (its) population size (is) estimated to number fewer than 250 mature individuals.” Currently, A. carneorum does not meet this criterion based on demographic survey data alone; some stands number 100s or up to a 1000 or more stems , and each stand may in effect represent a single genet . Thus, most of the stands that we surveyed probably have effective population sizes less than two and at least without intervention appear unable to generate seed.Our data imply that the conservation status of Acacia carneorum is not yet obligately asexual; it retains the capacity for episodic sexual reproduction in at least three known “fruiting” stands. However, it is a concern that even fruiting stands contain just one genet per stand, suggesting that recruitment via sexual reproduction has been persistently unsuccessful. Seeds generated within these stands are genotypically diverse, and paternity analysis has revealed genetic input from at least two neighboring stands . Thus, these stands could be used to produce seedlings for ex situ conservation or be protected from herbivory in situ by protective barriers  against excessive herbivory, primarily from feral herbivores (Auld, es Auld, , as wellNone declared.


Recent analyses of trait-disorder overlap suggest that psychiatric dimensions may relate to distinct sets of genes that exert maximum influence during different periods of development. This includes analyses of social communication difficulties that share, depending on their developmental stage, stronger genetic links with either autism spectrum disorder or schizophrenia. We developed a multivariate analysis framework in unrelated individuals to model directly the developmental profile of genetic influences contributing to complex traits, such as social communication difficulties, during an approximately 10-year period spanning childhood and adolescence.N ≤ 5551) were studied in participants from a United Kingdom birth cohort . Using standardized measures, genetic architectures were investigated with novel multivariate genetic-relationship-matrix structural equation models incorporating whole-genome genotyping information. Analogous to twin research, genetic-relationship-matrix structural equation models included Cholesky decomposition, common pathway, and independent pathway models.Longitudinally assessed quantitative social communication problems (A two-factor Cholesky decomposition model described the data best. One genetic factor was common to Social Communication Disorder Checklist measures across development; the other accounted for independent variation at 11 years and later, consistent with distinct developmental profiles in trait-disorder overlap. Importantly, genetic factors operating at 8 years explained only approximately 50% of genetic variation at 17 years.Using latent factor models, we identified developmental changes in the genetic architecture of social communication difficulties that enhance the understanding of autism spectrum disorder– and schizophrenia-related dimensions. More generally, genetic-relationship-matrix structural equation models present a framework for modeling shared genetic etiologies between phenotypes and can provide prior information with respect to patterns and continuity of trait-disorder overlap. SEE COMMENTARY ON PAGE 544The extent to which genetic etiologies are shared between traits and disorders naturally depends on the genetic composition of the two phenotypes. While psychiatric disorders are diagnostic entities defined by clinical criteria, including the age of onset, human behavior changes continously during development. This includes developmental alterations in complex genetic trait architectures as reported for cognitive 2 = 0.74) 2 ≤ 0.45) Difficulties in socially engaging and communicating with others, as observed in the general population, are heritable twin-h = 0.74 2Studying the genetic overlap between psychatric illness and social communication difficulties across multiple developmental stages, different developmental profiles for childhood-onset versus adult-onset psychiatric disorders have been identified Discontinuity in trait-disorder overlap may, however, also result because of attrition-related artifacts, such as decreasing power or inherent sample bias To study the developmental profile of genetic factors in unrelated individuals, we implemented multivariate genetic-relationship-matrix structural equation models (GSEMs). These models use genome-wide genetic relationship matrices (GRMs) http://www.bris.ac.uk/alspac/researchers/data-access/data-dictionary/). Ethical approval was obtained from the ALSPAC Law and Ethics Committee (IRB00003312) and the local research ethics committees. Written informed consent was obtained from a parent or individual with parental responsibility, and assent (and for older children consent) was obtained from the child participants.All analyses were carried out using children’s data from ALSPAC, a United Kingdom population-based longitudinal pregnancy-ascertained birth cohort (estimated birth dates 1991–1992) Social communication difficulties during childhood and adolescence were collected with the 12-item mother-reported Social Communication Disorder Checklist (SCDC)  r, 0.38–0.61) . The distribution of SCDC scores was positively skewed and predominantly leptokurtic . Each sc38–0.61)  as previALSPAC children were genotyped using the Illumina HumanHap550 quad chip genotyping platforms . After qMultivariate SEM techniques were used to assess the relative importance of genetic and residual influences to variation in longitudinal SCDC scores during child and adolescent development. Similar to GREML In short, GSEMs describe the phenotypic covariance structure using one or more additive genetic factors A that capture genetic variance, tagged by common genotyped SNPs, as well as one or more residual factors E that capture residual variance, containing untagged genetic variation and unique environmental influences (including measurement error). As SEM methodology has its origins in the method of path analysis g) and genetic covariances are modeled as the product of additive genetic factor loadings and genetic factor variances (the latter being standardized to unit variance). For example, using multivariate GSEM, a saturated model can be fit to the data through a decomposition of both the genetic variance and the residual variance into as many latent factors as there are observed variables (Cholesky decomposition model) (g) In our formulation, additive genetic variances (GSEM-Varn model) . Estimat1.The Cholesky decomposition model, as described above, is a fully parameterized descriptive model without any restrictions on the structure of latent genetic and residual influences (20 free parameters) and involves multiple independent genetic influences sharing genetic etiologies across development.2.The independent pathway model, in its simplest form, specifies a single common genetic factor and a single common residual factor, in addition to age-specific genetic and residual influences (16 free parameters).3.The common pathway model, in its simplest form, parameterizes a single latent factor, influenced by both genetic and residual sources of variance, in addition to age-specific genetic and residual influences, and is the most constrained model (14 free parameters). The model constrains the variance of the latent factor to one . Although the likelihood of this model can be estimated, the resulting Hessian is not invertible owing to singularity problems. For these reasons, the model constraint was relaxed within this work.Multivariate GSEMs of longitudinally assessed SCDC scores were fitted in two stages. In the first stage (I), we specified a priori three standard multivariate AE models, analogous to twin research: a Cholesky decomposition model (saturated model), an independent pathway model, and a common pathway model.Both the independent pathway model and the common pathway model are consistent with a shared single genetic factor across development and are nested submodels of the full Cholesky decomposition model. The goodness of fit of GSEMs to empirical data was assessed using likelihood ratio test (LRT), the Akaike information criterion 2 estimates. However, path coefficients for multivariate GSEMs were restandardized to enhance the interpretability.In the second stage (II), we adopted a data-driven approach and investigated the pattern of genetic factor loadings for the best-fitting model from stage I in detail. The smallest genetic factor loadings were successively dropped from the model, and the overall fit of the model was compared with the best-fitting a priori defined GSEM (or an adapted form) using LRTs. The statistical significance of factor loadings was assessed using a Wald test (two-sided test). Standard errors for genetic and residual variances and covariances and genetic correlations were derived from the variance-covariance matrix of the estimated factor loadings using the delta method. Standard errors for factor loadings were estimated by GSEMs. For rank-transformed measures with unit variance, such as the SCDC scores in this study, genetic variances are equivalent to SNP-hhttps://gitlab.gwdg.de/beate.stpourcain/gsem) (GRMs were estimated using the GCTA software in/gsem) . For theThe GCTA software package can be used to estimate the proportion of phenotypic variation that is jointly explained by SNPs on a genotyping chip using GREML (AE model) Univariate and bivariate GREML were carried out as part of sensitivity and simulation analyses. For comparison with GSEMs, GRMs were derived from directly genotyped SNPs but excluded individuals with a pairwise relationship > 0.025, as recommended http://openmx.psyc.virginia.edu/), were fitted using FIML and mxGREML and included a full Cholesky decomposition of both genetic and residual variances . Bivariate OpenMx SEM analyses were conducted as part of a simulation analysis. Genetic variances, genetic covariances, and genetic correlations were derived as described for GSEM above. All analyses were conducted on high-performance clusters at the University of Bristol and the Max Planck Institute for Psycholinguistics.OpenMx SEM models To evaluate the accuracy of multivariate GSEMs, we carried out data simulations .SCDC attrition scores were generated to investigate potential sources of bias. Analyses included sample-specific estimates of genetic correlations among SCDC attrition scores and between SCDC scores and subsequent sample dropout .N = 5000) with two standardized measures (10 replicates)  (We simulated a bivariate trait (licates)  and confmethods) . Computamethods) . Trivarig [SE] = 0.25 [0.061], p = 3.4 × 10−5; age 11, Varg [SE] = 0.22 [0.061], p = 2.9 × 10−4; age 17, Varg [SE] = 0.47 [0.086], p = 4.4 × 10−8) , as previously reported Using univariate GSEMs, common genetic variants explained a large proportion of phenotypic variation in SCDC scores during childhood as well as during later adolescence   but not 3 and A4  .We first examined the profile of genetic factors contributing to variation in SCDC scores during development  using three a priori defined multivariate GSEMs . Based o3 and A4 , a two-g3 and A4  that pro, p = 1) . This mo, p = 1) . Using L, p = 1)  until al1 accounting for shared phenotypic variation throughout development as well as a second genetic factor A2 influencing SCDC scores at 11 years and especially at 17 years of age   , were co= 0.12]) .The predicted bivariate genetic correlations by multivariate GSEMs (model 1 and 5)  were ovepone-tailed = .02). Nonetheless, SCDC attrition scores were genetically correlated across all SCDC measures in ALSPAC (pone-tailed < 10−3)  .Using multivariate SEM in combination with common variant-based genetic correlation matrices, we investigated the developmental structure of genetic factors contributing to social communication difficulties during childhood and adolescence. We showed that the genetic architecture of this population-based complex trait changes continuously during development and is consistent with multiple genetic influences operating at different stages during development. Thus, our study provides evidence against the hypothesis that social communication behavior during development is a genetically homogeneous phenotype.2 estimates. This is consistent with recent reports of low SNP-h2 for autistic symptoms at the beginning of adolescence The best-fitting model, specifying two distinct genetic factors, suggested that the genetic origins of child and adolescent social communication behavior lie in middle and late childhood. The first genetic factor, parameterized to account for all genetic influences at 8 years of age, explained a considerable proportion of phenotypic variance throughout development (> 20%) with the exclusion of SCDC scores at 14 years of age that have negligible SNP-hThe second genetic factor, parameterized to be independent of the first one and to capture novel genetic influences arising at 11 years of age, explained predominantly phenotypic variation at 17 years of age (approximately 19%). Thus, the model predicted changes in the composition of the genetic variance during development, and only approximately 50% of the genetic variation at 17 years of age was accounted for by genetic variation at 8 years of age. Within defined developmental stages, however, such as stages spanning midchildhood to very early adolescence , we found evidence for strong genetic correlations across measures. These results are consistent with recent longitudinal twin research that reported moderate to high genetic stability for autistic traits, including communication impairments, between midchildhood and early adolescence The identification of two distinct genetic factors, especially during later adolescence, suggests that SCDC scores at 8 or 11 years of age are, in terms of average composition, different from SCDC scores at 17 years of age. Developmental changes in the genetic architecture of social communication traits are consistent with biological maturation processes during childhood and adolescence. For example, synaptic pruning in the cerebral cortex is a signature late maturational process for generating a diversity of neuronal connections An important implication that flows from the observation of developmental variations in the genetic trait architecture is that measures assessed at different developmental stages may reveal different patterns of trait-disorder overlap, as previously shown for clinical autism spectrum disorder and schizophrenia Finally, our study proves that structural models of genetic influences in unrelated individuals, as captured by GRMs, are computationally feasible within a longitudinal context. Beyond the scope of bivariate GCTA (GREML), multivariate GSEMs allow for the modeling of complex latent genetic factor structures across different stages of development, in particular, their genetic variance composition, and can reveal developmental origins of genetic variation that are otherwise hidden. It is furthermore possible to envisage that the concept of GSEM can be extended to investigate multivariate models of cross-disorder overlap and other complex phenomena, such as reciprocal causation. Note that also novel OpenMx FIML and mxGREML algorithms are currently being developed.2 distribution A limitation of our study is the analysis of nonmissing data across all repeatedly assessed measures. Thus, weaker genetic links, spanning wider age gaps, may not have been sufficiently captured as a consequence of lower power, although genetic correlations predicted by multivariate GSEM and bivariate GCTA(GREML) were overall similar. In addition, cohort studies can be affected by attrition bias The genetic architecture of social communication difficulties, as tagged by common genetic variation, changes with age and involves multiple genetic factors operating at different developmental stages during a 10-year period spanning childhood and adolescence. The identification of distinct genetic trait factors is consistent with different profiles of trait-disorder overlap and underlines the importance of investigating genetic trait variances within a multivariate context.


The Hungarian draft is a horse breed with a recent mixed ancestry created in the 1920s by crossing local mares with draught horses imported from France and Belgium. The interest in its conservation and characterization has increased over the last few years. The aim of this work is to contribute to the characterization of the endangered Hungarian heavy draft horse populations in order to obtain useful information to implement conservation strategies for these genetic stocks.To genetically characterize the breed and to set up the basis for a conservation program, in the present study a hypervariable region of the mitochrondial DNA (D-loop) was used to assess genetic diversity in Hungarian draft horses. Two hundred and eighty five sequences obtained in our laboratory and 419 downloaded sequences available from Genbank were analyzed.Hd = 0.954 ± 0.004; π = 0.028 ± 0.0004) were identified in Hungarian population, although they were higher within than among the different populations . Fourteen of the previously observed seventeen haplogroups were detected.One hundred and sixty-four haplotypes and thirty-six polymorphic sites were observed. High haplotype and nucleotide diversity values (Our samples showed a large intra- and interbreed variation. There was no clear clustering on the median joining network figure. The overall information collected in this work led us to consider that the genetic scenario observed for Hungarian draft breed is more likely the result of contributions from ‘ancestrally’ different genetic backgrounds. This study could contribute to the development of a breeding plan for Hungarian draft horses and help to formulate a genetic conservation plan, avoiding inbreeding while. In recent decades, various types of animal species have been investigated with special emphasis on improving the efficiency of selection programs and the use of modern molecular genetic methods has increased considerably. As a consequence of the development of feeding technologies and the acceleration of transport and communication, local native breeds have been worldwide replaced by modern, high-productivity varieties. However, the genetic value of gene conservation divided the fate of varieties: those having excellent secondary traits and rare alleles resulting in considerable diversity contribute to both the current and future preservation of desirable properties . It is gEquus caballus) mitochondrial genome sequence has been available since 1994 . All horses in this study were client-owned, no harmful invasive procedure was performed on them, there was no animal experimentation according to the legal definitions in Europe , and in Hungary (40/2013. (II. 14.) Government Decree on animal research and therefore no ethical committee approval was required.Two hundred eighty five samples from registered mares from all over Hungary representing 35.63% of the Hungarian draft horse population were used. Hair samples from the tail complete with follicles were used and were stored airtight until examinations at the laboratory at room temperature. Following verbal consent, each breeder contributed to the tests as well as for the study being prepared. Samples were examined in the Laboratory of Animal Genetics at the University of Debrecen. Genomic DNA was extracted from hair-root samples , and wasKY512807–KY513091), 83 sequences examined by AB329597, AF064632–AF431969, AJ413825–AJ413900, AY246186–AY575139, DQ324048, EF014970–EF014989, EF494073–EF494083, EF495133–EF495151, EU093045–EU093073, EU256571–EU256622, GQ119632–GQ119636, GU339390, GU563634, GU563651, GU563669–GU563711, HQ439455–HQ593058, HQ848967–HQ848977, JN398377–JN398457, KC847166, KF192350–KF192499, KF849272–KF849290, KT757760–KT757761.We downloaded 419 sequences from 52 different breeds available at Genbank. We considered the presumed founding ancestors of Hungarian draft (2 (25 mM) Promega, 1 µl forward and 1 µl reverse primer (10 pmol/ µl) Sigma, 5 µl dH2O. The reaction mixture was incubated at 95 °C for 10 min, followed by 35 cycles each consisting of 20 s denaturation at 95 °C, 30 s annealing at 62 °C, 30 s of extension at 70 °C and then a final 10min extension at 72 °C. Sequencing was done by the Macrogen Company . Sequences were assembled and truncated to a length of 222 bp  to maximize sample size comparisons. The correct reading of nucleotides and the comparison of sequences were done with CodonCodeAlignerV.6.0.2., whereas statistical analysis was performed with two versions of Mega (Mega6 (H ± SD) and nucleotide (π ± SD) diversities. Genetic distances among different mtDNA haplotypes were calculated by the two-parameter method of Kimura  and detecting shared haplotypes among populations. Median joining networks were constructed using NETWORK version 5.0.0.1  and also used by KY512807–KY513091) identified 55 haplotypes based on 35 variable nucleotide sites (222-bp sequence). Thirty-five polymorphic sites were detected, which represented 16.98% of the total mtDNA sequence analyzed (222 bp). The average ratios of the four nucleotides A, T, C, G were 32.7%, 28.1%, 27.4%, and 11.8%, respectively. We observed high haplotype and nucleotide diversity values . The average number of pairwise differences was k = 5.77497. During sequence analysis we considered only 204 from the included 222 positions, after excluding sites with gaps. Among the 55 haplotypes identified in these 204-bp sequences, we observed 22 unique haplotypes , whereas the most frequent haplotype was #34 identified in thirty individuals. Eight haplotypes comprise 55.78% of the total sequences, with the remaining 47 haplotypes including less than ten individuals in each group. The details of the variable positions in basepairs 15,531–15,752 are given in Hd = 0.972 ± 0.002; π = 0.03097 ± 0.002). The average number of pairwise differences was k = 6.164. Intra- and inter-breed variation is summarized in The haplotypes obtained were compared with an π = 0.0057 ± 0.008), whereas the highest values were in Fell horse (π = 0.047 ± 0.05). We observed the greatest number of polymorphic sites in Clydesdale, where the highest number of insertion/deletion positions (11) and transitions (42) as well as transversions (127) occurred.The lowest nucleotide diversities were found in Giara and Icelandic Horse (We classified the resulting haplotypes into haplogoups,  as previOur purpose was to explore the mitochondrial genetic relationships between European horse breeds based on the sequences determined in our 285 individuals and a total of 419 sequences downloaded from the Genbank database. These added up to 704 different sequences, representing 52 different breeds, including our 285 individual Hungarian cold-blooded/Hungarian draft horses. The breeds represent a wide geographic area as well as different horse types. We tried to select cold-blooded varieties, which play an important role in developing the breeds like Breton, Noriker, Belgian cold-blooded or Percheron, and also included other common horse breeds like Akhal Teke, and Shetland Pony. Hungarian draft is a horse breed with a recent mixed ancestry. It was developed in the 1920s by crossing local mares with draught horses imported from France and Belgium. The foundation stock of this breed was initially the native Hungarian mares which were made to breed with other various breeds like the Noriker, Percheron, Ardennes and also with the native Hungarian stallions.These sequences were aligned and trimmed to obtain the above-mentioned 212-bp HVR I region. The Hungarian draft breed shared haplotypes with twenty other populations included in this analysis. It is noteworthy that Exmoor Pony, a breed known to have originated in the UK also shared haplotypes with two heavy horses, Italian Heavy and Rhineland Heavy, but not with the two other native UK cold-blooded breeds, Clydesdale and Shire. On the other hand, shared haplotypes among populations are indicators of common founder lineages or gene flow. A network of 25 oriental and European breeds was drawn up on the basis of mtDNA sequences , which sFST values are shown in FST values. Our FST values fall into a wide range, 0.00–1.00 (varying from white, and light to dark blue colors in FST comparison values obtained were significant in 492 pairwise calculations. Despite historic admixture with domestic horses and probably due to their low effective population size, Przewalskii could be differentiated from domesticated horses. Four breeds could be separated from only one other population on any level of significance, Italian-Cleveland Bay horse FST = 0.4461, Syrian-Polish Heavy Horse FST = 0.2217, Scottish Highland-Hucul FST = 0.2373, Wielkopolski-Finn horse FST = 0.3062, Croatian Heavy draft was significantly different from 34 other horse populations; on the other hand, this breed also has only a recent mixed ancestry, which in this case means relationship with 18 other breeds, mainly other cold-blooded horses. The Hungarian draft horse population was significantly differentiated from 12 other populations, namely: ShetlandPony, Przewalski, Hucul, Murinsulaner, Croatian Heavy draft, Giara horse, Belgian, Breton, Cleveland, Clydesdale, Posavina, Shire. Surprisingly, a significant difference was observed between Belgian horses and Hungarian draft, even though the import of cold-blooded Belgian Heavy horses started as early as before the First World War and led to the establishment of a cold-blooded flock in Hungary at that time (Pairwise hat time .In general, horse breeds are genetically close due to their somewhat recent domestication history. However, some breeds are highly differentiated due to strong bottlenecks, intensive selection and also isolation and genetic drift. There is no other farm animal species that exhibits a similar level of mitochondrial DNA variation . No geneFST analysis supports this weak pattern of differentiation showing high rates of mtDNA sharing between populations. This state is also confirmed by the observation that the median-joining network does not have a start-like structure, suggesting that a large number of founders could have produced the Hungarian cold-blooded breed. Secondly, a high percentage of the variability among pairwise FST values is explained by actual migrations demonstrating, further, strong relationship between population structure and observed gene flow. The haplotypes found in Hungarian cold-blooded horses were dispersed in the tree across several haplogroups. Mitochondrial lineage diversity changed over time by breeding or hybridization and introgression; as a result, a breed is not necessarily isolated from other populations (The contents of A+T was the richest in the mtDNA D-loop region. It was in accordance with other studies, where A+T was 55.8%, whereas C+G was 44.2% , and alsulations . The Kimulations . Also, tulations . Geneticulations . Greaterulations . It has ulations , which aHungarian heavy draft counts 800 mares today, and only survives due to breeding programs; in this way, each haplotype frequency depends on the extent to which mares are involved in the breeding. Since breeders lack written documentation the present stud book contains unknown individuals in pedigree. However, we confirmed the multiple origins in the maternal lineage of domestic horse breeds reported by other researchers . We pres10.7717/peerj.4198/supp-1Table S1Nucleotide positions 15,531–15,752 as compared to GenBank reference sequence X79547 .Click here for additional data file.10.7717/peerj.4198/supp-2Table S2Click here for additional data file.10.7717/peerj.4198/supp-3Table S3Click here for additional data file.10.7717/peerj.4198/supp-4Data S1Click here for additional data file.