Jasus tristani and Jasus paulensis have a long pelagic larval duration (up to 20 months) and inhabit seamounts and islands in the southern Atlantic and Indian Oceans, respectively. We used a multidisciplinary approach to assess the genetic relationships between J. tristani and J. paulensis, investigate historic and contemporary gene flow, and inform fisheries management. Using 17,256 neutral single nucleotide polymorphisms we found low but significant genetic differentiation. We show that patterns of connectivity changed over time in accordance with climatic fluctuations. Historic migration estimates showed stronger connectivity from the Indian to the Atlantic Ocean (influenced by the Agulhas Leakage). In contrast, the individual‐based model coupled with contemporary migration estimates inferred from genetic data showed stronger inter‐ocean connectivity in the opposite direction from the Atlantic to the Indian Ocean driven by the Subtropical Front. We suggest that the J. tristani and J. paulensis historical distribution might have extended further north (when water temperatures were lower) resulting in larval dispersal between the ocean basis being more influenced by the Agulhas Leakage than the Subtropical Front. As water temperatures in the region increase in accordance with anthropogenic climate change, a southern shift in the distribution range of J. tristani and J. paulensis could further reduce larval transport from the Indian to the Atlantic Ocean, adding complexity to fisheries management.While there is now strong evidence that many factors can shape dispersal, the mechanisms influencing connectivity patterns are species‐specific and remain largely unknown for many species with a high dispersal potential. The rock lobsters  Jasus tristani and Jasus paulensis changed over time in accordance with climatic fluctuations. Historical connectivity between the ocean basis was more influenced by the Agulhas Leakage (from the Indian to the Atlantic Ocean) while contemporary connectivity is stronger in the opposite direction from the Atlantic to the Indian Ocean, driven by the Subtropical Front.Using a multidisciplinary approach combining genetic data (SNPs) with individual‐based modelling, we show that patterns of connectivity between rock lobsters  Stocks generally respond predictably to fishery pressure and are able to recover under moderate levels of exploitation  and the St Paul rock lobster (J. paulensis) are distributed in a narrow latitudinal band (~25° to 47° S) in the Southern Atlantic and Indian Oceans, respectively  and Gough Island  data have shown little to no genetic differentiation between these two species  and two populations of J. paulensis  following the manufacturer's instructions. Library preparation and sequencing were conducted at Diversity Arrays Technology, Canberra, Australia and followed standard protocols of DArTseq™ genotyping technology .Libraries were demultiplexed, and reads were filtered for overall quality  using m (minimum stack depth) from 3 to 7 and M  and n  from 1 to 9, as recommended by Rochette and Catchen  provided a balance between obtaining true polymorphism and introducing sequencing error  and that M = 3 and n = 3 was sufficient to stabilize the proportions of loci with 1–5 SNPs. Therefore, we retained M = 3 and n = 3 for the main analysis. The high coverage with the value of m = 3 (67×) and consistent results with m = 3 to m = 7 imply a true biological signal. As m = 3 also performs well for a broad range of data sets . Putative loci were searched against the catalog (sstacks module) and further filtering was then conducted in the populations module.To optimize the de novo assembly, we tested a range of parameters, including  Catchen  and Pari Catchen . The parRetained reads were present in at least 50% of samples within each species, detected in at least one species, had a rare allele frequency of at least 20%, and had no more than 2 alleles detected. Potential homologs were excluded by removing markers with heterozygosity >0.50 within samples following Hohenlohe et al. . Individhierfstat (version 0.04‐22) in R , GeneClass2  and M (migration rate) calculated from FST values. The first 10,000 runs were discarded as burn‐in for each chain and runs consisted of sampling increments of 100 iterations with 10 replicates. Model convergence of MIGRATE analyses was confirmed using TRACER 1.7  was estimated using a coalescent method implemented in MIGRATE 3.7  modeling framework, provided by the National Oceanography Centre, Southampton, UK. NEMO is a versatile modeling system that has been used for a range of modeling studies from regional to global scales, and the ability of the 1/12° configuration to represent key oceanographic features has been demonstrated  approach was used to investigate the transport and population connectivity of Jasus lobsters, using an established individual‐based model: the Hydrodynamics‐based Algorithm for Lagrangian simulations  according to the imposed three‐dimensional velocity field, using a second‐order Runge‐Kutta method, with additional horizontal and vertical diffusions included using a random‐walk approach . Diel vertical migration (DVM) was not included in the representation of larval transport; there are no data on DVM in J. tristani or J. paulensis phyllosoma, and observations of DVM in J. edwardsii suggest that well‐defined DVM is only observed in late‐stage larvae  from a given source population (columns). Summary matrices were then calculated describing the mean and standard deviation in connectivity, and the number of years with nonzero connectivity. Particles transported among J. tristani and J. paulensis sites (and vice versa) were identified and their trajectories analyzed to identify dominant inter‐species connectivity pathways.For each simulation year, the percentage of larvae from each release site successfully recruiting to one of the defined recruitment zones was calculated, and the results were combined into connectivity matrices, 2.4J. tristani and J. paulensis and the direction of contemporary and historical migration. Six models were built considering possible scenarios: (SI) Strict Isolation, where the environment  promoted allopatry; (IM) Isolation‐with‐Migration, with continuous gene flow since divergence; (AM) Ancient Migration, with an ancient gene flow event but recent isolation; (SC) Secondary Contact, with a recent gene flow event after past isolation; (PAM) Ancient Migration with two ancient gene flow events but recent isolation; and (PSC) Secondary Contact with two recent gene flow events after past isolation. All models were implemented allowing for asymmetric migration rates . In the models with two events of secondary contact (PAM and PSC), four parameters for migration rates were included (mA = m12 and mB = m21 in the first migration event and mC = m12 and mD = m21 in the second migration event) to determine changes in the direction of gene flow over time.Several hypotheses of divergence modes were tested, aiming to identify the best divergence mode of vcf2dadi in the R package radiator  in the diffusion approximation method implemented in the software ∂a∂i  and ∆AICi = AICi − AICmin. Therefore, the worst model has a score of 0 and the best model has a score of 1. To evaluate the relative probabilities of the different models within each species pair, Akaike weights (WAIC) were also calculated following:R corresponds to the total number of models considered (R = 6).To compare among nested models of increasing complexity and address over‐parameterization issues, we used the comparative framework of Tine et al.  by penalJasus spp. The ancestral effective population size (Nref) before split for each species pair was estimated following:θ being the optimal multiplicative scaling factor, μ the mutation rate , the high number of SNP markers provide the necessary statistical power to accurately determine population genetic structure  only the 17,256 neutral SNPs were used for the following analysis.ST values were low (0 < FST <0.05) with the highest values observed for the comparisons JTR_NI with J. paulensis populations (FST = 0.05), while the lowest pairwise FST values were observed for the comparisons of J. paulensis populations (FST = 0). Significant pairwise FST values were observed for all J. tristani and J. paulensis comparisons  to the Indian Ocean (J. paulensis). The total number of first generation migrants from J. tristani to J. paulensis varied from 12 to 17 while migrants from J. paulensis to J. tristani varied from 2 to 7 across the different SNP subsets  to the Atlantic (J. tristani). The total number of migrants per generation from J. paulensis to J. tristani varied from 62 to 107, while migrants from J. tristani to J. paulensis varied from 13 to 19 across the different SNPs samples  and stronger historical migration from east to west (transport of J. paulensis larvae from the Indian to the Atlantic Ocean). Contemporary migration rates  were higher than historical migration rates  than in secondary contact .The demographic model with a scenario of two gene flow events after past isolation (PSC) provided better fits to the data with good predictions of the joint site frequency spectrum asymmetry , while historical migration estimates showed stronger connectivity from east to west . These are consistent with FST values observed among populations of other lobster species. For example, American lobster (Homarus americanus) populations had a highest pairwise FST of 0.004  the highest FST = 0.001  the highest FST = 0.027  the highest FST = 0.018 , Groeneveld et al.  Silva et al.  than in secondary contact  since initial divergence. Also, the Tristan da Cunha and Gough Islands and the Amsterdam and St. Paul Islands have been grouped in the same zoogeographic province, called the West Wind Drift Islands Province as there is evidence of the same fish species that are endemic to both regions , our study suggests low but significant differentiation among species.Previous studies have shown mixed evidence regarding the taxonomic status of  Kensler  noted thd et al.  showed la et al.  showed e4.2J. paulensis into J. tristani). Groeneveld et al. (J. paulensis) are the putative ancestral populations. However, levels of genetic diversity can also reflect higher effective population sizes as there are probably more populations of J. paulensis distributed throughout the southern Indian Ocean, whereas J. tristani may be less widely distributed in the southern Atlantic , which is reflected in the higher genetic diversity in the Indian Ocean, and in the admixture analyses , J. tristani and J. paulensis are distributed throughout a narrow range of temperatures . This was also reflected in the admixture analyses but to a lesser extent than historical migration estimates. This pattern could be the result of a shorter period of contemporary connectivity (influenced by the STF), than historical connectivity, perhaps due to more northward historical distribution patterns.J. edwardsii; Lesser  but the Agulhas Current and Leakage also facilitate a stepping stone connectivity in the opposite direction, from the Indian to the Atlantic Ocean. This complex connectivity scenario has important implications in recruitment for fisheries management of the benthic stocks and how it could be altered under future environmental conditions.Our study identified a potential model of source‐sink dynamics among J. tristani) to the Indian Ocean (J. paulensis). Therefore, depletion of J. tristani populations can not only reduce local recruitment but, to a lesser degree, the recruitment of J. paulensis populations as well. In addition, reduced source populations could compromise the recovery of sink populations in cases where source populations are suddenly severely depleted due a catastrophic event or events. Information on spatial genetic structure and source‐sink dynamics should be used to design more biologically relevant management units at the population level and more integrated management strategies  may reduce the connectivity and recruitment in other (sink) populations. This may have demographic consequences for populations and compromise productivity or recovery ; Data curation ; Formal analysis ; Methodology ; Visualization ; Writing‐original draft (lead); Writing‐review & editing (lead). Emma F. Young: Conceptualization (supporting); Data curation ; Formal analysis ; Funding acquisition (lead); Writing‐original draft (supporting); Writing‐review & editing . Nicholas P. Murphy: Conceptualization ; Funding acquisition ; Writing‐original draft (supporting); Writing‐review & editing (supporting). James J. Bell: Conceptualization ; Funding acquisition (supporting); Writing‐original draft (supporting); Writing‐review & editing (supporting). Bridget S. Green: Conceptualization ; Funding acquisition ; Writing‐review & editing (supporting). Simon A. Morley: Funding acquisition ; Writing‐original draft (supporting); Writing‐review & editing (supporting). Guy Duhamel: Data curation ; Writing‐original draft (supporting); Writing‐review & editing (supporting). Andrew C. Cockcroft: Writing‐original draft (supporting); Writing‐review & editing (supporting). Jan M. Strugnell: Conceptualization (lead); Funding acquisition (lead); Supervision (lead); Writing‐original draft ; Writing‐review & editing .


HO and HE were 0.57 and 0.75, respectively. Differentiation statistics revealed that a high proportion (97%) of the total genetic variation was explained by differences between individuals within populations. Genotype assignment independent of the population of origin showed K = 2 to be the optimum number of genetic backgrounds present in the dataset. This result was further confirmed by mtDNA D-loop sequences comparison in which the matrilineal genetic origin of eastern Ethiopia sheep is from two haplotype groups (types A and B) among the five haplotypes globally observed. Taken together, our findings suggest that the sheep populations from three breeds originated from two ancestral genetic backgrounds that may have diverged prior to their introduction to Ethiopia. However, to obtain a complete picture of the evolutionary dynamics of Ethiopian indigenous sheep, more samples and populations from within and outside of the country will need to be analyzed.Ethiopia is home to a diverse gene pool of indigenous sheep populations. Therefore, a better understanding of genetic variation holds the key to future utilization through conservation. Three of these breeds, Afar, Blackhead Somali, and Hararghe Highland, are found in eastern Ethiopia where they contribute significantly to the livelihood of most pastoralist, agro-pastoralist, and smallholder farmers. These indigenous sheep are recognized on the basis of morphotype and their genetic distinction remains unknown. Here, to assess genetic variation, and matrilineal genetic origin and relationship of fat-rumed sheep found in eastern Ethiopia, 300 individuals from the three breeds were genotyped for 22 microsatellite markers and sequenced for the mitochondrial DNA displacement loop (mtDNA d-loop) region. The overall The online version of this article (10.1007/s11250-019-01827-z) contains supplementary material, which is available to authorized users. Animal genetic resources are critical for global food security and livelihoods. Livestock products have high densities of energy, protein, and other critical nutrients, which are necessary for infants and expectant mothers , east Hararghe , and Gabi Rasu  Fig. . DescripTo capture the wide spectrum of the genetic diversity of the three populations, a total of ten locations were considered . For eacTwenty-two fluorescent-labeled microsatellite markers recommended by the ISAG/FAO to be used for sheep genetic diversity studies were genotyped  FAO, 20), and 302 (1.5 μM), 2.5 μl of each primers (10 pmole), 0.09 μl of Taq , and 0.32 μl of BSA . PCR was carried out using the GenAmp® PCR System 9700 (Applied Biosystems) with the following conditions: initial denaturation at 94 °C for 5 min, followed by 35 cycles of amplification at 94 °C for 30 s, annealing at 62 °C for 30 s, extension at 72 °C for 1 min, and a final extension at 72 °C for 5 min. PCR products were purified using Invitrogen Pure Link PCR purification kit (Thermo Fisher Scientific) and sequenced for both strands at Sequencing Genotyping and Oligonucleotide (SEGOLIP) unit at the same laboratory mentioned above.To amplify the mitochondrial DNA displacement loop (mtDNA D-loop) region, primers were designed from the published ovine reference sequence  and observed (HO) heterozygosis were estimated from allele frequencies with FSTAT 2.9.3.2 (Goudet FIT) was partitioned into within (FIS) and among population (FST) components following Weir and Cockerham  to vary between 1 ≤ K ≥ 15, using a burn-in of 100,000 followed by 200,000 Markov Chain Monte Carlo (MCMC) iterations. Ten simulations were carried out for each K assuming populations are admixed, and allele frequencies are correlated. To estimate the most optimal K, the number of clusters (K) was plotted against DeltaK calculated as mean(|L”(K)|)/sd(L(K)) and the optimal number of clusters identified by the largest change in log-likelihood (L(K)) values between the estimated number of clusters . Geographic distances between populations were calculated using Google map as the distance between the main towns within each of the sampling locations.The total number of alleles (TNA), mean number of alleles (MNA), allelic richness (AR), polymorphic information content (PIC), effective number of alleles (ENA), and expected  was calculated using DnaSP v.5.10.01 (Librado and Rozas www.fluxus-engineering.com). The maximum parsimony (MP) calculation was used to remove unnecessary median vectors and to avoid reticulations, which could be switched off in the results display.Two mitochondria DNA (mtDNA) D-loop sequence data sets were used in this study. The first 31 sequences generated were used to estimate maternal genetic diversity and elucidate relationships between the eastern Ethiopia sheep populations. The second data set was retrieved from the Gene Bank which represent the five  globally defined domestic sheep mtDNA D-loop haplogroups . Electropherograms were visualized, edited, and aligned using the ClustalW  for most loci except MAF33, OarJMP29, MAF214, and OarAE54  are presented in For each population, the total and mean number of alleles, allelic richness, effective number of alleles, private alleles, observed and expected, and FIT), the inbreeding coefficient of the populations relative to all the three populations (FST), and the within populations inbreeding coefficients (FIS) were 0.26 (0.05), 0.03 (0.01), and 0.23 (0.05), respectively (FIT), indicating deficiency in heterozygotes in the total population compared to between populations. It also reflects that loci within populations are more heterogeneous than across all the populations. This is also supported by the low FST value (3%) and the branching pattern on the phylogenetic tree  except the ones between BHS (Babile) and HHL (Deder and Gorogutu) sheep. This was supported by results of the Mantel test  which defined 28 haplotypes. The overall average haplotype and nucleotide diversities were 0.99 and 0.027, respectively . The range in values for HE and HO observed in this study compare favorably with previously published data for other African sheep populations  which ranged from − 0.49 to 0.87. The difference between the observed and expected heterozygosity can be attributed to the non-random mating among the individuals of the population. Similar to our assumptions, the high FIS value (0.302 ± 0.057) in the Tibetan sheep was considered as the major cause of the Hardy–Weinberg disequilibrium in that population  produces clusters that are consistent with matrilineal genetic origin of eastern Ethiopia sheep. The first level of clustering (K = 2), which is revealed by DeltaK to be the most optimal, reflects that three sheep populations across ten locations derived from two ancestral populations (gene pool). The first genetic background predominated in sheep populations of AFR while the second background occurred at higher frequency in the BHS. All the BHS and AFR sheep across all locations clustered within their respective population groups, whereas the HHL sheep were grouped in both clusters. For example, sheep located in Deder shared a similar pattern with AFR sheep but Meta and Gorogutu shared the same pattern with BHS Sheep. This, therefore, HHL sheep may not be a pure/independent population, rather they are genetically an admixture with both AFR and BHS sheep. This result is in an agreement with the result of Nigussie et al.  in eastern Ethiopia sheep though Haplogroup B occurs at higher frequency than haplogroup (A). This result in a way corresponds to that of the STRUCTURE analysis which revealed the most optimal s et al.  who repos et al.  pointed s et al. . Undateds et al.  suggest s et al. . HoweverThe current result indicated that the within genetic diversity is high than between populations, which will be an opportunity to design a cost-effective and sustainable genetic improvement and conservation program for eastern Ethiopia sheep. Both autosomal microsatellite markers and mtDNA D-loop sequences diversity analysis revealed that eastern Ethiopia sheep breeds most likely derive from two ancestral genetic backgrounds. HHL sheep had high genetic admixture with both AFR and BHS though mtDNA D-loop sequences analysis indicated that the sheep clustered with AFR sheep population. These findings need to be confirmed with the analysis of more populations from adjoining area to address the breeding tract of HHL sheep. The result for the matrilineal genetic origin of indigenous sheep is preliminary; therefore, more samples from wider geographical location and east African countries are required to substantiate the evolutionary history of Ethiopian sheep.ESM 1(DOCX 872 kb)


Primula fasciculata that widely occurs in the Hengduan Mountains and Qinghai-Tibetan Plateau. We obtained genomic data for 234 samples of the species using restriction site-associated DNA (RAD) sequencing and combined approximate Bayesian computation (ABC) and species distribution modeling (SDM) to evaluate the effects of multiple glaciation periods by testing several population divergence models and demographic scenarios. The analyses of population structure showed that P. fasciculata displays a striking population structure with six groups that could be identified genetically. Our ABC modeling suggested that the current groups diverged from ancestral populations located in the eastern Hengduan Mountains after the largest glaciation occurred in the region (~ 0.8–0.5 million years ago), which is consistent with the result of SDMs. Each current group has survived in different glacial refugia during the LGM and experienced expansions and/or bottlenecks since their divergence during or across the following Quaternary glacial cycles. Our study demonstrates the usefulness of population genomics for evaluating the effects of past climatic changes in alpine plant species with shallow population structure.Understanding the factors that drive the genetic structure of a species and its responses to past climatic changes is an important first step in modern population management. The response to the last glacial maximum (LGM) has been well studied, however, the effect of previous glaciation periods on plant demographic history is still not well studied. Here we investigated the population structure and demographic history of  Plant populations are not randomly arranged assemblages of genotypes but are structured in space and time . BecauseQuercus aquifolioides , one of the most widely distributed alpine plant species in the Hengduan Mountains and QTP  at species level and investigated interspecific divergence and the factors that affected the maintenance of species boundaries, has indicated that P. fasciculata diverged from P. tibetica during the Pliocene and experienced expansion during the Quaternary  compare several detailed demographic scenarios for P. fasciculata using ABC modeling and test these hypotheses with species distribution models to evaluate the effects of Quaternary climatic changes on the evolution of this species.Here we focus on  and QTP Figure 1-streams . A recenaternary . HoweverP. fasciculata  used in The data set of http://www.worldclim.org/current) from the occurrences of the individuals sampled in the 54 populations. We did a principal component analysis (PCA) using the prcomp function in the stats package of R and identified the 12 populations based on the PC1 and PC2 axes  among populations were calculated in GENODIVE v.2.0b27  of the 12 populations were used in a Procrustes analysis using the R package vegan . This anvegan . The simgan (t0 . We usedutations .FST). We randomly selected 5,000 loci from the neutral data set 100 times to generate 100 FST distance matrices by using populations module in the STACTS v1.30  modeling. We stratified the procedure in three steps (Results) that were identified by the PCA analyses among 13 scenarios describing all possible topologies (Results), i.e., L3 was split into G3, G1, and G2; L1 was split into G4 and G5; L2 was not split and renamed as G6 . The parameters defining each scenario  were considered as random variables drawn from prior distributions. For each simulation, DIY-ABC drew a value for each parameter from its prior distribution and performed coalescent simulations to generate a simulated pseudo-observed data set (POD) with the same number of gene copies and loci per lineage/group as those observed. It then calculated summary statistic for each POD and the observed data. Based on a distance and a tolerance, it decided for each POD whether its summary statistic was sufficiently close to that of the observed data. We used 1% of the simulated data sets closest to the observed data to estimate the relative posterior probabilities for each scenario via logistic regression. Posterior distributions of historical demographic parameters based on the most likely scenario  differed from the optimal one by only 2. Other K values  also received considerable support. We decided to show all these K values in Although the optimal ry small Figure 3The first two PCA axes identified three main genetic lineages and explained 13.04% and 7.76% of the total variation, respectively Figure 4K = 4 in the STRUCTURE. We identified this population as G1. Looking at K values from K = 2 to K = 6, population PF08 was always represented as an admixed population, which was labeled as G2. The remaining populations of L3 were grouped as G3. The two populations (L1) that diverged from each other in the PCA , which increased to t0 = 0.777 when genetic variation in PC1 and PC3 spaces were considered  0.99, 1.00; –shrinkage,” while G2, G4 and G6 were better modeled by a scenario of “expansion–shrinkage–expansion.” The other two groups (G1 and G5) were better modeled by a scenario of “recent expansion” (We used a three-step procedure to estimate the demographic history of pansion” Table S6P. fasciculata and started to expand its distribution ca. 0.60 Ma (95% highest posterior density (HPD): 0.27–0.86 Ma; We estimated the divergence times and the population sizes as well as the timing and extent of these changes for the six groups. Group G3 was found to be the ancestor of P. fasciculata has retreated to eastern Hengduan Mountains to occupy a large region and some restricted refugia in the eastern Himalayas, while during the MIS19 (a much colder period of Quaternary), the predicted suitable habitat for the species was enormously reduced to a small region in the eastern Hengduan Mountains.The value AUC obtained for the final consensus model was 0.995, therefore it is an accurate model. The predicted current potential distribution fitted very well with the actual distribution of the species. The predictions to the LGM condition suggested that P. fasciculata in the highly fragmented biodiversity hotspot of the Hengduan Mountains. The patterns of genetic differentiation detected by different structure analyses were congruent, and we identified six groups of populations that capture the main characteristics of the population history of this species. The results of ABC modeling provided strong support for population divergence driven by Quaternary climatic fluctuations. The comparison of the different demographic scenarios shows that all six groups have experienced bottlenecks or stayed stable during the last glaciation, while five groups started to expand just after the LGM. These results obtained with genomic data were also supported by the SDM analyses. Taking together with a recent study that investigated factors in driving genomic variation in this species  = 0.03, Our results revealed exceptionally high levels of population divergence across the distribution of a 0.450;  and Bulbm 0.387; , but it m 0.387; , a valueP. tibetica  and expanded its distributions at the beginning of the Quaternary when the climate became cold  are congruent with a period when two other glaciation events and multiple interglacial periods occurred in the QTP and Hengduan Mountains . The glaFinally, the remaining two groups G2 and G5 were formed in different ways during the last interglacial when the climate was warm Figure 6Primula  are selected from our 54 sampled populations based on geographical distributions and ecological variables, bias on the genetic grouping and inferences regarding populations size and dating may exist because of the relatively small sampling size. More populations, especially the sampling sites with limited dispersal area are needed to further uncover its evolutionary history. Nevertheless, our results on P. fasciculata, considered in light of results recently reported for its closely related, Himalayan species P. tibetica  used in The dataset of GR and NS planned and designed the research. GR carried out the sampling and the laboratory work, performed the molecular analysis. RM performed the SDM analysis. GR and NS wrote the manuscript with the help of EC.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Clupea harengus L has a vast geographical distribution and a complex population structure with a few very large migratory units and many small local populations. Each population has its own spawning ground and/or time, thereby maintaining their genetic integrity. Several herring populations migrate between common feeding grounds and over-wintering areas resulting in frequent mixing of populations. Thus, many herring fisheries are based on mixed populations of different demographic status. In order to avoid over-exploitation of weak populations and to conserve biodiversity, understanding the population structure and population mixing is important for maintaining biologically sustainable herring fisheries. The aim of this study was to investigate the genetic population structure of herring in the Faroese and surrounding waters, and to develop genetic markers for distinguishing between four herring management units , namely the Norwegian spring-spawning herring (NSSH), Icelandic summer-spawning herring (ISSH), North Sea autumn-spawning herring (NSAH), and Faroese autumn-spawning herring (FASH). Herring from the four stocks were sequenced at low coverage, and single nucleotide polymorphisms (SNPs) were called and used for population structure analysis and individual assignment. An ancestry-informative SNP panel with 118 SNPs was developed and tested on 240 individuals. The results showed that all four stocks appeared to be genetically differentiated populations, but at lower levels of differentiation between FASH and ISSH than the other two populations. Overall assignment rate with the SNP panel was 80.7%, and agreement between the genetic and traditional visual assignment was 75.5%. The NSAH and NSSH samples had the highest assignment rate  and highest agreement between traditional and genetic assignment methods . The FASH and ISSH samples had substantially lower assignment rates  and agreement between traditional and genetic methods The Atlantic herring  This population occurs sporadically, and the exact spawning locations are unknown. Nevertheless, this fjord herring has been observed intermittently since at least the 1780s . During Recent studies of the herring genome have revealed that genomic differentiation between populations is generally low, but that there are specific regions in the genome that show elevated genetic differentiation associated with ecological adaption to e.g., spawning time and salinity . Thus, s22.1Herring samples were obtained from several sources. They were collected on a research cruise conducted by the Faroe Marine Research Institute (FAMRI), obtained from fishing boats, or kindly provided by NAFC Marine Centre in Scalloway and the Marine Research Institute in Iceland. The length, weight, sex, and maturity stage of the fish were recorded, and the otoliths extracted. The maturity stage was classified according to the local maturity scale which is based on 2.2Due to the uncertainties surrounding the FASH exact spawning place and time, collecting only spawning individuals for the baseline was not possible. Therefore, the genetic populations for the baseline were defined in a two-step process. First the individuals for sequencing were chosen based on traditional assignment see . This waThe DNA was extracted from tissue samples from 103 herring  using an AS1000 Maxwell 16 instrument  and the Maxwell 16 Tissue DNA Purification Kit (Promega). DNA concentration was measured using a Qubit 3.0 fluorometer . The DNA was fragmented to roughly 300 bp using a Covaris M220  and the libraries were prepared using the KAPA LTP Library Preparation Kit for Illumina Platforms , following the manufacturer’s instructions. The library from each individual herring was indexed using indexed adapters  and quantified using the KAPA Library Quantification Kit (KAPABiosystems), following the manufacturer’s instructions. After quantification, the libraries were pooled to equal proportions and sequenced on a NextSeq500  using the High Output v2 Kit (Illumina).2.3-6. SNPs where more than 90 individuals had missing genotype likelihood as well as those with a minor allele frequency lower than 0.05 were filtered out.The sequencing data were trimmed to remove adapters and low-quality bases (Q score < 20) using Trimmomatic v0.36 . AfterQC2.4ST  using the genotype likelihoods from the full SNP data set and PCAngsd from the ANGSD R package , which u package  and for  package  and the  package . Ninety 2.6Tissue samples from 240 herring  were sent to LGC Genomics  for genotyping, and 500 predetermined SNPs were genotyped using their SeqSNP service. These 500 SNPs were selected based on their discriminatory power between the populations; 154 SNPs were selected based on the pooled data ; and the2.7We investigated the agreement between the traditional assignment method and genetic assignment methods. The assessment samples were assigned to populations using the traditional assignment method described in 33.1ST values  for 500 SNPs were performed and after filtering 377 SNPs and 238 individual herring remained see . Of thes3.2The genetic baseline populations were defined using the 103 baseline individuals, the SNP panel data set and a DAPC analysis. The individuals constituting the baseline populations can be found in the The STRUCTURE analysis showed that the mean likelihood of K plateaued at K = 4 , indicatTo search for substructures in the second cluster, the NSSH sample was removed, and the STRUCTURE and Evanno analyses were run again. This time the most likely number of K was again two  with theThe DAPC analysis of the SNP panel data set showed similar results as STRUCTURE. There were three distinct clusters . The NSSST between the baseline populations was calculated using both the full data set and the SNP panel . For 192 of the 238 individuals (80.7%) the assignment to a population was identical regardless of algorithm . Out of Out of the 46 individuals not unambiguously assigned to a population, 43 pertained to problems with FASH or ISSH assignment , likely Twenty-five out of the 28 technical replicas resulted in identical assignment . The thr3.4The 13 spawning FASH individuals  collected for this study, were captured from mid-August to mid-October . The gen3.5ST values for all SNPs between all population pairs were calculated and the top 100 SNPs from each pairwise comparisons selected. After removal of duplicates 457 SNPs remained (including the 118 panel SNPs). These SNPs were spread across the genome, with a few hotspots , which makes the genotypes inferred from these data more uncertain, e.g., sequencing errors are more likely to be incorporated into the dataset as a heterozygous genotype, or only one allele from a heterozygote is sequenced, resulting in a homozygous genotype being called. Theoretically, these uncertainties should be distributed equally throughout the whole data set, provided uniform sequence coverage, which was not the case . This un4.2It was evident  that the4.3A major limitation of this study is the use of non-spawning individuals in some of the baseline populations. As previously mentioned, the uncertainties surrounding the FASH exact spawning place and time, made collecting only spawning individuals for the baseline impossible. Therefore, the samples were collected according to the available biological information about the stocks . The individuals to be used in the baseline were chosen based on traditional assignment see , to ensu4.4ST values when comparing populations using the full data set versus the SNP panel data set  and loc5The results from this study show that low coverage sequencing (average 2.6x) can be useful for high gene flow organisms like herring, in order to resolve population structure, but it is important to be aware of uneven coverage. We showed that there are most likely four different herring populations in Faroese waters , with low genetic differences between FASH and ISSH. Further studies, with larger sample size, spawning individuals and high-quality DNA are needed to finally confirm whether FASH and ISSH are two distinct biological populations and reveal their relative distribution in Faroese waters. The results are crucial for stock management for example for estimating the actual stock size and exploitation levels, which are important to prevent over-exploitation. Finally, the study has shown that the SNP panel developed in this study could be used in monitoring of herring fisheries as well as in stock management. In concert with SNPs identified in other studies, the SNPs identified here, could be combined into valuable high-resolution tools for assignment of mixed herring catches and thus help to assure sustainable herring fisheries.Supplementary FiguresSupplementary Tables


Penaeus monodon), is widely distributed throughout the Indian and Pacific Oceans including along the western, northern and eastern coastline of Australia, where it is an important aquaculture and fishery species. Understanding the genetic structure and the influence of environmental factors leading to adaptive differences among populations of this species is important for farm genetic improvement programs and sustainable fisheries management.Restrictions to gene flow, genetic drift, and divergent selection associated with different environments are significant drivers of genetic differentiation. The black tiger shrimp . Eighty-nine putatively outlier SNPs were identified to be potentially associated with environmental variables by using both population differentiation (BayeScan and PCAdapt) and environmental association  analysis methods. Clear population structure with similar spatial patterns were observed in both neutral and outlier markers with three genetically distinct groups identified . Redundancy, partial redundancy, and multiple regression on distance matrices analyses revealed that both geographical distance and environmental factors interact to generate the structure observed across Australian P. monodon populations.Based on 278 individuals obtained from seven geographically disparate Australian locations, 10,624 high-quality SNP loci were used to characterize genetic diversity, population structure, genetic connectivity, and adaptive divergence. Significant population structure and differentiation were revealed among wild populations (average P. monodon in the face of environmental changes, which can be used to advance sustainable fisheries management and aquaculture breeding programs.This study provides new insights on genetic population structure of Australian  Haliotis laevigata) [Crassostrea virginica) [Homarus americanus) [Pecten novaezelandiae) [Penaeus monodon; Fabricius, 1798), is unknown.Local adaptation can occur when geographically discrete environmental conditions impose selection pressure on resident populations of widespread species. Within heterogeneous populations, local adaptation can arise when strong environmental selection pressure outweighs the influence of genetic drift and gene flow towards homogeneity . The marevigata) , easternrginica) , Americaricanus) , and Newlandiae) . HoweverFST) of each locus across the genome without assuming the effect of environmental variables on outlier SNP generation [Single nucleotide polymorphisms (SNP) have been demonstrated to be the most prevalent type of polymorphism within investigated genomes . Moreoveneration , 19. Altneration , 21. EA neration –23. Giveneration . For exaneration , 20, 24.Penaeus monodon is widely distributed throughout the Indo-Pacific and is found along the western, northern, and eastern coasts of Australia [P. monodon comprises an offshore planktonic larval phase, estuarine juvenile and adolescent phases, and an inshore adult phase [P. monodon samples collected in Madagascar, Mozambique, and South Africa, despite geographic separations of up to 2000 km. Thus, P. monodon exhibits the capacity for moderate to high-level gene flow  across large geographic distances. In Australia, previous genetic work on P. monodon determined the existence of population structure using microsatellites, allozymes and mitochondrial DNA (mtDNA) markers [P. monodon from Western Australia was a separate genetic stock with reduced a number of allelic due to colonisation by east coast P. monodon individuals. Additionally, another previous study of Indo-Pacific P. monodon presented that P. monodon from Northern Territory separated into a discrete cluster while Queensland and Western Australia grouped with Thailand, Palau, PNG, Taiwan, Philippines, and Vietnam (Bac Lieu and Can Tho) in one cluster [ustralia –27. The lt phase , 29. A tlt phase  revealedlt phase  found ev markers , 31–33.  cluster .P. monodon in Australia using a high-resolution genome-wide SNP approach and identify any genomic signatures of local adaptation among geographically and environmentally discrete populations. More specifically, this study aimed to (1) assess the levels of gene flow, genetic diversity, and population structure among geographically discrete populations of Australian P. monodon for evidence of local adaptation, and (2) predict which environmental factors are most likely to influence Australian P. monodon population structure . The implications of P. monodon genetic structure and local adaptation for management and conservation of Australian populations are discussed.The main objective of this study is to resolve the population genetic structure of P. monodon individual  by DArTseq™ sequencing  were subjected to further analyses  ranged from 1.61 ± 0.48 for Nickol Bay  population to 1.75 ± 0.34 for Tiwi Island population (Northern Territory) (Table PR) and average MAF of polymorphic loci were slightly higher for Western Australia (0.03 ± 0.14 and 0.18) than for both northern Queensland  and Northern Territory  populations, respectively.For the retained 10,624 SNPs, mean allelic richness values (AO) across all seven populations ranged from 0.13 ± 0.15 to 0.16 ± 0.2 and average expected heterozygosity (HE) ranged from 0.15 ± 0.16 to 0.17 ± 0.17 while average multi-locus heterozygosity (Av. MLH) within populations ranged from 0.13 ± 0.01 to 0.16 ± 0.04 . Estimated NeLD varied across populations and ranged from 165.4 in Western Australia (95% CI = 159.9–171.2) to 24,121 in Bramston Beach , with Western Australia and Etty Bay populations having the lowest NeLD across all seven populations , a Northern Territory group , and Western Australia (Nickol Bay)  Fig. . The inday) Fig. b. Fine-s 60 Fig. c and d.P. monodon populations pairwise FST values based on unbiased [FST comparisons were significant (p ≤ 0.05) except for pairwise comparison between Townsville and Etty Bay populations in north Queensland. Not surprisingly, the most geographically separated sites  exhibited the largest significant pairwise FST (0.107) and standard genetic distance (0.028) values , while 0.2% of the total variance (p <  0.01) was explained by variation among populations within each group.AMOVA analysis also grouped populations by geographical region: north Queensland , Northern Territory , and Western Australia (Nickol Bay). Hierarchical population genetic analyses indicated majority of variation (95.6%) being explained among individuals within populations  detected 582 and 514 outlier SNPs, respectively, among the 10,624 SNPs retained for each individual  with 43.5 and 30.2% of inherent genetic variation explained by the first and second RDA axes, respectively. Based on these two significantly constrained axes the RDA model collectively yielded 963 candidate outlier SNPs. Lastly, the rigorous evaluation of both PD and two EA analyses (see Methods) identified 425 and 161 outlier SNPs respectively, of which 89 (21 and 55%) overlapped . Considering the 10,535 neutral SNPs, PC1 and PC2 explained 24.8 and 14.7% of the total genetic variance, respectively  for all pairwise comparisons when 10,535 neutral SNPs were assessed. Pairwise FST values ranged from 0.001 (Bramston Beach vs. Etty Bay and Joseph Bonaparte Gulf vs. Tiwi Island) to 0.1 . In a similar way, using 89 outlier loci, FST values between north Queensland populations and Western Australia (FST = 0.60–0.64) were in all cases higher than those between Northern Territory populations and Western Australia . FST values differed significantly between all populations within the Northern Territory (p ≤ 0. 04); however, no FST values differed significantly between populations within northern Queensland (p > 0.4).Pairwise p = 0.001 and 0.001, adjusted R2 = 0.03 and 0.2), respectively  > Env_1  > Env_3 . For outlier SNP, Env_2 and Env_3 were both significant for putative adaptive genetic variation (p = 0.001); however, Env_2 explained 13% of inherent genetic variation while Env_3 explained 2.8%. When both neutral and outlier SNP datasets were combined, surface temperature max and min (Env_2 and Env_3 respectively) were the highest ranked predictors contributing to the genetic variation of Australian P. monodon. Variance partitioning based on neutral and outlier loci RDA models revealed that environmental effects explained 4.3 and 20.2% of genetic structure variation, while geographic location explained 12.9 and 2.8%, respectively. The remaining genetic structure variance was explained by the combination of environmental effect and geographic location.ANOVA for RDA analyses demonstrated that five  and four  environmental factors (see Methods) were significantly associated with genetic variation in identified neutral and outlier SNP datasets . MRM models including surface temperature maximum (Env_2) and surface temperature minimum (Env_3) exhibited significant relationships with pairwise FST values , respectively . More specifically, SNP PM1958 matched “calcium-activated chloride channel regulator”, “calcium-activated chloride channel regulator 2-like”, and “epithelial chloride channel-like” contigs while SNP PM4714 matched “uncharacterized protein APZ42_034504” or “Predicted: uncharacterized protein LOC108744461 isoform X4” contigs (Table n = 78 or 87.6%) did not exhibit sufficiently strong matches to any contigs within the P. monodon transcriptome and were thus concluded to reside in non-coding  genomic regions.Among the 89 SNPs that were found associated with environmental variables, 11 (12.4%) yielded matches to 173 total contigs in the P. monodon) is an important aquaculture species in Australia; however, to date only limited information is available regarding the influence of environmental pressures on the genetic structure of geographically discrete populations. Based on results from this study, three distinct genetic groups  were revealed across the geographic distribution range of Australian P. monodon: north Queensland , Northern Territory , and Western Australia (Nickol Bay). Of note is that the Western Australia population exhibited the lowest level of genetic variation of all assessed populations, which could be due to restricted gene flow between Western Australia and Northern Territory populations. Using multivariate analyses that considered both geographic distance and environmental factors it was determined that the majority of SNPs are neutral , while a small subset of outliers are putatively adaptive (n = 89). Surface temperature maximum and minimum provided the strongest correlative explanation for the presence of these outliers (see below).The giant black tiger shrimp . Relative reduced genetic diversity  was observed for Australian P. monodon compared to other recently assessed crustacean species. More specifically, European green crab (Carcinus maenas) exhibited average HO and HE of 0.254 and 0.256 [Homarus gammarus) had HO and HE that ranged between 0.049–0.63 and 0.179–0.504, respectively [Panulirus homarus) had HO, HE, and Av.MLH that ranged between 0.166–0.184, 0.226–0.233, and 0.168–0.186, respectively [P. monodon could be due to technical artefacts of the RADseq-based genotyping  [O deficiency has also been observed in Bangladesh P. monodon using 10 microsatellite markers and 14 SNP loci [Genetic diversity and stock assessments of Australian nd 0.256  while Euectively  and scalectively . The smaalleles) , 42 and/alleles) , 44. HowSNP loci , so the O and HE) were equal or higher than other populations despite AR and PPL being slightly lower (0.01–0.02) (see Results). This slight reduction in AR and PPL is most likely due to the smaller sample size in our study for the Western Australia population [P. monodon from the same Western Australia region [P. monodon population genetics studies concluded that observed reductions in heterozygosity and number of alleles were most likely driven by founder effects or bottleneck events. As such, based on the entire Western Australia SNP dataset encompassing 10,624 genome-wide SNPs, Western Australia P. monodon appears from our dataset to not have undergone a bottleneck , but rather has retained similar heterozygosity to Northern Territory and north Queensland populations. Differences between our dataset and those using microsatellites, allozymes and mtDNA may simply be due to the level of genetic resolution of these markers when comparing allelic diversity, where our high-resolution sampling captured more of the true genetic diversity within the P. monodon genome. Likewise, Lemopoulos et al. [P. monodon populations exhibited FIS values > 10%, which exceeded a general FIS threshold of ~ 10% suggested by Moss et al. [Litopenaeus vannamei). However, slightly positive FIS values observed here are most likely a result of DArTSeq genotyping technical artifacts  rather than of biological origin. Furthermore, the large NeLD estimates determined for all Australian P. monodon populations are likely to reduce any possible effect of inbreeding [IS values in this study are lower than observed in wild Pacific Ocean L. vannamei collected from Panama to Mexico (FIS = 0.53) [IS = 0.36) [In the Western Australia (Nickol Bay) population, heterozygosity   and alon = 0.36) .FST analysis for north Queensland and Northern Territory Australian P. monodon populations revealed relatively weak genetic structuring (0–0.039), except for Western Australia (0.069–0.107), which had the highest levels detected  and Queensland (0.032) [P. monodon given the relatively short offshore planktonic larval phase (approximately 20 days), during which larvae and post-larvae disperse and migrate to nursery areas  [P. monodon populations presented here (see below) suggests that divergent selection may be contributing to genetic divergence despite genetic drift [P. monodon populations.Pairwise ing 0–0.09, except (0.032) . As suchtuaries) . Moreoveic drift . RegardlFST, DAPC and Netview analyses revealed regional structure in Australian P. monodon with three major population groupings: north Queensland , Northern Territory , and Western Australia (Nickol Bay)  [Scylla serrata) [Penaeus esculentus) [Lates calcarifer) [P. monodon genetic differentiation investigations based on allozyme, mitochondrial DNA and microsatellite markers also revealed significant genetic partitioning among Australian and south-east Asian P. monodon populations indicating significant bio-geographic barriers to dispersal [P. monodon populations appear to be driven by the presence of biogeographic barriers between eastern, northern, and Western Australia that effectively limited or prevented gene flow over evolutionary timescales.Assessment of population structure using ay) Fig. . One pos regions . Moreove regions –58. Simimmerson) , mud craserrata) , brown tulentus) , and barcarifer) . Previouispersal , 33, 34.PD and EA analyses collectively identified 89 overlapping outlier SNPs, which accounted for 21 and 55% of total outlier SNPs detected, respectively , European green crab (C. maenas), and Atlantic salmon ) that were determined to have low level gene flow and genome-wide divergence [P. monodon populations . This conclusion is supported by other studies that also demonstrated that association between environmental variables and outlier SNPs could be indicative of local adaptation [Analysis of population structure based on 89 outlier SNPs using PCA presented a similar spatial pattern as to PCA based on 10,535 neutral SNPs in the form of three groups: north Queensland, Northern Territory, and Western Australia (Nickol Bay). Several causes may contribute to the same population structure being determined when either neutral or outlier loci were used  and outlier (n = 89) SNPs inherent to wild Australian P. monodon. This significant positive correlation suggests that: 1) gene flow between sampled sites may be impacted by natural selection or 2) geographically discrete Australian P. monodon populations have adapted to region-specific environmental variables . Geographic isolation due to biogeographic barriers (see above) could have caused initial level of genetic differentiation between P. monodon populations with subsequent divergent selection imposed by environmental factors leading to increased differentiation across evolutionary time. This interpretation is supported by RDA analysis, which indicated that the effects of surface temperature maximum and minimum (Env_2 and Env_3 respectively) were more pronounced than geographic distance for both neutral and outlier SNP.RAD, pRAD and MRM analyses demonstrated a strong relationship between geographic distance, environmental distance, and genetic differentiation in both neutral  [C. maenas) [C. virginica) [Placopecten magellanicus) [Parastichopus californicus) [C. maenas [P. monodon broodstock emigrate out of estuaries into foreshores for spawning and, thus, extreme swings could influence individual fitness . Further studies are needed to elucidate the existence and extent of population-specific thermal adaptation among Australia P. monodon populations and the potential functional genomics implications of the identified 89 outlier SNPs.RAD and partial RAD analyses conducted on outlier SNP demonstrated that surface temperature maximum (Env_2) explained five and six times more genetic variation than was explained by surface temperature minimum (Env_3), respectively. Therefore, it is plausible that surface temperature maximum (Env_2) could be the predominant driver of local adaption among Australian nd Kelly  found thricanus) , Europea maenas) , easternrginica) , sea scalanicus) , and giaornicus) ). Winter. maenas . In Aust. maenas , while s. maenas . MoreoveP. monodon genome. Only 11 of these 89 outlier SNPs matched P. monodon transcriptome contigs [P. monodon transcriptome matches should be considered as candidates for future research into divergent selection driven local adaptation among these geographically discrete populations [The combination of PD and EA approaches identified 89 outlier SNPs that are potential targets for local adaptation within the Australian  contigs  with sub contigs , 74, allulations .CLCA)  Table . CLCA isecretion –78. In Phallenge . As suchThe remaining 78 outlier SNPs are presumed to be located in non-coding regions absent from the transcriptome, or in genes with low-level expression that were not captured within initial transcriptome assembly –82.P. monodon should be managed as three separate stocks: north Queensland, Northern Territory, and Western Australia. The levels of genetic diversity revealed in the present study using both neutral and outlier SNP are useful for aquaculture purposes such as selective breeding programs, maintaining stock diversity, and distinguishing hatchery stocks from wild populations. Moreover, in addition to reduced gene flow, thermal adaptation is also likely to contribute to a greater divergence in the Western Australia population. Regardless, the strong genetic structure and presence of rare alleles in all seven P. monodon populations suggests that these populations should be managed accordingly to maintain genetic integrity. Environmental factors can influence genetic diversity and population structure in marine species and an accurate understanding of this influence can both improve fisheries management and help predict responses to environmental change [P. monodon; however, the magnitude of influence for each of these factors is hard to determine conclusively. This study also provides evidence for environmentally-driven selection pressure on geographically discrete populations, which can be utilized to help ensure sustainable management of Australian P. monodon . Practically, a population that is potentially under local adaptive pressures may be an important source of private or rare alleles that can enhance population resistance to future environmental change  or assist natural migration [The black tiger shrimp is an important aquaculture and fishery species in Australia. Therefore, the identification of genetic stock units among wild populations is crucial for fishery and aquaculture broodstock management. Neutral and adaptive population structure findings suggested that Australian l change . This stigration , 84.P. monodon . This study provides novel insights that assist the development and implementation of P. monodon aquaculture and fishery management practices within Australia. Analysis of population structure using both neutral  and outlier (n = 89) SNPs suggest that Australian P. monodon should be managed as three separate stocks  and that geographically discrete P. monodon populations have likely undergone local adaptation to region-specific thermal regimes. Future studies should investigate the role that outlier SNPs potentially play in local adaptation in order to advance wild stock structure preservation and help facilitate selective breeding programs.We utilized a SNP dataset containing 10,624 loci to determine genetic population structure and local adaptation across seven populations of Australian black tiger prawn P. monodon (n = 283) were collected from seven locations around Australia between 2015 and 2017 , Etty Bay , Townsville , Joseph Bonaparte Gulf , Tiwi Island , Gulf of Carpentaria , and Nickol Bay   was extracted from each pleopod tissue sample following a modified cetyl trimethyl ammonium bromide (CTAB) protocol , 87. BridartQC python scripts (available at https://github.com/esteinig/dartqc). More specifically, SNP sequences matching the following criteria were removed from the dataset  platforms as per , 90, 91.PLINK 1.9 [2) threshold of 0.2), SNPs with the lowest call rate value were removed. Lastly, remaining SNP dataset for each population was then assessed for deviation from Hardy–Weinberg equilibrium (HWE) using R package dartR [p < 0.0001) were removed.All individuals and remaining loci were subsequently tested for missing data and linkage disequilibrium (LD) using LINK 1.9 . All indge dartR  and all O), expected heterozygosity (HE), and inbreeding coefficient (FIS) were calculated using the divBasic function of R package diveRsity version 1.9.90 [FIS values were determined using 95% confidence intervals and 1000 bootstrap replicates. Allelic richness (AR), private allelic richness (ARA), and percentage of polymorphic loci (PPL) were calculated for each SNP using rarefaction to avoid sampling size bias in HP-RARE version 1.1 [inbreedR version 0.3.2 [eLD) was also estimated for each population using NeEstimator version 2.1 based on LD and random mating model [Several measures were used to estimate population genetic variation and differentiation . Effectng model .StAMPP version 1.5.1 [FST), along with confidence intervals and probability (p) values between populations, were calculated according to Wright [mantel.randtest function in R package ade4 [Genetic differences among populations and individuals were calculated using R package on 1.5.1 . Pairwiso Wright  and updao Wright . Nei [36o Wright . Statistage ade4  with 999adegenet version 2.1.1, with number of possible clusters  of 1–20) determined by running 60 iterations of ‘find.clusters’ function [NetView [NetView was run through multiple K-nearest neighbour (kNN) values between 1 and 60 to assess both broad and fine scale population structure.To determine the number of genetic groups a Discriminant Analysis of Principal Components (DAPC) was used on individual genotypes in R package function . Bayesia[NetView , 102. NeP. monodon [http://www.bio-oracle.org/downloads-to-email.php) [https://www.qgis.org/) was used to extract values of 23 marine environmental variables  identified seven marine environmental variables with Pearson coefficient of correlation (r) between − 0.75 and 0.75. Marine environmental variables retained for further analysis were: 1) surface salinity mean (Env_1), 2) surface temperature maximum (Env_2), 3) surface temperature minimum (Env_3), 4) surface current velocity mean (Env_4), 5) surface phytoplankton mean (Env_5), 6) benthic temperature mean (Env_6), and 7) benthic current velocity mean (Env_7)  , 104. QGPCAdapt [vegan version 2.5–2 [LEA version 2.0.0 [The possible effects of divergent selection on the overall pattern of genetic differentiation was assessed by combining the results of two independent population differentiation (PD) and two independent environmental association (EA) approaches  control levels of 0.001, 0.005, 0.01, 0.05, and 0.1 using Bayescan 2.01 function plot_R.r [PCAdapt [BayeScan version 2.1 , 105 wasplot_R.r . The R p[PCAdapt  with K =vegan version 2.5.2 [vegan function ordistep, which applies a stepwise per mutational ordination method, was used to perform the ‘optimal’ model that corresponded to the highest adjusted coefficient of determination (adj R2). To test the significance of the final RDA model, the vegan function anova.cca was run with 999 permutations then outlier SNPs were identified on each of the first two constrained axes (p = 0.001) using the function outliers. Finally, LFMM models were run using R package LEA version 2.0.0 [p values. Candidate loci were determined for each marine environmental variable  and all candidate loci with FDR-adjusted p ≤ 0.05 were retained for subsequent analyses (see below).The linear model redundancy analysis (RDA) was run in R package on 2.5.2 . The vegon 2.0.0  for eachn = 89) and neutral  loci using R package adegenet version 2.1.1 [StAMPP version 1.5.1 [To examine adaptive and neutral patterns of variation within SNP obtained across all seven collection locations Fig. , PCA wason 2.1.1 . Retaineon 1.5.1 .P. monodon genetic structure, redundancy (RDA) and partial redundancy (pRDA) analyses were conducted for both retained outlier and neutral locus allele frequencies using R package vegan. Analyses of variance (ANOVA) with 999 permutations were used to assess the significance of each environmental parameter within the RDA for all RDA and partial pRDA tests. The vegan function ordistep with 999 permutations was used to evaluate each environmental variable and perform the ‘optimal’ model .To examine the relative contribution of geographical distances and environmental variables and select which environmental factors best explain Australian ecodist [FST values calculated in StAMPP and least-cost geographic distances calculated in marmap were used as response values and explanatory variables, respectively, and then MRM models were generated using the marine environmental variables that significantly correlated with genetic variation of retained outlier loci. Note that MRM models focused on the relationship between geographic distance and environmental variables to determine the significance of environment factors on gene flow among populations [To determine whether isolation by distance (IBD) could explain geographic patterns of differentiation in retained outlier loci (see above), multiple regression on distance matrices (MRM)  was condecodist  with 100ulations .P. monodon transcriptome [P. monodon transcriptome that exhibited strong pairwise similarity with any outlier SNP  were compiled and reported along with contig annotations provided by Huerlimann et al. [Each outlier SNP was assessed against an assembled criptome  using Gecriptome . Contigsn et al. . All SNPAdditional file 1. SNP data analysis workflow from raw  to final neutral  and outlier (n = 89) loci datasets.Additional file 2. Genotypic data. Genotypes of 278 individuals of P. monodon at 10,624 adaptive and neutral genome-wide SNPs are included in a standard STRUCTURE format.Additional file 3. Mantel correlograms for the relationship between genetic distance (FST) and geographic distance (km) among Penaeus monodon populations (n = 7) using 10,624 SNPs.Additional file 4. Genotypic data. Genotypes of 278 individuals of P. monodon at 89 adaptive genome-wide SNPs are included in a standard STRUCTURE format.Additional file 5. Genotypic data. Genotypes of 278 individuals of P. monodon at 10,535 neutral genome-wide SNPs are included in a standard STRUCTURE format.Additional file 6. Population differentiation estimates for Penaeus monodon populations sampled using neutral and outlier loci. Population pairwise FST  estimates computed by using the R package StAMPP v.1.5.1 . Pairwise FST values of 89 outlier loci are shown below the diagonal, and pairwise FST values of 10,535 neutral loci are reported above. All FST values were significant at p ≤ 0.05 following 1000 bootstraps performed across loci to generate confidence intervals. The only non-significant FST value (p > 0.05) is in bold type.Additional file 7. Characterization of high-quality BLAST matches of outlier SNP to Penaeus monodon transcriptome contigs.Additional file 8. Translation of eight outlier SNPs with transcriptome matches to protein sequences using all three reading frames.Additional file 9. Sampling codes and site locations with number of individuals (N).Additional file 10. Marine environmental variables (n = 23) for each of the seven geographically discrete sampling locations as determined using location-specific latitude and longitude.


Ailurus fulgens) have been listed as one of the endangered species in China and worldwide. In order to protect the populations as well as to avoid the inbreeding depression in wildlife, monitoring the genetic diversity and population genetic structure of red pandas is necessary. By analyzing the captive red pandas among the zoos in China using PCR and genotyping, we found a high genetic diversity among the populations as high as that of the wild population. This demonstrate the current scientifically-based breeding program can keep the high genetic diversity in captive red pandas.Red pandas (Ailurus fulgens) have been raised in zoos for 60 years. It is very important to understand the genetic diversity and population genetic structure of the captive red pandas. Based on 19 microsatellite loci, we investigated genetic diversity and population genetic structure of 116 captive red pandas, with samples taken from 11 captive populations in China. Our results revealed a high genetic diversity among the populations, with mean allelic richness varying from 3.505 (Beijing) to 4.026 (Mianning), and expected heterozygosities varying from 0.631 (Huangshan) to 0.782 (Wenling). In particular, significant deviation from Hardy–Weinberg equilibrium was found in populations of Fuzhou and Jiangsu. The genetic differentiation index across all populations was 0.055, indicating a significant genetic differentiation among the 11 populations. These populations could be divided into three genetic clusters using a microsatellite-based Bayesian clustering analysis, which were consistent with the clustering results of wild populations. We conclude that the genetic diversity among captive red pandas is as high as that of the wild population. More attention should be paid to develop a proper and scientifically-based management program to avoid inbreeding and maintain a high genetic diversity in captive red pandas.In China, red pandas ( Ailurus fulgens), a member of the order Carnivora, is an arboreal vegetarian mammal that depends almost mainly on a bamboo diet. The major threats to wild red pandas include habitat loss, fragmentation, and degradation  [Genetic clustering analysis was conducted using Structure 2.3.1 . Ten ind/s[L(K)] .HE ranged between 0.528 and 0.901, with a mean value of 0.730 ± 0.115. HO ranged between 0.513 and 0.926, with a mean value of 0.719 ± 0.145, and PIC ranged between 0.469 and 0.889, with a mean value of 0.708 ± 0.131. Except for the moderate polymorphism level observed on the locus Aifu-39, all other loci exceeded 0.5 and exhibited a high polymorphism level, as shown in A total of 175 alleles were detected among the 116 red pandas screened through the 19 loci. The number of alleles in each locus ranged between 5 and 18 with a mean value of 9.21 ± 3.6 alleles; p > 0.05), as shown in The expected heterozygosity of the captive populations in this study was compared with that in Liang’s study  using thThe genetic diversity of the captive populations was compared with that of the wild red panda populations in Hu et al. . The difp < 0.01), whereas other populations were in equilibrium, as shown in The present study verified the Hardy–Weinberg equilibrium of the 11 captive populations. The results revealed that the Fuzhou and Wuxi populations exhibited significant deviations from the Hardy–Weinberg equilibrium . This result was contributed by 47.36% of the loci, and the FIT of all loci ranged from −0.209 (Aifu-12) to 0.269 (Aifu-7).FIS of all loci had a nonsignificant −0.006. Further analysis of the FIS of each population revealed that the FIS ranged from −0.73 (Beijing) to 0.111 (Huangshan). Only the Wuxi and Huangshan populations had a significantly positive FIS, indicating low inbreeding levels in most populations, as shown in The mean FST of the corresponding subpopulations . Further analysis of the genetic differentiation between pairs of populations revealed that most captive populations exhibited substantial genetic differentiation, as shown in All loci contributed the significant ΔK occurred when k = 3 (ΔK = 21.4), as shown in Genetic structure analysis revealed that the maximum Effective genetic management depends on comprehensively understanding the genetic diversity of captive populations. By analyzing the number of alleles, gene polymorphism (PIC), genetic richness, genetic heterozygosity, and the Hardy–Weinberg equilibrium, we revealed the genetic diversity of captive red pandas in the specific zoos in China. Myotis davidii), You et al. [In conservation genetics research, NA is a critical indicator of the genetic variation of a population. The mean NA of the 11 captive populations ranged from 4.05 (Beijing) to 7.11 (Fuzhou), indicating the abundant genetic variation in captive populations. The mean NA of the Fuzhou population was 7.11, indicating the genetic diversity of this population. This may be attributed to the many red pandas in the Fuzhou population, which increased the NA. Liang et al.  reportedu et al.  reportedHE in 10 loci ranging from 0.668 to 0.779, as shown in HO and HE of 0.679 and 0.719 in the diversity of red pandas in Qionglai, Liangshan, Xiaoxiangling, Gaoligong, and Tibet (n = 105), whereas the present study reported the HO and HE of 0.719 and 0.730, respectively (n = 116). The mean NA of the wild populations in Hu et al. [The comparison of our results and those in the findings from Liang et al.  showed tHE of 0.620 and 0.779, respectively. This phenomenon is not observed in red pandas, and we thought that the high genetic diversity of captive red pandas can be attributed to the supplement of wild populations to achieve the genetic admixture of captive and wild individuals. Yan et al.  reportedRed pandas have a short life expectancy, high mortality rate, and low offspring survival rate, resulting in difficult self-sustainment among the captive populations in China . The susThe essence of the Hardy–Weinberg equilibrium in red panda populations refers to the unchanged genetic and genotype frequencies throughout generations in a large-scale and randomized mating population in the absence of external effects such as selection, migration, and mutation. The Hardy–Weinberg equilibrium is based on random mating in natural populations . EndangeF-statistic. The genetic differentiation coefficient (FST) is a critical indicator of genetic differentiation among subpopulations. It measures the variance of the average heterozygosity in a subpopulation relative to the total population, ranging between 0 and 1. Wright [FST value between 0 and 0.05 indicates a low level of genetic differentiation between subpopulations, 0.05–0.15 indicates moderate genetic differentiation, and 0.15–0.25 indicates a high level of genetic differentiation. The present study reported a moderate genetic differentiation among the red panda subpopulations in all microsatellite loci (FST = 0.055). Further analysis revealed that most population pairs had a significantly high FST, indicating a high level of genetic differentiation between captive populations.The present study estimated the genetic status among the red panda populations through an . Wright  argued tFIS indicated the decreased proportion of heterozygotes because of nonrandom mating among individuals in a subpopulation. The range of FIS is between −1 and 1. A positive FIS indicates severe inbreeding in a population, resulting in heterozygote deficiency, whereas a negative FIS indicates an outbreeding and heterozygote-surplus population. In this study, the captive populations in Fuzhou, Shanghai, Beijing, Guangzhou, and Wenling had negative FIS values, indicating a heterozygote surplus; whereas those in Wuxi, Panyu, Chonqing, Dazu, Mianning, and Huangshan had various degrees of heterozygote deficiency. The mean FIS of the total population was −0.067, revealing a low level of inbreeding in the total population. Wang et al. [FIS values of 0.3221 and 0.3983, respectively. By contrast, captive red panda populations exhibited lower FIS, indicating a low level of inbreeding. Inbreeding existed to an extent in some captive populations such as Huangshan (FIS = 0.111). The existing captive red pandas have a small population scattered in the zoos throughout China, and few subpopulations have 10 or more red pandas. Therefore, although the existing population has a high level of genetic diversity, the small population may lead to increased inbreeding between captive red pandas in the future.The inbreeding coefficient g et al.  examinedCluster analysis is an ideal instrument for genetic structure analysis of a population. The principle of clustering is to assume each analyzed individual has a common ancestor in all categories, and genetically similar individuals are clustered together to estimate the probability values of each category belonging to the said ancestor. When an individual has a more than 80% probability of being classified to a specific category, the individual is inferred to belong in this category. The present study conducted structure analysis to group the 11 captive populations into three clusters. Hu et al.  reportedThe genetic diversity among captive red pandas is similar to that of the wild population. A proper and scientifically-based management program should be established to avoid inbreeding and maintain the genetic diversity in captive red pandas.


Plasmodium. Here, the genetic diversity of PvHAP2 was studied in Plasmodium vivax parasite populations from the Greater Mekong Subregion (GMS).Antigens expressed in sexual stages of the malaria parasites are targets of transmission-blocking vaccines (TBVs). HAP2/GCS1, a TBV candidate, is critical for fertilization in Plasmodium vivax clinical isolates were collected in clinics from the China-Myanmar border region (135 samples), western Thailand (41 samples) and western Myanmar (51 samples). Near full-length Pvhap2 (nucleotides 13–2574) was amplified and sequenced from these isolates. Molecular evolution studies were conducted to evaluate the genetic diversity, selection and population differentiation.pvhap2 gene for a total of 227 samples from the three P. vivax populations revealed limited genetic diversity of this gene in the GMS (π = 0.00036 ± 0.00003), with the highest π value observed in Myanmar (0.00053 ± 0.00009). Y133S was the dominant mutation in the China-Myanmar border (99.26%), Myanmar (100%) and Thailand (95.12%). Results of all neutrality tests were negative for all the three populations, suggesting the possible action of purifying selection. Codon-based tests identified specific codons which are under purifying or positive selections. Wright’s fixation index showed low to moderate genetic differentiation of P. vivax populations in the GMS, with FST ranging from 0.04077 to 0.24833, whereas high levels of genetic differentiation were detected between the China-Myanmar border and Iran populations (FST = 0.60266), and between Thailand and Iran populations (FST = 0.44161). A total of 20 haplotypes were identified, with H2 being the abundant haplotype in China-Myanmar border, Myanmar and Thailand populations. Epitope mapping prediction of Pvhap2 antigen showed that high-score B-cell epitopes are located in the S307-G324, L429-P453 and V623-D637 regions. The E317K and D637N mutations located within S307-G324 and V623-D637 epitopes slightly reduced the predicted score for potential epitopes.Sequencing of the pvhap2 gene among P. vivax populations in the Greater Mekong Subregion. The relative conservation of pvhap2 supports further evaluation of a Pvhap2-based TBV.The present study showed a very low level of genetic diversity of  Plasmodium infections as the reservoir for malaria transmission [Plasmodium vivax has become the predominant parasite in the GMS, and its proportion has been increasing [Malaria remains an important public health problem in the six countries of the Greater Mekong Subregion (GMS), including Cambodia, China’s Yunnan and Guangxi provinces, Lao PDR, Myanmar, Thailand and Vietnam . Launchismission –6. In adcreasing . Thus, ncreasing –11.Arabidopsis thaliana and Lilium longflorum, is a class II viral fusion protein with a cysteine-rich extracellular region [Chlamydomonas and Plasmodium [Plasmodium, HAP2 protein is located specifically on the surface of activated male gametes [hap2 gene knockout in the rodent parasite P. berghei abolishes fertilization [P. falciparum HAP2, which is critical for membrane fusion during fertilization [Gametocytes developed in the human host are the obligative stage for the transmission to mosquitoes . After ar region –18. HAP2asmodium . The N-tasmodium . In Plas gametes . It is alization , 21. Reclization , 22, 23.lization . Meanwhilization . AltogetP. vivax populations from the GMS were assessed in this study. These analyses revealed limited genetic diversity of Pvhap2 gene as well as little differentiation of parasite populations in the GMS compared with the global sequences of a gamete fertilization essential gene, so as to provide useful information for transmission blocking vaccine development.Understanding the genetic diversity of a vaccine candidate in endemic areas is necessary for guiding the development of an effective malaria vaccine. Genetic polymorphism in vaccine candidates for asexual stages of malaria parasites has been a major limiting factor for the development of malaria vaccines . Thus, tP. vivax malaria attending clinics located in the China-Myanmar border area  in 2016, western Myanmar  in 2018, and western Thailand  in 2011, respectively , located on chromosome 8 at nucleotides 651,355 to 654,032 of the P. vivax Salvador-I  reference strain  was amplified by PCR using the primer pair pvsHap2-1F and pvsHap2-1R for the primary PCR, and the primer pair pvsHap2-nest-F and pvsHap2-nest-R for the nested PCR. Primary and nested PCR was conducted in a reaction volume of 20 μl including 1× KOD-Plus-Neo buffer , 200 μM dNTPs (Toyobo), 1 mM MgSO4 (Toyobo), 10 µM of each primer, 1 μl of gDNA or primary PCR product (1:1000 dilution), and 0.4 units of KOD-Plus-Neo DNA polymerase (Toyobo). The cycling conditions for both primary and nested PCR were as follows: one step at 95 °C for 5 min; 39 cycles at 95 °C for 30 s, 55 °C for 15 s, and 68 °C for 1 min; and an extension step at 68 °C for 5 min. The PCR products were separated in 1.5% agarose gel in the presence of SYBRTM Safe DNA  and visualized under UV light. PCR fragments were purified with QIAquick PCR Purification kit (Qiagen) according to the manufacturer’s protocol and sequenced in both directions using primers listed in Additional file pvhap2 sequences from Iranian P. vivax isolates (GenBank: KM594259-KM594288 and KU743361-KU743382) were included for comparison [Genomic DNA (gDNA) was extracted from dried filter blood spots by using the QIAamp DNA mini kit  and stored at − 20 °C until use. Primers for mparison .Pvhap2 sequences were aligned using Clustal W program in MEGA X [S), total number of mutations (η), nucleotide diversity (π), the average number of nucleotide differences (k), number of haplotypes (H), haplotype diversity (Hd) and the corresponding standard deviation were computed using DnaSP v6.10.01 [dN-dS difference using the Nei & Gojobori’s method with the Jukes & Cantor correction as implemented in MEGA X, and significance was determined by a Z-test [dN-dS shows positive natural selection, whereas a negative value indicates purifying selection. The Tajima’s D test [D* and F* tests [Plasmodium cynomolgi hap2 gene (PlasmoDB ID: PcyM_0814900) as an outgroup [P < 0.05), and the skew from randomness was calculated as the neutrality index.n MEGA X . The numv6.10.01 . Additiov6.10.01 . Naturala Z-test . A posits D test , Fu & LiF* tests  were perF* tests . McDonalF* tests  was perfoutgroup . Meanwhioutgroup . Fisher’FST) implemented in ARLEQUIN v3.5.2.2 software [FST values is defined as no differentiation (0), low genetic differentiation (≤ 0.15), moderate genetic differentiation (0.15–0.25) and high differentiation (≥ 0.25) [P-value < 0.05 was considered as indicating a significant difference. The median-joining method in NETWORK v5.0.0.3 [The genetic differences between populations was investigated by estimating the rate of fixation (software . Interpr(≥ 0.25) . P-valuev5.0.0.3  was usedv5.0.0.3 .http://www.cbs.dtu.dk/services/BepiPred/). For ABCpred server, a threshold of 0.7 was used to predict a peptide length of 16 residues. The program BepiPred was run with an epitope threshold of 0.6, and the epitope score represents the average of the scores of at least eight consecutive amino acids above the cut-off. The overlapped predicted regions from ABCpred and BepiPred, as well as mutation sites were marked on the schematic structure of Pvhap2 protein by using DOG 2.0 software [2 algorithm [The potential B cell epitopes of Pvhap2 were predicted by using the ABCpred server  and Bepisoftware , 38. Thelgorithm . The elelgorithm . The amipvhap2 gene (nt 13–2574 bp) in 227 clinical P. vivax samples collected from three regions of the GMS. From the alignment with the reference Sal I strain, 28 single nucleotide polymorphisms (SNPs) were identified. Among them, 16 SNPs were synonymous and 12 were non-synonymous  for the 227 pvhap2 gene (nt 397–2097 bp) obtained from this study was aligned with previously published global pvhap2 sequences [k) value was observed in the China isolates (1.267), followed by Thailand (0.963) and Myanmar (0.897) isolates, while the lowest k-value was detected in the PNG isolates (0.286)  and Thailand (− 0.0003) populations; however, neither value was statistically significant by Fisher’s exact test  observed in Myanmar isolates indicated purifying or directional selection  to study genetic differentiation between parasite populations. AMOVA revealed statistically significant differences in all studied populations. The pairwise comparison of parasite populations between the China-Myanmar border and Thailand populations, as well as between the Thailand and Myanmar populations showed little genetic differentiation with an FST value of 0.04077 and 0.09271, respectively , as well as between Thailand and Iran populations (FST = 0.44161), suggesting a high degree of differentiation between Southeast and West Asia populations.Pairwise comparisons were performed using the Wright’s fixation index  was represented by single parasite isolates , Peru (14.3%) and Mexico (46.6%) [P. falciparum (178–195 aa) and P. berghei (174–191 aa). Only one G626A SNP  was found within the hydrophobic “fusion loop” (cd loop) segment (167–198 aa) of loop region in Pvhap2. Given the essential function of the “cd loop” in mediating membrane fusion, these results suggest that functional conservation of the same region within the HAP2 protein in P. vivax may limit the accumulation of amino acid mutations [Sequence analysis of the Colombia .1%, PeruColombia .1%, Peru (46.6%) . Of note segment 7–198 aa utations . WhetherThe divergent prevalence of Pvhap2 mutations in different endemic areas probably reflects the different demographic histories of the parasites such as endemicity and selection imposed by the human host and mosquito vectors. In the GMS, the Y133S mutation was fixed or almost fixed, whereas the D637N mutation showed regional differences in prevalence. Difference in regional malaria epidemiology may be held accountable for this divergence in D637N prevalence. Pairwise comparison showed considerable differentiation between China-Myanmar border and western Myanmar populations. Alternatively, this divergence may reflect an evolutionary trend of the parasite populations through time, as the parasites from western Thailand (83.0%), China-Myanmar border (68.3%) and western Myanmar (36.5%) were collected in 2011, 2016 and 2018, respectively. It is possible that this longitudinal decrease in D637N prevalence may be associated with host immunity since HAP2 is shown to be naturally immunogenic and antibodies against this protein are present in endemic populations . InteresPvhap2 showed lower genetic diversity  [P. vivax such as pvs28/25   comparedf China, ; π = 0.0f China, ), pvs48/solates, ). This helection . Anotherelection . Thus, iFST and phylogenetic analysis. Within the GMS, however, low FST values among all GMS populations studied here may suggest extensive gene flow among these populations, possibly mediated or enhanced by migratory human populations [Despite the low level of sequence diversity, the overall genetic differentiation among GMS and Iran populations in this study was remarkable, which was cross-verified from ulations . These rpvhap2, indicating the importance of this protein in parasite’s survival and transmission. Little genetic differentiation identified among the three GMS populations suggests extensive gene flow between these areas. Notably, this study revealed a dominant haplotype (H2) of pvhap2 in GMS isolates. Additionally, the present study identified potential B-cell epitopes with relatively low diversity. These findings provide important information for designing an effective TBV based on Pvhap2.The present study revealed very low level of genetic diversity and purifying selection in Additional file 1: Figure S1. Geographical distribution of P. vivax populations contributing to this study. Plasmodium vivax isolates from three sampling sites, including China-Myanmar border, Myanmar and Thailand were analyzed in the present study.Additional file 2: Table S1. List of the primers along with their nucleotide sequences used to assess the genetic diversity of the pvhap2 gene.Additional file 3: Table S2.Pvhap2 gene polymorphism by country.