Mizuhopecten yessoensis is an important aquaculture species that was introduced to Western Canada from Japan to establish an economically viable scallop farming industry. This highly fecund species has been propagated in Canadian aquaculture hatcheries for the past 40 years, raising questions about genetic diversity and genetic differences among hatchery stocks. In this study, we compare cultured Canadian and wild Japanese populations of Yesso scallop using double-digest restriction site-associated DNA (ddRAD) sequencing to genotype 21,048 variants in 71 wild-caught scallops from Japan, 65 scallops from the Vancouver Island University breeding population, and 37 scallops obtained from a commercial farm off Vancouver Island, British Columbia. The wild scallops are largely comprised of equally unrelated individuals, whereas cultured scallops are comprised of multiple families of related individuals. The polymorphism rate estimated in wild scallops was 1.7%, whereas in the cultured strains, it ranged between 1.35 and 1.07%. Interestingly, heterozygosity rates were highest in the cultured populations, which is likely due to shellfish hatchery practices of crossing divergent strains to gain benefits of heterosis and to avoid inbreeding. Evidence of founder effects and drift was observed in the cultured strains, including high genetic differentiation between cultured populations and between cultured populations and the wild population. Cultured populations had effective population sizes ranging from 9 to 26 individuals whereas the wild population was estimated at 25,048–56,291 individuals. Further, a depletion of low-frequency variants was observed in the cultured populations. These results indicate significant genetic diversity losses in cultured scallops in Canadian breeding programs.The Yesso scallop  HOBS; Ne) relative to census size from sweepstakes reproductive success  have high genetic diversity  both in ess SRS; . This geess SRS; .Mizuhopecten yessoensis is an economically important aquaculture species and a valuable commercial protein source. Native to Japan (JPN), it has been grown in culture in British Columbia  and China since the 1980s , followed by 2 washes with 0.5 mL ice-cold 75% ethanol, followed by resuspending the dried DNA pellet in molecular-grade water. Commercial and breeding program samples were extracted from frozen tissue using the Monarch Genomic DNA Purification Kit (NEB), as per manufacturer's instructions using the enzymatic cleanup protocol with both proteinase K and RNase A, and eluting into 50 µL molecular-grade water.All samples were quantified by Qubit dsDNA-BR measured by BioSpectrometer (Eppendorf) with a reusable µCuvette . Any sample under 20 ng/µL was concentrated using the Genomic DNA Concentrator Columns (Zymo Research), eluted in 12 µL water, and quantified by Qubit. Quality was inspected in representative samples from each collection by 1% agarose gel electrophoresis. Samples were normalized to 20 ng/µL in a 10 µL volume, randomized in 96-well microtiter plates, and submitted to the Institute de Biologie Intégrative et des Systèmes (IBIS) at Université Laval for library preparation. Each plate contained a negative control well. Five samples with concentrations between 17.7 and 20 ng/µL were also included in a 10 µL volume .PstI and MspI, or NsiI and MspI, size selected to retain fragments from 100 to 250 bp. Digestions with NsiI and MspI were predicted in silico to generate 118,850 fragments in the 1.65 Gb genome, whereas PstI and MspI were predicted to have 50,836 fragments; PstI and MspI were selected for digestion.Selection of restriction enzymes and estimation of required sequencing depth was conducted using SimRAD  using thFileExporter plugin.Samples were multiplexed to 32 individuals per chip, with 6 sequencing chips used. Library preparation was conducted using the semiconductor platform-adapted ddRAD-seq approach  with thestacks_workflow repository (E. Normandeau), and all instructions for the analysis is provided . Sample barcodes and populations were designated in the metadata file (see Data availability). Raw sequence data were quality checked using FastQC . Samples with fewer than 500,000 reads were removed including the 3 negative control samples that had between 338 and 2,101 reads each. The gstacks module of Stacks was then used with the reference genome to genotype the samples using default settings . Following genotyping, the populations module of Stacks was used to filter, retaining loci genotyped in ≥70% of the individuals per population in all 3 populations . A global minor allele frequency (MAF) filter retained loci with MAF ≥ 0.01 (flag: --min_maf 0.01). To determine the impact of unequal sample size on the mean nucleotide diversity estimated per population, gstacks and populations were run as above but with a rarefied data set using 37 samples per population for all 3 populations. Using the full data set, the per-individual inbreeding coefficient was calculated using vcftools . Genotyping rate was calculated per sample using the percent_missing_by_ind function of simple_pop_stats, and individuals were retained if they had less than 30% missing data. MAF was calculated per locus, and any locus with MAF < 0.01 was removed. Per locus FST was calculated using pegas  equilibrium was conducted per locus using pegas. Loci showing significant deviation from HW proportions (P ≤ 0.01) in any 1 of 3 populations were identified and removed from the data set. Loci with global HOBS ≥ 0.5 were also removed.Single-SNP per locus genotypes were read into R using the adegenet . Data weng pegas . HOBS waprivate_alleles function of poppr  using the glPca function of adegenet. The purged close relative data set was then used to calculate mean population-level FST that was calculated using 1,000 bootstraps to generate 95% confidence limits using the boot.ppfst function of hierfstat .In total, 558,098,638 single-end reads were generated from 189 Yesso scallops sequenced on 6 Ion Torrent chips. Each chip produced on average (±SD) 93.0 ± 1.4 M reads. After quality trimming, 547,450,718 reads (i.e. 98.1%) were retained, and the mean read duplication rate per sample was 86.6%. The mean GC content per sample was 43%, which is higher than the reference genome . After demultiplexing by sample, 503.5 M reads were retained (i.e. 92.1% of trimmed reads), where the per-sample average (±SD) number of reads was 2.7 ± 1.2 M . SamplesMaterials and methods) or low global MAF (i.e. MAF < 0.01) resulted in the removal of 370,235 loci (96.6%). Within the retained 13,136 filtered loci, comprising 1.14 M genomic sites, 21,048 variants were identified within 9,377 polymorphic RAD-tags.Aligning samples against the reference genome resulted in per-sample average alignment rates ranging from 80.6 to 81.6% . GenotypWhen retaining multiple variants per RAD-tag, there was an average of 2.24 variants within each tag, and a single, 2, or 3 variants were observed in 3,721 (39.7%), 2,585 (27.6%), and 1,541 (16.4%) tags, respectively . Four toP < 0.01) in at least 1 population and were removed. For the BC farm, JPN, and VIU collections, this included 703, 1,773, and 1,015 non-conforming markers, respectively. Of the remaining 6,797 variants, 74 had excess heterozygosity (i.e. HOBS > 0.5) and were removed, leaving 6,723 variants remaining. As samples had been removed, a second MAF filter was applied, and this resulted in the removal of an additional 176 variants with MAF < 0.01. After all filters, the single variant per RAD-tag data set had 6,547 variants remaining for downstream analyses.Per individual missing data was on average 8.1 ± 12.2%. Samples with more than 30% missing data were dropped from the analysis, which removed 2 samples from VIU and 14 samples from JPN . Using the rarefied data set (n = 37 samples per population), these relative trends in polymorphism rate and HOBS remained consistent (HOBS is observed in the wild samples (JPN) relative to the cultured samples, and the BC farm collection has the lowest polymorphism rate.Considering all 21,048 variants (i.e. multiple SNPs per RAD-tag) prior to HW filters, the JPN collection had the highest polymorphism rate at 1.7%, with 19,411 variants . By compnsistent . TherefoUsing the filtered, single-SNP per RAD-tag data set, 4,401 of the total 6,547 variants (67.2%) had MAF ranging 0.01–0.10. Once the data were separated by individual population, and any monomorphic or low MAF variants were removed (i.e. population MAF < 0.01), the JPN, VIU, and BC farm data sets retained 5,423 (82.8%), 4,531 (69.2%), and 3,722 (56.9%) variants, respectively . In addin = 270) and BC farm (n = 58). Regionally, when VIU and BC farm were merged into a Canadian population then compared to JPN, there were 1,401 private alleles in JPN and 690 in Canada. Although less numerous than JPN, the Canadian private alleles were higher overall in MAF (median MAF = 0.032) than the private alleles in JPN  or the single-SNP per RAD-tag data set . The linkage disequilibrium (LD) method estimated 95% confidence interval (C.I.) of Ne for JPN as 25,048–56,291 , for VIU as 26.4–26.4 , and for BC farm as 9.3–9.3  when using a PCRIT value of 0.01  but were estimated at similar levels for VIU and BC farm as the microhaplotype data (i.e. 26.2–26.2 for VIU and 9.2–9.3 for BC farm). Using the rarefied data set , 95% C.I. of Ne for VIU and BC farm were again estimated to be 9.5 and 22.9–23.0, respectively, but the JPN population Ne was not estimable with the existing data . This resulted in the removal of 43 of 65 (66%) of the VIU samples and 28 of 37 (76%) of the BC samples. None of the JPN samples were removed, and so VIU, BC, and JPN populations had 22, 9, and 71 samples each for PCA and FST analyses (see below). After the putative close relatives were removed, an additional MAF filter was applied, removing any variants with MAF < 0.01, which resulted in the removal of 283 additional variants, leaving 6,264 variants in the data set. Per locus FST was also recalculated, and both the purged close relatives and all sample data sets are presented in To reduce the impacts of family structure on downstream PCA  and pairpulation , and so n = 6,264 variants), samples were separated by country (i.e. Canada or JPN) across PC1, explaining 5.7% of the overall variation (In an unsupervised PCA using the single-SNP per locus data with putative close relatives removed (ariation . The BC ariation . After Pariation  inset. Aariation .FST (FST = 0.061–0.068) and the BC farm and JPN wild (FST = 0.068–0.077). The 2 cultured populations had slightly lower differentiation from each other than either with the wild JPN samples but still relatively high differentiation (FST = 0.046–0.055). As observed in the PCA, the removal of putative close relatives reduced FST differences between the hatcheries .Filtered single-SNP per RAD-tag markers were inspected for per-locus Ne and significant genetic diversity decreases indicate that it is important to continue efforts to maintain, monitor, and optimally use the existing diversity present in the cultured populations.The present study was conducted to evaluate the degree to which cultured Yesso scallop populations in BC have lost genetic diversity since their founding in the 1980s. This benchmark analysis provides a starting point for continued monitoring and preservation of existing genetic diversity on an ongoing basis. Although the standard hatchery practice of crossing divergent lines to avoid inbreeding and to gain heterosis benefits is observed in the present data as elevated heterozygosity in cultured stocks, low n = 71) were estimated to have a 1.7% polymorphism rate when including all SNPs with MAF > 0.01 that were present in at least 70% of the samples from each of 3 populations and were calculated prior to removing variants that did not conform to HW proportions. This rate was estimated based on 0.08% (or 1.14 Mb) of the expected 1.43 Gb genome .The The BC cultured scallop populations characterized here have significantly lower polymorphism rates than the source population. The genotyped VIU collection, which likely represents the full complement of genetic backgrounds in the VIU breeding program , was estimated to have a polymorphism rate of 1.35%, which is lower than the source wild population (i.e. 1.7%). The BC farm, with an estimated polymorphism rate of 1.07%, is likely only a subset of the full genetic variation in the cultured lineages from the seed provider for the farm since this collection was obtained as farmed adults from a single sampling event. Additionally, the JPN collections are obtained from several years of sampling, and so this does encompass a larger number of spawning events. Notably, both cultured collections were also typified by a depletion of alleles in the lower MAF range (i.e. MAF 0.01–0.10).FST = 0.05–0.24; Ne but potentially also from any artificial selection that has occurred (Mytilus edulis were found to be significantly different from each other (FST = 0.03–0.08) as well as from the source wild population (FST = 0.07–0.08), which accompanies reduced Ne and allelic diversity (FST = 0.076) between hatchery and wild populations (FST: 0.05–0.08). As a notable contrast, 5 different farms characterized in China showed generally low differentiation between farms (FST = 0.02–0.03) but high differentiation between the farms and the source populations . The BC farm population, which may not fully represent the entire broodstock, as described above, was estimated at Ne = 9. These values are remarkably lower than that observed for the wild population , which required all data to estimate and was only estimable using microhaplotype information. Similarly, cultured stocks of Yesso scallop analyzed by Li et al.  relative to the wild population. Although these private alleles are likely to be in the JPN wild population, they were not represented in the sampling here and therefore not expected at the same high frequency as those in culture. In contrast, wild private alleles, although more numerous, were lower in frequency (MAF = 0.01–0.15).HOBS relative to the wild population. This is likely due to the shellfish breeding practice of outcrossing, or crossing inbred lines, to produce heterosis in the offspring  was observed in the longer established Suminoe oyster (HOBS in the above cases, all of which have a consistent decreasing trend in genetic diversity by other metrics, and given that decreases in HOBS can be counteracted relatively quickly through crossing of divergent lines (Seemingly paradoxical to known inbreeding expectations, with an initial decrease in polymorphism and a subsequent loss of heterozygosity expected , here thffspring . Heterosved here . Selecte strains , which t strains . Whethere oyster . Given tnt lines , as wellnt lines , loss ofnt lines .Breeding programs in general are challenged by losses of genetic variation over successive generations of breeding with or without selection, which in shellfish is further compounded by high fecundity and large differences in reproductive success, resulting in significant drift . This prNe around 9–26 individuals relative to that calculated from the wild population at 25,048–56,291 individuals, a general depletion of low-frequency variants in cultured populations and high-frequency private alleles, and high FST values between cultured and the wild population. The polymorphism rate was reduced in the cultured populations, but HOBS was elevated, likely due to the practice of outcrossing to induce heterosis, indicating that polymorphism rate is a better estimator for genetic diversity loss than HOBS in this species. Although significant diversity loss is observed relative to wild populations, existing cultured populations still have relatively high polymorphism rates, and efforts to monitor and preserve the standing genetic variation will be important to continue to ensure the long-term viability of the broodstock program and the adaptability of the cultured animals to environmental challenges and selective breeding efforts.The wild Yesso scallop characterized from JPN has a high polymorphism rate that is similar to other shellfish species and therefore likely will be significantly impacted by both inbreeding depression and heterosis. Cultured populations characterized from the industry in BC indicate strong founder effects and drift, with jkad242_Supplementary_DataClick here for additional data file.


Swertia perennis, a typical European subalpine springtime species, revealed the existence of at least five major phylogenetic lineages. A large phylogeographic separation exists among these geographical regions, which confirms the existence of glacial refugia in the Pyrenees, but also in the Eastern and Central Alps. The results obtained from the analyzes indicate the existence of three major differences between the populations of the Alps and the Pyrenees, but also between the populations of the Alps and those of other geographical regions . The studied populations from the Black Forest and from north-eastern and southern Poland are a relic of the former wider distribution of these  genetic groups within Swertia perennis. Our results also confirm the existence of biogeographical links between the Carpathians and the Hercynian Range and the Alps. Certainly, there was an exchange of genes between populations located in the eastern Alps, the Carpathians and the Hercynian ranges . This confirms previous results of comparative studies on the genetic diversity of populations of other vascular plant species.Phylogeographic analysis of  As a result, the Pleistocene experienced several glacial phases that strongly influenced the geographic range of many plant and animal species. Difficult climatic conditions and thousands of years of periods of low temperature during the climate cooling and higher temperatures during the interglacials resulted in alternately shrinking and splitting the ranges of species, which often led to the isolation of populations3. By contrast, during warmer periods, many populations of species moved back to their original ranges through return migration or recolonization. Under such conditions, the ranges of species expanded, which led to the restoration of gene flows6. However, genetic changes that occurred in populations isolated during glaciations often led to complex mechanisms of speciation. In refugial populations, long-term isolation can substantially affect genetic diversity and reduce population size7. Other evolutionary processes may, in turn, take place in a recently colonized area. Some of these processes are the founder effect or the bottleneck effect, which cause a reduction or impoverishment of the gene pool of the population9. In turn, the opposite process that may take place in the contact zones between migrating populations may increase genetic diversity11.The Quaternary history of the flora of Europe was largely shaped by climatic oscillations during the Pleistocene. (from 2.58 million to 11.7 thousand years ago). This was accompanied by a drop in temperature and an increase in snowfall, which favored the formation of a thick ice cover and decreasing of the temperature14. For this reason, and because of the difficulties resulting from the collection of research material, there is little research that would allow for an in-depth understanding of the processes that occurred in plant species populations during the Pleistocene. Although there are studies on the variability and genetic differentiation of populations, found in the Alps and the Carpathians, studies on the populations of species found in the lowlands and in other mountainous regions are limited. Although there are studies on the variability and genetic differentiation of populations found in the Alps and the Carpathians, studies on the populations of species found in the lowlands but also in the Sudetes, Pyrenees and Dinaric Mountains are not numerous. Each of these mountainous regions has historically played an important role as a refuge for species in Pleistocene Europe10. In the interglacials and just after the end of the ice age, many plant species migrated in different directions from these mountains, expanding their ranges. These processes took place both from the Apennines and the Pyrenees towards the Alps and the Balkans, from the Alps towards the north (Sudetes) through the Šumava Mountains, but also from the Dinaric Mountains towards the Carpathians and between the Sudetes and the Carpathians17.Tracking the changes and transformations of flora over thousands of years is difficult because of the very limited research possibilities to study fossil material  or perform molecular researchSwertia perennis, referring to the history of colonization and recolonization during the post glacial period. S. perennis is interesting for several reasons. It occurs in a large geographical area both in the European lowlands and at high altitudes in the mountains. S. perennis is also a disjunct species, which is widely distributed in Europe, but occurs either in the lowlands  or in the mountains . Based on the currently available data, determining the origin of S. perennis in the areas where it is found today but also the relationships between the widespread populations of S. perennis and the reasons for the discontinuity of its range is difficult. It is possible that the current populations in the Carpathians, the Sudetes, the Alps, the Pyrenees, and the Balkans are populations that survived in glacial refugia, where the plant may have survived the unfavorable glacial period, like many other Arctic-Alpine species19.Therefore, here, we present the results of research on the genetic differentiation of the population of a rare species from the Gentianaceae family: 20. In addition, several species such as Pulsatilla vernalis, Doronicum austriacum or Salix lapponum, have survived to this day in the currently fragmented European mountain ranges by inhabiting refuges in non-glaciated zones of the mountains and in areas on the outskirts of glaciations21. Discovered the main genetic groups of P. vernalis in the Alps as well as in the lowlands north of the European Alpine system. This suggests that colonization of the lowlands may have occurred independently, with later zone contact in the German and Polish lowlands. A common haplotype was found both for the population of D. austriacum from mountainous areas  and those located in the Świętokrzyska Upland (Poland), several hundred kilometers to the north, surrounded by lowland areas22. Similarly, S. lapponum is a species that at its current sites, both in the Sudetes and in the lowland of north-eastern Poland, shows a large phylogeographic similarity23.Lowland areas in Europe were glaciated the longest, and the last glaciation in Northern Europe (including northern Poland) ended about 10.4 thousand years ago21. For this purpose, we chose S. perennis, a widespread species found from northern Siberia to the Pyrenees. We collected samples not only in the plant’s meridional distribution (Carpathians–Sudetes–Alps–Pyrenees) but also in its latitudinal distribution (northern Poland–southern Poland–Czech Republic). The goal was to analyze the history of S. perennis in the late Pleistocene. Unfortunately, the analyzes performed could not reflect the entire whole biogeographical history of S. perennis in the World and were limited only to Europe (from the Pyrenees to the Carpathians). Unfortunately, the conducted analyzes were not able to reflect the entire biogeographical history of S. perennis in the world, but they are another contribution to understanding the biogeography of this species in the context of climate changes in the Pleistocene.Therefore, to fully understand the complexity of the problem resulting from genetic differences that arose in populations during the Pleistocene, particularly in Central Europe, genetic analyses covering the occurrence of the species in a wide distribution range are needed. This applies in particular to the study of species occurring both in the mountains and in lowlands, which are few. Closer phylogeographic relations between mountainous and lowland areas, whose flora developed differently, are not known. Both differences and similarities in halpotypes between such disjunct populations are found. The reason is probably the existence of different biogeographical patterns that shaped the origin of plant populations in the pastS. perennis exhibits (2) can genetic phylogeographical groups be distinguished among European populations?The main questions posed in this work were: (1) what level of genetic differentiation Swertia perennis Linnaeus, Sp. Pl. 226, 1753, syn.: Gentiana palustris All. Fl. Pedem. 1: 100, 1785 belong to the Gentianaceae family inhabiting higher mountains but it can also be found at lower elevations in the lowlands throughout Europe from the Urals to the Pyrenees. The species is distributed globally across the Northern hemisphere, but also shows discontinuous distribution from Asia to North America23. It inhabits wetlands, especially peat bogs in calcareous fens or high mountains medows; however, it is also present in, creek shores or wet rocks. It is a species of circum-boreal distribution, inhabiting Arctic, Siberian and Northern, Atlantic and Central European provinces25. In Europe, the species can be found in all mountain range systems: in Pyrenees, Alps, Carpathians, Herzynian mountains , Caucasus and Dinaric Alps. S. perennis is a long-lived perennial rhizome herb that usually produces one erect stem that grows to around 10–50 cm tall. It is a diploid (2n = 28) organism that flourishes in July or August25. The flower nectar chambers are visited by various insects including species of Coleoptera, Lepidoptera, Diptera and Hymenoptera 24.S. perennis was collected across all of Europe  and preserved in silica gel prior to analysis . For the PCR reactions, a GeneAmp 9700 cycler  was used. Finally, the PCR ISSR amplification products were separated in agarose gel with a GeneRuler 100 bp Plus DNA Ladder  and photographed.DNA was extracted from dried plant material, using a DNeasy Plant Mini Kit  according to the manufacturer’s protocol. The Inter Simple Sequence Repeat (ISSR) microsatellite markers were selected to study of S. perennis from all study populations. Seven primers amplified a total of 99 loci in 240 individuals from 43 European populations tested. ISSR markers after separation in agarose gel were analyzed using the CLIQS software26 and then corrected manually. The analysis of the fringe images allowed them to be encoded in a binary form , and the data obtained this way was used for statistical calculations. Molecular analysis of variance (AMOVA) studies were performed using the Arlequin 3.5.1 program to determine the distribution of genetic variation within and between populations, and to assess the genetic differentiation of the S. perennis populations studied27. The degree of population division was measured by the Fst index according to30. The effective and observed number of alleles31, as well as the gene diversity index by32, Shannon's information index33, and Nei’s genetic identity and genetic distance indexes34 were calculated using POPGENE v. 1.3235. The number of bands (private bands found in groups) was calculated using FAMD36. The STRUCTURES 2.3.4 program was used for analyses of Bayesian clustering based on the admixture model39. The split by the number of groups (K) was tested with 10 replicates per K, using 200,000 firing iterations followed by 1,000,000 MCMC iterations. The output with many K values and hundreds of iterations was analyzed using the CLUMPAK online software40, which produced a graphical representation of the results of Structure 2.3.4 and calculated the necessary statistics. The optimal value of K was estimated from Ln (K) and the calculation of ΔK38, using CLUMPAK. Coordinate analysis (PcoA) calculations and graphical visualization of data were obtained on the basis of calculations of the genetic distance matrix generated based on the obtained data, using the GeneAlEx program41, with the method described by42. To reconstruct the relationships among the analyzed populations, we used a consensus network split using the equal-angle approach as implemented in SplitsTree43, using 1000 bootstrap replicates. GeneAlEx with the Mantel test implemented was also used to test the relationship between the matrix of the logarithmic geographic distances and the matrix of genetic diversity values. All scientific names of plant taxa are given according to44. The abbreviated surnames of the authors of plant names are given as in in accordance with the recommendations of the ICN45. The reproducibility of SSR markers was assessed by repeated DNA extraction from eight randomly selected samples of leaves from which DNA was previously isolated. The PCR reactions were also repeated with all the primers selected for the study and the separation on agarose gel was repeated. Band analysis was also performed comparing the result with previously performed analyses. The results were identical with the approximately 3–5% difference found for the three primers.Amplification with ISSR primers was performed to obtain the DNA fingerprint profiles of S. perennis  value (2) was found in the ALP11 population . The genetic structure of the population was also studied by Bayesian analysis, using the STRUCTURE program. ΔK values for STRUCTURE results were estimated in the admixture analysis to assess population distribution and allele distribution between the groups  to 1.272 (CAR5). The smallest effective number of alleles  were found in the JUR2 population. However, the highest values of these coefficients were found in the CAR5 population. The calculated number of polymorphic bands (Pl) ranged from 2  to 27 in the CAR5 population. Private bands were rarely found in Analysis of the STRUCTURE program the division with these values showed that, in this case, a similar allele division was maintained (K = 5) in populations from the Pyrenees, the Alps, the Sudetes, and northern and eastern Poland, whereas in the Carpathians, it was divided into many subgroups. Therefore, an additional STRUCTURE analysis was performed, based on the attempt made to determine the most probable distribution of K values by means of impurity analysis. Unfortunately, the analysis did not indicate which K value would be appropriate, and the results of the admixture suggested a very high level of genetic differentiation within the Carpathians in the K = 6–10 groups Fig. . Hence, Genetic distinctness was evident in the populations of the Pyrenees and the Alps. In the remaining regions, gene addition was found, which may indicate a past history of gene flow between populations. PCoA based on genetic distance divided the populations into three groups: the Pyrenees population, the Alps populations, and the remaining populations from northern Poland, eastern Poland, the Carpathians, the Sudetes, and several populations from the Alps Fig. .Figure 3AMOVA analyses were performed using the Arlequin program based on the predictive division of the population into six and eight groups resulting from the geographical division Table .Table 3Rp < 0.01) Fst index was also found, which reflected the variability among the studied populations of S. perennis. AMOVA was high for all populations (Fst = 0.909) and for population groups (Fst = 0.834). This was also confirmed by the results obtained for the analyses of the genetic distance matrix of all populations  in all randomized locations, which indicates isolation by distance  collected in western Austria and eastern Switzerland, and between the samples collected from the Jura and the western Alps. Some Central Alpine populations show similarities to the Western Carpathians and the Šumava Mountains, and some to the Sudetes.The result of the SplitsTree analysis points to the division of the ons Fig. . DiffereS. perennis populations is very high (Fst = 0.909) compared to other plant species like Trollius europaeus L., Fst = 0.39; Cicerbita alpina, Fst = 0.30; Ranunculus platanifolius, Fst = 0.30 or Lonicera nigra, Fst = 0.3749 in a similar geographical area. The found high value of the Fst coefficient seems quite high. However, the previously cited results of these coefficients were calculated for data obtained using the AFLP method, which, although they concern other species of herbaceous plants, but the set of samples for testing was made in similar locations. Both methods are obviously not comparable in the case of Fst calculations, but the obtained values can be classified as small, medium or large. A similar value for AFLP was calculated for the population of Hieracium sect. Cernua (Fst = 0.99), and in the case of the ISSR method 0.4750 for Kirengeshoma and Fst = 0.42 for Bromus erectus51. It can be definitely said that the result obtained by us is of high value and clearly indicates a large variation between Swertia perennis populations.Genetic differentiation among S. perennis can be identified . Analyses of the Pyrenees population revealed a substantial discrepancy with other populations. Similar results were reported for the genetic variability of other plant species53, indicating a large diversity in the Pyrenean populations of different species. Nonetheless, the studied populations (including S. perennis) were not internally differentiated. This may be due to the Würm glaciation, which lasted from 115,000 to about 11,700 years ago and which probably affected the genetic disjunction of plant and animal populations. Despite the presence of local glaciations, the Pyrenees are thought to have been one of the Pleistocene glacial refugia, a potential refuge area for many Arctic-Alpine and mountain species55. Another reason could be the bottleneck effect, which is often the result of a sudden decline in numbers caused by environmental events caused by a sudden random event, e.g. a catastrophe. This results in a reduction in genetic variability (reduced genetic diversity in the population). In this case, genetic diversity can only increase through gene flow from another population, which was not the case with the population from the Pyrenees. That is why the studied populations from the Pyrenees show such a large genetic distinctness. It is also possible that these geographically isolated populations are descended from a limited number of individuals who established them. Thus, the occurrence of the founder effect and the related occurrence of genetic drift can also be considered as equally likely for the population from the Pyrenees57.Based on the results of STRUCTURE analyses Figs.  a, b, PcThis is confirmed by the results obtained by the PcoA analyses, which indicate the existence of three large differences between the populations of the Alps and the Pyrenees, but also between the populations of the Alps and those of other geographical regions . Population groups from the Alps, including those located in the Jura Mountains, the Western Alps, and the Central Alps, are poorly differentiated internally. However, the populations of the Alps  differ from others and show some similarities to the populations of the Carpathians and the Sudetes , Carpathians and Poland. Lowland populations are characterized by low genetic diversity within populations, but high genetic diversity between populations. This pattern has previously been attributed to relict populationsans Fig. a, b. WitS. perennis populations from the Carpathians supports the hypothesis of a different population history for the Carpathians as the main center of mountain flora in Central Europe. Gene exchange between these population groups may have occurred in the Hercynian mountain ranges. The results also suggest that the post-glacial range history of this species in Europe was influenced by glacial refugia of populations located both in the Alps and in the area associated with Central Europe, resulting in a visible addition of genes21. On the other hand, the high distinctness of the population of S. perennis from the Carpathians supports the hypothesis of a different population history of the Carpathians as the main center of mountain flora in Central Europe. As in the case of Ranunculus platanifolius47, the populations of S. perennis from the Carpathians show similarities to the populations from the Sudetes.The results also suggest that the post-glacial history of the range of this species in Europe was influenced by glacial refugia of populations located both in the Alps and in the area associated with Central Europe, resulting in a visible gene addition. On the other hand, the high distinctiveness of the S. perennis. This explains the isolation of the current localities of S. perennis in the Carpathians  and in lowland areas apart from mountains67. Thus, the existing populations of S. perennis in the Western Carpathians probably survived the glacial periods in many refugia. Also in the populations of Cicerbita alpina and Ranunculus platanifolius in deep valleys in the southern part of the Western Carpathians, a different genetic pattern was found47. Differences in the genetic structure of plant populations in the Carpathians are reflected in the presence of separate phylogroups, but also in their internal heterogeneity. This indicate on greater isolation of local Western Carpathian populations and/or the possibility of secondary contacts.The area of the Carpathians is not very extensive, but it is physiographically diverse and is crossed by numerous mountain ranges, which may be the habitat of genetically diverse populations of ans Fig. . Carpathans Fig. . DistingS. perennis post-glacial migrations are complicated, especially in Central Europe, where gene exchange and close phylogenetic relationships can be observed in some mountain ranges. Our research also confirms that both the Carpathians and the Sudetes as well as populations from Polish lowlands and in southern Germany were the refuges where gene admixture took place. The obtained results suggest that these sites played a crucial role in the overall survival of S. perennis. Certainly, there was gene exchange between populations located in the eastern Alps, the Carpathians and the Hercynian mountain ranges . This confirms earlier results of comparative studies on genetic differentiation of populations of other vascular plant species46. However, for more decisive conclusions, synthetic studies that are based not only on molecular markers but also on paleobotanical studies and population dispersion at a distance are needed.In conclusion, the history of Supplementary Information 1.Supplementary Information 2.


Understanding how populations adapt to their environment is increasingly important to prevent biodiversity loss due to overexploitation and climate change. Here we studied the population structure and genetic basis of local adaptation of Atlantic horse mackerel, a commercially and ecologically important marine fish that has one of the widest distributions in the eastern Atlantic. We analyzed whole‐genome sequencing and environmental data of samples collected from the North Sea to North Africa and the western Mediterranean Sea. Our genomic approach indicated low population structure with a major split between the Mediterranean Sea and the Atlantic Ocean and between locations north and south of mid‐Portugal. Populations from the North Sea are the most genetically distinct in the Atlantic. We discovered that most population structure patterns are driven by a few highly differentiated putatively adaptive loci. Seven loci discriminate the North Sea, two the Mediterranean Sea, and a large putative inversion (9.9 Mb) on chromosome 21 underlines the north–south divide and distinguishes North Africa. A genome–environment association analysis indicates that mean seawater temperature and temperature range, or factors correlated to them, are likely the main environmental drivers of local adaptation. Our genomic data broadly support the current stock divisions, but highlight areas of potential mixing, which require further investigation. Moreover, we demonstrate that as few as 17 highly informative SNPs can genetically discriminate the North Sea and North African samples from neighboring populations. Our study highlights the importance of both, life history and climate‐related selective pressures in shaping population structure patterns in marine fish. It also supports that chromosomal rearrangements play a key role in local adaptation with gene flow. This study provides the basis for more accurate delineation of the horse mackerel stocks and paves the way for improving stock assessments. SeveralOwing to advances in high‐throughput sequencing, recent genomic studies screening thousands to millions of genetic markers across the genome have revealed population structure and selection signatures in species previously assumed to be panmictic  is a marine benthopelagic shoaling fish widely distributed in the east Atlantic, from Norway to southern Africa  and in the Mediterranean Sea (FAO Major Fishing area 37)  identify the population structure underlying the stock divisions; (ii) estimate the extent of genetic differentiation between populations based on whole‐genome sequencing; (iii) identify the evolutionary processes, genetic basis, and environmental drivers of local adaptation; and (iv) design a genetic tool (SNP panel) that can be used for future population studies and genetic stock identification.22.13 piece of tissue was excised from the dorsal musculature of each specimen and stored at 4°C in absolute ethanol. Total genomic DNA was extracted from the majority of samples by Weatherbys Scientific Ltd, Ireland from 30 mg of tissue using sbeadex™ magnetic bead‐based extraction chemistry on the LGC Oktopure™ platform. Samples from the Mediterranean Sea were extracted using CTAB, and some samples from Portugal  were extracted with Chelex and proteinase‐K based extraction protocol  data to assess the genomic variation among samples. This method provides population‐level allele frequencies by sequencing to a high depth a single‐barcoded library prepared from a mixture (pool) of DNA of individuals from a population . For all population pairs, we calculated pool‐FST (F^STpool) and its 95% confidence interval (CI) using the R package poolfstat  had an unusual depth of coverage and insert size distributions  and the other of highly differentiated markers . Both marker sets were chosen based on the empirical distribution of allele frequencies and standard deviation (SD) cutoff values  per pool in 10 kb‐sliding windows with a step size of 2 kb using PoPoolation 1.2.2  by conducting a linear regression of the linearized genetic distances  to the raw read counts, in order to account for the random variation of read coverage and chromosome sampling during pooling and sequencing  per SNP between paired contrasts of grouped pools, as dAF = absolute (meanAF (group1) – meanAF (group2)). The contrasts evaluated were established based on geographic closeness, PCA clustering, and biological knowledge Table . To idenWickham, .2.6n = 28), the 9.9 Mb putative inversion underlying the north–south genetic pattern (n = 12), west of Ireland (n = 14), Alboran Sea (n = 13), southern Portugal (n = 4), north Africa (n = 4). Three to four individuals per location were genotyped twice to assess genotyping error rate. DNA extraction and SNP genotyping were undertaken by IdentiGEN, Ireland, using their IdentiSNP genotyping assay chemistry. The protocol utilizes target‐specific primers and universal hydrolysis probes. Following an end‐point PCR reaction, different genotypes are detected using the Araya fluorescence reader .To validate pool‐seq findings and to identify a panel of highly informative SNPs for genetic stock identification, we obtained the genotypes of 160 individuals  4.05 was used to calculate pairwise FST estimates  and linkage disequilibrium (LD) were assessed with Rousset, . MicrosaK), using the sNMF algorithm  plateaus or increases  21, we extracted the individual genotypes of 12 diagnostic SNPs within the inversion from the 100‐SNP data set Figure . We perf2.8R package vegan ; nitrate concentration (μmol/m), NO3; iron concentration (μmol/m3), Fe; current velocity (m/s), CVel; primary production (g/m3/day); seawater salinity (PSS); and dissolved oxygen concentration (μmol/m3). Prior to RDA, environmental data were standardized to zero mean and unit variance and some of the highly correlated variables were removed (|R2 ≥ 0.7|) (see Appendix N = 136). The sample NOS2 was excluded from this analysis as it is potentially a temporal replicate of NOS1, and thus, it cannot serve as a spatial replicate. The statistical significance of the RDA model, constrained axes, and environmental variables was assessed with 1000 permutations. Candidate SNPs corresponded to those with the highest loadings on significantly constrained axes . Based on the coefficient of determination (R2), we identified which of the environmental variables each candidate SNP is most strongly correlated with. We further explored the linear relationship between candidate SNPs and environmental predictors using a scatterplot, and the genetic patterns between samples with a heatmap plot depicting allele frequencies of candidate SNPs.To identify which environmental variables are related to adaptive genetic variation and local adaptation, we evaluated genome–environment associations (GEA) with a redundancy analysis (RDA) implemented in the n Dixon, . RDA is n Dixon, . We retrs Bosch, . The env2.9snpEff v.4.1  but revealed three subtle population structure patterns that were statistically significant , where samples north of this break  are genetically more similar to each other  or only the northern ones , implying that the observed patterns of population structure are not derived from spatially limited gene flow.A pattern of isolation‐by‐distance was not detected when considering all samples  or highly differentiated (under selection) markers. These marker sets were chosen based on the standard deviation in allele frequencies across populations Figure  the firsTaken together, these results indicate that the separation between the westernmost part of the Mediterranean Sea and Atlantic populations might be driven by neutral processes, while the latitudinal pattern and separation of North Sea samples could be the result of selective processes.3.2Z‐score threshold of significance. This analysis revealed a number of genomic regions with elevated differentiation for three contrasts: (i) the Mediterranean Sea versus others, (ii) north Africa versus others, and (iii) the North Sea vs. others  per SNP for paired contrasts to identify outlier loci (presumed under selection) that are characteristic of certain populations. The contrasts were chosen based on PCA clustering patterns Figure . Outlier Figures , 3, 4. A3.2.1opn1mw4 (green‐sensitive opsin‐4). In the putative selection signal on chr 21 . The current knowledge of known functions of candidate genes is presented in Table Besides genome‐wide differences, the contrast between the Mediterranean Sea and all other samples revealed two distinctive regions at chromosomes (chr) 5 and 21 Figure , as it r1 Figure , the Med3.2.2In the north Africa versus other samples contrast, we discovered that a single large region on chr 21 underpins the latitudinal genetic pattern distinguishing “northern” and “southern” samples with respect to a break along mid‐Portugal Figure . This loTo further characterize this structural variant, we leveraged the genotype data of 160 individuals for 12 SNPs within this region. As expected for a chromosomal inversion, in a PCA plot individuals were separated into three clusters depending on their genotype Figure , and ind3.2.3π; Figure The contrast between the North Sea and all other samples revealed seven genomic regions with elevated differentiation on chr 1, 4, 7, 11, 20, and 21 Figure . Furthergpr83 (G‐protein coupled receptor 83). In the second region  based on its location. The signals on chr 20  and taar7a (trace amine‐associated receptor 7A). Note that the locus on chr 21 is the same as the one described in the Mediterranean sample , western Ireland (WIE1–WIE2), northern Portugal (NPT1–NPT2), and southern Portugal (SPT1–SPT2). While the replicate samples from Portugal  were excluded from all analyses due to potential technical bias, for exploratory purposes, they were included in the heatmap plot representing allele frequencies of the most differentiated SNPs in putatively adaptive loci Figures  and 4b–e3.3p ≤ 0.01, Figure R2 = 0.87) and primary productivity  conducted on 136 putatively adaptive loci identified two main environmental variables strongly associated with genetic differentiation in the Atlantic horse mackerel: mean seawater temperature and temperature range (**3.4R2 = 0.9 ± 0.1), supporting the findings of the pool‐seq analysis , and 155 out of 160 individuals with a genotyping success >80%. The final data set comprised 17 SNPs of which 9 markers are from divergent genomic regions (outlier loci) and 8 markers are neutral .To minimize marker redundancy in the 63‐SNP panel, we performed a linkage disequilibrium (LD) analysis for all loci and samples. As expected, significant LD was found between a number of SNPs located in close proximity on the same chromosome Table . Though n Figure  for eachFST values indicated a lack of significant genetic differences between the North Sea samples or between the west of Ireland samples. There was also no significant genetic differentiation between individuals from the west of Ireland, the northern Spanish shelf, and northern Portugal , the separation between southern Portugal and north Africa disappears, and the only distinguishable groups are the North Sea and everything else  the North Sea; (ii) west of Ireland, northern Spanish shelf, and northern Portugal; (iii) southern Portugal; and (iv) north Africa Figure . The same Figure  or the Ne Figure , inset. e Figure . While te Figure , inset.K = 4, Figure Individual admixture analysis supports the same four groups identified with PCA  to examine the population structure, genomic basis, and environmental factors involved in genomic differentiation and local adaptation of the Atlantic horse mackerel. Our results revealed low genome‐wide differences among locations, but high differentiation at a relatively small number of putatively adaptive loci, including a putative chromosomal inversion. The spatial extent of population structure appears to be largely determined by local environmental adaptation rather than spatially constrained gene flow. Although the pool‐seq data results were validated with individual genotyping, the extent of population structure might have been underestimated because about 60% of the collected individuals were not in spawning condition and the pool‐seq method does not allow the identification of genetic heterogeneity within pools. Further studies including larger sample sizes of breeding individuals, and finer geographic coverage are needed to refine the identification of population baselines.4.1FST = 0.007 ± 4.4e‐05). This result is in agreement with previous studies using dozens of neutral genetic markers  the western Mediterranean and Atlantic populations, (ii) “northern” and “southern” populations with respect to a genetic break in mid‐Portugal , and (iii) the North Sea respect to other “northern” populations  and Atlantic populations  gene, which encodes a cone photopigment essential for the vision of blue‐green light. This gene contains two missense mutations . In contrast, the alternative haplotype dominates in North Africa, where temperatures are the highest (18°C) and oxygen content is the lowest (223 μmol/m3). Intermediate haplotype frequencies occur in the south of Portugal, where intermediate temperatures (13–14°C) and oxygen content (242 μmol/m3) are common.Our genomic data revealed a hitherto undescribed genetic break‐off mid‐Portugal, distinguishing populations “northern” or “southern” of this area. This latitudinal pattern was noticeable in pairwise‐The exception to this trend is the prevalence of the northern haplotype in the sample from the western Mediterranean, a location where seawater temperature is higher than expected , a pelagic fish with similar distribution and life history characteristics as the horse mackerel, reported a comparable latitudinal pattern  encodes a receptor that plays a role in the regulation of energy metabolism, feeding, reward pathway, and stress/anxiety responses in mice  encodes a transcriptional coactivator for steroid receptors that are presumably involved in glucose metabolism regulation  on chromosome 1 encodes a protein involved in the synthesis of sphingomyelin, a major component of cell and Golgi apparatus membrane. Previous studies indicate that this protein is crucial to maintain cell membrane structure and fluidity at low temperatures in fish  encodes an olfactory receptor specific for sensing amines in vertebrates  reported significant differences in the morphology of chemoreceptors  that can be used as a genetic tool for fisheries stock assessment. A panel of only 63 markers suffices to identify the main genetic subdivisions. In fact, using a reduced panel of only 17 markers, it is possible to differentiate individuals collected in the North Sea and North Africa from neighboring populations. These markers can help, for instance, to elucidate the extent of mixing between the Western and North Sea stocks in the English Channel (ICES Divisions 7.e and 7.d) and in ICES area 4.a in the northern North Sea. Therefore, this study can serve as a successful example of the utility of genetic tools for fisheries monitoring and management.The authors declare no competing interest.Data S1Click here for additional data file.


Despite the localisation of the southern Caucasus at the outskirt of the Fertile Crescent, the Neolithisation process started there only at the beginning of the sixth millennium with the Shomutepe-Shulaveri culture of yet unclear origins. We present here genomic data for three new individuals from Mentesh Tepe in Azerbaijan, dating back to the beginnings of the Shomutepe-Shulaveri culture. We evidence that two juveniles, buried embracing each other, were brothers. We show that the Mentesh Tepe Neolithic population is the product of a recent gene flow between the Anatolian farmer-related population and the Caucasus/Iranian population, demonstrating that population admixture was at the core of the development of agriculture in the South Caucasus. By comparing Bronze Age individuals from the South Caucasus with Neolithic individuals from the same region, including Mentesh Tepe, we evidence that gene flows between Pontic Steppe populations and Mentesh Tepe-related groups contributed to the makeup of the Late Bronze Age and modern Caucasian populations. Our results show that the high cultural diversity during the Neolithic period of the South Caucasus deserves close genetic analysis. Genomic analysis of three Neolithic individuals from Mentesh Tepe in Azerbaijan belonging to the beginnings of the Shomu-Shulaveri culture provides insight into the origin and population structure of the Shomu-Shulaveri community. Several centres of Neolithisation have been identified, such as the Levant or Southern China, from which the agropastoral way of life diffused to other regions. The mechanisms of this diffusion have attracted tremendous attention for the last few decades. In some places, the Neolithic gained ground through the acculturation of local hunter-gatherers ;3, Kmlo-2 Rock Shelter (Western Armenia) and Kotias Klde Cave (Western Georgia). Paleogenetic analyses of human bones excavated from the Kotias Klde Cave showed a genetic continuity with earlier Upper Palaeolithic  sites4 but a discontinuity with pre-LGM individuals5. Their genetic ancestry shares, to a certain extent, a common origin with ancient Iranian populations6 and differs from that of Anatolian and Levant hunter-gatherer groups7, demonstrating a high genetic differentiation at this time between geographically close populations8.The mechanism of Neolithisation in the South Caucasus, a region located between the Black and Caspian Seas on the southern slope of the Greater Caucasus Mountains, remains poorly understood. Mesolithic sites are known at Damjili Cave, unit 5 (Western Azerbaijan)9 and in Western Azerbaijan at Damjili Cave, unit 43. However, evidence of agriculture and herding remains scarce, suggesting that these sites represent a transitional phase between the Mesolithic and the Neolithic.The first settlements attributed to the Early Neolithic period belong to an aceramic culture, evidenced in several places in Central Georgia, as at Nagutni, in Western Georgia, as at Paluri11. Found in several clusters of settlements in the northern foothills of the lesser Caucasus, the SSC is characterised by circular mud-brick houses, domestic animals and cereals, handmade pottery, sometimes with incised and relief decoration, and obsidian and bone industries. Variants, such as the Aratashen/Aknashen culture12 (Ararat Plain) and other Neolithic contemporaneous cultures like the Kültepe13 Culture (Nakhchivan region) are also found in the South Caucasus. The slightly later Kamiltepe culture (Mil steppe culture), which probably includes the site of Polutepe, differs by its architecture, the use of flint tools instead of obsidian, and pottery-painted patterns that are rather related to Northern Iran and the Zagros15.In this context, the Shomutepe-Shulaveri culture (SSC) is the most ancient Caucasus culture with a complete Neolithic package11. Due to the rapid transition from the aceramic stage to the SSC, population continuity during the Neolithisation process is possible12. However, several cultural and biological features are nonlocal. Domesticated animals, such as cattle, pigs or goats, originate from Eastern Anatolia and from the Zagros moutains18. Similarly, the glume wheat and barley recovered in the SSC sites have been domesticated elsewhere in the Middle East19, even if the ancestors of the naked wheat Aegilops tauschii are found in the Caucasus and may have been local20. The material culture and architecture evidence technical transfers with neighbouring regions such as Southern Anatolia, Pre-Halafian and Halafian culture of Northern Mesopotamia and Zagros21. Taken together, these data suggest a strong cultural connection, and maybe a degree of admixture, with other groups from the Fertile Crescent. Indeed, the genome-wide data for one individual coming from the same collective grave at Mentesh Tepe as the samples we analyse here, and confirmed by that of other individuals from Polutepe in the Mughan steppe or from Aknashen and Masis Blur in Armenia22 already showed that southern Caucasian groups are part of a cline that connected Eastern Anatolian and Zagros populations and evidence a gene flow that began around 6500 years BC23.The origins of the SSC are still discussed25. At this time, the botanical assemblage is dominated by cereals, especially barley, naked wheat and emmer26, a common association during the Neolithic Southern Caucasus. Animal remains consist largely of domesticated ones , and wild animals are rare27. The diet of the Neolithic individuals from Mentesh Tepe relied mainly on C3-plants, such as wheat, barley and lentils, with some evidence of freshwater fishes; the consumption of animal proteins varies between individuals28. The pottery differs from classical SSC sites by being vegetal-tempered, a characteristic shared with Kamiltepe or the first occupation of the Nakhchivan site of Kültepe29. As observed in many SSC sites, houses are circular and made of mudbricks, with or without the addition of straw or other organic material21. Two SSC occupation phases are represented, separated in some places by a thick layer of ashes.One of the oldest sites of the SSC, Mentesh Tepe, is located in the Tovuz district of western Azerbaijan and has been excavated between 2007 and 2015 Fig. . Several30. The number of individuals in the burial, as well as their sex and age bias, suggest a dramatic event such as an epidemic, a famine, or a sudden episode, but no trace of violence has been evidenced on the bones.In a context where Neolithic burials are rare, Mentesh Tepe is exceptional for the discovery of a collective burial containing around 30 individuals Fig. , which i31.There is no specific orientation or position of the corpses, but some intentional arrangements are visible. The most striking is formed by two juveniles embracing each other Fig. . Such anTo better understand the origin of the Shomu-Shulaveri population and the structuration of this community, we performed paleogenetic studies of some of the individuals found in the collective burial. The genetic data obtained are then compared to those of another individual from the structure already published and to contemporaneous southwestern Asian genomes.30, but genetic data allowed a robust determination of the sex for some juveniles.From the 30 individuals from Structure 342 of the site, we sampled 23 petrous bones but could only obtain genome-wide data for one female  and two males , with coverage ranging from 0.1 to 0.3X, a number of SNPs hit on the 1240k from 61,151 to 205,055 and ancient DNA damages  an unsupervised ADMIXTURE33 analysis with the HO and the ancient dataset , even though the Aknashen individual has the highest IranN/CHG percentage. They are also very similar to the Chalcolithic and Bronze Age Anatolian populations from Arslantepe and with other Chalcolithic and Bronze Age Anatolian populations but are quite distant from the Late Neolithic Anatolian Tell Kurdu individuals  who instead clusters with the Neolithic Anatolian populations. Due to the lack of genomic data from South-eastern Mesopotamia, only the ancestry of groups that lived in North-eastern Mesopotamia during the PPN period and the Late Bronze Age could be considered. We observe that the South Caucasus population displays a different profile from the PPN Mesopotamian one, as it has more Anatolian affinity, but that it presents a profile similar to the LBA individual from North-eastern Mesopotamia (Nemrik9_LBA).We merged our genome-wide data with the Human Origins dataset (HO-dataset)ter Fig. , and falter Fig. . Both anTo formally test for the genetic affinity with earlier populations in Western Eurasia observed in the PCA in our samples, we performed D-statistics of the form D. The statistic deviates significantly from zero if the pair of Anatolian/Caucasian/Mesopotamian groups (Z) and Mentesh (MT) do not have the same genetic relation to Western European populations (Y). We confirm that the new Mentesh samples are very similar to MTT001 as almost no D-statistics of the form D significantly deviate from 0, showing a high level of homogeneity in this site   and replacing Iran_N by CHG  also provide acceptable models for Mentesh as a group but not always for all the samples when modelled individually. We also modelled Mentesh as a mixture between a Neolithic Anatolian group (Tell Kurdu or Barcın) and a North Mesopotamian group (Nemrik9 or Mardin). Eventually, we tested 3-populations models and obtained acceptable models for Mentesh, involving CHG (15–25%), an Anatolian population (Barcın 40% or Tell Kurdu 45%) and a Neolithic North-eastern Mesopotamian population (Nemrik9 29–45%). The same analysis was performed on the other Neolithic South-Caucasus groups , and we obtained models with a p > 0.05, involving the same kind of combinations as for Mentesh. The amount of Anatolian ancestry in these three populations is not significatively different than that in Mentesh Tepe , even though the Aknashen individual presents the highest CHG percentage.  Fig. . Replaci35 to estimate the date of the admixture event. To account for the low number of individuals representing the Neolithic Ganj Dareh in Iran and the poor coverage, we added other individuals from Neolithic sites in Iran . We found that the admixture between the Anatolian source and the Iranian source only took place 15 ± 5 generations before Mentesh Tepe occupancy (Z = 2.5 and nrmsd = 0.348). With 28 years per generation, this dates the admixture event around 6300 BC (Phase 1 occupancy: 5880 BC). However, the decay of ancestry covariance estimated by DATES for Mentesh poorly fits with the data  being null, we do not see any preferential gene flow from Mentesh Tepe into one Bronze Age population from the South Caucasus or Anatolia , highlighting a gene flow to the South Caucasus from the Steppes or from a population linked to CHG after the Neolithic period. This result can be interpreted with two different hypotheses: either a Neolithic population from North-Caucasus or an ancestral population from the South Caucasus carrying a small proportion of Steppe/CHG ancestry replaced the local Mentesh-like Neolithic population in South-Caucasus, or a population from the steppe north of the Caucasus migrated south and admixed with the local population.The PCA shows that all the Bronze Age individuals from Armenia plot together and are shifted toward the Steppe cluster. In the ADMIXTURE analysis, they all exhibit a red component, absent in the Neolithic Mentesh Tepe individuals but maximised in Steppe populations and present, also, in CHG individuals. Interestingly, individuals from Chalcolithic Armenia  do carry this Steppe/CHG component, whereas a Chalcolithic individual from Alkhantepe in Azerbaijan does not. D-statistics of the form D are almost all significantly positive  is an admixture between 25% Steppe and 75% Mentesh   are involving CHGs instead of a Steppe population. This suggests that the admixture at the base of the Kura-Araxes ancestry occurred between an unsampled population from the Caucasus with a profile more similar to the Caucasus Hunter-Gatherers and a Mentesh-like population from the Late Chalcolithic period. Thus, it is unlikely that Kura-Araxes populations had yet received a significant gene flow from the Steppe at this period. Models involving the only Middle Chalcolithic individual from the North Caucasus (from the Unakozovskaya cave)36 available to date did fit but not as well as the other models . Though we note that for Kura-Araxes the models involving the North Caucasus individuals were more successful than for the LBA populations, no nested model involving 100% of North Caucasus Neolithic was detected. In other words, the Bronze Age or Chalcolithic individuals found in Armenia cannot be modelled as 100% North-Caucasus Neolithic. This result suggests that the admixture scenario is more likely than the migration one, given the individuals sequenced for now.To test for these hypotheses, we used qpAdm with the rotating methodTo establish the kinship structure and social organisation of the four sequenced Neolithic Mentesh Tepe individuals, we first look at uniparental markers. Three different mitochondrial haplogroups were determined: U7, K1 and U1a1. Both MT23 and MT26 shared the same mitochondrial haplogroup, as well as the same Y chromosome haplogroup (J2b).38.Next, genetic relatedness analyses were performed to further investigate potential close familial ties within individuals of structure 342. To this intent and given the low coverage of our samples, kinship analyses were carried out using READ23  and Tell Kurdu from both Neolithic and Early Chalcolithic period23 Fig. . Here, tn = 17864) which is well over READ’s satisfactory threshold of around 1500 SNPs. These results were subsequently confirmed using TKGWV239, an alternative kinship estimation method which provided us with the same findings .However, close genetic ties could not be detected regarding the other pairs involving individuals MT7 and MTT001. The pairs MT23–MT7 and MT26–MT7 were both estimated with a normalised value neighbouring the observed median of ART individuals .On a regional scale, a certain degree of homogeneity is observed. The Polutepe group, though belonging to a different Neolithic culture, is highly similar to that of Mentesh Tepe, as is the Masis Blur Neolithic group, belonging to the Aknashen culture, a variant of the Shomu-Shulaveri culture. While the Aknashen individual is more Caucasian-like than the other South Caucasus Neolithic individuals, this does not constitute a significant difference, showing that a coherent culture displays a small genetic heterogeneity. Moreover, this period is characterised by several other archaeological cultures, such as Kültepe or Kamiltepe, that have not been studied yet through paleogenetic studies. As the Shomutepe-Shulaveri is already not completely homogenous, it would not be surprising that the population linked to these cultures are genetically different. In addition, the lack of archaeological background for Polutepe prevents us from drawing any conclusions about the associated culture. However, we do show here that the Aknashen culture presents a good degree of genetic homogeneity with the Shomu-Shulaveri individuals but is not fully parallel to the overall cultural unity Aratashen-Shomu-Shulaveri culture seen by the archaeologists.9. The genetic data from Mentesh Tepe shows that this SSC site shared one ancestry probably inherited from the Mesolithic Caucasian Hunter-Gatherers (or the Neolithic Iranian Farmers), one from the early Anatolian farmers and one from North-eastern Mesopotamia. The genetic proximity to southern populations could be an indication of the role that Halaf and Hassuna communities played in the formation of the SSC, as already identified through the material culture. Due to the poor DNA preservation, genetic data from the core of Mesopotamia remains scarce, and the genetic affinity of Halaf groups can only be determined by their proximity to South-eastern Anatolia and North-eastern Mesopotamia. Interestingly, Tell Kurdu, which has known a strong influence on the Halafian culture, appears as the best proxy for the ancestors of Mentesh Tepe individuals in the qpAdm analyses. The other part of Mentesh Tepe ancestry is best modelled by Iranian Neolithic groups from Ganj Dareh40. This genetic component is also shared with Caucasian and Iranian Hunter-Gatherer, suggesting that a large population inhabited the southern fringes of the Caucasus Mountains and coasts of the Caspian Sea at the end of the Neolithic period. The currently available data could not discriminate if this ancestry was local  or came from Iranian farmers, thus leading to a complete replacement of the more ancient groups.The genetic analyses performed on the Mentesh Tepe samples allow a better understanding of the diffusion of the Neolithic in this area. Based on the material culture, two models were considered: after a very short transitional phase of an aceramic culture, the Neolithic package appeared with the SSC, either from acculturation or from the migration of human groups from Western Asia23. analyses, which date the admixture event to 6500 BC, we obtain an admixture event around the middle of the 7th millennium, even if their work used two individuals from two different sites with diverse backgrounds, dates and possibly genetic origin, while we focused our analyses on a homogeneous population. However, due to the high number of missing data in the populations used to calculate the date of admixture, the fit is not perfect. Mentesh Tepe appears as a site that closely followed the process of migration and admixture of Anatolian farmers and of North-eastern Mesopotamian populations with the South Caucasus earlier Hunter-Gatherers’ descendants, which was contemporaneous with the establishment of the Shomu-Shulaveri culture and is coherent with the ancient dates obtained for this site.Like Skourtanioti et al.36.The genetic data from Mentesh Tepe also helps understanding better the subsequent periods, as previous studies exploring the Bronze Age in the South Caucasus only had available the Chalcolithic genome-wide data from the North Caucasus36 as a proxy for the Chalcolithic and Neolithic Caucasus shows a genetic profile that differs from the Mentesh Tepe one, with a slight amount of Eastern Hunter-Gatherer component likely due to a gene flow from the adjacent steppe. The comparison was not directly feasible in the former study due to the lack of Neolithic data from the southern slope of the Great Caucasus, which emphasises the need for a thorough sampling in ancient DNA studies, both in space and time.Indeed, we find that the North Caucasus Chalcolithic individual used previously42. Part of the Areni-1 pottery as well as the funerary architecture and pottery from Soyuq Bulaq connect them with the Maikop tradition43. The pre-Kura-Araxes South-Caucasus thus appears as a mosaic of populations of different ancestry, with the intrusion of more northern populations. Nevertheless, the precise dating of one individual from Areni 1 could be uncertain (cf. supplementary text).Thanks to the new data, we show that, after the end of the Neolithic, a genetic component related to the CHG reappeared in the South Caucasus and that, later in the Bronze Age, a genetic component related to the Pontic steppe groups arrived. Chalcolithic and Late Bronze Age groups in the South Caucasus can be modelled as a mixture of Mentesh Tepe-related populations and the Steppe population. Interestingly, Steppe ancestry is not spread homogeneously during the Chalcolithic period, whereas Areni-1 individuals display a high amount of steppe ancestry; none is found in the Alkhantepe genome. Such diversification in the population’s structures could mirror the one observed in cultural features. The funerary practices were also more diversified during the Chalcolithic, and while the dead at Alkhantepe are still buried in pits according to ancient practices, the first kurgans are observed in Kavtiskhevi (Georgia) and Soyuq Bulaq (Azerbaijan)44. This gene flow from the north mirrors the earlier (Maikop period) increase of the South Caucasus ancestries in the populations living on the northern slopes of the Great Caucasus after the end of the Neolithic, thus showing that the mountains act as a bridge rather than a frontier.Compared to the Mentesh Tepe Neolithic individuals, an archaic ancestry associated with CHG re-emerges during the Early Bronze Age/Kura-Araxes period. We also observed some Steppe ancestry in two Kura-Araxes groups , but at the lowest proportion than during the Late Bronze Age. At the end of the Kura-Araxes period, more frequent connections with the Steppes are also evidenced in the burial ritualsThe analysis of ancient DNA from the Mentesh Tepe Neolithic funerary pit reveals an uneven genetic homogeneity inside this Shomu-Shulaveri culture archaeological site. After the dramatic event that caused their death, the two brothers were buried, embracing each other, by people knowing their close relationship. The Shomu-Shulaveri culture took place between two intense population events: a population expansion of Neolithic groups from South-eastern Anatolia and Northern Mesopotamia at the beginning of the Neolithic and diffusion of steppe populations starting with the Middle of the Bronze Age period and following the resurgence of the CHG ancestry during the Kura-Araxes period. Even if the genetic analyses presented here help to better understand the timing and direction of these events, a more detailed analysis of other samples from the South Caucasus is called for, as this area is characterised by a high cultural diversity during the Neolithic period.23. Petrous bone fragments from 23 among 30 individuals of the SSC structure S342 have been selected , which has already been published elsewhere45, ancient DNA extraction was performed using a protocol adapted from ref. 46. Briefly, 50–200 mg of petrous bone was powdered by drilling and incubated in 1 ml of lysis buffer , 0.1% SDS, 65 mM DTT, and 0.5 mg/ml proteinase K), at 37 °C, for 14 h. After centrifugation, 1 ml of supernatant was recovered and purified with 13 ml of binding buffer . The mixture was then transferred to a High Pure Extender Assembly column  and centrifuged. Then, the column was washed using manufacturer’s recommendations . DNA was eluted in 100 µl of elution buffer. First, we tested the presence of human ancient DNA through PCR by targeting a portion of the mitochondrial genome using the same protocol as in ref. 45. Only samples with positive mitochondrial DNA amplification were used for library preparation.All pre-amplification steps were carried out in the clean room dedicated to ancient DNA of the Paleogenomic and Molecular genetics platform, set in the Musée de l’Homme (Paris). As in ref. The ancient DNA extract was converted to a TruSeq Nano Illumina library using the manufacturer’s protocol with slight modifications that account for the ancient DNA damage. First, DNA was not fragmented for obvious reasons; only 25 µl of ancient DNA extract was used; after end-repair, the libraries were purified using a MinElute column (Qiagen ©); the adaptor mix was gently pre-heated before adding ligase enzyme; libraries were amplified using 10–12 PCR cycles and purified on a MinElute column (Qiagen ©). Analyses on a LabChip ® GX provided an estimated size distribution of fragments with a peak length of 150–250 bp. Due to the limited preservation of genetic material in the site, reliable ancient DNA was found only in seven individuals, and genome-wide data were generated only for three of them.For the three selected samples (endogenous DNA content >1%), genomic capture was performed using the myBaits Expert Whole Genome Enrichment (WGE) kit (Arbor Biosciences), and the manufacturer’s instructions were followed. Baits were formed from the genomic DNA of three individuals of different  ancestries.Aliquots of the amplified libraries were first pooled and sequenced on MiSeq instrument (2 × 75 bp) on the IGenSeq platform. Captured libraries were sequenced on a NextSeq 500 (2 × 75 bp) instrument.23 was devised to make our results as comparable as possible with these previous analyses. All of our newly sequenced samples were thus specifically preprocessed through EAGER-v1.92.3747. From the raw shotgun sequencing data, sequencing adaptors were thus clipped, and paired-end reads were merged using AdapterRemoval-v2.2.1a48, all the while discarding any read shorter than 30 bp. Reads were then aligned against the 1000Genomes-phase2 reference genome (hs37d5) using BWA-v0.7.17-r118849, with a quality threshold for read trimming of q30. Samples were then merged across sequencing runs for each individual, using samtools merge v1.6.050. PCR duplicates were marked and removed using DeDup v0.12.147 prior to and following the previously described merging. PMD-damage statistics and frequency of nucleotide misincorporation were then estimated until the 50th nucleotide (starting from the 3′ and 5′ ends) using MapDamage v2.0.651 .Considering that our kinship analysis protocol makes use of previously published ancient individuals as a surrogate population, a separate data-preprocessing workflow mirroring that of ref. 52, and for the male samples, we calculated contamination using the X chromosome53 with ANGSD54. . We ran 20 replicate ADMIXTURE analyses for K between 2 and 14, from which we kept the most likely.We computed ADMIXTURE56 with Mbuti as outgroup for both statistics. We computed all the D-statistics for each Mentesh Tepe individual and for the group Mentesh Tepe formed by MT23, MT7 and MTT001. We computed D-statistics of the form D, with Z being Palaeolithic or Neolithic groups from Anatolia , Northern Mesopotamia  or Caucasus  and Y ancient Palaeolithic, Mesolithic or Neolithic populations from Western Eurasia to test the affinity of Mentesh Tepe to them when compared to an older contemporaneous group from the same region. We also performed D-stat of the form D to test the affinity of Mentesh Tepe with the populations that succeeded it in the region. These last results are shown in the supplementary information  individuals in one group. We also tested for 3-way admixture models; we could obtain model without nested 2-way models for Mentesh. All the results of the qpAdm modelling, including the data used to generate the figures, are in Supplementary Data 35 to estimate the time of admixture events between Iran_N and Anatolia_N in the Mentesh group. To convert the estimated admixture date in a generation into years, we assumed 29 years per generation13. The standard errors of DATES estimates come from the weighted block jackknife with “binsize: 0.001”, “maxdis: 1”, “runmode: 1”, “mincount: 1”, “lovalfit: 0.45” as parameters as in the example file at https://github.com/priyamoorjani/DATES/blob/master/example/par.dates. We tested three different configurations to account for the poor coverage of the ancient individuals: the first test was conducted with only ancient Iranian Neolithic individuals as a source and Barcın_N or Tell Kurdu_LN as the second source. The second, with modern populations from Armenia, Georgia and the Russian Caucasus added in the Iranian source. A third and final test was made with Barcın_N and Tell Kurdu_LN individuals put together as a source. We performed these three configurations for the Mentesh individuals grouped together and for all the Neolithic South-Caucasus individuals as a group.We used DATES v7531857 for Y chromosome, the comparison on ISOGG version July 11, 2020, was done manually.Y chromosome and mitochondrial haplogroups were determined by comparison of vcf files to databases. For mitochondrial DNA, a comparison was performed on Phylotree 17, using Haplogrep v2.2;59, we first filtered out from our 1240K dataset anything but biallelic autosomal SNP positions carrying a minor allele frequency greater or equal to 5% and subsequently extracted the genotypes of individuals MT23, MT26, MT7 and MTT001, along with 21 unrelated Late Chalcolithic and Early Bronze-Age individuals from Arslantepe (ART). The resulting dataset was then transposed to the tped/tfam format and used as input for READ. Next, we devised a two-pass estimation approach by running the READ method twice: A first run was therefore performed using only the Arslantepe individuals and with default parameters to compute a normalisation value . A second run was then carried out against the Mentesh Tepe individuals alone, using the previously obtained median P̄0 as a predefined normalisation value. We re-applied this protocol a second time using six individuals from the Late Neolithic and Early Chalcolithic site of Tell Kurdu to substantiate our initial results .We also used TKGWV2 to substantiate our initial results . ADMIXTURE analysis has been replicated 20 times. For Kinship analysis, READ analysis has been replicated twice: one with 21 individuals from Arslantepe and one with six individuals from Tell Kurdu.Further information on research design is available in the Peer Review FileSupplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Reporting Summary


Whole-genome SNP data from 613 Chinese indigenous pigs were collected to comprehensively estimate the genetic diversity, genetic relationship, and population genetic structure of the indigenous pig meta-populations. These Chinese meta-populations were characterized by genetic distance, genetic difference index, and runs of homozygosity (ROH). The contribution of genetic diversity of each meta-population was scaled based on genetic and allelic diversity, which provides a basis for improving the conservation project. Further, a selective sweep analysis revealed that genes related to fat deposition and heat stress may contribute to the adaptability of the indigenous pigs to cold and heat. These findings provide a theoretical basis for the improvement of conservation strategies and insights into the environmental adaptability of Chinese indigenous pigs.EPAS1, NFE2L2, VPS13A, SPRY1, PLA2G4A, and UBE3D) were potentially involved in adaptations to cold and heat. These findings from population genetic analyses provide a better understanding of indigenous pig characteristics in different environments and a theoretical basis for future work on the conservation and breeding of Chinese indigenous pigs. The diversification of indigenous pig breeds in China has resulted from multiple climate, topographic, and human cultural influences. The numerous indigenous pig breeds can be geographically divided into six meta-populations; however, their genetic relationships, contributions to genetic diversity, and genetic signatures remain unclear. Whole-genome SNP data for 613 indigenous pigs from the six Chinese meta-populations were obtained and analyzed. Population genetic analyses confirmed significant genetic differentiation and a moderate mixture among the Chinese indigenous pig meta-populations. The North China (NC) meta-population had the largest contribution to genetic and allelic diversity. Evidence from selective sweep signatures revealed that genes related to fat deposition and heat stress response ( The domestication of indigenous pigs in China began about 9000 years ago ,2,3,4, aBased on the region where they live and environmental adaptations, Chinese indigenous pigs can be divided into six geographic types . These sIn this study, a population genetic analysis was performed on six geographically isolated Chinese indigenous pig meta-populations. Genetic and allelic diversity of each indigenous pig meta-population was evaluated to determine their proportional contributions to the gene pool of all six meta-populations. Finally, the signatures of selective sweeps through Chinese indigenous pig meta-populations were compared to screen potential candidate genes associated with acclimation. A broad array of 60K genome-wide single-nucleotide polymorphism (SNP) data that included 613 pigs from 37 Chinese indigenous breeds was downloaded . Based ohttps://www.cog-genomics.org/plink/1.9/, accessed on 12 May 2022). The SNPs with call rates of less than 99% and minor allele frequency larger than 1% and located on sex chromosomes were removed. A set of 40,127 SNPs remained for subsequent analyses. Quality control of SNP data was performed using PLINK software  in R . Principal component analysis (PCA) was performed using PLINK, and the visualization of PCA was created with ggplot2 . The phylogenetic tree was analyzed with MEGA6 [http://www.bioconductor.org/packages/ggtree, accessed on 12 May 2022). ADMIXTURE software [http://www.royfrancis.com/pophelper/, accessed on 12 May 2022) was used to visualize the results of admixture patterns. The best number of ancestral components, K, was determined by cross-validation error analysis with K = 2 to 6.The geographical distributions of the six pig meta-populations were created using ‘tmap’ functions (th MEGA6  and drawsoftware  was utilOH) and expected heterozygosity (EH) of the six pig meta-populations were calculated using PLINK. Metapop2 software  was used to compute the pair-wise genetic differentiation index (STF) and pair-wise Nei’s minimum genetic distance (neiD) between subpopulations [SH) and average Nei’s minimum genetic distance between subpopulations (GD); SH and GD were used to evaluate the contribution of each pig meta-population to heterozygosity [TA) was divided into within and between subpopulations. Average allelic diversity within subpopulations was computed as the mean number of alleles in the subpopulations minus one (SA), and the average number of special alleles in a subpopulation contrasted with the other subpopulation mean over all possible subpopulation pairs (AD) and was defined as the average allelic diversity between subpopulations [Observed heterozygosity  to calculate runs of homozygosity (ROH) segments with default parameters as follows: the ROHs were divided into five categories: 0 to <6 Mb, 6 to <12 Mb, 12 to <24 Mb, 24 to <48 Mb, and ≥48 Mb. A sliding window analysis with window size of 15 SNPs was used, and the minimum number of homozygous SNPs was set to 20. Further, the homozygous threshold of windows overlapping was set to 0.05 with a minimum number of 1000 SNPs per kbps. Additionally, the maximum distance between two SNPs and the minimum length of a homozygous run was set to 1,000,000 bps and 250,000 bps, respectively. The individual genomic inbreeding coefficient based on ROH (ROHF) [We used the detectRUNS package (STF) and cross-population composite likelihood ratio test (XP-CLR) were employed to detect the genomic regions under selection for the differentiation of Chinese indigenous pigs. Selective sweep analysis was performed using VCFtools [STF and XP-CLR results were plotted using rMVP . Selective sweeps were scanned for STF and XP-CLR using a 10 kb non-overlapping window. Genomic signatures were defined as the top 1% of STF and XP-CLR. In addition, the intersection of genes located around the significant selection regions were annotated using BioMart .The population differentiation index  and expected heterozygosity (EH: 0.3224) were found for the NC meta-population; this meta-population had the lowest inbreeding coefficient (F: 0.3533) and average co-ancestry (iif: 0.6817). The minimum OH (0.2246) and EH (0.2733) were seen for the CC  and PC (Plateau of China) meta-populations, respectively. The maximum F (0.4748) and iif  (0.7676) were observed in the JDC and SC meta-populations, respectively (STF and neiD results between the six pig meta-populations are shown in STF and neiD between the SWC (Southwestern China) and PC meta-populations were the lowest among all pair-wise comparisons of the meta-populations with values of 0.0375 and 0.019, respectively. In addition, the SC and JDC meta-populations had the highest values of differentiation indices (STF: 0.1359 and neiD: 0.0709). The values for genetic differences were largely consistent with the physical distances between geographical coordinates, indicating that geographical distance influences the degree of genetic differentiation. Comparing the CC meta-population with the other meta-populations produced the lowest averages of STF (0.06486) and neiD (0.03254). This finding of similar genetic composition of the CC meta-population to the others may be the result of its location in the middle region of China leading to a high gene flow between the CC meta-population and the others. Similarly, the results for population structure indicate that the CC meta-population was admixed with other populations  followed by the JDC meta-population  (TA), respectively. The CC meta-population had the highest percentage of TH (−1.5417%), and the percentage of TA was the highest in the SC meta-population (−0.5499%). The SC meta-population had lower within-population diversity because it had the most significant negative contributions to both SH (−2.1954%) and SA (−2.0666%) (iif: 0.7676), which corresponds to the above results for population diversity (SH (3.1711%) and SA (0.8303%). Additionally, the largest within-population diversity corresponded to a low average co-ancestry (iif: 0.6817) in the NC meta-population (GD (−0.4937%) was observed in the CC meta-population, which is consistent with the CC meta-population having the smallest average Nei’s distance compared with the others (neiD: 0.02712) (K). For genetic diversity, the NC population contributed 100%, and all six meta-populations contributed greater than 10% to allelic diversity. Overall, the above evidence suggests that the NC meta-population should be given the highest priority in a conservation plan for Chinese indigenous pigs. We should strengthen the research and application of indigenous pigs in North China, promote the re-innovation of pig germplasms, and speed up the development of local pig industries. Maintenance of maximum genetic diversity is an important goal of conservation to maintain the potential for adaptation to environmental changes in the future . Expecte0.8306%) A. Furthe2.0666%) A,B. Moreiversity . The NC pulation . The max0.02712) . Based o0.02712) C. As expROHF) for the pig meta-populations within geographic regions is shown in A total of 207,141 ROHs were identified in the 613 pigs by detectRUNS, and the mean length for each ROH was 2.91 Mb. The number of ROHs on each pig chromosome is displayed in STF > 0.3430, XP-CLR > 6.1482) and NR vs. SR and CR , were used to explore the selection signatures related to heat and environmental adaptability, respectively  in southern China is over 20 °C, whereas the AMT in northern China is below −20 °C B. Pigs rectively B. Genes position . EPAS1, NFE2L2, and VPS13A. EPAS1 is associated with heat tolerance and can be directly implicated in the hypoxic response during thermal stress [EPAS1 may protect the reproductive capacity of pigs. A previous study indicated that NFE2L2 plays a protective role in bovine endometrial epithelial cells cultured under heat stress conditions [NFE2L2 may reduce the effects of heat on fertility in pigs in the southern region. In addition, VPS13A has been recognized as a key regulator of secretion and aggregation of blood platelets in heat stress conditions, which can reduce physiological damage, including the risk of coronary thrombosis, in animals under heat stress [VPS13A is likely to have a close relationship with heat stress resistance in pigs. These three genes possibly reduced or mitigated inflammatory responses produced by heat stress in local pigs, and they may be associated with high-temperature adaptation of pigs in southern China. Genes associated with heat stress response included l stress ,34,35. Tnditions . NFE2L2 t stress . This fiSPRY1, PLA2G4A, and UBE3D. Adipose tissue-specific SPRY1 expression in mice resulted in increased body fat, and conditional deletion of SPRY1 resulted in increased body fat [SPRY1 has protective effects on mice fed a high-energy diet, and SPRY1 expression can prevent high-fat, diet-induced obesity [PLA2G4A was a key regulator for fat deposition significantly involved in several biological processes of fat deposition [PLA2G4A was significantly associated with persistent weight stability and insulin sensitivity [UBE3D had a relationship with fat composition in sheep, and it was a cold-resistant candidate gene in Weining cattle [UBE3D may be related to the adaptation of pigs to low temperatures in the northern region. Thick layers of subcutaneous fat are an important adaptation to cold and heat; the fat can produce heat in response to cold exposure and protect animals against hypothermia [Three genes associated with fat deposition were body fat . Further obesity . This meposition . Furthersitivity . This reg cattle ,43. Therothermia . The aboEPAS1, NFE2L2, VPS13A, SPRY1, PLA2G4A, and UBE3D. These findings illuminate the genetic features of Chinese indigenous pigs leading to an improved conservation plan and provide theoretical support for the evolution of environmental adaptability.In summary, this study investigated contributions to genetic diversity of Chinese indigenous pig meta-populations and identified selective candidate genes involved in heat and cold environmental adaptation. We found that the NC meta-population has the highest genetic and allelic diversity for the six pig meta-populations. Numerous genes linked to fat deposition and heat stress were detected based on selective sweep analysis including