The Hmong-Mien (HM) and Sino-Tibetan (ST) speaking groups are known as hill tribes in Thailand; they were the subject of the first studies to show an impact of patrilocality vs. matrilocality on patterns of mitochondrial (mt) DNA vs. male-specific portion of the Y chromosome (MSY) variation. However, HM and ST groups have not been studied in as much detail as other Thai groups; here we report and analyze 234 partial MSY sequences (∼2.3 mB) and 416 complete mtDNA sequences from 14 populations that, when combined with our previous published data, provides the largest dataset yet for the hill tribes. We find a striking difference between Hmong and IuMien (Mien-speaking) groups: the Hmong are genetically different from both the IuMien and all other Thai groups, whereas the IuMien are genetically more similar to other linguistic groups than to the Hmong. In general, we find less of an impact of patrilocality vs. matrilocality on patterns of mtDNA vs. MSY variation than previous studies. However, there is a dramatic difference in the frequency of MSY and mtDNA lineages of Northeast Asian (NEA) origin vs. Southeast Asian (SEA) origin in HM vs. ST groups: HM groups have high frequencies of NEA MSY lineages but lower frequencies of NEA mtDNA lineages, while ST groups show the opposite. A potential explanation is that the ancestors of Thai HM groups were patrilocal, while the ancestors of Thai ST groups were matrilocal. Overall, these results attest to the impact of cultural practices on patterns of mtDNA vs. MSY variation. Thailand occupies the center of Mainland Southeast Asia (MSEA) and shares borders with several countries: Laos and Myanmar in the North and West, Laos in the Northeast, Cambodia in the East, and Malaysia in the South Fig. . With a The HM language family is one of the major language families in MSEA, comprising some 39 languages (35 Hmongic and 4 Mienic) distributed across China, northern Vietnam, northern Laos, and northern Thailand . AlthougThe HM and ST groups in Thailand are regarded as the hill tribes who inhabit the high mountainous northern and western region of Thailand. Consisting of ~700,000 people, there are nine officially recognized hill tribes: the AA-speaking Lawa, Htin and Khmu; the HM-speaking Hmong and IuMien; and the ST-speaking Karen, Lahu (or Mussur), Akha, and Lisu , 7. In aOur previous studies on the paternal and maternal genetic lineages and structure of many TK and AA groups indicated different and complex demographic histories in populations from Thailand and Laos –15. HoweSamples were collected from 416 individuals belonging to 14 populations classified into three linguistic groups: (1) HM groups, consisting of five Hmong populations (HM1–HM5) and two IuMien populations (Y1 and Y2); (2) ST groups, consisting of two Lahu populations (MR and MB), one Lisu (LS), and one Karen subgroup Skaw (KPW3); and (3) Tai-Kadai groups, consisting of one Shan population (SH2), one Phutai population (PT2), and one Lao Isan population (IS5); (Supplementary Table Shg19 [www.mbio.ncsu.edu/BioEdit/bioedit.html). The complete mtDNA sequence data set can be found at GenBank (accession number MT418943–MT419358). All reads that aligned to the region of the MSY that was targeted by the capture-enrichment array were deposited in the European Nucleotide Archive (study ID PRJEB36639). Final SNP genotypes and their chromosomal positions on hg19 are provided in Supplementary Table SGenomic libraries with double indices were prepared and enriched for mtDNA as described previously , 18. Thehg19 , 14. Seqhg19 –15. We tΦst), and analyses of molecular variance (AMOVA) were obtained with Arlequin 3.5.1.3 [Φst distance matrices for the MSY and mtDNA) (R function: isoMDS package: MASS) and to construct heat plots of the Φst distance matrix and the matrix of shared haplotypes . STATISTICA 13.0  was used to carry out a correspondence analysis (CA) based on MSY and mtDNA haplogroup frequencies. For the MSY, haplogroup assignment was performed by yHaplo [http://www.isogg.org/) and the available genetic markers in our target region. The derived SNPs for each sample are provided in Supplementary Table Shttp://www.phylotree.org) [SSSS−10 substitutions/bp/year [S−8 and 9.883 × 10−8 for data partitioned between the coding and noncoding regions, respectively [SSThe newly-generated 234 MSY sequences from 14 populations were combined with 928 sequences from 59 populations from our previous studies , 14 for  3.5.1.3 . To visu 3.5.1.3  was usedy yHaplo . Haplogr.org) SSS. For BSP.org) SSS and the .org) SSS. BEAST i.org) SSS. The bes/bp/year , 30. Aftectively . The RSRectively . The strWe combined the 234 newly-generated sequences with 928 sequences from our previous studies , 14 for SSWhen combined with our previous Thai/Lao data, there are a total of 90 haplogroups identified , Lawa (LW2), and Karen (KPA) from northern Thailand, and the Nyaw (NY) from northeastern Thailand. In contrast O1b*, which is the predominant lineage in SEA and at high frequency in AA speaking people , is the We generated 416 complete mtDNA sequences with mean coverage ranging from 35× to 7752×  , Lawa (LW3), Lisu (LS), and IuMien (Y2) from northern Thailand, and in Mon (MO5) from central Thailand. In contrast, the predominant SEA haplogroups  are at highest frequency in TK and AA speaking groups, indicating genetic similarity between these two groups. Strikingly, the populations with the highest frequencies of NEA MSY lineages are not the same as the populations with the highest frequencies of NEA mtDNA lineages  but no significant Tajima’s D values were observed in any of the ST-speaking groups . Interestingly, the ST-speaking groups show no significant Tajima’s D values while the two IuMien groups both show significant negative Tajima’s D values , Htin (TN1 and TN2), and Seak (SK), the ST speaking Lahu or Mussur (MR) shows low mtDNA haplotype and haplogroup diversities whereas the Lisu (LS) shows higher genetic diversities than the other ST populations Fig. . In contSSues Fig. .The AMOVA results indicate that the variation among populations accounts for 13.72% of the total MSY genetic variance  than for the MSY (4.88%), in contrast to the larger MSY than mtDNA variation observed when pooling data from other groups from Thailand and Vietnam. In particular, the mtDNA variation among Hmong groups from Thailand and Vietnam was only 1.08%, which is not significantly different from zero. When the Hmong and IuMien were separately compared with other linguistic groups, significant differences were observed for the Hmong but not for IuMien, similar to the MSY results and further supporting the difference between Hmong and Mien groups.Shared haplotypes within populations are an indication of smaller population size and increase relatedness among individuals, while shared haplotypes between populations are an indication of recent shared ancestry or contact. There were shared MSY haplotypes within the HM groups, and some sharing between them and a few TK-speaking groups, except for HM5 and Y1, who did not share any haplotypes with any other populations Fig. . The LisΦst values) indicate, in general, genetic heterogeneity among AA populations and homogeneity among TK populations, as well as genetic differences between the AA (except the Mon) and TK populations. For the newly-studied HM groups, significant genetic differences between the Hmong and almost all other populations were observed, whereas the Φst values for comparisons of the IuMien (Y1 and Y2) and Lisu with many populations were not significant , the hunter-gatherer group from southern Thailand, the Hmong groups (HM1-HM5) and the Karen (KSK3) , which is based on haplogroup frequencies. The results indicate that the genetic distinctiveness of the Hmong reflects high frequencies of haplogroups O2a2a1a2a1a2 (O-N5) and C2e2 (C-F845)  and Seak (SK)  over time were constructed for each ethnicity. For the MSY, different trends were observed for different groups  and TK populations  [The BSPs for each ethnicity show that several groups, i.e., Lahu, Shan, Phutai, show a common trend of kya Fig. . Howeverand Lue)  are simiand Lue) . InteresThere are nine official hill tribes in Thailand: the AA-speaking Lawa, Htin, and Khmu; the HM-speaking Hmong and IuMien; and the ST-speaking Karen, Lahu, Lisu and Akha. The Lahu, Karen, and Htin are matrilocal  whereas the others are patrilocal. Our previous study  had inveh and MPD values do not differ significantly between patrilocal and matrilocal groups , IuMien (Y1–Y2), Lisu LS), Khmu (KA), Lawa (LW1-LW3), and Palaung (PL) groups practice patrilocality, whereas the Htin (TN1–TN3), Karen  and Lahu (MR and MB) are matrilocal. If genetic variation was influenced by residence pattern, then lower within-population genetic diversity coupled with greater genetic heterogeneity among populations is expected for matrilocal groups than for patrilocal groups for the mtDNA, whereas the opposite pattern is expected for the MSY . However, Khmu , while for the matrilocal groups the mtDNA genetic variation is much higher, but still less than that for the MSY . Much stronger contrasting between-group variation is seen in two patrilocal groups, i.e., Hmong  and Lawa  and one matrilocal Htin group  . When Hmong, IuMien, and Lahu from Thailand and Vietnam were combined, contrasting patterns of genetic variation between the MSY and mtDNA were still in accordance with expectations based on residence pattern, albeit the IuMien show only slightly higher between-group differentiation for the MSY than for mtDNA  was proposed to be characteristic of all modern ST populations , subsequST-speaking groups in Thailand have not been studied in the same detail as those in China; here we analyzed three groups: Lisu, Lahu (Mussur), and Karen. Lisu and Lahu speak Lolo-Burmese languages, while the Karen languages belong to a different branch, Karenic . HistoriThe genetic distances between the Lisu and Lahu are significantly different from zero for both mtDNA and the MSY, and they also share both mtDNA and MSY haplotypes Fig. , indicatSThe estimated coalescent ages of the predominant lineages in ST populations provide an upper bound for their divergence/contact from other groups. MSY haplogroup O2a2b1a1-Page23, equivalent to O-M117, which was previously reported to be abundant in TB groups in southwestern China and in Han Chinese , was datPreviously, postmarital residence pattern has been shown to influence genetic variation in the hill tribes of Thailand , 11, 12.In general, the within-population genetic diversity values were not in agreement with expectations (Supplementary Table SNonetheless, one striking pattern remains in our data, and that concerns NEA vs. SEA ancestry. Previous genetic studies supported a north-south division in East Asian peoples and with some spread of northern ancestry to the south , 49. TheSSHowever, the HM and ST groups are a dramatic exception to this general pattern of higher SEA than NEA ancestry for both paternal and maternal lineages Fig. . The estGiven that both HM and ST groups originated from southern China or northwestern China, it is likely that the ancestral HM and ST groups both had relatively high levels of NEA ancestry for both the MSY and mtDNA, as this has been reported for contemporary Chinese populations (Supplementary Table SHowever, the interaction between NEA and SEA groups was undoubtedly a complex admixture process that started in prehistoric times and has continued into historic times, and there are many factors in addition to residence pattern that could have led to sex-biased admixture . AdditioWe have carried out the most extensive study to date, using high-resolution methods, of the maternal and paternal lineages in HM and ST speaking groups of northern Thailand. We find unexpected differences between the Hmong and IuMien, which may reflect different cultural practices, and genetic heterogeneity among ST groups. Compared with previous studies, we find less contrast in genetic diversity and differentiation between matrilocal and patrilocal groups among the hill tribes. However, a novel finding of this study is the contrast between HM and ST groups, both assumed to have origins in southern China, in frequencies of NEA maternal and paternal lineages. We suggest that this striking difference reflects ancestral patrilocality for HM groups vs. ancestral matrilocality for ST groups. Overall, our results further attest to the impact of cultural practices on patterns of mtDNA vs. MSY variation in human populations.Supplementary FiguresSupplementary Tables


Differentiation comparisons of 26 populations from the 1000 Genomes Project distinguished among East Asian, South Asian, African and European populations. Overall, these markers enrich the current microhaplotype marker databases and can be applied for individual identification, paternity testing and biogeographic ancestry distinction.Microhaplotype markers have become an important research focus in forensic genetics. However, many reported microhaplotype markers have limited polymorphisms. In this study, we developed a set of highly polymorphic microhaplotype markers based on tri-allelic single-nucleotide polymorphisms. Eleven newly discovered microhaplotypes along with nine previously identified in our laboratory were studied. The microhaplotype genotypes of unrelated individuals and familial samples were generated on the MiSeq PE300 platform. These 20 loci have an average greater than 3.5 effective number of alleles. Over the whole set, the cumulative power of discrimination was 1–3.3 × 10 There aet al.  proposed [et al.  optimize [et al. . Recombi [et al. . Microha [et al. –13.3–104 times those of SNPs [Nonetheless, at present, STRs are the preferred markers used in forensic genetics owing to their multi-allelic nature and thus high number of polymorphisms . Capilla of SNPs , which l of SNPs . STRs of of SNPs . STR det of SNPs –22. Ther of SNPs –25. TherA number of microhaplotypes have been proposed –28, but 2.2.1.Ae) needed to be greater than 3.0; and heterozygosity for each microhaplotype less than or equal to 0.6. The naming of these microhaplotypes followed the principles proposed by Kidd [SNPs, with a preference for tri-allelic ones, were selected according to the following criteria: (i) for Chinese Han populations (CHB and CHS from the 1000 Genomes Project), a minor allele frequency (MAF) greater than 0.10, and (ii) SNPs on the same microhaplotypes with an identical allele frequency were excluded. Then each microhaplotype needed to be less than 200 bp, with a molecular distance between loci on the same chromosome greater than 2.0 Mb to minimize the effects of linkage disequilibrium. The effective number of alleles , including 50 unrelated individuals and 12 parent/child duos.2.3.et al. [−1. PCR was performed on a T100 Thermal Cycler  with the following conditions: 95°C for 5 min, 10 cycles at 95°C for 15 s, 60°C for 30 s, 72°C for 60 s, 2 cycles at 95°C for 15 s, 80°C for 30 s, 60°C for 30 s, 72°C for 60 s, 8 cycles at 95°C for 15 s, 60°C for 30 s, 72°C for 60 s, 2 cycles at 95°C for 15 s, 80°C for 30 s, 60°C for 30 s, 72°C for 60 s, 8 cycles at 95°C for 15 s, 60°C for 30 s, 72°C for 60 s, 10 cycles at 95°C for 15 s, 80°C for 30 s, 60°C for 30 s and 72°C for 60 s. The PCR products were purified by gel-cut recovery. All samples were sequenced on the MiSeq PE300 platform according to the manufacturer's recommendations.All samples were amplified in a SmartChip using the Takara/WaferGen SmartChip TETM system . Parallel nanolitre polymerase chain reaction (PCR)-based target enrichment for amplicon sequencing was performed using a method similar to that described in De Wilde et al. . The PCRhttps://sourceforge.net/projects/bbmap). The phase-known genotype data were ascertained using GATK v. 4.0 [The base coverage threshold of sequencing was set to 30×. The raw data were processed with bcl2fastq software for each sample and run through the BBDuk software of BBMap v. 37.75  with a 9700 Thermal Cycler . PCR products were separated and detected using an ABI PRISM 3130xl Genetic Analyzer . The genotypes were analysed using GeneMapperID v. 3.2 (Applied Biosystems).2.5.Sequencing accuracy was validated through T vector molecular cloning and Sanger sequencing. Ten randomly selected loci were typed using the S14 sample and checked for consistency against the sequencing result of the S14 sample using the MiSeq PE300 platform.2.6.p-value of exact tests for Hardy–Weinberg equilibrium (HWE). Kidd & Speed [Ae) for a locus as the equivalent number of neutral alleles of equal frequency, calculated using the formula 1/∑pi2 . The probability of detecting DNA mixtures was calculated as well. Linkage disequilibrium (LD) between loci was estimated with χ2-tests using Arlequin v. 3.5 software [r2) for loci pairs were calculated using the SHEsis online tool [K value (K = 2–7); CLUMPP v. 1.1.2 [F-statistic values were calculated using Arlequin v. 3.5 software, and R software v. 3.4.2 was used to describe F-st among populations with the ‘pheatmap’ package. For the 12 parent/child duos, paternity was separately verified using the combined paternity index (CPI) based on STR genotyping and MiSeq sequencing results.The forensic parameters were evaluated using modified Powerstats software v. 1.2  based on & Speed  defined software , and corine tool . SNP infine tool ,39. We aine tool  was usedv. 1.1.2  and Distv. 1.1.2  were usev. 1.1.2  to estabv. 1.1.2  and MEGAv. 1.1.2 . F-stati3.3.1.After excluding loci according to the screening criteria and sequencing quality control threshold, 20 microhaplotypes were successfully sequenced on the MiSeq PE 300 platform. The accuracy of MPS sequencing was verified on the S14 sample for 10 randomly selected loci. The results are presented in The newly proposed markers identified in this study are mh02zha012, mh04zha001, mh04zha002, mh04zha007, mh08zha011, mh09zha008, mh11zha006a, mh10zha002, mh14zha003, mh17zha001 and mh22zha008. p = 0.05/20 = 0.0025). The LD p-values of microhaplotype markers on the same chromosome showed no significant deviation from expectations, suggesting that these sites were in linkage equilibrium. To further evaluate LD, we calculated another parameter, r2 . The r2 values between marker pairs on the same chromosome were all under 0.04, supporting the previous conclusion of LD tests.The HWE and LD test results are given in electronic supplementary material, table S2. There was no significant deviation from HWE after Bonferroni correction  to 4.995 (mh19zha007), with an average value of 3.724, suggesting wide applicability of this system in forensic practice [Ae value and the matching probability  of the set with other microhaplotypes proposed in Ae values correlate with the ability of microhaplotype loci to detect and deconvolute DNA mixtures [Ae value of 3.0 is applied for detecting a mixture of two unrelated individuals, the probability of there being a third allele was 0.4444 under the simple HWE model [Ae value of 4.0, the maximum probability would be 0.65625. We used the minimal integral value of Ae for our probability calculation. The cumulative probability of detecting a mixture with the set of microhaplotypes was equal to 1 − (1 − 0.4444)15 (1 − 0.65625)4 = 0.999997927. Ae values convey information on the polymorphisms of markers, and we assessed how these could be used to analyse DNA mixtures. The larger the Ae values reflect the better capacity of detecting mixed DNA samples. The PD of 20 loci ranged from 0.801 to 0.916 with an average value of 0.866. The cumulative PD value of the set was 1–3.3 × 10−18. The PE values of those loci ranged from 0.342 to 0.755 with a mean value of 0.523, and the cumulative PE was 1–1.928 × 10−7. The Ho values of all loci were greater than 0.6. The observed alleles of microhaplotypes and allele frequencies are illustrated in electronic supplementary material, figure S2; most microhaplotypes had at least four alleles, although the maximum number was 12 for locus mh04zha004.The practice . We compmixtures . For insWE model . Hence, −4 (MSL population) to 1.04 × 10−12 (STU population). The combined MP of CHB population for unrelated individuals was 8.73 × 10−11, suggesting this set can be used independently for personal identification. Alleles observed in the global populations and allele frequencies are presented in electronic supplementary material, table S4, the allele frequencies of tri-allelic SNPs for every microhaplotype are given in electronic supplementary material, table S5. We speculate that the tri-allelic SNPs in microhaplotypes contribute significantly to the polymorphism of each locus in the Chinese Han population. The Ae values of 20 loci were calculated for 26 populations, and average Ae for five main regions are depicted in Ae > 3.0 at all loci. More population genetic studies of this highly polymorphic panel will be done in the future, so that this panel could be applied in forensic casework.The principal forensic statistics are summarized in electronic supplementary material, table S3. The combined MP of 26 populations was calculated following the method proposed by Balding & Nichols , and ran3.2.K = 2, the AFR populations  were distinguished from the others. At K = 3, it was possible to find genetic differences between AFR and EAS. At K = 4, the four populations of AFR, SAS , EAS and EUR  were separated, but AMR  populations were not separated from EUR. At K = 5, populations of AMR and EUR formed two mixed clusters that could be attributed to the immigration history of the AMR population from Europe. Another reason for poor differentiation might be the small number of loci and the deficiency of markers' ancestry information. Because the set of microhaplotypes was not specifically designed for inferring ancestry, we focused more on Ae values than Rosenberg's informativeness (In) values [F-st is illustrated in electronic supplementary material, figure S3. The AFR populations clustered in the upper left part of the figure with negligible F-st values. Conversely, there was a high F-st value between AFR and EAS populations, representing significant genetic differentiation among African and East Asia populations. A phylogenetic tree was constructed using the NJ method ; it produced five main branches  extending from a rooted tree starting with AFR populations. Taken together, these results indicate that our system unambiguously differentiated between four major populations: East Asian, African, South Asian and European/American.The results of STRUCTURE analysis are shown in ) values . The hea3.3.10 values of CPI using a single marker type (microhaplotype or STR) with those using STR markers with our set of microhaplotypes and show the results in The specific genotypes and CPI values of 12 parent/child duos based on microhaplotype sequencing and CE of STR markers are shown in electronic supplementary material, table S6. The genotypes of 20 microhaplotype loci for all duos are in accordance with Mendel's law of inheritance. The CPI value of eight duos  exceeded the threshold value of 10 000, which could be direct confirmation of paternity. Furthermore, we compared the log4.We developed a set of highly polymorphic microhaplotypes and evaluated their use for forensic analyses. The lengths of loci were limited to 200 bp and most amplicons were less than 300 bp, making them amenable to the MPS method. Moreover, several loci with small amplicons can be applied for the analysis of slightly degraded DNA samples. These markers will be particularly helpful for mixture analyses and for identifying individuals from East Asian populations. The population specificity of these markers will be helpful for inferring biogeographic ancestry. We believe that this microhaplotype set is a useful addition to forensic genetic testing.


Platycladus orientalis range‐wide collection to evaluate its diversity and to predict genetic offset under future climate scenarios. Platycladus orientalis is a widespread conifer in China with significant ecological, timber, and medicinal values. We found population structure and evidences of isolation by environment, indicative of adaptation to local conditions. Gradient forest modeling identified temperature‐related variables as the most important environmental factors influencing genetic variation and predicted areas with higher risk under future climate change. This study provides an important reference for forest resource management and conservation for P. orientalis.Understanding and quantifying populations' adaptive genetic variation and their response to climate change are critical to reforestation's seed source selection, forest management decisions, and gene conservation. Landscape genomics combined with geographic and environmental information provide an opportunity to interrogate forest populations' genome‐wide variation for understanding the extent to which evolutionary forces shape past and contemporary populations' genetic structure, and identify those populations that may be most at risk under future climate change. Here, we used genotyping by sequencing to generate over 11,000 high‐quality variants from  For large conifer genomes, GBS has become a practical method for generating genome‐wide variant data for population genetic studies  from Heilongjiang Province, China, were collected , following a previously established protocol  from the tail of each read were removed using Trimmomatic v0.36   opposed to others. We used the CLUMPAK (Cluster Markov Packager Across K) web server to align and visualize the bar graphs . For each best fit K, we conducted three independent runs to evaluate the convergence.To avoid overestimating the number of potential clusters caused by the presence of isolation by distance (IBD), as is often found in continuous populations, we further used conStruct  , Wright's inbreeding coefficient (FIS), and observed heterozygosity (Hobs), were calculated using the “populations” module in Stacks.Population differentiation  ≤.8 were retained for the following analysis  with absolute value of Pearson correlation coefficient   and geographic distance and environmental distance with significance determined using 999 permutations in the R package vegan  isolation by distance (IBD), (b) isolation by environment (IBE), and (c) the correlation between environmental and geographic distance. Pairwise Rousset,  was calcRousset, . Mantel 2.6K = 3 was selected based on scree plot  as significant putative adaptive loci.We applied three methods to detect outlier SNPs, BayeScan  in the R package vegan  passed initial quality filters Table . A total3.2K = 4 as the most likely number of evolutionary clusters among the 22 populations sampled, including one population of T. koraiensis , a cluster A with 6 populations , and a large third cluster with all the remaining populations  and C  clusters  and being a likely introduced population. DAPC showed clear separation among the clusters identified by ADMIXTURE  highlighted the divergence between T. koraiensis and P. orientalis.Using the unlinked SNPs 3,911), ADMIXTURE identified s Figure A. Under s Figure B. Under 1, ADMIXTs Figure B. One poK = 6   Figure . The pat) Figure .FST) among clusters  was significant when using unlinked 3,911 SNPs, with FST values ranged from 0.030 to 0.140  was noticeably higher than within P. orientalis (0.030–0.069). On all 11,049 SNPs, FST values changed little (Table π) in P. orientalis populations ranged from 0.0021 to 0.0029 when considering all 11,049 SNPs, and the value in T. koraiensis was similar  and the T. koraiensis population (LYL) were removed from this analysis, leaving 17 populations for all following analyses. We performed GF analyses to test whether a subset of genomic variation can be explained by environmental effects and to visualize climate‐associated genetic variation across the species range. Mapping of genetic variation across environmental space revealed significant differences of genetic composition along the latitudinal and longitudinal axes of P. orientalis range  and Euclidean geographic distance  and 2.4% (p > .05) of the variation, respectively.From the full set of SNPs , we detected 579 5.24%) outlier SNPs by the three detection methods used. BayeScan, Pcadapt, and Bayenv2 identified 214 (1.94%), 211 (1.91%), and 228 (2.06%) significant outlier SNPs, respectively Figure . We perf4% outliep < .05) of the variation in the Bayenv2 outlier SNPs, but the effect of geography was insignificant , while the corresponding values were −0.2% to 0.5% (p > .05) and 1.8%–2.0% (p > .05) for the Pcadapt and BayeScan outliers . Geological events and subsequent climatic changes during Pliocene–Pleistocene in northern and western China were identified as major forces that have shaped the distribution and genetic differentiation of forest species in northern China (Xia et al., P. orientalis. It is suggested that P. orientalis became fragmented into relatively small and isolated populations since at least 1,500 years ago (Xie et al., Within n Figure . conStru4.2P. orientalis using Mantel test indicated that geographic and environmental distances were almost equally important to the observed genetic differences. The relationship between geographic and environmental distances was highly correlated, which made it difficult to correctly parse out the factor that plays the key role in shaping the genetic variation. We thus further applied RDA to subdivide the genetic variation to environment and geography. On all SNPs, 13.1% of the variation could be jointly explained by the environment and geography, with environment exclusively contributing 6.8%, leaving 86.9% of the genetic variation unexplained. For the outlier SNPs, especially the Bayenv2 outliers, 19.3% of the variation could be explained by both environment and geography, of which a significant 8.1% of variation was attributed to environment, while geography was insignificant. These results suggest that environment was more important than geography in the population differentiation of P. orientalis. The genomic signature of IBE in P. orientalis was similar to that of Pinus tabuliformis in northern China (Xia et al., Environment has been widely reported as a strong selective pressure on natural populations (Joshi et al., P. orientalis (Fu & Shen, P. orientalis (Fu & Shen, P. orientalis (Chen, Yang, Li, Xu, & Wang, P. orientalis responding to low temperature is not yet understood. Dissection of this adaptive mechanism should be the objective of future studies.Local adaptation studies on climate change contribute to understanding the ability of populations to sustain or adapt to rapid climate change (Fournier‐Level et al., P. orientalis only one of the top ten‐ranked environmental variables is in the water regime indicating that precipitation has less impact on genetic adaptation than temperature. This is likely due to the strong drought tolerance of P. orientalis, as it can survive under annual precipitation of less than 200 mm and soil moisture content below 5% (Dong et al., Water availability is commonly recognized as another critical factor delimitating tree species' distribution in northern China (Mao & Wang, 4.3P. orientalis under future conditions using GF modeling. The same strategy has been applied to a variety of species (Bay et al., Based on the current genetic–environmental associations, we attempted to assess the potential genetic offset in P. orientalis would be less affected in the northwestern Loess Plateau and most of northern China, while relatively high genetic offset was predicted in the northern and southern margins. These high‐risk regions would need to adapt fast, either actively or passively, to keep pace with climate change; otherwise, populations in these regions may decline. Similar to other conifers, P. orientalis has a relatively long lifecycle with long generation intervals, in which the rate of emergence and spread of novel adaptive alleles in populations through de novo mutations are likely to be too slow to respond to rapid future climate changes.Our GF analyses suggested that P. orientalis populations to validate and adjust model predictions.The prediction accuracy of the GF models has been verified on bird populations (Bay et al., 4.4Platycladus orientalis is one of the most widely distributed coniferous trees in China (Dong et al., P. orientalis range, which offers an opportunity for optimal seed zone delineation, allocation of seed sources, seed movement for reforestation, and genetic conservation. For example, traditional seed transfer guidelines are based on provenance trials and climate models to select the range of seed transplants. However, establishment and maintenance of provenance trials are expensive and time‐consuming, resulting in limited information available, which makes it challenging to develop either population response functions or transfer functions (Mátyás,  Mátyás, . The knoConsidering that future climate may dramatically change in certain parts of the species' range, we propose adopting assisted gene flow to increase genetic diversity and adaptation to the anticipated climate changes. Assisted gene flow is a managed migration of individuals or gametes between populations within species range, which may be effective in accelerating adaptation to future climate (Aitken & Whitlock, The authors declare no conflict of interest.JFM, XRW, TW, and YAE planned and designed the research; YJ, SSZ, SQJ, and WZ collected samples and executed the GBS prep; WZ and PAM contributed some scripts; KHJ, WZ, and XRW analyzed the data and wrote the manuscript; JFM, YAE, TW, WZ, and XRW edited and improved the manuscript; all authors approved the final manuscript. Click here for additional data file.


Brassica oleracea), “wild” populations occur throughout Europe but little is known about their genetic variation or potential as resources for breeding more resilient crop varieties. The main aim of this study was to characterize the population structure of geographically isolated wild cabbage populations along the coasts of the UK and Spain, including the Atlantic range edges. Double‐digest restriction‐site‐associated DNA sequencing was used to sample individual cabbage genomes, assess the similarity of plants from 20 populations, and explore environment–genotype associations across varying climatic conditions. Interestingly, there were no indications of isolation by distance; several geographically close populations were genetically more distinct from each other than to distant populations. Furthermore, several distant populations shared genetic ancestry, which could indicate that they were established by escapees of similar source cultivars. However, there were signals of local adaptation to different environments, including a possible relationship between genetic diversity and soil pH. Overall, these results highlight wild cabbages in the Atlantic region as an important genetic resource worthy of further research into their relationship with existing crop varieties.There has been growing emphasis on the role that crop wild relatives might play in supporting highly selected agriculturally valuable species in the face of climate change. In species that were domesticated many thousands of years ago, distinguishing wild populations from escaped feral forms can be challenging, but reintroducing variation from either source could supplement current cultivated forms. For economically important cabbages (Brassicaceae:  Brassica oleracea) populations found around the coasts of the UK and the north coast of Spain, including many populations at the edges of the B. oleracea range that have not been seen in the literature before. This study system is particularly interesting because existing evidence suggests that domesticated plants escaped from gardens and fields, and established these wild populations relatively recently across a large environmental gradient. We suggest that these wild cabbage populations hold agriculturally useful genetic resources for an economically important plant genus.We investigated the population structure of cabbage ( While vBrassica oleracea  that could have established feral populations. This alternative hypothesis that B. oleracea originated elsewhere and escaped into the wild in the Atlantic region has been supported by recent linguistic and historical research  recognition systems where haplotype blocks encode distinct proteins for pollen‐pistil recognition  in a potentially feral crop wild relative because it is diploid and a reference genome is available  how are populations structured in the Atlantic region and how much differentiation exists between isolated populations; and (c) are there signals of local adaptation to the environment? The results provide insights into the utility of B. oleracea as a crop wild relative genetic resource for agriculture, as well as shed light on the most likely region of B. oleracea domestication.Overall, current knowledge on genetic variation of es Table . Therefo2B. oleracea were chosen from the UK and Spain to cover both a latitudinal and longitudinal gradient of the Atlantic range for genetic analyses . Leaves were collected from four individual plants from each population for DNA extraction, as has been successfully applied to the study of population structure in wild relatives in the Brassicaceae  with the default settings. The populations pipeline (Stacks) was used to filter the data, and calculate summary statistics. Three datasets were generated with different filtering parameters depending on the downstream analysis. Firstly, for dataset 1 , which was used to estimate genetic diversity within individuals and in phylogenetic analyses, all individuals were filtered as a single population, and loci were retained if they had a minimum individual stack depth of five, a minimum minor allele frequency of 0.01, a maximum observed heterozygosity of 0.7 and were present in 60% of individuals. Secondly, dataset 2 was generated using the same filtering as dataset 1 but SNPs linked within each RAD locus were avoided by only retaining one SNP at random per locus, required for population structure analyses , which was used to calculate genetic distance between populations, individuals were assigned to their population of origin and loci were retained if present in 50% of the populations. This filtering was designed to reduce the inclusion of duplicate loci and balance the amount of missing data with the number of informative loci , observed heterozygosity (HO), percentage of polymorphic loci , and nucleotide diversity .Reads were demultiplexed and trimmed to 100 bp using cutadapt Martin, . These w%; Table , the inb2.3K values were tested  using an admixture model that assumed correlated allele frequencies. For each K, five independent replicates of 100,000 MCMC repetitions, after a burn‐in of 10,000 iterations, were run. The most likely K was selected using the log‐likelihoods and deltaK  from four soil pH classes: Neutral (6.6–7.3), Slightly acidic (6.1–6.5), Moderately acidic (5.6–6.0), and Strongly acidic (5.0–5.5) based on USDA  were carried out using dataset 1 following Forester et al.  with the33.1A total of 115,746,909 reads from 76 individuals  were lower than in the studies cited in Table HOp < 0.001) greater than HE for all populations, and average FIS was similar in the two geographic regions . There was thus no evidence of inbreeding  but heterozygosity excess was apparent in all populations. The Fortrose population contained 10‐fold more private alleles compared with all other populations and had the highest values for both HE and HO. Values considering all sites were lower but did not change conclusions about relative patterns of diversity  were dominated by a single genetic ancestry, and two individuals from Fortrose were distinct from both the third individual from Fortrose and all other samples . Furthermore, no relationship was found between the first record for each of UK populations .Based on the RAxML tree, the majority of individuals clustered by population, with the exceptions of: (a) two individuals that did not cluster with any population , and (b) an individual from Fortrose  that clustered more closely with other Scottish populations than other individuals from Fortrose Figure i. The mos Figure ii. The d3.3HE, HO, and π) and soil pH  of the variation in the SNPs using RDA analysis; the strongest association of genotype with the environment was with annual precipitation Figure ii. For eB. oleracea reference genome, and of the top 18, six were annotated as part of the receptor‐like kinase family  were most closely associated with "Mean Temperature of Wettest Quarter", followed by "Precipitation Seasonality" (349), "Longitude" (333), "Annual Precipitation" (269), and "Annual Mean Temperature" (259). These were fairly evenly distributed across the genome with no indications of any single SNP with a large effect Figure . A few S9 were mo4B. oleracea crops occurred in the Mediterranean, domesticates were moved by people across Europe, escaped, and established wild populations in the Atlantic region. For example, there was no indication of isolation by distance from northern Scotland to Spain (>14˚ latitude), which might be expected if these plants were natural colonizers following common phylogeographic patterns , one to three new populations have been recorded every thirty years within the UK Table . However773, one HO was significantly greater than HE) and the general mix of shared genetic ancestry across such a wide geographical area in distinct populations could also be an indication of continued introgression into these wild populations from agricultural and horticultural sources. It would be interesting to identify popular cultivars in the local areas of these populations, including any changes in the preferred cultivars through time, to investigate patterns of introgression in more detail. Such direct comparisons with cultivars could identify the most likely founder of these populations.The genetic ancestry and clustering analyses hint that populations could have been established by escapees from different cultivars. The majority of individuals were assigned to multiple sources of genetic ancestry Figure ii; howevB. oleracea around the coasts of the UK; out of sixteen populations, fourteen were C:01 and two were C:04. The two populations with the C:04 haplotype were in Tyne & Wear, in the northeast of England; in the current study, this area is represented by the Tynemouth and Whitby populations. In line with the rarity of the chloroplast haplotypes identified in this region in the previous study, these two populations clustered most closely with populations not sampled by Allender et al.  to different environmental conditions, and could also be thought of as a way to compare invasion success within a species. B. oleracea would be interesting to more explicitly test origins of the populations studied here, there are several issues with ddRAD data that would make this challenging and potentially hard to interpret. A benefit of ddRAD sequencing is the generation of discrete loci that are standardized to the same length. However, the resulting short sequence segments normally contain only one or a few SNPs, which does not allow accurate assignment of paralogs in highly duplicated and rearranged genomes such as found in the Brassicaceae  found no evidence of population structuring in French Atlantic populations. These results could also be correlated with annual precipitation; perhaps the strong variation in annual precipitation in the UK  is causing more differentiation between these populations, whereas along the French range annual precipitation has a smaller gradient. One reason for the importance of annual precipitation other than water availability could be the influence of precipitation on soil pH. Soil pH is primarily determined by bedrock, but is also altered by precipitation through leaching of compounds such as calcium carbonate  across four continents. These results highlight the fundamental importance of the immune system to fitness and suggest that it could be related to environmental differences across different spatial scales. Given that immune system genes are among the best candidates for local adaptation, there is a potential connection between plant genetic diversity, soil pH, and pathogens. It would be interesting to investigate whether less acidic soils host more pathogens, increasing the selective pressure on the plants and decreasing the plant genetic diversity in these soils. Overall, the impact of climate change on the spread of virulence of plant pathogens and herbivores, and the phenological mismatches that may occur between interacting species remain unknown ; Data curation (lead); Formal analysis (lead); Funding acquisition (supporting); Investigation (lead); Methodology ; Project administration (supporting); Validation ; Visualization (lead); Writing—original draft (lead); and Writing—review and editing . Christina Cobbold: Conceptualization (supporting); Funding acquisition (lead); Investigation (supporting); Methodology ; Supervision ; and Writing—review and editing . Umer Zeeshan Ijaz: Data curation ; Funding acquisition (lead); Investigation (supporting); Methodology ; Supervision ; and Writing—review and editing . Elizabeth Kilbride: Methodology (supporting); Resources ; and Writing—review and editing (supporting). Karen Moore: Data curation (lead); Methodology ; Validation ; and Writing—review and editing . Barbara Mable: Conceptualization (lead); Formal analysis ; Funding acquisition (lead); Investigation ; Methodology ; Resources ; Supervision (lead); Validation ; Visualization ; and Writing—review and editing .Table S1‐S2‐Fig S1Click here for additional data file.


Vipera berus) in southern Britain, where numbers are declining. We found no evidence for loss of heterozygosity in any of the populations studied. Genetic diversity was comparable across sites, in line with published levels for mainland Europe. However, further analysis revealed a striking level of relatedness. Genetic networks constructed from inferred first degree relationships suggested a high proportion of individuals to be related at a level equivalent to that of half-siblings, with rare inferred full-sib dyads. These patterns of relatedness can be attributed to the high philopatry and low vagility of adders, which creates high local relatedness, in combination with the polyandrous breeding system in the adder, which may offset the risk of inbreeding in closed populations. We suggest that reliance on standard genetic indicators of inbreeding and diversity may underestimate demographic and genetic factors that make adder populations vulnerable to extirpation. We stress the importance of an integrated genetic and demographic approach in the conservation of adders, and other taxa of similar ecology.Genetic factors are often overlooked in conservation planning, despite their importance in small isolated populations. We used mitochondrial and microsatellite markers to investigate population genetics of the adder ( Population genetics remain overlooked in conservation planning , althougVipera berus  is a terrestrial snake with an extremely wide geographic range [V. ursinii rakosiensis.Tingley et al  have stric range , which aic range , 10, lific range , 12. Theic range . The potic range –16. Újváe) [e estimates tend to be low in relation to census population size in natural populations [Small population size is an important factor in loss of genetic diversity, exacerbated by bottleneck events. This has led to the concept of a minimum viable population size , 19, base)  and the e) , 22. Ne ulations , 24, infulations , 26. Botulations , 28. Smaulations .Ophidiomyces ophiodiicola) in UK adders in the wild [We report the results of the UK Adder Genetic Project (UKAGP), a study into the genetic status of lowland adder populations in southern mainland Britain, where national distribution surveys have indicated a decline in comparison with historic records –33. A nathe wild . This coThe aim of this study was to document population genetic structure and differentiation, and to estimate indicators of inbreeding and genetic diversity in lowland adder populations in southern mainland Britain. To assess the ecological and conservation significance of our study, we interpret our results in comparison with published studies of adder populations in mainland Europe, and with reference to the size, and thus the likely risk status, of the study populations.Ethics and animal welfare: the project was reviewed prospectively and approved by the ZSL Ethics Committee. Sampling was undertaken in concordance with ZSL ethical guidelines. Cloacal swabs were collected without anaesthetic from adult snakes, and buccal swabs from juveniles , by ecolSamples were collected from 220 adders at 16 sites in southern mainland Britain between March and May 2011 . No permDNA was extracted using a DNeasy blood and tissue kit (Qiagen), following the manufacturer’s protocol for swabs.haplotype function of pegas package v0.10 [www.fluxus-engineering.com). Phylogenetic analysis was carried out as described in Supporting Information (We designed primers from a 918-bp mtDNA control region (CR) sequence and a 1043-bp mtDNA cytochrome b sequence (Cytb) , based oge v0.10 , implemege v0.10 . A haploge v0.10  was consge v0.10  in NETWOormation , S1 Fig.V. berus) [V. ursinii) [In preliminary studies we tested published microsatellite primers that had been developed for adders (. berus) , 44, selursinii) , selectiReplicates and template negative controls were included in all plates to confirm reproducibility of results. Results were analysed for genotyping errors and null alleles in Micro-Checker v 2.2.0.3 . We usedST values between populations were estimated in FSTAT.In this study, we use the term population to refer to all individuals sampled at a single study site in the sampling time period. Pairwise Fmantel.rtest function of ade4 v1.7–13 [ST to estimate genetic distance. Geographic distance was estimated at https://andrew.hedges.name/experiments/haversine (accessed 23 April 2108) using the Haversine great circle method, a measure of the shortest distance between two points on a sphere [To test for isolation by distance  we appli v1.7–13 , implemea sphere .locprior option [5, followed by 106 iterations for 10 independent replicate runs for values of K from 1 to the number of populations being studied. Results were uploaded to StructureHarvester [greedy function of CLUMPP [find.clusters function was applied to determine the optimal number of clusters (k) in each population, independent of the number of sampling sites. The dapc function was then applied, using the α- score function to determine the optimum number of principal components to retain in each analysis. Probabilities of assignment of individuals to each of the different clusters were visualised using the compoplot function of adegenet [To investigate genetic differentiation, we used STRUCTURE v2.3 , 52, usir option . The inir option . We usedarvester  to derivarvester . Resultsf CLUMPP  to derivf CLUMPP  in adegef CLUMPP  implemenadegenet .IS, a measure of intrapopulation heterozygote deficiency due to inbreeding, were calculated using the boot.ppfis function of hierfstat package v0.04–22 in R [For each population, we estimated F-statistics  and alle–22 in R . Mean al–22 in R  in PopGe–22 in R , implememRatio function of the strataG package v 2.0.2 [We used BOTTLENECK v 1.2.02 to test for significant heterozygosity excess, applying a one-tailed Wilcoxon test with 1000 iterations, using the two-phase model (TPM)  . A mode- v 2.0.2 , implemee. The linkage disequilibrium method [Two single sample methods were used for estimation of Nm method , 69 was m method , assuminm method , which em method , using tinbreeding function of adegenet, version 2.0.1 [FPED, which denotes the probability that both alleles at a single locus are identical by descent from a single ancestor [familysim function of the package related v1.0 [grouprel function of related v1.0.We applied the on 2.0.1  in R, toancestor . Pairwisancestor . To calited v1.0 , implemeFor sibship and parentage analysis we used a full-likelihood method, implemented in COLONY 2.0.6.4 , assuminWe compared our results for F-statistics and allele richness from UKAGP study populations with summary statistics from two published studies of adder populations in mainland Europe , 77, andxy between small and large populations defined in this way, using a Wilcoxon rank sum test implemented in R.In the UK, the MTAC survey demonstrated opposite average population trends between sites with small and large adder populations, the threshold being a mean normalised peak count of 10 adders, below which there was significant decline over time . This prAlthough exceeding the MTAC peak count threshold, the springtime counts for the UKAGP populations in the “large” category were still relatively low, with a maximum of 27. In the absence of a very large well-characterised UK population for comparison, we analysed 50 genotypes from the Belgian site , focusinV.berus, as described by Ursenbacher et al 2006 [Eight different Cytb/CR haplotypes were identified in 97 individuals across the 16 sites . Six hap al 2006  S1 Fig)V.berus, There was no evidence for null alleles, allele drop out or linkage disequilibrium between loci. No two samples had identical genotypes. Results from duplicate samples confirmed consistency of genotyping. Samples that failed to amplify at a minimum of 6/8 microsatellite loci were excluded from analysis. In all, 186 samples (84.5%) were retained for downstream analysis. There was no significant divergence from HWE. Two small populations TM (n = 5) and CC (n = 4) had missing data for more than one individual at a single locus (CA71 and Vu4 respectively). For these, genetic diversity was calculated with the omission of the relevant locus, which had little impact on summary statistics for the other populations .The WMids Haplogroup includes ten geographically neighbouring sites, separated by up to 100km , Table 4ST matrix between these sites , but more divergent for K = 6  were at similar levels across the study sites, and broadly equivalent to published results from European populations [Fulations . This isle locus . Estimatulations , 77, inculations  , with no significant difference between large and small populations ; Table 7Rxy frequency distribution curves of simulated pairs show clear differences between the defined relationships, with a dominant density peak at Rxy = 0 in the unrelated pairs, and a progressive right shift of the curve for the simulated half-sib and full-sib pairs . The RxyWhen the GS50 sample was analysed as a single group in COLONY, the patterns of inferred parentage differed between individuals from the SW and NE sampling sites , providiST results for individual sites. By contrast, DAPC analysis revealed clustering within the all individual study populations, including GS50 , nor in the GS50 SW or NE samples, consistent with the low Fing GS50 , despiteIS measure of inbreeding, to allow comparison with published studies of adders in mainland Europe. We also applied the MTAC criterion of a threshold peak count to categorise study sites into small or large, predicted to be at high or low risk of decline respectively [The aim of our study was to investigate the genetic status of lowland adders in the UK, in response to concerns about declining numbers, especially affecting small, fragmented populations. We initially adopted a standard panel of microsatellite-based summary statistics, including genetic diversity and the standard Fectively . This inThe interpretation of genetic results requires a biologically relevant comparator, especially for single time-point samples. Estimates of allele richness in the UKAGP study populations were similar to those of the large Belgian population , and of IS levels were not significantly above zero in any of the UKAGP study populations, irrespective of size, indicating retention of heterozygosity, despite the clear risk of inbreeding depression in fragmented, isolated populations. Our findings are again consistent with published studies of remnant adder populations of varying sizes in mainland Europe [Notably, Fd Europe , 77. In d Europe , 81, andd Europe . We therd Europe .In all the UK study populations, genetic estimates of the mean inbreeding coefficient FPED were at a level consistent with the degree of genetic identity expected in the offspring of a one-off mating between half siblings . HoweverBy contrast pairwise relatedness in simulated pairs of genotypes was significantly and predictably influenced by the defined relationship, providing a very useful template against which to interpret Rxy results for the study populations. We found a range of patterns of relatedness, with variable loss or blunting of the modal unrelated peak seen in the simulated unrelated pairs of genotypes, and right shift of the Rxy distribution curve seen in the half- or full-sibling simulated pairs. Pairwise Rxy thus generated the most informative results in our study, with the potential caveat that results may be influenced by cryptic genetic differentiation within the sample. Repeat studies will be necessary to determine trends and timescales in changing patterns of relatedness.COLONY results, like Rxy, provide a snapshot estimate of contemporary relatedness between individuals within the sample, but in the format of best maximum likelihood combinations of inferred genotypes of sibling and parent-offspring dyads. This approach generated dramatic networks of inferred half-sibships in our study populations. However, in the absence of pedigree data to inform COLONY analysis, loose networks of inferred half sibships may simply reflect identity by state, rather than true first-degree relationships, especially for inferred half sibships in larger populations . While w [We found the membership of inferred dominant large sibships in small populations to be concordant with the assignment of individuals to clusters in DAPC, in keeping with DAPC clusters reflecting allele frequency patterns driven by a polygynandrous mating system in a small population, rather than discrete panmictic subpopulations. An equivalent phenomenon of clustering in DAPC, but not STRUCTURE, has also been described in the Prairie rattlesnake viridis) . This isNegaprion acutidens) in French Polynesia [As a single year snapshot, our study is only a starting point, and inevitably limited by population size, and thus sample size, especially in the study of the most vulnerable populations. We have nevertheless exposed a previously undocumented degree of consanguinity in wild adder populations, despite their showing no increase in homozygosity to suggest inbreeding depression. In models of inbreeding, a system of half-sib mating is more likely to maintain heterozygosity than one of maximum avoidance of inbreeding , 85. Pololynesia . Despiteolynesia .While polyandry may delay, it will not prevent the eventual loss of heterozygosity in isolated populations, where movement of adders is prevented by loss of connectivity between patches of fragmented habitat. It is therefore interesting to compare the large, thriving Belgian site, with high levels of relatedness but no loss of heterozygosity, with the well-documented Swedish population of adders with unequivocal evidence for inbreeding depression –16. Both“Unfortunately, the best way to find tipping points so far has been to cross them–a dangerous proposition” . Our finWe are using radio-telemetry of adult snakes to inform habitat management, especially with respect to connectivity , in combWe plan to use genomic sequencing to increase the number of informative markers available, especially important for genetic diversity and pedigree studies. In addition, the development of a genomic SNP panel will help to increase consistency and comparability across sites and laboratories, currently hampered by the limited numbers of microsatellite markers, and potential inconsistencies in their application –104. ThiOur results suggest that the most immediate threat to small adder populations is demographic rather than genetic. For larger populations high levels of relatedness indicate that genetic factors are likely to represent a real threat, albeit less imminent, but also less visible and thus more insidious . ContinuS1 FigBootstrap consensus tree of UKAGP concatenated Cytb/CR haplotypes (500 replicates) relative to homologous sequences in Genbank.(PPTX)Click here for additional data file.S2 FigBar charts showing proportional membership coefficients of individuals to each of the inferred clusters for K = 3–6, grouped according to their study population, with locprior option (left) and without (right). Hierarchical clustering is apparent with both approaches. The colour schemes differ between bar charts.(PPTX)Click here for additional data file.S3 Figlocprior option. In each panel the populations are superimposed on a Venn diagram of overlapping circles according to their broad proportional membership of the three clusters inferred in STRUCTURE for K = 3. The colour schemes are independent for STRUCTURE and DAPC. Top left panel: pie charts for K = 3 in STRUCTURE. Top right panel: pie charts for K = 3 in DAPC. Bottom left panel: pie charts for K = 6 in STRUCTURE. Top right panel: pie charts for K = 6 in DAPC.Pie charts for each population of the WMids Haplogroup, showing the proportion of group membership assigned probabilistically to K = 3 or K = 6 clusters in analysis applying the (PPTX)Click here for additional data file.S4 FigDAPC scatterplots (K = 3) for individual study populations with sample size ≥ 10, including GS50 SW and NE, showing clearly separated clusters.(PPTX)Click here for additional data file.S5 FigFor each population, the network of COLONY-inferred sibship dyads is shown, together with a table (upper) of inferred parentage for each individual, and their assignment to one of the clusters of individuals linked at a minimum of half-sibling level. Dominant hypothetical parents are highlighted in the parentage table and network. The lower table for each population shows bar plots of the probability of assignment of each individual (in same order as in COLONY) to DAPC clusters. For population CH (top left), the assignment of individuals to K = 2 clusters in DAPC is concordant with the COLONY-defined clusters, assignment of individuals to the largest of K = 3 clusters in DAPC is concordant with the hypothetical dominant parent of the inferred family structure in COLONY. Different patterns of concordance are evident in populations MHS, BM, EH and MF. By contrast, for the larger population HL (bottom right), the membership of DAPC clusters shows poor concordance with the inferred parentage of the larger, looser COLONY network.(PPTX)Click here for additional data file.S1 Table(PPTX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S1 Text(TXT)Click here for additional data file.


Crassostrea gigas, endemic to coastal Asia, has been translocated globally throughout the past century, resulting in self‐sustaining introduced populations . Oyster aquaculture industries in many parts of the world depend on commercially available seed (hatchery‐farmed) or naturalized/wild oysters to move onto a farm . It is therefore important to understand genetic variation among populations and farm types. Here, we genotype naturalized/wild populations from France, Japan, China, and most extensively in coastal British Columbia, Canada. We also genotype cultured populations from throughout the Northern Hemisphere to compare with naturalized populations. In total, 16,942 markers were identified using double‐digest RAD‐sequencing in 182 naturalized, 112 hatchery‐farmed, and 72 naturalized‐farmed oysters (n = 366). Consistent with previous studies, very low genetic differentiation was observed around Vancouver Island (mean FST = 0.0019) and low differentiation between countries in the Japan–Canada–France historical translocation lineage . Chinese populations were more differentiated (China–Japan FST = 0.0241). Hatchery‐propagated populations had higher interindividual relatedness suggesting family structure. Within‐population inbreeding was not detected on farms, but nucleotide diversity and polymorphism rate were lower in one farm population. Moving oysters from nature onto farms did not result in strong within‐generation selection. Private alleles at substantial frequency were identified in several hatchery populations grown in BC, suggesting nonlocal origins. Tests of selection identified outlier loci consistent with selective differences associated with domestication, in some cases consistently identified in multiple farms. Top outlier candidates were nearby genes involved in calcium signaling and calmodulin activity. Implications of potential introgression from hatchery‐farmed oysters depend on whether naturalized populations are valued as a locally adapted resource or as an introduced, invasive species. Given the value of the industry in BC and the challenges the industry faces , this remains an important question.Pacific oyster  Identifying genes involved in either domestication or local adaptation will be informative for guiding selective breeding. Identifying haplotypes specific to broodstock lines will also be useful for tracking introgression of international commercial hatchery populations into naturalized or wild populations. Best practices for broodstock development could both require maintenance of genetic diversity as well as the incorporation of alleles conferring higher survivorship under adverse conditions or higher fitness in specific environments. Although relatedness can be determined through pedigrees or low‐density panels , and the global, per annum value of farmed bivalves is 20.6 B USD , low population differentiation among sites has been observed using 52 coding SNP markers  of 16,942 RAD loci, representing ~0.255% of the Pacific oyster genome. Second, to better characterize differences between hatchery‐farmed and naturalized oysters, several farm populations grown in BC, France, the United Kingdom (UK), and China were genotyped at these same markers. This permitted the identification of loci that may be subject to different selective pressures between hatcheries/farms and nature . Third, to characterize the selective landscape associated with growth on a farm, naturalized oysters that had recently been moved to a farm were compared to their source populations in three parallel comparisons across the Northern Hemisphere . Collectively, these analyses provide insight into the relative genomic impacts of hatchery practices, farming, and translocation history on the contemporary genetic diversity that exists in Pacific oysters throughout the Northern Hemisphere.22.1Crassostrea gigas individuals were sampled from a variety of sources, including those growing in nature, either as wild (endemic) populations or as naturalized (introduced) populations (both referred to as NAT), those growing in farms, either from commercial hatcheries (HAT‐F) or from NAT populations moved from nature onto farms (NAT‐F) as described by local farmers in each area, as well as those from hatcheries destined for farms but not yet transferred onto a farm (HAT). NAT samples were obtained from introduced populations  or wild populations , and medium and large were collected for HIS . BC HAT‐F populations were sampled from farms located at Deep Bay (DPB) and Rosewall Creek (ROS), BC. The location on the farm that the DPB oysters were obtained is unknown (samples were provided directly by the farm), but those from ROS were obtained by sampling throughout the farm site on the beach. BC NAT‐F samples were sampled from a population grown in mesh bags on a farm in Baynes Sound from several locations throughout the farm, with the naturalized source population being Pendrell Sound, BC.All collections for this study were conducted in 2017 and used adult oysters. BC NAT collections were conducted from sites around Vancouver Island during summer Figure ; Table 1International NAT samples included those from France and China. France (FRA) samples were collected as tissue samples of naturally set adult oysters in Northwest France from a beach near Sainte‐Barbe . China (CHN) NAT samples were collected in the Qingdao region. NAT‐F collections were also made from a farm in western France and a farm in China. China HAT‐F samples were also collected, one from a short‐term culture  and one from a long‐term culture .HAT samples were obtained from a hatchery in Guernsey, UK (Guernsey Sea Farms), which maintains their own hatchery broodstock originally derived from BC stock . Adult samples of unknown origin (farmed or wild) were also obtained from local markets in Japan. All international samples were collected by local researchers (see Acknowledgements) or obtained from commercial markets, obtained in accordance with local regulations, and shipped according to international regulations.2.2DNA was extracted using the BioSprint automated extraction protocol in plate format (QIAGEN), using a proteinase‐K digestion and an RNase incubation to reduce RNA presence in genomic DNA, as per manufacturers’ instructions. Sections of tissue used for extraction were ~4 × 4 mm. Purified genomic DNA was quantified with a Qubit  and quality‐checked for fragmentation using 1% agarose gel electrophoresis. Genomic DNA was normalized to 20 ng/μl for library preparation.2.3PstI and MspI indicated low cut frequency due to the low GC content in the oyster genome (33.5%); therefore, NsiI, a 6‐base cutter with fewer GC recognition sites was used in place of PstI. Using NsiI and MspI with a size selection of 100–250 bp, 15,770 and 15,319 sites were predicted using the Zhang et al.  combined in a double‐digest, then ligated via sticky ends to a unique forward adapter and a common Y‐adapter specific to the Ion Torrent Proton (Thermo Fisher) following previously published procedures , as per manufacturers’ instructions. Base calling was done with Torrent Suite 5.10.1 (Thermo Fisher) and multiplexed FASTQ output files with barcodes still present were exported using the Torrent 2.5stacks_workflow  using process_radtags of Stacks v2.3e  to make a list of markers to exclude . The populations module using the blacklist was then used to output multilocus genotype files with either a single SNP per RAD‐tag or as microhaplotypes . The single SNP output from populations used the following filtering parameters: ‐r 0.7 ; ‐‐min_populations 15 ; ‐‐min‐maf 0.01 ; ‐‐write‐single‐snp ; a blacklist including all loci identified as out of HWE; ‐‐fasta‐loci ; ‐‐vcf ; and ‐‐plink . For the microhaplotype data output from Stacks as a VCF, the parameters were the same as above except allowing multiple SNPs per marker and including the ‐‐radpainter flag to output into fineRADstructure input   into the number of alleles per locus, per individual. The recoded VCF was input to adegenet , were calculated using a single marker per locus  considering variant positions only, in the populations module of Stacks. Per population, per marker estimates of nucleotide diversity (π) were calculated using VCFtools (‐‐site‐pi) . Relatedness was also calculated based on shared microhaplotypes using fineRADstructure  were retained based on the flattening of the slope on scree plots to keep only those to the left of the plateau as suggested, retaining k = 3, 5, 6, 6, 3 for DPB, ROS, GUR, QDC, and RSC, respectively . However, scree plots did not indicate a plateau .To identify markers that changed significantly in allele frequency following transplantation from naturalized populations onto farms, a pairwise comparison was conducted between NAT and NAT‐F oysters for populations in BC, China, and France. The comparison was between a nearby, naturalized population and the transplanted farm population , France (FRA n = 23), China , and Japan , to compare to expected source populations. Finally, 112 hatchery‐origin oysters grown on a farm (HAT‐F or HAT) were genotyped  were genotyped from coasts of Vancouver Island, British Columbia . Loci were then filtered for missing data by only retaining markers that were genotyped in at least 70% of the individuals per population in all populations, that had a minor allele frequency , and that were not out of HWE equilibrium in three or more populations (p < .01 in each). This resulted in a total of 81,443 SNPs found in 17,084 loci when considering microhaplotypes. When filters were applied to only the first SNP per RAD locus (see above), a total of 16,942 markers were retained. These 16,942 markers comprised 1,532,384 bp of total sequence, representing 0.255% of the expected 600 Mbp Pacific oyster genome.On average, samples had a total of 2.17 M reads . Aligning to the most complete reference genome . Moreover, no differentiation was observed  between PEN and Pipestem Inlet (PIP), even though they are on opposite sides of Vancouver Island. Discriminant analysis of principal components (DAPC) also demonstrated highly overlapping genotypic distributions among these locations , and medium and large were collected for HIS . The difference between sizes was therefore greater for HIS than for PEN. There was no differentiation between PEN_2 and PEN_3 (FST not significantly different from 0 (FST 95% CI: 0–0.0011), and very low differentiation between HIS_3 and HIS_6 . Size‐matched spatial differences between PEN_3 and HIS_3 (FST 95% CI: 0.0024–0.0048) were greater than temporal differences within a site for PEN but not significantly different for HIS.To evaluate the relative impacts of temporal versus spatial effects on population signatures in these BC collections, multiple size classes that were assumed to be different brood years (but see Methods and Discussion for criticisms to this approach) were obtained for HIS and PEN Table . Small (3.3FST = 0.0022–0.0035 (95% CI) between PEN and HIS. This level of genomewide differentiation was similar to that between naturalized oysters in BC and France . In general, to understand this divergence, HAT/HAT‐F oysters were compared to their closest wild/naturalized (NAT) populations in terms of physical distance Table .FST = 0.0075–0.0096), with a level of differentiation slightly higher than that observed between PEN and JPN  and formed a single grouping along the first axis of the DAPC , followed by QDC  and GUR . Other HAT‐F populations, such as ROS and RSC, did not have a substantial number of private alleles ( < 2).To identify private alleles by group, similar populations were grouped together to be analyzed, producing collections BC, DPB, ROS, GUR, RSC, QDC, CHN, FRA, JPN Table . FRA andFST 95% CI: CHN‐RSC = 0.0074–0.0100; CHN‐QDC = 0.0181–0.0212; Table Farmed oysters in China were distinct from those in the France/UK and Canada  indicates that the majority of the variation in the genotypic data  is explained by PC1‐3, although the percent variation explained (PVE) was low  and the effect of sample size on π. Lower π in DPB was not associated with elevated within‐population inbreeding (FIS); in fact, DPB had the lowest FIS observed in the study, although its 95% confidence interval overlapped with that of all other populations except France (Table Genetic diversity as measured by nucleotide diversity (π) was lowest in the DPB HAT‐F population, whereas all other collections had similar median π Figure . JPN wasce Table A. Severace Table .Comparing different loci, per‐locus nucleotide diversity was highly correlated across populations, with some loci showing consistently high or low π in multiple populations, even outside of the JPN‐CDN‐FRA lineage , the resulting offspring grown together on a farm tend to be comprised of highly related individuals, due to family structure  were identified per contrast between paired populations of HAT/HAT‐F oysters and associated NAT populations , or those that were consistently identified in multiple population comparisons with at least two pieces of evidence . Top outliers with the strongest evidence and consistent observations included three markers: CLocus_104468, CLocus_625182, and CLocus_704500, which were identified in RSC and GUR, QDC and RSC, and QDC and DPB, respectively. CLocus_104468 was found to be in an intron of fibrocystin‐L, CLocus_625182 was found to be in an intron of prion‐like‐(Q/N‐rich) domain bearing protein 25, and CLocus_704500 was not near any genes. Both CLocus_104468 and CLocus_625182, significant using all three outlier approaches for RSC and QDC, respectively, were found on chrD, at position 32,708,807 bp and 9,881,897 bp, respectively. An additional 10 outliers were identified using two statistical approaches . Three parallel comparisons were made from farms and source populations in Canada, France, and China. As expected, oysters that remained in the source population versus moved onto a farm typically exhibited negligible genetic differentiation  found in FRA and BC.Outlier loci were not identifiable for these comparisons with Appendix . RDA fouAppendix , and theAppendix . BayeScas Figure . In thiss Figure . Two mar4C. gigas allowed us to obtain a detailed overview of the genetic differentiation of oysters in populations across the Northern Hemisphere, including whether consistent evidence exists for reduced diversity in farmed populations. There were three major findings that emerged from our study. First, the most distinct populations in terms of genetic differentiation were commercial hatchery‐farmed populations (HAT‐F) as well as populations from China. HAT‐F populations often had elevated relatedness as has been observed previously  and/or high dispersal, for example as observed in blue mussel . The present study uses short time scales and has expected noise that comes with assuming brood year by oyster size  are less likely to survive perturbations, or whether they have in fact undergone more selection and may be fitter under local conditions. These questions are important to address in future work.In the current study, the most divergent hatchery populations were those with identifiable family structure, and this was consistent for all countries . This may indicate founder effects of the broodstock line, selection within hatcheries or farms, or genetic drift due to low effective population size of the brood line. Interestingly, the most divergent hatchery strain (DPB) also had low nucleotide diversity, which may indicate a lower number of founding individuals or more ongoing drift in this particular lineage. Hatchery propagation is known to potentially impact both the genetic diversity and the allelic composition of Pacific oyster. For example, hatcheries may cull the slowest growing oysters to keep the crop synchronized, but given that this growth rate is in part genetically determined, this imposes selection on the hatchery population and has been shown to reduce diversity and induce temporally varying genetic structure  in DPB may reflect the practice of crossing inbred lines, possibly with the aim to reduce the negative impact of recessive mutations in the Pacific oyster .Higher interindividual genetic variation was observed using multivariate approaches  within farm populations, particularly those with family structure, than within naturalized populations Figure . A similBreeding practices can influence the underlying genetics of hatchery strains, which can affect their potential to undergo selective breeding, as well as to have the necessary genetic variation to handle various biotic and abiotic stressors or threats, such as disease, when there is a genotype–environment interaction. An Australian hatchery strain was found to have lower genetic variation but to have less family structure and no sign of inbreeding; other strains in the same study showed higher family effects than this strain; thus, this is likely occurring through the careful selection of unrelated individuals for crosses , calmodulin‐like, calcineurin subunit B type 1 , and GTP‐binding protein RAD   toll‐like receptor 3 was also found to be near an outlier.The identification of outliers in multiple populations of oysters has highlighted regions of the genome that may have been subject to different selection pressures under domestication. Many outlier markers were near or within genes involved in either calcium signaling or resilience to environmental stressors  and potentially has led to the reduction of cold tolerance . Alternatively, the outliers may reflect selection in the oysters that remained in the naturalized populations, with relaxed selection in farmed oysters.The lack of genomewide differentiation between the naturalized and the transplanted populations indicates that the sampling of the naturalized population to be moved onto the farm did not impose a bottleneck or founder effect and that specific families were not greatly enriched on the farm during the transplantation. Given that mesh bags of naturally set oysters are moved onto farms, and that these were sampled in the present study, suggests that oysters naturally settling near one another are not highly related. This is also reflected by the lack of family structure in the NAT‐F populations relative to that viewed in the HAT/HAT‐F populations.5Consistent with previous studies, Pacific oysters along the Japan–Canada–France translocation lineage showed very low genetic differentiation. However, Chinese oysters, including wild individuals, were more distinct from this group. This could be due to long‐term genetic isolation and/or the effect of farmed oysters sourced from Chinese broodstock introgressing into wild populations. Whether due to founder effects or drift the magnitude of the genetic differentiation between some commercial hatchery populations and naturalized populations were similar to differentiation in the most distinct populations across the sampled Northern Hemisphere range. As previously observed, commercial hatchery populations had more distinct family structure and higher interindividual relatedness, and one farm exhibited lower genetic diversity. The most differentiated hatchery populations also had a higher prevalence of private alleles. Whether the distinctiveness and low genetic diversity of some hatchery‐propagated populations are considered to be a risk for introgression into naturalized populations depends on whether Pacific oyster in Canada is considered a genetic resource for broodstock and a harvestable food resource, or whether it is considered an invasive species. Commonly used broodstock sourcing sites Pendrell Sound and Pipestem Inlet was the only pair of populations that had no genetic differentiation. Valuable next steps would include controlled challenge studies to determine the impact on survival of the candidate loci identified as outliers here and studies to examine the strength of heterosis as a function of the differentiation documented between populations.Box 1Working in Laboratoire Bernatchez, or Louis Bernatchez’ laboratory, was one of the highlights of my career so far. I learned many things during my three years in beautiful Quebec City; I will underscore several items that have helped shaped me into the scientist I am today. The first thing that struck me while at Louis’ laboratory was the depth of knowledge of the literature within the group. Each day, numerous papers would get shared around the laboratory, from a broad range of species (not just freshwater and marine organisms!). People passionate about each field  would share key papers from each subdiscipline. This level of knowledge and sharing of the literature was so impressive that I wanted to share these curated lists with the rest of the scientific community, thus starting the @LaboBernatchez Twitter account to share “recently shared papers” with the world. The second key item that I learned while studying in Labo Bernatchez was a strong foundation in coding. So much of our work depended on being able to run and rerun the same analysis again and again with different parameters, and watching the bioinformatics professional in the group, Eric Normandeau, switch between different research domains seamlessly (from transcriptomics to scoring SNPs to annotating genomes), through the use of careful version control in GitHub and well‐documented README files, I knew this was the essential piece that I needed in order to keep up. That is reflected in the manuscript contributed to this Special Issue (present study), where all code, all input data, and all instructions to use the code and data are provided as part of data and GitHub repositories associated with the project (see Data Availability Statement). Combine those two items with a new appreciation of the meaning of collaboration, a global network of contacts, and a now‐functional ability to present, listen, and converse in French, this was truly a life‐changing experience. Labo Bernatchez continues to be a top laboratory in molecular ecology and evolutionary genomics, fostering some of the most brilliant minds and ideas that are coming out of academia today.None declared.Supplementary MaterialClick here for additional data file.Appendix S1Click here for additional data file.Appendix S2Click here for additional data file.Appendix S3Click here for additional data file.