Massicus raddei  (Coleoptera: Cerambycidae), is widely distributed in Asia , but pest outbreaks have occurred only in Liaoning Province and Jilin Province of China. In order to explore possible mechanisms of local population outbreaks and characterize the genetic diversity and genetic structure of M. raddei across its range in China, three mitochondrial genes  were sequenced and analyzed for seven M. raddei populations collected from six provinces in China. From these different populations, we found a high haplotype and nucleotide diversity. Haplotype networks and phylogenetic analyses both demonstrate apparent genetic diversification between SC (southern China) and NC (northern China) population groups. A set of 21 pairwise comparisons for Fst (pairwise fixation indices) and Nm (genetic flow index) showed significant genetic differentiation and limited gene flow except for two pairs, Shandong (SD) and Liaoning (LN), and Anhui (AH) and Henan (HN). This pattern suggested that the periodic outbreak of the LN population could not be attributed to the absence of genetic flow with other spatial populations and that regional environmental factors might be responsible. AMOVA  showed that the greater molecular genetic variation was among populations. Based on Tajima's D statistic, Fu's Fs, and the mismatch distribution test, we determined that the seven populations sampled were stable and had not experienced any recent population expansion. The fact that all the sampled populations showed only unique haplotypes and lacked shared or ancestral haplotypes, as well as the nonstar‐like distribution of haplotype network for concatenated genes, collectively provided powerful evidence of the stable and isolated nature of most populations. The high genetic differentiation and spatial genetic structuring among populations are both likely related to the beetle's moderate flight capacity, regional variation in host tree species and microclimate, as well as the geographic distance between sampling sites.The oak longhorned beetle (OLB),  Massicus raddei across the natural distribution of this species in China. High genetic diversity and distinct spatial genetic structuring were identified through the mitochondrial analyses, and the assumption that particular periodic behaviors and outbreaks of M. raddei in northeastern China mainly result from the absence of gene flow and local isolation was preliminarily denied.The present study used three genes of mitochondrial DNA to explore the patterns of genetic structure among seven populations of  Eight microsatellite loci and a partial mtDNA COI gene were used as molecular markers to elucidate patterns of genetic structure among 32 populations of the endangered beetle Rosalia alpina Linnaeus (Coleoptera: Cerambycidae) across central and south‐east Europe, to clarify its biology and recent population expansion, and to devise more effective conservation measures  in its main distributional habitats. Moreover, Javal et al. (Anoplophora glabripennis Motschulsky (Coleoptera: Cerambycidae) across Europe based on COI gene. Lee et al. (A. glabripennis) populations of South Korea and confirmed that ALB invasion has occurred even within the species’ native ranges based on COI mitochondrial sequences. Studies on M. raddei have focused mostly on its biology, damage pattern, field surveys for natural enemies, and integrated control measures  Cerambycidae”  after the epicuticle was removed, using the DNeasy Tissue & Blood Kit (QIAGEN) in accordance with the manufacturer's instructions. Extraction yield was measured using a Nanodrop‐1000 Spectrophotometer (DS‐11 Envivx). Subsequently, products were diluted to 30–40 ng/μL and stored at −20°C before polymerase chain reactions (PCR).M. raddei   and an extension at 72°C for 40 s, and then a final elongation at 72°C for 10 min. The products were visualized by 1% agarose gel electrophoresis and gel imaging system , and sequencing was implemented by Invitrogen Biotechnology Co., Ltd.Polymerase chain reactions (PCR) were performed using a S1000 Thermal Cycler (BIO‐RAD) in a total volume of 25 μl containing 12.5 μl 2× Green‐Mix, 1 μl forward and reverse primer (10 μmol/L), 1 μl genomic DNA, and 9.5 μl ddH2.3https://www.geneious.com; Kearse et al., http://blast.ncbi.nlm.nih.gov.). Sequences of COI (679 bp), COII (369 bp), and Cytb (462 bp) of M. raddei were deposited in NCBI Genbank . Subsequently, these three partial genes from the same individuals were concatenated to yield a combined sequence of 1,512 bp as a single locus because of the lack of recombination of mtDNA .Sequencing data were analyzed and edited using Geneious R11.1.5 (2.4Hd), nucleotide diversity (Pi), and the nucleotide average difference (K) for concatenated genes with the DnaSP version 5.0 , haplotype diversity (2.5Fst) and genetic flow indices (Nm) were estimated with DnaSP version 5.0  was conducted with 10,000 permutations to estimate the hierarchical genetic structure using the Arlequin version 3.5 ], and the natural logarithm of geographical distance data (In Km) between all the sampling sites were analyzed for their degree of correlation by two mantel tests, with significance tests performed with 9,999 permutations. The analysis was implemented in GenAIEX 6.41 , ML phylogenetic analyses were used to identify major clades and evaluate the relationships among the seven populations by concatenated genetic sequences. An ML phylogenetic tree was constructed using General Time Reversible model with 1,000 bootstrap replications , the optional alternative model (GTR + G) was used to construct Maximum‐Likelihood (ML) phylogenetic trees using haplotypes detected from private genes  and concatenated genes. Bootstrap nodal support was evaluated from 1,000 bootstrap replicates. We aimed to examine whether there exists the high diversification between SC and NC population groups, and then further investigate the extent of nucleotide variation among different populations in each single gene sequences.Haplotype networks of 2.7D and Fu's Fs values were estimated to test demographic expansion . The expected values were calculated assuming that sudden population growth model through coalescent simulations. According to simulations, demographically stable or admixed populations must present a multimodal distribution, whereas populations that have undergone a recent expansion generally exhibit a unimodal distribution . Thirteen haplotypes, consisting of one shared haplotype and 12 exclusive haplotypes, were observed. Based on the COI partial gene sequences, only one common haplotype was shared between Shandong (SD) and Liaoning (LN) populations, while 19 unique haplotypes were detected. The COII gene sequences contained 15 haplotypes, comprised of three shared haplotypes and 12 unique haplotypes. Among these haplotypes, the two shared haplotypes in the SC population group were shared by the YNW, YNP, and AH (first haplotype) and YNW and HN (second haplotype). One common haplotype in the NC population group was shared with LN and NMG. Furthermore, 25 unique haplotypes without links to the shared or ancestral haplotypes were observed for concatenated gene sequences . In terms of a single population, haplotype diversity (Hd) ranged from 0.000 to 1.000. Nucleotide diversity (Pi) and the average number of nucleotide differences (K) ranged from 0% to 1.38% and from 0.00 to 20.83, respectively. Of these, the HN and YNW populations displayed the highest haplotype diversity, with each individual possessing one unique haplotype. HN population also developed the largest nucleotide diversity (0.0138) and differences (20.83) . Although AH and HN populations have developed moderate genetic differentiation, comparatively high genetic diversification was also observed between SD and LN populations. Based on the Nm values, frequent genetic flow was under a way between the two sets of populations, respectively (Table p < .001) (Table Pairwise 1) Table .R = .289, p = .138 and R = .257, p = .100 were detected between average pairwise genetic distances and geographic distances and between lnKm and Fst/(1 − Fst))  with strong bootstrap supports. Based on concatenated genes, SC and NC population groups were not divided into two apparent clusters. However, there was still an obvious division observed between SC and NC population groups despite of low bootstrap values in some branches. In contrast, the Median‐Joining networks and ML phylogenetic trees of the haplotypes based on single genes of COI and Cytb were not in the line with results obtained from COII and concatenated genes, which resulted from the lower nucleotide diversity and less genetic variation . Fu's F statistic of entire populations showed a significantly positive result with a value of −2.500 (p < .05), whereas at the single population level, insignificant values were observed (p > .10)  Table . Distrib4M. raddei. High genetic diversity and distinct genetic differentiation among sampled populations were identified through the mitochondrial analyses, and the assumption that particular periodic behaviors and outbreaks of M. raddei in Liaoning Province mainly result from the absence of genetic flow with other populations and local genetic isolation was preliminarily denied.The present study used three genes of mitochondrial DNA to provide basic molecular information to allow better insight into factors contributing to genetic diversification among populations and possible mechanisms triggering the evolution of different patterns of adult emergence of 4.1M. raddei are not invasive species being recently introduced, but how long they have settled in China poses a need for subsequent confirmation. Carter et al. (A. glabripennis (Coleoptera: Cerambycidae) in invasive range comparing with native populations due to population bottleneck and genetic drift. 37 different mitochondrial haplotypes were obtained from a sample of 131 beetles in Asia, whereas only 12 unique haplotypes were detected in 258 beetles from North America. Javal et al. , following its invasion into Siberian and European parts of Russia from its native range. Men et al. (Dendrolimus kikuchii Matsumura (Lepidoptera: Lasiocampidae) had similar genetic characteristics among various spatial populations, which might be native to southern China.The largest nucleotide diversity and differences are detected in HN population, and four beetle specimens occupy one unique haplotype, respectively. All results provide a possibility that Xixia County might constitute the ancestral range of this species in Henan Province. The assumption requires more samples of Xixia County and adjacent areas to be further evaluated and verified. High number of unique haplotypes, lack of ancestral and shared haplotypes reveal that r et al. , Carter r et al.  identifil et al.  also obsg et al.  reportedg et al.  identifin et al.  also rep4.2M. raddei investigated, most have developed strong genetic differentiation and a low level of genetic flow, and the major proportion of genetic diversification was found among populations. Although M. raddei adults have a moderate flight ability allowing short‐distance dispersal  might occur among spatial populations of M. raddei. Previous studies have well documented that among polyphagous insects, ecological divergence occurs as a consequence of the selection pressure imposed by the host traits. Even in the absence of physical barriers, different host resources can act as the selection basis to form new ecotopes of herbivores and subsequently strengthen their genetic differentiation. Consequently, geographical populations exploiting different resources might be genetically distinct , obvious HAD phenomenon was still not present between them. Recent invasive events or local microclimate might be as the cause, which needs more samples of Yunnan Province and adjacent areas to facilitate a more systematic investigation.With reference to phylogenetic analyses of haplotypes and concatenated sequences, distinct genetic differentiation is detected between NC and SC population groups. Indeed, different geographic populations of es EPPO, . Q. glauMassicus raddei have a wide natural distributional range, but only have developed high population density and reached destructive damage to forest ecosystems in Liaoning and Jilin provinces. By coincidence, among those regional populations, a periodic (once per 3 years) adult emergence phenology was also observed ; writing – review and editing . Yufan Zhang: Data curation ; investigation (lead); writing – original draft (lead). Atif Manzoor: Software (supporting); writing – review and editing (supporting).Figure S1Click here for additional data file.Table S1Click here for additional data file.


Jassa marmorata and Jassa slatteryi. Both species are common elements of marine biofouling on nearly all kinds of artificial hard substrata in temperate to warm seas. We hypothesized that the two species’ modern occurrences across the oceans are the result of human shipping activities that started centuries ago. Mitochondrial DNA sequences of the CO1 fragment of specimens from distinct marine regions around the world were analysed, evaluating genetic structure and migration models and making inferences on putative native ranges of the two Jassa species. Populations of both species exhibited considerable genetic diversity with differing levels of geographic structure. For both species, at least two dominant haplotypes were shared among several geographic populations. Rapid demographic expansion and high migration rates between geographically distant regions support a scenario of ongoing dispersal all over the world. Our findings indicate that the likely former native range of J. marmorata is the Northwest Atlantic, whereas the likely former native range of J. slatteryi is the Northern Pacific region. As corroborated by the genetic connectivity between populations, shipping still appears to be the more successful vector of the two species’ dispersal when compared to natural mechanisms. Historical invasion events that likely started centuries ago, along with current ongoing dispersal, confirm these species’ identities as true “neocosmopolitans”.The geographic distributions of some coastal marine species have appeared as cosmopolitan ever since they were first scientifically documented. In particular, for many benthic species that are associated with anthropogenic substrata, there is much speculation as to whether or not their broad distributions can be explained by natural mechanisms of dispersal. Here, we focused on two congeneric coastal crustaceans with cosmopolitan distributions—the tube-dwelling amphipods  Human-mediated dispersal of organisms can exceed natural mechanisms, carrying species over large distances and often across natural barriers . For exaThanks to the rise of scientific documentation and description, human-mediated introductions of marine species across the globe within the last half-century have been increasingly documented. In clear contrast, the dispersal of marine species in earlier centuries is nearly undocumented and probably a great many introductions since the 1820’s have yet to be recognized. Many coastal benthic species that occur in anthropogenic habitats, especially those associated with the hulls of wooden ships , had probably been distributed across the oceans by human shipping activities centuries before their geographic distributions were scientifically documented for the first time. As a consequence, modern geographic distributions of some marine species show cosmopolitan occurrences e.g., , classifJassa marmorata Holmes, 1905 and Jassa slatteryiJassa species are effective colonizers of clean hard substrata through immigration of nearby adult animals, but primarily by recruitment of drifting juveniles through the water column , mediated by transoceanic shipping activities that started centuries ago.The long-standing difficulties in the taxonomy of the genus  records , 1990. FJassa species in order to evaluate their genetic structure and infer their putative former native ranges.Molecular approaches have often been used to retrace species’ native ranges, particularly in the case of ecologically harmful invasive invertebrate species such as woodwasps e.g.,  and ladyJ. marmorata and 128 of which were identified as J. slatteryi, following the original descriptions provided by Samples were collected opportunistically from artificial substrates  within distinct marine regions via different sampling methods. A total of 419 specimens were collected, 291 of which were identified morphologically as If available, at least 10 specimens  from each sampling location were used for molecular analysis. Two to three pereopods  of each specimen were used for DNA isolation. DNA extractions were performed using the QIAmp DNA Mini Kit  according to the manufacturer’s protocols. Polymerase chain reaction (PCR) amplifications at the cytochrome c oxidase 1 fragment were carried out using the universal primers LCO1490 (5′ GGTCAACAAATCATAAAGATATTGG 3′) and HCO2198 (5′ TAAACTTCAGGGTGACCAAAAAATCA 3′) . The 25 http://blast.ncbi.nlm.nih.gov/Blast.cgi) was used as a sequence control. DNA barcode data from all genetically analyzed specimens were made publicly available in the project “JASSA” in the Barcode of Life Data System  from a previous work (MH052599–MH052642). Detailed information on the specimens and sequences are provided in The software CodonCode Aligner v.8.0  was used to check electropherograms for ambiguous bases, remove primers, and to check for stop codons to prevent inclusion of pseudogenes in the analyses. Clustal W  implemenem BOLD;   was collapsed to unique haplotypes, subsequently treating gaps as missing data using ALTER . A shortIntra- and interspecific species distances were calculated in MEGA 7.0 based on the Kimura-two-parameter (K2P) model with pairwise deletion using the full length of 657 bp sequences; these were then used to estimate genetic divergence between taxa. The K2P model was preferred over more complex and fitting models in order to ensure comparability of the results with those of published literature and other species delimitation analyses .In the program DNASP 5.10 , metricsTo test for departure from neutrality, In order to test for a possible differentiation among regions or populations, one-way and two-way analyses of molecular variance AMOVA;  were perAs possible differences between populations can be related to the remoteness of some locations, the relationship between geographic and genetic distances was tested with a Mantel test implemented in Alleles in Space AIS;  with 10,eμ for haploid mtDNA) and migration rate (M = m/μ). The product is the number of effective migrants per generation (2Nem). The probabilities were estimated using 10 replicates, wherein each replicate was run for 50,000,000 generations and sampled every 1,000 steps, thereby recording a total of 50,000 steps. Each replicate included four MCMC chains with relative temperatures of 1.0, 1.5, 3.0 and 100,000. Convergence was assessed to ensure that the effective sample size (ESS) was higher than 200. Migration rates were only considered when 95% of the confidence intervals (CIs) were different from zero.In addition to a putative poor differentiation, a high degree of migration between populations can provide further information on the present connectivity between populations, supporting a neocosmopolitan scenario. Migration rate among regions was assessed using MIGRATE-N 3.6.11  on the CJ. marmorata, and 85 sequences and 595 bp for J. slatteryi with the median-joining method  and Bayesian inference (BI) of the CO1 gene fragment. The ML was inferred with PhyML . Tree files were combined using LogCombiner on XSEDE (1.8.4) with 15% of burn-in. The maximum clade credibility tree was generated using TreeAnnotator on XSEDE. All the XSEDE analyses were performed on the CIPRES Science Gateway  and four (J. slatteryi) areas of possible occurence as this was the maximum number of regions where a single haplotype was found for each species respectively. In order to avoid overinterpretation, only the nodes with at least 70% of bootstrap support and 90% of posterior probabilities were considered. The best-fitting model was selected based on Akaike’s information criterion (AIC).The ancestral ranges of the haplotypes were estimated with the R package BioGeoBears . This paJ models  and a maJ. marmorata and 0.57% ± 0.17 for J. slatteryi.The distance between species was 15.2% ± 1.7 and the average distance within species accounted 0.17% ± 0.07 for J. marmorata (19 haplotypes) exhibited lower haplotype diversity (Hd = 0.492) and lower nucleotide diversity (π = 0.002) than J. slatteryi   .J. marmorata, the haplotype diversity was variable throughout marine regions, ranging from 0.884 in the NW Atlantic region, to 0.305 in the NE Pacific region. Nucleotide diversity was low among the regions, ranging from π = 0.001 in the NE Atlantic to π = 0.006 in the NW Atlantic region.Within J. slatteryi also exhibited a wide range. The highest regional value of Hd = 0.731 occurred in the NE Pacific  while the lowest was detected in the SE Pacific (Hd = 0.433). Nucleotide diversity ranged from π = 0.022 in the NW Pacific to π = 0.002 in the SE Pacific . In the comparison of populations, two main groups were distinguished within J. marmorata: a general group with the majority of the populations, and another with Argentina and Chile. The Virginia population showed the highest average differentiation and appeared to be an outgroup  and the NE Pacific  (ifornia)  and S4. ifornia) .J. marmorata, no emigration was observed from Chile, but from almost every other population. In contrast, for Connecticut and Virginia there was only one population donor although multiple receivers were detected  and for the NW Pacific populations met the assumptions of the migration model; conversely, more emigrants were found from South Korea to Japan  segregation but included multiple missing haplotypes and three haplotypes which were present in more than two regions (The haplotype network of Atlantic . The sec regions .J. marmorata or J. slatteryi (p > 0.1).There was no significant correlation between genetic and geographic distances in either J. mamrorata and J. slatteryi as well as each species  for the cluster grouping  species .J. marmorata  and J. slatteryi  (The DEC+J model significantly increased the likelihood of the DEC model in both  −78.23) .J. marmorata, while the reconstruction of the most recent common ancestor’s  geographic range was inconclusive, it seemed to exclude the Mediterranean Sea and SW Atlantic but an ancestral divergence of a clade in the NW Atlantic was evident  of two cosmopolitan amphipods that are common elements of marine biofouling. The examination of the genetic variability between these two species in our analyses indicated high levels of intraspecific divergence within the J. marmorata populations was derived almost equally from both variation within populations and variation among populations within marine regions. The highest genetic diversity  was found in the NW Atlantic region, and the lowest occurred in the NE Pacific region.The results indicated that the overall percentage of variation present in J. marmorata samples taken from assumed potential source populations along the Atlantic coast of the United Stated showed varying results: one population from coastal Connecticut revealed no genetic diversity at all in the population, whereas the samples analyzed from coastal South Carolina were shown to have been derived from a separate Jassa species-level lineage  whereas other populations were less structured (Spanish Mediterranean coast and Connecticut). These high levels of structure suggest a more limited gene flow or different selective pressures. Some J. marmorata populations were not panmictic throughout a marine region, but displayed high Fst values, indicating an absence of genetic connectivity between them. Populations from Chile-Peru as well as Connecticut-Virginia, on the other hand, could each be assigned to the same marine regions, and were characterized by high heterozygosity values. J. marmorata populations from California and Oregon exhibited genetic connectivity with populations in Norway and Iceland, which is unlikely to result from natural dispersal. Furthermore, negative Tajima’s D, Fu’s Fs statistics, as well as the results of the mismatch distributions for J. marmorata indicated a population size expansion, perhaps after a bottleneck. It can be assumed that the amount of samples was rather limited considering that the genetic diversity of these populations has probably been shaped largely by stochastic events. A lack of samples from entire marine regions remains an open issue that impedes clear inferences on the connectivity of globally distributed populations.The obtained data for J. slatteryi was highest within populations and lowest among marine regions. In J. slatteryi, there was discordance between haplotype and nucleotide diversity. Haplotype diversity was higher in the Mediterranean Sea and the NE Pacific whereas nucleotide diversity was higher in the NW Pacific. The lowest genetic diversity was found in the SE Pacific region.The magnitude of genetic variation within Jassa slatteryi had been previously considered to be either cryptogenic or introduced to the NE Pacific . Surprisingly, there was also high genetic diversity in the populations from the Mediterranean Sea region. The highly divergent haplotypes found in the Mediterranean Sea and the private haplotypes from the SW Atlantic might be artifacts due to small sample sizes and a resulting undersampling in the putative native regions due to the nature of our opportunistic sampling campaign. The overall genetic diversity of J. slatteryi populations was different from that of the J. marmorata populations as the overall nucleotide diversity was higher, indicating larger differences between haplotypes. This was displayed by the haplotype network where the respective haplotypes were separated by more mutations than in J. marmorata.Our results corroborate the previous assumption that J. slatteryi also varied in their level of geographic structure, ranging from highly structured populations (Japan) to lower levels of structure  and higher gene flow between populations. In contrast to J. marmorata populations which showed only limited or no gene flow between populations of the same marine region, J. slatteryi populations did not reflect this structure between populations within the same regions. Furthermore, our findings indicate gene flow between geographically remote populations over large distances which is highly unlikely via natural dispersal . The mismatch distribution for J. slatteryi suggested a geographic expansion but not a demographic expansion.Populations of J. marmorata and J. slatteryi populations in regions where both species co-occurred. J. slatteryi populations in Chile, California, South Korea, the Spanish Atlantic and the Mediterranean coasts were particularly closely related to each other , whereas the J. marmorata population from Chile showed more structure with very little gene flow between this and other populations. The Fst values also indicated that, for example, while there is measurable gene flow between J. marmorata populations from the neighboring regions of Chile and Argentina, populations of J. slatteryi from the same regions are not panmictic. However, both Jassa species showed evidence of gene flow between the Mediterranean coast of Spain and California. Both J. marmorata and J. slatteryi exhibited high haplotype diversity and low nucleotide diversity, indicating rapid demographic expansion from a small effective population size . The results of this study indicate that these shipping routes played a major role for the establishment of both J. marmorata and J. slatteryi populations in the SE Pacific and SW Atlantic regions. The models of J. marmorata and J. slatteryi in the NE Atlantic region. Our data indicate both Jassa species occur along the West and East coast of the United States, which is likely due to the connection of both coasts via shipping routes through the Panama Canal. This high invasion risk canal is also passage for shipping routes to and from the NE Atlantic regions . regions . The JasJassa species achieved their neocosmopolitan distribution. It is most likely that the species’ distributions are a result of human-mediated introductions which started centuries ago. Our findings indicate that the likely former native range of J. marmorata is the NW Atlantic region, and the likely former native range of J. slatteryi is the Northern Pacific (NW and NE) region. Due to the relatively fast travel speeds, extensive surface area, and independence of stochastic ocean currents , accession numbers, and references.Click here for additional data file.10.7717/peerj.9613/supp-5Supplemental Information 5p-values are highlighted in bold. Regions represented by only one population are annotated in the same line with "/". OR: Oregon; CA: California; NS: North Sea; NO: Norway; IC: Iceland; SPA: Atlantic Spain; CT: Connecticut; VA: Virginia; SPM: Mediterranean Spain; CH: Chile; PE: Peru; JA: Japan; SK: South Korea.Significant Click here for additional data file.10.7717/peerj.9613/supp-6Supplemental Information 6p-value < 0.05) are highlighted in bold. JM: Jassa marmorata; JS: Jassa slatteryi. NEP: North East Pacific; NES: North european seas; NWA: North West Atlantic; MED: Mediterranean Sea; SEP: South East Pacific; NWP: North West Pacific; LUS: Iberian Peninsula.Significant Fst values  are highlighted in bold. JM: Jassa marmorata; JS: Jassa slatteryi. SPM: Mediterranean Spain; OR: Oregon; CA: California; NS: North Sea; NO: Norway; IC: Iceland; SPA: Atlantic Spain; CT: Connecticut; VA: Virginia; MED: Mediterranean Sea; CH: Chile; PE: Peru; JA: Japan; SK: South Korea; AR: Argentine; AF: Africa.Significant Fst values . JM: Click here for additional data file.10.7717/peerj.9613/supp-9Supplemental Information 9Jassa marmorata; JS: Jassa slatteryi. OR: Oregon; CA: California; DE: Germany; NS: North Sea; NO: Norway; IC: Iceland; SPA: Atlantic Spain; CT: Connecticut; VA: Virginia; MED: Mediterranean Sea; CH: Chile; PE: Peru; JA: Japan; SK: South Korea.The source of migration is given in the top row; consistent results are highlighted in bold . JM: Click here for additional data file.10.7717/peerj.9613/supp-10Supplemental Information 10Click here for additional data file.10.7717/peerj.9613/supp-11Supplemental Information 11DNA barcode data from all genetically analyzed specimens is also publicly available in the project “JASSA” in BOLD: dx.doi.org/10.5883/DS-JASSA.Click here for additional data file.


Morone saxatilis , is an anadromous fish species that supports fisheries throughout North America and is native to the North American Atlantic Coast. Due to long coastal migrations that span multiple jurisdictions, a detailed understanding of population genomics is required to untangle demographic patterns, understand local adaptation, and characterize population movements. This study used 1,256 single nucleotide polymorphism (SNP) loci to investigate genetic structure of 477 Striped Bass sampled from 15 locations spanning the North American Atlantic coast from the Gulf of St. Lawrence, Canada, to the Cape Fear River, United States. We found striking differences in neutral divergence among Canadian sites, which were isolated from each other and US populations, compared with US populations that were much less isolated. Our SNP dataset was able to assign 99% of Striped Bass back to six reporting groups, a 39% improvement over previous genetic markers. Using this method, we found (a) evidence of admixture within Saint John River, indicating that migrants from the United States and from Shubenacadie River occasionally spawn in the Saint John River; (b) Striped Bass collected in the Mira River, Cape Breton, Canada, were found to be of both Miramichi River and US origin; (c) juveniles in the newly restored Kennebec River population had small and nonsignificant differences from the Hudson River; and (d) tributaries within the Chesapeake Bay showed a mixture of homogeny and small differences among each other. This study introduces new hypotheses about the dynamic zoogeography of Striped Bass at its northern range and has important implications for the local and international management of this species.Striped Bass,  Lawrence River, Quebec, to the St John's River, Florida, as well as a native population in the Apalachicola–Chattahoochee–Flint river system in the Gulf of Mexico  and Blueback Herring  can be used to construct large panels of single nucleotide polymorphisms (SNPs) throughout the genome of an individual organism  high‐resolution genotyping ; and (b) transcriptomic profiling (measuring gene expression of thousands of genes simultaneously); began to be widely applied to study wild populations in nature. Another important transition occurred in approximately 2010, which is when applications of DNA barcoding greatly expanded. Louis Bernatchez has always been on the forefront of these changes and applying them to nonmodel organisms, often in natural settings. Many of these species had few genomic tools developed and many of the datasets were “messy” in comparison with zebrafish in laboratory settings, requiring innovated data analysis strategies. Louis has always embraced these situations as the variability is not a nuisance but rather fundamental to the way nature works. Highly duplicated genomes, traits controlled by many loci, panmictic or nearly panmictic species, are just a few of the messy systems that Louis has studied which have resulted in substantial insights about how selection, drift, migration, and mutation affect Earth’s biodiversity.2.2® 96 Tissue (Macherey‐Nagel) kit on an epMotion 5075t (Cat. 5075000302), or the E.Z.N.A. Tissue DNA Kit (Omega Bio‐Tek). Libraries containing 96 individuals each were prepared using a double‐digest restriction‐site‐associated DNA sequencing (ddRAD‐seq or ddRAD) protocol developed by Poland et al.  at Génome Québec Innovation Centre.DNA was isolated using either NucleoMagd et al.  and modid et al. . Samples2.3https://github.com/enorman deau/stacks_workflow, downloaded August 2016). Cutadapt v. 1.13  of 0.2 and a minimum read length (m) of 50. FastQC v. 0.11.5 (Babraham Bioinformatics) was used to assess sequence quality before and after. Sequences were then trimmed to a uniform length of 85 bp and demultiplexed using the process radtags module of Stacks v. 1.46  of 19, a maximum seed occurrence of 55, and no filtering on output alignment score, and otherwise default parameters. The stacks module pstacks identified reference aligned loci with a minimum depth (m) of 4 using the “snp” model type and an alpha of .1. Loci were assembled into a catalogue using cstacks, sstacks, and rxstacks with default settings, and unclear or unlikely haplotypes, as well as SNPs with a log likelihood <45, were pruned from the dataset. Using the populations module, SNPs were further filtered to remove all loci with a stack depth <5, with >20% missing data in any given location, and any loci not amplified in all locations. We examined the output of populations and removed loci with an Fis < −0.3 to eliminate possible paralogs, and used VCFTools 0.1.13  was assessed using mantel tests implemented in Arlequin v. 3.5.2.2 , with 10 repetitions per K value, on all individuals as well as on only US individuals. To ensure results were not biased by differences in sampling size, sNMF was run a second time with a maximum of 30 individuals from any given genetic cluster found in the initial run. The probability of a K being valid was calculated using the cross‐entropy criterion. The K values with the lowest minimal cross‐entropy value were considered most probable as the true number of ancestral populations . The number of putative clusters with the lowest Bayesian information criterion value was chosen to evaluate population groupings. Another DAPC analysis was conducted with samples taken from Canadian rivers excluded, using the same methods described above.The R package LEA v. 2.0 , and average read depth for loci across all individuals was 55 (range = 17–131). Sibship analysis found two possible full sibship pairs in the Kennebec River and three in the Chesapeake Bay; one individual from each pair was removed. In addition, the Cape Fear dataset contained three possible full sibship pairs, one trio, and one group of five individuals. One individual from each group was retained, and the rest were excluded from downstream analyses.The initial SNP catalogue contained 756,713 loci. After filtering for Ln Likelihood less than −40, the catalogue contained 670,167 loci. After filtering out loci with stack depths of less than five, more than 20% missing data, more than two alleles, and loci present in fewer than 17 populations, the SNP catalogue contained 7,884 loci. After filtering by HO = 0.26–0.33) compared to rivers south of the Bay of Fundy . The remaining two outliers, 25891_222 and 27535_2519, were located on short  scaffolds with no known genes.Outlier analyses identified 35 total outlier loci: BayeScan identified 13 loci as possible outliers, compared to 25 loci found by outFLANK, and 3 loci were identified by both analyses. All 35 potential outliers were excluded from downstream genetic structure analyses, while the three loci identified by both approaches were examined further as putative adaptive loci. These three loci were located on three different scaffolds and were given names according to their scaffold number and base pair position on the scaffold (scaffold_bp). Locus 4437_41108 is located 41,108 base pairs into a large  scaffold, Msax_4437, inside an intron of insulin‐like growth factor 2b Examination of allele frequencies of the three putative outliers revealed that all three loci possessed one allele that was at or near fixation in individuals within Gulf of St. Lawrence and Shubenacadie River and at very low frequencies in US locations Figure , with ma3.3FST was 0.086 and highly significant (p < .001), while pairwise values varied from 0 to 0.20. Correction for multiple testing did not change significance of any pairwise FST values. Canadian populations tended to be highly genetically distinct, while populations in the US migratory range were less genetically differentiated. The highest pairwise FST values occurred between Canadian rivers and all other locations , with FST values ranging from 0.09 to 0.20. Pairwise FST between Mira River and Bras d’Or–Miramichi River was 0.007 and nonsignificant . Within the Delaware River–Chesapeake Bay region, 20 of 28 comparisons were significant  but significant (p < .01) pairwise FST compared with Hudson River individuals. Similarly, the Roanoke River and Cape Fear River had small (FST = 0.004) but significant differences . Within Chesapeake Bay, there was no significant isolation‐by‐distance pattern ; however, when US locations in North Carolina, Hudson River, and Kennebec River were included, IBD became significant . When only Canadian populations were considered, there was no significant IBD . When all samples were run using DAPC, the most likely number of clusters was four, as estimated using the Bayesian information criterion  with the lowest entropy across 10 runs was 6  primarily belong to the seventh cluster  and differentiation patterns differed among Canadian and US locations. Significant isolation by distance was found when all locations were considered , Alewife . Within Canada, the highest observed heterozygosity was seen in the Saint John River, only slightly lower than values seen in the United States, suggesting this population has retained substantial genetic diversity despite its apparent decline in numbers since the 1970s . Phylogeographic theory predicts that populations founded after the last glacial retreat will show less intraspecific divergence than their southern counterparts , the Shubenacadie River, and the Saint John River were consistently, highly differentiated from each other and from US populations . Recent evidence that Striped Bass move between the US Atlantic coast and the northeastern coast of Nova Scotia is scarce. In 1983, a single fish tagged in the Kouchibouguac River in the Gulf of St. Lawrence in 1983 was later recaptured in the Wye River, Maryland  and support three regions with low but significant genetic divergence: (a) Hudson River and Kennebec River, (b) Delaware River and Chesapeake Bay, and (c) North Carolina Rivers. Overall, our results suggest individual spawning populations within the Delaware River and Chesapeake Bay make up a large metapopulation connected by extensive gene flow, with lesser amounts of gene flow between this region and populations to the north and south.In contrast to Canadian Striped Bass, Connectivity among populations of Striped Bass along the Atlantic Coast has been investigated in several previous studies  but significant, and similar to values seen among some rivers within the Chesapeake Bay, indicating a similar level of relatedness despite the large geographic distance between them (approximately 620 km from mouth to mouth). This similarity was also seen in a recent microsatellite study that examined Kennebec River juveniles , North Carolina, and South Carolina to a region of origin (Gauthier et al., rubias is insensitive to the presence of admixed individuals. When performing mixed‐stock analysis on locations with large numbers of hybrid individuals, assignment may be better conducted using a genetic clustering algorithm such as those found in LEA or STRUCTURE. Overall, our SNP panel constitutes a significant improvement over other genetic markers in assigning Striped Bass to regional areas along the Atlantic coast and will be invaluable to the development of a highly accurate and reliable genetic tool for mixed‐stock analysis of the species across the central and northern portion of their range.Statistical biases when using large panels of SNP loci have been identified in assignment tests that use simulated individuals to predict assignment accuracy of a set of loci (Anderson, Waples, & Kalinowski, 5Striped Bass have been thought to exhibit a high degree of natal homing (Pess et al., This study represents the first contribution that used genotyping by sequencing to facilitate highly accurate mixed‐stock analyses of Striped Bass along the Atlantic Coast, including stock compositions in the Bay of Fundy and ongoing characterization of Striped Bass along the Nova Scotian coast. This mixed‐stock analysis method will be especially valuable if climate change influences shifts in the range of Striped Bass and results in increased mixing of different spawning populations across international borders, allowing for early detection and appropriate responses in management and policy.None declared.Fig S1‐S4Click here for additional data file.Table S1Click here for additional data file.


In this study, we analyzed the whole-genome sequencing data from The 1000 Genomes Project (Phase 3) and the pharmacogenetic information from Drug Bank, PharmGKB, PharmaADME, and Biotransformation. Here we show that ancestry-informative markers are enriched in pharmacogenetic loci, suggesting that trans-ancestry differentiation must be carefully considered in population pharmacogenetics studies. Ancestry-informative pharmacogenetic loci are located in both protein-coding and non-protein-coding regions, illustrating that a whole-genome analysis is necessary for an unbiased examination over pharmacogenetic loci. Finally, those ancestry-informative pharmacogenetic loci that target multiple drugs are often a functional variant, which reflects their importance in biological functions and pathways. In summary, we develop an efficient algorithm for an ultrahigh-dimensional principal component analysis. We create genetic catalogs of ancestry-informative markers and genes. We explore pharmacogenetic patterns and establish a high-accuracy prediction panel of genetic ancestry. Moreover, we construct a genetic ancestry pharmacogenomic database Genetic Ancestry PhD ( Hsin-Chou Yang et al. examine population structure in several genomic databases and identify that pharmacogenetic loci are enriched for markers of genetic ancestry. Their results suggest that genetic ancestry must be carefully considered in population pharmacogenetics studies. With the aid of a growing number of discovered genetic variations, more and more ancestry-informative markers (AIMs) are identified to trace the origin of individuals with differential genetic backgrounds31. AIMs have been applied in various fields of studies, such as population genetics36, medical genetics39, forensic sciences42, and animal sciences44.Genetic ancestry has been long recognized as a key topic in population genetics. It has been proved an influential factor that should be modeled or controlled for in -omics studies, including genetic association studies54 and pharmacoepigenetic studies57 have received considerable attention because of their value in precision population health. This contributes to the reasons why AIMs play central roles in both pharmacodynamics (PD) and pharmacokinetics (PK). In the context of PK, AIMs are relevant to differences in drug absorption, distribution, metabolism, and excretion (ADME); downstream outcomes of PK/PD—drug response or pharmacogenomic effect (FX); adverse drug reactions (ADRs); and a drug’s effective dose in different ancestry groups52. An example of an ancestry-informative pharmacogenetic locus (AI-PGx) is the single nucleotide polymorphism (SNP) rs1045642 on the MDR1 gene, also known as the ABCB1 gene. The minor allele frequency of rs1045642 differs significantly between continent ancestry groups and is associated with ADR to amitriptyline and nortriptyline and a specific drug response to morphine51. In the data produced by The International HapMap Project58, the frequency of allele A was 0.542 in the Center d’Etude du Polymorphism Humain Utah collection; 0.489 among Japanese in Tokyo, Japan; 0.400 among Han Chinese in Beijing, China; 0.108 among Yoruba in Ibadan, Nigeria (P = 2.13 × 10–13 in Fisher’s exact test). Because of potential applications of AI-PGx in precision population health, there is an unmet need to comprehensively identify AI-PGx in the human genome and to establish knowledge databases for differential populations and super-populations .Trans-ancestry pharmacological effects in population pharmacogenetic59. Homozygosity disequilibrium analysis (HDA) has been used to study genetic ancestry, autozygosity, and natural selection in general population61; chromosomal aberrations responsible for complex disorders and cancers62; gene regulation of expression quantitative trait loci63; and pharmacogenomic and pharmacoepigenomic effects64. In contrast to single SNPs, the context of HD can serve as a multilocus AIM and AI-PGx to provide genetic information supplemental to single-locus AIMs and AI-PGx. In addition, HD that allows for a small proportion of heterozygotes due to genotype errors, mutations, and missing genotypes provides an analogous but more flexible homozygosity enrichment set than the conventional run of homozygosity that has been broadly applied to study genetic ancestry, demographic history, and disorders/traits in population genetics74 and medical genetics79.Homozygosity disequilibrium (HD) is defined as a non-random pattern of a sizable run of homozygosity that its homozygosity intensity exceeds the equilibrium homozygosity intensity in the human genome83 has drastically accelerated the discovery and application of single nucleotide variations (SNVs), including SNPs and rare variants (RVs). The 1000 Genomes Project84 generated a rich whole-genome sequencing dataset consisting of more than 77 million SNVs in 2,504 individuals from 26 global populations representing five major continents—Africa (AFR), Americas (AMR), East-Asia (EAS), South-Asia (SAS), and Europe (EUR). Since then, several studies have used this dataset to investigate population genetics87. In this study, we examined the genomic structure, inferred demographic history, identified AIMs and ancestry-informative genes (AIGs), and established population prediction panels for global continents and populations by combining the valuable genomic resource of The 1000 Genomes Project with additional public pharmacogenetic databases. Based on these materials, we identified AI-PGx across the human genome, explored their features in pharmacogenomics, and established genetic ancestry pharmacogenomic databases.The advent of parallel sequencing technologies88   further displays genetic distances among the studied ancestry continents and populations Fig. . The AfrGenomic profiling of homozygosity intensity Fig.  and popuN = 575,750) of regions of HD exhibited significantly different medians of homozygosity intensity. In the non-American-ancestry groups, the proportions were much lower: 0.006% (N = 4951), 0.037% , 0.037% , and 0.006% (N = 4731) in AFR, EAS, EUR, and SAS, respectively. The HDA revealed differential genomic structures and demographic histories of the studied ancestry groups.In general, the pattern of HD differed across the continental ancestry groups; 84.358% of regions of HD showed a significant difference in medians of homozygosity intensity among different continental ancestry groups. Moreover, compared with other continents, the American-ancestry group exhibited the largest within-continent variation in HD. Among the populations within the American-ancestry group, 0.740% (PAIM) are provided , PK (N = 503), and CYP endogenous substrates (N = 334) , missense (N = 514), and gene upstream (N = 355)  Fig. . The top55) Fig. . For ancPAIM) and the proportion of AIMs only based on PGx  in each of the six analysis groups  = , , , , , and  in the whole-continental, African-, American-, East Asian-, European-, and South Asian-ancestry groups, respectively. One-sided Fisher’s exact tests showed that PAIM | PGx was significantly higher than PAIM with a P value of 0, 2.88 × 10–195, 0, 7.93 × 10–90, 5.25 × 10–48, and 2.71 × 10–14 in the whole-continental, African-, American-, East Asian-, European-, and South Asian-ancestry groups, respectively. The results revealed that AIMs are highly enriched in PGx loci, especially in ancestry groups with a high genetic differentiation.Based on all autosomal SNVs, we calculated the proportion of AIMs  under HD among continents. In specific continent, we found 2.904% (N = 1076), 34.562% , 4.802% (N = 1779), 2.440% (N = 904), and 0.432% (N = 160) AIGs under HD in AFR, AMR, EAS, EUR, and SAS, respectively. The results suggested a pattern of differential autozygosity in global continents and populations and provided a catalog of AIGs under HD.Manhattan plots from a genome-wide homozygosity association study are displayed Fig. . Among 3LOC100132249) are summarized in a staked-bar/box-whisker plot  and four genetic variation categories  at the website 74. Combined with molecular genetics and archeological evidence, these results revealed that this group had a larger effective population size and was more ancient than other continental ancestry groups. In contrast to the indigenous residents in Africa, the African-ancestry diaspora populations exhibited a much more diverse genetic admixture. For the American-ancestry group, PCA and HCA showed two clearly separate clines (PUR–CLM and MXL–PEL) and HDA showed that this group exhibited the largest spread in the number and length of regions of HD among the studied ancestry groups. This revealed a large genetic differentiation and complex admixture in the Americas in this data set. For the South Asian-ancestry group, PCA showed that individuals from each population had been divided into two subgroups. This special substructure was also observed in previous research and was explained by a social hierarchy of individuals in the Indian subcontinent85. More investigations are warranted to clarify the intrinsic reasons for this special pattern. HDA found a number of outlier individuals who carried many more or larger regions of HD in this group. For the East Asian-ancestry group, PCA showed the Northeast Asian population JPT was separated from others, starting from East Asian populations (CHB and CHS) to Southeast Asian populations (CDX and KHV) along the latitudinal cline. HDA showed that they carried the largest number and widest regions of HD among the studied ancestry groups. This may be partially explained by a recent bottleneck in East Asia90. For the European-ancestry group, PCA and HCA exhibited a latitudinal cline of Northern Europe (FIN) to Western Europe (GBR and CEU) to Southern Europe (IBS and TSI). HDA showed the distributions of the number and length of regions of HD were in between those of the East Asian-ancestry and South Asian-ancestry groups. In each continental ancestry group, we found a number of individuals who exhibited an extremely long genomic segment or multiple genomic segments under HD. For example, a 410-Mb region under HD was observed in individual HG04070 (ITU)  are Native Americans. These Native Americans population had undergone recent and severe bottlenecks and exhibited much higher lengths of runs of homozygosity than other populations . In The 1000 Genomes Project, the American-ancestry populations  are admixed Americans. This ancestry admixture reflects in the large variability of the lengths and numbers of HD , and this result is consistent to our finding. The CLM participants in The 1000 Genomes Project were Colombians with admixed ancestry recruited from the second-largest city in Colombia and they differed from the Colombian participants with the Native Americans ancestry in the Human Genome Diversity Panel. As expected, the Colombian participants in the Human Genome Diversity Panel exhibited much higher lengths of runs of homozygosity compared to the CLM in The 1000 Genomes Project .In this study, we found that East Asians carried the largest number and widest regions of HD in The 1000 Genomes Project. This finding is not in conflict with the previous findingThis study generated catalogs of AIMs and AIGs that provide genetic ancestry information for global continents and populations and established a prediction panel for global continents. We found more than 28 million among-continental AIMs and 0.17–3.83 million within-continental AIMs with the African-ancestry group having the most AIMs and the South Asian-ancestry group having the least AIMs. This reflected the genetic diversity in these continents. In addition, we found 36,812 AIGs under HD among continents and 160–12,805 AIGs under HD within continents, with the American-ancestry group having the most AIGs under HD within continents in reflection of the spread of genetic admixture in this continent. Furthermore, we established a marker panel that can be used to predict continental ancestry groups with a high accuracy according to the information of genetic ancestry from 31 AIMs and 1 AIG. Compared with previous prediction panels, we combine both of AIMs and AIGs under HD from whole-genome sequencing data to establish prediction panels for global continents.94, our postulated genetic ancestry prediction panel is either more accurate or parsimonious. Our panel that contains 31 AIMs and 1 AIG obtained a training accuracy of 96.6% and a testing accuracy of 95.6% in correctly assigning individuals to their continental ancestry group. The QIAGEN140-SNP Identification Multiplex panel94 required a larger number of SNPs (140 SNPs) and had a lower training accuracy of 95.87% and a testing accuracy of 92.03% than ours. The Ion AmpliSeq HID Phenotyping Panel95 used the 24 SNP HIrisplex System and had a notably reduced training accuracy of 81.1% and testing accuracy of 85.2% than ours. The 165 SNP Precision ID Ancestry Panel constituted by 123 SNPs from the group of M. Seldin24 and 55 SNPs from the group of K. Kidd96 with 13 overlapping SNPs, had a higher training accuracy of 99.29% and testing accuracy of 100%, but this panel required more SNPs than ours.Compared to the three available commercial panelsWe observed a substantially high enrichment of AIMs in PGx. This study finds this informative phenomenon through a formal analysis of whole-genome sequencing data and pharmacogenomic data, and it illustrates that differential drug responses, ADRs, and drug’s effective doses among ancestry groups are related to genetic ancestry. This explains the necessity to differentiate medical treatments according to not only population ancestry but also the individual’s genetic ancestry and genotype information. Genetic ancestry plays a critical role in precision population health and the development of gene therapies and cancer testing and should include more population groups with different genetic backgrounds to benefit more people around the world.C677T and Ala222Val) is a C > T missense SNP on the methylenetetrahydrofolate reductase (MTHFR) gene at 1p36.22. MTHFR is known to be associated with catalysis of the conversion from 5,10-methylenetetrahydrofolate to 5-methyltetrahydrofolate and folate metabolism. Allele T leads to a valine substitution at amino acid 222 and encodes a thermolabile MTHFR enzyme with reduced activity. Methylenetetrahydrofolate reductase deficiency decreases concentrations of folate and red blood cells and alters susceptibility to multiple diseases, such as hyperhomocysteinemia, occlusive vascular disease, neural tube defects, colon cancer, and acute leukemia. Drugs targeted by this AI-PGx include methotrexate, vitamin B-complex, and so on. Supplementation with folate for individuals who carry homozygote TT can improve methylenetetrahydrofolate reductase deficiency.Our pharmacogenomic database revealed that an AI-PGx with multiple drug targets and differential allele frequencies among global continents was often located in protein-coding regions  and related to important biological function(s). For example, rs1801133  and the maximum allele frequency difference among the five studied continents was ∆ = 0.384; the allele frequencies of allele T were 0.090, 0.119, 0.296, 0.365, and 0.474 in AFR, SAS, EAS, EUR, and AMR, respectively , synonymous rs6305 on HTR2A , missense rs6025 on F5 , and missense rs149157808 (merged to rs28371733) on CYP2D6 . Drug targets and allele frequency distributions of the aforementioned AI-PGx are provided in our pharmacogenomic database.This study showed that rs1801133 had a significant difference in allele frequency among global continents  and significantly different allele frequencies within the European-ancestry group . In the East Asian-ancestry group, the allele frequencies of T were 0.134, 0.192, 0.286, 0.380, and 0.466 in CDX, KHV, CHS, JPT, and CHB, respectively; the maximum difference was ∆ = 0.332. In the European-ancestry group, the allele frequencies of T were 0.727, 0.702, 0.676, 0.556, and 0.533 in FIN, CEU, GBR, IBS, and TSI, respectively; the maximum difference was ∆ = 0.195. These examples illustrate again that, if the information of genetic ancestry and individual genotype is available, they should be considered in the use of medication. Other examples include: (1) in AFR—rs2231142 on ABCG2 , rs1799971 on OPRM1 , and rs4149056 on SLCO1B1 ; (2) in AMR—rs738409 on PNPLA3 , and rs11854484 on SLC28A2 ; (3) in EAS—rs4961 on ADD1 ; (4) in EUR—rs1137101 on LEPR , rs1801394 on MTRR , and rs4149117 and rs7311358 on SLCO1B3 ; (5) in SAS—rs6280 on DRD3  and rs2229774 on RARG .In addition to a significant difference in allele frequency among global continents, AI-PGx with target multiple drugs may also exhibit significantly differential allele frequencies within specific continent(s). For example, missense rs1801133 also had significantly different allele frequencies within the East Asian-ancestry group . The American-ancestry group exhibited a substantial number of AIGs under HD , which reflects the heterogeneous genetic background of this continental ancestry group in this data set.Top AIMs with the maximum difference in allele frequencies across the studied continental ancestry groups and within a specific continental ancestry group are also provided in our pharmacogenomic database , exogenous chemicals , and salt homeostasis97. Our database showed that all CYP3A family members exhibited significantly different levels of homozygosity intensity across the five studied continental ancestry groups; CYP3A4 , CYP3A43 , CYP3A5 , and CYP3A7 .We found that the distributions of HD in PGx regions coincided with the patterns of HD in the whole genome, but the discrepancies of homozygosity intensities shrunk among the studied continental and population ancestry groups. In addition, our HDA identified AIGs with reported evidence of natural selection such as the Lactase (LCT) on 2q21.3 encodes an enzyme that aids in lactose digestion. A lactase deficiency results in a lactose intolerance that has an autosomal recessive mode of inheritance and is associated with homozygosity of the gene. The prevalence of lactose intolerance has a wide global spectrum and Caucasians have a lower prevalence than that of other populations103. LCT was also identified as an AI-PGx responsible for decitabine. Our results showed a significant difference in homozygosity intensities across the continental ancestry populations  and a significant difference in homozygosity intensities in the European-ancestry group . CEU and GBR had strikingly high median homozygosity intensities (0.991 in CEU and 0.995 in GBR), which were also much higher than the values in other European-ancestry populations  and in other continental ancestry groups . The patterns of prevalence of lactose intolerance and homozygosity intensity therefore coincide.Some examples demonstrated a high selective pressure in continental ancestry groups and specific population ancestry groups. For example, Minichromosome maintenance complex component 6 (MCM6) gene on 2q21.3 is another key gene associated with lactose intolerance in early adulthood104. This AI-PGx is responsible for the PK/PD of multiple medications, including tropine, estradiol, and so on. Our results showed that similar to LCT, MCM6 also exhibited strikingly high median homozygosity intensities that were considerably higher than the values in other European- and non-European-ancestry groups with 0.993 in CEU and 0.994 in GBR. Different lengths of homozygosity regions may reflect different population histories. A short segment of homozygosity in tens of KB may be formed by a pair of ancient haplotypes that contribute to local LD patterns74; however, it is not always the case. CEU and GBR did not exhibit a striking difference in LD patterns compared to other European- and non-European-ancestry groups in LCT and MCM6. A previous study demonstrated that LCT and MCM6 acted as a selective pressure in the European-ancestry group100. This study further showed the detailed patterns of differential signatures of selective pressure in ancestry populations within this continent.The N = 1566) and missense . However, the majority of previous population pharmacogenetic studies focused only on PGx in protein-coding regions105 or differentially expressed regions106. The studies provided biological interpretations to protein-coding PGx but failed to provide a whole picture of the PK/PD mechanisms. The current study provides the unbiased investigation of PGx by analyzing a whole-genome sequencing data set of global populations. In addition to protein-coding PGx, the identification of AI-PGx in non-protein-coding regions provides information supplemental to previous studies.Both protein-coding and non-protein-coding regions are necessary for a complete population pharmacogenomics study. According to the four popular pharmacogenetic resources used in this study, the largest and second largest functional groups of PGx were intron . The genetic variants were annotated based on the Genome Reference Consortium Human genome build 37 (GRCh37). After removing duplicate variants, multi-allelic variants, insertions, and deletions, 77,818,183 SNVs remained. We further removed SNVs with a minor allele frequency of 0; in total, 245,462, 38,873,037, 51,903,729, 56,021,288, 53,228,676, and 55,685,350 SNVs were removed in the whole-continental, African-ancestry, American-ancestry, East Asian-ancestry, South Asian-ancestry, and European-ancestry groups, respectively. In the subsequent analysis, SNVs were classified into four genetic variation categories: (1) All SNVs for SNVs with a nonzero minor allele frequency ; (2) SNPs for SNVs with a minor allele frequency of >0.01; (3) RVs for SNVs with a nonzero minor allele frequency of ≤0.01; and (4) SNPs and/or RVs for SNVs with a nonzero minor allele frequency of ≤0.01 in some continental or population ancestry groups and of >0.01 in others.All 2504 samples were sequenced across the whole genome using either Illumina HiSeq 2000 or Illumina HiSeq 2500 by The 1000 Genomes Projecthttp://www.drugbank.ca/), which consisted of 106 PGx SNPs categorized as ADR only, FX only, or FX & ADR and 16,885 PGx genes categorized as downregulated, upregulated, or both (update: 02/04/2018); (2) PharmGKB (https://www.pharmgkb.org/), which consisted of 2597 PGx SNPs and 1226 PGx genes categorized as PK only, PD only, PK & PD, or non-PK & non-PD (update: 05/04/2018); (3) PharmaADME (http://www.pharmaadme.org/joomla/), which consisted of 138 PGx core SNPs and 32 PGx core genes categorized as Phase I, Phase II, or Transporter (update: 05/04/2018); and (4) Biotransformation genes, which consisted of 1001 PGx SNPs grouped into eight subcategories49. The combination of all PGx records in the four public PGx resources consisted of 3,259 and 16,221 distinct autosomal PGx loci and genes, respectively. Functional annotation of PGx from The 1000 Genomes Project was as follows: NMD transcript, noncoding transcript, noncoding transcript exon, regulatory region, intergenic, upstream gene, 5′ UTR, initiator codon, synonymous, missense, stop gained, splice donor, splice acceptor, splice region, intron, stop lost, 3′ UTR, and downstream gene. The frequencies of the relationships between drug category and functional annotation of PGx are provided  Druded Fig. . Here, t107  were summarized by taking an average (median and mean) homozygosity intensity of all individuals in the population or analysis group, respectively. Third, for all gene regions and the gene regions that contain PGx, the genomic distributions of the number and length of the regions under HD  in each population and continental ancestry group were summarized in violin plots.HD, originally coined by Yang et al108 was performed to examine whether SNVs had a significant difference in allele frequency among the studied populations in a continent and among the five-continent ancestry groups. A false discovery rate109 for multiple testing was performed to calculate the adjusted P values. An SNV with an adjusted P value of <0.05 was assigned as an AIM. The number of AIMs (NAIM) and the proportion of AIMs (PAIM) were calculated.This study identified AIMs in each of the six analysis groups on the basis of the four genetic variation categories, resulting in 24 analyses in total. In each of the 24 analyses, an average individual-level allele frequency of every SNV in each of the six analysis groups was calculated. Two-sided Fisher’s exact testNPGx) based on the four studied PGx resources; (2) the number of ancestry-informative PGx (NAI-PGx); and (3) the proportion of ancestry-informative loci in PGx (PAIM | PGx = NAI-PGx / NPGx). The excess of PAIM | PGx compared with PAIM was examined using one-sided Fisher’s exact test. Odds ratio and P value were also calculated. If PAIM < PAIM | PGx, it indicated that AIMs were enriched in PGx. All of the aforementioned analyses were performed using R packages.We examined enrichment of AIMs in PGx. Three statistics were calculated: (1) the number of PGx , a flexible discriminant analysis was applied to build a classification model with the highest training accuracy by sequentially selecting the AIMs or AIGs with the maximum increment of training accuracy. The AIM or AIG with the minimum ratio of the within-continent and between-continent sum of squares for genotypic values was selected if more than one AIM or AIG had the same training accuracy. The procedure continued until the training accuracy reached 1.0 or the increment of training accuracy was less than 0.001. Third, the model with the highest training accuracy was then used to classify individuals into continents in the remaining 10th subset  and calculate a testing accuracy. Fourth, the previous steps were repeated until each of the ten subsets of data had been analyzed as a testing dataset, resulting in ten classification candidate models. Finally, among the ten classification models, the model with the highest testing accuracy was selected as the final classification model and the AIMs and AIGs formed a continental prediction panel.Genetic ancestry prediction panel was developed by using BIASLESS version 1.0Further information on research design is available in the Peer Review FileSupplementary InformationReporting Summary


The Union Territories of Jammu and Kashmir (J&K) and Ladakh in North India owing to their unique geographic location offer a wide variety of landscape from plains to high altitudes and is a congruence of many languages and cultural practices. Here, we present the genetic diversity studies of Gujjars from Jammu region of J&K and Ladakhi population based on a battery of autosomal single nucleotide polymorphisms (SNPs) and short tandem repeats (STRs), Y-chromosomal STRs and the control region of the mitochondrial genome. These two populations were observed to be genetically distant to each other as well as to other populations from India. Interestingly, Y-STR analyses showed a closer affinity of Gujjars to other nomadic populations of Pashtuns from Baghlans and Kunduz provinces of Afghanistan and Pashtuns and Sindhis of Pakistan. Gujjars exhibited lesser genetic diversity as compared to Ladakhi population. M30f and M9 were the most abundant mitochondrial haplogroups observed among Gujjars and Ladakhis, respectively. A lower matrilineal to patrilineal diversity was observed for both these populations. The current study presents the first comprehensive analysis of Gujjars and Ladakhis and reveals their unique genetic affiliations with other populations of the world. The two northernmost Union Territories of India viz., Jammu and Kashmir (J&K) and Ladakh, owing to their geographical location, are believed to have served as a corridor for ancient human migrations between main land of Indian subcontinent and North-East Asia, Eurasia or Africa3. The populations of J&K and Ladakh offer a unique platform for looking into the past anthropological and demographic events which may have shaped the extant human population diversity. However, there is scant information about these populations in phylogenetic studies reported in the literature6.The Indian subcontinent, which represents about one-sixth of the world population, is a unique conglomerate of multiple cultures, languages and genetic diversity. Together with sub-Himalayan countries and the present day Pakistan, Bangladesh and Sri Lanka, the Indian subcontinent is one of the oldest geographical regions inhabited by modern humans and is a witness to ancient human migratory histories7. Few research groups had previously reported on the genetic diversity studies among Gujjars11, however, considering the unique geographical distribution of Gujjars and their under-representation in previous genetic studies, it would be interesting to examine their genetic diversity and to get a deeper insight into their relationship with other populations.In this study we have attempted to understand the genetic relationship of Gujjars (GJ) from Jammu region of J&K and Ladakhis (LL) with other populations of Indian subcontinent. Gujjars inhabit the north-western region of the Indian subcontinent spanning across the regions of J&K, Himachal Pradesh (HP), Rajasthan (RJ), Haryana and Gujarat in India, and in the neighboring countries of Pakistan and Afghanistan. In the Union Territory of J&K, Gujjars constitute the third-largest population group and follow a nomadic/semi-nomadic lifestyle and are dependent on rearing of cattle, goats and sheep12. Although currently it is regarded as a remote area, Ladakh was at the cross-roads of trading routes, described as the Silk Route for several centuries. A previous study based on patrilineal markers (Y-STRs and Y-SNPs) had suggested Ladakhi population as a genetic mosaic, owing to multiple contributors from the past migratory events13. Although, the authors in this report attempted to provide insights into the genetic diversity of Ladakh population, their study was limited by the use of only patrilineal markers13. In the light of the recent findings that migrations through this region were not male-exclusive5, it was interesting to investigate the matrilineal genetic diversity in Ladakhis. The mitochondrial (mt) DNA analysis not only supplements our current knowledge of patrilineal history of contemporary Ladakh population but also would reflect on their matrilineal relationship with other populations.Ladakh located on high altitude ) faces harsh weather conditions. It is considered to be one of the last inhabited regions by prehistoric humans14 and Y-chromosomal STRs15 that included populations from J&K (JK) (other than GJ and LL populations), suggested that JK individuals were not genetically isolated from other populations of India. This observation was in concordance with a previous report16. Interestingly, in another study from our group, which was aimed at designing an autosomal single nucleotide polymorphism (SNP)-based panel for human identification (HID), it was observed that GJ and LL exhibited lower genetic affinity with other populations in their geographical proximity17. In the present study, based on the analysis of autosomal SNPs and STRs, Y-chromosomal STRs and control region of mtDNA, we sought to gain a broader picture of genetic diversity of Gujjars (GJ) and Ladakhis (LL)  viz., Africa (YRI), Europe (GBR), East Asia (CHB), Pakistan  (PK) from Human Genome Diversity Project (CEPH Stanford data) were analyzed to assess the genetic relatedness of GJ and LL with the reference populations. Of the 275 SNPs shortlisted for the HID purposes as described in our previous study, 21 SNPs which had failed Hardy-Weinberg equilibrium (HWE) test were discarded and further analysis was based on 254 SNPs.Autosomal SNPs data of samples from Gujjars (GJ), Ladakhis (LL), other populations of Jammu and Kashmir (JK), Uttarakhand (UK) (from our previous study)ST = 0.017), yet, the range of FST was quite variable, e.g., from as less as 0.003 (between JK and UK population) to 0.032 (between GJ and YRI population)  and lower (avg FST  = 0.016) values of average FST, respectively. The genetic affiliations between these populations was better visualized from the Principal Coordinate Analysis (PCoA) based on SNPs , Caucasians (CAUCA), African-Americans (AFAM) and Asians (ASIA), LL showed greater affinity towards Asian populations, while GJ was found to be genetically distant from all the reference populations , Han (China) and Magar  populations based on pairwise RST.To further investigate the genetic proximity of GJ to Pashtuns, we interrogated R47/48 (GJ) and 41/69 (LL) males were assigned haplogroups with > 99% probability as mentioned in the Athey’s algorithm. The distribution of Y-STR based abundant haplogroups in GJ and LL samples is represented in Fig. Genealogical studies were performed to get an estimate of time to the most recent common ancestor (TMRCA) of Gujjars and Ladakhis based on the most abundant Y-STR haplogroup present in these populations. The TMRCA of R1a Gujjars was estimated to be 25.61 kya (thousand years ago). TMRCA was also calculated for R1a individuals from different parts of the country, which was found to be lower than TMRCA of R1a Gujjars. TMRCA for major haplogroups of Ladakh population viz., R1a, Q and L haplogroups was estimated to be approximately 18 kya, 11 kya and 10 kya, respectively.ST of 0.09 was observed across all the populations. However, the average pairwise FST value for GJ was comparatively higher (0.11) than the average FST value of 0.08 for LL samples. As expected, the FST for LL individuals was similar (FST = 0.02) to the previously reported Ladakh population .GJ showed the lowest mtDNA nucleotide diversity among all the reference Indian populations  for GJ and LL is shown in Supplementary Fig. Bayesian Evolutionary Analysis Sampling Trees (BEAST) analysis of the major mtDNA derived haplogroup M in both GJ and LL populations showed that they clustered according to their population affiliations  was observed for these populations. Further, no correlation was observed among the mtDNA and Y-STR based pairwise-distance matrices for these populations . The independence of the mtDNA and Y-STR data was corroborated by Mantel test, wherein a weak negative correlation was observed (r = −0.02) and was not statistically significant (p = 0.417).For the comparative analyses based on mtDNA and Y-STR data, both GJ and LL populations were compared to reference populations from seven States of India viz., Uttar Pradesh (UP), Assam (AS), Jharkhand (JH), Maharashtra (MH), Rajasthan (RJ), Andhra Pradesh (AP) and Karnataka (KA). A high ratio of F13. Even though both the populations are in geographic proximity, their origin and settlement in India are vastly different. The GJ community is primarily nomadic and is believed to practice a highly endogamous culture, conservative lifestyle, religious practices and traditional occupation as compared to other populations in the vicinity. In contrast, the enormous diversity in terms of cultural, religious and food practices in the populations of LL is believed to be the result of congruence of different ancestral groups13.The present study was undertaken to understand the genetic diversity of Gujjars (GJ) and Ladakhis (LL). Both the populations selected for the study possess interesting history and their arrival in the Indian subcontinent is believed to be a consequence of different demographic eventsST  even among African, European and East Asian populations and hence, are less polymorphic across populations.On similar lines, comparison with other populations from YHRD; LL samples were found to be closely associated with Chinese (Uighyurs and Han) and Nepalese (Magar) populations, which might be due to their close geographic proximity. These observations suggested the influence of past demographic events which might have led to the isolated nature of GJ and LL populations. Although LL individuals were found to be genetically different from other populations based on Y-STRs, this observation was not reflected by the analysis of autosomal SNPs used in this study. The reason for this could be due to the fact that the SNPs employed here were originally shortlisted for human identification purposes in such a way that their allele frequency remains similar  in Ladakh region. Since these estimates were based on STRs, which have high rates of mutation, Y-SNP analysis would help to get a finer resolution regarding the haplogroups.GJ showed limited number of Y-STRs-based haplogroups when compared to LL Fig. . Athey’s13. Pairwise FST values among the populations based on mtDNA sequences were similar to that of SNP-based analyses, wherein the mean FST for GJ was higher than that of LL. This indicated that GJ individuals were relatively more genetically distant to other reference populations when compared to LL.Analysis of the mtDNA sequences from the GJ, LL and other reference populations showed considerable variations among the populations. However, since most of the previous studies with the reference populations targeted a particular haplogroup/caste/tribe, those samplings may not be considered as purely random. This may be further aggravated by the large differences in sample size leading to non-uniform distribution of different haplogroups. Although merging of different populations from the same state might have brought uniformity to some extent, however, since most studies were aimed at the abundant haplogroups, the rarer haplogroups could have been neglected and thus the mtDNA genetic relationship among the populations of different states may not be completely accurate. However, in the present study, when GJ and LL populations were compared based on mtDNA, the results were in concordance with those of autosomal SNPs, i.e., both GJ and LL were genetically distant than other populations. The LL population displayed higher genetic diversity as compared to GJ (0.009 for GJ and 0.01 for LL) suggesting higher heterogeneity in LL samples, consistent with the previous reportST (0.02) for the LL samples when compared to the previous report on Ladakh population20 based on the mtDNA analysis added reliability to the current dataset and suggested that the results drawn from this study might be a true representative of the LL population and therefore, the genetic data generated here could be used as a reference for future studies  population, which supported a previous finding based on HLA genotyping26  between the Y-STR and the mtDNA pairwise distances indicates that as the matrilineal distance increases, the patrilineal distance decreases and vice versa. One of the reasons why the value of Mantel test was not significant even though the populations were patrilocal could be the nomadic/semi-nomadic lifestyle of the GJ leading to male migration. However, similar observations were reported in various other populations as well34. Further analysis with increased sample size encompassing ancestry informative SNPs, complete mtDNA sequences and Y-chromosomal SNPs might shed more light on investigations into matrilocality versus patrilocality among these populations.The higher ratio of male to female FIn summary, the present study represents the first detailed analyses of two interesting populations residing in J&K and Ladakh territories of India (GJ and LL) using multiple sets of DNA-based markers. In agreement with the previous studies, the LL individuals were observed to be genetically diverse, possibly due to their presence in the historically important ancient trade routes, which might have paved the settlement of diverse genetic groups, and enriching the genetic pool at this location. On the other hand, the GJ displayed very low genetic heterogeneity, which may be because of their endogamous and conservative lifestyle, along with genetic isolation for the past several hundreds of years. The present study would help in better understanding of the human genetic diversity in the Union Territories of J&K and Ladakh in north India and their genetic relatedness to populations from neighboring regions and provides deeper insights into our knowledge about ancient settlements in this part of the world.35. A detailed description of sampling sites along with the coordinates of latitude and longitude is provided in Fig. A total of 185 unrelated adult volunteers from two population groups viz. Gujjars (GJ)  and Ladakhis (LL)  were selected for this study. The individuals informed that they have been residing in the same geographic region for at least since three generations. All the participants voluntarily contributed 2 ml of saliva samples after signing an informed consent. The saliva samples were collected in an unstimulated fashion in sterile tubes that were sealed and transported to the laboratory at room temperature for DNA extraction using the salt precipitation method as described previously® Fusion (PP Fusion) and PowerPlex® Y23 (PPY23)  chemistries respectively, were amplified according to the manufacturer’s instructions in a GeneAmp® 9700 thermal cycler . The amplified products were size fractionated and detected by capillary electrophoresis on the ABI Prism 3130 xl Genetic Analyzer (Thermo Fisher Scientific) and analyzed using GeneMapper® ID version 3.2.1 (Thermo Fisher Scientific). The control DNA 2800 M was genotyped employing both the chemistries for quality control purposes.22 autosomal STRs and 23 Y-chromosomal STRs present in the PowerPlex36 employing 2X SapphireAmp® Fast PCR Master Mix . The amplification was performed using GeneAmp® PCR System 9700  with the following thermal profile: initial denaturation at 94°C for 60 seconds; 30 cycles of denaturation at 98°C for 5 seconds, annealing at 58°C for 5 seconds, extension at 72°C for 5 seconds and final extension at 72°C for 5 minutes. The amplicon sizes of the PCR products were examined by electrophoresis on a 2% agarose gel followed by solid phase reversible immobilization (SPRI) purification37.The control region of mitochondrial genome (1.1 kilobase (kb)) was amplified for 183 samples  with L15996 and H583 primers® PCR System 9700 (Thermo Fisher Scientific). In addition to L15996 and H583, three internal primers along with L27, H7 and H409 primers were used for sequencing the control region36. The reaction conditions used for cycle sequencing were: initial denaturation at 96°C for 30 seconds, 25 cycles of denaturation at 96°C for 30 seconds, annealing at 50°C for 15 seconds and extension at 60°C for 4 minutes. The products of Sanger sequencing were ethanol precipitated, denatured and electrophoresis was performed on the automated sequencer ABI Prism 3730xl (Thermo Fisher Scientific) as per the manufacturer’s instructions.Cycle sequencing of the purified products was carried out using the Big Dye™ Terminator Sequencing kit  on a GeneAmp17. Post filtering, a total of 275 SNPs were shortlisted based on high heterozygosity (≥0.4) and low Wright’s F-statistic, FST ≤ 0.02 and were genotyped in various Indian populations (N = 462), including samples of GJ (N = 45) and LL (N = 56) from J&K. The SNP genotyping data from these two populations were reanalyzed here along with other samples from JK (N = 38), UK (N = 30), YRI (N = 88), GBR (N = 88), CHB (N = 97) and PK (N = 24). The SNP genotype data is available on figshare (https://figshare.com/s/3e7f91b7ecd6298826e9).In a previous study from our group, a stringent bioinformatic approach was used to shortlist candidate autosomal SNPs from public databases in order to design a panel for the purposes of forensic HID38 and population pairwise FST was calculated using Arlequin 3.5.1.239. PCoA plot was generated based on relative distance using the function prcomp implemented in R (https://cran.r-project.org/).The SNPs which failed the HWE test for GJ or LL populations and SNPs with higher proportions of missing data (>5%) were discarded. To glean the differences between allelic distribution, cluster analyses was carried out with STRUCTURE v2.3.414 (Table http://www.yhrd.org)19 and accession numbers YA004401 and YA004402 were assigned for LL and GJ populations, respectively.Amelogenin and DYS391 from PP Fusion and DYS385 a/b (multi-copy marker) from PPY23 chemistries were excluded from further analysis. For comparison purposes, Indian populations previously studied by our group were also incorporatedST based on both autosomal STRs and Y-STRs was calculated employing GenALEx v6.541 to examine the genetic relationship of these two populations with those from other regions of the country included in our previous study14. PCoA based on pairwise distance matrices to visualize the genetic relationship among the populations were plotted using GenALEx v6.5. To test for the presence of clustering among populations, STRUTURE 2.3.4 run was iterated fifteen times with a burn-ins of 1,000 iterations and 10,000 Markov Chain Monte Carlo (MCMC) iterations, assuming an admixture model of the concerned populations. Further, the best cluster was calculated using STRUCTURE Harvester employing Evanno’s method42 and the STRUCTURE results were processed with distruct43.Nei’s genetic distance and pairwise FST and MDS was performed to infer the genetic relationship among the various populations19. For comparison of patrilineal affinities with Indian populations, Y-STR data from our laboratory was included15, whereas for comparison with populations from neighboring countries, published data was incorporated18  for population growth rate per generation (α), gamma  for the time in coalescent units when exponential growth began and normal  for effective population size (N). Mutation rates and prior distribution for each marker was considered based on an earlier study47. MCMC simulations were iterated for 100,000 cycles and the first 1000 were discarded as burn-ins. The product of generation time ‘N’ (30 years) and the height of the tree ‘T’ yielded the TMRCA.To estimate the TMRCA of the major haplogroups in the populations, Bayesian Analysis of Trees With Internal Node Generation (BATWING) analysis48. The sequences were aligned to rCRS and all the five overlapping products from internal primers were concatenated to construct a single consensus sequence employing mtDNA profiler: mitochondrial DNA sequence analysis tool (www.mtprofiler.yonsei.ac.kr). For comparison of mtDNA-based genetic relationship with other previously studied Indian populations, whole mtDNA sequences that were publicly available for other Indian populations56 were downloaded from mtDB-Human Mitochondrial Genome Database (http://www.mtdb.igp.uu.se/)57  and Ladakh population (LL) along with mtDNA variations are available on figshare (c).MtDNA sequence quality was checked using BioEdit v7.2.5 programST) were calculated with Arlequin 3.5.1.239. AMOVA was carried out by merging the reference populations sampled from the same state, followed by grouping the populations based on the major geographic regions of the respective state, viz., north, west, east and south India. A neighbor-joining tree based on pairwise FST for the present and reference populations was constructed using neighbour function in Phylip and was visualized in Figtree ver. 1.3.1 (http://tree.bio.ed.ac.uk/software/figtree/). MSN for GJ and LL populations was constructed using POPART v1.759. Nucleotide diversity and Tajima’s D-statistics for the populations were also calculated using POPART v1.7. Haplogroup assignment based on the mtDNA sequences for all the samples was performed by MITOMASTER60.Molecular diversity indices, haplotype diversity, AMOVA and pairwise Wright’s F-statistics (F61. For the present dataset, the best nucleotide substitution model was determined by MEGA6. To perform Bayesian MCMC analysis, HKY + G nucleotide substitution model was utilized and a fixed substitution rate of 9.88 × 10−8 substitutions/site/year was applied as suggested previously62. The MCMC run was executed for 10 million generations and the sampling was carried out at every 1000 steps, while the initial 10% of the run was discarded as burn-ins. The input for BEAST analyses was prepared through BEAUti and the output was analyzed by Tracer v1.6. The tree obtained from coalescence analysis was plotted using Figtree ver. 1.3.1.MCMC-based coalescence analysis was performed in order to estimate TMRCA for the most abundant mitochondrial haplogroup common in both the GJ and LL populations employing BEAST v.2.4.8ST among all populations was calculated using Arlequin 3.5.1.2 and the corresponding distance matrices were constructed for comparative analyses of mtDNA and Y-STR diversity for the populations from India including GJ, LL, UP, AS, JH, MH, RJ, AP and KA (abbreviations explained in Table 15. The correlation between the two matrices was examined by utilizing cor.test function implemented in R. Mantel test was carried out by employing mantel.r test function implemented in ade4 package in R63.Pairwise FElectronic Supplementary Materials.