The current study aimed at genomic characterization and improved understanding of genetic diversity of two Indian mithun populations  using genome wide genotype data generated with Illumina BovineHD BeadChip. Eight additional populations of taurine cattle (Holstein and NDama), indicine cattle (Gir) and other evolutionarily closely related species  were also included in this analysis (N = 137) for comparative purposes. Our results show that the genetic background of mithun populations was uniform with few possible signs of indicine admixture. In general, observed and expected heterozygosities were quite similar in these two populations. We also observed increased frequencies of small-sized runs of homozygosity (ROH) in the farm population compared to field mithuns. On the other hand, longer ROH were more frequent in field mithuns, which suggests recent founder effects and subsequent genetic drift due to close breeding in farmer herds. This represents the first study providing genetic evidence about the population structure and genomic diversity of Indian mithun. The information generated will be utilized for devising suitable breeding and conservation programme for mithun, an endangered bovine species in India. Bos frontalis), also known as gayal, a unique bovine species, has a limited geographical distribution primarily restricted to the North-Eastern Hilly (NEH) region of India, Myanmar, hilly provinces of Bangladesh, Bhutan and Yunan province of China1. Though it is difficult to ascertain the actual population of Mithun in the world, India possesses 0.30 million mithun which constitutes ~97.57% of the world population 2. Mithun has a very special socio-cultural status among the indigenous tribal population of NEH region of India. Having the very high value in the barter system, owning mithun is considered as a sign of prosperity.Mithun  and quantity (higher dressing %)6. Mithun have higher feed conversion efficiency than local cattle7. The current population size of mithun in India is not large. However, considering its importance in widening the biodiversity base and adaptation to a humid sub-tropical climate and a hilly topography, it could be exploited as an alternative means of livelihood as well as for improved food and nutrition security in its present habitat and in similar environments elsewhere.Mithun are traditionally raised as a meat animal in a free-range system in the sub-tropical rain forest with almost zero input. As a part of socio-cultural practices, mithuns are sacrificed during rituals and festivals to offer feastGenetic improvement of mithun, however, is impeded by the way mithun is presently reared. Under traditional system of rearing, mithuns are let loose in the forest with minimal intervention. Recording of traits and pedigree information is almost non-existent. The size of a typical mithun herds ranges from 50 to 100. Herds are generally served by a few dominant bulls. The mithun population may therefore suffer from inbreeding. Under these circumstances, the genetic diversity of the mithun population needs to be described. This will assist ongoing mithun conservation and genetic improvement programme and for devising a suitable breeding policy.8. The availability of high-throughput SNP genotyping platforms makes it feasible to undertake high-resolution scans by using large numbers of SNP markers distributed across the whole genome. SNPs are useful in studying livestock genetic diversity and population structure9. Although a large number of SNPs have been identified in bovine genome-sequencing projects, few of these have been validated outside Bos taurus, as for example in mithun10. The Illumina BovineHD BeadChip  with 777k SNPs was introduced and utilized for genotyping studies in various breeds of cattle, but has also been used in other members of the bovidae, for example, yak, gaur, buffalo and wild anoa16.Among a wide range of molecular markers developed, single nucleotide polymorphisms (SNPs) are the most abundant, widely dispersed throughout genomes, and have variable distribution among species17. Hence, we used the unsupervised clustering analysis (carried out with the ADMIXTURE software)18 to estimate individuals’ ancestries from SNP genotypes to assess the extent of admixture in mithun with other cattle species  and expected (He) heterozygosities estimated in the Indian mithun population (farm and field population) ranged from 0.25 to 0.17, and 0.25 to 0.18, respectively (Table IS) based on the observed versus expected number of homozygous genotypes, were found to be 0.06 ± 0.02 and 0.02 ± 0.01 for the farm and field animals  in the autosomes of 48 farm mithun and 24 field mithun animals were determined using PLINK1.9The total length of ROH per animal was averaged within population in six sized windows: 250 kb–1 Mb, 1 Mb–2 Mb, 2 Mb–4 Mb, 4 Mb–8 Mb, 8 Mb–16 Mb and >16 Mb. The average total length of ROH per animal was 823.7, 242.1, 92.30, 47.03, 66.22 and 110.9 Mb in the farm and 735.8, 245.0, 121.3, 84.4, 86.0 and 87.4 Mb in the field population for these six length categories.Summary statistics of ROH observed are outlined in Table Unsupervised clustering analysis using ADMIXTURE software program were carried out for 10 populations . The admixture analysis including other species grouped mithuns from farm and field together, and were fairly homogeneous Fig. , K = 4–7The principal component analysis was done using 50k markers for eight Bos species and two mithun populations (firm and field). The first two principal components (PC1 and PC2) grouped farm and field mithuns in a cluster with gaur, yak and bison, separated from three other major clusters of two taurine cattle (NDama and Holstein), and indicine cattle  positioned mithuns along with gaur, yak, bison and Bali cattle in distinct clusters from indicine and taurine cattle populations. Principal component 2 (PC2) separated two taurine breeds into separate clusters. Principal component 3 (PC3) further separated Gir from other populations Fig. .Figure 520 analysis was used to study the population splits and subsequent gene flows. We constructed a phylogenetic tree of all the bovine species without adding any migrations and assuming wild buffalo (OWB) as outgroup using Treemix analysis . Myanmar , Bangladesh , China  and Bhutan  also home small populations of mithun in Asia2. Information regarding genetic diversity and population structure of Indian mithun remains scanty. A conservation programme for mithun has been initiated at Khonoma and Thevopisu villages, Nagaland in their native breeding tracts in India. Genetic improvement in mithun through a systematic breeding program could not be initiated under field conditions due to the small number of animals, uncontrolled mating, and their scattered distribution across remote and inhospitable hilly terrain.India has the largest mithun population (~97.57%) in the world , observed heterozygosity (Ho) and estimates of inbreeding (FIS and FST). FST between two populations was low (0.03) indicating a close genetic connection between them21. Mithun is assumed to be a close evolutionary relative of gaur23. Hence, the BovineHD BeadChip was used to genotype mithuns and available literature on cattle was referred for comparison in the present study. Genetic diversity in terms of average heterozygosity in both mithun populations (0.17–0.25) was found to be similar to zebu and African taurine cattle14, and was in the same range as for taurine cattle from West Africa (from 0.18 for African Lagune to 0.22 for Somba cattle) or East Africa (0.24 for Sheko cattle)10. However, SNPs that are included in the analyses are polymorphic within Mithun, but originate from bovine and are thus expected to be old SNPs (polymorphic in the common ancestor of Mithun and Bos taurus) leading to biased allele frequency spectrum.Genetic diversity of two groups of Indian mithun population (farm and field groups) were assessed by expected heterozygosity  than in the farm population (0.06). We see some evidence of mating between relatives in the farm population, while there is little evidence of mating among close relative in the field population. The FIS values in our study were similar to that reported24 in mithuns from Bangladesh using the Illumina BovineSNP50 BeadChip, mean expected heterozygosity of 0.148 ± 0.14 with a heterozygote deficiency of 0.06 (FIS).We used Fo and He of mithun population in our study compared to taurine and indicine cattle breeds as reported by the Bovine HapMap Consortium13 and Korean cattle populations16 may also be attributed to ascertainment bias in SNP discovery of the BovineHD BeadChip. Previously low heterozygosity estimates of Tunisian cattle populations were likewise attributed to ascertainment bias in the design of the BovineSNP50 BeadChip as polymorphic sites of African origin present in the genome of Tunisian cattle were not included in the chip14. The Bovine HapMap Consortium reported that nucleotide diversity in (Brahman) cattle, a crossbred of indicine breeds, is more than twice that observed within Holstein and Angus breeds26 as expected.Lower heterozygosity values H29.Our study revealed a higher number of short and medium ROH  than longer categories (8–16 Mb and >16 Mb) in both the farm and field mithun population. This reflects ancestors shared between the parents long ago, can be indicative of selective sweep, ancient inbreeding or bottleneck. The farm population showed greater ROH length and numbers compared to field population, which was as per expectation due to higher level of inbreeding and small population size parental population in the farm. Similar observations in various cattle breeds were reported as short ROH are generally due to older haplotype relatedness, while longer ROH result from more recent inbreeding31. Long ROH indicates consanguinity between an individual’s parents27. Shorter ROH indicate more distant demographic and selective events after repeated fragmentation of chromosome segments by recombination27. In particular, recent inbreeding resulting from the mating of closely-related ancestors leads to a high occurrence of long ROH. On the other hand, very long ROH sometimes occur in outbred populations32.Length and frequency of ROH provide information about demographic history as well as recent inbreeding in individuals33. Level of inbreeding can be estimated accurately in cattle population without knowledge of the pedigree using Illumina BovineHD SNP genotyping assay34. FROH, the genomic inbreeding coefficient, was found to be highly correlated with pedigree inbreeding in a small group of populations34.In human, short ROH (<1.5 mb) is due to ancient linkage disequilibrium through inheriting parental common haplotypes, whereas, bigger ROH is due the history of related parents in recent timesROH for the various categories  in the farm and field mithun population were generally low, probably indicating larger effective population sizes in the past. We observed relatively higher mean FROH for the 250kb–1Mb category in the farm mithun (0.335). Such short ROH reflect events at least 25 generations ago. With generation interval of approximately 5–10 years in mithun, this translates to selection 100 or 200 years in the past. It seems that probably mithun population has experienced a population bottleneck in the past.Estimates of mean FROH) with lengths above 1 Mb was in intensely selected Brown Swiss breed (0.151–0.156)35 and in Holstein cattle (0.116)29, while lower FROH for unselected cattle breeds Pinzgauer and Tyrolean Grey domestic cattle 35, and the lowest in unselected, preserved Polish Red breed (0.057)29.Our results fit well with other genotyping studies in various breeds of cattle using Illumina BovineHD BeadChip and validated through Illumina BovineSNP50 BeadChip, viz. average autozygosity calculated from ROH  and a diverse field population (n = 24) from four locations of North Eastern Hill Region  falling in the native mithun breeding tracts of India. All the experiments were performed in accordance with relevant guidelines and regulations approved by the Institutional Animal Ethics Committee, ICAR-NRC on Mithun, Nagaland.Blood samples were collected from jugular vein in a vacutainer tube (BD) containing EDTA by a qualified veterinarian and were transported to the laboratory in cool pack as soon as possible. The DNA was isolated using a standard blood DNA isolation kit (Promega #A1620) as per the manufacturer’s instructions.Genomic DNA from each mithun was quantified to assure a concentration of at least 50 ng/µl genomic DNA required by the Illumina® Infinium® SNP genotyping platform. A total of 72 mithun  were genotyped by Sandor Lifesciences Pvt. Ltd., Banjara Hills, Hyderabad, India with the Illumina® BovineHD BeadChip assay, following the manufacturer’s protocol. Genotypes were called using the validated standard cluster file provided by the manufacturer. Only the autosomal SNPs were considered in this study.https://support.illumina.com/array/arraysoftware/genomestudio/documentation.html). Genome Studio was also used to generate PLINK1.9 data files (in.ped and.map format)19 for further analyses. The SNPs located on sex chromosomes, not polymorphic or without known position in the cattle genome were excluded from further analysis. The Bos taurus genome assembly  was used as a reference genome due to the absence of a published mithun genome.Samples and marker based quality control was performed using the Illumina’s Genome Studio software . SNP markers showing deviation from Hardy-Weinberg proportions (HWP) based on parental genotype data (p < 0.001) were removed. The attributes considered for quality control finally include filtering of SNPs with call rate ≥95%, MAF ≥5% and HWP ≥0.001 using PLINK1.9 software. After this filtration 139,350 polymorphic SNPs remained for analysis.To identify closely related individual, a pair-wise identity-by-state (IBS) distance analysis was performed using PLINK1.9The parameters estimated to study the genomic diversity in mithun populations included estimates of heterozygosity, detection of runs of homozygosity (ROH) and estimates of inbreeding coefficients.o) and expected (He) heterozygosities, as well as the estimates of inbreeding for mithun populations were estimated using PLINK1.9 software19. The FST was estimated using PLINK software, which uses method introduced by Weir and Cockerham (1984)36.The observed  were detected to determine the extent of autozygosity in the mithun populations. A ROH is defined as a contiguous length of homozygous genotypes. A ROH of sufficient length indicates the two copies of the chromosome in this region are identical-by-descent (IBD)19 was used to identify ROH by running a sliding window that scans the genomic distribution of SNP data to identify stretches of homozygous SNPs. A minimum number of 10 consecutive homozygous SNP and zero heterozygotes were allowed in each window. A maximum gap between SNP of 1,000 kb was allowed. With high-density SNP data this approach mostly detects truly autozygous segments37. Moreover, this strategy is particularly suitable for livestock populations because they have much higher levels of autozygosity than model organisms making identification of longer ROH easy37.PLINK1.9Detected ROH were classified in windows: ROH in the range of 250 kb-1 Mb, 1 Mb–2 Mb, 2 Mb–4 Mb, 4 Mb–8 Mb, 8 Mb–16 Mb and >16 Mb.ROH of each individual was estimated as the sum of length of autosomal ROH divided by the total length of the autosomes covered by markers34.In the present study, genomic autozygosity, Fhttp://widde.toulouse.inra.fr/widde/]38 and were included along with two mithun populations to explore the evolutionary relationship of mithun with other bovine species  was included assuming contribution of gaur to mithun genomes as its presumed wild relative; while yak , bison  and Bali cattle  were taken into account to find out possible genetic contribution towards mithun genome, if any. As it is assumed that there might be some introgression due to crossing between mithun and cattle under the field conditions, the indicine cattle, Gir (Bos indicus) genotype data was included. Taurine cattle  were included to identify any taurine introgression and anoa, the wild buffalo  was included as an outgroup in the present study.The main goal of the present study was genomic characterization of two mithun populations (farm and field). Another eight Illumina® BovineHD BeadChip or BovineSNP50 BeadChip genotype data sets were collected from previously published work, available at [es Table . The wil19 if they did not have call rate of at least 95%. No filtering was done at this step for MAF, except SNPs monomorphic across all breeds. Summary statistics such as heterozygosity and inbreeding coefficient were computed and results are presented  showing admixture were removed from the Treemix analysis. Nodes robustness was estimated with 500 bootstrap replicates and plotted using the Treemix bootstrap function of BITE40. Admixture detection was done using the F3 statistics computed using threepop program from the Treemix software package.Phylogenetic analysis was carried out using TreemixApproval of Institutional Animal Ethics Committee was obtained for this study including collection of mithun blood samples and DNA extraction from the Institute research farm. Blood samples of the mithun were collected from the farm and with the permission of the mithun owners from the field by qualified veterinarians.Data supporting this paper were generated by ICAR-NRC on Mithun. The phenotype and genotype data are available with the Data Cell of the Institute and should be requested directly from the corresponding authors or the ICAR-NRC on Mithun.


Pleurobranchaea maculata is a rarely studied species of the Heterobranchia found throughout the south and western Pacific–and recently recorded in Argentina–whose population genetic structure is unknown. Interest in the species was sparked in New Zealand following a series of dog deaths caused by ingestions of slugs containing high levels of the neurotoxin tetrodotoxin. Here we describe the genetic structure and demographic history of P. maculata populations from five principle locations in New Zealand based on extensive analyses of 12 microsatellite loci and the COI and CytB regions of mitochondrial DNA (mtDNA). Microsatellite data showed significant differentiation between northern and southern populations with population structure being associated with previously described regional variations in tetrodotoxin concentrations. However, mtDNA sequence data did not support such structure, revealing a star-shaped haplotype network with estimates of expansion time suggesting a population expansion in the Pleistocene era. Inclusion of publicly available mtDNA sequence sea slugs from Argentina did not alter the star-shaped network. We interpret our data as indicative of a single founding population that fragmented following geographical changes that brought about the present day north-south divide in New Zealand waters. Lack of evidence of cryptic species supports data indicating that differences in toxicity of individuals among regions are a consequence of differences in diet. It is nmolluscs , 3. Littmolluscs , 3, 4.P. maculata [P. maculata that have recently invaded coastal waters of Argentina also contain TTX [In late 2009 this otherwise little-known sea slug attracted attention after it was implicated in dog deaths on beaches in Auckland . Analysemaculata . This wamaculata . P. macutain TTX , 7.P. maculata, there is mounting evidence that toxin accumulation occurs through feeding [P. maculata [P. maculata populations [TTX is a potent neurotoxin found in numerous terrestrial and marine organisms, but neither the origin of TTX nor the causes of variation in TTX levels among species are understood. The structure of TTX suggests a microbial origin  and whil feeding –14. An amaculata , 16. Obsulations , and verulations  suggestsP. maculata populations from northern regions of the North Island  have high TTX concentrations , while populations from the South Island (Nelson and Kaikoura) have trace amounts of TTX (<0.5 mg kg-1) or none at all [-1 [-1) supporting the notion of a geographical cline [Studies of individual and temporal differences in TTX concentration have established that f TTX <0. mg kg-1 e at all , 14. A rP. maculata is unknown, but variation in the established differences in toxicity between northern and southern populations suggests that geographic variability in TTX concentration correlates with genetic structure–even the possibility that northern and southern populations define separate species. Here we test this hypothesis using a combination of microsatellite and mitochondrial DNA (mtDNA) sequence markers.The genetic structure of et al. [et al. [et al. [A total of 156 samples were collected from nine regions around New Zealand between 2009 and 2013 . DNA waset al. . The Tau [et al. .P. maculata individuals from Wellington (WL) as only one individual had been previously tested [At the outset of this study there was limited knowledge of the toxicity of y tested . To obtay tested . The TTXy tested .Population-level analyses were performed only for five populations which are Ti Point (TP), AKL, TR, WL and Nelson (NL) due to the small sample sizes of the other locations. TP, AKL and TR, which included highly toxic individuals  were desPm01, 02, 07, 08, 09, 10, 11, 13, 17, 19, 20 and 23) [et al. [Twelve microsatellite markers (CytB) and cytochrome oxidase I (COI) genes, respectively, were amplified and sequenced in all 156 P. maculata individuals. For details regarding the primer pairs and amplification see Methodology and Table C in A 1060 bp and 1153 bp region of mitochondrial cytochrome B (FIS [Na), allele frequencies, observed heterozygosity (Ho), expected heterozygosity (He) [PA) per locus and population were calculated using GenAlex v6 [AR) and private allelic richness (PAR) were calculated using the rarefaction method implemented in ADZE v.1.0 [He and AR were used to compare the amount of genetic diversity among populations from different regions using one-way Wilcoxon-Mann-Whitney Test [Microsatellite genotyping data were tested for scoring errors due to stuttering, null alleles, and large allele dropout using MICRO-CHECKER v.2.2 . DepartuFIS  with 9,9FIS . CorrectFIS . LinkageFIS  for eachity (He) –25, and nAlex v6 . AllelicZE v.1.0 . He and ney Test .Sin), haplotypes (Hap) and segregating sites (S), the average number of nucleotide differences between sequences (k) [h) and nucleotide diversity (π) [CytB and COI for each sampling location using DnaSP 5.10.0.1 [For mtDNA, several estimates of genetic diversity, including the number of singletons (nces (k) , haplotysity (π)  were cal5.10.0.1 .FST [Dest) in GenAlex. The statistical power to detect true population differentiation and α-error probability were assessed in POWSIM v4.1 [K) and individuals were assigned to populations using a Bayesian assignment approach. The most likely value of K was resolved using the ΔK method [For microsatellite data, global differentiation and pairwise differentiation between each pair of populations was investigated using various differentiation estimators, including a log-likelihood ratio (G)-based test , fixatioFST , and JosFST  differenSIM v4.1 . STRUCTUSIM v4.1  was usedK method  with theK method , and theK method . DestrucK method  was usedhttp://www.leigh.net.nz/software.shtml) was used to create a median joining haplotype network (MJN) [P. maculata samples obtained from individuals isolated in Argentina . A saturation test was performed in DAMBE v6.2.9 [Pinv) was estimated by Jmodeltest v0.1.1 [Pinv values  obtained from the most likely models suggested by the software  and default settings for other parameters were used for the calculations. Haplotype-frequency-based FST [ΘST [ΘST, the Tamura-Nei mutational model [For the mtDNA sequences, POPART v1 (rk (MJN) . We creaE v6.2.9  using thE v6.2.9 . The prot v0.1.1  with theFST [ΘST  were calFST [ΘST  to estimal model  was usedDM), which calculates the mean character differences between individuals, and clonal distances (DCL), which assumes a stepwise mutational model (SMM) [DSEQ) was calculated as a standardized bp difference between every pair of individuals in Mothur v1.33.3 [Patterns of differentiation were also analysed using a multivariate approach. For microsatellite data, the Manhattan distance (el (SMM) , were us v1.33.3 . Statist v1.33.3  with PER v1.33.3 . Pattern v1.33.3 . Permuta v1.33.3 , 53 was  v1.33.3 , 54 was  v1.33.3 . All perP. maculata individuals from this study and three P. maculata individuals from Argentina using COI sequences [Pleurobranchaea meckeli (KU365727.1), Pleurobranchaea nevaezelandiae, Pleurobranchus peronii (KM521745.1), Berthella ocellata (KM521694.1) and Berthellina citrina (KM521694.1) were used as out-groups. The analysis involved 200 informative positions of 616. The phylogeny was tested with 1,000 bootstrap replicates.A maximum likelihood (ML) tree using the Tamura-Nei mutational model  with defequences  , i.e., northern and southern populations, using the microsatellite allele data (twelve loci). IMa2 uses Bayesian inference and MCMC simulations of genealogies to estimate several demographic parameters, including population size (Θ) of the extant (ΘNorth and ΘSouth) and ancestral populations , the split time (t) for the branching event, and asymmetric migration rates between the extant populations . A step-wise mutation model was applied. Upper bounds of prior distributions of parameter values were evaluated in several trial runs. When the highest posterior probability peaks of all parameters fell well within the prior boundaries in these test runs, we ran five IMa2 runs with prior settings chosen according to these trial runs (three with wider and two with narrower prior boundary settings), all with different random seed numbers. The runs began with a burn-in period of 106 steps followed by 108 steps where every 103 genealogy was sampled. We achieved adequate convergence and mixing of the Markov chains as indicated by visual inspection of trend line plots, sufficient effective sample size values and similar posterior probability distributions in the five runs. We present parameter estimates corresponding to the average highest peak of the posterior probability distribution of the five runs. The parameter estimates are scaled to the mutation rate (μ) and generation time (g) and to convert them to biologically interpretable demographic units, we calculated the population sizes (N) as Θ/4μ, split time (T) in years as tg/μ, and population migration rates per generation (2NM) as Θm/2. The mutation rate is uncertain for P. maculata microsatellites and was set to 10−4 per generation, a commonly assumed mutations rate for microsatellites [2NM is forward in time .We used isolation-with-migration models  implementellites , 65, andD [Fs [COI and Cytb sequences. The null hypothesis of expansion was statistically tested with the sum of squared deviations (SSD) from the expected values [raggedness index [P. maculata COI sequences (1153 bp) in DnaSP using P. meckeli COI sequences as an outgroup species. Fisher's exact test (two-tailed) was used to identify significant deviations from neutrality.Deviations from neutrality and demographic changes within and across the populations were calculated with Tajima’s D , Fu’s FsD [Fs  and mismd values  and Harpss index . McDonalss index  neutraliCOI and CytB sequence data using Bayesian skyline plot analyses [8 and a pre-burn-in of 107, with sampling every 104, were used. Results were inspected and the Bayesian skyline plot analysed and reconstructed in Tracer 1.7 [Population size reconstruction based on the analyses  was perfanalyses . Since tanalyses , 73. In acer 1.7 .Previous analyses have established that northern WH, AKL, TR, and Coromandel populations have high levels of TTX, marking these populations as “toxic”, while southern populations from NL and Kaikoura (KK) are recorded as containing either trace, or no TTX , 11, 14.He across populations ranged from 0.407 to 0.843, with an average of 0.655. Rarefaction curves for AR across each locus levelled off for each sampling location indicating that a reasonable portion of the available allelic diversity was sampled at each location  or He (P≥0.504). No significant linkage disequilibrium was found after Bonferroni correction (P>0.05) , 98 of which are singleton mutations ; which defined 103 and 74 distinct haplotypes for COI and CytB sequences, respectively. In contrast to the range of values obtained for h (0.922–0.989), the corresponding ranges for k (2.9–4.8) and π (0.254–0.414%) were low for both genes. The mean values for k and π are 7.30  and 0.330% , respectively for the concatenated sequences. Similar diversity was observed between sampling locations.The basic diversity values for FST and Dest, was low to moderate: 0.064 and 0.122, respectively, and highly significant (P≤0.0001). Differentiation was significant for most loci  showed that southern populations (WL and NL) were significantly differentiated from northern populations   was found based on some estimators , suggesting a low risk for Type I error. The differentiation pattern was therefore considered real and reinforced by FST values between the significantly differentiated populations being for the most part greater than 0.01.Global genetic differentiation, estimated using ost loci . PairwisDM (DCL (not shown). Note that although stress is relatively high (0.23) for the 2-dimensional MDS ordinations, the north-south distinction was also very clearly apparent in the corresponding 3-dimensional MDS ordinations (not shown here), which had lower stress (0.18). Additionally, PERMDISP analysis showed no significant differences in dispersion for either DM  or DCL , meaning that average nucleotide distances from individuals to their own population centroid did not differ among the groups (i.e. that within-group genetic variability did not differ among populations). Discriminant analysis with CAP supported , with the CAP model showing a leave-one-out allocation success of 97.26% , while there was no discrimination among the three northern  and two southern populations (WL and NL) , justifying their pooling into a single group. Bayesian clustering of individuals based on allele frequencies as implemented by STRUCTURE showed a ΔK value and mean log probabilities of data (LnP (x/K)) that were maximal at K = 2 . MJN analysis of CytB  and 22  individuals  (based on a slightly shorter COI fragment (see ISS (ISS.c) values  and CytB . However ΦST values were significant for COI , but not for CytB . Pairwise FST values were low (0.033–0.074), yet significant for all the comparisons (P< 0.03) ; a weak significant differentiation was observed only between TR and NL for CytB , but not for CytB , which was consistent with the results of ΦST analysis. Pairwise PERMANOVA tests showed significant but weak differentiation between the AKL and TP (P = 0.0276), and AKL and TR populations (P = 0.0218)  or CytB . The CAP results were consistent with the structures revealed by PERMANOVA: among the five populations, AKL is weakly distant from TP and TR based on COI data.Analysis based on P< 0.03) . Differe3)  against WL and NL (the southern population cluster) to test whether the north-south differentiation identified with microsatellite data could be observed with the mtDNA data. The results indicate no significant differentiation between the clusters , whereas there was good bootstrap support (between 74–100%) for five other species in the Pleurobranchidae family.A phylogenetic analysis of P = 0.01): two individuals sampled from the northern cluster (TR) were migrants from the southern cluster, and two individuals sampled from the southern cluster (WL) are migrants from the northern cluster  than in the north (1.2 migrants per generation). Note, however, that the estimated 95% highest posterior density (HPD) intervals were wide for all parameters .D and Fu’s Fs revealed significant negative values in pooled samples (COI and CytB haplotypes (SSD and raggedness patterns (P. maculata (Pn/Ps = 5/136) and between species (Dn/Ds = 5/100) was statistically similar . Reconstruction of the demographic history with Bayesian skyline plots showed signs of an expansion at around 5 kya for COI and CytB, but the 95% highest posterior density intervals are wide  samples formed another (with few examples of migration). However, this disjunction was not supported by FST analysis of mtDNA data, which indicated divergence among all populations.This study marks the first attempt to describe and account for patterns of genetic diversity in Discordance between results obtained from nuclear versus mitochondrial markers is not uncommon, with explanations ranging from variation in lineage sorting to differences in rates of nuclear versus mitochondrial evolution. However, data from microsatellite markers that come from multiple unlinked nuclear loci are expected to provide a more accurate representation of population structure that mtDNA that are linked at a single locus . Taken tP. maculata inhabited NZ waters before the end of the last glacial maximum (LGM) when sea levels were low and NZ was a single land mass [According to this scenario, and mass , 78. At and mass , 80, andand mass .COI and CytB haplotypes  [Haliotis iris) [Cellana ornate) [A north-south genetic differentiation has been observed in other marine organisms from NZ . Co. lucasi  and the rostrata . The Cooliculus) , blackfois iris)  and the  ornate) . It may  ornate) .P. maculata is the distribution of invasive species [P. maculata. The Asian date mussel, Arcuatula senhousia has been established in the Auckland region since the 1970s, forming large transient beds in sub- and inter-tidal areas of the Hauraki Gulf, Manukau Harbour and Whangarei Harbour [A. senhousia beds in the Hauraki Gulf appears to have preceded increases in the density of P. maculata populations. Interestingly, subsequent decline of near-shore beds of A. senhousia post 2010 was followed by rapid decline in the density of P. maculata populations [P. maculata may have been facilitated by availability of prey species comes from off-shore mussel farms in Tasman Bay , where culture of the green shell and blue mussels have created new habitats for P. maculata, with high-density populations being found beneath mussel farms [P. maculata was identified in Argentinean waters with the species rapidly spreading along the Atlantic coast [One additional factor that has likely promoted recent population subdivision in  species  that con Harbour . Expansiulations . Furtherel farms . Recentlic coast , 7. The P. maculata (2–4 weeks) exhibit structural patterns of diverse types ranging from no structure, to a north-south disjunction, IBD and differentiation within and between sampling locations [P. maculata [Life history traits such as the nature of egg and larval stages are of understandable importance in shaping population structure of the species. Species with benthic eggs tend to have more structured populations than ones with pelagic eggs , 102 andocations . The thrmaculata , 4, 91 lmaculata .P. maculata is a complex of two cryptic species, but no such evidence exists, at least for the samples included into this study. Our phylogenetic analysis indicates that all P. maculata populations–including samples from Argentina waters [P. maculata. Similar lack of differentiation between toxic and non-toxic populations has been shown for Taricha granulosa newts from various localities in western North America [Notophthalmus viridescens [Our prediction that the previously recorded cline in TTX might be explained by genetic structure holds only for microsatellite markers. Had this also held for mtDNA markers a case may have been made that a waters –are cons America , 106 andidescens .P. maculata accumulating TTX via feeding [P. maculata found no evidence for a bacterial origin of the toxin [Stylochoplana species that co-occurs with TTX-containing P. maculata [Having called into question substantive genetic differences between north and south populations, differences in TTX levels are thus likely attributable to exogenous factors, such as differences in associated bacteria, exposure, or diet. Work to date is strongly suggestive of diet as the major source of TTX, with  feeding , while o feeding . Recent he toxin , but TTXmaculata .S1 File(DOCX)Click here for additional data file.


Microtus californicus). We tested the prediction that introgression of nuclear loci would be greater than that for mitochondrial loci, assuming ongoing gene flow across the contact zone. We also predicted that genomic markers would show a mosaic pattern of differentiation across this zone, consistent with genomes that are semi‐permeable. Using mitochondrial cytochrome b sequences and genome‐wide loci developed via ddRAD‐seq, we analyzed genetic variation for 10 vole populations distributed along the central California coast; this transect included populations from within the distributions of both parental lineages as well as the putative contact zone. Our analyses revealed that (1) the two lineages examined are relatively young, having diverged ca. 8.5–54 kya, (2) voles from the contact zone in Santa Barbara County did not include F1 or early generation backcrossed individuals, and (3) there appeared to be little to no recurrent gene flow across the contact zone. Introgression patterns for mitochondrial and nuclear markers were not concordant; only mitochondrial markers revealed evidence of introgression, putatively due to historical hybridization. These differences in genetic signatures are intriguing given that the contact zone occurs in a region of continuous vole habitat, with no evidence of past or present physical barriers. Future studies that examine specific isolating mechanisms, such as microhabitat use and mate choice, will facilitate our understanding of how genetic boundaries are maintained in this system.To examine the processes that maintain genetic diversity among closely related taxa, we investigated the dynamics of introgression across a contact zone between two lineages of California voles ( This species is divided into northern and southern clades that come into contact in Santa Barbara County  sequences indicate that these lineages are ca. 4.6% divergent  have revealed a discordance between these markers  in the north to Irvine (Orange County) in the south. This transect consisted of 10 collecting localities that included the northern and southern lineages of this species as well as populations from the putative contact zone in Santa Barbara County  sequences for the cyt‐ Neuwald . Cyt‐b s Neuwald . Sequenc Neuwald .M. californicus genome. Details of the procedures used to develop genomic libraries are provided in the To provide a nuclear perspective on introgression across the contact zone, we used double digest restriction site‐associated DNA sequencing (ddRAD‐seq) as described by Peterson, Weber, Kay, Fisher, and Hoekstra  to ident2.3M. californicus reference genome  using the program Novoalign . Only reads that mapped uniquely to the reference genome were retained. Next, we used Picard (http://www.picard.sourceforge.net) to identify overlapping reads (read groups) and GATK . For analyses of genotypes, we only considered genotypic assignments with a posterior probability of ≥.95.For analyses that required data to be input as discrete variants , we identified single nucleotide polymorphism (SNP) markers contained in our genomic dataset. SNPs were identified and individual genotypes were assigned using ANGSD  were generated using Mega 6.06  was set to a lognormal distribution with a mean (±SD) value of 0.015 (±0.2) that incorporated the divergence date for the M. agrestis lineages. Additional details regarding the parameterization, assignment of prior values, and calibration of this analysis are provided in the The divergence time between lineages based on cyt‐xa Table . No foss2.7M. californicus, we used DnaSP v5.10.1 , nucleotide diversity (π), and Watterson's theta (θw). We also used DnaSP to identify fixed differences and shared polymorphisms between mitochondrial lineages, as well as to conduct neutrality tests based on Tajima's D  was determined following Evanno, Regnaut, and Goudet . We then calculated the average allele frequencies for each SNP among the three most northern populations and the two most southern populations sampled; these populations were targeted because earlier studies suggested that they are likely to represent pure parental lineages. SNP markers were considered to be diagnostic if the average allele frequency was >0.8 in one lineage and <0.2 in the other . After demultiplexing and removing adaptor sequences and low‐quality reads, 33,302 Mb of sequence remained in the cleaned dataset. Of those, 29,860 Mb (89.6%) mapped to the draft genome for M. californicus. Coverage per individual ranged from 4.6× to 159.9× (mean ± SD = 47.3 ± 32.1X). A total of 132 (95%) of the individuals sequenced were characterized by >10× coverage (mean ± SD = 48.68 ± 31.69X) and thus were retained in the dataset for subsequent analyses. From these animals, a total of 3,475,028 biallelic  sites were identified for use in analyses of genomic variability. From this dataset, a total of 56,343 SNPs were identified for potential use in analyses requiring discrete measures of genetic variation.The raw data generated by the ddRAD‐seq procedure consisted of 80,791 Mb of sequence representing a total of 281,696,020 sequence reads . There were seven fixed differences and no shared polymorphisms between the northern and southern mitochondrial lineages. Reciprocal monophyly of northern and southern lineages was well supported , with a width of 65.99 km (95% CI: 44.37–104.5 km); this places the center of the mitochondrial cline between populations 6 and 7 , with a width of 10.72 km (95% CI: 10.71–11.48 km); for 3,090 (76%) of SNP markers, the cline width was less than 7 km .Consistent with these findings, the shape of the genetic cline between lineages differed markedly between mitochondrial and genomic markers. Cline analysis revealed that the center of the cyt‐7 Figure ; for them Figure . Only 1.n = 45) in the three populations displaying evidence of mitochondrial introgression had Q‐values (proportion of the northern genotype) that were >0.9 or <0.1  of these markers had cline centers that fell within the geographic distribution of the northern lineage; the remaining 57 (64.8%) had cline centers that fell within the distribution of the southern lineage Table . All ani1 Figure , providi3.4w) were greater in the northern lineage, although differences between lineages were not significant  and polymorphism , 7 (COPNR), and 8 (CSMR)  varied among the populations sampled but there were no conspicuous geographic patterns to this variation. Pairwise comparisons of populations revealed FST values based on mitochondrial haplotypes ranging from 0.00 to 1.00  were observed primarily between pairs of populations from different lineages. Exceptions occurred when (1) populations within the putative contact zone that were strongly biased toward one mitochondrial lineage were compared with populations from the other lineage or (2) populations within the same lineage containing large numbers of private mitochondrial haplotypes were compared. Consistent with these findings, AMOVA analyses revealed significant differences within and among populations as well as between lineages, with the greatest variation (57.7%) occurring between lineages  Table . Estimat 7 Table . The numes Table .K = 2) within each lineage. In contrast to the apparent absence of gene flow across lineages, gene flow among populations within the same lineage was evident . Accordingly, evidence from both mitochondrial and genomic data indicates that the northern and southern lineages of M. californicus are the products of a recent divergence event. Interestingly, while the apparent timing of the cyt‐b divergence between lineages reported here is consistent with other analyses of divergence in Microtus, this level of sequence differentiation is greater than expected based on analyses of other mammal species   within the Pleistocene, which encompasses the last glacial maxima in western North America .Raw read data are available at the National Center for Biotechnology Information Sequence Read Archive (Bio project no. PRJNA432693). Mitochondrial cyt‐None declared.D. Lin, E. Lacey, and R. Bowie designed research. D. Lin and C. Conroy performed research. K. Bi, C. Conroy, and J. Schraiber contributed to analytical tools. D. Lin, K. Bi, and J. Schraiber analyzed the data. D. Lin, K. Bi, C. Conroy, E. Lacey, J. Schraiber, and R. Bowie wrote the manuscript. Click here for additional data file. Click here for additional data file. Click here for additional data file.


Juglans regia L.) is an economically important hardwood tree species cultivated worldwide for its high quality wood and edible nuts. It is generally accepted that after the last glaciation J. regia survived and grew in almost completely isolated stands in Asia, and that ancient humans dispersed walnuts across Asia and into new habitats via trade and cultural expansion. The history of common walnut in China is a matter of debate, however. We estimated the genetic diversity and spatial genetic structure of 31 walnut populations sampled across its Chinese range using 22 microsatellite markers . Using historical data and population genetic analysis, including approximate Bayesian analysis (ABC), we reconstructed the demographic history of J. regia in China. The genetic data indicated the likely presence of J. regia in glacial refugia in the Xinjiang province (Northwest China), Northeastern China , Central China (Qinling and Baishan Mountains and Xi’an), and Southwestern China . Based on DIY-ABC analysis, we identified three ancient lineages of J. regia in China. Two lineages (subpopulation A and subpopulation B+C) diverged about 2.79 Mya, while Southwestern China, and Qinling and Baishan Mountains lineages diverged during the Quaternary glaciations (about 1.13 Mya). Remnants of these once-distinct genetic clusters of J. regia may warrant ecological management if they are to be retained as in situ resources. A population size expansion in Northeastern China was detected in the last five centuries. The present distribution of walnut in China resulted from the combined effects of expansion/contraction from multiple refugia after the Last Glacial Maximum and later human exploitation.Common walnut ( Juglans regia L.) is an economically important hardwood tree species cultivated worldwide for its high quality wood and edible nuts  (J. regia) in Asia has emerged as a complex interaction of biogeography, climate change, and human forces (000 BCE) . The evon forces , 2017; tn forces . Humans n forces . For exan forces , 2017. Tn forces .Juglans in China and adjacent areas has been the basis of several recent studies , and Shaanxi   . The timientists . Among sientists . There irn China . Startinrn China . Today, rn China , and altrn China , the natrn China .J. regia in China, (ii) estimate the degree of population differentiation among J. regia populations from different regions, and, finally, based on this data (iii) describe the lineage divergence, location(s) of glacial refugia, and phytogeographic history of common walnut in China.The aims of this study were as follows: (i) determine the genetic diversity and population structure of J. regia individuals were collected from 31 populations across the species’ entire geographical distribution in China (Supplementary Table S1). All sampled trees were mature adults, apparently healthy, growing in a mountain forest, along a forest road, or near a village but not in an orchard or on farmed land. Sampled trees were separated by at least 50 m. Fresh leaf samples were stored at 25°C in silica gel until DNA extraction, as previously described , four originally from Chinese walnut (J. cathayensis) . We obtained genotypes using the ABI 3730XL sequencing system  and analyzed them using GENEMARKER ver. 2.2.0 software . On the other hand, loci under selection can be used specifically to investigate “population structure on ecological, rather than evolutionary timescales” , the remaining 14 markers  were again evaluated for FST values and Hardy–Weinberg equilibrium in each population using default parameters in GENEPOP ver. 4.5.1  (Supplementary Table S4).We calculated election . Loci unelection   were used for analyses related to contemporary distribution and population assignment only . All loci were annotated using Pfam, GO database, and blastx of NCBI. For each population and locus we calculated the number of alleles (Na), number of effective alleles (Ne), Shannon’s Information index (I), observed heterozygosity (HO), and expected heterozygosity (HE), and Fixation index (FIS), Nei’s genetic distance, and the percentage of polymorphic loci (PPL) using GENEALEX ver. 6.5.0.1 (Supplementary Table S1). The inverse distance weighted (IDW) interpolation function implemented in the GIS software ArcGIS ver. 10.3  was used to show the geographic patterns of observed heterozygosity (HO), number of alleles (Na), the percentage of polymorphic loci (PPL), allelic richness (Rs), and the number of private alleles (PrA) was computed for all 31 common walnut populations using ArcGIS ver. 10.3 software.The nine loci determined to be  6.5.0.1   of pairwise population differentiation (FST), generated with GENALEX (K = 4) and a second analysis that excluded loci under selection  using ARLEQUIN ver. 3.5  in DIYABC ver. 2.0.4 . Population A consisted of eight demes  from the Xinjiang Uygur Autonomous Region, Tibet, Yunnan province, and Northern China ; population B consisted of six demes  from Yunnan province and Sichuan province (Southern China), and population C consisted mostly of seventeen demes  from the Qingling-Bashan Mountains  .To decipher the demographic history and postglacial dispersal routes of r. 2.0.4  based onFST, classification index, and (dμ)2 distance . These statistics were used in 13 scenarios for analysis using DIYABC. In these scenarios, t# represented the time scale measured in number of generations, N# represented effective population sizes, NA represented ancestral population size, and t2 > t1, t3 > t1, t2 < t3. We performed every scenario with 1,000,000 simulations. In concordance with Supplementary Figure S4). In order to identify the best-supported scenario, we estimated posterior distributions using a weighed polychotomous logistic regression value of 1% of simulated posteriors data sets closest to the observed data from posterior probabilities of scenarios (the mean of the closest 1% of simulated posteriors). The goodness of fit of the best supported scenario was evaluated by the option ‘model checking’ with principal component analysis (PCA) . The median value of the mean mutation rate for SSR was 2.14 × 10-6 (95% CI: 1.08 × 10-6–9.48 × 10-6), the mean P (the parameter of the genomic distribution to generate multiple stepwise mutations) was 0.626 (95% CI: 0.173–0.9), and Single Nucleotide Indel (SNI) was assessed to be 4.17 × 10-8 (1.01 × 10-8–1.83 × 10-6).For each of the three derived populations , we estimated the following summary statistics: (i), mean number of alleles; (ii), mean genetic diversity; (iii), mean size variance (one sample summary statistics); for each of the population pairs, distance  . The divergence time for the major lineages were estimated as implemented in BEAST ver. 2.3.2  and a mutation rate 10 × 10-8 were used to estimate divergence time  for J. regia based on maximum entropy modeling using MAXENT ver. 3.3.3 . We analyzed four time frames: current , during the last glacial maximum (LGM) , during the last interglacial (LIG) , and future climate environmental layers  based on data from the Worldclim database3. The biologically relevant climate variables of the LGM were accepted from the community climate system (CCSM) and the Interdisciplinary Research on Climate model (MIROC). To identify a best fit model, we defined two datasets: one consisted of the 19 bioclimate layers used to simulate the current distribution of the species; a second used 15 bioclimate layers selected based on the bioclimate variable analyses. To reduce the potential impact of correlation among different bioclimatic variables (J. regia and the two climate data sets were standardized (with respect to measurement units) and analyzed using “R” software . Overlap among niche models was assessed using I, calculated in ENMTOOLS ver.1.4.1  using R ver. 3.1.0. We computed climatic (Euclidian) distance matrices based on population scores for both PCoA axes (PC1 and PC2), and for each bioclimatic variable. Tests were performed for the whole data set, including both northern and southern lineages, using partial Mantel tests . Population “I,” “II,” “III,” and “IV” indicate metapopulations as identified using STRUCTURE analysis based on all 22 SSR loci  . Based on EST-SSR data, the parameters θ (four times effective population size multiplied by mutation rate per site per generation) and M (immigration rate divided by the mutation rate) were calculated using software MIGRATE ver. 3.6. First, we implemented FST estimations for the parameters θ and M. Then we started four independent runs by 10 short chains of 5,000 steps and 3 long chains of 50,000 steps. We recorded genealogies with a sampling increment of 100 and 10,000 burn-in records. Last, we calculated the average values as the true value. To estimate contemporary gene flow among the meta-populations A, B, and C , we used BAYESASS ver. 3.0 (We used MCMC maximum likelihood (ML) to estimate historical gene flow among the populations A, B, and C, and among populations I, II, III, and IV using MIGRATE ver. 3.6 . Populatver. 3.0  to calcuFST-outliers  when a false discovery rate (FDR) was considered . The 13 EST-SSRs that were not under selection were assumed to be neutral SSRs . Eighteen loci out of the 22 polymorphic EST-SSRs showed significant departures from Hardy–Weinberg equilibrium (HWE) across all samples (Supplementary Table S8).Among the 22EST-SSR loci we examined, nine showed evidence of positive selection with 95% confidence intervals (CI) based on J. regia demes . The number of alleles (Na) for each locus ranged from 2 to 19 (Supplementary Table S2). Eight of the 22 loci  were monomorphic in more than 10 of the 31 populations. The observed heterozygosity (HO) and expected heterozygosity (HE) varied from 0.13 to 0.40 (-x = 0.23) and from 0.08 to 0.50 (-x = 0.30), respectively (Supplementary Table S1). The percentage of polymorphic loci per population (PPL) ranged from 26.1 to 95.7%, with a mean of 65.8%.A total of 602 individuals were successfully genotyped. We observed an average of 6.79 alleles per each of 22 SSR loci for a total of 95 alleles in 31 Na), percentage of polymorphic loci (PPL), and allelic richness (RS), although only the populations from Southwestern China showed high observed heterozygosity (HO) values . The sampled site with the highest diversity was MT based on PPL (95.65%), I (0.89), and HO (0.21). The site with the lowest diversity based on these measures was LM (near the Tibet/Yunan border)  .Genetic diversity appeared to be highest in two distinct locations . Trees sampled from these demes were especially genetically diverse as measured by number of alleles . Private alleles at the GZ sample site ranged in frequency from 0.028 to 0.227; in MT, private allele frequency ranged from 0.033 to 0.335; in HL at JH91908 four private alleles were observed at a frequency of 0.031, 0.031, 0.045, and 0.094. Fourteen sampled sites had at least one private allele at a total of 22 different loci .Of 190 alleles detected, 56 were unique to a single geographic site. The samples from MT had 21 private alleles at 13 loci, while GZ had 11 private alleles at 10 loci . The current potential distribution as predicted by the model was highly consistent with the actual species distribution. During the LIG, the range of J. regia contracted considerably, and the species was probably restricted to parts of Yunnan (Southwestern China), the Southern Qinling-Bashan Mountains, Beijing, Shandong, and Changbai Mountains (Northeastern China) . Later, during the LGM, our models show a general southward range shift; although the species did not disappear from the northeastern part of its range, it was likely limited to a small region (near present-day Beijing) . The distribution map reconstructed from the SDM model showed that the occurrence of J. regia from the LIG and into the future was/will be highest in Southwest China  and Northeast China  (Prob. >0.75). Accordingly, we predicted a gain of suitable habitat in Northeast China . The species will be most common in Northeast China in the future based on the SDM model .The SDM model yielded high AUC scores (AUC = 0.993) and thus was expected to perform well for Supplementary Table S7; mean temperature of the driest quarter) was the most important layer for determining the distribution of J. regia. Subsequent layers, including BIO18 (precipitation of warmest quarter), BIO11 (mean temperature of coldest quarter), and BIO15  also affected the species distribution. The four most important ecological factors were precipitation of the warmest quarter (16.9%), mean temperature of the driest quarter (21.6%), precipitation seasonality (coefficient of variation) (15.1%), and mean temperature of the coldest quarter (10.4%) (Supplementary Table S7).According to the relative importance tests, BIO9 . Based on these three metapopulations , we evaluated 13 scenarios related to the phylogeny and demography of J. regia in China . DIYABC unambiguously indicated support for scenario 3 , the 95% confidence intervals of this model did not overlap with the 12 other scenarios . In all 13 scenarios, there was a significant difference between the observed and simulated data based on posterior distributions of scenarios. The PCA of posterior distributions of scenarios showed that the observed data was always near the cluster of simulated data, indicating that scenario 3 was generally suitable for the observed data . Scenario 3 posited population A at t2 , and a more recent separation of population B and population C at t1  .Applying Bayesian analysis of genetic structure to the trees from all 31 sampled sites using only the 13 neutral loci, the most likely number of populations was NE) of the ancestral population (NEa) was 56,600 , the effective population size of population A (NE1), and population B (NE2), population C (NE3) were 302,000 , 369,000 , and 510,000 , respectively . The size of all three populations has undergone cyclical shrinkage and expansion during the Quaternary . Population A, B, C started to expand their distribution c.2.79 Mya, 2.89 Mya, and 1.13 Mya, respectively. At the time of the divergence of population C, the sizes of populations A, B decreased considerably while population C expanded .In scenario 3, the median values of the effective population size , Southwest China (YN and GZ), and East China (SD) . The outgroup J. nigra  contained a private haplotype (H12) . The minimum spanning network of 11 chloroplast genome haplotypes showed that H5 was the ancestral haplotype, but the conclusion that H5 was ancestral was not supported by the BEAST tree . Most of the other J. regia chloroplast haplotypes were derived by a single mutation from H5. The coalescence time for the 11 chlorotypes was 2.73 Mya (95% HPD: 0.38–5.18 Mya) , dating to the Plio-Pleistocene. The microsatellite data supported diversification of J. regia about 2.79 Mya  based on neutral loci. Most chlorotypes diverged during the Quaternary glacial period .We identified 11 chloroplast haplotypes among the 17 K = 4 was the best representation of the underlying hierarchical structure for the 31 common walnut demes we sampled . Most of the samples clustered into predicted populations that corresponded to the geographic region from which they were sampled . For K = 4, eight populations from Xinjiang , two populations (XZ and LM) from Tibet and Yunnan (Southwestern China), and populations from Hebei and Shandong provinces  clustered into population I ; samples from seven demes from Yunnan and Sichuan province  clustered into population II ; samples from nine demes from Northern China clustered into genetic population III , and samples from six demes from Qinling-Bashan Mountains were clustered into genetic population IV . Populations MT, LNS, AH, SX, NY, and HB contained members from population I (purple) and population (blue), while CQ, SC, and AH were mostly comprised of population II (yellow) and population III (blue) . Principal coordinate analyses (PCoA) accounted for 54.82% of the observed variance on the first two coordinate axes  and revealed four clusters of samples corresponding to the same groups identified by STRUCTURE .The STRUCTURE analysis based on 22 loci  indicated FST) was also high (0.15) among the four subpopulation I, II, III, and IV based on the 22 loci (Table 2). The matrix of FST values between the 31 demes (Supplementary Table S11) and the matrix of FST values between the four populations (as determined by STRUCTURE) showed that population I and population II had the highest genetic differentiation (FST = 0.148). The highest FST (0.544) between two demes was found between JJ and LM (Supplementary Table S11).AMOVA analyses showed that genetic differentiation . Analyses using the software BARRIER identified five statistically significant (0.25 < P < 0.41) genetic barriers . The main genetic boundary appeared between LM  and BS  corresponding to a barrier between the Himalayan samples and those of Yunan . Within the demes from Southwest China a second genetic barrier appeared . Haplotype H6 was private to Tibet (XZ); most members of population I, including samples from Xinjiang (XJ and BM) and Yunnan (BS) contained H5 . Estimates of θ were lowest for population I . Scaled immigration rates (M) revealed asymmetric historical gene flow from population I to population II (M = 5.14), population III (M = 0.92), and population IV (M = 0.53) (Table 3). The scaled immigration rate (M) was 15.8 from population B  to population C , while immigration rate (M) was 1.5 from population B to population A . These results revealed the existence of generally asymmetric historical gene flow among metapopulations. Gene movement occurred mainly from population B into population C (15.8 vs. 11.9) and from population I into population II (5.14 vs. 3.81) (Table 3).The Mantel test for matrix correlation revealed a strong and significant correlation between genetic distance and geographic distance  predicted by STRUCTURE analysis of the J. regia samples  based on neutral loci only. Although many geographically proximal demes were also proximal in the PCoA space, there were notable exceptions, and apparent genetic similarity was not always reflected in similarity of ecological niche. For example demes GS (green), EM (yellow), and LM (purple) all deviated sharply from their predicted niches . The genetic barrier between LM and BS  was not reflected in the niche PCoA. J. regia is adapted to a surprisingly large range of conditions .The PCoA analysis showed that interactions of temperature and precipitation were the most important factors driving the distribution of J. regia. The ranges of the three predicted populations  of common walnut overlap, but they may not share niches within the same locations . Inspection of the spatial overlap between Ecological Niche Models (ENMs)  revealed that factors other than those described in the ENM may maintain parapatry for J. regia genetic sub-groups.The loadings of the J. regia in China was comparable to values reported for wild populations throughout the species’ range at EST-SSR markers. We found an average of 6.79 alleles per locus at 22 EST-SSR loci, a value lower than reported in some recent studies of J. regia based on nuclear SSRs. For example, J. regia distributed in five major regions  based on 643 genotypes comprising 317 diverse accessions at the National Clonal Germplasm Repository, USDA-ARS, Davis, California, and Na in our study was similar to values described by Na = 7.5), HO and HE) are common measures of genetic diversity  was 0.21 (0.00–0.65), 0.29 (0.00–0.64), 0.24 (0.00–0.59), 0.20 (0.00–0.54) in populations I, II, III, and IV , respectively (Table 2). By comparison, HO in J. regia sampled across Eurasia was about 0.5 . Our results may have differed from others in part because we used EST-SSRs, which often have lower levels of polymorphism than nuclear microsatellites that are not located in genes, and therefore EST-SSRs reveal lower heterozygosity than SSR loci in intergenic regions. We also observed numerous demes that were fixed for a single allele at a locus (Supplementary Table S8).Genetic diversity of iversity . In our ear SSRs , and eveEST-SSRs . The avebout 0.5 , and HO ral Asia  based onFST among populations  was surprisingly high (∼0.30). We also observed a surprisingly large number of private alleles, especially in MT and GZ, possibly reflecting genetic isolation . IBD was significant for J. regia in China , so geographical distance likely restricted gene flow and increased genetic differentiation . In general, it appears that forces such as isolation and adaptation that tend to increase genetic differentiation of local demes have been stronger than homogenizing forces such as gene flow . Local gene flow dynamics among wild demes of J. regia in Asia are not well studied, although there is some evidence for landscape scale gene flow processes among Chinese Juglans species  with low genetic differentiation among 31 collection sites   , human d ranges) , climate ranges) , and eve ranges) .Supplementary Figure S8). These indicated that gene flow from population A (Western China and Northern China) into population C   was greater than gene flow from population B (Southern China) into population C (0.0752 versus 0.0614). This result may reflect the relative strength of monsoonal air flow in the spring when J. regia blooms . These climate factors correspond to temperate versus subtropical climates at the sampling sites . The southwesterly migration of different regions before 1.13 Mya is probably affected by one factor: natural gene flow, which was shown by the results of the analyses using MIGRATE software. Population C is predicted to have large amounts of bidirectional gene flow with population B , from the Himalayas, and (possibly) from other species, increases the likelihood that this type of natural gene flow occurred over the past 2 Mya, shaping the diversity of walnut in China.Our analyses of contemporary gene flow (using BAYESASS) among (inferred) populations were based on neutral SSRs only. The values of contemporary gene flow ranged from 0.0069 to 0.0752 . Although we found a large number of private alleles in MT, this population did not present the highest average pairwise FST, so the presence of private alleles may not indicate genetic isolation. The niche PCoA did not put MT and GZ in extreme niches, so the private alleles they maintain are likely not the result of adaptation to a specific environment. Although we did not determine chloroplast haplotypes for any samples from MT, some GZ trees had a rare chloroplast haplotype (H10). It is possible that these demes are not genetically or ecologically isolated but actually reflect gene flow into J. regia from J. cathayensis or J. mandshurica. Hybrids are often fertile in Juglans .It is not clear from our data to which extent isolation and ecological specialization have contributed to genetic differentiation of nd there . Our res Juglans . AlthougFigures 1, 3, 4). The effects of Quaternary climatic oscillations on the distribution and phylogeographic structure of species in the mid- to high-latitude regions of Europe and North America  was likely about 2.79 million years ago (Pliocene of the Tertiary), although a more recent date for speciation within Juglans has been proposed (J. regia) was distributed in Northeast China, Western Yunnan province, and probably Xinjiang province, based on a species distribution model , chloroplast data , and the current genetic structure of common walnut in China based nuclear SSRs .Common walnut is considered a relict species of the Tertiary  and nati America , in high America , and Nor America  have alr America , and ourproposed . In ChinJ. regia in China was split between a northern and a southern range . By the LGM, refugia corresponding to three modern regional centers of genetic diversity were present , including locations in Northern, Central, and Southern China. These results showed that intraspecific divergence in J. regia was partly driven by climate change. The size of at least one population (A) of this species experienced slight bottlenecks during the last glaciations . A possible recent expansion of population size in China detected by SDM was also supported by our genetic data . The expansion to Northern China during warmer periods is also supported by the comparison of the SDMs between LIG and the present . After the Last Glacial Maximum (LGM), J. regia survived and grew spontaneously in almost completely isolated stands in Asia, from Xinjiang province of Western China through Central Asia to the Caucasus , distributed in Southern China, Northern China, and Central China (Qinling Mountains) . This result, as well as the projected habitat at the LGM , indicated that multiple potential allopatric refugia existed in these areas for this species . In this respect, results of the SDM were consistent with ABC analysis in terms of the general locations of refugia for common walnut. The current structure reflects expansion from ancient refugia . Based on fossil and genetic evidence, common walnut survived in multiple refugia in Europe  . These private alleles may have been the result of bottlenecks followed by founder effects that resulted in the restriction of certain alleles to specific lineages. Other authors have argued that temperate forests retreated southward to c. 30°N during the Quaternary glacial periods as revealed by paleo-vegetation data from East Asia (PAL) haplotypes . J. regia because of the rich genetic diversity found there. Our results indicated that Southwestern China and Northeastern China harbored the highest number of alleles, heterozygosity, and frequency of private allele .We conclude that although the number and location of refugia for temperate angiosperm trees in China is uncertain, there appear to have been multiple refugia containing st China , 2017, ist China , and SouJ. regia into China have focused on two likely sources: Central Asia and parts of Southern China in or near the Himalayas. Common walnut, according to these theories, was transported by humans from these centers of origin into China. These models cite as evidence legends about Zhang Qian and the long history of trade along the Persian Royal Road and Silk Road. Although there is no doubt that walnut was traded along the silk road, a recent introduction of J. regia into China is not consistent with the genetic analysis . We also conclude based on our niche models that habitat for J. regia was present in China continuously throughout the Pleistocene. J. sinensis (Northern China) and J. duclouxiana (Southern China) that are no longer recognized. Nevertheless, these former species, which were considered native plants by Dode, probably reflect indigenous J. regia populations. We identified Qinling Mountains  and Changbai Mountains (Northeast China) as the most likely locations for refugia for J. regia (and possibly other Juglans species native to China) based on their correspondence with refugia for related species . The presence of high levels of genetic diversity in Chinese J. regia and strong regional genetic structure also favor an hypothesis that J. regia has a long evolutionary history in China. There is, in fact, little scientific evidence that indicates J. regia is not native to China. The presence of population A in Northeastern and Western China, and its geographic coincidence with ancient capital cities and roads  may indicate human dispersal was responsible for the disjunction in the distribution of this genetic cluster. But the presence of suitable habitat for common walnut near Beijing during the previous LGM  increases uncertainty about how long common walnut has been present in Northeastern China . The haplotypes of amino acid positions based on chloroplast genomes were submitted to the National Center for Biotechnology Information (NCBI). The accession numbers were MF953875–MF953885. The Unigene sequences and BLAST search results for 22 SSR-containing ESTs were provided as Supplementary Tables S2, S5.The whole chloroplast genome data deposited in the National Center for Biotechnology Information (NCBI) GenBank (PZ, XF, MM, and KW designed and performed the experiment as well as drafted the manuscript. XF, HZ, SZ, YH, LF, and PZ collected the samples. XF, HZ, LF, SZ, and PZ completed the sequence assembly and analyzed the data. XF, XL, LF, KW, and PZ conceived the study and revised the manuscript. All the authors have read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Single nucleotide polymorphisms (SNPs) in commercial arrays have often been discovered in a small number of samples from selected populations. This ascertainment skews patterns of nucleotide diversity and affects population genetic inferences. We propose a demographic inference pipeline that explicitly models the SNP discovery protocol in an Approximate Bayesian Computation (ABC) framework. We simulated genomic regions according to a demographic model incorporating parameters for the divergence of three well-characterized HapMap populations and recreated the SNP distribution of a commercial array by varying the number of haploid samples and the allele frequency cut-off in the given regions. We then calculated summary statistics obtained from both the ascertained and genomic data and inferred ascertainment and demographic parameters. We implemented our pipeline to study the admixture process that gave rise to the present-day Mexican population. Our estimate of the time of admixture is closer to the historical dates than those in previous works which did not consider ascertainment bias. Although the use of whole genome sequences for demographic inference is becoming the norm, there are still underrepresented areas of the world from where only SNP array data are available. Our inference framework is applicable to those cases and will help with the demographic inference. This effort led to the characterization and selection of sequence variants or single nucleotide polymorphisms (SNPs), that were later included in commercial genotyping arrays. However, since another goal of this project was to establish associations between these variants and common diseases, most of the SNPs were confined to those of intermediate frequency in genic regions, therefore creating a representation bias. Operating under the “common disease, common variant” model, SNPs with these characteristics were selected to increase the power of association tests2. Once array data became available from several populations, they were used for a different purpose: inference of population genetic or demographic parameters. Unfortunately, the approach used to identify variants for these SNP arrays may adversely affect the quality of such inferences.One of the main goals of the HapMap project was to describe common patterns of variation in the human genome, initially from individuals with African, European and Asian descent4. This phenomenon is known as ascertainment bias, which results in the deviation of population genetic statistics and in the loss of important genetic information specific to the history of a population5. This deviation is particularly relevant as several available human datasets derived from genotyping arrays have been regularly used for demographic inference6. Inference for population genetic studies usually rely on the site frequency spectrum and other summary statistics to study demography. Thus an excess of common sites leads, for example, to the underestimation of the overall heterozygosity and FST, and the overestimation of Tajima’s D9.When SNPs are found in a small sample of individuals from selected populations and then genotyped in other populations, sites with intermediate frequencies in the discovery panel will be reliably scored in other populations, while variants that are rare or absent in the discovery panel will not be accurately accounted for when they are common (or rare) in the population of interest. As a consequence, the observed patterns of nucleotide diversity will be skewed11.While complete sequencing of all samples in a study eliminates the effect of ascertainment bias, this option is not always available given the much higher cost relative to SNP array genotyping and there are still populations of special interest from which only SNP data are available. In addition, ancient DNA sequences have been enriched for a panel of known SNPs which, in turn, introduces ascertainment bias in the analyses of this type of samples12, or to directly account for the underlying bias in the arrays13. Some of these methods depend on knowing exactly the methodology used to determine SNPs, which is rarely known for available arrays. For example, in the original phase of the HapMap project, the criteria for selecting variable sites changed during the course of the SNP discovery process4, making methods for correction even more challenging. Indeed, investigators have acknowledged that these methods are difficult to generalize across studies9.To address these ascertainment bias issues, several methods have been proposed to correct the site frequency spectrum generated by the SNP array data14. We apply this method to estimate parameters of the Out-of-Africa model and of the admixture model of the Mexican population. This methodology has the capacity to address diverse demographic questions when only SNP array data are available and is applicable to different array platforms.In this study, we develop an inference pipeline that explicitly models the SNP discovery protocol by varying the haploid sample size and the site frequency cut-off. We recreate the SNP distribution of two different Affymetrix arrays and use a bootstrap strategy to jointly estimate the ascertainment and demographic parameters in an Approximate Bayesian Computation frameworkIn this study we investigated the underlying ascertainment of the Affymetrix Axiom Genome-Wide Human Array (Axiom array) data and the admixture dynamics of the modern Mexican population (Supplementary Table n = 9), Utah residents with European ancestry , and Han Chinese  sequenced by Complete Genomics (CGI genomes)15. This was matched with Axiom SNP array data from the same YRI, CEU and CHB individuals. To infer the time of contact of Europeans with the indigenous populations in Mexico, we collected data from individuals with Mexican ancestry  and from Iberian Populations in Spain , all part of the 1000 Genomes Project16. We also used previously published genotypes from the Nahuas of Puebla 17. All three populations were genotyped on the Affymetrix Genome-Wide Human SNP Array 6.0 (Affy 6.0).We gathered high coverage whole genomes of unrelated Yoruba .We assumed that the YRI, CEU and CHB populations, as part of Phase I of the HapMap project, were the first populations genotyped and that the variable sites discovered in these populations are the basis for most of the currently available SNP arraysons Fig.  with a s21 for Python, which allows faster processing of the coalescent simulations. We performed one million simulations of the whole set of 10 kb loci, according to the demographic scenario in Fig. A set of values was drawn for the prior distribution of the parameters, both demographic and ascertainment, and one unique simulation was performed with that set of values. In this manner, we can jointly infer all the parameters of the model. The parameters of the model and their priors are shown in Table We established the following strategy to model the SNP ascertainment process. For each locus in the 10 kb loci from the aforementioned coalescent simulations, we created a “pseudo-array” with the same number of sites as the Axiom array and with a similar physical distance distribution between the SNPs. To accomplish this, we randomly selected from the prior distribution a number of haploid samples from each of the HapMap populations and simulated segregating sites. In this discovery set, sites with frequency greater than the cut-off were selected and, of these available sites, sites with the closest physical position to the corresponding Axiom SNPs were kept for subsequent analysis. A simulation was repeated until sufficiently many segregating sites were obtained (Step 1 A of pipeline). One simulation included: the simulation of the whole genome data (10 kb loci) in the sample that includes 9 YRI, 9 CEU and 4 CHB diploid individuals, of the discovery panel and of the ascertained data (ascertained SNPs in each of the 10 kb loci in the discovery panel).The summary statistics calculated in the simulated (Step 1 A) and the observed data (Step 1B) were later transformed into Partial Least Squares components in order to obtain linear combinations of the summary statistics. We calculated the mean and standard deviation of site frequency spectrum and haplotype based summary statistics across the 10 kb loci extracted for the CGI genomes and for the Axiom SNPs that are present in those loci. The summary statistics used were the number of segregating sites, singletons, doubletons, Tajima’s D, number of distinct haplotypes and number of the most frequent haplotype per population, FST and number of shared and private haplotypes between pairs of populations.22 and to optimize the parameter estimation, we varied the number of PLS components and the number of retained simulations (Step 2). For each choice, we re-sampled the one million simulations with replacement and used ABCtoolbox to estimate the parameters. This step was repeated 1000 times for each choice. We then computed the covariance matrix of the posterior estimates (to maintain the structure of the joint estimation of the parameters) and simulated 1000 random values using its covariance structure.We inferred the ascertainment and demographic parameters for the Out-of-Africa model with the software ABCtoolboxWe then performed simulations using these 1000 parameter values, standardized the observed and simulated summary statistics and calculated the Euclidean distance between them. Lastly, we selected the number of PLS components and number of retained simulations that minimized this distance and confirmed that its parameter values generated summary statistics like the observed ones. We then retained the posterior mode and 95% High Posterior Density Intervals (HPDI) for each parameter (Step 3), as estimated by ABCtoolbox. For more details on the different steps of the methodology, refer to the supplemental materials.https://bitbucket.org/cdquinto/ascertainment-bias-scripts/.The list of the 10 kb loci and all the scripts used in this work can be found in the following link: We examined the admixture process giving rise to the present-day Mexican Mestizo population with the proposed demographic pipeline. We used the data described above from the HapMap, IBS, MXL and NXP populations.We established the model shown in Fig. To validate this method we used simulated summary statistics with their associated known parameter values from each model and treated them as observed data (pseudo-observed datasets). We applied our pipeline to these datasets to verify our ability to successfully recover the true parameter values of each pseudo-observed dataset in the 95% HPDI as reported by ABCtoolbox. In addition, we used our inference pipeline in 1,000 pseudo-observed datasets for each of the tested demographic models to calculate the ration of the posterior estimates and their true value.et al. in 2010 to account for ascertainment bias using ABC13. Firstly, they simulated a demographic model involving the 3 HapMap populations and sampled a random number of haploid samples from each population in order to find polymorphic sites within regions of the ENCODE project data23. The physical distance distribution of these sites was then fitted to the observed distribution of the Affy 6.0 platform and, with ABC, the parameters of the previously mentioned demographic model as well as the number of ascertained samples were inferred. The summary statistics used were: observed heterozygosity, number of distinct haplotypes, number of the most frequent haplotype per population and the joint frequency spectrum. Secondly, the resulting posterior distribution of the demographic parameters and samples sizes were utilized as the prior for the consecutive investigated models involving the different Oceanian populations. Herein, we refer to this series of steps as Wollstein’s method.Likewise, we compared our pipeline and results to the methodology described in Wollstein 13: mean number of segregating sites, mean FST, mean number of different haplotypes, mean number of the most frequent haplotype in both the 10 kb loci and array data. We also retained the same percentage of simulations as in the aforementioned paper .We followed Wollstein’s steps to infer the ascertainment and demographic parameters of the Out-of-Africa and Mexican admixture models using our observed data. We reduced our summary statistics in order to replicate the set used inWe finally compared the mode and the width of the 95% HPDI of the parameters’ estimations obtained by the two methods and verified if the true values of pseudo-observed datasets were recovered by Wollstein’s method. The complete description of the validation and comparison of the pipeline can be found in the Supplemental Material.We applied the inference pipeline to the Out-of-Africa model and obtained posterior estimates of the discovery set and demographic parameters Table , Fig. 4.Moreover, we estimated that African and Eurasians populations diverged 69.7 kya , and that the time of divergence between CHB and CEU was 35.0 kya (95% HPDI.: 29.0–35.9 kya). We also found particularly large effective populations sizes for YRI  and CEU ; whereas we estimated 4.7 K (95% HPDI.: 3.3–6.2 K) for CHB, and the size of the ancestral population of Eurasians was 4.5 K (95% HPDI.: 2.2–5 K).We first confirmed that the MXL samples show a signal of admixture with the IBS and NXP populations as sources. In the PCA plot, the IBS and NXP samples formed tight clusters on opposite sides of the plot and the MXL individuals are found between these two clusters , while the split between Han Chinese and the indigenous Mexican population was dated to be 17.2 kya (95% HPDI: 10.5–37.3 kya). The estimates of the effective population sizes of the Nahuas and Iberian populations were 13.5 K (95% HPDI: 2.4–81.3 K) and 52.5 K (95% HPDI: 2.5–100 K) respectively; while the Mexican Mestizo population size was 16.2 K (95% HPDI: 2–100 K). Moreover, the time of admixture between the Iberian and Nahua populations was estimated to have occurred almost 22 generations ago, or 550 years ago (95% HPDI: 413–600 ya); with the Iberian population contributing 53% (95% HPDI.: 43–65%) of the genomes.The estimations of the parameter values of the pseudo-observed datasets were always within the 95% HPDI and the true values were most of the time recovered by the mode of the posterior distributions. Supplementary Figs We calculated the ratio of our posterior estimate and the true value of the parameters for 1,000 pseudo-observed datasets. For both tested models, the mode of the distribution of this ratio peaks close to the expected value of one and most of the obtained distributions peak at this value  in the discovery sample for HapMap and Perlegen. According to their results, between 5 and 10 samples were used in HapMap while 20 samples were used in Perlegen. Our estimates of the number of haploid samples used to find SNPs oscillate between 3 and 17, which may likely reflect the mixture of ascertainment strategies  the effects of ascertainment bias are expected to be stronger given the divergence between indigenous Mexican populations and those that were part of the SNP discovery panels . The choice of the Nahuas from Puebla as a representative of the indigenous population was motivated by its location in central Mexico and the number of available samples. We also made the assumption that MXL individuals were Mestizo individuals with predominately more ancestry from central groups which held a large territory, as opposed to more isolated and small populations (e.g. Mayans).We chose to examine the case of admixture in Mexico for two reasons: (1) at the time of this work only SNP genotypes from indigenous Mexican populations were publicly available31. When an admixture event takes place, the chromosomes that are brought together are shuffled by recombination during meiosis. As the time since admixture increases, chromosomes from the admixed population become mosaics of shorter segments of DNA from the sources populations. The ancestry switch points are inherited like point mutations and can be used as markers to define ancestry blocks or haploblocks.Regarding the admixture dynamics of this historical encounter, our results indicate an admixture time of 21.5 generations ago , in agreement with recorded history and with previous estimates of admixture timing using alternative approaches such as ancestry block length distribution31). The small difference in the estimates could be due to the fact that the MXL samples were collected outside of Mexico and probably experienced more recent gene flow from Europeans in the U.S.30. The estimate of the divergence between CHB and NXP (18 kya) corresponds to the estimated time of crossing of the Bering land bridge during the Last Glacial Maximum by the ancestors of Americans34. However, we inferred a very old split time between Northern/Western European and Iberian populations (24 kya), as opposed to previous results that point out to a much recent divergent time between European populations  and the high degree of relatedness in those populations35.In contrast with such methods, our approach enables bias-free inference of additional demographic parameters, like the effective population sizes and times of divergence which have been typically inferred from sequencing data only until now. Our inference of 53% Spanish contribution to the Mexican Mestizo genetic pool (95% C.I.: 43–65%) is consistent with other published estimates for Mexican Americans  which are lower estimates compared to previous results using data generated by 1000 Genomes ProjectIn general, the results of the validation of our pipeline show that it is possible to estimate without bias demographic and ascertainment parameters , which probably provided a small amount of information to ABCtoolbox for the inference. Another important difference is the estimation of the ascertainment and demographic parameters of models at the same time and the incorporation of bootstrap sampling to have information of the joint distribution of the parameters.In sum, we showed that our developed pipeline enables to do demographic inference using SNP array data ABC, can be applied to data from different commercial arrays and that the only requirement for its application is to have whole genome information of the populations used to define SNPs and the SNP array data of the populations of interest.Dataset S1Dataset S2Dataset S3Dataset S4Supplementary material