The resulting admixture among these groups yielded a significant reservoir of Native American ancestry in the modern US population. We analyzed the patterns of Native American admixture seen for the three largest genetic ancestry groups in the US population: African descendants, Western European descendants, and Spanish descendants. The three groups show distinct Native American ancestry profiles, which are indicative of their historical patterns of migration and settlement across the country. Native American ancestry in the modern African descendant population does not coincide with local geography, instead forming a single group with origins in the southeastern US, consistent with the Great Migration of the early 20th century. Western European descendants show Native American ancestry that tracks their geographic origins across the US, indicative of ongoing contact during westward expansion, and Native American ancestry can resolve Spanish descendant individuals into distinct local groups formed by more recent migration from Mexico and Puerto Rico. We found an anomalous pattern of Native American ancestry from the US southwest, which most likely corresponds to the Nuevomexicano descendants of early Spanish settlers to the region. We addressed a number of controversies surrounding this population, including the extent of Sephardic Jewish ancestry. Nuevomexicanos are less admixed than nearby Mexican-American individuals, with more European and less Native American and African ancestry, and while they do show demonstrable Sephardic Jewish ancestry, the fraction is no greater than seen for other New World Spanish descendant populations.European and African descendants settled the continental US during the 17 Nuevomexicanos, that is distinct from other Spanish descendant groups.The post-Colombian settling of North America brought African, European, and Native American populations into close proximity for the first time. The inevitable admixture among these groups resulted a reservoir of Native American ancestry in modern US populations, outside of traditional Native American groups. Here we characterize that Native American ancestry in a geographically diverse set of African descendant, Western European descendant, and Spanish descendant populations. We show that Native American ancestry in the US population is not monomorphic, strongly related to geography, and suggestive of frequent historical admixture between European settlers and local Native American groups. We also show the presence of a unique, admixed Spanish population in the Southwestern US, the modern  Native Americans inhabited the area that now makes up the continental US for thousands of years prior to the arrival of the first European settlers. The ancestors of modern Native Americans are thought have arrived in the Americas from Asia, by way of the Bering Strait, in several successive waves of migration . The curth century. The first Africans were brought to Jamestown in 1619 by Dutch pirates who traded them to the British settlers as indentured servants [Europeans arrived in the Americas more than 20,000 years after the first Native Americans. The first European settlers to reach the continental US were Spaniards led by the conquistador Ponce de León, who claimed Florida for the Spanish crown in 1513 . Britishservants .e.g. malaria, measles, and smallpox–for which the indigenous population had little or no immune defense [The arrival of Europeans and Africans in the Americas, and the conflict that followed, would prove to be catastrophic for the indigenous population. It has been estimated that 10–100 million Native Americans may have died in the first 150 years after Columbus’ arrival in the New World, amounting to a 95% reduction in the population . This ma defense , 9.The story of conflict between Native Americans and European settlers and enslaved Africans, along with the devastating consequences for the indigenous population, is by now well-known. However, there is another, perhaps less appreciated, aspect of the encounter between these population groups that has also had profound consequences for the genetic demography of the Americas. Here, we are referring to the process of genetic admixture, whereby individuals from previously isolated population groups reproduce, resulting in the novel combination of ancestry-specific haplotypes within individual genomes. Admixture has been a fundamental feature of human evolution and migration . WheneveAs European and African descendants settled the continental US, they inevitably came into contact with established Native American populations resulting in admixture and the introduction of Native American genomic sequence into the expanding US population. Accordingly, the genomes of European and African descendants in the US are expected to contain some fraction of Native American ancestry. In other words, a significant reservoir of Native American ancestry currently exists outside of recognized indigenous communities. In this study, we ask how the historical processes of migration and settlement affected the distribution of Native American admixture across the continental US . We addrThe first aim of our study was to characterize the major genetic ancestry groups for the continental US based on observable patterns of ancestry and admixture seen for the 15,620 individuals from the Health and Retirement Study (HRS) analyzed here. The Health and Retirement Study data is sponsored by the National Institute on Aging (grant number U01AG009740) and is conducted by the University of Michigan. Having defined the US genetic ancestry groups, we then considered the distribution of Native American admixture within and between ancestry groups and among geographic regions. We provide a detailed description , along wThe ancestry distribution of HRS individuals among the three largest US genetic ancestry groups is shown in Individuals from the African descendant group show medians of 85% African ancestry, 14% European ancestry, and 1% Native American ancestry . Most ofIndividuals assigned to the three main genetic ancestry groups show distinct geographic distributions across the continental US, which are largely consistent with demographic data for the country. The proportion of African descendants is highest in the three southern census regions, Western European descendants in the two north central regions, and Spanish descendants in the Mountain census region, which includes Arizona and New Mexico .We compared the patterns and extent of sex-biased admixture among the three US genetic ancestry groups by comparing the continental ancestry fractions–African, European, and Native American–seen for the X chromosomes versus the autosomes. For any given ancestry component, a relative excess of X chromosome ancestry is indicative of female-biased admixture, whereas an excess of autosomal ancestry reflects male-biased admixture . This waFor each US genetic ancestry group, we considered three distinct characteristics of Native American ancestry across the continental US: (1) the relative levels of Native American ancestry genome-wide, (2) the patterns of Native American allele frequencies, and (3) the phylogenetic relationships among US populations based on their Native American ancestry.As we showed previously, overall Native American ancestry is highest for the Spanish descendant group , followed by the African descendant  and Western European descendant groups  . Among aWe characterized the ancestry-specific and genome-wide haplotype heterozygosity (HH) for each of the admixed populations to interrogate how admixture has affected the diversity of the populations . Where pK = 9, ADMIXTURE is able to resolve the two Northern Mexican Native American reference populations, as well as reveal a unique ancestry in the Spanish descendant population from the Mountain (MNT) region. This population shows a distinct pattern of Native American ancestry under any K  between pairs of populations based on their masked Native American haplotypes  and D-statistics of the form D using the masked Native American haplotypes and AdmixTools [f3 and D-statistics agree well with the inferred phylogeny . The results of the ADMIXTURE and phylogenetic analyses are consistent with historical records indicating the presence of a unique group of Spanish descendants in the American Southwest, known as the ‘Hispanos of New Mexico’ or Nuevomexicanos. This population is descended from very early Spanish settlers to the Four Corners region of the US, primarily New Mexico and southern Colorado, and distinct from Mexican-American immigrants who arrived later [The ADMIXTURE results for the Spanish descendant group in the Mountain region (MNT) point to the presence of two distinct sub-populations, one of which is clearly of Mexican descent, whereas the second group has a pattern distinct from any other group analyzed here Figs  and 6A. ed later .Nuevomexicano population have maintained a distinct cultural identity for centuries, and the ability to isolate individuals from this group based on analysis of their genotypes allowed us to address open questions related to their ancestry. In addition to characterizing their distinct pattern of Native American ancestry, we also compared the levels of Native American admixture between Nuevomexicanos and the other nearby Spanish descendant groups, which show a Mexican pattern of Native American ancestry. Consistent with previous results [Nuevomexicanos have significantly more European ancestry and less Native American ancestry than other Spanish descendant groups from the Western Census regions NuevomexiWe were able to delineate three predominant genetic US ancestry groups–African descendant, Western European descendant, and Spanish descendant–using comparative analysis of whole genome genotypes from >15,000 individuals from across the continental US. Each of these different groups of people experienced distinct historical trajectories in the US, which we found to be manifested as group-specific patterns of Native American ancestry.Individuals from the African descendant group show low  and relaOf the three US ancestry groups characterized here, the Western European descendant group shows the lowest levels of Native American ancestry , consistNuevomexicanos, from subsequent waves of Spanish descendants who arrived later from Mexico.As can be expected, the Spanish descendant group shows by far the highest  and mostThe three main US ancestry groups are also distinguished by their patterns of sex-biased ancestry in a way that reflects the unique history of each group . WesternNuevomexicanos from the HRS dataset, using their distinct Native American ancestry, allowed us to address a number of open questions and controversies regarding the history and culture of this interesting population. Nuevomexicanos from the American southwest are historically defined as the descendants of early Spanish settlers, those who arrived in the period from 1598 to 1848, as opposed to immigrants from Mexico who arrived the region considerably later. The two distinct patterns of Native American ancestry seen for Spanish descendant individuals from the Mountain census region are very much consistent with this historical definition. The Nuevomexicanos show a pattern of Native American ancestry that is intermediate to the Canadian and Mesoamerican reference populations analyzed here, whereas the Mexican American individuals from the same region are more closely related to Mesoamerican reference populations. This is consistent with early admixture with local Native American groups in the US southwest, for the Nuevomexicanos, versus admixture with Mesoamerican groups in Mexico for the later Mexican immigrants. A more precise characterization of Nuevomexicanos’ Native American ancestry would require access to genomic data from US Native American reference populations, which are not readily available owing to cultural resistance to genetic testing for ancestry among these groups [Our ability to distinguish e groups .Nuevomexicanos have identified strongly with their European (Spanish) ancestry, while downplaying ancestral ties to Native Americans [Sistema de Castas, whereby mixed-race individuals were categorized into a complex hierarchical system, with tangible legal and social implications, based on their parents’ ancestry [Mestizo with an explicit recognition of their Native American heritage [Nuevomexicanos and Mexican ancestry groups yielded results that are partly consistent with this historical narrative. On the one hand, Nuevomexicanos do have a substantial amount of Native American ancestry, with a median of just under 40% . Nevertulations . Our resmericans . Interesn Mexico .Nuevomexicano model suggests a slightly older admixture, though with the same ordering, 11–12 generations ago, while the best Nuevomexicano model for 10–11 generations produced a significantly worse model (log-likelihood of -413 vs. -390). Regardless, this suggests that the timing of admixture in the Mexican populations and the Nuevomexicano population was similar, consistent with historical records, while the Native American source populations were different, consistent with their geographical origins. While the admixture timing estimates for these groups are within the range of previous estimates, they are younger than what has been previously reported for Mexican populations [We investigated this apparent differing population history by inferring the timings and proportions of admixture with the TRACTS utility . The besulations . NeverthNuevomexicano history relates to the influence of Conversos on the culture and traditions of the local community. Conversos are Jewish people who converted to Catholicism under intense pressure from religious persecution in Spain, and elsewhere in Europe, and many Spanish Conversos immigrated to the New World [Conversos apparently maintained Jewish religious traditions over the centuries since their immigration from Spain. For example, the persistence of rituals and symbols related to Jewish traditions in New Mexico has been taken as evidence for an influential presence of Conversos among the Nuevomexicanos, a position championed by the historian Stanley Hordes[Converso identity among Nuevomexicanos back to the colonial assertions of pure Spanish ancestry given that the Sephardim are Spanish and would presumably be loath to marry outside of their religious group [Perhaps the most controversial aspect of ew World . Despiteey Hordes. On the ey Hordes. Neulandus group .Nuevomexicanos, via comparative analysis of their European haplotypes to both European and Sephardic Jewish reference populations, in attempt to assess the genetic evidence in support of the Converso narrative. While we did find more Sephardic Jewish ancestry among Nuevomexicanos compared to Spaniards or other Europeans, they did not show any more Sephardic Jewish ancestry than Mexican descendants from nearby regions  were merged with whole genome sequence variant data from the 1000 Genomes Project (1KGP) [n = 1,718) and whole genome genotype data from the Human Genome Diversity Project (HGDP) [n = 230) (n = 314). These Native American genotype data were accessed according to the terms of a data use agreement from the Universidad de Antioquia. Whole genome genotype data from 5 populations of Sephardic Jewish individuals (n = 40) were also included as reference populations [Whole genome genotype data of US individuals from the Health and Retirement Study (HRS) dataset (t (1KGP) , 38 (n =t (HGDP) , 39, 40 n = 230) . Individulations . The genulations , keepingulations , and priulations . The merulations . SNPs thThe RFMix algorithm  is able For each admixed population, rephased genotypes from the final output of RFMix were used to compute the haplotype heterozygosity (HH) for both the masked ancestry-specific genomes and for the unmasked whole-genome. Haplotypes were found by considering sets of 5–15 consecutive variants with a maximum recombination rate between any two variants of 0.5 cM/mB as in , resultiConverso) ancestry in individuals from the Spanish descendant group in HRS (as defined in the genome-wide ancestry section below), and Latin American populations from 1KGP, was inferred via ancestry-specific haplotype comparisons with Sephardic Jewish reference populations using the program ChromoPainter2 [The extent of Sephardic Jewish , Western (French and British), Spanish, and Southern  European reference populations from the 1KGP and HGDP datasets. The ADMIXTURE results at K = 5 were used as one of the ADMIXTURE components was substantially different between the Spanish and Italian reference populations (We developed a novel machine learning based approach to distinguish Spanish from other (primarily Western) European descendants in the HRS dataset via analysis of European-specific haplotypes. First, ADMIXTURE was run with ulations  Figs. A ulations .Admix) is defined as:Fanc,total, Fanc,X, and Fanc,auto are the genome-wide, X chromosome, and autosome ancestry fractions, respectively.Sex-biased ancestry contributions were inferred by comparing the RFMix characterized fractions of each continental ancestry component on the X chromosomes versus the autosomes as previously described , 46. ForST was found between each population using smartpca from the EIGENSOFT package [ST distance matrix was used to create a neighbor-joining tree [ST, and counting the occurrences of each clade using prop.part and part.clades of the Ape package [We used the RFMix defined Native American haplotypes for individuals from the HRS and reference populations to infer the phylogenetic relationships between populations. Using the masked Native American haplotypes, the F package . The resing tree  with theing tree . Clade b package .Nuevomexicano, Mexican (1KGP), MA Spanish descendant, and Puerto Rican (1KGP) populations, three possible orderings of admixture were evaluated with TRACTS: (1) European, Native American, and African; (2) European, African, and Native American; and (3) African, Native American, and European. For each ordering, TRACTS was used to evaluate possible admixture timing from 14 to six generations ago, in 1000 bootstrap attempts. From the bootstrap attempts, the most likely series of admixture events was chosen to represent the population.The TRACTS method was used to infer the timing of admixture events with ancestry tracts defined by RFMix . For theS1 TextThe methods used to assign genetic ancestry groups to HRS individuals and the CLUMPP-ADMIXTURE analysis.(DOCX)Click here for additional data file.S1 TablePopulations included and the number of individuals in each population are shown. How or if a population was used for local or sub-continental ancestry analysis is indicated.(DOCX)Click here for additional data file.S2 TableA support vector machine (SVM) was created to characterize the European ancestry of individuals using ADMIXTURE values generated using masked European genotypes. 10-fold cross-validation was used to evaluate the performance of the SVM. Values shown are the numbers of individuals assigned to each ancestry in the validation procedure. Correct assignments are on the diagonal.(DOCX)Click here for additional data file.S3 TableThe SVM was used to characterize New World individuals with known European ancestry. Values shown are the number of individuals assigned each European ancestry from each population. For each population, the known European ancestry is listed in the parentheses.(DOCX)Click here for additional data file.S4 TableHaplotype heterozygosities (HH) were found for both the ancestry-specific and genome and whole-genome for each of the admixed populations.(DOCX)Click here for additional data file.S1 FigThe census regions, in semi-clockwise order, are: West North-Central (WNC), East North-Central (ENC), Northeast (NE), Mid-Atlantic (MA), Southeast (SE), East South-Central (ESC), West South-Central (WSC), Mountain (MNT), and Pacific (PAC).(TIF)Click here for additional data file.S2 FigAfrican (blue), European (yellow), Native American (red), and East Asian (green) ancestry components are shown. The HRS individuals were divided into two groups based on their self-identified status as African-Americans and all others. HRS individuals shown in comparison to African (African American–ASW), European , Latin American (Mexican–MXL and Puerto Rican–PUR) 1KGP reference populations.(TIF)Click here for additional data file.S3 Figk = 2, 3, 4, and 5 populations.Individuals were placed into genetic ancestry groups using the SVM classifier as described in the Materials and Methods. ADMIXTURE was run and HRS individuals from the two genetic ancestry groups (AD-purple and WD-lime) are plotted according to their geographic region of origin along with individuals from European  and Mexican 1KGP reference populations. ADMIXTURE was run using (TIF)Click here for additional data file.S4 FigIndividuals were placed into genetic ancestry groups using the SVM classifier as described in the Materials and Methods. ADMIXTURE was run and HRS individuals from the Spanish descendant group are plotted according to their geographic region of origin along with individuals from European , Mexican, and Puerto Rican 1KGP reference populations. ADMIXTURE was run using k = 2, 3, 4, and 5 populations.(TIF)Click here for additional data file.S5 FigK = 2 to K = 5, using different seeds for each run. The CLUMPP utility was used to identify corresponding inferred ancestries across ADMIXTURE runs that used the same K. The means of the CLUMPP characterized ancestries are shown here.ADMIXTURE was run on masked European haplotypes from WD and AD individuals 20 times for (TIF)Click here for additional data file.S6 FigK = 2 to K = 5, using different seeds for each run. The CLUMPP utility was used to identify corresponding inferred ancestries across ADMIXTURE runs that used the same K. The means of the CLUMPP characterized ancestries are shown here.ADMIXTURE was run on masked European haplotypes from SD individuals 20 times for (TIF)Click here for additional data file.S7 FigK = 2 to K = 5 and taking the mean of each component across individuals. The Euclidean distance in these vectors was found between all populations, and rescaled from 0 to 1. The similarity between populations was found as 1—the distance.For each population, the mean CLUMPP-ADMIXTURE vector was found by concatenating the vectors from (TIF)Click here for additional data file.S8 FigEuropean CLUMPP-ADMIXTURE similarities were found between African descendant (AD), Western European descendant (WD), and Spanish descendant (SD) populations. Differences in similarity between two population groups were assessed using a Wilcoxon rank-sum test.(TIF)Click here for additional data file.S9 FigHaplotype heterozygosity (HH) values were found for the ancestry-specific genomes and whole-genomes of admixed populations. For each of the admixed populations, the HH value for each of the three continental ancestries were compared to the whole-genome HH value. Significance was assessed using a Wilcoxon rank-sum test.(TIF)Click here for additional data file.S10 FigK = 2 to K = 9, using different seeds for each run. The CLUMPP utility was used to identify corresponding inferred ancestries across ADMIXTURE runs that used the same K. The means of the CLUMPP characterized ancestries are shown here.ADMIXTURE was run on masked Native American haplotypes from AD and WD individuals 20 times for (TIF)Click here for additional data file.S11 FigK = 2 to K = 9, using different seeds for each run. The CLUMPP utility was used to identify corresponding inferred ancestries across ADMIXTURE runs that used the same K. The means of the CLUMPP characterized ancestries are shown here.ADMIXTURE was run on masked Native American haplotypes from HL individuals 20 times for (TIF)Click here for additional data file.S12 FigK = 2 to K = 9 and taking the mean of each component across individuals. The Euclidean distance in these vectors was found between all populations, and rescaled from 0 to 1. The similarity between populations was found as 1—the distance.For each population, the mean CLUMPP-ADMIXTURE vector was found by concatenating the vectors from (TIF)Click here for additional data file.S13 FigNative American CLUMPP-ADMIXTURE similarities were found between African descendant (AD), Western European descendant (WD), and Spanish descendant (SD) populations. Differences in similarity between two population groups were assessed using a Wilcoxon rank-sum test.(TIF)Click here for additional data file.S14 Figf3 statistic. The f3-statistics were computed as f3.Masked Native American genotypes were used to compare admixed US populations to Native American reference populations using the outgroup (TIF)Click here for additional data file.S15 FigD statistic. The D-statistics were computed as D to determine whether admixed populations were more closely related to the modern Pima population or to another Native American population.Masked Native American genotypes were used to compare admixed US populations to Native American reference populations using the (TIF)Click here for additional data file.S16 FigNuevomexicano population. (B) Inferred admixture timing in the 1KGP Mexican population.(Left side) Observed (points) and predicted (solid line) ancestry tract size distributions; the shaded areas represent 95% confidence intervals. (Right side) Admixture event timings are shown together with ancestry proportions. Each inferred admixture event is indicated by a circle, which is scaled according to the size of the contribution to the population and also shows the relative ancestry proportions. The y-axes of the charts show the inferred continental ancestry fractions, and the x-axes show time as the number of generations ago (GA). (A) Inferred admixture timing in the MNT (TIF)Click here for additional data file.S17 Fig(Left side) Observed (points) and predicted (solid line) ancestry tract size distributions; the shaded areas represent 95% confidence intervals. (Right side) Admixture event timings are shown together with ancestry proportions. Each inferred admixture event is indicated by a circle, which is scaled according to the size of the contribution to the population and also shows the relative ancestry proportions. The y-axes of the charts show the inferred continental ancestry fractions, and the x-axes show time as the number of generations ago (GA). (A) Inferred admixture timing in the Mid-Atlantic Spanish descendant population. (B) Inferred admixture timing in the 1KGP Puerto Rican population.(TIF)Click here for additional data file.S18 Figp < 1e-10, Pearson linear correlation).The RFMix utility was modified to reduce the computational time. The resulting genome-wide continental ancestry fractions  were compared to estimates given by ADMIXTURE for (A) African ancestry, (B) European ancestry, and (C) Native American ancestry. All correlations between RFMix and ADMIXTURE continental ancestry fractions were significant ((TIF)Click here for additional data file.S19 Figp < 1e-10, Pearson linear correlation).The modified RFMix utility was used to infer local ancestry as above in (TIF)Click here for additional data file.


PPP1R3B, is related to glycogenesis and has been associated with an increased risk of type 2 diabetes and obesity. These findings point to a selection event after admixture, which is still not entirely understood in recent admixture events.Admixed American populations have different global proportions of European, Sub-Saharan African, and Native-American ancestry. However, individuals who display the same global ancestry could exhibit remarkable differences in the distribution of local ancestry blocks. We studied for the first time the distribution of local ancestry across the genome of 264 Brazilian admixed individuals, ascertained within the scope of the Brazilian Initiative on Precision Medicine. We found a decreased proportion of European ancestry together with an excess of Native-American ancestry on chromosome 8p23.1 and showed that this is due to haplotypes created by chromosomal inversion events. Furthermore, Brazilian non-inverted haplotypes were more similar to Native-American haplotypes than to European haplotypes, in contrast to what was found in other American admixed populations. We also identified signals of recent positive selection on chromosome 8p23.1, and one gene within this locus,  To date, the majority of GWAS have been performed in European and Asian populations6 with a poor representation of other geographical areas and admixed populations. Even though ample GWAS reproducibility in medical genetic studies has been claimed8, it has also been shown that GWAS findings in European populations may not be transferable to admixed American cohorts due to the differences in population substructure, including a specific distribution of ancestry tracts in the genome caused by genetic drift9. In addition, admixed populations are likely to hide a larger number of genetic variants that have functional effects10, and local ancestry information could aid in identifying causal variants in complex traits11. Local ancestry has been inferred in different admixed American individuals, which showed ancestry-specific population substructures and differences in local ancestry proportions18.Population structure due to genetic ancestry has been a significant confounding factor in genome-wide association studies (GWAS)21. The proportion of global ancestries among the three populations is different between Brazilian and other admixed American populations23 and even among Brazilian individuals from different geographical regions20. However, local ancestry inference has been poorly explored in Brazilian individuals20, and since a specific demographic history can impact human populations differently, their study could potentially add relevant information about the distribution of local ancestry along the human genome. Furthermore, it has been established that ancestry proportions across different regions of the genome could present deviations, which can be due to the lack of methodological power to discriminate between two different ancestries in local ancestry inference, genetic drift after the admixture, or signals of recent natural selection26.Brazilian individuals encompass recent demographic admixture, including European, sub-Saharan African, and Native-American populationsTherefore, our primary goals are to describe the distribution and to search for possible deviations of local ancestry blocks across the genome of Brazilian admixed individuals. Furthermore, if deviations in local ancestry are found beyond chance, we aim to explore the issue further by searching for signals of recent natural selection and the possibility of association with disease.22. We observed similar population distributions when Native-American samples from Mao et al.27 are included in the PCA plot  populations  error at K = 10 in the global ancestry inference , which compared the genetic distance between the state of birth groups based on a set of 10,000 random SNPs across the genome and separately by chromosome; and  the Kruskal–Wallis test which compared each EUR, AFR, and NAT proportions (ADMIXTURE K = 5) with the state of birth of individuals in the BRS sample. The AMOVA results from the set of 10,000 random SNPs showed that 99.98% of the variation component was observed within groups and 0.02% was observed among groups , indicating consistency between the algorithms  also showed a decreased European ancestry proportion Fig.  with an 29, which is consistent with the presence of long-range LD haplotypes30. Therefore, to discard false-positive results for the excess of Native-American ancestry on 8p23.1 based on inversions, we controlled the local ancestry inferences for the presence (I) or the absence (NI) of the inversion in both the target and reference populations by a haplotype-based method implemented in the invClust package in R software31. To do that, we analysed the distribution of NI, and I haplotypes on chromosome 8p23.1 in the BRS sample and other admixed American populations and compared NI, and I haplotypes with the ancestry identified on chromosome 8p23.1 . However, we observed a decrease in European ancestry and an increase in sub-Saharan ancestry compared to the genome-wide values in the same genomic region. We performed the same analysis for Colombian, Mexican, and Puerto Rican populations as well as for the 62 admixed Peruvians   as well as the inversion (I/I). Then, we recalculated the local ancestry distributions and deviations separately. Heterozygous individuals for the inversion (NI/I) were not included because of the inability to perform an accurate identification of the phased chromosome containing the inversion30. Therefore, we performed multidimensional scaling (MDS) analysis based on the IBS values and Hudson’s Fst estimations on chromosome 8p23.1 to verify whether we had any bias in our previous analyses and to provide further proof of ancestry deviation by considering information unbiased by haplotype and LD information. The MDS plot from chromosome 8p23.1 in NI/NI individuals shows three clusters: one for European samples, one for sub-Saharan African samples, and one for Native-American samples  regions, unsampled in the 1 KGP database33), or to a false positive in the local ancestry estimation18.Fst estimates on chromosome 8p23.1 show that the NI/NI genotypes from the BRS individuals are more similar to those from Native-American Peruvian and Native-American populations than to those from Sub-Saharan, Colombian, Puerto Rican, and European populations, in agreement with both the local ancestry and the MDS analyses , than to European and African samples  although the European genome-wide ancestry found in BRS is much higher than the Native-American ancestry. The conclusions about I/I haplotypes are more challenging, due to the low frequency of the I allele in Native-American populations24 since this deviation is equivalent to a statistical significance p-value < 1 × 10−5 from neutrality simulation tests24. To check whether the ancestry deviation detected on chromosome 8p23.1 might be related to a positive selection event, we tested our sample for positive selection by five neutrality statistics: intrapopulation absolute integrated haplotype scores 36, interpopulation integrated haplotype score (|ΔiHHderived|)37, interpopulation extended haplotype homozygosity (XP-EHH)36, and population branch statistics (PBS)38 based on Hudson’s Fst39.Local ancestry proportions more than 4.42 SDs from the genome-wide average could be indicative of positive selection40, defined as Fisher combined score (FCS). We assigned as outlier those SNPs within the 1% highest FCS value across the genome. In that way, we found 46 blocks of 100-SNPs with the highest proportion of outlier SNPs: 11 blocks identified as European ancestry; 19 as African; 12 as Native-American; one as European or African; one as African or Native-American; and two as European or Native-American .One tail p-values from the five different statistics were combined using the method proposed by Fisher41, cystic fibrosis42, and resistance to hepatitis C virus infection43. More recently, genome-wide studies have shown different risk allele frequencies from SNPs associated with the disease among populations worldwide3, as well as genotypes associated with target treatments in specific populations45. In this context, local ancestry information across the genome is likely to become relevant to determine whether a genetic variant is expected to be useful in precision medicine, especially for an admixed population. Although local ancestry information is available for admixed American populations9, it is still poorly explored in Brazilian individuals20. Therefore, in order to fulfil the lack of data, the present study provides information on ancestry tracts across a sample of Brazilian individuals.Population genetics studies have led to a better understanding of human diseases in the context of human evolution, such as the heterozygous advantage phenomenon in sickle-cell anaemia28. This result is also consistent with previous findings in other admixed American populations, such as in Mexico, Puerto Rico, Peru, Colombia, Ecuador, Chile, and Argentina46, including ancestry estimations by different haplotype-based methods48Our results showed that the BRS sample is composed of a high proportion of European ancestry followed by sub-Saharan African and Native-American ancestry, showing remarkable variability among BRS individuals. At a genome-wide level, BRS sample is closer to Puerto Ricans and Colombians than to Peruvian and Mexican populations, which is consistent with a large amount of European ancestry detected in other Brazilian samples from different geographic regions49. Therefore, we further focused our analyses on both homozygous NI/NI and I/I genotype only since we cannot define the ancestry tract for each haplotype from the NI/I individuals. This approach considerably reduced the sample size for analyses performed independently in I and NI individuals.Average genome-wide local ancestry showed similar results compared to global ancestry inference in the BRS sample, which indicates consistency between the two ancestry inferences. The local ancestry screening along the genome shows that BRS samples have decreased European ancestry tracts on chromosome 8p23.1, followed by an excess of Native-American ancestry tracts in this same region. This locus is known to present inversion events, which can generate long-range LD regions and could lead to a possible confounding of local ancestry inferenceBrazilian NI/NI samples have extreme lower values of European ancestry in the 8p23.1 region compared to average values along the chromosome 8 and genome-wide. These observations point to the predominance of Native-American origin rather than European of NI haplotypes in the Brazilian population. The Brazilian I/I sample also shows lower values of European ancestry but followed by an increase in sub-Saharan African ancestry. Together, these results point to the presence of selection pressure against the European haplotypes in this genomic region in favour of the Native-American ancestry tracts for the NI haplotypes, and probably the sub-Saharan African tracks for the I haplotypes.Results in other admixed American populations show variability. Regarding NI/NI individuals, Mexican and Peruvian populations have more similarity on chromosome 8p23.1 with Native-Americans than with European populations, while Colombian and Puerto Rican populations have more similarity with European populations. Therefore, genomic ancestry distribution results cannot be extrapolated to different populations, and differences on 8p23.1 ancestry among populations point to different demographic histories and, more importantly, different selective pressures in the different populations.et al. showed that NI/I and I/I genotypes are rare in Native-American populations due to demographic events50, and their study suggests that a serial founder model of migration from Africa explains the demographic distribution of the chromosomal 8p23.1 inversion. We did not find evidence in favour or against this demographic explanation for the I and NI allelic frequency distributions in a pre-Columbian expansion. However, the increase of Native-American component on 8p23.1 in Brazilian, Mexican, and Peruvian samples is difficult to explain by demographic events alone, even considering extreme genetic drift, and it points to a selection event after admixture26.Salm 52, but it is not entirely understood in more recent admixture events53.Therefore, our findings suggest that either the inversion event occurred more than once, or that genetic drift has produced a high divergence between European and Native-American haplotypes in this genomic region before the European arrival in the Americas. Finally, we suggest that the European NI haplotypes on chromosome 8p23.1 were disadvantaged in favour of the Native-American NI haplotypes in the context of new heterogeneous genomic diversity landscapes and changing environmental conditions which arose after post-Columbian admixture events in the Americas, in Brazil and Mexico. Selection after admixture has been described in archaic admixture between modern humans and Neanderthals54, and on chromosome 8p23.1 in Mexican populations55. However, follow-up studies have depicted a critical view of these results, reporting the need for higher thresholds than p > 10−5 and the risk of errors in local ancestry inferences24, pointing to long-range LD as the possible origin of biased local ancestry predictions30. Moreover, they have highlighted the need for parallel analysis with convergent results to have enough evidence of a selection event after an admixture process. Taking these perspectives into account, we have corroborated our local ancestry deviation, reinforcing it with non-haplotype-dependent analyses and selection tests leading to the conclusion of selected Native-American ancestry in the 8p23 region in admixed Brazilians. Therefore, we consider this finding as one of the first pieces of substantial evidence of selection after admixture in admixed Americans, beyond the HLA region58.Deviation in local ancestry genomic distribution has also been found on chromosome 6p22 in African-AmericanPPP1R3B gene found within the candidate region encodes a regulatory subunit 3B of the phosphatase-1 protein. PPP1R3B plays an essential role in glycogen synthesis, and a previous study showed that PPP1R3B overexpression reduces hypoglycaemia after long-term fasting59. Indeed, the main clinical phenotypes associated with metabolic disorders, taking admixture into account, are related to type 2 diabetes, insulin secretion, body mass index, obesity, and adiposity62. In this way, we can hypothesise that during the Brazilian population admixture process after 1500 CE, some PPP1R3B haplotypes from the Native-American ancestry gene pool could have been advantageous, due to increased fat and glucose storage under a restrictive diet environment, such as severe famine periods63, which increased the frequency of the Native-American PPP1R3B haplotypes in the Brazilian population. Because of the long-range LD haplotype, due to inversion events, the locus on chromosome 8p23.1 from Native-American ancestry also had the frequency increased by PPP1R3B hitchhiking. Future gene expression studies considering genetic ancestry should confirm or disprove this hypothesis.The selection test revealed signs of positive selection on chromosome 8p23.1, in comparison with Peruvians and Native-Americans. Interesting, the In conclusion, we found an excess of Native-American haplotypes in the BRS sample at a locus on chromosome 8p23.1, and the presence of different European and Native-American haplotypes, due to long-range LD effects, helped us to understand further the local ancestry deviation. This unusual local ancestry deviation, which could be due to a recent adaptation after the admixture, was further studied, and the hypothesis proposed was supported by additional selection tests. The study of admixed samples allowed us to identify a process of adaptation in a region where the role of selection and demography was unclear. Our results also emphasize the need to study additional admixed American populations independently, avoiding extrapolated conclusions from one to another, since the admixture process can show relevant differences, as we have demonstrated in the present work. Moreover, we propose that individual local ancestry information can be used to obtain relevant information about human health, assisting in the identification of populations that may be at higher risk for specific disorders, which can, in turn, be applied in the recent efforts to develop and implement precision medicine.http://bipmed.org). We defined them as the Brazilian-São Paulo sample (BRS). Among them, 202 had information about their state of birth  ascertained at the University of Campinas hospital  within the scope of the Brazilian Initiative on Precision Medicine Project  64. The quantification and quality of each DNA sample were evaluated by Qubit® 2.0 Fluorometer  and Epoch 2 microplate spectrophotometer . Genotype calling was performed by Genome-Wide Human SNP Array 6.0 platform  in the Laboratory of Multiuser Equipment facility at UNICAMP. We called the genotypes from fluorescent signals by CLRMM algorithm65, which were converted to PLINK format66 by in-house scripts.The genomic DNA of the BRS sample was obtained from peripheral blood using the phenol-chloroform procedure66 software to perform SNP and individual filtering.67 Among the 906,600 genotyped SNPs, we removed 1,428 unmapped, 37,149 non-autosomal, 134,006 ambiguous , and 128,380 SNPs in Hardy–Weinberg disequilibrium 67. We also checked for SNP and individual missing data > 2%, but all SNPs and individuals passed this filter because the genotype call rate by the CRLMM algorithm was 100%65. After genotype filtering, 605,637 SNPs remained (Supplementary Table 9). We performed the same SNP filtering procedure in the Native-American sample, and 719,472 SNPs remained . We removed 11 individuals with heterozygosity rate higher (n = 5) or lower (n = 6) than two SDs from the mean. We also removed eight individuals from pairs of individuals who presented identical-by-state alleles > 0.85, which could indicate duplicated samples67. Next, we excluded individuals from identical-by-descent and genomic relatedness matrix estimations > 0.125, which is the expected genomic relatedness for third-degree relatives. To maximise the sample size, we used a greedy algorithm implemented in PLINK 1.9 software. After individual filtering, 222 individuals remained suitable for further analysis  or sample contamination (high heterozygosity rates)2 = 0.567. In the end, we had 304,541 SNPs for the population structure and global ancestry inference and 596,275 SNPs for local ancestry inference  < 0.01. To avoid bias in population structure, MDS, and global ancestry inference, we removed 291,734 SNPs in linkage disequilibrium (LD) by the following parameters: window size = 50 SNPs, shift step = 5 SNPs, and r68 from K = 2 to K = 13 ancestral components. We also performed three different runs with different random seeds for each K to observe the consistency of estimates. The BRS sample has a sample size two times larger than other 1 KGP populations, which could lead to bias in admixture results69; therefore, we divided the BRS sample into two random subsamples (n = 111) and ran ADMIXTURE separately for each subsample.We compared the BRS sample and the 26 populations from 1 KGP using principal component analysis (PCA) in PLINK v1.9 software. Global ancestry was inferred by unsupervised ADMIXTURE maximum likelihood approachpoppr.amova70 and Kruskal-Wallis test based on EUR, AFR, and NAT ancestry proportions (ADMIXTURE K = 5). Kruskal-Wallis p-values were corrected by Bonferroni adjustment under three comparisons. The analyses were performed by an in-house script developed in R software.Brazilian individuals could present different ancestries based on their geographical location. Therefore, we evaluated possible clustering based on available state of birth information by PCA. In addition, we evaluated whether the state of birth groups are genetically similar by the Analysis of Molecular Variance (AMOVA) using 71. After phasing, we converted the output data from SHAPEIT2 to the input files required by RFMix v.1.5.4 software32, following a pipeline reported by Martin et al.9 (https://github.com/armartin/ancestry_pipeline).We phased the SNPs without LD pruning with SHAPEIT2 v2.r387 software using the default parameters32, we selected 23 random individuals among Europeans  and 23 among Africans  , 43 random individuals among Europeans, and 43 among Africans to evaluate the consistency of local ancestry inference using the first approach  who presented more than 0.95 of Native-American ancestry proportion at K = 5 in ADMIXTURE . To avoid biases due to unbalanced reference panel sizeshttps://www.r-project.org/) to analyse the proportion of European, African, or Native-American ancestry among BRS individuals for each SNP across the genome. We inferred local ancestry and estimated the proportion of SNPs associated with each ancestry across the genome for Colombian (CLM), Mexican (MXL), and Puerto Rican (PUR) populations from 1 KGP to compare results with the BRS sample. We also analysed the local ancestry of all PEL individuals in the second local ancestry approach, where we used Native-American individuals from Mao et al.27 as a Native-American reference.We ran RFMix in the PopPhased mode with a minimum window size of 0.2 cM, one EM iteration, and node size 5, maintaining the reference panel after the initial inference step and saving the forwards–backwards probabilities. We developed an in-house R script (invClust package in R)31. We set invClust parameters as follows: two multidimensional scaling components; method = 1, which performed an EM algorithm for three genotypes; 1,000,000 iterations for the EM algorithm; and 10e−6 for EM convergence tolerance.Since our results show evidence of haplotype inversions, we identified non-inverted (NI) and inverted (I) haplotypes by a haplotype-based method developed by Cáceres and González  values from IBS estimations for each individual, using the R software, in the 8p23.1 and additional seven genomic regions at random across the genome with the same size of the 8p23.1 region (1883857 bp). To evaluate the similarity between the MDS plot from 8p23.1 and each MDS plot from the random genomic region, we performed Procrustes axis rotation. We compared the Procrustes results from the MDS plots by estimating the Procrustes correlation test under 10,000 permutations. All Procrustes analyses were performed by the vegan package in R74.In order to evaluate which haplotype from the 1 KGP populations has a high similarity with the BRS sample, we separated the NI/NI and I/I genotypes from all populations and estimated Hudson’s Fstread.vcfR and vcfR2DNAbin from the vcfR R package75. The DNA sequences were plotted by image.DNAbin function from the ape R package76.The SHAPEIT software was used to convert the phased haplotypes to VCF files, separating individuals with the NI/NI and the I/I genotype and by continental populations. The VCF files were converted to DNA sequence by 36, interpopulation integrated haplotype score (|ΔiHHderived|)37, interpopulation extended haplotype homozygosity (XP-EHH)36, and population branch statistics (PBS)38 based on Hudson’s Fst39. Integrated haplotype scores and extended haplotype homozygosity were estimated by rehh package in R software57. PBS and Hudson’s Fst were calculated by an in-house script in R software based on previously reported formulae39. Interpopulation statistics require a reference population and PBS statistics for an outgroup population. Therefore, the sample we used as a reference population for the scans of positive selection in the BRS group were 404 European , 504 sub-Saharan African , and the 23 Native-American Peruvian (PEL) individuals of the 1 KGP for the first local ancestry approach, and the 43 Native-American individuals27 for the second local ancestry approach. PBS was evaluated using 96 Punjabi from Lahore, Pakistan (PJL) as outgroup population. We performed the same positive selection test for Colombian, Mexican, and Puerto Rican populations, to compare results with our sample, and also changed the Native-American reference panel 40 and showed in the following formulae:FCS is Fisher combined score, k is the number of combined statistics, and pi the empirical p-value, i.e., the genomic rank of the ith statistics divided by the total number of SNPs40. Since small values of pi lead to large FCS values, we defined outlier SNPs as those with values of FCS higher than 99% of the SNP FCS values across the genome . To search for regions with a signal of positive selection, we split the genome into 100-SNP blocks. Then, we estimated the proportion of outliers within each block, and if a block was found to present a proportion of outliers higher than the 99.5th percentile (the highest 0.5%) among the proportions of all blocks, it was defined as a region under positive selection40. Although FCS violates the assumption of dependency among empirical p-values, it has a chi-square distribution with 2k degrees of freedom under neutrality40. Previous studies have also shown that combining different neutrality tests increases the power to detect signals of positive selection40.The ancestral and derived allele assignation followed the alignment from 1 KGP data. We combined the one tail p-values of the different statistics by the method proposed by FisherSupplementary information


Anopheles gambiae s.s. is distributed across most of sub-Saharan Africa and is of major scientific and public health interest for being an African malaria vector. Here we present population genomic analyses of 111 specimens sampled from west to east Africa, including the first whole genome sequences from oceanic islands, the Comoros. Genetic distances between populations of A. gambiae are discordant with geographic distances but are consistent with a stepwise migration scenario in which the species increases its range from west to east Africa through consecutive founder events over the last ~200,000 years. Geological barriers like the Congo River basin and the East African rift seem to play an important role in shaping this process. Moreover, we find a high degree of genetic isolation of populations on the Comoros, confirming the potential of these islands as candidate sites for potential field trials of genetically engineered mosquitoes for malaria control.The mosquito  Anopheles gambiae specimens sampled from west to east Africa. This study suggests a high degree of genetic isolation of populations on the Comoros, highlighting the importance of physical barriers to gene flow in this African malaria vector species.Schmidt et al. present population genomic analyses of 111 mosquito  The various processes underlying these population dynamics can be described at the organismal level as migration of individuals between populations or on the genetic level as ancient and recent gene flow between them. Gene flow essentially reflects the direct exchange of heritable information due to migration between related populations in an evolutionary sense3. Barriers to migration play a pivotal role in shaping patterns of inter-population gene flow. These comprise different elements of the landscape such as physical  and ecological barriers , with the latter often being species-specific and erratic7. The most obvious and universal barrier to migration is realized in islands, hence the term “island biogeography”8. Isolation of island populations depends on the dispersal ability of the species, the remoteness of the island, and various external factors like sea currents9, wind directions10, and anthropogenic activity11. Knowledge about the connectivity between island and mainland populations can help to facilitate an understanding of how newly introduced genetic variants can spread throughout the distribution area of a species.Evolutionary processes within species depend on the spatial and genetic connectivity of their populations. This is especially true when considering large spatial scales and long time periods, i.e., the full geographical and historical space of a speciesAnopheles gambiae s.s. Giles (referred to as A. gambiae henceforth) is a common mosquito occurring throughout sub-Saharan tropical Africa12. It is of high importance in public health for its role as the principal vector of human malaria, a disease causing >200 million cases and half a million deaths per year according to World Health Organization (WHO) estimates13. A recent modeling effort suggests that malaria is not expected to be eliminated with currently available tools in highly endemic areas15 due to lack of a vaccine16, increasing drug resistance17, spreading insecticide resistance alleles18, and decreasing effectiveness of physical prevention provided by bed nets19. Consequently, renewed efforts to explore the use of genetically engineered mosquitoes (GEMs) for malaria control have been initiated23, including the development of models to explore the dispersal capacity of gene drive systems through natural populations26. Knowledge of the varying degrees of connectivity between different populations throughout the species’ range as well as nucleotide diversity that could provide a potential mechanism for gene drive resistance28 therefore will be important for assessing the outcome of field release trials of GEM or conventional control measures.A. gambiae has led to extensive studies linking patterns of genetic diversity to different ecological conditions30 as well as diverse population genomics studies32. Recently, a broad-scale population genomic analysis across most of continental Africa revealed pronounced structure and varying levels of gene flow among geographic populations33. However, oceanic island populations were not included in this study. A number of individual studies of island populations of A. gambiae have been undertaken, most recently analysis of several small islands in Lake Victoria, eastern Africa, indicating genetic isolation of these island populations34. However, these islands are located <50 km from the mainland and there is a known negative correlation between species diversity and distance to mainland35 including a study specifically on mosquitoes36, underlining the importance of remoteness for isolation.The importance of A. gambiae populations suggest a high degree of genetic isolation37. Here we present individual whole-genome resequencing data for populations from the three Comoro islands Grande Comore (Comorian: Njazidja), Anjouan (Nzwani) and Mohéli  that comprise the Union of the Comoros, as well as populations from four continental African countries. We chose Mali as a representative of the core distributional area of A. gambiae in western Africa plus sites in Cameroon, Tanzania, and Zambia, which span sub-Saharan Africa  in the dataset.Sequencing of the 111 samples resulted in 3.7 billion reads in total with a mean genome coverage of 11.7× per sample  than within the populations of the Comoros (623).Genetic variability was estimated for each population by dividing the number of biallelic SNP sites in the heterozygous state by the total number of loci. Genetic variability was highest in west Africa , intermediate in east Africa , and lowest for the three Comoro islands Fig. . Plottinles Fig. . The remles Fig. . ConsistFST44 as a measure of relative population differentiation was estimated for each population pair  show a separation between west  and east  African populations. With K = 5, the samples from the Comoro populations are assigned to three genetic clusters, two present in Grande Comore and the third corresponding to all individuals from the two smaller islands Mohéli/Anjouan. One of the genetic clusters in Grande Comore is also present in East African populations (Zambia/Tanzania), suggesting closer genetic relationship between the main island and mainland than between the smaller islands and mainland , the three Comoro islands are distinct and separate from Tanzania and Zambia, which themselves form a single group. Cross-validation error analysis revealed K = 5 as the best fit . West AFig. K =  show a s46 follow a similar trajectory in all populations in the deep past  continue to decline. Somewhat later, ~40,000 years ago, the Tanzanian population increases while the population sizes in the Comoros continue to decline. The islands of Anjouan and Mohéli show similar patterns, reaching their minimal turning point at about the same time with the island of Grande Comore, increasing slightly later. Approximately 25,000 years ago, all populations have reached or passed their minimal turning point and are increasing in size again.Historical effective population sizes estimated by multiple sequentially Markovian coalescence using MSMC249. A higher relative cross-coalescence (RCC), calculated using the MSMC2 algorithm, indicates less time to the last common ancestor shared by the two populations in a specific comparison. All populations shared common ancestors in the deep past, reflecting high connectivity, probably as one super-population. Around the time when we see the potential initial founder effect  are inconsistent with their true geographic location  that are widely separated in both geogenetic and geographic space (separated by 1200 km). This pattern is even more prominent within the Comoros, which are geographically proximal (~40 km). Grande Comore is clearly separated from Mohéli and Anjouan in geogenetic space  populations, with steeper subsequent decline in population size for the latter group. This event may be attributed to crossing the East African rift, which has previously been shown to be associated with strongly increased FST values in A. gambiae33, hence suggesting a hurdle for gene flow. Presumably, the Comoros were colonized by founders migrating across the Mozambique Channel from easternmost continental African populations. Our data suggest that roughly ~70,000 years ago the Comoros A. gambiae population split from Tanzania, followed by another split ~40,000 years ago between Grande Comore and Mohéli and Anjouan. That Grande Comore was the most recently colonized is supported by the fact that it is the youngest of the islands geologically55 and is the latest to have experienced a dramatic increase in population size  prior to human habitation. This would be consistent with the assumption that A. gambiae adapted only recently  to humans as hosts58 and was not restricted to non-human primates before59.Our data indicate that A. gambiae or Anopheles coluzzii adult collections from the same village60. We minimize this potential bias by genotyping Divergence Island SNPs (DISs)61 prior to whole-genome sequencing. This assay provides a more in-depth genetic background characterization than traditional PCR-based methods and can distinguish hybrid from parental form individuals63. All Comoros samples had typical A. gambiae genotypes64. The temporal dynamics of A. gambiae populations, their resting/biting behavior, and ideal method of adult surveillance are some of areas that need further study.Our study has some minor bias regarding sampling of specimens at different sites. For instance, our 2011 Comoros collection is composed of larvae only since we were not able to locate indoor-resting adults. It has been shown in Goundry, Burkina Faso that larval collections contained hybrid populations that are distinct from typical A. gambiae for malaria control in Africa66. Identification of confined field trial sites aimed at evaluating the performance of GEMs includes measures of gene flow between target and off-target sites. Our results indicate that gene flow is extensive among mainland sites but highly restricted between mainland and sites on oceanic islands. The Comoro Islands have relatively small A. gambiae populations  from the Vector Genetics Laboratory archive mosquito DNA collection for the study . Individual genomic DNA libraries were constructed using 10 ng DNA, the KAPA HyperPlus Kit (KAPA Biosystems), custom IDT 48 dual index barcodes (Integrated DNA Technologies), and Ampure SPRI beads (Beckman). We applied the library construction protocol described in ref. 72. Library concentrations were measured using Qubit as described above. Individually barcoded libraries were combined by equal quantity for pooled sequencing based on the Qubit results. Sequencing was performed using an Illumina HiSeq 4000 instrument at the UC Davis DNA Technologies Core.DNA extraction was performed using the protocol described in refs. 73. PCR duplicates were removed using Picard Tools v2.2.4 (http://broadinstitute.github.io/picard/) and reads were realigned around indels with GATK v3.574. Afterwards the processed reads were mapped to the reference genome AgamP438 using BWA-MEM v0.7.1539 with default settings. Freebayes v1.0.140 was used for variant calling, applying default parameters but “theta = 0.01” and “max-complex-gap = 3.” Variants without support from both overlapping forward and reverse reads were removed. Only biallelic SNPs with minimum depth of 8 were called for genotypes and used for analysis.Demultiplexed raw reads were filtered and trimmed  using Trimmomatic v0.3675. Including all SNPs would have arbitrarily weighted the similarity/difference to the reference genome instead of the variability that is indicative of population size and history76. The data were plotted grouped by origin using the boxplot function in R v3.2.577 applying default settings (Tukey boxplot). The spatial patterns of population genetic structure were analyzed using the R package SpaceMix v0.1341 to create a geogenetic map of the polymorphism data. In these, allele frequency covariance is displayed in a way that distances between samples reflect geogenetic, rather than geographic, distance. Neighborhood sizes Nxσ2, which give an estimate of dispersal distances, were estimated using Raddle42 with settings recommended by the authors (https://github.com/NovembreLab/raddle). Raddle searches for rare alleles in the data and calculates dispersal capacities by their distribution within and across populations.The genetic variability was calculated for each mosquito as the number of biallelic SNP sites in heterozygous state divided by the total number of loci in the genomeFST were calculated using Hudson’s estimator, which is not sensitive to the ratio of sample sizes and does not systematically overestimate FST44, implemented in scikit-allel v1.2.078 utility package. As a proxy for relatedness of the populations in terms of relative genetic variance, a phylogenetic tree was calculated from the FST data using a Neighbor-Joining79 algorithm implemented in the program Neighbor (distance matrix model F84) from the package PHYLIP v3.69680. To estimate robustness of the topology, we re-calculated the tree 100 times with different subsets of randomly chosen 50K SNPs each for the distance matrix and derived the consensus rate in each branching point.As another measure of population differentiation due to genetic structure, pairwise fixation indices 45. Admixture analyses were performed for a broad range of assumed ancestral populations (K = 1–10) and analyzed and plotted in R. The best-fitting K was determined by ADMIXTURE’s cross-validation procedure and the resulting error values.Populations were screened for their admixture profiles using ADMIXTURE v1.3.081 to remove samples with >5% missing data  and sites with >5% missing data . Afterwards the variants of all specimens were phased into haplotypes using SHAPEIT2 v2.982. Phasing was done for 2L, 2R, 3L, and 3R separately (the X chromosome was not used here since recombination is different in sex-determining regions83). The SHAPEIT output was converted to VCF files and samples were separated again. Of those, only four samples per population (when possible) were used for the estimation of population sizes (N = 83) and two per population for the inter-population cross-coalescence, respectively (haplotypes N = 8 each), due to high computational demands. Reliable estimations can be calculated from haplotype numbers as low as 2 or 4 due to the mosaic nature of recombining nuclear genomes46.The VCF files were filtered with VCFtools v0.1.12bNe and historic cross-coalescence was performed using the multiple sequentially Markovian coalescent pipeline MSMC2 v2.0.246 following the guide (https://github.com/stschiff/msmc2). For the MSMC2 analyses, mappability masks were prepared for the reference genome following the procedure of Heng Li’s SNPable program (http://lh3lh3.users.sourceforge.net/snpable.shtml). In addition, mask files for the specimen’s VCF files were produced using BEDOPS v2.4.3084. The MSMC2 main runs were conducted separately for each population to estimate within-population coalescence and in addition between a selected set of populations for cross-population coalescence with 20 Baum–Welch iterations each. The results were then converted to real time in years (assuming ten generations per year), population sizes, and RCC following the procedure published by the authors of MSMC2 (https://github.com/stschiff/msmc/blob/master/guide.md). A mutation rate of 2.85 × 10−9 was assumed for the conversion, which is the median of the published mutation rates from the insect species Drosophila melanogaster (2.8e−985), Heliconius melpomene (2.9e−986), Chironomus riparius (2.1e−987), and Bombus terrestris (3.6e−988). All time slices with λ < 5 were excluded to account for possible phasing error affecting resolution89.Estimation of historic effective population sizes p values are reported in the text. FST tree topology was tested by bootstrapping (100 times with different subsets of randomly chosen 50K SNPs each). Coalescence estimates with MSMC2 were generated with 20 Baum–Welch iterations each.One hundred and eleven specimens from 22 populations from west to east Africa were used for this study. Statistical analyses were performed in R and Python with programs cited or custom scripts provided under “Code availability.” All tests and corresponding Further information on research design is available in the Supplementary InformationReporting SummaryPeer Review File


X-chromosomal short tandem repeats (X-STRs) may assist resolution of complex forensic kinship cases and complement autosomal and Y-chromosomal STRs in routine forensic practice and population genetics. In the present study, we investigated the allele/haplotype diversity and forensic genetic characteristics of 19 X- STRs in 206 Guizhou Han and 1344 Meta-Han Chinese individuals using AGCU X19 PCR amplification system. Population relationships within five Han Chinese population groups , between Guizhou Han and other 19 Chinese reference populations belonging to four language families , as well as between Meta-Han Chinese and other 15 minorities  were performed using Reynolds’s, Nei’s and Fst genetic distances, principal component analysis (PCA), multidimensional scaling (MDS), Structure and Neighbor-Joining tree. Mean paternity exclusion chance (MEC) in Duos > 0.99999999453588 and in trios > 0.99999999999781, as well as power of discrimination (PD) > 0.99999999999980 in Guizhou Han on the basis of allele frequencies. Consistent high MECs and PDs can be observed in Meta-Han Chinese population based on both allele diversities of 19 markers and haplotype diversities of seven linkage groups (LG). DXS10135 and LG1 are the most informative and polymorphic in Han Chinese group. The comprehensive population comparisons reveal that Han Chinese is a homogenous population and has the genetically closer relationship with Hmong-Mien-speaking groups than Tibetan-Burman-speaking and Turkic-speaking populations. In summary, AGCU X19 PCR amplification system is highly polymorphic and informative in Guizhou Han and Han Chinese populations. The comprehensive population data from 20 Chinese populations analyzed in this study may be used as a reference Chinese frequency database of X-STRs for forensic casework applications. Previous studies revealed that STRs are associated with the susceptibility and morbidity of more than 30 Mendelian hereditary disorders  and otheneration .® Argus X-12 Kit [® Argus X-12 Kit in China [Tang Dynasty (https://en.wikipedia.org/wiki/Guizhou).X-chromosomal STRs (X-STRs) with the unique pattern of inheritance (father transmits it to daughter and mother transmits one of them to her offspring) can complement autosomal and Y-chromosomal STRs in forensic identity (predominantly in identification cases of missing person and mass disaster victim) and complex kinship analyses, especially in the deficiency and incestuous cases . RecentlX-12 Kit  and eighX-12 Kit . The impin China , 17, whiin China –29.Han Cin China , 31. ChiPrevious population genetic studies have been concentrated on forensic characterization and genetic polymorphisms of AGCU X19 system in the Chinese Uyghur, Hui, Tibetan, Yi, Gelao, Miao, Li, Kazakh, Xibe and Han . HoweverThis study was specially approved -1-044) by the Biomedical Research Ethics committee of Zunyi Medical University. All subjects were kept informed of the purpose and signed the informed consent before taking part in sample collection. Each subject was confirmed the offspring of indigenous Han nationality and without consanguineous marriage with minority groups at least three generations.Peripheral blood samples were collected from 206 unrelated Han Chinese individuals  residing in Guizhou province, southwest China. We used PureLink Genomic DNA Mini Kit (Thermo Fisher Scientific) to extract and isolate human genomic DNA, and used an Applied Biosystem 7500 Real-time PCR System (Thermo Fisher Scientific) and Quantifiler Human DNA Quantification Kit (Thermo Fisher Scientific) to measure the DNA concentration on the manufacturer’s protocol. Finally, we diluted the DNA to 2.0 ng/μL and stored at -20°C until amplification.2O). The PCR conditions for 10 cycles  and 20 cycles  and followed a final extension for 30 min at 60°C and finally holding at 4°C for preservation. Capillary electrophoresis separation of amplified products was conducted on the Applied Biosystems 3130 Genetic Analyzers  with the POP7® polymer and a 36cm capillary array. GeneMapper ID-X v.1.4 software (Thermo Fisher Scientific) was utilized to analyze the electrophoretogram and assign the genotypes of 19 X-STRs.We genotyped 206 Guizhou Han individuals on the ProFlex 96-Well PCR System (Thermo Fisher Scientific) using the AGCU X19 kit  on the basis of the recommendations. We employed a total of 10 μL as the final PCR reaction volume, including 4 μL of reaction mix, 0.2μL of A-Taq DNA polymerase, 0.8 μL of template DNA, 2 μL of primers and 3 μL of sdH2O (sterile deionized HChrX-STR.org 2.0 database (http://www.chrx-str.org/). Gene diversities (GD) of X-STRs and haplotype diversity (HD) of seven linkage groups were calculated using Nei’s formula [We separately calculated allele frequencies in the males, females and pooled Guizhou Han Chinese population (206 subjects) and Meta-Han Chinese population (1344 subjects) using the modified PowerStatesV1.2 spreadsheet . Arlequin software (version 3.5.2)  was used formula  as emplo formula , 39.We used the Reynolds’s and Nei’s pairwise genetic distances, as well as Fst and corresponding p values to estimate the genetic differences and similarities using the PHYLIP version 3.5 packages  and ArleThe experiment was conducted at the Institute of Forensic Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University. Control DNA 9947A and sdH2O included in the AGCU X19 kit were chosen as controls for allele assignment. This laboratory has been approved the accreditation of ISO/IEC 17025 and CNAS . Besides, our experiment followed the recommendations of the Scientific Working Group on DNA Analysis (SWGDAM)  and the https://en.wikipedia.org/wiki/Han_Chinese). Here, to implement X-STR typing into routine forensic practice and establish Chinese reference database as well as investigate genetic diversity and forensic characteristics of Han Chinese population, we newly generated 19 X-STRs data from 206 Guizhou Han subjects (S1 Table) and combined previously published 4868 genotypes [China, composed of 56 officially recognized ethnic groups and a population over 1,404 billion, harbors substantial genetic, linguistic, physical, cultural and diversity , 31 , Tungusenotypes ). To chaenotypes : 201, HaWe then combined in total of 3730 previously reported genotypes from 15 different populations , 27–29 tS2 Table presents the observed heterozygosity (Ho), expected heterozygosity (He) and the p values on the basis of distribution of He and Ho. The Ho values span from 0.4808 (DXS7423) to 0.9135 (DXS10135) with the average of 0.7556 and He values vary from 0.4760 (DXS7423) to 0.9194 (DXS10135) with the average of 0.7663. X-STR locus of DXS10101 (0.0123) is not found to be in HWE before Bonferroni correction (p = 0.05). No significant deviations are observed after applying the multiplex correction. As listed in , all pairs of markers except for DXS101-DXS10101 (0.0000) and DXS10134-DXS10135 (0.0001) in Guizhou Han females and DXS10103-DXS10101 (0.0000) and DXS10103-DXS10101 (0.0000) in Guizhou Han males are found to deliver statistically significant results after Bonferroni correction (p = 0.00029).We first evaluated the Hardy-Weinberg disequilibrium (HWE) and Linkage Disequilibrium (LD) of 19 X-chromosomal STRs in Guizhou Han Chinese population and investigated the genetic differences among the 104 females and 102 males. S5 Table), however no deviations are found after correction. Additionally, significant associations in the pairwise LD analyses are found in four pairs after Bonferroni correction (p = 0.00029): DXS10103-DXS10101 (0.0000) and DXS10159-DXS10164 (0.0000) in Meta-Han females (S6 Table) and DXS10103-DXS10101 (0.0000) and DXS10164-DXS10075 (0.0001) in Meta-Han males (S7 Table).To validate the HWE and LD measures in a bigger population size, we subsequently replicated the analyses in a Meta-Han Chinese population. One new dataset consists of a total of 1344 Han Chinese individuals  from five geographically distinct populations: 206 aforementioned Guizhou Han individuals, 201 Sichuan Han individuals  in the western China, 474 Guanzhong Han individuals  in the northern China, 308 southeastern Han individuals , and 155 Hainan Han Chinese males in the southern China. Ho and He span from 0.4629 (DXS7423) to 0.8914 (DXS10148) and 0.4975 (DXS7423) to 0.9193 (DXS10135), respectively. DXS10079 (p = 0.0343) and DXS10101 (p = 0.0083) are found to be deviated from the HWE before adjustment for multiple testing of Bonferroni correction (p = 0.05) in this Meta-Han Chinese group . We estimated the allele frequencies and corresponding forensic parameters in the females, males and pooled population, including gene diversity (GD), polymorphism informative content (PIC), mean paternity exclusion index in duos (MEC_Desmarais_Duos) and trios , as well as power of discrimination in males  and females . A total of 188 alleles with the corresponding allelic frequencies spanning from 0.0048 to 06683 in females, 179 alleles with frequencies from 0.0098 to 0.6275 in males, and 205 alleles with allelic frequencies from 0.0032 to 0.6548 in pooled Zunyi Han population are found. Five alleles (12.3 (0.0032) at HPRTB, 15.2 (0.0032) at DXS7424, 16.1 (0.0032) at DXS10074, 17.3 (0.0032) at DXS10162 and 19.3 (0.0032) at DXS10074) are not observed in females but in males. Twelve alleles (7 (0.0032) at DXS10164, 15.3 (0.0032) at DXS10074, 18.2 (0.0032) at DXS10075, 23.2 (0.0032) at DXS10159, 27.2 (0.0032) at DXS10148, 29.3 (0.0032) at DXS10101, 30.1 (0.0194) at DXS10148, 30.3 (0.0032) at DXS0.0032, 34.2 (0.0032) at DXS10101, 35.2 (0.0032) at DXS10134, 37.3 (0.0194) at DXS10134, 41.3 (0.0032) at DXS10134) are observed in females but not in males .The Fst and corresponding p values of 19 X-STRs between females and males in Guizhou Han are presented in S12 Table present the forensic parameters. The maximums and minimums are consistently observed at loci of DXS10135 and DXS7423, respectively. In pooled Guizhou Han, HD and PIC span from 0.4848 to 0.9201 and 0.4075 to 0.9112, respectively. The PD values vary from 0.4832 to 0.9171 in males and from 0.6572 to 0.9872 in females. The MECs in duos range from 0.2726 to 0.8427 (MEC_Desmarais_Duos) and in Trios span from 0.2278 to 0.8326 (MEC_Krüger), 0.4073 to 0.9111 (MEC_Kishida) and 0.4075 to 0.9112 (MEC_Desmarais). Besides, high combined MEC_Desmarais_Duos, MEC_Kishida, MEC_Krüger, PD_Female, PD_Male are achieved as > 0.99999999453588, > 0.99999999999782, > 0.99999999999781, > 0.99999996992057, > 0.9999999999999999999996 and > 0.99999999999980 (Table 1).S13 Table, there is no gender differentiation in the Meta-Han Chinese population. Thus, allele frequencies and corresponding forensic parameters among females, males and pooled population are estimated subsequently. There are 261 alleles with the corresponding allele frequencies spanning from 0.0010 to 0.6381 in females, 258 alleles with allele frequencies varying from 0.0012 to 0.6264 in males and 293 alleles with the allele frequencies ranging from 0.0005 to 0.6330 in pooled Meta-Han population . A total of 8 alleles are not observed in 819 males: 23.2 (0.0011) at DXS10159, 42 (0.0005) at DXS10134, 18.2  at DXS10162, DXS10103 and DXS10075 respectively, 32.3 (0.0011) at DXS10135, 14.2 (0.0011) at DXS10075 and 18.1 (0.005) at DXS10162, are only observed in the females. Similarity, 15 alleles in 525 females are not observed: 18.3 (0.0005) at DXS10074, 19.3 (0.0005 and 0.0005) at DXS10162 and DXS10074 respectively, 25.2 (0.0005) at DXS10159, 26.2 (0.0005) at DXS10101, 28.3 (0.0005) at DXS10148, 15.2 (0.0005) at DXS7424, 16.1 (0.0005) at DXS10074, 17.3 (0.0011) at DXS10162, 34.1 (0.0005) at DXS10134 and 34.1 (0.0005) at DXS6809, 35.1 (0.0005) at DXS10134, 36.2 (0.0005) at DXS10135, 41 (0.0005) at DXS10134 and 43.3 (0.0005) at DXS10134. A total of 261 alleles with corresponding allele frequencies varying from 0.0010 to 0.6381 in Meta-Han females, 258 alleles with corresponding allelic frequencies spanning from 0.0012 to 0.6264 in Meta-Han males and 293 alleles with corresponding allelic frequencies ranging from 0.0005 to 0.6330 in Meta-Han pooled population are observed. DXS8378 and DXS7423 are the less polymorphic loci with only 6 alleles are found in the 1344 Meta-Han Chinese individuals and DXS10135 is the most polymorphic and informative locus with 32 alleles observed.In our genetic diversity and forensic characteristic analyses of Meta-Han Chinese population, as shown in S17 Table, GDs span from 0.4975 in Meta-Han females to 0.9202 in Meta-Han pooled population and PICs vary from 0.4186 in Meta-Han females to 0.9148 in Meta-Han males. PDs vary from 0.6686 in Meta-Han females to 0.9881 in Meta-Han males and 0.4971 in Meta-Han females to 0.9204 in Meta-Han males respectively focused on the forensic female population  and male population . The mean paternity exclusion chances in duos vary from 0.2826 in 525 Han females to 0.8484 in 819 Han males and in trios span 0.2358 in females (MEC_Krüger) to 0.9150 (MEC_Kishida). The combined MEC_Desmarais_Duos, MEC_Kishida, MEC_Krüger, PD_Female, PD_Male are achieved as lager than 0.99999999417952, 0.99999999999761, 0.99999999999760, 0.99999996804817, 0.9999999999999999999995, and 0.99999999999979, respectively (Table 1).As shown S18 Table, a total of 90, 61, 90, 34, 37, 68, 24 different haplotypes of LG1-7 are observed in 102 males, in which 79 in LG1, 39 in LG2, 79 in LG3, 11 in LG4, 17 in LG5, 47 in LG6, 9 in LG7 are unique. The most common haplotypes are 24.1-23-10  in LG1, 24-19-10  in LG2, 15-19-16-17  in LG3, 33–16  in LG4, 15–24  in LG5, 16-13-32  in LG6 and 36–15  in LG7. The HD values are larger 0.95 with the exception of LG7 (0.9177). Forensic parameters on the basis of genetic variation of the single locus are listed in  PICs vary from 0.9020 to 0.9876. The PD_Male and PD_Female are respectively span from 0.9087 to 0.9877 and 0.9850 to 0.9997. The MECs in duos span from 0.8290 to 0.9756 and MECs in trios vary from 0.8178 (MEC_Krüger) to 0.9876 (MEC_Desmarais). The combined values of PD_Male, PD_Female, MEC_Krüger, MEC_Kishida, MEC_Desmarais and MEC_Desmarais_Duos are 0.99999999998132, 0.99999999999999999997, 0.99999999742339, 0.99999999997377, 0.99999999997663 and 0.99999999802622, respectively.19 X-chromosomal STRs can be grouped into seven linkage groups on the basis of physical distances, previously linkage analyses and population genetic researches. The haplotype distributions of Guizhou Han population are presented in S20 Table). The most common haplotypes are 26.1-24-10  in LG1; 24-18-10  and 25-18-10  in LG2; 14-20-17-17  and 15-19-18-17  in LG3; 34–16  in LG4; 16–24  in LG5; 16-13-31  in LG6; and 36–15  in LG7. The HD and PIC values vary from 0.9310 to 0.9971 and 0.9258 to 0.9958, respectively (S21 Table). The values of PD_Male and PD_Female span from 0.9299 to 0.9958 and 0.9910 to 1.0000, respectively. The MEC values on the basis of formula devised by Krüger, Kishida, Desmarais for trios and Desmarais for duos vary from 0.8594 to 0.9851, 0.9254 to 0.9893, 0.9258 to 0.9958 and 0.8668 to 0.9917, respectively. The combined power of discrimination in males and females are 0.99999999999915 and 0.9999999999999999999999, respectively. The cumulative mean paternity exclusion chance in duos is 0.99999999990756, and the cumulative MECs in trios are larger than 0.99999999960026.In Meta-Han Chinese males , there are 394, 164, 394, 67, 69, 235 and 51 different haplotypes are found, in which 215 in LG1, 65 in LG2, 225 in LG3, 14 in LG4, 20 in LG5, 112 in LG6, 16 in LG7 are unique (S22 Table). Guizhou Han is genetically close to Guanzhong Han (Nei’s genetic distance: 0.0104), and keeps a relatively distinct genetic relationship with Hainan Han which is the southernmost Han Chinese population (0.0236). Population differentiation within five Han subpopulations is further dissected and visualized using principal component analysis, multidimensional scaling plot, Structure and one Neighbor-Joining tree . In the PCA dimensional plots constructed on the basis of PCA1 (45.08%) and PCA2 (23.86%), Guanzhong Han is located in the first quadrant near the X axis, Sichuan Han and South Han are located in the second quadrant near the Y axis. The remaining two groups are respectively located in the third quadrant (Hainan Han) and fourth quadrant (Guizhou Han) . Consistent population distribution patterns are observed in the MDS based on the pairwise Nei’s genetic distances . Phylogenetic relationship reconstruction reveals two genetically close clusters: one cluster comprises Guanzhong Han and Guizhou Han; and the other comprises Hainan, Southern China and Sichuan Han . No population substructure is identified in the model-based genetic structure dissection . Genetic cluster analyses among Han Chinese populations show that Han Chinese are relatively homogeneity with the modest levels of genetic differentiation (average±standard deviation (sd): 0.0136±0.0044).To explore the genetic homogeneity and heterozygosity among Han Chinese populations along different administrative divisions, we calculated the Nei’s genetic distances between Guizhou Han and other four Han Chinese populations (S23 Table. The Reynolds’s genetic distances range from 0.0022 (between Guanzhong Han and Guizhou Gelao) to 0.0161 (between Hainan Li and Xinjiang Uyghur2) whose average±sd is 0.0080±0.0032. Guizhou Han has the smallest genetic distance (0.0028) when compared with Guanzhong Han and has the largest genetic distance (0.0122) when compared with Xinjiang Uyghur2 with average±sd (0.0062±0.0027). The first ten PCAs can extract a total of 85.236% genetic variations from the 20 populations. Fig 3 presents the population structure revealed by the combinations of PCA1 and PCA2, as well as PCA2 and PCA3 . Four population clusters can be identified in Fig 3A: Turkic-speaking population cluster comprises three Xinjiang Uyghur populations  and one Ili Kazakh; Tibeto-Burman-speaking cluster consists of two Tibet Tibetan populations (Tibet1 and Tibet2), one Muli Tibetan and one Dujiangyan Tibetan; Tai-Kadai-speaking population cluster is made up of only one Hainan Li; and admixture-language-speaking population cluster comprises five Han Chinese population  which speak Sinitic language, Liangshan Yi (Tibeto-Burman), two Sinitic-speaking Hui populations (Ningxia and Wuzhong), one Tai-Kadai-speaking Gelao (Guizhou) and one Hmong-Mien-speaking population (Guizhou Miao). PCA1 distinguishes Turkic-speaking population cluster from others, which locates on the left side of the X-axis in Fig 3A. And PCA2 differentiates Tibeto-Burman-speaking cluster, as well as Tai-Kadai-speaking population cluster from others, which respectively locate upside of the first quartile and the downside of fourth quartile, as well as other populations cluster together and locate intermediate of aforementioned two clusters. PCA3 can clearly separate Hainan li from other populations . For further validation, we subsequently drew the MDS and N-J tree on the basis of Reynolds’s genetic distance matrix. Consistent population distribution patterns can be observed in the MDS analysis . In the N-J tree, Uyghurs, Kazakhs and one Wuzhong Hui form the nethermost cluster, Tibetans, Yis and one Ili Xibe form the intermediated cluster, and the remaining Hans, Lis, Miaos, Huis and Gelaos form the upper cluster. Guizhou Han first grouped with Guanzhong Han and then grouped with the other Sinitic-speaking population sub-cluster .The pairwise Reynolds’s genetic distances between Guizhou Han and other 19 Chinese adjacent populations are calculated on the basis of genetic variations of 19 X-chromosomal STRs and are listed in Fig 6A was constructed on the basis of the first two components which reveals that the Meta-Han Chinese population constitute the same genetic group with admixture-language-speaking population cluster, suggesting there are a high level of gene flow between Han Chinese populations and other adjacent groups  and may have a common ancestry. As shown in S24 Table, the smallest genetic pairwise Reynolds’s genetic distance is observed between Meta-Han Chinese population and Guizhou Gelao (0.0067) and the counterpart is observed between Meta-Han and Xinjiang-Uyghur2 (0.0144) with the relative larger genetic heterogeneity (0.0104±0.0024) comparing with the overall genetic distances among all 120 pairs within 16 populations (0.0090±0.0030). In the MDS  and phylogenetic tree reconstruction  also consistently reveal that the Meta-Han group exhibits a closer affinity to other Sinitic/Tai-Kadai/Hmong-Mien-speaking populations. Collectively, we observe that genetic differences exist between Meta-Han group and Turkic/Sino-Tibetan-speaking populations and genetic similarities can be found between Han Chinese and Tai-Kadai/Hmong-Mien-speaking populations.Considering the homogeneity within Han Chinese populations, we integrated the 1344 genotype data from five different geographical divisions into one group as the Meta-Han Chinese population. Pairwise Reynolds genetic distances, PCA, MDS and N-J tree are performed to assess and dissect the genetic relationship between Meta-Han Chinese group and 15 Chinese relative populations. The first ten principal components  from a national scale can extract a total of 90.265% genetic variation. ® Argus X-12 and AGCU X19 STR Kits. In this study, we first established one reference databases of Han Chinese population, extending already investigated data with additional 206 unrelated Chinese Han citizens and the total of 1,344 samples typed by AGCU X19 kit, to promote and implement X-STRs typing into Chinese routine forensic practice. Allele and haplotype frequencies and corresponding forensic parameters, as well as HWE and LD were first analyzed in the Guizhou Han and the comprehensive Han Meta-population. The combined MECs and PDs in Guizhou Han and Meta-Han Chinese combined with our previous studies [Clearly understanding the patterns of genetic variations of Han Chinese (the largest ethnicity in China and world) is important in the exploration of the population origin, migration, evolution and admixture in the prehistory and history, and providing investigative leadings and evidences in forensic cases. Although autosomal STRs have been the gold standard in forensic science and much effort has been made based on the genetic variations of autosomal or Y-chromosomal STRs in diverse populations –56, X-ST studies , 27 indiThe peopling history of East Asia is complex , 58. TheTo implement 19 X-chromosomal STRs PCR amplification system into routine forensic practice, we genotyped 206 Guizhou Han Chinese individuals and combined with previously reported 4868 genotypes from 19 Chinese populations to extend and establish the reference database of Chinese populations along linguistic divisions. We used the Nei’s genetic distances, PCA, MDS and N-J tree to test the genetic homogeneity of the Han Chinese population from different geographical administrative divisions. Due to no significant genetic difference exists among them, we estimated the allele and haplotype frequencies as well as forensic parameters in Guizhou Han and Meta-Han Chinese population on the basis of allele frequencies and haplotype frequencies. DXS10135 and LG1 are the most informative and polymorphic in Han Chinese group. The cumulative power of discrimination and mean paternity exclusion chance according to the allele and haplotype diversity are high enough to complete autosomal and Y-chromosomal STRs in the forensic routine practices  and population genetics. Subsequently, we compared Guizhou Han with 19 Chinese reference populations, as well as Meta-Han Chinese population and other 15 Chinese minority groups based on allele frequency distributions via pairwise Reynolds's genetic distances, PCA, MDS, and N-J tree. Population comparisons revealed the tight grouping within linguistic close populations of Tibetan-Burman, Turkic-speaking groups. Besides, Han Chinese is a homogeneous population and Guizhou Han and Meta-Han Chinese population have genetically close relationship with Tai-Kadai-speaking, Hmong-Mien-speaking populations. We concluded that the reference databases of AGCU X19 kit in Han Chinese populations, Tibetan-Burman-populations and Turkic-speaking populations are universally suitable and applicable for Chinese forensic casework.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.S7 Table(XLSX)Click here for additional data file.S8 Table(XLSX)Click here for additional data file.S9 Table(XLSX)Click here for additional data file.S10 Table(XLSX)Click here for additional data file.S11 Table(XLSX)Click here for additional data file.S12 Table(XLSX)Click here for additional data file.S13 Table(XLSX)Click here for additional data file.S14 Table(XLSX)Click here for additional data file.S15 Table(XLSX)Click here for additional data file.S16 Table(XLSX)Click here for additional data file.S17 Table(XLSX)Click here for additional data file.S18 Table(XLSX)Click here for additional data file.S19 Table(XLSX)Click here for additional data file.S20 Table(XLSX)Click here for additional data file.S21 Table(XLSX)Click here for additional data file.S22 Table(XLSX)Click here for additional data file.S23 Table(XLSX)Click here for additional data file.S24 Table(XLSX)Click here for additional data file.