Spodoptera frugiperda (J.E. Smith)) is a highly polyphagous agricultural pest with long-distance migratory behavior threatening food security worldwide. This pest has a host range of > 80 plant species, but two host strains are recognized based on their association with corn (C-strain) or rice and smaller grasses (R-strain). The population genomics of the United States (USA) fall armyworm remains poorly characterized to date despite its agricultural threat.The fall armyworm . The genomic resources presented provide foundational information to understand gene flow at the whole genome level among S. frugiperda populations. Based on the genomic similarities found between host strains and laboratory vs. field samples, our findings validate the experimental use of laboratory strains and the host strain differentiation based on mitochondria and sex-linked genetic markers extends to minor genome wide differences with some exceptions showing mixture between host strains is likely occurring in field populations.Our research is the largest diverse collection of United States The online version contains supplementary material available at 10.1186/s12864-021-07492-7. Spodoptera frugiperda  (Lepidoptera: Noctuidae), are a highly polyphagous agricultural pest affecting key food and fiber staples such as corn (Zea mays L.), cotton (Gossypium spp. L.), sorghum (Sorghum bicolor L.), rice (Oryza sativa L.), and vegetable crops [S. frugiperda in its native subtropical range in the Americas results in 15–100% yield loss, depending on the level of infestation [Larvae of the fall armyworm, estation . In mostestation .S. frugiperda in the Western hemisphere has been provided mainly by genetically modified corn and cotton producing insecticidal Cry and Vip3A proteins from the bacterium Bacillus thuringiensis (Bt). However, resistance to Cry1F, Cry1Ab and Cry1A.105 insecticidal proteins quickly developed in Puerto Rico and the continental USA (Florida and North Carolina) [S. frugiperda injury could be 21–53% of their annual corn production, which equals to US$2.5–$6.2 billion in losses [Bt toxins could have been carried by invasive S. frugiperda to the Eastern hemisphere.For more than a decade, effective control of arolina) , 5, as warolina)  and Argearolina) . More rearolina) , subsequarolina) , and morn losses . While yn losses , resistaS. frugiperda are composed of sympatric mixtures of two genetically differentiated strains based on host preference, a “rice” (R) strain feeding on rice, millet and smaller grasses, and a “corn” (C) strain feeding preferentially on corn and sorghum [COI (cytochrome oxidase subunit I) and sex-linked (Z chromosome) Tpi (triosephosphate isomerase) genes [Tpi marker agrees with assortative mating and host assessments [S. frugiperda samples from corn in the Eastern Hemisphere as C-strain, yet a predominant COI-R strain marker is obtained in these collections [Populations of  sorghum , 14. The sorghum  and diff sorghum . Signatu sorghum . Being me) genes –21. Howeessments  in descrlections . These clections  or be drlections . AdditioCOI host strain marker sites have been commonly grouped into “haplotypes” with relative proportions associated to geographic origin [, enabling differentiation of “Texas” and “Florida” overwintering S. frugiperda populations in the USA. Migratory studies with these haplotypes determined that the “Texas” population is distributed throughout South, Central and North America [S. frugiperda from Africa, India and Southeastern Asia supports introductions in Africa of individuals from the “Florida” population and subsequent spread to Asia [Nucleotides found at c origin , enablin America , 26, whi America , 28. Hap to Asia , 12.S. frugiperda from three continents, with an emphasis on C-strain individuals from North and South America. Genome-wide comparisons allowed testing for gene flow between geographically distant S. frugiperda populations and comparing genetic diversity between field and laboratory-reared S. frugiperda. Based on Fst values there is limited genetic differentiation between Texas vs. Florida populations and samples from these locations display similar genetic nucleotide diversity. Host strain differentiation was supported by nuclear and mitochondrial genome differences. Comparison of mitochondrial genomes detected two clusters where the clusters correlate by and large with host strain and resistance to the Cry1F toxin. The exceptions to this correlation suggest that C-strain individuals based on nuclear genome have likely mated with R-strain individuals. We also observed no detectable reduction in genetic diversity in well-established S. frugiperda laboratory-reared colonies, which supports using laboratory strains of this pest as a model for field populations.In this work, we collected and sequenced 55 genomic DNA (gDNA) samples of S. frugiperda samples used in this study, almost half  originated from field collections representing eight states in the USA. The average mapping rate, defined as the proportion of sample sequencing reads mapping to the reference genome assembly, was 88.2% with a range of between 60.62 to 96.82%, according to SAMtools flagstat. Identification of the C- and R- host strains for each individual was performed using nuclear Tpi and mitochondrial COI genetic markers described elsewhere [COI1164 marker identified all samples as C-strain. In contrast, typing based on the nucleotides at the TPi183 site resulted in 42 samples identified as C-strain individuals (C/C), 8 as R-strain individuals (T/T), 3 as interstrain hybrids (C/T) and 2 that were undetermined due to low sequencing coverage at this locus. All eight identified R-strain samples were from the USA .Of the 55 ost half , 47.3% oBt (data not shown for PC2 to PC10). We noted that PCA using the first two principal components uncovered weak substructure within the USA and Brazil samples where the majority of samples clustered in a larger cluster and two subclusters formed. The first subcluster was comprised of Brazil, Florida, Texas and Tennessee samples . The second subcluster included five samples, three from Florida  and two from Brazil   was analyzed for k = 1 to k = 10, but all samples clustered as one group even when the number of groups was increased. This lack of clear population structure was further confirmed using principal component analysis (PCA) on a filtered set of 2,762,958 nuclear SNPs (see Methods). PC1 through PC10 was plotted and no structure was found based on country of origin, host strain or susceptibility to the Cry1F insecticidal protein from Given the overall lack of clear population structure observed based on origin, we next applied genome sketching  and tradMash distance is a proxy for genome pairwise distance, with 0 representing identical genomes . The Mash distances across all countries surveyed were averaged to be < 0.05  based on 2,726,958 SNPs. This very low Fst value suggests no population separation at the nuclear level, supporting recent population separation or ongoing gene flow between Texas and Florida overwintering populations, independently of host strain.Previous work based on the ratios of four pre-determined in chain . As mentst value based on 406 mitochondrial SNPs found across all samples in these groups. The mitochondrial Fst for Texas R-strain and Florida C-strain samples was estimated at 0.7961. This Fst estimate being close to 1 suggests genetic differentiation between the R and C strains is the strongest at the mitochondrial level, possibly due to two main ancestral lineages. Although we see a stronger difference between geographic regions than between R and C strains in this study, the estimated mitochondrial to nuclear Fst ratio of 46.8 is consistent with previously reported sex-based differences in dispersal [Based on the same Texas R-strain and Florida C-strain groupings, mean diversity was determined. Mean diversity for the field samples from Texas R-strain and Florida C-strain samples was estimated at 0.0016 and 0.0013, respectively  originating from the same location in the USA  and all field USA populations surveyed, we estimated a weighted Fst (fixation index). As shown in Table st range of 0.0310 to 0.0961. Moderate differentiation was also observed when comparing the MS laboratory strains with other more recently established (< 10 years) laboratory populations from North Carolina (NC) and Florida (FL), with an Fst range of 0.0131to 0.1302. The samples least differentiated (Fst = 0.0131) from the MS laboratory populations were FL laboratory, while the most distinct (Fst = 0.0961) were the samples collected from Maryland (MD). Overall, these moderate Fst values [st = 0.0761 on average) and to either of the two overwintering populations (Fst = 0.0398).Laboratory-reared strains of t values  suggest S. frugiperda samples surveyed. Mean nuclear diversity was 0.0013 for Benzon (USAMSs1) and SIMRU (USAMSs2) laboratory strains (n = 2), compared to 0.0016 for field Texas samples (n = 8), and 0.0014 for field Florida/eastern USA samples (n = 16).Additionally, we surveyed overall genomic diversity using 3 kbp windows and estimated mean diversity for the same two MS laboratory strains compared to all field and laboratory S. frugiperda samples  by comparing genetic diversity of whole mitochondrial (mt) genomes. Using a total of 390 SNPs, we generated a haplotype network as described in Materials and Methods , and haplogroup B containing USAFLu5 and USAFLu9. Clusters with samples only from Brazil included haplogroups C, D and E containing samples from Cry1F-resistant strains from Bahia , Sao Paulo (BraSPr1 and BraSPr2), and Minas Gerais (BraMGr1 and BraMGr2), respectively. Samples from Kenya clustered in haplogroup F (KenXXu3 and USAMAu2) and G (KenXXu1 and KenXXu2). Non-unique haplotype samples from Puerto Rico clustered in haplogroups H (PueGUr1 and USAFLu3) and I (PueLAu1 and USAFLu7) with samples from Florida. Haplogroup J contained two samples (BraMGr4 and BraMGr5) from the Cry1F-resistant strain from Minas Gerais also represented in haplogroup E, and one sample from USA (USAFLu6). There were two main “haplotype” clusters that separated in the network, with average genetic difference between the two clusters of 29.53, supporting evidence for two main maternal lineages within the samples surveyed. Using these two haplotype clusters as labels, we mapped the mt haplotype clusters to the two whole genome sequence (WGS) “haplotype” clusters found in Mash distance-based tree against a mitochondrial variant UPGMA distance-based tree  where the insect is a devastating exotic pest. While using the C-strain S. frugiperda genome for alignments could have underestimated diversity in R-strain individuals, this approach is supported by previous surveys using both corn and rice genomes with minimal differences found based on the number of variants called and Fst values [We report on the genome-level comparison of 55 t values .S. frugiperda samples was surveyed using parametric and non-parametric methods. Based on FastStructure (parametric), no population structure was found either within samples from the Eastern Hemisphere or between the Eastern and Western Hemisphere samples. Since methods based on parametric estimation of allele frequencies methods are sensitive to sample size, one possibility is that the lack of structure detected is a reflection of the relatively small number of R-strain samples  analyzed. A more even distribution would address potential limited sampling of some locations surveyed, especially samples representing the Eastern Hemisphere (e.g. Africa). However, results from a previous study of genetic S. frugiperda diversity in the Western Hemisphere using amplified fragment length polymorphism (AFLP) already supported a large interbreeding population [The population structure of the 55 genomic pulation .COI haplotypes [S. frugiperda samples from Mississippi) [Both PCA and FastStrucutre analyses found little to no population structure based on the nuclear genome. Furthermore, the Mash distance-based hierarchical clusters showed weak correlation between nuclear genome and host strain. In contrast, the Hamming Distance tree based on the mitochondrial genome detected two subpopulations within the samples surveyed, which were associated with host strain and the southern Texas and Florida subpopulations previously described based on four mitochondrial plotypes . Furtherplotypes . The lacissippi) , that fiissippi) . In testS. frugiperda populations [S. frugiperda from Puerto Rico and Florida [Our analysis included samples collected from locations (Maryland & Tennessee) close to predicted hybridization zones for TX and FL overwintering ulations , and as ulations . Future  Florida .st estimates obtained between C- and R- host strains (0.7961) were highly similar to the Fst value (0.938) from a prior analysis of 9 corn and 9 rice sympatric samples from Mississippi (USA) [st found in our study comparing all corn versus rice strains (0.0173) is also similar to the previous study’s estimate (0.019) [Mitochondrial Fpi (USA) . Moreoverains 0.03 is alsoWolbachia [Wolbachia genomes on NCBI as of May 9, 2020 using BLAST (data not shown). This observation is in agreement with leg and head tissues being mostly used for isolating genetic material. In testing for Wolbachia infection, we extracted genomic DNA from a limited number of available abdominal samples and performed sequencing of PCR amplicons using primers amplifying the outer surface protein precursor (wsp) Wolbachia gene. Amplicons of the same size  [S. frugiperda laboratory lines. Both laboratory lines, Benzon (USAMSs1) and SIMRU (USAMSs2), originated from field collections in Mississippi and they were the least differentiated from the TX/MN field collections. Interestingly, more recently established laboratory populations from NC and FL [st values compared to field samples. This moderate differentiation between laboratory and field samples could be indicative of good culture management involving maintenance of large populations and/or regular introgression of field individuals. Another possibility could be that some of the field samples compared display low diversity, yet this is unlikely given samples from Central and South America (for Texas population) and the Caribbean (for Florida populations) based on current S. frugiperda migratory models [S. frugiperda, differentiation probably occurs during adaptation to mass rearing, resulting in selection of specific alleles that may not be representative of field populations. Thus, while our observations suggest laboratory strains contain genetic diversity similar to field populations, laboratory strains may have specific allele frequencies that may not be representative of field populations.Previous reports providing comparisons of genetic diversity in laboratory-reared and wild populations of the lepidopteran lostella , Tephritlostella , and loclostella  support lostella  and in lritidae)  high genC and FL  presentey models . It is iS. frugiperda whole and mitochondrial genome diversity. Findings from the study support lack of clear S. frugiperda population structure among the locations sampled. Only mitochondrial genomes indicated two different maternal lines mostly separating host strains. The genomic resources generated allow further exploration of gene flow and how it may affect management of S. frugiperda as an expanding global superpest using laboratory-reared and field-collected individuals.Overall, this study provides a diverse characterization of S. frugiperda samples sequenced are found in Supplementary Table B. thuringiensis for these laboratory samples was tested in bioassays presented elsewhere [S. frugiperda by morphology features [Details of the 55 laboratory and field-collected lsewhere –46. Larvfeatures  and storWe followed a sample naming protocol that included the first three letters representing the country of origin , the next two letters representing the first two letters of the state/province of origin , and the third letter representing the Cry1Fa susceptibility phenotype when known . A number was used when necessary to differentiate samples with the same geographic origin and Cry1F susceptibility phenotype.CoI1164 and Tpi183 sequences, as described elsewhere [COI1287 marker identified all samples as C-strain, so the nuclear Tpi183 marker was used as a more reliable marker of host strain [COI1164 marker was not used because it did not fall under any of the pre-defined COI haplotypes [Host strain (C versus R) was determined based on sequence identity at specific marker positions in reference mtlsewhere . The mitt strain . The mitplotypes  for any S. frugiperda larvae after dissecting the gut tissue or from legs or heads of adults using the Pure Link Genomic DNA mini kit (Invitrogen), following manufacturer’s protocols, and then quantified using a Nanodrop spectrophotometer (Thermo Scientific). Extracted DNA was then sheared randomly to between 250 and 500 bp using a Covaris M220 focused ultrasonicator , according to manufacturer’s instructions. The fragmented DNA was then ligated with dual-indices using a KAPA Hyper prep PCR-free library kit (Roche) according to the manufacturer’s directions. The ligated fragments were quantified using quantitative PCR and a KAPA Library quantification kit (Roche), and then submitted for sequencing on an Illumina HiSeqX device running a 2 × 150 bp paired-end configuration . Raw paired-end sequence reads for each sample (55) are available for download in the NCBI Sequence Read Archive (SRA) under SRR12044614-SRR12044668 with associated metadata available under NCBI BioProject id PRJNA640063. The raw data was processed to remove low quality reads using the CLC Genomics Workbench v9.5.2 (Qiagen) trim function with default parameters.Genomic DNA was isolated from individual carcasses of fifth instar https://jgi.doe.gov/data-and-tools/bbtools/) with options trimq = 15 and filter = 23. Further quality control and confirmation that adapter and non-relevant  sequences were removed from filtered reads were performed using FastQC [S. frugiperda corn and rice genomes [S. frugiperda corn reference genome (v3.1) downloaded from https://bipaa.genouest.org/sp/spodoptera_frugiperda_pub/  using acaffolds .st estimates.Variants were then called using the SAMtools mpileup utility , resultiD) as D = 1-J. All S. frugiperda individuals were sketched with Mash [Spodoptera species  as outgroups.The Mash distance method was used to measure pairwise dissimilarity of genomic sequences . One keyith Mash  using a ith Mash . All samst values as indicators of total genetic variance in a subpopulation were calculated using VCFtools [st when the window was varied. For Fst calculations using the mitochondrial genome, each base pair position was compared. Nuclear genome (pi) values were calculated using 1 kbp windows when comparing corn vs. rice host strain, and 3 kbp when comparing Fst values for overwintering FL/TX vs. USA laboratory samples. We also used 3 kbp windows for Fst comparisons across the nuclear genome with populations defined by mitochondria haplotype network groupings, Mash genome defined “haplotypes”, and host strain haplotypes. For all nucleotide diversity analyses, a total of 2,762,958 filtered SNPs as described in the Filtering and Mapping section was used to assess diversity using VCFtools [Weighted FVCFtools  by the WVCFtools .COI sequence MH932092.1 as the seed. A continuous complete mitochondrial chromosome of around 15 kbp was generated for all 55 samples, except for BraBAr5, which was excluded from further analysis. The whole mitochondrial FASTA sequences were assembled for each sample and then aligned via bwa-mem (https://github.com/lh3/bwa) with default options. Mitochondrial variants were called using SAMtools mpileup based on the S. frugiperda corn reference genome at LepidoDB (https://bipaa.genouest.org/sp/spodoptera_frugiperda_pub/). The vcf files were compressed using SAMtools bgzip, indexed using SAMtools tabix and merged using SAMtools BCFtools merge [S. frugiperda corn genome at LepidoDB. The vcf files were then filtered by removing 16 multi-allelic sites and 92 uninformative sites due to being found in 2 samples or less via the informloci function in the adegenet R package [Mitochondrial sequences were extracted from the whole genome sequence reads using NOVOplasty  with thels merge . Missing package , resultiPLINK –make-bed  was usedAdditional file 1: Supplementary Table 1. Describing details of samples analyzed, and Table 2 showing Mash distances based on country. Supplementary Figure S1. Showing clustering based on Mash distances, Figure S2 showing amplification of Wolbachia in selected samples, Figure S3 showing the amplified sequences in Fig. S2, and Figure S4 showing the geographic locations sampled.


FROH) were observed among domesticated bovid populations from different geographic areas, except for Bali cattle. Our findings indicated that some Italian breeds show the highest estimates of levels of molecular inbreeding among the cattle populations assessed in this study. Patterns of genetic differentiation, shared ancestry, and phylogenetic analysis all suggested the occurrence of gene flow, particularly among populations originating from the same geographical area. For European cattle, we observed a distribution along three main directions, reflecting the known history and formation of the analyzed breeds. The Italian breeds are split into two main groups, based on their historical origin and degree of conservation of ancestral genomic components. The results pinpointed that also Sicilian breeds, much alike Podolian derived-breeds, in the past experienced a similar non-European influence, with African and indicine introgression.The availability of genotyping assays has allowed the detailed evaluation of cattle genetic diversity worldwide. However, these comprehensive studies did not include some local European populations, including autochthonous Italian cattle. In this study, we assembled a large-scale, genome-wide dataset of single nucleotide polymorphisms scored in 3,283 individuals from 205 cattle populations worldwide to assess genome-wide autozygosity and understand better the genetic relationships among these populations. We prioritized European cattle, with a special focus on Italian breeds. Moderate differences in estimates of molecular inbreeding calculated from runs of homozygosity ( Genetic drift, together with natural and artificial selection, led to the development of divergent breeds3 that differ in several phenotypic traits, including coat color, body size, behavior, and production traits2. Until the first industrial revolution in the late eighteenth century, cattle genetic diversity, especially in Europe, was mostly shaped by selection and adaptation to the natural environment of local breeds4. However, during the last two centuries, locally well-adapted but, less successful populations were progressively replaced by commercial, highly productive breeds with the aim of increasing profitability for farmers5. Cattle are the species with the highest number of breeds at risk of extinction, indicating that cattle genetic diversity is gradually being depleted7.The domestication of cattle approximately 10,000 years ago changed the social and economic life of most human populations13. According to these studies, the European cattle gene pool is mainly of Bos taurus taurus origin. There are a few exceptions to this rule, as in the case of Turkish and some Italian beef breeds  which show evidence of mixed B. t. taurus and B. t. indicus ancestries. Similarly, Iberian cattle were shown to have African taurine introgression14, while the Italian Podolica breed is an ancient cross-breed in which indicine introgression has occurred14. However, these comprehensive studies did not include several local European cattle characterized in recent investigations16, including many Italian cattle breeds17. As a consequence of this gap, the relationships between these cattle and other breeds existing worldwide have not been clarified and addressed, and a comprehensive description of the distribution of the diversity of present-day European cattle is still lacking.The availability of genotyping assays has made it possible to conduct a detailed evaluation of cattle genetic diversity globallyIn the present study, we collected a large genomic dataset containing newly generated genotyping data from several European cattle, especially Italian breeds. We aimed to contextualize the genetic variation of the newly added breeds with respect to domesticated bovid populations worldwide. Specifically, we determined the population structure and genetic relationship of the new breeds with other populations from around the world.15. However, the genetic relationships with some local breeds that were not previously included in these global studies remain ambiguous. In this study, we assembled a large cattle dataset to provide a closer examination of inbreeding, admixture patterns, and genetic relationships among cattle worldwide, with a special focus on Italian breeds. After quality control, the merged dataset used in the analyses consisted of 3,283 individuals from 205 domesticated bovid populations   are one of the most powerful approaches to detect inbreeding effects and conceivably recent population bottlenecks17. We estimated FROH to identify genome-wide autozygosity for all populations with a sample size ≥ 10, which led to the retention of 151 populations   to the present day. Except for Bali cattle (B. javanicus) (FROH = 0.26) and the Hariana zebuine breed (FROH = 0.10) . Similarly, a recent SNP-based analysis performed on whole-genome sequencing data from European and African taurines, in addition to four indicine populations, reported that genome ROHs did not vary greatly among the individual cattle studied14. The same study reported that several southern European cattle displayed numbers and cumulative lengths of ROHs comparable to that of N'Dama cattle from Africa. Our results confirmed these findings, as we found that African populations (both taurine and zebuine cattle) showed mean FROH values comparable with those of most European breeds.Currently, ROH-based inbreeding estimates ; Tagil, Kalmyk (Russia); Red Metohian Busha, Anatolian cattle ; and Piedmontese, Reggiana, and Rossa Siciliana  showed the lowest FROH levels (< 0.02)  and French  breeds. Similarly, high levels of admixture may explain the low FROH values observed within some European  and admixed African populations  that have not undergone breeding programs. Conversely, some European breeds such as Toro de Lidia (Spain), Pontremolese, Garfagnina, and Mucca Pisana  showed the highest estimates of molecular inbreeding, with mean FROH values > 0.05. This was most likely due to a reduction in their population sizes and uncontrolled mating of related individuals. Indeed, Pontremolese, Garfagnina, and Mucca Pisana have experienced a population decline during the second half of the twentieth century, resulting in these breeds being classified as endangered17. Toro de Lidia is a genetically isolated breed due to the peculiarity of the breeding objectives of this breed (aggressiveness). Moreover, the breed comprises several lineages as a result of different behavioral selection objectives. This resulted in a considerable increase in the average relatedness value within lineages (0.144), a very high score when compared with other cattle breeds18. Our observation of high FROH value for Bali cattle suggests that the greater genetic distances from the SNP discovery panel breeds introduce a significant ascertainment bias into autozygosity estimates. In this study, owing to the minimum 4 Mb size imposed on ROH segments, we tried to avoid small autozygous segments so as to reduce ascertainment bias that would lead to overestimation of molecular inbreeding13. The data suggest that any potential bias did not appear to affect markedly our results. For its part, the high FROH value (0.10) observed for Hariana cattle  can be attributed to a significant deficit in heterozygosity, as previously reported for this breed based on microsatellite data19.Notably, we found marked differences in els < 0.0 , to refine and extend previous studies, and to provide a new global picture of cattle genetic diversity, we carried out a multidimensional scaling (MDS) analysis, graphically depicted the estimated Reynold’s genetic distances by Neighbor-Net analysis, and inferred ADMIXTURE events. In addition, a subset of data containing only European and Eurasian populations (147 breeds) was analyzed separately to obtain a fine resolution at the local level, with a special focus on Italian cattle breeds.To identify the genetic relationships among the 205 cattle breeds belonging to three domesticated subspecies (B. t. indicus and B. t. taurus were separated by the first axis (PC1) with the hybrid breeds spread between the two. These observations are consistent with documented knowledge of cattle history21. The second axis splits African taurine cattle from European, American, Eurasian taurine, indicine, and hybrid populations, suggesting a strong effect of genetic drift in African taurine when compared with Eurasian taurine cattle13. Mediterranean breeds clustered together and were closer to the African taurine group than to North European breeds  West African taurine , (ii) European, and (iii) indicine populations. Guelmoise (Algeria) and Oulmes-Zaer (Morocco) breeds fell at an intermediate position between South European and West African taurine populations , together with Turkish Grey cattle, were positioned further from the European breeds and were slightly tilted toward zebu populations, suggesting an indicine influence. The Chinese Qinchuan cattle was placed close to Anatolian breeds  and/or higher degree of inbreeding  showed characteristic longer branches, indicative of strong genetic drift or selection pressure. The longest branches reflect the combined effects of ascertainment bias, reproductive isolation, bottleneck, reduced population size, and distinctiveness.To provide additional insight into cattle phylogeny, relationships, and patterns of divergence, we constructed a Neighbor-Net network based on Reynolds genetic distances among the 205 cattle populations . At K = 3, African B. t. taurus populations formed a separate cluster (gold). African indicine and African hybrids shared a roughly similar proportion of African taurine and Asian zebuine ancestry, suggesting that elements of taurine descent (gold) may still be present in the genome of African zebuine cattle. The results of the f3 test highlighted clear signs of African taurine  and African zebuine (ZFU) admixture  shared ancestry with cattle from Asia. Interestingly, we also detected an African component in Russian cattle that has not been previously described. This result was corroborated by f3 statistics, which showed evidence for African taurine (Guelmoise and Kuri) and African indicine (ZBO and ZFU) ancestry in several Russian cattle breeds  .Within-continent and national substructures were identified when the number of ancestral populations was increased . Breeds were progressively assigned to separate clusters. For example, the Holstein group, Marismeña, and Ukrainian Whiteheaded formed distinct clusters at K = 10, while the Mucca Pisana separated from the other breeds at K = 15 Fig. . The res9. Italian breeds (in green) were split into two main groups when plotting the breed-average coordinates of eigenvalues of C1 and C2 , and Asian  taurine populations Fig. . Europea C2 Fig. . The fir14, and Romagnola10. Our analyses extend these findings to the Sicilian breeds. This result suggests that Cinisara, Modicana, Rossa Siciliana, and Sardo-Modicana, as well as Chianina, Calvana, Marchigiana, Maremmana, Podolica, and Romagnola, have experienced a similar non-European influence. This is evident from the graph in Fig. f3 statistics obtained when Sicilian breeds (Cinisara and Rossa Siciliana) were used as reference populations were indicative of a zebu ancestry , Sicilian , and Sardo-Modicana breeds presented minor contributions from indicine (dark blue) and African taurine (gold) cattle Fig. . Africanry Table . A recen14. Additionally, Northern-Italian breeds share ancestry with French and Swiss cattle, reflecting a historical gene flow among these populations. These findings suggested that continental European breeds contributed to this Northern-Italian cattle gene pool. With increasing K clusters, several European breeds  are increasingly distinguished. For instance, at K = 50, Calvana, Garfagnina, Romagnola, Pontremolese, and Mucca Pisana breeds separated as distinct breeds  share indicine ancestry with Balkan cattle  Anatolian breeds and the Yakut; (ii) Red Pied cattle breeds ; and (iii) Lincoln Red, Shorthorn, and Holstein  were genetically close to several European breeds of the Alpine area (Red Pied breeds and Brown Cattle), suggesting a contribution of the central European gene pool to the composition of these breeds. Indeed, spotted, brown, and grey Alpine cattle have influenced several Northern-Italian breeds4. The oldest known herdbook for cattle dates from between 1775 and 1782, and originated from the Swiss canton of Schwyz where the gray-brown mountain cattle (Braunvieh) evolved. In 1875, Schwyz cattle and two Braunvieh populations were recognized and combined as the Swiss Brown, comprising a common herdbook; these cattle were the ancestors of several Alpine, Italian, and Spanish brown cattle4.In a European context Figs.  and 8, w10. Our results supported this hypothesis, with the absence of indicine introgression in Iberian breeds  tended to cluster with Iberian and some French breeds (Maraichine and Charolaise) Fig. . It has eds Fig. . Our fineds Fig.  and 8 alIn this study, we have assembled a large cattle dataset to refine and extend previous studies and to understand better the genetic origins of European cattle, with a particular focus on Italian breeds. The genome ROH coverage differs within and among breeds and subspecies, and reflects the complex breeding history of cattle. Our findings indicated that, when compared with cattle worldwide, some breeds from Tuscany show high levels of inbreeding. In addition to confirming previous reports, our results also showed new insights into the complex origin of some Italian breeds. In a worldwide context with other breeds, the Italian cattle clustered with other European and Asian taurine breeds. Overall, the grouping of the breeds on the Neighbor-Net network was consistent with their geographic origins. The genetic diversity of European cattle is described by a gradient of distribution along three main directions. Within the European context, we identified two main groups for the Italian breeds based on their historical origin and degree of conservation of ancestral genomic components. In this study, we also observed that some Italian breeds not included in previous studies, such as the Sicilian breeds, experienced a non-European influence in the past, similar to that observed for Podolian-derived breeds. A common genomic component between Balkan and several Italian cattle breeds (beef breeds and Sicilian cattle) was also revealed. However, further study will be necessary to test and validate our results.24 work were merged with PLINK34. Spanish breeds  were genotyped for this study. Detailed information about all the breeds and samples is shown in Table For analyses of comparative population genomics, SNP genotyping data from previously published17, using a sliding window approach of 50 SNPs in PLINK v.1.0735. The minimum length that constituted a ROH was set to 4 Mb. Moreover, (i) one missing SNP was allowed in the ROH and up to one possible heterozygous genotype; (ii) the minimum number of consecutive SNPs that constituted a ROH was set to 50; (iii) the minimum density was set at 1 SNP every 100 kb; and (iv) 1 Mb was set as the maximum gap between consecutive SNPs. The genomic inbreeding coefficient (FROH) was calculated for each breed.For this analysis, breeds with less than 10 animals (Supplementary Table 35 and plotted using a MDS plot in the R environment.Pairwise genetic relationships were estimated using a matrix of genome-wide identity-by-state (IBS) genetic distances calculated by PLINK36 was used to estimate Reynolds genetic distances and neighbor networks were constructed from the estimated genetic distances using SPLITSTREE37.To assess reticulated relationships between populations, the ARLEQUIN software38 by applying the default settings. Different K values with the mixed ancestry model (K = 2 to 50) were estimated to examine patterns of ancestry and admixture in the dataset. The BITE R package39 was used to graphically represent the results. All these analyses were conducted on both datasets, i.e., for both the global and European breeds.Population structure was assessed by the maximum likelihood-based approach implemented in the ADMIXTURE software v1.3.040 was run to verify whether populations suspected of being admixed indeed showed significant signatures of admixture. This program calculated f3 statistics for all possible triplets from the selected populations. If population A was a mixture of two other populations, B and C, the Z-score computed for each tested triplet would have a significant negative value.Finally, the THREEPOP program implemented in TreemixSupplementary Table S1.Supplementary Table S2.Supplementary Figure S1.Supplementary Figure S2.Supplementary information.


D. raddei and male D. valentini gave rise to the parthenogenetic Caucasian rock lizard Darevskia unisexualis. A previously identified genetic polymorphism in the species consisted of one common and two allozyme clones. Analysis of microsatellites and single nucleotide polymorphisms (SNPs) from the three species yields estimates of clonal diversity and tests the hypothesis of a single origin for D. unisexualis.The hybridization of female D. unisexualis, 17 D. valentini, and 45 D. raddei nairensis identified 12 presumptive clones, including one widespread and 11 rare clones. Most individuals in some localities had a rare clone. Clone-specific alleles in D. unisexualis were compared with those of the parental species. The results inferred a single hybridization event. Post-formation mutations best explain the less common clones.Genotyping and sequencing of four microsatellite-containing loci for 109 specimens of D. unisexualis from its bisexual ancestors. SNP analyses fail to reject the hypothesis of a single interspecific origin of D. unisexualis, followed by microsatellite mutations in this initial clone. Microsatellites detect higher clonal diversity in D. unisexualis compared to allozymes and identify the likely origins of clones. Our approach may be applicable to other unisexual species whose origins involve interspecific hybridization.Interspecific analyses identify alleles inherited by  Darevskia (Lacertidae)  suggest that clonal diversity in parthenogenetic D. unisexualis, including one widespread common and 11 rare clones. Assessments confirm that formation of the parthenospecies resulted from the hybridization of female D. raddei nairensis and male D. valentini. Several overall rare clones are numerous and dominate in some populations. Clonal diversity in D. unisexualis appears to result from microsatellite mutations in the initial clone. Parent-specific microsatellite and SNP markers identify multiple clones that allozymes could not. This approach should prove to be equally applicable to detailing the origin and variation of other unisexual species.Analyses of four microsatellite loci and single nucleotide polymorphisms (SNPs) in their flanking regions reveal 12 presumptive clones in parthenogenetic D. unisexualis (n = 109). Analyses also included its parental species: six populations of matrilineal D. raddei nairensis (n = 45), and four populations of D. valentini (n = 17). All samples were from Armenia  was used for amplifications in a 20 μl reaction volume, which included approximately 50 ng of DNA and 1 μM of each primer. PCR amplification conditions were used as described previously . Both alRS) was adjusted for sample size. Expected heterozygosity was calculated by using an in-house R programming language script (available at https://github.com/andrewgull/PopGenScripts) employing packages Poppr and Mmod [The number of alleles . CYTB was chosen because it was used previously for the same species comparisons [We amplified and sequenced a 320 bp fragment of mitochondrial parisons , 56. PCRparisons . Amplifiparisons . An Applparisons .Additional file 1: Table S1. Allelic variation of microsatellite containing loci in Darevskia unisexualis, D. valentini, and D. raddei nairensis.Additional file 2: Table S2. Population indices of gene diversity for four loci in four sampled populations of D. raddei nairensis.Additional file 3: Table S3. Population indices of gene diversity for four loci in four sampled populations of D. valentini.


Dromedaries have been essential for the prosperity of civilizations in arid environments and the dispersal of humans, goods and cultures along ancient, cross-continental trading routes. With increasing desertification their importance as livestock species is rising rapidly, but little is known about their genome-wide diversity and demographic history. As previous studies using few nuclear markers found weak phylogeographic structure, here we detected fine-scale population differentiation in dromedaries across Asia and Africa by adopting a genome-wide approach. Global patterns of effective migration rates revealed pathways of dispersal after domestication, following historic caravan routes like the Silk and Incense Roads. Our results show that a Pleistocene bottleneck and Medieval expansions during the rise of the Ottoman empire have shaped genome-wide diversity in modern dromedaries. By understanding subtle population structure we recognize the value of small, locally adapted populations and appeal for securing genomic diversity for a sustainable utilization of this key desert species. Sara Lado et al. report a fine-scale analysis of genetic diversity in dromedary species across Asia and Africa. Using a genome-wide approach, the authors are able to infer patterns of migration following domestication, which follow known historic caravan routes, and identify major population expansions and bottlenecks through history. Camelus dromedarius) has a special position in human migration and trading1. Its physiological adaptations to harsh and dry environments allowed humans to traverse hostile lands such as deserts like no other livestock, including the horse2. Reversing a historian’s observation “the sea unites rather than divides”3 to the desert, dromedaries facilitated the expansion of civilizations5. Their superior and unique features predestined dromedaries for the use as military animals, and for the advance of international trading along ancient caravan routes, such as the Silk Road and Incense routes7. Interspecific hybridization between dromedaries and the closely related two-humped Bactrian camels (Camelus bactrianus) produced even more robust and enduring animals with an aptitude for the extreme climatic conditions of the Silk Road9. Nowadays, first generation hybrids and their backcrosses are valued in many countries for increased milk or wool production10, as well as in famous camel wrestling events11. These remarkable commercial networks in human history facilitated domestic animal exchange across large geographical distances and acted as gene-flow corridors, not only for camels but also for other livestock12. While camels were the chosen animals for transportation, horse movements along the complete network of the Silk Road mainly occurred in forms of tributes and gifts13.As one of the most recently domesticated animals , the dromedary (Protylopus) of camelids emerged in the North American savannah during the Eocence (~45 Mya). After their split into New (Lamini) and Old World (Camelini) camels around 15 Mya, the ancestors of the Old World camels crossed into Eurasia via the Bering Land Bridge (~6.6 Mya) and further diverged into one- and two-humped camels . Early-domestic dromedaries  cohabited the coastal Southeast of the Arabian Peninsula for nearly one millennium together with wild specimens, which did not survive the beginning of the Common Era (CE)19. The early dispersal of modern dromedaries from the Arabian Peninsula to the Levant, North Africa, South Asia, and finally to Australia (introduced in the late 19th century)20 was followed by cross-continental back-and-forth movements along historic trading routes. This led to a blurring of genetic stocks21 culminating in a panmictic dromedary population at the mitochondrial DNA level1. Previous studies using a limited number of microsatellite markers and mtDNA detected only weak population structure in the global dromedary population22. As few genomic studies have been completed with the dromedary, this major ungulate species has been left out of the livestock genomic revolution. However, two draft reference genomes at the scaffold level have been released24, as well as a draft reference genome at the chromosome level25, which will facilitate further genomic investigations.The early progenitors , and we decided to remove these samples from downstream analysis due to potential introgression from Bactrian camel (Supplementary Data We performed double-digest restriction site associated DNA (ddRAD) sequencing on 122 dromedary DNA samples from 18 countries  and observed (HO = 0.25 ± 0.17) heterozygosities in the global dromedary population . Separating the samples according to their continental origins, both Asian  and African dromedaries  showed similar genomic diversity. The mean HE  and inbreeding coefficients  were higher in Asian than African dromedaries, but mean HO  was not different between continents, according to Welch′s t test. Complete diversity and inbreeding values are given in Supplementary Table HE = 0.22–0.32)26 or cattle (HE = 0.24–0.30)27, we consider the genome-wide diversity in dromedaries as moderate at the best. Several bottlenecks during the last glacial period  and during domestication left modern dromedaries with a minimum of only six maternal lineages1 and limited genome-wide diversity. This will have implications on future intensification of breeding and genomic selection in dromedaries from regions with increasing desertification.With 22,721 SNPs, we estimated expected , and indices between dromedaries from different countries  were lowest in geographically close populations  and increased with geographic distance (Pakistan/Tunisia: FST = 0.0328)  revealed that most of the variation, ~94.3%, is explained within individuals  <0.05) between African and Asian dromedaries . In addition, we examined  regions 200 kbp upstream and downstream of the FST-outlier loci and detected fifty-three genes related to a number of biological functions .We screened for loci deviating from neutraliy using BayeScan 2.1Hadhana, and is one of the twelve recognized dromedary ecotypes in Saudi Arabia, limited to mountain regions in the South of the Arabian Peninsula, Al-Baha32. In this case, the geographic accessibility might have an important role in the observed genetic distinctiveness. A possible explanation for the close relationship of Hadhana and African dromedaries might be the historic sea route from Jiddah in Saudi Arabia to Aydhab and Port Sudan. On the western coast of the Red Sea existed a trading route connecting the Horn of Africa to Petra and Damascus via Port Sudan, Aydhab and Myos Hormos, near today’s Kosseir  and incorporated continental information (Africa/Asia) for each sample Fig. . Princip.3% Fig. . While t33  from two to seven confirmed the already observed differentiation between African and Asian dromedaries (K = 2), the clustering of the Saudi Arabian Hadhana breed with Africa (K = 4), the separation of Kenyan and Hadhana individuals, and the higher number of distinct clusters on the Asian continent (K = 7). We find a more homogenous gene pool in African animals with the exception of the East African group1 6. Even earlier dispersal of taurine cattle along the Northern coast of Africa and through the Mediterranean sea to Europe was described during the Bronze age35.To formally test our qualitative observations of weak population structure among African and Asian dromedaries Figs. , 3, we vert Fig. . This paert Fig. 1,7. The 36  over time with an Extended Bayesian Skyline approach (EBS)40 , we tested whether the dromedary populations from the two continents experienced a similar demographic history. First, we investigated the global population and second, African and Asian individuals separately. With the latter approach, we accounted for a potential confounding effect of population structure for the inference of NE40. Due to the observed substructure in Kenyan and Hadhana individuals, we excluded these two populations from the continental groups.To complete our understanding of the moderate genome-diversity observed in the global dromedaries, we investigated the demographic history and inferred effective population size  characterized by climatic cycles, and the Galerian Mammal Age (1.2–0.60 million ybp), which influenced the distribution and evolution of biota and resulted in some species being adapted to arid, cold climates43. Moreover, this timeframe also overlaps with the maximal diversity of the family Camelidae , supporting the adaptation of the dromedary ancestor to environmental changes with an expansion of its population during the middle Pleistocene transition44. Population expansion was followed by a drastic decline in NE beginning 700,000 ybp until the dromedary population collapsed during the last glacial period 45. This is a finding shared by previous Old World9 and New World camelid23 studies and those focusing on Late Quaternary Megafauna46.Irrespective of the continental origin, all inferences showed similar patterns of an initial population expansion from one million ybp until ~700,000 ybp Fig. . Our gen16. Previous BSP analysis using mtDNA likewise did not show a population decline during the time of domestication1. It is possible that the detection of a bottleneck with the EBSP analysis related to domestication has been superimposed by the drastic decrease in NE ending ~30,000 ybp , followed by the extension to Southern Asia and the Red Sea coasts47.After the Pleistocene bottleneck, the dromedary population slowly increased until reaching a stable Our study shows that assessing the evolutionary history of species using genome-wide approaches allows detailed inferences of population structure, migration, and potential signals of environmental adaptation. As the movements of dromedaries parallel those of humans, knowledge on dromedary spatial genetic signatures also sheds light into past human history. We detected genetic admixture across continental populations (Asia and Africa), which highlights the strong anthropogenic influence on these animals.48, and in many other (livestock) species50. With a genome-wide dataset we detected patterns of high effective migration in the global dromedary population and revealed pathways of dispersal after domestication, mirroring ancient caravan routes  or cultural nature. Domestic animals, and in particular camels, have been linked to this process of human development and were essential for its success. By establishing trading routes and reusing them over millennia, corridors of gene flow were opened that shaped genetic diversity and structure not only in dromedaries, but also in Bactrian camelstes Fig. . While tDromedaries and Bactrian camels are the most important livestock species in desert areas; their impact on land and water resources for food production is less than that of any other livestock. With increasing desertification and global climate change their importance will grow even more. For this reason, it is essential to understand the demographic history that has shaped modern dromedary populations. The genome-wide diversity present today, which we have characterized in this study, is a result of genetic shuffling due to historic and recent movements along trading routes. This constant mixing might have led to a unique genetic makeup that could make camels more resilient to global environmental changes, and that needs to be preserved.We selected a total of 122 dromedary samples from a pool of previously extracted DNA collected during projects supported from the Austrian Science Foundation, FWF P24607-B25 (PI: P.B.) and the European Research Council, EU ENPI CBC MED PROCAMED I.B.1.1/493 (PI: E.C.) under all legal requirements. The samples originated from 18 countries, which were representative of the species’ distributional range. In view of potential hybridization between one- and two-humped camels known to occur specifically in Central Asian regions, we included a Bactrian camel to control for introgression and as outgroup for phylogenetic analysis. Detailed information about all samples is provided in Supplementary Data C. dromedarius genome assembly (NCBI accession: GCA_000803125.1)24 highlighted SphI-BstYI as the best combination of restriction enzymes able to produce DNA fragments between 400 and 530 bp. ddRAD barcoded libraries were pooled and sequenced on an Illumina HiSeq 2500 in high output mode using 125 bp paired-end reads. Initial raw data analysis as well as SNP calling was performed by IGA Technology Services in-house bioinformatics pipeline. Briefly, all reads were trimmed to 110 bp, and quality controlled reads were aligned to the North African dromedary genome assembly (NCBI accession: GCA_000803125.1) using BWA-MEM51. ddRAD reads were then processed with Stacks v.1.3552. Out of nearly half a million RAD loci, 88,836 SNPs passed the imposed filtering criteria. The pstack module was run with a minimum coverage of 3 reads to call a haplotype, while SNPs were called using a bounded model to highest = 0.5 and alpha = 0.05. Cstacks and sstacks modules were run with default parameters. Population module was run requiring a minimum genotyping of 75% of individuals to score loci, along with a calling likelihood filtering threshold of −25.Library preparation and sequencing was performed at IGA Technology Services , to generate genome-wide data from ddRADseq. In silico analysis of the 53, first excluding individuals with more than 25% missing genotypes (--mind 0.25), next setting a threshold of 0.01 for minor allele frequency (--maf 0.01) and finally, removing SNPs with a missing genotype rate of more than 25% (--geno 0.25). Due to high individual missingness, 13 samples were removed from the dataset as well as 35,013 SNPs after filtering for minor allele frequency and missing genotype rate. To exclude any potentially related individuals, a symmetric identical by state matrix was created with PLINK with a cutoff value of 0.05; one sample from Jordan (JO434) and three from Nigeria  were removed from further analyses 25 or Bactrian camel (NCBI accession: GCF_000767855.1)23 genomes using BBSplit v. 38.79 (https://sourceforge.net/projects/bbmap/), with the following settings: minratio = 1.0, ambiguous = toss, ambiguous2 = toss. We preprocessed these two genome assemblies with dustmasker v. 1.054, and the percent Bactrian camel was estimated as the number of reads that unambiguously mapped to the Bactrian camel genome divided by the total number of unambiguously mapped reads to both dromedary and Bactrian camel multiplied by 100. We removed three individuals from Iran  and six from Kazakhstan   and observed (HO) heterozygosities, AR, and inbreeding coefficients (FIS) were calculated with the R package Hierfstat v0.04-2259. We have used a parametric Welch t test implemented in R v.3.5.1 using the t.test function to compare mean HE,HO, and FIS between African and Asian dromedaries. Pairwise FST and AMOVA59 were analyzed with the program Arlequin 3.5.2.260.SNPs were tested for Hardy–Weinberg Equilibrium and linkage disequilibrium using VCFTOOLS v0.1.1528 to identify FST outlier loci putatively under of selection using default settings with a FDR61 of 0.05. To understand if SNPs putatively detected under selection were linked to significant biological pathways, we screened the respective RAD sequences for genes using the annotation of the new CamDro2 assembly25 and assessed their protein function with Genecards (http://www.genecards.org). To consider also regions in potential linkage disequilibrium, we included genes 200 kbp upstream and downstream of the SNPs under selection in the analysis.We applied BayeScan 2.133 to assess ancestry and possible structure among dromedary populations , using the lowest fivefold cross-validation error to choose the best number of clusters (K), from K = 1 to K = 10. To understand the phylogenetic relationship among individuals, we applied the Neighbor-Net method62 implemented in SplitsTree463, which is a neighbor-joining algorithm for constructing phylogenetic networks from a genetic  distance matrix created in PLINK.Individual-based PCA was performed with Adegenet v2.1.1 using the s.class option to represent principal components of known groups. Furthermore we applied ADMIXTURE v1.334 to investigate effective migration patterns in the global dromedary population. Based on a stepping-stone model, and assuming that migration is symmetric, EEMS uses Markov Chain Monte Carlo to estimate the diversity and migration parameters and produces maps which represent the posterior mean of effective migration and effective diversity across space. We performed three distinct runs, each consisting of 10 million MCMC iterations, discarding the initial 5 million as burn-in and saving every 49,995 iterations for a grid with 500 demes. All runs reached convergence and results were similar across replicates. The habitat polygon was obtained using Google Maps API v3 Tool (http://www.birdtheme.org/useful/v3tool.html) and results were plotted using the R package rEEMSplots as suggested in Petkova et al.34.We used the software estimated effective migration surfaces (EEMS)64, setting the Coalescent EBS40 as a tree prior and following Huson and Bryant63. EBS analysis was conducted on the global population where we randomly selected 50 RAD loci containing at least four but not more than six SNPs across at least 75% of the individuals using a custom R script (https://github.com/jelber2/RAD-Scripts/blob/master/RAD_Haplotypes.R)65. We repeated the EBS analysis for African and Asian dromedaries separately, but due to slight population structuring, Kenya and Hadhana populations were excluded from this analysis. We ran EBS analyses three times per continent, with 50 random RAD loci used in each run. A RAD locus-specific clock rate (per generation) was estimated by calculating the average number of differences between dromedary and Bactrian camel sequences, dividing by the length of the RAD loci, taking the average among SNP classes (number of SNPs per RAD locus from 4 to 6), dividing by the split time between the Bactrian camel and dromedary of ~4.4 million ybp23, and using a dromedary generation time of 5 years1. Each EBS was run for 2,100,000,000 chains using the RAD locus-specific clock rate of 1.809442e-08 to calibrate the time scale.We assessed the demographic history of the species employing a coalescent-based multi-locus analysis with variable loci using BEAST2 v. 2.5.1t test implemented in R v.3.5.1 using the t.test function to compare mean HE, HO, and FIS, and data are expressed as mean and SD. Pairwise FST values were performed including a minimum of three individuals per countries and their significance levels are represented with “+”. AMOVA were analyzed with the program Arlequin. P values below 0.05 are considered as statistically significant for all statistical tests in this work. All analyses are reproducible with access to genetic data (see “Data availability”).Sample size is outlined in Supplementary Data Further information on research design is available in the Supplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Reporting SummaryPeer Review File


SLC24A5, SNX13, TYRP1, and UVRAG) associated with skin pigmentation, three of which already have been reported to be under selection. These selective sweeps point toward adaptations to the intense UV radiation of the Sahel.Africa contains more human genetic variation than any other continent, but the majority of the population-scale analyses of the African peoples have focused on just two of the four major linguistic groups, the Niger-Congo and Afro-Asiatic, leaving the Nilo-Saharan and Khoisan populations under-represented. In order to assess genetic variation and signatures of selection within a Nilo-Saharan population and between the Nilo-Saharan and Niger-Congo and Afro-Asiatic, we sequenced 50 genomes from the Nilo-Saharan Lugbara population of North-West Uganda and 250 genomes from 6 previously unsequenced Niger-Congo populations. We compared these data to data from a further 16 Eurasian and African populations including the Gumuz, another putative Nilo-Saharan population from Ethiopia. Of the 21 million variants identified in the Nilo-Saharan population, 3.57 million (17%) were not represented in dbSNP and included predicted non-synonymous mutations with possible phenotypic effects. We found greater genetic differentiation between the Nilo-Saharan Lugbara and Gumuz populations than between any two Afro-Asiatic or Niger-Congo populations. F3 tests showed that Gumuz contributed a genetic component to most Niger-Congo B populations whereas Lugabara did not. We scanned the genomes of the Lugbara for evidence of selective sweeps. We found selective sweeps at four loci ( The peoples who remained were more genetically diverse and have continued to diversify in response to changing environmental and disease pressures and admixture events.,,,,,,,,,The Nilo-Saharan family comprises 206 languages spoken by 34 million people (1996 estimate) and is divided into approximately 12 subgroups.The samples used for this study were obtained from the TrypanoGEN biobank,Peripheral blood was collected from the participants at the field sites, frozen, and transported to reference laboratories. DNA was extracted using the whole blood MidiKit (QIAGEN). The DNA was quantified using the Qubit (QIAGEN) and approximately 1 μg was used for sequencing at the University of Liverpool, UK. DNA from Cameroon and Zambia was sequenced at Baylor College, USA.300 participants’ DNA samples  were selected and subjected to whole-genome sequencing . The whoFrom the 1000 Genomes project,From the African Genome Variation Projectr2 < 0.1. Individuals with >10% missing data were also removed. Data were phased with Shapeit2 v2.r837,The data were filtered to minimize batch effects potentially introduced by the presence of samples sequenced at different depths by different labs. For descriptive statistics of the TrypanoGEN dataset all loci were retained. For all other analyses, sites that met any of the following criteria were removed; missing data > 10%, loci with < 3 SNP calls, minor allele frequency (MAF) < 0.01, Hardy-Weinberg equilibrium p < 0.001. For population analyses, the remaining SNP loci were thinned in order to retain only loci with For signatures of selection, the filtered and phased variant call format files were further filtered using VCFtools v.0.1.16,To infer the population structure based on the underlying genetic variation among the populations, we carried out multidimensional scaling (MDS) using PLINK 1.9Admixture was tested for 1 to 9 genetic components (K) using ADMIXTURE 1.23admixr.All plausible pairs of available populations that might be sources of the selected East African Populations  were tested for evidence of contribution to those populations using the F3 test in AdmixToolsSTST matrix was generated between our sequence data, 1000 Genome project,,We followed the workflow of Cadzow et al. for allele frequency statistics.rehh3ST and Tajima D.The sequence data were scanned for regions that might be under selection using the Extended Haplotype Homozygosity (EHH) test within and between populations.We sequenced the genomes of 50 individuals from the Nilo-Saharan Lugbara population and 250 from 17 linguistic groups from Guinea, Côte d’Ivoire, Cameroon, Democratic Republic of Congo, Uganda, and Zambia  and S2.The samples from Zambia and Cameroon were sequenced to 30× coverage while other populations were sequenced to 10× coverage. The call rate was 97.4% in the 10× samples and 99.4% in the 30× samples. The 30×-sequenced samples had higher proportions of heterozygotes (9.3%) compared with the 10× sequenced samples (7.5%) and there was a concomitant higher frequency of low Hardy-Weinberg p values in the 10× data . There wcf. 3.0%). There was a larger proportion of rare alleles (MAF < 5%) in the set of novel SNPs than in the known SNPs  of less than 0.1 . We clasown SNPs , as exper2 > 0.1) yielding a final dataset of 1,465,578 SNP and 731 samples that were used for MDS, Admixture, and F3 analysis.Bi-allelic loci from the 286 TrypanoGEN samples were merged with 1,000 Genomes and African Genome Variation Project data to obtain 10,857,449 loci that were present in all three datasets for population analysis. These were filtered to remove linked loci  for all numbers of genetic components (K3-9) . AlthougWith K > 5 the Niger-Congo populations separated into an east African cluster of the Ugandan Basoga and Kenyan Luhya, a central African cluster of the Zambia, Cameroon, and Democratic Republic of Congo, a Nigerian cluster of the Esan and Yoruba, and a far west-African cluster of the Côte d’Ivoire, Sierra Leone, Guinea, and Gambia populations. We also observed at K ≥ 8 a homogeneous group of seven Soli/Chikunda (Niger-Congo B)-speaking individuals within the Zambia population with no admixture with other populations and who were also outliers on the MDS coordinates plot A, the soThe admixture hypotheses generated by Admixture were tested with the three populations (F3) test implemented with AdmixTools.Pairs of each African population and each West Eurasian population were plausible sources to the Amhara (AMH) population consistent with the Admixture plot which suggests that the Afro-Asiatic populations have a large West Eurasian admixture component as previously reported .No pairs of populations were jointly source to either of the Nilo-Saharan populations (UNL and GUM) . HoweverThese observations are most consistent with the population structure indicated in the Admixture plot at K = 6. At K = 6 the dominant ancestry component in Lugbara and Gumuz (dark blue in ST distances between the Ugandan Lugbara and the other African populations to determine the genetic distance between them (ST was relatively high (mean FST > 0.015) between the Nilo-Saharan Lugbara samples and the Niger-Congo populations, except for the Uganda Basoga population (mean FST = 0.011) and Kenyan Luhya population (mean FST = 0.012). The Lugbara and Gumuz populations are about 1,000 km apart compared with the approximately 4,000 km, which separates the West and East African Niger-Congo A and B populations. However, FST between Niger-Congo A and B (0.008) was lower than between Lugbara and Gumuz . Those alleles with extreme EHH were then validated using the allele frequency-based Frehh3 software for which we observed a normal distribution of the absolute iHS values . Both genes are involved in diverse ranges of intracellular activities making it difficult to predict a specific effect on phenotype.,HLA region had some of the highest frequencies of SNP with |iHS| > 2 as well as some of the highest values of iHS (> 6) and has been found to have signatures of selection previously.We compared the regions under selection within the Lugbara and Basoga populations. The Basoga population was selected due to their geographic proximity to the Lugbara (500 km) , the minS values . The ManS values  shows 12ST (top 5% quantile), high Tajima’s D, and high cross population EHH (xpEHH > 2.5). The three different metrics were combined by ranking genes on each individual metric and then obtaining the sum of the ranks for each gene  exceeds that between African Niger-Congo A and Niger Congo B populations  and also exceeded that within European, East Asian, and South Asian populations but not the American population in the 1000 Genomes data  were in the top 1% of gene regions under selection in Lugbara and were also under selection in Basoga  had some evidence of selection (We identified signatures of selection in multiple genes associated with immune responses and other conditions. However, the multiple and diverse functions of individual genes make it hard to predict the specific adaptations or phenotypes that might have driven selection at these loci. Nevertheless, there was a group of genes associated with skin tone and hair form which are plausibly associated with the particularly dark color of the skin of Nilo-Saharans and the intense UV radiation they experience. t Basoga . This gen Basoga  and a fuelection .81 ThereUVRAG may be an important contributor to the exceptionally dark skin tones of the Nilo-Saharans in conjunction with SNX13 and TYROBP in particular and possibly also IRF4, TYRP1, HERC2, SLC24A5, and OPRM1.Nilo-Saharans have some of the darkest skin tones in the worldHair form is probably related to thermoregulation by helping keep the head cool during exercise.In conclusion, the Nilo-Saharan language speakers are an under-represented source for discovery of genetic variation. They are more genetically differentiated than the neighboring Afro-Asiatic and Niger-Congo groups but have been much less studied. They have contributed a large component to the genome of Afro-Asiatic speakersThe authors declare no competing interests.


Margaritifera margaritifera) were sampled from 11 populations, located in four different river systems in Southeast Norway, and analyzed for 11 simple sequence repeat (SSR) (microsatellite) markers. All study populations have landlocked brown trout  as the only possible host. Several populations had experienced recruitment failure, probably due to low pH (about 6.0) and calcium concentration. STRUCTURE clustering analysis revealed two genetic clusters, of which one cluster occurred mainly in the western river systems, and totally dominated in one population ) that had higher genetic diversity than the others. Cluster 2 completely dominated in the populations of the eastern river systems, and all of them had low genetic diversity. Bottleneck events were indicated in all populations and the inbreeding coefficient FIS was significant in all populations, except for the southernmost population ), which was the only population with genotypes in Hardy–Weinberg equilibrium. FIS were especially high in the populations of the eastern river systems, and maximum shell length was negatively correlated to FIS. If artificially breeding and stocking should become necessary for future preservation, it should be based on single populations; alternatively, the eastern populations should be based on cross-breeding of populations within the cluster to increase their genetic diversity.A total of 312 specimens of freshwater pearl mussel ( Margaritifera margaritifera) is one of the most threatened , is suboptimal for pH-sensitive organisms such as freshwater pearl mussels. Water quality and mussel reproduction in the study streams should be monitored, and lime treatment should be considered.Recruitment failure, low genetic diversity, and a high portion of siblings in the majority of the study populations suggested that they are prone to further loss of genetic variation and may be threatened by extinction in the future, i.e., within 100 years, except for the B-SØR, which appeared to be a healthy population. Habitat conservation is necessary, and in several study locations, the water quality, expressed as pH and [CaExcess of homozygotes and significant inbreeding coefficients suggested inbreeding, with a possible risk of inbreeding depression and future negative effects on reproductive performance . Restora


Besides, we also explored their forensic characteristics and extended the Chinese National Database based on STR data. We identified 231 alleles with the corresponding allele frequencies spanning from 0.0005 to 0.5624 in the forensic low-density dataset, in which the combined powers of discrimination and the probability of exclusion were 1–1.22E-24 and 0.999999998, respectively. Additionally, comprehensive population comparisons in our low-density data among 57 worldwide populations via the Nei’s genetic distance, PCA, MDS, NJ tree, and STRUCTURE analysis indicated that the highland Tibeto-Burman speakers kept the close genetic relationship with ethnically close populations. Findings from the 1240K high-density dataset not only confirmed the close genetic connection between modern Highlanders, Nepal ancients , and the upper Yellow River Qijia people, suggesting the northeastern edge of the TP served as a geographical corridor for ancient population migrations and interactions between highland and lowland regions, but also evidenced that late Neolithic farmers permanently colonized into the TP by adopting cold-tolerant barley agriculture that was mediated via the acculturation of idea via the millet farmer and not via the movement of barley agriculturalist as no obvious western Eurasian admixture signals were identified in our analyzed modern and ancient populations. Besides, results from the qpAdm-based admixture proportion estimation and qpGraph-based phylogenetic relationship reconstruction consistently demonstrated that all ancient and modern highland East Asians harbored and shared the deeply diverged Onge/Hoabinhian-related eastern Eurasian lineage, suggesting a common Paleolithic genetic legacy existed in high-altitude East Asians as the first layer of their gene pool.The Tibetan Plateau (TP) is considered to be one of the last terrestrial environments conquered by the anatomically modern human. Understanding of the genetic background of highland Tibetans plays a pivotal role in archeology, anthropology, genetics, and forensic investigations. Here, we genotyped 22 forensic genetic markers in 1,089 Tibetans residing in Nagqu Prefecture and collected 1,233,013 single nucleotide polymorphisms (SNPs) in the highland East Asians (Sherpa and Tibetan) from the Simons Genome Diversity Project and ancient Tibetans from Nepal and Neolithic farmers from northeastern Qinghai-Tibetan Plateau from public databases. We subsequently merged our two datasets with other worldwide reference populations or eastern ancient Eurasians to gain new insights into the genetic diversity, population movements, and admixtures of high-altitude East Asians  East Asia, one of the oldest centers of plant and animal domestication, is home to almost one-quarter of the world’s population and encompasses substantial genetic, cultural, linguistic, and physical diversity. Understanding the peopling processes of East Asia or some unique harsh environment area is therefore of interest for elucidating how these extensive diversities arose and evolved. However, the comprehensive genetic history of East Asia is poorly understood due to the lack of ancient DNA from a denser genetic sampling or sparse sampling of modern East Asians and combined analyses of spatiotemporally diverse East Asian populations . GeneralEndothelial PAS domain protein 1 (EPAS1) adaptive alleles , a high-altitude arid steppe bounded by the world’s tallest mountains, represents one of the most challenging environments with low temperature and hypobaric hypoxia for human settlement. As one of the last populated areas occupied by modern humans, the exact timing of the peopling of the TP and the migration trajectories of Tibetans have appealed to growing academic interests. The recovered paleoproteomic results of a Xiahe Denisovan mandible from the TP indicated that archaic hominins occupied the TP in the Middle Pleistocene epoch and successfully adapted to the high-altitude environments with the accumulation of  alleles . Archeol alleles . Conside alleles . The mat alleles . The coa alleles . It also alleles . Besides alleles . Linguis alleles . Further alleles .via the archeologically attested similarities of cultures from Nepal and upper Yellow River (Qijia people) and further explore the extent to which it was mediated via the population movement though a northeast geographical corridor; and (IV) evaluate to what extent of the barley/wheat agriculture spread in the Ganqing region was mediated via cultural diffusion or demic diffusion from the Fertile Crescent.Taken together, current archeological, anthropological, genetic, and linguistic findings suggested that the initial Paleolithic occupation of the TP combined with later multiple migrations at different times and from different regions may have created the complicated and mosaic demographic history of Tibetans. However, available genetic data are insufficient to address the discrepancy between demographic history constructed by different regional studies and hamper the exploration of genetic variations of Tibetans based on the forensically related markers. Hence, extending the existing forensic reference database and dissecting the genetic differentiation among different Tibetan groups or between Tibetans and other reference populations based on the combined resolution of modern and ancient genomes is indispensable. Here, we mainly aimed to focus on the following topics: (I) explore the pattern of genetic diversity of highland East Asian based on short tandem repeat (STR) and single nucleotide polymorphism (SNP) data; (II) dissect the potential gene flow events between highland Tibetan-Burman speakers and close lowland East Asian populations; (III) explore whether there is a genetic continuity between modern Highlanders and ancient populations who were linked Here, we carried out the present study in 1,089 unrelated Tibetan individuals  residing in Nagqu – the northeastern prefecture-level city of Tibet Autonomous Region . All parHuman genomic DNA was extracted using the PureLink Genomic DNA Mini Kit (Thermo Fisher Scientific) and quantified by employing the NanoDrop-2000 on the basis of the manufacturer’s instructions. Twenty autosomal short tandem repeats (A-STRs) recommended by the Chinese National Database (CND) as well as two gender-determining genes (Amelogenin and Y-indel) were amplified simultaneously using the STRtyper-21G PCR assay on a ProFlex PCR System (Thermo Fisher Scientific) following the manufacturer’s recommendation. ABI 3130 Genetic Analyzer (Thermo Fisher Scientific) was utilized to separate the PCR products, and the GeneMapper ID-X v.1.4 software was used to visualize the electrophoresis results.via the Gendist package implemented in the PHYLIP v.3.695 , and LD was observed in the locus pair of TPOX-Penta E  populations were observed within ST populations. Geographically, different components within the same language family gradually appeared with the increase of K values and the proportions of shared components were variable within ethnically different groups. Nagqu Tibetan consistently harbored a unique component and showed a closer genetic affinity with Chengdu Tibetan and Lhasa Tibetan.Generally, the patterns revealed by MDS and NJ tree were in accordance with those observed in the PCA and heatmap. To directly dissect the Nagqu Tibetan ancestry component and explore the genetic similarity based on the shared ancestral components with different predefined clusters . We founK = 3) were assumed . Also, this three-population model showed that yellow ancestry was enriched in Taiwan Iron Age population (average proportion is 0.954 as blue component in K = 3) maximized in the inland middle Neolithic northern East Asian Miaozigou individuals associated with Miaozigou culture (0.983), followed by the late Neolithic Shimao and Neolithic Wuzhuangguoliang people in Shaanxi and other ancient Tibetan and northern Chinese ancients (larger than 0.869). Modern Tibetan harbored 0.866 Miaozigou-related component and others from Hanben- or Mbuti-like component, and Sherpa derived 0.878 of their components related to this group. We identified two southern East Asian components when the model of four predefined cluster sources was used: island/coastal southern East Asian components maximized in Taiwan Iron Age Hanben people (0.962) and late Neolithic Xitoucun and Tanshishan  and inland southern East Asian component enriched in Tai-Kadai Dai which also existed with a high proportion in Chinese southern Tibeto-Burman Lahu, modern Austronesian Ami. and Hmong-Mien Miao and She. Similar to the patterns in K = 3, the third component was maximized in the inland northern Neolithic people. Based on the shared component in To study the demographic history and deep population history of East Asian Highlanders of Tibetan and Sherpa, we used the Tibetan and Sherpa individuals included in the Simons Genome Diversity Project (SGDP) as the new studied subject. We merged them with other publicly available modern and ancient Eurasian genomes based on the 1240K overlapping SNPs. The final dataset included 44 populations: 356 modern individuals from 21 East Asian groups and 1 central African Mbuti and 112 Chinese ancients from 22 spatiotemporally diverse archeological sites . We prunvia the outgroup 3f-statistics in the form of 3f. Pairwise-shared genetic drift among 63 ancient and 22 modern East Asian populations were also calculated 3via f and submitted in 3f values or green color in 3f values or red color meant less shared ancestry among them. The red color with Uyghur and the green color with the late Neolithic Wuzhuangguoliang people observed in the heatmap, respectively, showed their genomic differentiation and similarities with reference East Asians. The cluster patterns in the heatmap showed that Tibetan clustered with Nepal ancients and kept a close relationship with Sherpa. Focused on the genetic variations of Sherpa and Tibetan (We subsequently estimated the shared genetic drift between the highland East Asians (Tibetan and Sherpa) and other 350 lowland modern East Asian individuals from 20 populations, 118 lowland ancient East Asians from 33 populations, and 8 highland East Asian individuals from 3 Nepal populations   Tibetan , we founvia admixture 3f-statistics in the form of 3f. The observed statistically significant negative 3f values with absolute Z scores larger than three indicated that the targeted investigated population was a mixed population with the possible ancestral populations related to the two used sources. No negative 3f values were identified in 3f among 1,653 pairs of modern and ancient East Asians, but eight population pairs with negative 3f values were observed in 3f with one source from Nepal ancients and the other from modern/ancient northern East Asians  and NJ algorithm. Here, we could identify southern Neolithic to Iron Age populations grouped together and then grouped with southern Chinese modern Austronesian, Tai-Kadai, Hmong-Mien, and Sinitic language speakers, which formed the southern East Asian branch. Hanben and Gongguan people from Taiwan kept the closest relationship with modern Austronesian Ami and Atayal. Sherpa and Tibetan possessed a strong genetic affinity and grouped first with three Nepal ancients and then with lowland Tibeto-Burman-speaking Tu, Naxi, and Yi and formed the TP branch. The observed Tibeto-Burman branch showed a close genetic relationship between modern lowland/highland Tibeto-Burman language speakers and ancient highland Nepal ancients. The northern ancient branch comprised early Neolithic to Iron Age individuals from Shandong and Henan provinces in the middle and lower Yellow River basin and from Shaanxi and Qinghai provinces in the upper Yellow River basin and West Liao River. Amur River ancient clustered with modern Tungusic and Mongolic speakers formed an Amur branch. The overall patterns observed in the 3f-based phylogenetic relationship showed the TP branch was placed in the intermediate position between the northern East Asian branch and the southern East Asian branch, but far away from the Amur branch. The second NJ tree based on the Fst genetic distance matrices clustered one modern population branch and one ancient population branch  to explore the differentiated shared drifts between Highlanders and lowland East Asian reference groups compared with other East Asian reference groups in the form of 4f. The observed significant negative 4f values with the absolute Z scores larger than three (green color in the heatmap) denoted that our studied Tibetan and Sherpa shared more genetic drifts with Modern/Ancient Chinese population2 relative to the Modern/Ancient Chinese population1; otherwise, significant positive 4f values (red color in the heatmap) denoted more shared alleles between Highlanders and Modern/Ancient Chinese population1 rather than Modern/Ancient Chinese population2. No significant negative or positive 4f values  denoted two Chinese reference populations formed one clade relative to our studied Highlanders. As shown in 4f was conducted to explore the relationships between Highlanders and northwestern Chinese populations (modern Uyghur and Iron Age Shirenzigou people). The results of significant negative 4f values showed that Tibetan shared more derived alleles with both northern and southern Neolithic to present-day East Asians than with Xinjiang Iron Age to modern populations, which suggested little genetic materials associated with western Eurasian in Tibetans (4f: 3.546*standard error (SE)], Shirenzigou_IA (4.624*SE), and Shirenzigou_IA_E (8.593*SE) 4via f. Compared with Shirenzigou individuals with stronger western Eurasian affinity, we found that Tibetan shared more alleles with modern Uyghur, Shirenzigou_IA, and Shirenzigou_IA_E. Moreover, genetic similarities between Tibetan and Shirenzigou_IA_E were further confirmed 4 =via f 5.827*SE/9.473*SE. To study the genetic links between Tibetans and early East Asians, we carried out 4f and found that Tibetan shared more derived alleles with Neolithic to present-day East Asians compared with deep East Asian lineages. Here, early East Asians were represented by Onge from South Asia, Hoabinhian people from Laos and Malaysia, and Tianyuan from Beijing. Compared with early East Asians, Tibetan shared more alleles with modern and ancient East Asians with negative 4f values in the 4f. Some cases with the more shared genetic drifts between Tibetan and Jomon people were identified when we used Xinjiang Iron Age to modern groups or 40,000-year-old Tianyuan people as the reference populations, such as 4 =f 5.529*SE. In summary, compared with northwestern Chinese populations with signatures of western Eurasian admixture and early East Asians, we found a strong genomic affinity between our studied Tibetan and northern/southern lowland East Asians. These observed genetic close relationships between Highlanders and East Asians showed that the gene pool of modern Tibeto-Burman speakers mainly originated from East Asians, not from South Asia or Central Asia, although too many natural corridors and historic or prehistoric trade routes connected the TP and Central Asia or the Indian subcontinent f showed that Tibetan shared more alleles with northern East Asians. Positive values in 4f suggested Tibetan shared more alleles with Iron Age Hanben Taiwanese and their descendants than with their ancestors (Early Neolithic people), and positive 4f values in 4f denoted Tibetan shared more alleles with inland southern East Asians than with island/coastal southern East Asians. Consistent positive 4f values in 4f showed that Tibetan had a strong genetic affinity with 1,500-year-old Samdzong people and Qijia people from Lajia, as well as the modern Sherpa. This obvious genetic affinity between modern Tibetans and ancients from Nepal and Qinghai showed the direct genetic contribution between Qijia culture-associated ancestral population and modern Tibetan or Nepal high-altitude people and modern Highlanders. Affinity between Tibetan and modern Tibeto-Burman speakers and other northern East Asians was further confirmed via positive 4f values in 4f. Focused on the Sherpa, as shown in 4f values in 4f, which suggested Sherpa shared more derived alleles with lowland and highland northern East Asians compared with the early Asians, northwestern Chinese populations with western Eurasian admixture, ancients from coastal southeast China, islanders of Taiwan and Japanese Archipelago, and even some southern Chinese indigenous populations of Atayal and Dai. Red colors were observed when we used the following groups as the Modern/Ancient East Asian1: middle Neolithic populations , late Neolithic people , Qijia people , ancient Tibetans , and modern Sino-Tibetan , which showed that Sherpa shared more alleles with them compared with southern East Asians or early Neolithic northern East Asians.Focused on the population substructure within East Asians, the negative 4f statistics in the form of 4f. As shown in 4f values, which suggested that Tibetan harbored more ancestry derived from the groups related to the modern/ancient East Asian2. Here, we found that Tibetan possessed more ancestry from both northern and southern modern/ancient East Asians compared with northwestern Chinese populations and early Asians . Most negative 4f values were also observed in 4f, suggesting that Tibetans harbored more northern East Asian ancestry. Red colors showed strong genetic affinity among lowland East Asians or more shared ancestry among them relative to Tibetan. We expected to observe the significant negative 4f values if the included modern/ancient East Asian2 was the direct ancestor of Tibetan. Interestingly, 4f showed negative 4f statistical values. However, no similar signals were identified in the late Neolithic Lajia or Jinchankou populations. Furthermore, no significant 4f values should be observed when Nepal ancients were the unique ancestral source, or negative 4f values could be obtained if there were some additional admixture gene flow into modern Tibetan in 4f. No statistically significant 4f values were observed here suggesting that ancestral populations related to the Nepal ancients were the direct ancestors of modern Tibetans. Although differentiated shared alleles were observed between Highlanders of Tibetan and Sherpa illustrated 4via f, similar patterns of shared genetic drift were identified in Sherpa populations  were used as the base outgroup set. Our qpWave results of p_rank0 <0.05 showed at least two ancestral populations could be used to model the ancestry composition of our included Tibetan and Sherpa. We first used the Ancient Ancestral South Indian of Onge as the southern source population represented as the deep diverged eastern Eurasian ancestry, which recently was hypothesized as the representation of indigenous South Asians in the study of the formation of human populations in South Asia . When we substituted Hoabinhian from Laos with Onge as the southern ancestral source, the aforementioned six models  could be fitted well of two included Highlanders with relative higher ancestry proportion from Hoabinhian-related ancestry. Another three models . Middle Neolithic Xiaowu (0.882) and Xiaojingshan_EN (0.818) could be used as the northern East Asian sources for the model of the formation of modern Tibetan, and DevilsCave_N could be used as the source for modeling modern Sherpa with 0.719 derived from northern sources.Subsequently, to explore the plausible models of admixture fitted well of Highlanders and estimate the corresponding ancestry proportion, we used the statistical tool of uth Asia . FifteenFinally, to reconstruct a deep population admixture history of the Highlanders of Tibetan and Sherpa based on the 1,233,013 SNPs, we used the basic phylogenetic framework from Wang et al. with the terminal modern populations of Mbuti, Onge, archaic population of Denisovan, and Paleolithic to Iron Age populations of Loschbour, Tianyuan, Liangdao2_EN, Lajia_LN, Chokhopani, and two eastern Mongolia Neolithic people . After aThe Qinghai-Tibet Plateau and the surrounding great mountain ranges are home to cultural, genetic, and linguistic diversity since prehistoric or historic times, although nature environments, such as high-altitude hypoxia, resource scarcity, cold stress, and rough terrain, to some extent hindered the process, scale, and speed of the population’s settlement in this world’s high plateau. Archeological documents from Xiahe Denisovan mandible in northeastern TP  and abundant blade tool assemblage in the Nwya Devu site  successively demonstrated that humans colonized this high-altitude area from late Middle Pleistocene  to late Paleolithic stage , suggesting Qijia people as the representative of Neolithic millet farmers played an important role in the formation of modern Tibetans although they shared more alleles with Neolithic Yangshao, Longshan people from Central Plain in Henan Province, and Houli people from Shandong Province. Our autosome-based genetic links between ancient populations from northeast TP were consistent with recent archeological, Y-chromosomal, and mitochondrial evidence for the colonization and peopling of the Qinghai-Tibet Plateau  and northern China Yellow River dryland millet charred cereal grains (foxtail millet and broomcorn millet) were identified in the Neolithic archeological sites in the northeastern TP , suggestoup data . Li et aDifferent from subpopulation structures observed in Highland East Asians , our previa the cultural diffusion model and not via the demic diffusion model. Finally, the observed shared deeply diverged Onge/Hoabinhian-related eastern Eurasian lineage into modern Tibetan, Sherpa, and 2,700-year-old Chokhopani demonstrated that a common Paleolithic genetic legacy widely existed in all highland East Asians.Our population genetic or genomic analyses showed that both high-density and low-density datasets in the present study revealed the close genetic relationship between Highlanders and lowland Tibeto-Burman-speaking populations, forensic-related STR-based analysis showed limitations for finer-scale genetic structure dissection due to its relatively lower resolution with the forensically developed systems. We used STR-based datasets to evaluate the genetic diversity and forensic characteristics as well as to uncover the genetic similarities and differentiation between the studied Tibetan group and 56 reference populations and found that the STR amplification system was informative and discriminative in Nagqu Tibetan and could be applied in the construction of the Chinese national STR datasets. Comprehensive worldwide or nationwide population comparisons demonstrated that Nagqu Tibetan keeps the genetic affinity with ethnically close Chengdu Tibetan, Liangshan Tibetan, and Tibet Tibetan. Furthermore, population structure and demographic history reconstruction based on the high-density 1240K dataset showed that Highlanders of Tibetan and Sherpa possessed a close genetic relationship with Qijia culture-related people (Lajia and Jinchankou), suggesting that the northeastern edge of the Tibetan Plateau is an important geographical corridor for population movements and admixtures in the progress of permanent human settlement of the TP. No western Eurasian admixture signatures were identified in modern and ancient populations of the core region and northeastern edge of the TP, suggesting that the late Neolithic upper Yellow River millet farmers’ adoption of barley and wheat agriculture from the Fertile Crescent of southwestern Asia was mediated The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Ethics Committee of Zunyi Medical University. The patients/participants provided their written informed consent to participate in this study. Informed consent was obtained from all participants included in the study.GH, MW, and XZ conceived the idea for the study. GH, PC, XZ, YL, and MW performed or supervised wet laboratory work. GH, MW, XZ, PC, ZW, JL, LY, FW, YL, and RT analyzed the data. GH, MW, YL, and XZ wrote and edited the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.