The Norwegian White sheep (NWS) and New Zealand Terminal Sire Composite (NZC) sheep breeds have been developed based on crossing of multiple breeds, mainly of Northern European origin. A close genetic relationship between these populations could enable across-country genomic evaluations. The main objectives of this study were to assess the genetic connectedness between Norwegian and New Zealand sheep populations and estimate numerous genetic diversity metrics for these two populations. A total of 792 NWS and 16,912 NZC animals were genotyped using a high-density Illumina SNP chip panel (∼606K SNPs). The NZC animals were grouped based on their breed composition as: Finn, Lamb Supreme, Primera, Texel, “Other Dual Purpose”, and “Other Terminal Sire”. The average level of linkage disequilibrium ranged from 0.156 (for Primera) to 0.231 (for Finn). The lowest consistency of gametic phase was estimated between NWS and Finn (0.397), and between NWS and Texel (0.443), respectively. Similar consistency of gametic phase was estimated between NWS and the other NZC populations (∼ 0.52). For all composite sheep populations analyzed in this study, the majority of runs of homozygosity (ROH) segments identified had short length , indicating ancient (instead of recent) inbreeding. The variation in the number of ROH segments observed in the NWS was similar to the variation observed in Primera and Lamb Supreme. There was no clear discrimination between NWS and NZC based on the first few principal components. In addition, based on admixture analyses, there seems to be a significant overlap of the ancestral populations that contributed to the development of both NWS and NZC. There were no evident signatures of selection in these populations, which might be due to recent crossbreeding. In conclusion, the NWS composite breed was shown to be moderately related to NZC populations, especially Primera and Lamb Supreme. The findings reported here indicate a promising opportunity for collaborative genomic analyses involving NWS and NZC sheep populations. The Norwegian White Sheep (NWS) is a composite breed that accounts for 70–75% of the total Norwegian sheep population. This breed is well known for its prolificacy and high growth rates. Sheep in Norway originates from the Northern European short tail breeds . In the The development history of the New Zealand sheep breeds is somewhat similar to the NWS, with regards to the founder breeders used in their formation and crossbreeding schemes . ConsideThe genetic connectedness between these two populations can be determined based on the consistency of gametic phase (assessed based on linkage disequilibrium – LD between single nucleotide polymorphisms – SNPs and quantitative trait loci – QTL), as well as other genetic diversity metrics, including admixture and population structure . TherefoAll data used in this study were obtained from existing databases made available by the Norwegian Association of Sheep and Goat Breeders  and Animal Genomics . Therefore, no Animal Care Committee approval was necessary for the purposes of this study.® HD SNP Beadchip; A total of 792 NWS and 16,912 NZC animals were genotyped using a high-density (HD) SNP panel  were excluded. The number of genotyped animals in each population, based on birth year, and the descriptive statistics of the quality control are shown in The genotypic quality control was performed using the PLINK 1.9 software , separatThe extent of linkage disequilibrium (LD) was calculated for each breed group using the –r2 flag available in the PLINK 1.9 software . Therefowhere D = f(AB) − f(A)f(B), and f(AB), f(A), f(a), f(B) and f(b) are observed frequencies of AB, A, a, B, and b, respectively. Within-population quality control was used to calculate LD for each breed .Average LD values were obtained through a binning approach, in which SNP pairs were sorted into one of 20 bins, based on pair-wise marker distances. The 20 distance bins (described later) were defined to represent the LD decay, as suggested by D values were calculated using the –dprime-signed option available in the PLINK 1.9 software  metric, as used in the calculation of LD. The software . ThereafThe proportion of polymorphic SNPs  for each population was calculated based on SNPs with MAF greater than 0.01 (1%). The distribution of SNPs was calculated for 10 MAF range bins: from 0.01 until 0.50 defined every 0.05 points in MAF.O) per animal, within population, was calculated as the total number of heterozygotes divided by the total number of genotypes. The HO was compared to the expected heterozygosity (HE) under Hardy-Weinberg Equilibrium. These estimates were calculated after performing the genotypic quality control for each population . Thus, the DST was calculated using the –genomic option available in the PLINK 1.9 software . A genotypic quality control considering all sheep populations together was used to estimate the average pairwise genetic distance.where IBS1 and IBS2 are the number of loci that share 1 or 2 alleles identical-by-state (IBS), respectively, and m is the total number of loci. LD pruning was performed prior the calculation of the genetic distance, by using the –homozyg option available in the PLINK 1.9 software .Runs of homozygosity were identified using the software , consideG, calculated as in n = 27,114 animals).Three different measurements of genomic inbreeding were calculated for all sheep populations: (1) genomic inbreeding based on excess of homozygosity; (2) genomic inbreeding based on the variance of additive genotypes; and (3) ROH-based inbreeding. The genomic inbreeding based on excess of homozygosity was calculated as currently performed in PLINK 1.9 , using aG, calculated as in Principal component analysis was performed to investigate the genomic similarities between NWS and NZC sheep populations, using the –pca flag available in the PLINK 1.9 software . Principk value was defined through a 10-fold cross-validation procedure, with k ranging from 1 to 25. Thus, the k value with the lowest cross-validation error was assumed as the optimal k value to represent the optimal number of ancestral breeds. Standard errors were estimated using 100 bootstrapping replicates, and the convergence acceleration method used was the quasi-Newton method, with q = 3 secant conditions (The genomic make-up (population structure) of each animal was assessed using the ADMIXTURE software . In summnditions .The genomic dataset after performing quality control considering all populations together and linkage disequilibrium pruning was used. As sample size can affect the Admixture analysis, a randomly selected sample of 150 animals from each sheep population was used for the analyses.–matrix option in PLINK 1.9 (plot(hclust) function available in R  divided by the allele frequency variance. This was implemented using the –fst option available in PLINK 1.9 . Low inbreeding coefficients were obtained for ROH-based inbreeding. Finn had the highest, and “Other Terminal Sire” and NWS the lowest levels of genomic inbreeding.The HPurpose” . All popDue to the lack of reports on inbreeding levels in NWS, a detailed description will be provided here. The average (SD) pedigree-based inbreeding coefficients for the NWS were 0.009 (0.019) and 0.027 (0.025), considering all and only genotyped animals, respectively (up to 27 generations back). Pearson correlations between estimated inbreeding coefficients using different methods for the NWS are shown in As expected, inbreeding coefficients estimated based on the excess of homozygosity and ROH had the highest correlation 0.99; . On the The majority of NWS genotyped animals were born in 2016 (∼ 35%). In addition, a strong decrease in inbreeding estimated based on the variance of additive genotypes was observed after 1998. The average inbreeding coefficients estimated based on the ROH was almost constant over time (∼ 0.01). A slight increase in pedigree- and excess of homozygosity-based inbreeding was observed over time, but still with a low average of 0.0011 and 0.0008 over years, respectively.The LD decay pattern for all populations is shown in The consistency of gametic phase between NWS and the NZC sheep populations is shown in The descriptive analysis of the ROH is summarized in As expected, a higher number of ROH segments were observed when including all the available genotypes in the analysis. However, the average number and size of segments, the average number of SNPs in a ROH, proportion of sites homozygous and the proportion of ROH segments in each ROH length category were similar in both scenarios  than Primera (16.4) and Lamb Supreme (32.4). In general, there was a large variability in the average genome size covered by homozygous segments across populations . The maxThe majority of ROH segments observed in the composite breeds had short length , indicating ancient inbreeding. Primera, Lamb Supreme, and “Other Dual Purpose” had the highest proportion of short segments compared to the other sheep populations, which is likely associated with ancient inbreeding. In all populations, only a small proportion of ROH segments were longer than 10,000 kb. Primera and Lamb Supreme had the lowest proportion of long segments , k = 21 had the lowest cross-validation error .A summary of the Fl <0.10; , indicatThe average distance between SNPs was similar across populations as all individuals were genotyped using an HD SNP chip panel. Finn presented the greatest average distance between SNPs . Thus, even though the authors did not include the NWS and NZC populations (those studied here), HO estimates found in this study seems to corroborate with their report. On the other hand, higher heterozygosity estimates were reported by Heterozygosity measures the level of genetic variation within a population. Thus, usually populations developed based on a large number of ancestral populations or under intensive crossbreeding schemes have high HO and HE . In thish >0.32; . The HO Usually average pairwise genetic distances have been used to access the genetic distance among populations e.g., . HoweverInbreeding can be defined as the probability of an individual receiving, at a given locus, the same ancestral-allele from both parents . SeveralSimilar average inbreeding coefficients were estimated based on the excess of homozygosity and variance of additive genotype, which may be related to the fact that both approaches use the same SNP information . The inbDifferences in the pattern of inbreeding coefficients over the years  highlighThe accuracy of genomic predictions and the power of QTL detection in genome-wide association studies are partially determined by the levels of LD in a population . UsuallyThe performance of across-population genomic predictions are highly dependent not only on the levels of LD, but also on the consistency of gametic phase. The consistency of gametic phase measures the association between SNPs and QTLs alleles across breeds, as well as the QTL effects between breeds . Thus, iSimilar LD estimates, but higher consistency of gametic phase were found by The ROH pattern contributes to a better understanding of population history . ROH canThe similar ROH results observed when using all genotyped animals or a random sample  indicateA partial discrimination between NWS, Finn, and the other NZC populations was observed when analyzing the first and second, and second and third principal components . Howeverk = 21) is likely due to the fact that the populations studied here are, mainly composite breeds formed based on multiple (>20; The choice of the optimal number of ancestral populations is a notoriously difficult statistical problem, which also requires knowledge on the populations’ history . The larple >20;  founder ST values (ST statistics considering only the selected SNPs was low (The genomic population tree constructed based on the genomic distance estimated between NWS and the different NZC sheep populations showed that there is some differentiation between Finn and NWS and the other NZC sheep populations . In thisT values , and the was low . These fRelatively high genetic diversity was observed within each sheep population. The NWS breed seems to be moderately related to the NZC sheep populations, especially Primera, Lamb Supreme and “Other Dual Purpose”. The moderate genetic relationship between populations from both countries is likely due to the high number of ancestral breeds used in their development. The findings reported here indicate a promising opportunity for collaborative genomic analyses involving NWS and NZC sheep populations.The data supporting the results of this article are included within the article and in its HO, JM, JJ, TB, and LB conceived and designed this study. HO carried out the analyses. HO and LB wrote the manuscript. HO, JM, JJ, TB, TM, NP, SC, and LB interpreted and discussed the results. All the authors reviewed and approved the final manuscript.JM and SC were employed by the company AgResearch and NP was employed by the company Focus Genetics. The remaining 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.


Populus davidiana is among the most geographically widespread and ecologically important tree species in China. Whole‐genome resequencing data of 75 individual examples of P. davidiana throughout China were conducted, finding that all examples from different regions were clearly divided into either Northeast (N), Central (C), and South (S) populations. The ancestors of P. davidiana diverged into Northern group, comprising both N and C and Southern populations approximately 792,548 years ago. This time point of differentiation suggests that divergence of P. davidiana populations might have been triggered by the mid‐Pleistocene transition. The three populations experienced considerable periods of bottleneck following divergence, with population expansion beginning around 5,000 years ago after the end of the last glacial maximum. We found N to be the center of origin of P. davidiana in China. The migration route of P. davidiana in China was from N to S. Although the majority of the regions of genomic differentiation between N and S populations can be explained by neutral processes, a number of tested outlier regions were also found to have been significantly influenced by natural selection. Our results highlight that linked selection and rates of recombination were important factors in genomic differentiation between the N and S populations. Finally, we identified a substantial number of functional genes related to climate change during population differentiation and adaptive evolution.Forest trees are an excellent resource from which to understand population differentiation and heterogeneous genome variation patterns due to the majority of forest trees being distributed widely and able to adapt to different climates and environments.  Finally, multiple genes related to adaptation to local environments were identified in P. davidiana. This study provides insights into the evolutionary history and genetic diversity of the species, in addition to describing examples of the mechanisms by which a species can adapt to regions with variations in climate. At the same time, the study also provides the important reference value for understanding the mechanism of the formation of the geographical distribution patterns of other plant populations in China.22.1P. davidiana . After investigating the empirical distribution, we removed sites showing extremely low  or high  read coverage. In addition, reads or sites that included >20 mapping quality scores of zero within the whole sample were discarded. Finally, only top‐quality reads were retained for downstream analyses.A paired‐end sequencing library was prepared for every  Tuskan, . SAMtool Tuskan,  was used Tuskan,  used to 2.3We employed two complementary approaches for SNP and genotype calling. ANGSD v0.928  ranging from 1 to 6. Principal component analysis (PCA) was performed using PCAngsd software (http://www.popgen.dk/software/index.php/PCAngsd). TreeBest software was employed (http://treesoft.sourceforge.net/treebest.shtml) to construct neighbor‐joining (NJ) phylogenetic trees, with Populus tremula used as an out‐group. We downloaded the data of P. tremula from the Short Read Archive (SRA) at NCBI, and the accession numbers are SRR2744682, SRR2745906, SRR2745908, and SRR2745909. In addition, all sample covariance matrix was estimated using PCAngsd software and a relevance clustering thermal map was plotted using Omicshare Tools software (http://www.omicshare.com/tools/Home/Soft/getsoft/type/index).NGSadmix software  AsymmetricMigration without population expansion; (b) NoMigration without population expansion; (c) AsymmetricMigration with population expansion; (d) complex model, including a bottleneck in N; and (e) complex model, including a bottleneck in S  was then evaluated over a historical time frame through utilization of multiple sequential Markovian coalescent (MSMC) analyses  of observing more extreme interspecific FST values among simulated data sets than among the observed data. The determination of significance was based on running 500,000 coalescent simulations of the most acceptable demographic null model. We then corrected for multiple testing by using the false discovery rate (FDR) adjustment, and only windows with FDR lower than 1% were considered as candidate regions affected by selection.VCFtools software  between SNPs with pairwise distances larger than 1 kbp  interspecific shared polymorphisms were calculated among all segregating sites; (c) the difference between two nucleotides (dxy) was calculated based on the posterior probability of the sample allele frequency at each locus over a window size of 10,000 bp; (d) the relative node depth (RND) was calculated by dividing the dxy of the Northeast and South populations by the dxy between the Northeast population and P. tremula. For all population genetic parameters, Wilcoxon ranked‐sum tests were used to examine the significance of differences between outlier regions and the remainder of the genome.To assess selection signals in both highly and poorly differentiated regions, multiple population genetic parameters of the two unions of outer regions were compared with the rest of the genome in the Northeast and South populations. Firstly, ANGSD was employed to estimate sample allele frequency probabilities between populations of the Northeast and South with a nonoverlapping window size of 10 kbp for calculating θπ, Fay and Wu  and Fu a2.8FST > 0.95) between this population and both of the other two populations (B) (such as N and S populations). We then performed the HKA test by comparing the ratio of A/B to the genome‐wide average, computed as the sum of A and B values across all genes analyzed and testing the null hypothesis A(gene)/B(gene) = A(genome‐wide)/B(genome‐wide) using a Pearson's chi‐square test  for the HKA test and a ranked PBS above the 95th percentile were considered as positively selected genes.We applied the Hudson–Kreitman–Aguadé (HKA) test  to perform a Fisher's exact test  Thees Table  was used3.1P. davidiana. For K = 2, all 75 P. davidiana individuals were clearly divided into two groups: Northern and Southern populations. Further population sub‐structuring was observed in the Northern population when K = 3, where individuals from populations of the Northeast and Central regions of China clustered into two subgroups. When K = 4, no additional genetic structuring was observed , Central (C), and South (S). We found that the first three components explained 46.84%, 41.06%, and 12.10% of total genetic variance according to a Tracy–Widom test, respectively . The ancestry of the Northern population differentiated into N and C populations approximately 78,933 ago . The current effective population sizes (Ne) of N (Ne‐N), C (Ne‐C), and S (Ne‐S) are 45,278 , 63,365 , and 12,666 , respectively. The effective population sizes of the three populations are all significantly lower than their common ancestor . The migration rate (m) is also clear among the three populations, the lowest generation migration rate (m) between N and S populations (1.74 × 10–7 and 2.46 × 10–7), which is not unexpected given the large geographical distance and disjunct distributions between the two populations.A coalescent simulation‐based method was employed to infer demographic histories of s Figure . A detaiNe) over historical time was also evaluated using MSMC software  high and low interspecific 3.4W), Tajima's D, Fay & Wu's H, nucleotide diversity (π), recombination rate (ρ), gene diversity/heterozygosity (HE), and linkage disequilibrium (LD) (r2) to infer whether the N population possessed the greatest genetic diversity and represented the origin of P. davidiana in China as previously suggested . We also compared alleles fixed in the N or S populations and interspecific shared polymorphisms between the two populations. The results indicated that the proportion of interspecific shared polymorphisms in the highly differentiated regions was extremely low  over a window size of 10,000 bp. Because ρ = 4Nec, a decrease in Ne in a region associated with selection reduces the local estimate of ρ. To eliminate this effect, we evaluated the effect of recombination rate on genomic differentiation by calculating ρ/θπ in poorly and highly differentiated regions. In particular, we found a significant negative correlation between FST and the rate of recombination. The rate of recombination of the highly differentiated regions was extremely low, with a poorly differentiated region with a higher recombination rate  was used to analyze the differential enrichment of candidate genes, and we found that GO terms related to electron transport, apoptotic process, programmed cell death, and cell death were significantly overrepresented  group, a Northeast (N) group, and a Central (C) group. We calculated FST and dxy values across the genome and found that there was clear genetic differentiation among the three populations. To comprehend how diverse evolutionary forces drove the differentiation and demographic history of the three different populations, we applied a whole‐genome resequencing approach based on a large number of unbiased SNPs distributed across the genome.High‐throughput sequencing technology is a valuable tool for deciphering population structure and demographic history, enabling discovery of fine‐scale genetic variation and population divergence across the genome. We used whole‐genome resequencing to explore genetic architecture, demographic history, and genomic differentiation of 4.1P. davidiana in Fastsimcoal 2.6.1 software  and ecologically important tree species in China and has been shown to be clearly divided into N, C, and S populations. Therefore, it is of interest to study the origin and migration route of P. davidiana, because of the important reference value for understanding the mechanism of the formation of the geographical distribution patterns of other plant populations in China.P. davidiana in China. Throughout the genome of the three populations, we found that the values of π  and absolute divergence (dxy) will be higher, depending on intraspecific genetic diversity in areas with lower rates of recombination  in both N , and purifying selection (deleterious mutations). These findings have implications for rethinking the evolutionary history of other temperate forest species in East Asia as well as the role of these evolutionary factors in maintaining high species diversity in this region.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.Z.H. performed the experiments and wrote the study. J.G.Z. designed the research.Figure S1Click here for additional data file.Figure S2Click here for additional data file.Figure S3Click here for additional data file.Figure S4Click here for additional data file.Figure S5Click here for additional data file.Figure S6Click here for additional data file.Figure S7Click here for additional data file.Figure S8Click here for additional data file.Tables S1‐S4Click here for additional data file.


P = 2.4×10−7) are variants in a region on chromosome 12 comprising multiple regulatory elements. This region includes rs12821256, which regulates the expression of KITLG, a well-known gene involved in lighter hair and skin pigmentation in Europeans as well as in thermogenesis. Another variant from that region is associated with the long noncoding RNA RP11-13A1.1, which has been specifically involved in the innate immune response against infectious pathogens. Our results suggest that these genes were relevant for adaptation in Chileans following the Columbian exchange.Detection of positive selection signatures in populations around the world is helping to uncover recent human evolutionary history as well as the genetic basis of diseases. Most human evolutionary genomic studies have been performed in European, African, and Asian populations. However, populations with Native American ancestry have been largely underrepresented. Here, we used a genome-wide local ancestry enrichment approach complemented with neutral simulations to identify postadmixture adaptations underwent by admixed Chileans through gene flow from Europeans into local Native Americans. The top significant hits ( The admixture process continued with several minor migratory events at later times (mainly from Europe), including a small African component brought in during the 17th century  and a small African component (0.03) . These p century .rs12821256, an enhancer variant that regulates the expression on KITLG. Although rs12821256 is a causative variant of lighter hair color in northern Europeans  and 772,277 SNPs.We used whole-genomes from 11 Huilliche/Pehuenche individuals from n = 88), European (n = 911), and African (n = 229) populations, as described in L = 10 and an HMM with number of hidden states S = 15. We used phased samples from the parental populations and unphased data from the Chilean population. Phasing was performed using Beagle  using the sample-sites.py script provided in the Ohana GitHub repository https://github.com/jade-cheng/ohana  analysis on the clean data set of 420,688 SNPs and 796 individuals  was carried out with Plink, using the following filters to obtain an LD-pruned subset of variants. We only included variants with minor allele frequency >0.01, SNP calling rate >99%, and we pruned SNPs using an independent pairwise approach with window size of 50 kb, a step size of five SNPs, and a t-tests to identify SNPs with deviations in the mean European ancestry. At each SNP, we compared its European ancestry proportion with the genome-wide ancestry mean p0=0.52. We performed a statistical hypothesis test H0: p1=p0 versus H1: p1 > p0 at each SNP, where ip represents the European ancestry at the ith SNP. We assumed that Xij is a random variable following a Binomial distribution with parameters n = 2 and ip, where Xij takes values in {0,1,2} representing the number of European alleles at SNP i and individual j. ip is the proportion of European ancestry at SNP i. The asymptotic results of the maximum likelihood estimator for ip were used to assume a normal distribution on this estimator and to design our hypothesis test. Variants reaching a significance threshold of P < 10−5 were considered to be under PAS , we calculated the mean local ancestry for every SNP using an in-house R script  online. nder PAS .Ne) of our Chilean sample as follows. The initial ancestry proportion p0 of European component changes over time due to genetic drift. At the sampling time, the European proportion p1 is a random variable with the variance approximately equal to  and probability p1. So, the observed European ancestry is distributed as follows:The expectation of Ne so that the variance of k matches the empirical variance of European ancestry across all the SNPs in our Chilean sample of size N = 370 haplotypes. For different times since admixture, we got the following values for the effective population sizes: Ne = 4,500 at T = 10, Ne = 6,000 at T = 12, and Ne = 7,000 at T = 15.We adjusted T generations ago . The admixture proportions were set to 0.521 (European), 0.442 (Native American), and 0.037 (African), as those estimated by LAMP-LD in our Chilean sample. We assumed the Ne of the admixed population to be constant. We explored three different diploid (haployd) sample sizes: N = 93 (186), 185 (370), and 370 (740). For the case of 185 individuals, we simulated sexual populations , whereas for the other sample sizes, we simulated hermaphroditic populations. We simulated 189 chromosomes each of length = 20 Morgans, so that the total length of the simulated genomes is equal to the length of our data. Then we mapped the physical positions of the SNPs in our data on the simulated chromosomes, which gives the same correlation structure in the SNP ancestry as in the real data. We calculated t-test P values for the SNP with maximal European ancestry deviations for different combinations of N, Ne, and T (Table 1), similarly as indicated in We performed forward-time local ancestry simulations using SELAM . We assuAll variant annotations used in this study corresponded to the GRCh37 (hg19) assembly. Variant annotations including the Sequence Ontology (SO) consequence type, associated alleles, Gencode biotypes, and combined annotation dependent depletion (CADD) scores were retrieved using the web tool Variant Effect Predictor (VEP) from Ensembl . Downstrn = 99×4 = 396; We first estimated genetic affinities between the admixed Chilean population from the Atacama Desert and their proxy parental populations from Europe, the United States, and Africa by PCs clustering A. From ETo test for affinities between Chileans and Europeans, we performed a second PCA considering only Chileans and the aforementioned European populations . We find that IBS is the most closely related European populations to Chileans, as it can be seen along the PC2 axis in To evaluate genetic relationships between Chileans and Native Americans, we performed a third PCA comparing Chileans with the aforementioned Native American populations . We find that the Aymara and Quechua cluster closest to Chileans, as revealed by the PC2 axis of qpas from the Ohana suite software  inferred with ADMIXTURE (qpas (K = 5) estimates mean ancestry proportions of 20.7% Mapuche and 25.9% Aymara, 43.3% Southern European, 6.5% Northern European, and 3.5% African among Chileans. When summing up the contribution of each of these subancestry components, we obtain global European, Native American, and African proportions of 49.8%, 46.6%, and 3.5%, respectively. These estimates are slightly different to the genome-wide local ancestry means estimated by LAMP-LD , probably reflecting differences between the underlying algorithms. Of note, most populations included in this study have slight traces of other ancestry types. For instance, IBS and TSI have 1.5–1.7% African proportions, whereas FIN shows 6.3% of an ancestral component related with Native American populations (K = 6–7 identifies separate clusters within the Southern and Northern European components (K = 8 differentiates between Mesoamerican (Maya-Nahua) and Andean (Aymara-Quechua) subancestry components  and one processed pseudogene  , we simulated sexual populations , whereas for the other sample sizes, we simulated only hermaphroditic populations. We numerically estimated three effective population sizes (Ne) based on the values of T, obtaining Ne = 4,500 at T = 10, Ne = 6,000 at T = 12, and Ne = 7,000 at T = 15 . We simulated scenarios of neutral evolution under different combinations of N, T, and Ne. For each scenario, we calculated t-test P values for the SNP with maximal European ancestry deviations (Table 1). No P value achieved the significant threshold of P < 10−5  , but theons ago) . We explP < 10−5 . Hence, P = 2.4×10−7) were 65 variants associated with the lncRNA RP11-13A1.1, the processed pseudogene RP11-13A1.2, the lncRNA RP11-13A1.3 as well as several regulatory regions, including enhancers, promoter-flanking regions (PFR), CTCF-binding sites (CTCF-BS)—regions that binds CTCF, the insulator protein that demarcates open and closed chromatin  that overlaps a PFR as well as a CTCF-BS  and SLC38A2 (P = 2×10−6) in blood. In addition, rs12821256 has promoter histone marks in skin cells  and enhancer histone marks in 14 diverse tissues. We next queried the GWAS Catalog    and respiratory system disease (P = 2×10−14). Further, we found associations between SLC38A2 variants and hair color (P = 2×10−11) as well as lean body mass (P = 1×10−15). Remarkably, rs12821256 has been reported under strong selection in Europeans and East Asians (see Discussion).We queried the HaploReg tool  for eQTL Catalog  to ident×10−100)  as well ×10−308) . We did The emergence of vast human population genetic data and the development of advanced analytical methods have enabled the detection of genetic variants involved in adaptation and complex diseases across diverse worldwide populations . Most of(K = 5) than other more geographically heterogeneous Chilean samples with predominant Mapuche ancestry (K = 5–8). This is probably due to at least two effects resulting in a very low genetic diversity: 1) The Mapuche suffered a ∼95% decrease in their effective population size following the European colonization, based on an analysis that used imputed SNP array data from Mapuche genomes  The  genomes ; UnfortuIn the present study, we analyzed how gene flow from Europeans into Native Americans contributed adaptive genetic variation through PAS to admixed Chileans. We found that the strongest signals are associated with genes previously involved in pigmentation, thermogenesis, and immune defense against pathogens , all of After the Columbian exchange, European colonization of the Americas resulted in the exposure of immigrants and natives to novel selective pressures. For example, many infectious diseases brought in by Europeans decimated native populations. These include smallpox, measles, influenza, and several others . In contRP11-13A1.1 lncRNA. lncRNAs are an important class of noncoding RNAs that regulate gene expression through diverse mechanisms  UV radiation ranges from extremely high in Northern Chile to extremely low in Southern Chile, and the highest levels of surface UV irradiance in the world have been measured in the Atacama Desert  Thera Desert . It seemadiation .RP11-13A1.1, we speculate that it may have contributed to immune defense against European infectious pathogens during the Columbian Exchange. This is because of two reasons. 1) The regulation of RP11-13A1.1 has been well characterized in the context of immune defense against pathogens. 2) European infectious pathogens were an extreme selective pressure for Native American populations. However, it is also possible that RP11-13A1.1 variants were hitchhiked into high frequency by KITLG-associated variants affected by PAS.Regarding In conclusion, European variants of the haplotype in chromosome 12 underwent PAS most likely due to pleiotropic advantages that were relevant for Chileans following the Columbian Exchange. evaa136_Supplementary_DataClick here for additional data file.


Melayu Kedah and Melayu Kelantan), western (Melayu Minang) and southern parts (Melayu Bugis and Melayu Jawa) of Peninsular Malaysia. We analyzed 54,794 autosomal single nucleotide polymorphisms (SNPs) which were shared by 472 unrelated individuals from 17 populations to determine the genetic structure and distributions of the ancestral genetic components in five Malay sub-ethnic groups namely Melayu Bugis, Melayu Jawa, Melayu Minang, Melayu Kedah, and Melayu Kelantan. We also have included in the analysis 12 other study populations from Thailand, Indonesia, China, India, Africa and Orang Asli sub-groups in Malay Peninsula, obtained from the Pan Asian SNP Initiative (PASNPI) Consortium and International HapMap project database.The Malays consist of various sub-ethnic groups which are believed to have different ancestral origins based on their migrations centuries ago. The sub-ethnic groups can be divided based on the region they inhabit; the northern (Melayu Kedah and Melayu Kelantan which are genetically different from the other Malay sub-ethnic groups, but similar to Thai Pattani. More than 98% of these northern Malays haplotypes could be found in either Indians or Chinese populations, indicating a highly admixture pattern among populations. Nevertheless, the ancestry lines of Malays, Indonesians and Thais were traced back to have shared a common ancestor with the Proto-Malays and Chinese.We found evidence of genetic influx from Indians to Malays, more in These results support genetic admixtures in the Peninsular Malaysia Malay populations and provided valuable information on the enigmatic demographical history as well as shed some insights into the origins of the Malays in the Malay Peninsula. The knowledge of population genetic structure and genetic ancestry hold great potential towards better understanding of the differential susceptibility to disease, response to drugs and complex interaction of genetic and environment factors , Indonesia, Singapore, Brunei, Southern Myanmar and Southern Thailand  populations in the Peninsular Malaysia such as the Semang and Proto-Malays have also raised questions as to what extent they have contributed to the uniquely admixed gene pool of Malays  followed by Chinese (24.6%) and Indians (7.3%)  and southern parts (Melayu Jawa and Melayu Bugis) of the Peninsular Malaysia  was also established .The genetic variations within and between five Peninsular Malaysia Malay sub-ethnic groups and other studied populations were characterized by the pair-wise Fst between populations, followed by the non-parametric Multi-Dimensional Scale (MDS) analysis. The table of pair-wise Fst value that has been multiplied with 1000 is shown in Table Melayu Kelantan and Melayu Kedah were genetically close to each other (Fst = 0.015). Meanwhile, the genetic divergence between Melayu Jawa and Melayu Minang (Fst = 0.021) showed that they were closer to Melayu Kedah and Melayu Kelantan than to each other. Interestingly, these four Malay sub-ethnic groups were significantly closer to Proto-Malays Temuan, Indonesian Jawa and Chinese Wa from Yunnan, China. The genetic distances between the Proto-Malays Temuan, Indonesian Jawa, and Chinese Wa to each other were also substantially lower than to any other populations, even between population within their groups. Although the Thai Pattani samples were also close to these group, but they were much closer to Melayu Kedah relative to the other Peninsular Malaysia Malays (Fst = 0.018).Semang group, both the Melayu Kedah and Melayu Kelantan samples were relatively closer to the Jahai and Kensiu than any other Malays. Similarly, both the Semang samples were closer to the samples of Indonesian Jawa and Chinese Wa than any other populations. It is also noted that the genetic distance between Melayu Kedah and Melayu Kelantan with the Indians, especially with Telugu were considerable smaller than to any other populations.In relation with the Yoruba, Indians and Semang that are far more diversified than the modern Malays. The same pattern also can be seen in the 3D MDS plot  and three dimensions (3D) based on Fst genetic distance method as shown in Figure K = 2 to K = 10 is shown in Figure K color segment that represent the individual’s estimated Q fractions in K clusters. Each population was labeled below the figure and separated by the solid line. The results showed that individuals from the same pre-defined population shared almost similar Q values. Any pre-defined populations which shared similar distinctive Q values were merged into inferred cluster, as shown in Table The assignment of each of the 472 individuals sampled from 17 pre-defined populations into genetically inferred clusters of Ln(Pr) of K and by careful observation and comparison of each of the Qs of Ks from multiple runs between the same and different sampling datasets using the SSC. The value of Ln(Pr) was observed to increase until K = 6 and started to become relatively inconsistent at K = 7 onwards. The SSC scores were greater than 0.95 in all cases of K < 6 while for larger Ks (K > 5) the SSC were slightly lower with a minimal value of 0.90. In analysis of K > 6, the splitting orders of clusters varied across different runs and different datasets. However, for the same cluster mode, the SSC of membership coefficient estimates were still high (>0.90). Therefore, the K = 6 was considered as the most statistically supported by the data in all of the sampling datasets, depicted by six different colors in such a way as that given by the Q value  in black color, and non-African populations that were grouped into a yellow colored cluster. At K = 3, a newly cluster in green color represent Semang samples from Peninsular Malaysia (Jahai and Kensiu). The green component of the Semang also could be seen slightly in the Proto-Malay Temuan, Thai Pattani and two northern Malay sub-ethnic groups; Melayu Kedah and Melayu Kelantan. The Indian populations remained in the yellow cluster, although parts of their genome were also partitioned into the green component. At K = 4, all the Indian samples were assigned into a red colored cluster, which is exclusively separated from the yellow colored cluster of the non-African populations. It should be noted here that Indian proportions in the red colored fractions also appeared in three Peninsular Malaysia Malay sub-ethnic groups; Melayu Kedah, Melayu Kelantan and Melayu Minang, as well as in Thai Pattani. At K = 5, another cluster in pink color was apparent. This cluster mainly existed in the Proto-Malay Temuan and small proportions could be seen in both of the Chinese samples (Jinuo and Wa).At K = 6, where a new cluster, denoted by the light blue color was confined mainly in the Peninsular Malaysia Malays, Thais and Indonesians samples. The light blue fractions are prominent in the Proto-Malay Temuan but rather slightly in the Chinese samples. Interestingly, the Indian components could be seen clearly in the samples of Melayu Kedah, Melayu Kelantan, Melayu Minang, and Thai Pattani. The yellow colored cluster mainly belonged to the Chinese samples although the proportions were also associated with the Malays, Proto-Malays, Thais and Indonesians indicating a common ancestor origin for these populations. The proportions of Q for each population in each of the six inferred clusters are shown in Table The structure of the Malays became apparent at Q plot at Figure Semang Jahai for K = 7, while purple proportions mainly in Chinese Jinuo at K = 8. The splitting order of clusters varied greatly across different runs and different data sets. The newly derived clusters of K = 9 and K = 10 started to lose biological meanings as the real clusters and produced relatively lower Q proportions with unstable patterns in the graph of Ln(Pr).Higher K values revealed other clusters as shown in the Ks, the more it resembles or represent the modern genome of studied populations. As shown at K = 10  the Melayu Jawa were similar to the Indonesian Jawa, with significant components of Chinese in their genomes, 3) the Melayu Kelantan and Melayu Kedah were more resemble to Thai Pattani, with significant admixture from Indian components.Nevertheless, the higher the number of 0 Figure , the genMelayu Kedah, Melayu Kelantan and Thai Pattani. We further confirmed the HS pattern of peninsula Malays (PMY) data which comprised of all five Peninsular Malaysia Malay sub-ethnic groups including Thai Pattani compared with the Orang Asli Proto-Malays (PM) and Semang (NG). The PMY  from STRUCTURE analyses consistently showed that the last split of branches was the clade of Malays, Indonesians and Thais (MIT), clustered together with PM. However, the trees revealed two slightly different topologies. The Cavalli DC showed simultaneously evolutionary divergence between the group NG and CN populations and the group of MIT and PM. Whilst, the topology of Nei’s DA support the subsequent divergence of evolutionary processes of the group of populations, which is more similar to the pattern of splitting orders in STRUCTURE analysis. In the phylogeny analysis of HSAs, Figure The phylogenetic trees based on Cavalli DC and Nei’s DA genetic distances that were reconstructed using allele frequencies in each ancestral component inferred by Bayesian algorithm from the STRUCTURE analyses as well as the HSAs phylogeny are shown in Figure Quantifying genetic distance is the main aspect in population genetic study, especially to characterize population structure and identify substructures   with the Proto-Malay Temuan, Indonesian Jawa and Chinese Wa from Yunnan, China. This may imply a common origin for all of those populations regardless of their historical migration patterns as been reported  to 7hit Paul . These eMelayu Kelantan, the total population is about 1.67 million and the percentage of the Indian community is only 0.2% of the population. In the Kedah state which is the origin of the Melayu Kedah, the total population is about 2.04 million and the percentage of the Indian community is also slightly higher than in Kelantan with 6.6% of the population  was predominant in all the Malays, Thais, and Indonesians as well as in Proto-Malays itself. This is in accordance to the historical and anthropological evidences of the migration pattern of Proto-Malays from Yunnan, southern mainland China  polymorphism and population structure analysis of Malays  Committee, School of Medical Sciences, Universiti Sains Malaysia (USM) and Medical Ethics Committee, Pusat Perubatan Universiti Malaya (PPUM)), China  at Shanghai, PR China), Indonesia , India ) and Thailand ; three Orang Asli sub-groups ; one Thai population (Pattani), three Indonesian populations ; two from Yunnan, China (Jinuo and Wa); two from India that were assigned based on their language (Telugu and Marathi); and one from Nigeria, Africa (Yoruba). The map of the Asian continent depicting geographic locations of the sampling populations is shown in Figure In this study, samples were carefully selected by the inclusion and exclusion criteria that emphasized the three generations without any different ethnic admixture. All datasets used in the study were derived from 17 populations representing six linguistic families that consisted of 472 unrelated individuals from five Malay sub-ethnic groups  Consortium, except for Yoruba that were obtained from the International HapMap Consortium.The Affymetrix GeneChip Mapping Xba 50 K Arrays were used to genotype single nucleotide polymorphisms (SNPs) on a genome-wide scale for 109 unrelated individuals of the five Peninsular Malaysia Malay sub-ethnic groups and one Thai A total of 58,960 SNPs that have been genotyped for all the sampled individuals were screened under the strict criteria of data quality control. Samples with a call rate below than 90% were excluded from further analysis. A total of 4,166 SNPs (7%) were filtered out , leaving a total of 54,794 autosomal SNPs as the final genotype data for each individual to be used in further analyses.The genetic divergence between studied populations were determined by Fixation Index Statistic (Fst) as described by Weir and Hill (Weir and Hill K) with estimated membership coefficient (Q) for each cluster which were fitted with posterior probabilities of Pr (X|K) solely based on the given genotyped data (X) without incorporating any other population information  which showed by the graph of Ln(Pr) over the run of analysis is the most probable number of ancestral clusters  as the threshold value in the re-sampling procedures and a total of five sampling datasets (S1-S5) have been produced. The average of the BMD is 550 kb and each dataset contain approximately 3700 number of SNPs that were evenly distributed across 22 autosomal chromosomes. The sub-datasets were created because of STRUCTURE’s limitation which does not deal with strong background linkage disequilibrium in the data  with 30,000 burn-in length and 20,000 MCMC iterations in each analysis to STRUCTURE software. The distribution of the alpha parameter showed a relatively constant distribution indicating convergence after 20,000 iterations. The estimated Q matrices from the STRUCTURE outputs were carefully observed and compared using the symmetric similarity coefficients (SSC) and there were no big differences in the estimation of Qs for all runs. The SSC was computed via permutation analyses of Q matrices for any number of clusters from multiple runs or multiple datasets generated by STRUCTURE software. The analyses were implemented by Cluster Matching and Permutation Program (CLUMPP) , 2) haplotypes are common in populations A and B but not in population C (HAB), 3) haplotypes are in common in populations A and C but not in population B (HAC), and 4) haplotypes are common in all populations (HABC). The haplotypes for populations B and C can be similarly defined.In this study, we adopted three sizes of sliding window  to estimate the HS in three-population framework. According to , the Chinese (CN) group consisted of Jinuo and Wa, while the group of Indians (IN) comprised of Marathi and Telugu. This analysis was done to indicate the proportion of HS among the three groups and to identify which group was a bigger genetic contributor to Malays group. To represent the divergence pattern of the northern Peninsular Malaysia Malays, we combined the Melayu Kedah, Melayu Kelantan and Thai Pattani into a group named northern Malays (NMY) and implemented the second HSA with the Chinese and Indians groups. The third HSA was done to examine the relationship of the peninsula Malays (PMY) group consisted of Peninsular Malaysia Malays and Thai Pattani with the indigenous populations, which are the Orang Asli Semang (Jahai and Kensiu) grouped as Negrito (NG) and the Proto-Malays Temuan labeled as Proto-Malays (PM).In the HSAs of this study, the particular populations were merged into a group pursuant to the result of STRUCTURE analysis. For the first HSA, all Peninsular Malaysia Malays, Thai However, the varying sample size among populations could affect the HS results. Taking this point into consideration, we performed a procedure called Non-Replace Sampling (NRS) in the HaploSharing program. The number of haplotypes in each genomic window on 76 chromosomes  were counted for each population. The bootstrap replicate for the sampling procedure was 100 times and the results were averaged for each window. Any haplotype that observed less than twice in this analysis was excluded.Yoruba (YRI) samples were added to serve as an outgroup for the rooted trees. The haplotype sharing distances among the group of populations from 100 kb bins were calculated based on Fst  (Cavalli-Sforza and Edwards The phylogenetic trees of HSAs were based on four-population framework of HS estimation as African


Leishmania major Yakimoff & Shokhor and transmitted by Phlebotomus papatasi (Scopoli) is a public health concern in Morocco. The disease is endemic mainly in pre-Saharan regions on the southern slope of the High Atlas Mountains. The northern slope of the High Atlas Mountains and the arid plains of central Morocco remain non-endemic and are currently considered high risk for ZCL. Here we investigate and compare the population genetic structure of P. papatasi populations sampled in various habitats in historical foci and non-endemic ZCL areas. A fragment of the mtDNA cytochrome oxidase I (COI) gene was amplified and sequenced in 59 individuals from 10 P. papatasi populations. Haplotype diversity was probed, a median-joining network was generated (FST) and molecular variance (AMOVA) were analyzed. Overall, we identified 28 haplotypes with 32 distinct segregating sites, of which seven are parsimony informative. The rate of private haplotypes was high; 20 haplotypes (71.4%) are private ones and exclusive to a single population. The phylogenetic tree and the network reconstructed highlight a genetic structuration of these populations in two well defined groups: Ouarzazate (or endemic areas) and Non-Ouarzazate (or nonendemic areas). These groups are separated by the High Atlas Mountains. Overall, our study highlights differences in terms of population genetics between ZCL endemic and non-endemic areas. To what extent such differences would impact the transmission of L. major by natural P. papatasi population remains to be investigated.Zoonotic cutaneous leishmaniasis (ZCL) caused by  Phlebotomus papatasi  is the proven vector of L. major Yakimoff & Shokhor 1914, which causes zoonotic cutaneous leishmaniasis (ZCL) in North Africa and other geographic areas, with rodents acting as reservoir hosts [L. major zymodeme MON 25 is transmitted by P. papatasi [Meriones shawi grandis is the reservoir host [L. major as well as favorable eco-climatic conditions. These areas should therefore be classified as at high risk for ZCL [The Leishmaniases are a group of parasitic diseases caused by various species of protozoa belonging to the genus Leishmania  and transmitted by several species of phlebotomine sand fly vectors  ,2,3. Phlir hosts . In Moro for ZCL ,11.Phlebotomus papatasi present a wide geographical distribution [P. papatasi exceeds that of L. major and its reservoir hosts, extending to southern Europe and the eastern regions of the Indian subcontinent, where it is often abundant in domestic and peri-domestic biotopes far from rodent colonies [L. major between rodents and from rodents to humans [cytochrome b mtDNA sequences showed that Phlebotomus papatasi populations are relatively homogeneous [P. papatasi is also demonstrated with another molecular marker, the second internal transcribed spacer ribosomal DNA (rDNA ITS2) [P. papatasi populations from the North African and the Mediterranean subregions have provided evidence for genetic differentiation [P. papatasi populations at a microgeographic scale [ribution  and adapribution ,14,15,16colonies . It is ho humans . The undogeneous  despite ogeneous ,21,22,23NA ITS2) . Microsantiation ,26 and hic scale .P. papatasi are one of the most frequently trapped sandfly species [P. papatasi are recorded in urban areas and in unhealthy suburbs of large cities such as Marrakech and Agadir, particularly in wild dumps and areas of sewage application, far away from human dwellings [P. papatasi are frequently recorded, with symmetrical anomalies being observed in the lateral lobes, along with abnormal styles, which correlate with environmental human-caused disturbances [cytochrome c oxidase I gene as a molecular marker, we investigated the genetic structure of wild P. papatasi populations sampled in endemic and non-endemic areas for ZCL in Morocco.In Morocco,  species ,28,29,30 species . High dewellings . Phenotyurbances . Here, uL. major in Ouarzazate, southern Morocco. The second one, in central and southwestern Morocco, is a non-endemic area for L. major. The two areas are separated by the High Atlas Mountains (Sand fly specimens were collected between April and November from 2006 to 2008. They were captured using CDC-miniature light traps placed in houses and sticky traps (castor oil papers) placed in intra-domestic, peri-domestic, and sylvatic habitats . Collectountains .The head and the abdominal Terminalia of each specimen were dissected with sterilized microneedles and mounted on a slide in Canada balsam . MorpholTotal DNA was extracted using the QIAmp kit , according to the manufacturer’s protocol.COI gene [Polymerase chain reaction (PCR) was performed in a total volume of 50 µL. The primers LepF (5′-ATTCAACCAATCATAAAGATATTGG-3′) and LepR (5′-TAAACTTCTGGATGTCCAAAAAATCA-3′) were used to amplify a 681 bp fragment of the COI gene . Each amhttp://ribosome.toulouse.inra.fr/multalin/multalin) using the cytochrome c oxidase I gene as a molecular marker. Gaps were treated as missing data. Sequence analysis and Kimura’s two-parameter genetic distance calculation were performed using MEGA v. 4.0 [FST) [Fst values > 0.25 indicate strong genetic differentiation. The median-joining algorithm, implemented in the software package NETWORK v. 3.1 [https://www.phylogeny.fr/) [P. papatasi COI gene sequences from Morocco, or from Spain (LC090048.1), Serbia (KY848828.1), Turkey (MH780862.1), Cyprus (MF968974.1), and of P. ariasi (KJ1481169.1), P. sergenti (KC755398.1), or P. perniciosus (KJ1481136.1).Sequences (681 bp) were manually edited and aligned with the online service Multalin   . SignifiK v. 3.1 ,42, was eny.fr/) . AlignmeCOI gene produced a band of approximately 700 bp in a 1% agarose gel. The 681 bp sequences were manually trimmed, resulting in good quality sequences with a length of 654 bp. Sequence alignment of the 654 bp COI sequences did not identify any nucleotide insertion/deletions (indels), and sequences can be fully translated with the Drosophila mitochondrial code. Seven parsimony-informative sites were recorded in more than one haplotype. All sequences are available in GenBank under accession numbers MT074053-054, MT075056-57, MT075059-61, MT075064-75, MT075057, and MT075078-85. Sequence analysis delineated the presence of 28 haplotypes with 32 distinct segregating sites  are high. Finally, Tajima’s D and Fu’s Fs neutrality values are negative and significant in all sampled populations. These pinpoint probable episodes of population expansion . See The PCR amplification of the 3′ end of the mitochondrial ng sites . A relatP. papatasi samples are presented in P. papatasi sampled in endemic and non-endemic areas. The other 24 of the 28 identified haplotypes (85.7%) are private ones. Thirty private haplotypes  are associated with Ouarzazate, five  with Nfifa, one (H9) with Labrouj, and one with Taferiat (H5). The rate of private haplotypes is high in populations from the ZCL-endemic areas of Ouarzazate. Here, 13 of the 15 identified haplotypes (86.6%) are unique. In non-endemic areas, the corresponding rate is only of 42.8% (6 out of 14 haplotypes).The 28 haplotypes recorded within the 59 Phlebotomus papatasi belongs to the Phlebotomus subgenus. The consensus tree  ariasi, P. (Laroussius) perniciosus or P. (Transphlebotomus) sergenti which belong to the Laroussius and Transphebotomus subgenus. Overall, within P. papatasi samples collected in Morocco, the presence of at least two P. papatasi populations that fall into two groups which show some genetic relatedness is depicted . Most specimens sampled in the endemic area of Ouarzazate are grouped together and constitute the “Ouarzazate group”, with a bootstrap value of 56%. This group is interrupted by two individuals from Labrouj in the central Morocco area. The other specimens from non-endemic areas, or “Non-Ouarzazate” areas, constitute a geographically mixed group.sus tree A recoverThe median-joining analysis displays a highly structured network. Within each major clade, the internal and terminal nodes can be interpreted as old and recently derived haplotypes. The network display places all haplotypes into two groups: “Ouarzazate” or endemic; and “Non-Ouarzazate” or non-endemic. Within the Non-Ouarzazate group, two groups (named Mix1 and Mix2) are distinguishable . These tP. papatasi was defined by F statistics. AMOVA results presented in The genetic differentiation among populations of FST values were calculated in a pairwise manner for the main populations . With a STF value of 0.31026, the matrix of significance for 110 permutations reached p = 0.05, which highlights some genetic differentiation between the two groups that were analyzed using our 59 samples.The L. major is largely widespread. The central Moroccan plateaus overlooking the northern slope of the High Atlas, notably the Haouz plain and Chichaoua, remain non-endemic but, according to entomological data, is considered high risk.Natural barriers like high mountains play an important role for limiting dispersal and isolating populations. The Atlas, a natural barrier, may have played a role in limiting the diffusion of zoonotic cutaneous leishmaniasis to the Saharan regions of Morocco. This Atlas consists of three parallel ranges: the Anti-Atlas in the southwest, which gives way to the High Atlas, where the country’s highest peak rises to 4165 m; the Middle Atlas is located northeast of the High Atlas. Rolling plateaus east of the mountains gradually lead into the Sahara, located in southeastern and southern Morocco. Here, in sub-Saharan areas, cytochrome c oxidase subunit I (Cox1 or COI) as molecular marker to gather clues on the influence of the Atlas Mountains on the evolutionary history of P. papatasi populations in Morocco. The cytochrome c oxidase gene is used as a DNA barcode to guide the identification of new animal species and to delineate cryptic taxon and the association between their life stages [Lutzomyia longipalpis [L. umbratilis sibling species [COI in combination with the cytochrome b (Cytb) gene can differentiate two closely related species, P. chabaudi and P. riouxi [COI gene in Morocco, reminiscent of a genetic structuration in P. papatasi populations. Samples collected were indistinguishable by morphological observation and genomic analysis of the 18srRNA (data not shown). The haplotype distributions displayed in the median-joining network as well as the phylogenetic analysis supported the notion of a geographic clustering of P. papatasi, in Morocco. Two groups, with distinct genetic structures, can be differentiated. The first one encompasses P. papatasi samples from the ZCL endemic areas of Ouarzazate. The second one, designated the “Non-Ouarzazate” group, includes a mix of individuals from non-endemic areas sampled on the northern slope of the High Atlas Mountains and the arid plains of central Morocco, as well as samples from endemic areas.We have investigated the genetic structure of 10 populations collected from both endemic and non-endemic areas for zoonotic cutaneous leishmaniasis using the e stages ,45,46. Tgipalpis , and to  species . In Old . riouxi . Here, wP. papatasi populations. A complex scenario of P. papatasi populations might have taken place in Morocco, with expansion events allowing the appearance of multiple haplotype subgroups. The Ouarzazate population of P. papatasi, which derives from the populations of the southern slope of the High Atlas Mountains, is expected to have experienced a period of isolation due to the physical barrier of the Atlas Mountains. Then, population expansion and genetic diversification might have occurred, maybe following a climatic shift. These assumptions are supported by the high rate of private haplotypes recorded in this group (87%), which could reflect some local adaptation. The diversity of the ecological conditions may affect the distribution and density of P. papatasi, including the bioclimate, anthropization, urbanization, and elevation [P. papatasi density is high and in mountainous localities where P. papatasi is less frequent [P. papatasi from endemic areas, and the more distant Mix2. The Mix1 and Mix2 populations are not geographically isolated by the High Atlas Mountains. It therefore appears that ecological habitats do not play a key role in shaping the genetic variation of P. papatasi populations. This finding agrees with other reports from Hamarsheh et al. [P. papatasi linked to latitude rather than to ecological environment.The median-joining network, as well as nucleotide analyses, provide additional evidence of a probable demographic expansion of levation . The biofrequent . Despiteh et al. ,25,26 thP. papatasi populations and therefore have had an impact on the current and future ZCL incidence in Morocco. To this aim, the use of microsatellite markers may help to more precisely define the demographic history of Moroccoan P. papatasi populations.It will be now of interest to gather more precise information on the evolutionary scenarios that have governed the dispersal of COI gene), highlights clear differences in the genetic structure between P. papatasi populations living the L. major-endemic area of Ouarzazate and those of non-endemic areas of central Morocco. In addition, morphological differences have been reported in P. papatasi populations in Morocco [L. major by its specific vector P. papatasi [PpGalec) with a mutation affecting an amino acid involved in substrate recognition. Although performed on a limited number of samples, this further highlights an unequal distribution of this mutation in populations from endemic and non-endemic areas for zoonotic cutaneous leishmaniasis [P. papatasi populations from the southern and the northern slope of the High Atlas Mountains in Morocco.This work, based on a maternally inherited marker (the  Morocco . Indeed, Morocco , which ppapatasi . This stmaniasis . All the


Novel genetic markers like microhaplotypes and compound markers show promising potential in forensic research. Based on previously reported single nucleotide polymorphism (SNP) and insertion/deletion (InDel) polymorphism loci, 29 genetic markers including 22 microhaplotypes and seven compound markers were identified. Genetic distributions of the 29 loci in five continental populations, Kazak and Mongolian groups in China were investigated. We found that the expected heterozygosity values of these 29 loci were >0.4 in these populations, indicating these loci were relatively high polymorphisms. Population genetic analyses of five continental populations showed that five loci displayed relatively high genetic variations among these continental populations and could be useful markers for ancestry analysis. In summary, the 29 loci displayed relatively high genetic diversities in continental populations and Chinese two groups and could be informative loci for forensic research. In forensic research, human identification and paternity testing are two important research items. Since short tandem repeats (STRs) are highly polymorphic and widely distributed in the human genome, they are universally employed in forensic practice ,2. HowevForensic geneticists recently explored the application values of some novel genetic markers in forensic practice. Liu et al. proposed a novel compound marker that was a combination of one InDel and one SNP in a genomic region; they evaluated the power of the novel genetic marker to detect the DNA mixture, and their results revealed that the novel marker was able to disentangle the unbalanced mixture . Microhahttps://www.ncbi.nlm.nih.gov/snp) based on previously reported SNPs [In the present study, 29 novel microhaplotypes and compound markers (InDel-SNP) were selected from the dbSNP database . A NanoDrop 2000 instrument  was utilized to determine the concentration of each DNA sample. PCR primers for each region were designed on Primer 6.0 software. The primer sequences used in this study are given in Five continental populations  were used as reference populations for the initial evaluations of genetic distributions of selected SNPs/InDels. Genetic genotypes of all the selected SNPs/InDels in these continental populations were obtained from 1000 Genome Project Phase 3 .2O. PCR was conducted on the GeneAmp PCR System 9700 based on the following parameters: 98 °C for 2 min; 8 cycles of 98 °C for 20 s, 65 °C for 30 s, 72 °C for 20 s; 72 °C for 5 min and hold at 4 °C. Then constructed DNA libraries were separated using 2% agarose gel electrophoresis. We further purified DNA libraries using CMPure MagBead DNA Purification kit . The Qubit dsDNA HS Assay kit  was employed to quantify the concentration of each library.The sequencing library of each sample was prepared according to the following instructions. The total PCR system was 25 μL, consisting of 12.5 μL 2× Platinum multiplex PCR master mix, 3 μL GC enhancer, 2.5 μL primer mix (2 μM), 10 ng genomic DNA and ddH2O (up to 25 μL). We performed thermal cycling with the following conditions: denaturation for 2 min at 95 °C; 35 cycles of 30 s at 95 °C, 90 s at 60 °C and 30 s at 72 °C; extension was performed for 5 min at 72 °C. After PCR, we used 2% agarose gel electrophoresis to segregate DNA segments, and magnetic beads were used to purify DNA samples using CMPure MagBead DNA Purification kit . Next, we conducted the second round amplification based on KAPA HiFi HotStart ReadyMixPCR kit . The reaction reagents were 12.5 μL 2× KAPAHIFI mix, 2.5 μL Barcode (50 μM), 2.5 μL PE 1.0 (50 μM), 5 μL purified PCR product and 2.5 μL ddHhttps://support.illumina.com.cn/sequencing/sequencing_instruments/nextseq-500/documentation.html?langsel=/cn/).We denatured and diluted libraries using the standard normalization method. The final concentration of the library pool was 1.8 pM. Moreover, 1% PhiX control was used as the quality control and added to the library pool. The detailed instructions were referenced in the NextSeq System Denature and Dilute Libraries Guide (http://code.google.com/p/cutadapt/). We compared clean data with the reference genome (h19) using the BWA (http://bio-bwa.sourceforge.net/). We annotated all detected SNP and InDel loci with GATK (https://software.broadinstitute.org/gatk/) and VarScan software packages [The NextSeq 500 High Output kit v2.5  was used to conduct paired-end sequencing (150×) of each sample on the Illumina NextSeq 500 platform . The Local Run Manager was used as the run mode to perform sequencing reactions. The number of cycles was 300. We removed reads with self-ligation primer, low quality, multiple N and very short sequences using Cutadapt  of different continental populations was built using STRAF online program based on estimated haplotypic data. We conducted genetic structure analyses from K = 2 to K = 5 with five independent replicates using STRUCTURE software v2.3.4 [K value with the STRUCTURE HARVESTER online program (http://taylor0.biology.ucla.edu/structureHarvester/). We processed the data of the STRUCTURE replicated run to reduce stochastic effects with CLUMPP software v1.1 [In) value of each locus in five intercontinental populations with the INFOCALC program [p-values for HWE and linkage disequilibrium (LD) tests of these 29 loci in Kazak and Mongolian groups using STRAF online program. The Ae values of 29 loci in Kazak and Mongolian groups were calculated based on the description mentioned above. The allele coverage ratio (ACR) of each SNP/InDel was estimated according to a published description [We used PHASE software version 2.1  to conduare v3.3 . Expecteare v3.3 ; Ae was are v3.3 . Boxplotare v3.3  and pheaare v3.3  in R sofe v2.3.4 . The detare v1.1 . Then thare v1.1 . We calc program . Finallycription .In the present study, 29 microhaplotypes and compound markers were identified from previously reported SNP ,21,22,23Based on the population genetic data reported in 1000 Genomes Phase 3 , we asseWe also calculated the cumulative discrimination power (CDP), cumulative match probability (CMP) and cumulative probability of exclusion (CPE) values of the selected 29 loci in these five continental populations . The CDPK = 2 in comparison with other continental populations mainly showing yellow ancestral components. When K increased to 3, the East Asian population could be separated from other populations. As K reached 4, African, East Asian, European and South Asian populations displayed different ancestral components: African for brown, East Asian for green, European for yellow, South Asian for pink; American population showed admixed ancestral proportions. The STRUCTURE HARVESTER results are shown in K) values could be discerned at K = 3–5, indicating that K = 3 was the most suitable for the data in this study. The population genetic analyses mentioned above suggested that these 29 loci showed different genetic distributions in these continental populations, which might be useful for differentiating these continental populations. We also estimated the In values of the 29 loci among five continental populations .Based on haplotypic frequencies of the 29 loci, we conducted PCA of five continental populations . We founulations  and founDepth of coverage (DoC) and ACR were used to evaluate the sequencing results of the developed multiplex system . The meaWe also estimated Q30 of sequencing data for each individual. We found that they were greater than 90%, implying high accuracy. Some same individuals were analyzed by the developed system twice, and identical results of these loci were observed for the same individuals. Therefore, the developed system showed good performance and high genotyping accuracy.p-values) of the 29 loci in Kazak and Mongolian groups are given in p = 0.05/29 = 0.0017), the MH01ZBF002 locus deviated from HWE in the Kazak group, and the MH01ZBF002 and MH08ZBF002 loci deviated from HWE in the Mongolian group. LD analyses of pairwise loci in Kazak and Mongolian groups are given in p = 0.05/406 = 0.00012). However, one pair (MH06ZBF002 and MH07ZBF002) deviated from linkage equilibrium in the Mongolian group.HWE test results , suggesting that they could be used as informative markers for ancestry inference of these continental populations.Population genetic analyses among five continental populations were conducted based on the 29 loci. According to PCA results, most East Asian and African individuals could be differentiated from other individuals at the first three PCs . Moreoveulations . The In ulations  revealedUsing the system presented in this study, most loci showed high DoC values. However, the rs1382755 and rs33911727 loci at MH04ZBF001 locus had low DoC values, implying low performance of the region during multiplex PCR and sequencing. We also observed that most loci showed relatively high ACR values (>0.66), indicating that they may be useful to analyze mixed sample. Next, we investigated genetic distributions of the 29 loci in Kazak and Mongolian groups. The results revealed that all loci had at least three allele variations in both groups, and MH20ZBF002 locus had the most alleles. The average Ae values of the 29 loci were 2.92 and 2.93 in the Kazak and Mongolian groups, respectively. According to the previous research published by Kidd et al. the cumulative probability of resolving a mixture is 0.9471 if there are five loci with Ae values of 3.00 . In thisIn values and could be used for ancestry inferences of these continental populations. Further evaluations of the 29 loci in Kazak and Mongolian groups yielded a similar conclusion: the 29 loci could be a valuable tool for human identification and paternity testing. The power of the 29 loci to detect the mixture needs to be validated.We selected 29 novel loci including 22 microhaplotypes and seven compound markers for forensic application in the East Asian populations. We found that most of these 29 loci were relatively high polymorphisms in different continental populations. Moreover, five loci showed relatively high