South Eastern Bantu-speaking (SEB) groups constitute more than 80% of the population in South Africa. Despite clear linguistic and geographic diversity, the genetic differences between these groups have not been systematically investigated. Based on genome-wide data of over 5000 individuals, representing eight major SEB groups, we provide strong evidence for fine-scale population structure that broadly aligns with geographic distribution and is also congruent with linguistic phylogeny . Although differential Khoe-San admixture plays a key role, the structure persists after Khoe-San ancestry-masking. The timing of admixture, levels of sex-biased gene flow and population size dynamics also highlight differences in the demographic histories of individual groups. The comparisons with five Iron Age farmer genomes further support genetic continuity over ~400 years in certain regions of the country. Simulated trait genome-wide association studies further show that the observed population structure could have major implications for biomedical genomics research in South Africa. Despite linguistic and geographic diversity in South Eastern Bantu-speaking (SEB) groups of South Africa, genetic variation in these groups has not been investigated in depth. Here, the authors analyse genome-wide data from 5056 individuals, providing insights into demographic history across SEB groups. Three sets of migration events have dramatically reshaped the genetic landscape of this geographic region in the last two millennia. The first of these was a relatively small scale migration of East African pastoralists, who introduced pastoralism to Southern Africa ~2 kya7. This population was subsequently assimilated by local Southern African San hunter-gatherer groups, forming a new population that was ancestral to the Khoekhoe herder populations12. Today, Southern African Khoe and San populations collectively refer to hunter-gatherer (San) and herder (Khoekhoe) communities. While Khoe-San groups are distributed over a large geographic area today , these groups are scattered, small, and marginalised14.The sarchaeological record and rock art evidence trace the presence of San-like hunter-gatherer culture in Southern Africa to at least 20–40 thousand years ago (kya)17. The earliest communities spread along the East coast to reach the KwaZulu-Natal South coast by the mid-fifth century AD while the final major episode of settlement is estimated to be around AD 135019. These archaeologically distinct groups gradually spread across present-day South Africa, interacting to various degrees with the Khoe-San groups, eventually giving rise to South Africa’s diverse BS communities. The third major movement into Southern Africa was during the colonial era in the last four centuries when European colonists settled the area. During this period slave trade introduced additional intercontinental gene flow giving rise to complex genomic admixture patterns in current-day Southern African populations23.The introduction of pastoralism in the region was closely followed by the arrival of the second set of migrants i.e., the Bantu-speaking (BS) agro-pastoralists. The archaeological record suggests that ancestors of the current-day BS populations undertook different waves of migration instead of a single large-scale movement27. A new lexicon-based linguistic phylogeny included in this study  groups has not been systematically investigated. One of the very early studies based on the Y-chromosome and a few autosomal markers, which included almost all the main SEB groups and covered most of the provinces from South Africa, indicated the possibility of genetic structure within the SEB populations29. However, many subsequent studies using genome-wide datasets did not investigate genetic differentiation or population structure within SEB groups, which consequently led to its consideration as a group without clear internal substructure30. Moreover, studies including multiple SEB groups were often limited in terms of sample size or SEB group diversity32.South Africa has 11 official languages of which nine are Bantu languages belonging to this family’s South-Eastern branch. Within these nine languages two large subclusters are traditionally distinguished: Nguni  and Sotho-Tswana . Venda and Tsonga tend to be seen as independent linguistic entitiesHere we describe a systematic study of genetic diversity of South African SEB groups based on an analysis of 5056 individuals (AWI-Gen study) genotyped on the Illumina H3A-genotyping array (~2.3 M SNPs). Although the eight SEB groups have very specific geographic distributions of linguistic majority areas (LMAs) within the country, for our study they were sampled at three sites; Soweto (SWT) in Gauteng, Agincourt (AGT) in Mpumalanga, and Dikgale (DKG) in Limpopo province Table .Table 1DThis study reports a fine-scale population structure among SEB groups that parallels both linguistic affinities and geographic distribution in the country. Simulated association studies demonstrate that the fine-scale structure has a potential to influence genetic association in cohorts that include multiple SEB groups and/or SEB groups from multiple study sites within South Africa.The principal component analysis (PCA) of 4319 unrelated SEB participants (AWI-S2 dataset) reflects the linguistic phylogeny with partial separation of Tsonga, Sotho-Tswana  and Nguni (Zulu and Xhosa) speakers Fig.  suggesti33. These recent admixtures could result in incomplete boundaries observed between the SEB groups in the PC plot. In order to minimize the effect of such recent admixture on population structure detection, we analysed 2702 SEB participants   and geographical distances between the SEB groups also show a moderate correlation . Procrustes transformation analysis further highlights the correlation between PC and geography   Fig.  and lingST) Fig.  of the S32, we estimated the level of Khoe-San ancestry proportions in each SEB group (based on the Merged dataset 2-EC downsized) using an unsupervised clustering approach37. ADMIXTURE analysis at K = 3 highlights the separation of BS, Khoe-San and Eurasian ancestry   Fig. . The varies Fig. . To inveies Fig. .Fig. 2GeADMIXTURE analysis on the full set of unrelated samples . Ancestry-specific PCA after masking haploid genomes shows that the core differences within SEB groups, although reduced, persist even after accounting for differential Khoe-San gene flow Fig. . The obs38 . Moreover, for all SEB groups, a single admixture event model was detected to be the best-guess conclusion by fastGLOBETROTTER  onto the genetic variation of present-day SEB individuals  of each SEB group over the last 100 generations by analysing the sharing patterns of identity-by-descent (IBD) segments using IBDNe50. As depicted in Fig. Ne for all the SEB groups was very similar for the 100th to the 40th generations before present. It needs to be noted that most of the present-day SEB groups did not exist, as such, more than 50 generations ago and the older estimates here correspond to possible ancestral populations of these groups. The period of around 40 generations ago also corresponds to the estimated time scale for the oldest Khoe-San admixture dates  when Nguni-speakers first began to move North-west into the interior, becoming the first BS in South Africa to occupy grasslands51. The comparison of Sotho and Zulu Ne profiles between our study and samples from a previous study32 shows a high concordance, demonstrating an overall robustness in these estimates  to the edi Fig. . MoreoveAPOL1 variant rs73885319 among Sotho and Xhosa . Similarly, alleles in genes such as HERC2 (associated to skin colour), PCSK9 and SLC22A1 (associated to lipid level phenotype) also showed three-fold or higher allele frequency differences between SEB groups  between the SEB groups. The comparison Fig.  shows alups Fig. . A detaiPopulation structure accompanied by high allele frequency differences could have major implications for genome-wide association studies (GWAS). Therefore, to assess the extent to which the observed structure could bias association results, we conducted four categories of simulated traits GWASs (binary trait) using study sites  and/or ethno-linguistic labels as ‘trait-proxies’ (see Methods). Category 1 was aimed at stimulating a scenario where cases and controls are sampled from different study sites. The Fig. P-value threshold of 5 × 10−8. While the genomic inflation normalizes with homogenization of the dataset, GWASs for category 3 and to a lesser extent category 4 generated false positives in a few simulations . The addition of rare variants (MAF < 0.05) in a real GWAS, as well as increasing this dataset size by imputation, as is commonly performed in GWASs, could further increase false positives. Many of the signals from the simulated-trait GWASs have been previously reported as trait/disease genetic associations in the GWAS catalogSYT1, PAH, CAPN2, and SLC8A3 genes) to reach outlier threshold in some of the SEB groups but not in others, suggesting that the fine-scale structure can also influence evolutionary analyses. A population branch statistics (PBS) approach further detected SNPs showing high differentiation between Tswana and Tsonga  and Pedi (around ZBTB20 gene) also harboured selection outliers hinting at possible post-admixture selection scenarios , and also reflects the geographic distribution of LMAs to a large extent. The resolution of this structure within the SEB groups was enhanced considerably by taking ethno-linguistic concordance of individuals and their geographic locations into account. However, it needs to be noted that self-identity itself is complex, with about one third of the participants having more than one parent or grand-parent with a different ethnic self-identity. Moreover, while the PCA and PCA-UMAP shows clear population structure, there are exceptions highlighting the fluidity of cultural identity. Thus, self-selected group-identity encompasses significant group-related genetic variability, and it is important to emphasise that cultural identity and genetic variation are not necessarily aligned. Studies on population structure in South Africa should not be seen as justifying the ethnic nationalism generated by the country’s colonial and apartheid past. Our aim was to explore the role of genetic diversity in explaining population history and in health research. We recognise, and our study shows, that self-identity can involve considerable fluidity and that biological reductionist approaches pose dangers for the interpretation of our findings.32, our data also shows that differential Khoe-San gene flow plays a major role in the population structure of SEB groups. However, the persistence of the structure even after accounting for differential Khoe-San admixture suggests the contribution of other demographic factors in the genetic differentiation of these groups. The SEB groups start to show clear divergence in population size dynamics from about 40 generations ago. This timeframe converges with the earliest dates of Khoe-San admixture and probably points at the initiation of migration events that gradually separated these groups. On the other hand, a rather wide variation in Khoe-San admixture dates (spanning ~20 generations) among SEB groups possibly reflects the complexity of the settlement of different parts of the country by the ancestral BS populations. Comparison of present-day SEB groups with Iron-Age farmer genomes provided evidence for genetic continuity in a geographic region in Central-East South Africa for at least the last 300–500 years. Our results, while attesting to the well-known pattern of Khoe-San female-biased gene flow, showed notable differences in the extent of this bias among different SEB groups demonstrating that the nature of interaction between Khoe-San and BS could have varied temporally and geographically.In alignment with results from previous studiesThe dataset we generated for this study has provided a much better contextualization for previously sequenced Iron-Age genomes from Southern Africa. The SEB are unique in Africa, as being among the very few populations that contain considerable gene flow from the Khoe-San. These data therefore are of major importance in terms of understanding the interaction between the Khoe-San and other Southern African populations. They will play an important role in providing insights through comparative analyses once more genetic data from hunter-gatherers and ancient genomes from this geographic region become available.Our analyses including allele frequency comparisons, genome-wide scans for selection and Khoe-San ancestry distribution show the SEB groups to be highly diverged at certain genomic regions. Based on simulated-trait GWAS, we further illustrate that the fine-scale population structure within the SEB groups could impact a GWAS by introducing a large number of false positives. A combination of cautious study design to minimize geographic and ethno-linguistic biases and stringent measures for population structure correction is therefore recommended for GWASs involving SEB groups. Moreover, while GWAS can address the false positives introduced due to population structure using genomic control, PC or other approaches, it is impossible to identify and control for population structure in candidate gene studies. Therefore, utmost care should be taken during study design to ethnically and geographically homogenise samples in order to control for false positives in association studies using limited markers.A major limitation of our study is that the sampling sites do not cover the full geographic spread of SEB groups in the country, possibly causing some of the groups to be suboptimally represented in our dataset. Nevertheless, our results suggest that we are at a critical point in history where the population structure is still observable with efficient sampling and in-depth ethno-linguistic characterization, even if it is gradually diminishing due to migration and intermingling between different SEB groups. We hope that our findings will motivate studies with larger sample sizes and wider geographic representation to help unravel the demographic events that contributed to the peopling of South Africa.56. Of these SWT is urban, whereas DKG and AGT are rural/semi-urban sites. The study included a total of 5268 individuals (mostly within the age range of 40–60 years) representing eight major South African SEB groups: Tsonga, Pedi, Venda, Sotho, Tswana, Swazi, Zulu, and Xhosa. This study was approved by the Human Research Ethics Committee  of the University of the Witwatersrand (Wits) (protocol number M121029), and renewed in 2017 (protocol number M170880). In addition, research at the Dikgale Study Centre was approved by the Medunsa Research and Ethics Committee of the University of Limpopo (MREC/HS/195/2014:CR). Community engagement preceded sample collection and all participants provided broad consent for medical and population genetic studies. Details of community engagement, written informed consent, and genomic DNA extraction from blood samples have been described elsewhere57. In brief, the community engagement was tailored for each of the study sites according to their setting in a rural or urban area. It involved meetings with community leaders and elders, providing an opportunity to ask questions about the study and the potential benefits for the communities. The opportunity was also used to raise awareness of the rise in cardiometabolic diseases, as the AWI-Gen study aims to identify environmental and genetic risk factors. At the end of the first round of data collection there was community level and individual level feedback and further discussions. The outcomes of this population genetics study will be shared in future community interactions. The samples were genotyped on the H3Africa array (~2.3 M SNPs) using the Illumina FastTrack Sequencing Service2. The default Illumina pipeline was used for the genotype calling (build GRCh37/hg19).The volunteers included in this study were sampled across three study sites Fig. ; Agincouhttps://en.wikipedia.org/wiki/Languages_of_South_Africa#/media/File:South_Africa_2011_dominant_language_map.svg. Attribution: “the user acknowledges Stats SA as the source of the basic data wherever they process, apply, utilise, publish or distribute the data, and also that they specify that the relevant application and analysis (where applicable) result from their own processing of the data”. The language centroid points were calculated for this study by the author of the original map, Adrian Frith. The geometric medians of the population of speakers of each language  were calculated. Since the large population sizes of the cosmopolitan Gauteng Province  will distort the picture, geometric medians excluding Gauteng have been calculated.The map in Fig. 58 and involved removal of duplicate SNPs, multi-allelic SNPs, INDELs and SNPs with a missingness >0.05, MAF < 0.01 and SNPs that failed HWE test . Individuals with missingness >0.05, discordant sex information and lacking self-reported ethnicity information were also removed. The genotype dataset post-QC consists of 5056 samples and 1,733,001 autosomal SNPs (AWI-S1)  using PLINK. The same parameters for LD-pruning were used for the datasets described below.Quality control (QC) on the AWI-Gen genotype dataset was performed using PLINK v1.9).958 and 59 and GENESIS60 and PC-Relate option for plotting. After these QC-steps, no first-degree or second-degree relatives were found in the dataset used for the analyses below  segments for each sample pair, based on the LD-pruned AWI-S1 dataset. For each pair of related individuals (PIHAT > 0.18), the sample with higher missingness was dropped, resulting in the removal of 737 SEB participants in the process leading to AWI-S2 dataset  groups as well as between SEB and non-SEB groups has been common in recent South African history. Since admixture events could influence fine-scale comparisons between SEB groups, we identified the participants that were ethno-linguistically concordant (EC), i.e., they have reported the same ethnicity for themselves, both parents and the four grandparents . This set of 2,702 EC participants was defined as AWI-S3 dataset, details are listed in Table The representation of various SEB groups in the AWI-Gen study was notably skewed toward Tsonga, Pedi, and Zulu  (Table 21), 1000 Genomes Project Phase 3 (KGP)61, and African Genome Variation Project (AGVP)32 using PLINK (hereafter Merged dataset 1), and only the SNPs that overlapped between all datasets were retained . This layered merging was performed to retain the maximum number of SNPs possible for each analysis.For comparison of our population with previously studied populations, the AWI-S2 data  was merged with additional worldwide datasets from 34. Lastly, to better discern fine-scale population structure among SEB groups we performed haplotype-based clustering analysis using ChromoPainter jointly with fineSTRUCTURE35  was performed on the basis of the LD-pruned AWI-S2 dataset using the program smartPCA implemented in the EIGENSOFT suiteST statistics (Weir and Cockerham 1984) between pairwise SEB populations included in the Merged dataset 2 (EC downsized) using PLINK. The relationship between the SEB groups based on pairwise FST values was represented with a UPGMA tree using the program MEGA X64.To investigate genetic affinities between the different SEB groups, we estimated Weir and Cockerham’s F65. The lexical data were binary recorded in 1304 partial cognate sets (form-meaning associations). The resulting matrix was analysed with Bayesian inference methods as implemented in MrBayes (v3.2.7)66 using a restriction-site model67.The linguistic phylogeny is based on lexical data for 100 concepts in 69 Bantu varieties, 34 of them part of South-Eastern Bantu languages and 35 outgroup languages belonging to different major Bantu brancheshttp://www.or.uni-bonn.de/~vygen/files/fl.pdf), and considered them as the midpoints of each group. The great circle geographic distance between each midpoint was estimated using an online tool . All individuals belonging to a particular SEB group were assigned to the same geographic location (geometric median), and the Procrustes transformation analysis was performed using the R package MCMCpack68. The correlations between PCA (PC1-PC2) results and geographic location of each SEB group was estimated using R package vegan69 (9999 permutations). For the Mantel test, the genetic distance matrix was based on weighted mean FST estimates for each pair of SEB groups. This once again was performed using the R package vegan69, using 9999 permutations to test the correlations between the geographic distances and FSTbased genetic distances.Procrustes transformation analysis and Mantel tests were implemented to investigate possible relationships between the geographic and genetic distances between the SEB groups. As many of the groups were sampled from sites that are quite distant to their native geography, such as Zulu and Xhosa, we calculated geometric medians of the population of speakers for each language using Weiszfeld’s algorithm (37 on the Merged dataset 2 (EC downsized). The number of K-groups analysed varied from K = 3 to K = 8, and 50 independent runs with a random seed for each K-group was performed. The K-group with the lowest cross-validation error was considered “optimal”. PONG70 was used for merging and visualizing the clustering outputs of all the runs from the ADMIXTURE analysis, and major modes were used for the ADMIXTURE plots. To compare the differential contributions of the main ancestries , the average admixture proportions of each ancestry was computed from the ADMIXTURE results at K = 3 for each SEB group in the Merged dataset 1. For each study site, we further estimated the average admixture proportion for each ancestry at K = 3 in each SEB group. We applied a t-test to compare whether there are significant differences in Khoe-San ancestry proportion across the three sites for a given SEB group.For global ancestry inference, we used an unsupervised clustering algorithm implemented in ADMIXTURE (v1.3)71, on the basis of the Merged dataset 1 (EC). As reference panels, we selected: YRI for Central-West African ancestry; CEU for Eurasian ancestry; and combined Ju|’hoansi, /Gui //Gana, and Karretjie21 for Khoe-San ancestry. The merged dataset was first phased using SHAPEIT272 with a reference panel of worldwide haplotypes61, and then analysed with two runs of expectation maximization (EM = 2), forward-backward and PopPhased options. The genetic map from HapMap Phase 2 build GRCh37/hg19 was used for the analysis.For local ancestry inference, we used RFMix (v1.54)73.To investigate whether the differential Khoe-San gene flow is the only factor leading to the observed population structure, SEB haploid genomes were masked for regions of Khoe-San and European ancestries identified using RFMix. We then analysed haploid regions with more than 50% Bantu-related ancestry using the ancestry-specific PCA approach38, MALDER (v1.0)43, and MOSAIC (v1.3.7)44. The details for each method is described in Supplementary Note To reconstruct the timeframe of admixture events between the major ancestry components in SEB populations, we used three admixture dating methods, fastGLOBETROTTER, the recent implementation of GLOBETROTTER10, and one 700-years-old sample from Pemba, Tanzania (AWI-AG dataset)11. We used smartPCA to project the ancient samples onto the modern samples . To better visualize genetic affinities between ancient and modern samples, we performed the PCA-UMAP analysis using the PC coordinates for the first ten PCs and UMAP tool for the analysis 34, and a custom Python script for the plotting.To compare the genetic affinities of modern SEB groups to Iron-age Bantu-related samples from Southern Africa, we analysed the AWI-S3 dataset together with five ancient samples: four associated with Iron-Age (300–500 year old) farmers in South AfricaplotY tool (https://github.com/shaze/ymthaplotools), based on a modified version of the tree and mutations table of AMY-tree74. The results were then validated using SNAPPY75. MtDNA haplotyping was performed using Haplogrep 276, using Phylotree mtDNA tree build 17rsrs-RSRS77. The details for mtDNA and Y-haplogroup detection are described in Supplementary Note Y-haplogroup analysis was carried out using our new in-house 78. To investigate the extent of sex-bias in the contributions of different ancestral populations to admixed SEB groups, the AWI-S3 and YRI and CEU from KGP were merged with available data62, consisting of the 33 Khoe-San samples. The ancestry proportions for each were estimated using ADMIXTURE at K = 3 for three datasets: the X-chromosome dataset, the autosomal dataset, and the merged autosome-X-chromosome dataset. Admixture difference (ΔAdmix) ratios were then calculated using the method proposed by (ref. 79). A positive ΔAdmix ratio indicates an excess of female-specific admixture contributions, while a negative value indicates an excess of male-specific admixture. To test statistical significance of the difference between the ΔAdmix for each ancestry between pairs of populations, we used the Wilcoxon rank-sum test.Recent literature has suggested the comparison of X-chromosome and autosomal contributions from the two source population as a robust method to test for possible sex-biased admixture80 for each group (with default parameters). The output was then used as input for the program IBDNe50, which computes the effective population size (Ne) for each SEB group for the last few hundred generations. To avoid the conflation effect of short IBD segments81, only IBD segments longer than 4 cM were retained for the Ne estimation, and the remaining parameters were set as default. Ancestry-specific effective population size (ancestry-specific IBDNe)50 was estimated for the different SEB groups to identify the contribution of Khoe-San and BS ancestries to the Ne dynamics of various SEB groups. This analysis was performed on the dataset that was used to estimate the overall Ne. We followed the pipeline provided by the authors, which implements both IBD and local ancestry information from the genotype data. The first step in this approach was to phase the data using Beagle (v5)82, and then IBD segments were detected using RefinedIBD and local ancestry information was inferred with RFMix . Finally, IBDNe was used to estimate the ancestry-specific Ne from the detected IBD segments and the ancestry blocks inferred from the local ancestry analysis.To estimate and compare the variation in recent population size of the different SEB groups, IBD segments were detected from the downsized Merged dataset 1 using the program IBDseq55 (accessed on 19 April 2020), in the six major SEB groups . Standard error for allele frequencies was estimated using 50 bootstrap iterations in a subset of 30 individuals from each SEB group.We used PLINK to estimate allele frequencies of all SNPs in our dataset that are included in the GWAS catalogn = 800) are a mixture of samples from AGT and SWT and the controls (n = 800) are sampled from SWT only. Three sets of cases with varying proportions of AGT and SWT representation  were generated and 50 iterations were performed for each set. The third category of trait simulation was aimed at studying the impact of ethno-linguistic stratification within a sampling site, SWT. For two sets  generated in this category, the assignment was done in a way in which one of the ethno-linguistic groups (Tswana in set 1 and Tsonga in set 2) was absent in cases but present in controls. The fourth category was generated by randomly assigning case and control labels to the samples from a single site at a time. GWAS for each of the case-control pairs in all the sets under the four categories were conducted using the association testing function in PLINK. Genomic inflation scores were recorded for each run, and signals at a genome-wide significance threshold of P-value = 5 × 10−8 were identified, as well as a less stringent suggestive significance threshold . To assess the extent of population structure correction possible with a genomic control based approach, for each run the inbuilt correction testing function was implemented using the “adjust” flag in PLINK. To assess the impact of PC-based correction, for each of the case-control iterations, PCs for the dataset were estimated using PLINK and the first three principal components were used as covariates in logistic regression based association testing in PLINK. QQ plots were generated using a custom R script. Possible phenotypic roles of the associations detected in these simulated-trait GWASs were assessed using the GWAS catalog55.To simulate various possible scenarios for genetic association studies using ethno-linguistically and geographically mixed sets of SEB participants, four categories of artificial “case-control” trait simulations were performed. In the first category, the sampling site was used as the basis for assigning the case and control status. Here, the “case” label was assigned to 800 randomly sampled individuals from one of the three sites and the “control” label assigned to 800 randomly sampled individuals from a different site. Independent comparisons, 50 iterations each for AGT-DKG; DKG-SWT; AGT-SWT were performed. The second category corresponds to a scenario in which cases . The AWI-S4 dataset was used for this analysis, and only SNPs with MAF < 0.05 were considered. We included six SEB groups, and we removed Venda and Swazi samples due to their small sample size. For each SEB group, the raw iHS were normalized across 40 frequency bins. A random sampling of scores across populations was performed to assess P-values for various score cutoffs. Based on this |iHS| > 4 was considered as outliers . The mapping of SNPs to genes was performed based on information retrieved from Ensembl Biomart . We also used the population branch statistics (PBS) analysis84 to identify SNPs under positive selection. PBS is a summary statistic that utilizes pairwise Fst values among three populations to quantify genetic differentiation along each branch of their corresponding three-population tree. Since the overall genetic distance between the SEB groups is not very high, we considered only two groups from our study: the one with the highest Khoe-San ancestry (Tswana), and the other with the lowest Khoe-San ancestry (Tsonga). CHB from KGP was selected as the outlier population for this study. For each exonic SNP (identified using Ensembl Biomart as mentioned above) with MAF > 0.01, FST values were estimated between the three pairs of the populations  using VCFtools85. PBS scores were then estimated in Tswana-Tsonga-CHB and Tsonga-Tswana-CHB comparisons using the method described in (ref. 84).To identify SNPs under positive selection, we calculated the integrated haplotype homozygosity scores (iHS)To identify genomic regions showing enrichment of Khoe-San ancestry in the SEB groups, local ancestry inference was performed using RFMIX as described above. To avoid statistical noise, regions around centromeres and telomeres (2 Mb from each side) for each chromosome were excluded from the analysis. Only SNPs with a high confidence value for Khoe-San ancestry  were retained for the analysis. Ancestry regions (containing at least 3 SNPs) exceeding the average genome-wide Khoe-San ancestry estimate by at least +3SDs were considered as candidates for preferential Khoe-San gene flow. We then investigated whether there are regions of adaptive introgression in the genomes by overlapping the regions under positive selection (as described above) and regions showing Khoe-San enrichment.Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Reporting Summary


To obtain refreshed insights into the paternal lineages of Tunisian populations, Y-chromosome diversity was assessed in two populations belonging to an Arab genealogical lineage, Kairouan and Wesletia, as well as in four Tunisian Andalusian populations, Testour, Slouguia, Qalaat-El-Andalous and El Alia. The Arabs from Kairouan revealed 73.47% of E-M81 and close affinities with Berber groups, indicating they are likely arabized Berbers, clearly differentiated from the Arabs from Wesletia, who harbored the highest frequency (71.8%) of the Middle Eastern component ever observed in North Africa. In the Tunisian Andalusians, the North African component largely prevailed, followed by the Middle Eastern contribution. Global comparative analysis highlighted the heterogeneity of Tunisian populations, among which, as a whole, dominated a set of lineages ascribed to be of autochthonous Berber origin (71.67%), beside a component of essentially Middle Eastern extraction (18.35%), and signatures of Sub-Saharan (5.2%), European (3.45%) and Asiatic (1.33%) contributions. The remarkable frequency of T-M70 in Wesletia (17.4%) prompted to refine its phylogeographic analysis, allowing to confirm its Middle Eastern origin, though signs of local evolution in Northern Africa were also detected. Evidence was clear on the ancient introduction of T lineages into the region, probably since Neolithic times associated to spread of agriculture. The emergence of agriculture and animal domestication was splendidly captured by the cave paintings and rock engravings in the mountains of Jebel Ousselat5. In historical times, Tunisia was primarily inhabited by autochthonous Berber populations that faced the arrival of successive waves of invaders, most of them coming from the Middle East: i) Phoenicians, originally from Lebanon, who founded Carthage in the twelfth century BC; later ii) the Arab Muslims who conquered the region in the seventh century that would lead to the foundation of Kairouan, the first Islamic city in Northwest Africa; and later on iii) the Bedouins in consequence of the invasion of Banu Hilal tribes coming from the Arabian Peninsula in the eleventh century. This last event would instigate the Arabization and Islamization of many Berber populations. More recently, in the sixteenth century, when Tunisia was incorporated into the Ottoman Empire, a substantial migration of Turkish people coming from other eastern Ottoman territories took place. In the beginning of the seventeenth century a new influence to the Tunisian population scenario took place with the entry of the “Andalusians”, the name given to the Muslims fleeing from Spain after the decline of the Muslim rule of the Iberian Peninsula, initiated with the conquest of al-Andalus in 711 and lasting till the fall of Granada in 14926. The following decades were turbulent times for the Muslims communities in Spain, whose persecution would culminate in the years 1609 to 1614 with the final expulsion of the Moriscos (designation given to Muslim converted to Christianity) from the whole nation, a massive event during which thousands of people were driven from their homes, the majority to North Africa7.North Africa experienced a rich and complex population history since ancient times, which is understandable given its strategic position across the southern part of the Mediterranean Sea. Tunisia, the northernmost country in Africa, holds a privileged geographic position representing a crossroad between Africa, Europe and the Middle East. According to archaeological and historical data, the settlement of human populations coming from the east has occurred in Tunisia in different prehistoric and historical migration waves. During the Neolithic period, the Capsian culture that flourished in the Maghreb, between 10,000 and 5000 years ago, left remarkable sites in nowadays Southern Tunisia, as attested by the archaeological traces in GafsaSeveral studies have already been undertaken to understand the genetic structure of North African human populations and discern the origin and timing of the migrations that modeled current-day populations. In this work we sought to finely dissect variability of the paternal genetic pool in Tunisia in order to infer the past population interactions, especially addressing the paternal Middle Eastern inputs.22 have shown that in North Africa the most widespread and common Y lineages are the E- M81, which reaches an average frequency of 45% across the region21 and is thought to have a very recent origin probably in Northwest Africa19, and J-M267, accounting for around 30% of North Africans and assumed to have spread out of the Arabia Peninsula into North Africa, as is shown by, for instance, its east–west decreasing prevalence23. There is wide agreement about the sources of J-M267 in North Africa, which imply multiple and temporarily different expansions of Middle-Eastern and Arabic populations through the Mediterranean. In fact, although this haplogroup is considered to be one of the signatures of the spread of Islam from the Arabian Peninsula30, it also retains clues on a much earlier expansion during Neolithic times as part of the previously mentioned Capsian cultural complex that was introduced in North Africa along with agriculture31.Previous works with Y-chromosome markers32, whose recent spread in the Mediterranean is believed to have been facilitated by the maritime trading culture of the Phoenicians15. Besides, G-M201 and T-M184 haplogroups, also supposed to have originated in the Middle East, were occasionally detected in some North African populations and their distribution also suggests their involvement in the Neolithization of North Africa34.Another haplogroup consistently associated to the package of lineages accounting for the rapid expansion of Neolithic farmers from the Middle East is J-M17236. Contrarily, Wesletia is a rural city surrounded in the east by the mountains of Jebel Ousselat, which preserve the richest rock art in Tunisia since the Neolithic period5. In previous works, we have already characterized both populations for 15 autosomal STRs loci37 and mtDNA HVS-I and HVS-II regions38, which disclosed the clustering of North African and Middle Eastern populations37 and further showed that in the Middle Eastern maternal genetic pool of Tunisian populations prevailed an ancient substrate suggesting that migration waves during Paleolithic and Neolithic periods must have been more important contributors to that component than the much more recent Islamic expansion that brought the massive Bedouin dispersal in the 11th CE38.In this context, we sought to refine the knowledge on the extent of the paternal Middle Eastern contribution in Tunisia. Because previous studies on Tunisian populations have mainly included cosmopolitan citizens, Berbers or “Andalusian” groups, we set as goal to enrich the catalogue of Tunisia populations by studding Tunisians claimed to have stronger Middle Eastern contribution. To that end, we assessed Y-STRs and Y-SNPs diversity in samples from Kairouan and Wesletia known to belong to Arab genealogical lineages. Kairouan, founded in the 7th CE, was indeed the first Islamic Arab city in North Africa that would become the capital of Ifriqiya  for five centuries (7th to 11th CE). It houses the Great Mosque, one of the most important monuments in Arabo-Islamic architecture that functioned as a centre of Islamic thought, attracting scholars from all over the Islamic World40. In addition, a comprehensive phylogenetic analysis of the Middle Eastern Y-chromosome haplogroup T was performed after recruiting available data from the literature to further elucidate the extent of gene flow from the Middle East into North African populations.In the present work we aimed at discerning the paternal Middle Eastern lineages in present-day populations of recent Arab ancestry from Kairouan and Wesletia and compare the findings with those obtained in four previously studied Tunisian Andalusian populations In the present work, we analyzed 23 Y-STRs and 29 Y-SNPs in Tunisian Arabs from Kairouan and Wesletia , contrasting with the much lower proportion of Middle Eastern 14.28%) or the residual value of Sub-Saharan lineages (2.04%). Contrarily, in the Arabs from Wesletia, haplogroups typically found in Middle Eastern populations were very well represented, summing up a combined frequency of 71.8%, whereas the North African substrate was detected at the moderate frequency of 23.9% and the European only attained the frequency of 4.3% , and El Alia by exhibiting the highest proportion of the Middle Eastern ones 41.87%). In Testour and Slouguia, the most frequent haplogroups are North African , the Middle Eastern assumes intermediate values , while the European, Sub-Saharan and Asian are present in both populations with low frequencies  is marked by the high average frequency (around 72%), although showing remarkable disparities between populations Fig. .Regarding the Middle Eastern contribution, J-M267 haplogroup is present at the average frequency of 15% in Tunisian populations, but as well shows large inter-population variability, ranging from 52.2% in Arabs from Wesletia, 43.75% in Tunisian Andalusians from Zaghouan to 0% in Sub-Saharans from Jerba and Berber populations . In turn, J-M172 is comparatively less frequent, varying from 16.6% in Andalusians from Testour, 8.2% in Cosmopolitans from Sousse, to 0% in 13 out of 21 Tunisian populations. Other Middle Eastern subclades such as G-M201, T-M184 and T-M70 were residually observed, except T-M70 in the Arabs from Wesletia detected at the remarkable frequency of 17.4%.As for the Sub-Saharan influence , it is very strong in the sub-Saharans from Jerba, among whom E-V38 and R-V88 represent 33.3% and 35.7%, respectively, of the male lineages.The proxies of the European component here identified were R-M207, R-M198, R-P25, I-M253 and I-M438 sub-clades, each one occasionally detected in some populations; their combined average frequency did not exceed around 3%. Sporadic occurrences of a few Asian haplogroups like C-M217, L-M20, F-M89 and Q-M346 were also found with the average frequency of ~ 1%.In sum, the Tunisian paternal pool is mainly dominated by a North African component (71.67%), followed by a Middle Eastern contribution (18.35%) and, at much lesser extent, by lineages of Sub-Saharan (5.2%), European (3.45%) and Asiatic (1.33%) origin.Considering the 23 Y-STR analyzed in the two Tunisian Arab samples, we found 45 91.83%) and 39 (84.78%) different haplotypes in Kairouan and Wesletia, respectively. Both populations display high haplotype diversities (0.996 ± 0.004 in Kairouan and 0.992 ± 0.006 in Wesletia) and the mean number of pairwise differences in Wesletia is higher (12.639) than in Kairouan (9.929). Diminishing the resolution to 17 Y-STR  are also shown in Table RST genetic distances (based on 12 Y-STR) and corresponding P-values between 18 Tunisian populations are given in Supporting Information Table P = 0.00277), the Arabic populations  differed significantly from each other as well as from most of the remaining Tunisian populations. Non-significant differentiations emerged between i) Wesletia and Tunisian Andalusians from Zaghouan, ii) Wesletia and Cosmopolitans from Tunis on the one hand and Kairouan and Cosmopolitans of Sousse on the other, or iii) Kairouan and Berbers from Takrouna as well as Zriba and Berbers from Takrouna. Remarkably, most of the genetic distances between Cosmopolitans and Tunisian Andalusian populations were statistically non-significant.Pairwise RST genetic distances were used to generate a multidimensional scaling (MDS) plot outlining the relationships between Tunisian populations  is near the Tunisian Andalusians.The 12. Yet, the variance among populations within groups was highly significant, reflecting the complexity of other factors that account for the pattern of genetic differentiation between Tunisian population44.To further assess the genetic structure of Tunisian populations, we implemented hierarchical AMOVA pooling populations according to the broad ethnical classification into four groups: Berbers, Arabs, Tunisian Andalusians and Cosmopolitans. Results revealed no statistically significant variance among groups Table , sustainThen, to assess the influence of geography, we conducted SAMOVA taking into account the geographic location of the populations. Out of the different runs performed from K = 2 to K = 7 groups without a priori classification, the maximum significant value of variation among groups was obtained when 5 groups were considered and only accounted for 15.43% of total variation  were integrated in the diversity context of other populations not only from Tunisia but also from North Africa, Sub-Sahara, Europe and the Middle East, we recruited the available population data  and Libyan (2.28%)34. In this context, it was rather unexpected to have found out that 17.4% of the Tunisian Arabs from Wesletia belonged to sub-haplogroup T-M70.T-M184 haplogroup, thought to have had origin and initial expansion in the Middle EastTo examine in depth this haplogroup in a broad scenario encompassing Tunisian and worldwide populations, a phylogeographic analysis of T lineages was undertaken, constructing three median joining networks: one based on a set of 17 Y-STR loci typed in 114 samples from North Africa and Middle East regions; other with the same samples but reducing the set of loci to 13 Y-STRs; and a third with the 13 Y-STRs used in the second network integrating 453 samples from worldwide populations Fig. .Figure. https://isogg.org/tree/).All networks illustrate the extensive divergence between haplotypes belonging to haplogroup T as well as the scarcity of shared haplotypes between populations and the faint geographic structure. The networks containing only North African and Middle Eastern individuals Fig. A,B show 34, after which it underwent a complex dispersal into Europe along with its spread in East and North Africa. But instead of a star-like structure , the network displayed a number of signals of evolutionary events leading to local diversification of lineages.The network with worldwide individuals Fig. C, encomp50. Connected to these haplotypes are many other from Iranian, Jordanian, Lebanese, Saudi, as well as from Turkish and Greek Cypriot profiles. Haplotypes from Israel-Palestine and Kuwait scattered throughout the network.A subset of haplotypes from the Middle East presents several branches with many one-step neighbour haplotypes particularly from Armenia and North Iraq, which seems to be coherent with a presumed expansion of haplogroup T from the region of Fertile CrescentThere are also various branches integrating haplotypes from Europe that are quite divergent between each other, indicating that the diversification of the T lineages introduced from the Middle East already occurred in loco in some European populations, namely from Italy, Spain and Portugal. Other European haplotypes  are dispersed throughout the network, as well are the few haplotypes from American and Asian populations which were randomly connected with both European and Middle Eastern representatives.52 and the other uniquely shared haplotypes from Madagascar . Although these two nodes encompass haplotypes quite divergent, both present core haplotypes considerably ramified.The network discloses in addition notable signatures of founder effects in two populations from East of Africa, illustrated by two large nodes, one of which only containing shared haplotypes from Djibouti The haplotypes from the studied Arab Tunisian populations (Kairouan and Wesletia) are connected to the Madagascar branch, to which are also related the Egyptian chromosomes, suggesting that all these haplotypes have a common source. Still, other haplotypes from Morocco, Egypt, Libya and Tunisians from Sousse are ubiquitously distributed across distinct branches from the network.59, while the TMRCA estimate for the branch including Arab Tunisian samples from Wesletia and Kairouan was 9.237 ± 6.450YBP.The TMRCA of Y-chromosome haplogroup T was 45.582 ± 5.162, in accordance with the range of values estimated by Hallast et al.The interpolation map of the Middle Eastern haplogroup T in worldwide populations . Actually, whereas in the Arabs from Kairouan clearly prevailed a male genetic background associated to a Berber ancestry  coming from the Arabian Peninsula in revenge for the Zirid dynasty that broke away from the Fatimid Empire60, an event that would be considered the major driver of the linguistic and cultural arabization of the indigenous Berber populations.At first glance, this is counterintuitive because Kairouan is recognised as having been an important Arabo-Muslim base in North Africa during five centuries (7th–11th), and even today it continues to be one of the holiest cities in Islam61. Still today, several families from the region claim to have Middle Eastern origin.In turn, Wesletia and surrounding villages were the first localities in the area to be occupied by Arab Muslim troops from the beginning of the Arab conquest in 666. These rural cities reached the peak of their development until the 13th CE providing the neighbouring regions fruits, vegetables and good water qualityOur sampling criterion relied on the self-provided patronymic information given by the voluntary participants in the study, which admittedly might be misleading on family ancestry as a consequence of the acculturation behaviour that was strongly rooted everywhere in Tunisia. However, since the criterion was applied both in Kairouan and Wesletia, the detection of very disparate amounts of Middle Eastern and Berber backgrounds in populations from the two regions might indeed reflect a higher influence of people from the Arabian Peninsula in Wesletia than in Kairouan.The differences captured between Wesletia and Kairouan highlight the genetic heterogeneity of Tunisian populations, even sharing a common language and being located in close geographical proximity. That is also the case of the two Tunisian Andalusian populations Qalaat-El-Andalous and El Alia that are only 15 km away. In Qalaat-El-Andalous, two unique haplogroups E1b1b (89.5%) and J1 (10.5%) were found, while in El Alia 9 different haplogroups were observed encompassing almost equally frequent North African (44.19% of E1b1b1) and Middle Eastern representatives , largely exceeding the much less frequent European , Sub-Saharan  and Asian lineages . Notwithstanding, there were also two Tunisian Andalusian populations where the Middle Eastern component dominates, as in Zaghouan (46.55%) and in El Alia (41.87%). Tunisian “Andalusians”, is the common description of those Tunisian populations that incorporated the Muslim people living in Al-Andalus who were expulsed from the Iberian Peninsula and entered the North of Tunisia at the beginning of the seventeenth century. That wave of migrants was essentially constituted by the descendants of the Muslims who entered and settled in Hispania following the Umayyad conquest of the territory (711–718 CE). It seems thus very likely that these newcomers to Tunisia had retained a strong Middle Eastern signature of their origin, which resisted even after admixture with populations mainly from Northern of Tunisia in which they were assimilated.Considering Tunisian populations as a whole, the majority part of their paternal haplogroups are of autochthonous Berber origin (71.67%), which co-exists with others assumedly from the Middle East (18.35%) and to a lesser extent from Sub-Saharan Africa (5.2%), Europe (3.45%) and Asia (1.33%).65. In the Tunisian population landscape, clearly conflicts the low values of diversity in the Arabs from Zriba and in the Tunisian Andalusians from Qalaat-El-Andalous. This can be explained because both are small sized populations that underwent remarkable drift effects40, as also applies to the Berber communities from Jradou, Bou-Omrane and Bou-Saad, among which drift effects and the endogamy promoted by cultural isolation must have interplayed to reduce diversity13.Most of Tunisian populations here analysed exhibited high levels of Y-STR diversity, reflecting the wealth of demographic histories underlying current-day populations. This finding is in agreement with previous studies using different types of markers that reported the highest genetic diversities in North African populationsDespite the remarkable differentiation between Tunisian populations, no clear factor of differentiation emerged when assuming the conventional assignment in Arabs, Andalusians, Berbers and Cosmopolitans, or when accounting for their geographical region of origin. This indicates that such kind of categorization or geography are irrelevant to explain substructure amongst Tunisian populations, which must thus rely in a complexity of interacting factors difficult to discern.50, the T lineages originated in the Near East during the Paleolithic and were introduced in Armenia by migrations dating back to ∼12–13 kya, affording afterwards the time needed for considerable in situ diversification. The analyses undertaken in this study, pointed toward an ancient introduction of T lineages into North Africa, as illustrates the high level of molecular divergence among haplotypes. In the network of worldwide chromosomes, a branch is defined containing the T haplotypes from the Arab Tunisian population here studied, demonstrating a certain extent of local diversification  in comparison with the much smoother Middle Eastern and North African mtDNA contributions 67, the predominant component in the paternal pool in Tunisia was North African (71.67%) followed by an essentially Middle Eastern contribution (18.35%). This suggests that a sex-biased pattern of gene flow has modelled current day Tunisian populations, adding another level of complexity to their past that still needs to be further investigated. The phylogenetic analysis of haplogroup T revealed that it started to be introduced in North Africa in very remote times, probably through the Neolithic eastern genetic flow associated to the spread of agriculture, which was later reinforced by other population influxes into the region.In conclusion, the panorama obtained by the analysis of Y-chromosome polymorphisms confirms the mosaic structure of Tunisian population68 All study methods were carried out in accordance with the approved guidelines and regulations.Blood samples were collected from 95 unrelated healthy males originating from two regions of central Tunisia: 49 from Kairouan and 46 from Wesletia (Al-Waslatiyah). Only individuals providing patronymic evidence to belong to the Arab population were sampled. All subjects were volunteers who gave informed consent to participate in this study, which was performed with ethical approval of the local health authorities  and the approval of ethics committee for research in life and health sciences (CER-SVS/04/2020) of the Higher Institute of Biotechnology of Monastir. DNA extraction procedure was based on the salting out protocol70. We applied the haplogroup nomenclature proposed by http://www.phylotree.org/Y/.Samples were typed for 23 Y-STR loci  using the kit PowerPlex Y23 System . The amplified products were separated in an ABI 3130 Genetic Analyzer (Applied Biosystems) and analysed with GeneMapper v4.0 software (Applied Biosystems) using the supplied allelic ladders and internal size standard. In order to assign Y-chromosome haplogroups, the samples were genotyped with a set of three different multiplexes that included a total of 29 Y-SNPs: 10 Y-SNPs in Multiplex 1 + M13, 6 Y-SNPs in Multiplex 2 and 13 Y-SNPs Multiplex E40. We predicted their Y-haplogroups (which were not previously described) using Whit Atheys’ Haplogroup Predictor.A total of 132 Tunisian Andalusian samples originating from Testour (48), Slouguia (22), Qalaat-El-Andalous (19) and El Alia (43) were previously typed for 12 Y-STR71. The population genetic structure of the 18 Tunisian populations was assessed through several approaches: pairwise genetic distances of Slatkin’s (RST) and analysis of molecular variance (AMOVA) pooling populations according to ethnical criteria, using the Arlequin software version 3.5.1.271, along with Spatial Analysis of Molecular Variance (SAMOVA) performed with SAMOVA v1.072. With the use of additional geographic information, SAMOVA maximizes the proportion of genetic variance due to differences among populations (FCT) for a given number of genetic clusters . Different numbers of groups (K = 2 to K = 7) were tested and then we considered the best grouping that yielding the highest FCT value.Diversity parameters  were calculated using Arlequin software version 3.5.1.2RST pairwise genetic distances for 12 Y-STR , using IBM SPSS Statistics version 19.0 software.The genetic relationships between Tunisian populations and between Tunisian and other worldwide populations  were assessed by generating a Multidimensional Scaling (MDS) based on http://www.fluxus-engineering.com/)74 using in the first step the set of 17 Y-STR loci  common to 114 samples from North Africa and Middle East regions which their haploroup classification to haplogroup T were based on Y-SNP data. Then, considering the same sampling, we generated a network using 13 Y-STR loci by omitting 4 Y-STR loci . In a second step, we generated a network based on 13 Y-STR loci coincidentally typed in 453 individuals belonging to haplogroup T from 36 worldwide populations , collected from the literature. For some populations, haplogroup classification, when not originally reported, was inferred from their Y-STR haplotypes using the online program Whit Atheys’ Haplogroup Predictor.The median joining network of Y-chromosome haplogroup T was constructed with the Network software version 10.2  for the 13 Y-STRs in the case of a modal haplotype as a root, and as proposed by Hallast et al.https://www.goldensoftware.com/products/surfer. The assumed geographic location corresponded to the centre of the distribution area from where the individual samples were collected.The T-M70 haplogroup frequencies obtained were used as the input data for a grid-based contour map, to study its geographic distribution. Surfer 8.0, mapping software from Golden Software, LLC, USA, was used for the frequency spatial distribution mapping. Kriging gridding method was adopted for the interpolation of geographical data Supplementary Legends.Supplementary Figure S1.Supplementary Figure S2.Supplementary Tables.


Admixed populations arise when two or more ancestral populations interbreed. As a result of this admixture, the genome of admixed populations is defined by tracts of variable size inherited from these parental groups and has particular genetic features that provide valuable information about their demographic history. Diverse methods can be used to derive the ancestry apportionment of admixed individuals, and such inferences can be leveraged for the discovery of genetic loci associated with diseases and traits, therefore having important biomedical implications. In this review article, we summarize the most common methods of global and local genetic ancestry estimation and discuss the use of admixture mapping studies in human diseases. Admixed populations are the result of gene flow between reproductively isolated groups, owing to events that have occurred throughout human history, including migratory events, the discovery of new territories, or the slave trade. As a result of the intermixture and recombination, over time, the genomes of individuals in the hybrid population will contain a mosaic of ancestries from different population sources in their chromosomes. The length of the chromosome segments inherited from the different ancestral populations will be proportional to the time elapsed since the admixture event. These tracts shorten over the generations by the meiotic recombination process, so that the most recently admixed populations, such as the Canary Islanders in Spain or the Latino populations, would retain longer ancestral tracts, while the populations that mixed more distantly in time, such as the Uyghur in China, would harbor shorter ancestry segments in their chromosomes ,2.As such, the admixture proportions and the elapsed time since the admixture event can be inferred based on linkage disequilibrium (LD) ,4. When Several studies support that genetic ancestry and admixture can partially explain the differences in the prevalence of complex diseases and treatment responses between population groups, due to the unequal distribution of allelic frequencies of the underlying causal variants across populations. As such, and given that the prevalence of many traits and diseases differs between populations, the analysis of genetic ancestry differences between affected and nonaffected subjects, as well as the link of these differences with the pathogenesis, and with evolutionary, environmental, or behavioral factors, plays an important role in biomedical research. Some examples are the high prevalence of multiple sclerosis among Europeans , of diabCYP2C19) and Paraoxonase 1 (PON1) genes, which are highly structured by ancestry, modify the response of the antiplatelet agent clopidogrel in Puerto Ricans [Many studies also support the existence of genetic variants affecting drug metabolism, transport, and toxicity, which vary widely among populations and ethnic groups ,11. In to Ricans . Additioo Ricans  or to tro Ricans .Given the importance of genetic ancestry in medicine, here, we provide an updated review of the most common methods for global and local genetic ancestry estimation, and of their use in admixture mapping approaches, highlighting a few key findings from the recent literature. We also discuss the potential of Next Generation Sequencing (NGS) data for ancestry estimation.Global ancestry (GA) is the fraction of genomic ancestry from each admixed individual that can be ascribed to each of the ancestral populations contributing to the recently admixed population A. The esLocal ancestry (LA) is a term commonly used to refer to the ancestry in each of the chromosome blocks, also known as ancestral tracks, in recently admixed individuals B. For thSome of the genetic characteristics of recently admixed populations described above (chromosome blocks and ALD distribution) allow the estimation of ancestry with a relatively small number of genetic markers. These markers are usually single nucleotide polymorphisms (SNPs) with a different distribution between populations, known as ancestry informative markers (AIMs). Their number will also depend on the assessed population, the ancestral groups, and the time since the admixture event . In ordeThe use of genotyping microarrays has also led to the development of improved methods to infer LA, such as LAMP-LD , RFMix , and HAPIn order to identify the optimal approach for each scenario, benchmarking the different algorithms and reference panels is necessary. Previous reviews have compared the characteristics and effectiveness of local ancestry estimators ,44,45,46Regarding the necessary requirements for the use of each estimator, it must be considered, for example, whether a phasing step is needed prior to ancestry estimation. This step is crucial for an accurate estimate of ancestry and is closely linked to the density of available markers . TherefoMoreover, the inherent characteristics of the population under study can also influence software selection. For example, although RFMIX  or LAMP-Finally, another important aspect that must be considered is the computational requirements of each software, such as the execution time or memory used, which is strongly conditioned by the sample size of the admixed sample under study ,44. In tThe distribution of allelic frequencies in recently admixed populations is closely related to those frequencies found in their ancestral populations ,49. WhenAdmixture mapping studies can be performed using case-only or case-control approaches. The case-only design is based on the comparison between observed and average expected ancestry to detect loci enriched in one of the ancestries, while the case-control approach consists of comparing LA pointwise between cases and controls ,54. AlthSince 2005, genome-wide association studies (GWAS) have allowed the identification of thousands of genetic loci associated with many traits, including complex diseases . HoweverOn the contrary, admixture mapping studies lose efficiency when allele frequencies are similarly distributed among ancestral populations and when the LD in the parental populations is unknown ,65. FurtBased on admixed populations, several studies have implemented admixture mapping approaches to reveal novel risk factors associated with complex diseases, including cancer, hypertension, and autoimmune, respiratory, and infectious diseases. Primarily, these studies have been widely applied in African Americans and Hispanic/Latino populations. The genetic contribution of each of the parental populations has been estimated for African Americans from four different states, providing an estimate of average ancestry corresponding to 76.4% African, 20.9% European, and 2.7% Native American . A similMycobacterium tuberculosis in the South African Colored population [Staphylococcus aureus in African Americans [Zhu and colleagues performed an admixture mapping in African Americans, identifying an excess of African ancestry at 6p24 and 21p21 associated with hypertension . Gignouxpulation  and the mericans , allowinmericans ,11. Thermericans .EYS) gene significantly associated with type 2 diabetes [PLCG2) gene, located in that region, was enriched of deleterious variants among asthma cases [Additionally, although less frequently, admixture mapping studies have also been performed in admixed populations other than Latinos or African Americans. For instance, Sun and colleagues recently performed an admixture mapping approach in the Hawaiian population in order to determine the relation of the genetic admixture with different cardiovascular traits. Interestingly, this admixed population  has a hdiabetes . Moreovediabetes . This stma cases . Likewisma cases , and thema cases . GeneticThe technological development of NGS over the years, together with the reduction in the sequencing costs, offers a great opportunity for genetic ancestry studies to develop further. Among the major advantages of this technology compared to microarrays  is its hHowever, although the application of NGS implies important advances, its use to estimate LA still has important limitations . First, Although the blooming use of NGS represents a great opportunity for population studies improvement, only two NGS-based admixture mapping studies have been published to date ,91. Liu Genetic ancestry studies and admixture mapping approaches have expanded genetic knowledge in biomedical research, revealing new loci associated with traits and diseases that could not have been detected by conventional association studies. Despite this, the available genomic resources need to be improved to obtain more accurate ancestry inferences. For instance, WGS data for African or Middle Eastern populations are still limited. Interestingly, the genetic features of Arab populations could be a valuable opportunity, especially in the study of recessive traits given the high rate of consanguinity that could be overlooked in other population groups . In thisConsequently, a more equitable representation of the ancestral groups in association studies will improve the development of personalized medicine. The unequal proportion in European-based GWAS is known to generate a bias in the findings that could lead to misinterpretations and hamper the risk prediction, prognosis, or response to drugs in under-represented populations . In thisThere are other approaches currently used in biomedical research that would benefit from taking genetic ancestry into account. Gene expression studies, which seek to explain how the expression of certain genes influences the development or severity of a trait, are an example. Based on the evidence that gene expression levels also differ between populations, in recent years, there has been a need to incorporate ancestry in expression studies and generate ancestry-dependent transcriptomic profiles ,98,99. AIn summary, promoting genetic studies in admixed populations, and the use of admixture mapping studies, combined with the alternative approaches described, promise the identification of novel disease associations and a better understanding of complex trait genetics. Eventually, these results will translate into a more equitable representation of the catalogs of genetic variation across populations.


Abies alba Mill.) at its southwestern range margin . We sampled 45 populations throughout the French Pyrenees and eight neighboring reference populations in the Massif Central, Alps, and Corsica. We genotyped 1,620 individuals at three chloroplast and ten nuclear microsatellite loci. We analyzed within‐ and among‐population genetic diversity using phylogeographic reconstructions, tests of isolation‐by‐distance, Bayesian population structure inference, modeling of demographic scenarios, and regression analyses of genetic variables with current and past environmental variables. Genetic diversity decreased from east to west suggesting isolation‐by‐distance from the Alps to the Pyrenees and from the Eastern to the Western Pyrenees. We identified two Pyrenean lineages that diverged from a third Alpine–Corsica–Massif Central lineage 0.8 to 1.1 M years ago and subsequently formed a secondary contact zone in the Central Pyrenees. Population sizes underwent contrasted changes, with a contraction in the west and an expansion in the east. Glacial climate affected the genetic composition of the populations, with the western genetic cluster only observed in locations corresponding to the coldest past climate and highest elevations. The eastern cluster was observed over a larger range of temperatures and elevations. All demographic events shaping the current spatial structure of genetic diversity took place during the Mid‐Pleistocene Transition, long before the onset of the Holocene. The Western Pyrenees lineage may require additional conservation efforts, whereas the eastern lineage is well protected in in situ gene conservation units. Due to past climate oscillations and the likely emergence of independent refugia, east–west oriented mountain ranges may be important reservoir of genetic diversity in a context of past and ongoing climate change in Europe.In a conservation and sustainable management perspective, we identify the ecological, climatic, and demographic factors responsible for the genetic diversity patterns of the European silver fir ( Abies alba Mill., decreases from east to west in western Europe, suggesting isolation‐by‐distance from the Alps to the Pyrenees and from the Eastern to the Western Pyrenees. The two Western and Eastern north‐Pyrenean lineages identified diverged long ago, during the Mid‐Pleistocene Transition, while population size fluctuation occurred later, possibly during the Late Glacial Maximum, with a contraction in the west and expansion in the east. East‐west oriented mountains such as the Pyrenees are important reservoirs of genetic diversity in a context of climate change, making likely the emergence of independent refugia, lineages, and possibly local adaptation, needing conservation measures.Genetic diversity of the silver fir,  It is an economically important species as well as a keystone species which maintains high biodiversity forest ecosystems  and until the industrial revolution  as external references. We addressed the following questions:Abies alba ancestors expanded southward across Europe from circumboreal high latitudes and diversified during the general cooling of the late Eocene and Neogene period, starting some 25 million years ago and culminating during the Pleistocene  to allow sampling trees at least 20 meters apart from each other. Sampling consisted of three cambium disks per tree, frozen in liquid nitrogen in portable devices during collection. DNA extraction followed the protocol of Demesure et al. .2.2Among the 52 sampled populations, 50 were analyzed at both chloroplast (cpSSR) and nuclear (nuSSR) microsatellites and two from Western Alps at only one type of marker (VES at nuSSR and LURE at cpSSR).bies nordmanniana and transferable to A. alba . One nuSSR (SF1) displays a trinucleotide pattern, whereas all the others are dinucleotides. The ten nuSSR were genotyped in two multiplexes using the Multiplex PCR Kit (QIAGEN). The final PCR volume was optimized to 10 μl, and the PCR mix for both multiplexes was 5 μl of the QIAGEN Multiplex PCR Master Mix, 2 μl of DNA (10 ng/μl), and 0.2 μM primers. For both multiplexes, the PCR thermal profile was the following: an initial step at 95°C for 15 min, followed by 35 cycles at 95°C for 30 s, 57°C for 90 s, and 72°C for 90 s, with a final 30‐min extension step at 60°C. PCR products were scored at INRAE in Avignon (France) using an AB 3730 XL automatic sequencer (Applied Biosystems) with LIZ‐600 as internal size standard.Three cpSSRs, Pt30141  were estimated using Spagedi1.4b , A. cilicica ((Antoine & Kotschy) Carrière), A. cephalonica (Loud.), and A. bormuelleriana (Mattf.) as outgroup, obtained from the study of Awad et al. .Pairwise differentiation between populations and between mountain ranges was computed using GENEPOP 4.7.5 Rousset, . A phylod et al.  producedd et al. . Linear  Slatkin ) on geog2.3.3http://www.github.com/dipetkov/eems).Spatial population structure in the Pyrenees was explored by estimating effective migration surfaces  steps, followed by a run of 500,000 steps. We used Structure Harvester 0.6.94 ; Lesp for the Eastern Pyrenees (Q = 0.88); and Vent for the Southern Alps (Q = 0.93). An alternative sampling scheme would have been to pool a subset of individuals across populations belonging to the same genetic cluster, but this could have impacted the detection and the quantification of population size changes  representing the contact zone between the genetic lineages in the Pyrenees. To confirm that the results were not biased by our choice of populations, we duplicated the analysis by choosing another set of 4 populations: Issa (Q = 0.92), Bele (Q = 0.87), Sigu (Q = 0.60), and Punt (Q = 0.86) for the Western Pyrenees, Eastern Pyrenees, admixed Pyrenees and Southern Alps, respectively  framework implemented in DIYABC 2.1.0 , at T1 a first event of divergence gave rise to both the Alps and the Eastern Pyrenees lineages, then at T2 the two lineages came into contact to give rise to the Western Pyrenees lineage. At T3, a secondary contact took place between the Pyrenean lineages to give rise to the admixed population of the Pyrenees. Finally, scenario S4 (Alps–West Pyrenees Split followed by two admixtures) is as in S3, but the Alps and Western Pyrenees lineages diverged first and came into contact later to give rise to the Eastern Pyrenees population. In the four scenarios, fluctuation of population sizes was allowed along final branches .In scenario S1 (Simple split), a single event of divergence took place at time TSSRµNu) was drawn from a log‐uniform distribution constrained between 10–4 and 10–3, while for cpSSR, SSRµCp was sampled between 10–8 and 10–3. Priors for times of demographic events were drawn from a normal distribution of mean equal to 25,000 and standard deviation equal to 12,500. Priors for demographic sizes were drawn from a normal distribution of mean equal to 5,000 and standard deviation of 2,500. We applied the procedure “evaluate scenario‐prior combination”  and θ1 = N1μ1 (past) , relative root mean square (RMSE), 50% and 95% coverage and Factor 2). The goodness‐of‐fit of the combination of the best model and parameter posterior distributions was appreciated using the model‐checking procedure in which the observed values of four additional summary statistics  could be used as a proxy to model the organization of the genetic diversity in the Northern Pyrenees.Environmental characteristics were obtained for each Pyrenean population. Slope, altitude, latitude, longitude, and nineteen bioclimatic variables from contemporary periods (1970–2000) and older periods , Last Glacial Maximum approx. 22,000 years BP, Last interglacial approx. between 120,000 and 140,000 years BP) were extracted from the WorldClim database . All appendices are deposited open access at the following address: https://doi.org/10.15454/X6ZJU0.33.1e at nuSSR between 0.72 and 0.74) than in the Pyrenees (0.55–0.65), while genetic diversity in the Pyrenees was highest in the eastern part of the range . The haplotype frequency distribution was L‐shaped, with a large number of low‐frequency haplotypes (60 with frequency lower than 0.01) and one common haplotype (frequency = 0.48). Population and mountain‐range genetic diversity levels are reported in Appendix e Figure .Figure FST, RST) between mountain ranges was significant at both nuSSR and cpSSR and globally stronger between the Alps and the Pyrenees than among zones within the Pyrenees  on geographical distances. However, there was no significant difference between the slopes of the linear regressions of pairwise statistics based on allele identity (FST) and allele size (RST) meaning that populations could have been isolated for a relatively short period of time (in comparison to the mutation rate) or that gene flow between populations was strong. The EEMS confirmed the progressive colonization of the Pyrenees. However, in some parts, the presence of deviations from the step‐by‐step model was observed as either a stronger restriction of gene flow or an excess of gene flow (Appendix Pairwise genetic differentiation (3.2K = 3 (deltaK = 247)  for all four scenarios. For scenario S2, type I error (rejection of S2 when true) and the type II error (acceptation S2 when not true) were respectively 0.56 and 0.65. Model adequacy of the best scenario S2 is nonetheless acceptable since among thirty tests, only four observed summary statistics (the mean genic diversity between pairs of Pyrenees clusters) deviated significantly from its simulated distribution , population size, admixture, and mutation rates (see Appendix Ncurrent/Npast) was greater than 1 over 95% of its distribution for both nuSSR and cpSSR  indicating a population expansion event  indicating a population contraction event. In the Western Alps and the admixture zone of the Pyrenees  cannot be excluded from the 95% posterior distribution envelope.In the Eastern Pyrenees, the mode of posterior distribution of the ratio of current to past size  were negatively associated with both longitude and mean temperatures during the LIG wettest quarter  were associated with lower temperature , whereas the populations belonging to the eastern cluster (Qw < 0.25) were associated with a larger range of temperatures .Among the significant regressions, high values of the membership coefficient for the western genetic cluster . However, we also detected a contact zone allowing gene flow between genetic clusters in the Northern Central Pyrenees which could explain the absence of phylogeographic signal (absence of difference between FST and RST) and the deviation from a stepping‐stone model in EEMS.Our study shows that genetic diversity of silver fir decreases from east to west, Alpine populations being more diverse than populations in the Northern Pyrenees. This confirms the observations made on coarser sampling grids , demonstrating the roles of east‐west land barriers in models of simple processes of differentiation, colonization, and mixing , and it was five degrees higher during the last interglacial period, although mean temperature was much colder then, and probably more selective for trees . The strength of such environmental contrasts could have contributed to the maintenance of a strong differentiation between Pyrenean lineages. Whether long‐lasting differentiation in different environments has led to differential selection remains to be tested, but indications that regional environmental gradients lead to diversifying selection can be found in 4.3https://www.cbd.int/). Since 2016, recommendation WCC‐2016‐Rec‐104 of the International Union for Conservation of Nature (IUCN) “Integrating autochthonous forest genetic diversity into protected area conservation objectives” recommends that geographic entities that have in situ genetic diversity conservation as their main purpose should be recognized as protected areas . Identifying which forests deserve the status of gene conservation unit (GCU) warrants the identification of well‐defined evolutionary lineages and the selection of forests where management is compatible with the occurrence of genetic processes governed by natural selection , located in seven environmental zones , following the classification of Metzger et al.  from the Eastern Pyrenees belong to the eastern lineage with a membership coefficient of 0.77 to 0.88. One GCU (the forest of Saint Lary) from the Central Pyrenees is admixed between the two lineages. The remaining two GCUs (the forests of Hautacam and Barousse), also from the Central Pyrenees, belong to the western lineage with a membership coefficient of 0.77 to 0.80. While the Eastern Pyrenees  and the eastern lineage are properly covered, with GCUs from forests at both low and high elevation, the Western Pyrenees  are not covered and the western lineage is poorly covered, with low elevation populations and the western margin of the distribution lacking. Geographical edges of distribution ranges demonstrate original local adaptations  structure in the Pyrenees and suggests that geography alone cannot be used as proxy for sampling for conservation. As genetic differentiation can occur over short distance along elevation gradients in silver fir , the Pyrenean ibex (Capra pyrenaica pyrenaica Schinz), and the bearded vulture (Gypaetus barbatus L.). Many strategies are developed to protect these species in the Pyrenees such as the strict protection of old‐growth silver fir, beech, and pine forests  for building and/or refining conservation strategies in mountain forest tree species.The authors have no competing interests to declare.Caroline Scotti‐Saintagne: Data curation (lead); Formal analysis (lead); Methodology (lead); Writing‐original draft ; Writing‐review & editing . Thomas Boivin: Formal analysis ; Methodology ; Writing‐review & editing . Marie Suez: Formal analysis (supporting); Methodology (supporting). Brigitte Musch: Conceptualization ; Funding acquisition ; Writing‐review & editing . Ivan Scotti: Methodology ; Writing‐review & editing . Bruno Fady: Conceptualization (lead); Funding acquisition (lead); Writing‐original draft ; Writing‐review & editing .Appendix S1‐S5Click here for additional data file.


Aedes aegypti, raising the medical importance of this mosquito. We genotyped 12 microsatellite loci in four populations of Ae. aegypti from Sudan, two from the East and two from the West, and analyzed them together with a previously published database of 31 worldwide populations to infer population structure and investigate the demographic history of this species in Sudan. Our results revealed the presence of two genetically distinct subspecies of Ae. aegypti in Sudan. These are Ae. aegypti aegypti in Eastern Sudan and Ae. aegypti formosus in Western Sudan. Clustering analysis showed that mosquitoes from East Sudan are genetically homogeneous, while we found population substructure in West Sudan. In the global context our results indicate that Eastern Sudan populations are genetically closer to Asian and American populations, while Western Sudan populations are related to East and West African populations. Approximate Bayesian Computation Analysis supports a scenario in which Ae. aegypti entered Sudan in at least two independent occasions nearly 70–80 years ago. This study provides a baseline database that can be used to determine the likely origin of new introductions for this invasive species into Sudan. The presence of the two subspecies in the country should be consider when designing interventions, since they display different behaviors regarding epidemiologically relevant parameters, such as blood feeding preferences and ability to transmit disease.Increases in arbovirus outbreaks in Sudan are vectored by  Aedes aegypti is an invasive mosquito found across the tropical and subtropical world; and the principal vector of worldwide epidemics of arboviruses such as dengue, yellow fever, Zika, and chikungunya virus  in Africa with darker body color and Ae. aegypti aegypti (Aaa) outside Africa with lighter body color (Ae. aegypti aegypti subspecies (Aaa) that has spread throughout the tropical and subtropical world by humans (Ae. aegypti formosus (Aaf), occurs in natural breeding habitats such as forests  for the invasion of Ae. aegypti to the Sudan.At the global scale, studied. . In Sudastudied. . The curStudy sites were divided into two major regions, including four cities, two cities in Eastern Sudan , and two in Western Sudan . Coordinates and geographic locations are shown in Aedes biology  using standardized sampling methods for Ae. aegypti surveillance . After their emergence, adult mosquitoes (F0) were identified morphologically according to the descriptions of Mattingly and Huang  from any locality or residential area within each city. To avoid loss in sampling due to mortality we collected six individuals per trap per the same breeding site and reared to adults in the laboratory. Only one individual mosquito was genotyped per trap or from the same breeding site. Given that ntainers . The usend Huang . SubsequAe. aegypti mosquitoes using the DNeasy Blood and Tissue kit (Qiagen) according to manufacturer instructions, with an additional RNAse A (Qiagen) step. Individual mosquitoes were genotyped at Jeffrey Powell’s laboratory at Yale University as described by Gloria-Soria et al. (http://www.geneious.com). Raw microsatellite calls used for this project are provided in https://vectorbase.org). Sudan genotypes can be retrieved as Vectorbase bioproject VBP0000762.Total nucleic acids were extracted from 201 individual a et al. . Microsaa et al.   to calculate the percentages of related individuals within each population, in order to confirm that the assumption of independent genotypes was not violated. For each pair of individuals, maximum log-likelihood estimate (MLE) or R was calculated for four relatedness categories: unrelated, parent–offspring, full-siblings, and half-siblings. The MLE has a lower mean square error and performs well with a relatively small sample size .F; homozygosity), was compared with the 95% confidence intervals for the expected sum of the squared allele frequencies (expected F). The sums of squared alleles were adjusted for sample sizes and the number of alleles. The 95% confidence intervals and standard errors for observed F were calculated using 1,000 simulated samples.The exact Hardy-Weinberg equilibrium (HWE) test and linkage disequilibrium (LD) among all pairs of loci were estimated using GENEPOP v4.2.1 . SignifiMNa), mean number of effective alleles (MNe), allelic richness (AR), private allelic richness (PAR), observed heterozygosity (Ho), expected heterozygosity (He), and inbreeding coefficients (FIS) were estimated using Genetic Analysis in Excel (GenAlEx) version 6.3  and 2) the expected heterozygosity based on observed alleles (Heq). He > Heq therefore indicates a recent Bottleneck event and He < Heq a recent population expansion . Bayesian clustering analysis implemented in Structure software version 2.3.2  and Discriminant Analysis of Principal Components (DAPC), using the R packages ade4 ; ic range . These pon 2.3.2 , which ion 2.3.2  was calcon 2.3.2 . To avoion 2.3.2 , we stanon 2.3.2  method. ges ade4 , LEA  for Ae. aegypti populations worldwide the program cannot detect bottlenecks that occurred more than ∼1,200 generations ago , with 10,000 permutations, in GenAlEx version 6.3 (Pairwise geographic distances (km) among study areas of the mosquito populations were calculated using (GenAlEx) version 6.3  and isolsion 6.3 .Ae. aegypti in Sudan following a hierarchical approach using Approximate Bayesian Computation methods (ABC),  and East Sudan (ES), Africa, and Out-of-Africa populations (first round); or from WS and ES, West Africa (WA), East Africa (EA), America, and Asia (second round). Datasets for three runs were generated by selecting individuals from different populations within each region; datasets for the other three runs were generated by drawing individuals from one representative population per region, switching populations for each of the three runs (Ae. aegypti (Africa vs out-of-Africa). Scenario 1: WS and ES Ae. aegypti derived from Africa Ae. aegypti; Scenario 2: WS and ES Ae. aegypti derived from Out-of-Africa Ae. aegypti; Scenario 3: WS Ae. aegypti derived from Africa Ae. aegypti and ES Ae. aegypti derived from Out-of-Africa Ae. aegypti; Scenario 4: ES Ae. aegypti derived from Africa Ae. aegypti and WS Ae. aegypti derived from Out-of-Africa Ae. aegypti; (Ae. aegypti is affected by the climatic conditions (−6 - 1 × 10−5 based on previous publications and rates reported in the literature for other Diptera species was used (We performed demographic inference of s (ABC), , as impls (ABC), . The pro. A mutawas used . DetailsAe. aegypti ranged between 1.6% in Nyala population and 5.3% in Port Sudan population, indicating that the great majority of individual mosquitoes are not siblings (The maximum likelihood estimation (MLE) showed that the percentage relatedness (first-degree relatives) for siblings .He) within populations for all loci was 0.599. Shannon’s Information Index (I) ranged from 0.056 (AG5) to 2.286 (AC5) with a mean value of 1.228 (IS) ranged from—0.0004 (B2) to 0.526  with an average value of 0.261 allele per locus . Small deviations from HW may be caused by rare null alleles  within-population comparisons deviated significantly from Hardy–Weinberg Equilibrium  observed, followed by Nyala - Port Sudan and Nyala - Kassala . All pairwise comparisons were significant (p > 0.05) in all 6 pairwise comparisons. Generally, there was a lower degree of genetic differentiation among Western Sudan populations  and Eastern Sudan  than between East and West Sudan (FST) of all four Ae. aegypti populations in Sudan was positively correlated with geographic distance , indicating isolation by distance.Mosquitoes from Eastern Sudan sites were significantly differentiated from Western Sudan , with FSst Sudan . GeneticBayesian clustering analysis on the global dataset points to K = 2 being the optimum number of clusters, when evaluated using ΔK  method, Ae. aegypti had undergone a recent bottleneck. The excess in heterozygosity was not significant (p < 0.05) in any population using the two-phase mutation and the stepwise mutation models under Wilcoxon sign-rank test and shift mode test, except for the Port Sudan population, which was significant under the one tail TPM model. However, the Mode Shift showed a normal L-shaped distribution   and East Sudan (ES) populations, with 5 of six runs supporting Scenario 3: > 0.9992 , in whic> 0.9992 . A follo> 0.9992 , favored 0.5363) , likely Ae. aegypti in the country, the principal vector of arboviruses. Ae. aegypti has been reported broadly in several geographical areas in Sudan since the 1940s, including the eastern regions , central Sudan (Wad Medani and Khartoum), western regions , and the Nuba mountains in the south  among mosquito populations across study sites, which is consistent with the findings from a recent study of Ae. aegypti in a large Sahelian zone in Sudan that used morphology and the Cytochrome oxidase-1 mitochondrial marker (CO1)  and among eastern Sudan regions  might be explained by human-driven passive dispersal facilitating gene flow. While Ae. aegypti can only move hundreds of meters around their larval habitats, studies have demonstrated that Ae. aegypti travels long distances by taking advantage of human assisted transportation routes through land, sea, or air  . Genetic, or air . This is, or air ; while l, or air  and port, or air . Occasio, or air . The dic, or air , and is , or air .Ae. aegypti in Sudan is consistent with reports from parts of coastal East Africa dating back as far as the 1950s, based on morphology and habitats  is in remarkable agreement with historical records. Assuming ∼10 generations/year, our results are consistent with the Sudan populations originating between 70 and 80 years ago. These findings may be in part explained by the historical migrations from West Africa to the Western Sudan either through pilgrimage to Saudi Arabia or through the trade with West and East Africa. Asian countries might be an important source of mosquito vectors and possibly dengue virus serotypes into Sudan, driven by the international trade and travel to these countries through the major port of Sudan in the Red Sea (Port Sudan).The demographic analysis support that the two major genetic groups of ductions , one froAe. aegypti populations in Africa are known to historically bred almost entirely in forests. Today, populations of Ae. aegypti in Africa can be found in urban habitats  cities. habitats , even ifhabitats . Our fins (et al  suggestis (et al . The pres (et al .et al., (Ae. aegypti genetic structure and demographic history of the country. Unfortunately, more extensive sampling is currently limited by the political conflicts in the region. Here, we have shown that microsatellites have sufficient resolution to uncover the large-scale population structure in an area where the research budget is limited. However, they did not provide enough resolution to narrow down the precise source of the introductions that led to Sudan’s populations. Many more genome-wide markers will be needed in the near future to address this question, together with a larger reference panel that includes nearby regions. At the moment, the costs associated with this kind of study are high and beyond our budget.Our results recapitulate the regional genetic structure detected by Abuelmaali et al., , and expAe. aegypti in Sudan, genetic diversity and connectivity patterns among regions that can be used to monitor new introductions, changes in gene flow patterns, and the success of control strategies. Populations of Ae. aegypti considerably vary in their competence to transmit arboviruses (Our findings provide base-line data of the current population genetic status of the mosquito vector, oviruses  and resioviruses . The preoviruses . Geneticoviruses  will nee