Trypanosoma cruzi circulates in semiarid areas of northeastern Brazil in distinct ecotopes  where Triatoma brasiliensis Neiva, 1911 is the most important Chagas disease vector. In this study, we analyzed microevolutionary and demographic aspects of T. brasiliensis populations at the ecotypic, micro and macro-geographic scales by combining morphometrics and molecular results. Additionally, we aimed to address the resolution of both markers for delimiting populations in distinct scales.The protozoan T. brasiliensis from distinct ecotypic and geographic sites in the states Rio Grande do Norte (RN) and Paraíba (PB). The geometric morphometry was carried out with 13 landmarks on the right wings (n = 698) and the genetic structure was assessed by sequencing a region of cytochrome b mitochondrial gene (n = 221). Mahalanobis distance (MD) and coefficient of molecular differentiation (ΦST) were calculated among all pairs of populations. The results of comparisons generated MD and ΦST dendrograms, and graphics of canonical variate analysis (CVA).We sampled populations of R2 < 0.35). The factorial graphics built with the CVA evidenced population delimitation for the morphometric data at micro-geographic scales.Little structure was observed for both markers for macro-geographic scales. Mantel tests comparing geographic, morphometric and genetic matrices showed low correlation (all T. brasiliensis carries in its genotype a source of information to allow the phenotypical plasticity across its whole distribution for shaping populations, which may have caused a lack of population delimitation for CVAs in morphometric analysis for macro-geographic scale analysis. On the other hand, the pattern of morphometric results in micro-geographic scales showed well-defined groups, highlighting the potential of this tool to inferences on the source for infestation.We believe that  These insect vectors may transmit the disease when they are infected with the parasite Trypanosoma cruzi  (Kinetoplastida: Trypanosomatidae), the etiological agent of the illness. Although the disease’s incidence has declined in recent decades due to intense programs to combat the main domiciliated vectors, it is estimated that six to eight million people are still infected worldwide [Chagas disease is among the most important neglected diseases in Latin American countries. The transmission classically occurs  Chagas, 909 , T. brasiliensis Neiva, 1911, T. pseudomaculata Corrêa & Espínola, 1964 and T. sordida  [T. brasiliensis and T. pseudomaculata, native triatomines of difficult control and widespread in the semiarid Northeast. Whereas T. pseudomaculata is more associated with birds [T. cruzi infection), T. brasiliensis is associated with mammals, which are potential parasite reservoirs [T. brasiliensis is the most frequently Chagas disease vector collected in human domiciles and is considered the most important triatomine species in the region [More than 65 species of triatomines have been recorded in Brazil, but few exhibit synanthropic behaviors, such as l, 1859) . The BraT. brasiliensis species complex is based on a group of monophyletic taxa that occur in rocky outcrops of the semiarid region of the Brazilian Northeast, including seven species: T. brasiliensis; T. bahiensis Sherlock & Serafim, 1967; T. juazeirensis Costa & Felix, 2007; T. lenti Sherlock & Serafim, 1967; T. melanica ; T. petrocchiae Pinto & Barreto, 1925; and T. sherlocki Papa, Jurberg, Carcavallo, Cerqueira & Barata, 2002; in which T. brasiliensis is divided into two subspecies: T. b. brasiliensis Neiva, 1911 and T. b. macromelasoma Galvão, 1956 [T. brasiliensis (s.l.) exhibited numerous chromatic forms and emphasized it should be better investigated. This led Costa and colleagues to conduct a set of investigations based on biology, ecology, phylogeny, experimental crossings [T. melanocephala Neiva & Pinto, 1923 and T. vitticeps  (as uncertain). The chromatic variation combined with genetic information resulted in raising T. juazeirensis [T. melanica [T. b. macromelasoma), also because little genetic distance was observed with T. b. brasiliensis [T. sherlocki was included in this species complex by using the information on the variation in cytochrome b (cytb) and 16S mitochondrial genes [T. lenti and T. bahiensis [T. petrocchiae was also considered a member of this monophyletic group, in a study combining geometric morphometrics and information on multiple mitochondrial genes (12S + 16S + cox1 + cytb) [T. melanocephala and T. vitticeps [T. b. brasiliensis) as T. brasiliensis.The current status of the ão, 1956  observedrossings  among otrossings  based oneirensis  and T. mmelanica  to the siliensis . Later, ps Stal, 59  . The nonitticeps , which witticeps . RecentlT. brasiliensis species complex, molecular studies have been mainly based on the variation of mitochondrial DNA genes [T. brasiliensis populations at the ecotypic, micro and macro-geographical scales by combining morphometrics and molecular approaches.For the NA genes , 17, 25 NA genes . MoleculNA genes , 27, butTriatomine captures were conducted in the State of Rio Grande do Norte (RN), in the municipalities of Caicó, Currais Novos and Marcelino Vieira, whereas in the State of Paraíba (PB), insects were collected in the municipalities of Condado, Cajazeiras, Santa Teresinha, São José de Espinharas, São Mamede and Emas Fig. . The samWe based the population defined as the set of insects collected at the same site and capture spot. Each insect/wing was cataloged at the population level, receiving identification acronyms, namely: (i) the first two letters refer to the locality: Caicó-RN (CC); Currais Novos-RN (CN); Marcelino Vieira-RN (MV); Condado-PB (CD); Cajazeiras-PB (CZ); Santa Teresinha-PB (ST); São José de Espinharas-PB (SJ); São Mamede-PB (SM); and Emas-PB (EM) Fig. ; (ii) gevia TPS dig 2.17 [For this study, the anterior wing of 5–30 individuals per population was used. Considering the epidemiological importance, we did not exclude populations with lower numbers, as for the bug collected inside domiciles in Condado-PB. Photographic records of right wings were made using a stereomicroscope Zeiss™ Discovery V.12  with the image capture system AxioCam 5.0™ and ZEN™ software version 2.0 . Thirteen anatomical landmarks  and canonical variate analysis (CVA), which were assessed by the combination of variations in the 13 anatomical coordinates of each individual. All of these statistics were performed using the MorphoJ software [P-values were considered significant at P < 0.05.For shape measurements, Mahalanobis distances (MD) between all pairs of populations were calculated and their significance was assessed using a non-parametric test based on permutations , which were illustrated in dendrograms. The choice for MD instead of Procrustes distances (PD) was based on some studies that say that PD produces estimates that are statistically inconsistent if the variation is not isotropic –36. Addisoftware . P-valueb (cytb) gene was amplified by the polymerase chain reaction (PCR), using the primer pair CYTB7432F, and CYTBR and amplification conditions set by the authors who designed the primers [cytb sequences via Bayesian inference with unique haplotypes. The matrix was run in BEAST 1.8.0 [http://tree.bio.ed.ac.uk/software/figtree/). The evolutionary model was chosen according to the Bayesian information criterion (BIC) in MrModeltest [The total DNA of 3 legs was extracted using the Qiagen Blood and Tissue kit , following the manufacturer’s specifications. The mitochondrial cytochrome  primers , 38. Bot primers , 38. For primers . Edited  primers  program ST 1.8.0  with 10 ST 1.8.0  was usedodeltest  by usingodeltest .T. brasiliensis were calculated for insects from each population. The null hypothesis of neutrality was tested using Tajima’s D statistic [P-values < 0.05.For population analysis, summary statistics of genetic diversity of tatistic  and Fu’statistic . The foltatistic  was perftatistic  was usedtatistic  softwaretatistic  was testP < 0.0001). The cross-validation test wrongly classified 27–28% of the individuals according to sex , as expected. Thus, we assumed that sexual dimorphism may have influenced differentiation and dealt with sexes separately. We excluded populations with low number of samples (n < 5). Therefore, the resulting dataset included 349 samples from 24 populations for males  irrespective of sex. Both sexes were larger in peridomestic insects , it was not possible to observe any grouping delimitation in the CVA plot in relation to sex . Populations from São José dos Espinhais-PB also showed some well-delimited groups for both sexes, even though there was some overlap Fig. .Fig. 5LeFor Cajazeiras, clear delimitation was observed for males. For females of Cajazeiras, the peridomestic populations CZ18P and CZ19P were delimitated and there was an overlap for the remaining, that was composed of populations from distinct ecotopes. For Condado municipality, we only had a sufficient number for morphometric analysis for females but we observed that this peridomestic population CD46D) was delimited in a group separated from others, which were isolated in distinct areas of the graph . The exceptions were for the wild populations of Caicó , the peridomestic area of Currais Novos . The highest haplotypic diversity was observed for the populations CC51S (0.9556 ± 0.0594). The π value for SJ41S population was also the highest (0.003853 ± 0.002802), followed by MV29P (0.002486 ± 0.001997) and MV216S (0.001491 ± 0.001424), being below 0.001 for the remaining populations. The number of samples, haplotypes and remaining molecular statistics are provided in Additional file T. brasiliensis. The phylogenetic reconstruction by using unique haplotypes based on Bayesian inference revealed that posterior probabilities were low in general. For cases where these were higher than 80%, sequences had K2P lower than 0.009, confirming we were dealing with haplotypes with low variation. The molecular evolution model was HKY + G based on BIC . Following the hypothesis that the genetic structure may be driven by ecotypic force (predominant migration between the same ecotopes), a second test was performed by nesting groups of populations defined by their ecotopes within each municipality. For this kind of nesting, the percentage of variation between groups was even lower (10%) and the variation within groups was higher (41%). For both kinds of nesting populations, most of the variation was observed within populations (46–49%).Analysis of molecular variance (AMOVA) Table  demonstrP < 0.05) but in general, correlations were low. The highest correlation occurred between the MD between males and females (R2 = 0.74), as expected. The remaining correlations exhibited low values, such as between the ΦST/MD and the geographical distance (R2 < 0.36). For both sexes, the correlations between MD and ΦST were also low (R2 = 0.13–0.15).All Mantel tests comparing the matrices showed a significant correlation , but it was a pioneer study by using geometric morphometrics for assessing the variation among ecotypic populations [T. brasiliensis  on a wider geographical scale ranging from 95 km (North-South) to 240 km (East-West). Additionally, the morphometric information is here associated with genotypic variations. Some authors [cytb) does not suffer the influence of the environmental variation [The differences between the distributional patterns of morphometric and genetic variation have been extensively explored for insect vectors –53. For  studied . Populat studied . Therefo studied . The firphometry . The seculations . Our stu authors , 57 concariation . Thus, tT. brasiliensis collected in different ecotopes. The peridomestic individuals were significantly larger than the sylvatic individuals. Costa et al. [T. brasiliensis are usually found hungry in the field, which may explain the difference in size. We suggest that domestic insects did not have numbers enough for inferring the size. Geometric morphometrics effectively minimizes the size factor, but it does not eliminate it totally [Considering all the samples grouped according to the ecotopes and for each sex, we observed a relative ecotypic grouping in the PCA. Similarly, in Ceará, Batista et al.  found sia et al.  mentione totally . The ecoT. infestans in Argentina, the resolution of geometric morphometry to detect differences in macrogeographical scales was more evident in comparisons of individuals separated by municipality [In the dendrograms derived from the Mahalanobis distances (MD), it was possible to observe that some groups were formed by nearby populations, indicating geographical structure. The District of Patos is composed of the municipalities of Emas, São José de Espinharas, Santa Teresinha, Condado, all in the State of Paraíba with related bug populations. However, the Mantel test showed a weak correlation between the morphometric and geographical distance matrices, which may indicate that forces, other than geographical, maybe also driving the morphometric variation. For cipality . HoweverPrincipal components analysis (PCA) is the most popular method to characterize shape variation . HoweverT. dimidiata , Stevens et al. [T. juazeirensis, a member of the T. brasiliensis complex, Carbajal de la Fuente et al. [T. brasiliensis via active search, and they found more than twice males than females in sylvatic environments. This may indicate the existence of distinct patterns of migration between sexes.For both sexes, no structure was observed for the populations from Currais Novos; and for Caicó there was grouping delimitation only for females in CVA graphs. The lack of correlation between the CVA graphs in regard to sexes for Caicó may indicate a different migration pattern. However, for overall results, the Mantel test showed 73% correlation between males and females for MDs. For s et al.  found noe et al.  found moe et al.  capturedT. brasiliensis sequences . This value is expected intraspecifically since the values reported between the subspecies T. b. brasiliensis and T. b. macromelasoma were higher (K2P = 0.03) [We found low variation among  = 0.03) . Therefo = 0.03)  presente = 0.03)  which us = 0.03)  and also = 0.03) .T. brasiliensis. In this case, it is known that the ecotope does not influence the genotype, but for some triatomine vectors, gene flow occurs predominantly between ecotopes, as observed for T. infestans in the Andean valleys [The hierarchical molecular variance analysis (AMOVA) was conducted to evaluate the role of geographical and ecotypic forces in the genetic structure of  valleys . Here, n valleys , 25, alt valleys .ST matrices revealed that the geographical forces play a role in the distribution of genetic variation. However, most populations of Currais Novos were in the same cluster for morphometrics of males from Marcelino Veira. This was observed for some populations of Cajazeiras and Emas, although AMOVA has not shown statistical significance for geographical structuring. On the other hand, this corroborates the results of morphometry, in which Mantel tests did not evidence correlation between matrices of geographical and Mahalanobis distances.Both molecular and morphometric markers can be applied to understand macroevolutionary and microevolutionary processes in vector insects . ConcernT. dimidiata  in the Yucatan Peninsula in Mexico [T. infestans in Argentina [T. dimidiata the authors did not find remarkable conformity between markers [The comparison of the results of the molecular and morphometric tools showed low conformity by Mantel tests. But in all dendrograms, the populations of Cajazeiras and Currais Novos trend to be grouped in the same branch. This indicates that in some cases the markers are in agreement. This conformity has already been tested by other authors and for other species, such as n Mexico  and T. irgentina . For T.  markers , which m markers .T. infestans in Argentina, concluding that geometric morphometrics represents a useful tool. However, the populations analyzed by these authors all came from artificial ecotopes. We must emphasize that our results suggest that the ecotope seems to have an important effect on phenotypic variation. Therefore, the morphometric associations between populations of different ecotopes on the microgeographical scale can present important clues to detect infestation processes when analyzed within the same municipality. In this sense, geometric morphometrics was more sensitive to delimit populations on a microgeographical scale than on a macrogeographical scale, which was evidenced in the factorial maps. For example, in the maps illustrating the analysis of canonical variables for both males and females, the peridomestic populations of Cajazeiras and São José de Espinharas showed associations with sylvatic populations. These findings corroborate the idea that this tool can be used to detect infestation foci [The strategy of observing variations within a smaller geographical scale aimed to seek clues about the processes of (peri) household infestation. To our knowledge, this is the first attempt in the literature to test geometric morphometrics for this purpose focusing on vectors of Chagas disease in Brazil. Schachter-Broide et al.  applied ion foci . Howeverion foci  to validT. brasiliensis may carry information that allows equal morphological plasticity throughout the entire collection range which prevents structuring on the macrogeographical scale. However, demographic events, such as bottlenecks or founder effects, might be acting to maintain homogeneity in groups of insects collected at the same point, as recently observed for Brazilian populations of T. sordida [cytb is the most frequent in the entire collection range, and this phenomenon is well known for T. brasilensis [cytb gene on a microgeographical scale. The power demonstrated by geometric morphometrics to delimit populations on a microgeographical scale indicates that this tool has a higher resolution in revealing population structure than the molecular marker used here.The genome of  sordida . Retentisilensis , 17, 25.T. brasiliensis in the present and also for future climate scenarios - global warming. Model projections indicated the potential for expansions of T. brasiliensis for 2050, highlighting the need for constant monitoring T. brasiliensis distribution [The ecologic niche model was used for the first evaluation of the distributional potential of members of ribution .The most remarkable result obtained here was the resolution in the population delimitation on a microgeographical scale for geometric morphometrics, which was sometimes in agreement with the results of molecular variation. At this scale, populations from distinct ecotopes appeared to be more associated in some cases, which questions the role of the ecotope on the distribution of morphometric variation. This indicates that geometric morphometrics can be used to assess population structure, microevolutionary processes and to look for foci of reinfestation. The combination of molecular and morphometric markers have the potential to be used in decision-making for more rational, targeted and resource-saving control measures (as for spraying domiciles with insecticides). Additionally, the geometric morphometrics based on the venation of the wings of triatomines is a low-cost and quick-to-perform technique. Therefore, the results presented here indicate that there is potential applicability of this technique in vector control measures in the near future.Additional file 1: Figure S1. a PCA for the morphometric variation of all T. brasiliensis. Red dots represent females and blue dots represent males. b The canonical variable using the discriminating function of the variation between the sexes. c Boxplots representing variations in the size of the wing centroid.Additional file 2: Table S1. Statistics for the Mahalanobis distance of males. Table S2. Statistics for the Mahalanobis distance of females. Table S3. Summary statistics of genetic diversity. Table S4. Estimates of evolutionary divergence between cyt B fragments (447bp). Table S5. ΦST pairwise population comparisons for all populations.Additional file 3: Figure S2. Graphics of Canonical Variance for males (a) and females (b) T. brasiliensis populations grouped by municipality. See Fig. Abbreviations: CC, Caicó-RN; CD, Condado-PB; CN, Currais Novos-RN; CZ, Cajazeiras-PB; EM, Emas-PB; MV, Marcelino Vieira-RN; SJ, São José de Espinharas-PB; ST, Santa Teresinha-PB.Additional file 4: Figure S3. Left: The locations where T. brasiliensis populations were collected in CurraisNovos, State of Rio Grande do Norte. Right: Canonical variate analysis (CVA) based on CVA1 and CVA2 for both sexes.Additional file 5: Figure S4. Bayesian phylogenetic tree based on the cytb gene (447 bp) for T. brasiliensis using the molecular evolution model TN93 + I. Posterior probabilities are indicated at the nodes. Outgroup: T. petrocchiae (GenBank: MN177967).


HBB gene cluster, a genomic region known to be under strong balancing selection in sub-Saharan Africa due to its co-evolution with malaria. This study further supports the role of evolution in shaping genomes of Cameroonian populations and reveals fine-scale hierarchical structure among and within Cameroonian ethnicities that may impact genetic association studies in the country.Inferences from genetic association studies rely largely on the definition and description of the underlying populations that highlight their genetic similarities and differences. The clustering of human populations into subgroups (population structure) can significantly confound disease associations. This study investigated the fine-scale genetic structure within Cameroon that may underlie disparities observed with Cameroonian ethnicities in malaria genome-wide association studies in sub-Saharan Africa. Genotype data of 1073 individuals from three regions and three ethnic groups in Cameroon were analyzed using measures of genetic proximity to ascertain fine-scale genetic structure. Model-based clustering revealed distinct ancestral proportions among the Bantu, Semi-Bantu and Foulbe ethnic groups, while haplotype-based coancestry estimation revealed possible longstanding and ongoing sympatric differentiation among individuals of the Foulbe ethnic group, and their Bantu and Semi-Bantu counterparts. A genome scan found strong selection signatures in the HLA gene region, confirming longstanding knowledge of natural selection on this genomic region in African populations following immense disease pressure. Signatures of selection were also observed in the  Although genome-wide association studies (GWASs) have been successful in uncovering the contribution of single nucleotide polymorphisms (SNPs) to common diseases in populations with European and Asian ancestries, studies on African populations have been less successful. Population structure/substructure, characterized by extensive genetic variation and low linkage disequilibrium (LD) among and within African populations is a major cause of the poor performance of GWAS and other statistical genetics tools in Africa3.Advances in the genome-wide analysis of human genomic variation in disease states have led to the discovery of thousands of disease-associated loci7, the fine-scale genetic structure of specific African populations at fine geographic ranges remains largely understudied. Previous genetic association analyses in Africa have relied on principal component analysis (PCA)8, with the top principal components (PCs), included as covariates in the association analyses to control for confounding by population structure15. More recently, approaches like mixed models (MM)18 and Bayesian statistics19 have proven particularly effective in accounting for genetic structure among and within populations in GWASs. However, given the extensive genetic heterogeneity in Africa that usually accompanies ethno-linguistic, cultural, and religious disparities, scars of genetic substructure remain ubiquitous in GWAS on the continent leading to the high false discovery rates (FDRs) and deficiency of significant SNPs from such studies15.Population structure analysis is crucial to the design, analysis, and interpretation of genetic association studies. While human genetic structure is well understood at the continental scale20. The genomes of African populations have also been shaped by evolutionary and selection pressures from infectious diseases and the environment. The relatively high frequency of the Sickle cell trait and the glucose-6-phosphate dehydrogenase (G6PD) deficiency in sub-Saharan Africa (sSA) perpetrated by malaria22 are classic examples of such disease pressure. Perhaps, it is the clustering of African populations into thousands of demes of local ethnicities20 that mirrors the extent of the genetic diversity within the continent.The human genetic diversity in Africa remains complex and multi-factorial, accrued from ancient and recent migration events. The outcome of this has been significant genetic admixture despite cultural, ethno-linguistic, and eco-geographic barriers to gene flow20. While this is expected, the ancestral relationship and extent of genetic differentiation between individuals of different ethnicities in Cameroon have not been explored. Considered the World’s most culturally diverse nation, with a complex colonial history24, Cameroon’s populations may have been subject to both genetic admixture and numerous barriers to gene flow, leading to differences in allele frequency and haplotype structure between ethnic populations. A recent severe malaria GWAS in Africa highlighted some heterogeneity in the effect and genotype frequency of some key malaria-associated loci in Cameroonian individuals relative to their sSA counterparts15. Hence, detailed analysis of the fine-scale structure of the ethnic groups may shed some light on specificities of the sub-populations.In Cameroon for instance, there are over 250 tribes distributed within 3 broad ethnic groups; Bantu (BA), Semi-Bantu (SB), and Sudanese (which includes the Foulbe). A recent population genetic study revealed that chunks of the genome of individuals of the BA and SB ethnicities are shared with their African counterpartsST), model-based clustering, and PCA were used to determine the relationship between structure and ethnicity while information derived from patterns of haplotype sharing among individuals (coancestry) was used to depict fine-scale structure and ancestral admixture. Additionally, the genetic architecture of individuals in the study population was compared to those from the 1000 Genomes Phase 3 populations and genome scan was performed to map genomic regions with significantly differentiated alleles and extended haplotype homozygosity that may be due to selective forces.This study aimed at characterizing the genetic structure of the Bantu, Semi-Bantu, and Foulbe (FO) ethnic groups from Cameroon. SNP data from 1073 individuals living in the South West, Littoral, and Centre regions of Cameroon were analyzed. The Wright’s F-statistic   thus employed in this analysis. Estimates for within- and among-continent population comparisons similar to those previously reported were observed (supplementary Data ST SBvsYRI = 0.002) than did the BA (FST BAvsYRI = 0.003) contrary to previous estimates20. We further found that the FO ethnicity is relatively less genetically related to the YRI (FST = 0.004) compared to Cameroonian SB and BA populations. In addition, contrary to prior estimates that the Cameroonian BA population was genetically closer to the Luhya population of Kenya (LWK), we observed no difference in FST among the LWK population and all Cameroonian populations herein studied 20. Interestingly, the FO, like the LWK population appeared to be more genetically close to populations of European and Asian ancestries as compared to the BA and SB, supporting possible influence of Eurasian migration back into Africa28. PCA generally showed positive concordance with FST results  and 2504 individuals from the 1,000 Genomes reference panel phase 3 (1KGP3) populations were studiedSL) see “” sectionsmartpca’s default 5 outlier removal iterations (numoutlieriter) resulted in ancestry outliers  and 2 (PC2) . In principle, AIMs are loci with substantial allele frequency difference across populations30. They can be used to assign individuals into clusters based on biological  or geographical  boundaries31. Although pre-calculated lists of AIMs exist, they mostly apply to cross-continental populations32. Furthermore, there are several measures of marker information content as listed in Table 31. We therefore used the FST measure in this study to generate AIMs by selecting all SNPs with FST greater than or equal to the highest FST (genetic distance) between Cameroonian ethnic groups. FST between the BA and SB was 0.001 whereas FST between the FO and both BA and SB was 0.003. Hence, AIMs were considered as SNPs with pairwise population FST (Hudson) ≥ 0.003. This strategy saw an increased resolution of the clusters, clearly separating the FO from the BA and SB based on PC2  , we computed ancestral proportions (Q estimates) for our three ethnic groups with five k-parameters using model-based clustering implemented in Admixtureded Fig.  and/or h35 for fine scale resolution of Cameroonian ethnicities. In this analysis, we thinned the samples using Plink1.9′s—thin-indiv-count (which removes samples at random until only the required number is left) to obtain 50 BA and 50 SB individuals together with the initial 25 FO individuals to make a final analysis set of 125 individuals. We observed isolation of a subgroup of FO individuals from the BA and SB by PC1 , haplotype-based approaches have the potential to exploit the linkage information in genotype datasets to estimate genetic distances with finer resolutions. Therefore, we used a haplotype-based approach of coancestry estimation by “chromosome painting” implemented in fineStructure36 and BioMart-annotated gene list. Although the strongest signature occurred on chromosome 1 around the REG4 gene , the most consistent signatures were recorded on chromosome 6 spanning the HLA region which has been reported in several previous studies of selection40. The SNP rs10947368, a missense variant on HLA-DOA emerged with the strongest signal within the HLA region  (Table HBB) gene cluster of chromosome 11, a region with longstanding knowledge of balancing selection under the influence of malaria42. However, the strongest signal on chromosome 11 was a relatively uncommon missense variant (rs7943508) in the APLNR gene, implicated in hypertension and some cancers44. Generally, selection signatures were not isolated, but occurred in clusters of consecutive SNPs positions, consistent with “genetic hitchhiking” around a positively selected variant, typical of selective sweeps. Our genome-wide significance threshold was estimated at 6.033 × 10−08  and low genetic diversity. A genome-wide scan for such regions harboring signatures of selection by the standardized integrated haplotype score (iHS) which measures the EHH identified strong signatures on multiple chromosomes. This included missense and regulatory region variants in genes overwhelmingly associated with response to infections. The scan identified a total of 133 SNPs within 57 overlapped genes and 173 overlapped transcripts across chromosomes 1 to 12, 14, 16, 17, 19, and 20 with significant signatures of selection at iHS threshold of ± 4  which accounts for unequal effective population sizes (Ne) and hierarchical population structure46. We used the MSL population as an out-group as earlier mentioned. Several genomic regions with subtle allele frequency differences between the ethnicities were observed although none of these regions remained significant after correction for multiple testing by the Benjamin–Hockerbeg method48. However, evidence of positive selection remained apparent in the HLA region on chromosome 6. Specifically, positions on chromosomes 2, 6, 8, 10, 17, 18, and 22 were subtly differentiated among the ethnicities . Also, allele frequency differences may arise due to changes in population dynamics like rapid population growths or contractions, and selection pressures among others. Therefore, to isolate only selection pressure effect on allele frequency differences among Cameroonian ethnicities, we computed the extended Lewontin–Krakauer F0.8 see “” section0.8 see “.ACSM6 gene associated with acetyl coenzyme-A production (p = 1.62 × 10−06), and on the CYP2C8 gene, a cytochrome P450 superfamily enzyme member associated with drug metabolism (p = 6.09 × 10−06). Multiple missense variant signals were also observed in the ABCC11/12 gene on chromosome 16 (p = 5.39 × 10−07), an ATP binding cassette subfamily member involved in multi-drug resistance. In addition, signals were observed on the MTTP gene on chromosome 4 (p = 3.73 × 10−06) involved in triglyceride transfer and lipoprotein assembly, the TMEM199 gene on chromosome 17 (p = 5.35 × 10−06) whose deficiency is associated with abnormal glycosylation49, and the TCN2 gene on chromosome 22 (p = 6.76 × 10−06) involved in the absorption of vitamin B12 . Our threshold for genome-wide significance was estimated at 6.02 × 10−08 , we screened for selection using clusters of loci (haplotypes) by the haplotype variant of the FLK test (hapFLK). This resulted in a strong signature on chromosome 6 as was recorded by iHS, while several other regions on multiple chromosomes showed suggestive signals Fig. . Of note−08 see “” section51. We found chromosome 6 to be strongly selected in the BA and SB and only subtly selected in the FO. This could be due to the low sample size of the FO population. The BA population showed additional signatures on chromosome 6 involving the missense variant rs9276 on the HLA-DPB1 consistent with previous reports of selection in this gene in Bantu-speaking African populations (BSP)52 and the variant rs1419638 on the OR5V1 gene, as well as on chromosome 7 unique to the BA ethnic group. Likewise, the SB showed specific signatures on chromosomes 16 and 20, while strong signatures specific to the FO population were recorded on chromosomes 1, 7, 9, 10, 16, and 19 . This study sought to ascertain the fine-scale genetic structure in Cameroon by analyzing the genotype of 1073 individuals from the South West, Littoral, and Centre regions.An in-depth understanding of population structure is critical in genetic studies, particularly in highly diverse and admixed African and American populations because of its potential to impact genetic association studies that usually have small effect sizes and rare variant analysesST observed among Cameroonian ethnic populations sampled in this study indicates substantial mixing and common recent coancestries. However, the fact that the estimates were non-zero values supports the existence of distinct ancestral proportions. The FO (belonging to the Sudanese ethnic division) appear to have North African roots related to Fulani migration from north eastern Nigeria and Chad. The BA, on the other hand, are thought to have been the earliest inhabitants of Cameroon, with traces of their ancient civilization still prominent in the pigmies of the South and East. In fact, some studies have associated the spread of the BA ancestral proportions found in Central, South and East Africa to a Bantu expansion that originated somewhere around South Western Cameroon57. The SB mainly inhabit the Western highlands and grass fields of the West and North West of Cameroon. Together with the BA of the South West, the SB of the North West also appear to have endured a four-decade complex cohabitation with Eastern Nigerian populations during colonial era23. Therefore, the ancient interactions, and interactions of the recent past of Cameroonian populations with other populations may have paved the way for substantial genetic admixture and drift.The subtle differences in pairwise F58. Following these events, parts of Nigeria, Chad, Central African Republic, Congo, Gabon, and Equatorial Guinea today were parts of Cameroon under the German and subsequently French and British rule23. These were therefore good recipes for genetic admixture and drift.The close similarity in ancestral proportions among the BA and SB is expected Fig. a. The di23, it is possible that some isolation of the populations was automatically imposed. Large disparities in ancestral proportions among the FO and the BA and SB ethnicities may, thus be due to a longstanding barrier to gene flow due to a sympatric differentiation among the populations (That is they live together but do not interbreed to a great extent). Such a relationship has been documented in the Malian and other African populations64. The separation of the FO ethnicity into two distinct clusters by chromosome painting, one close to the BA and SB and the other quite apart, further indicate that the FO is not homogeneous due to some limited but detectable coancestry with the other ethnic groups. Indeed, it is known that non-FO individuals integrated in to the Fulani–Hausa populations as herdsmen and these may have contributed to the admixed FO sub-group.The considerable dissimilarity in the ancestral proportion of the FO ethnicity from the others is not surprising as was apparent in their allele frequency distribution. However, their splitting into two distinct clusters may provide evidence of genetic heterogeneity within the ethnic group. They are believed to have northern African roots associated with the ancient Sao civilization that flourished around the shores of Lake Chad around ninth–fifteenth century AD, and a Hausa-Fulani land invasion from Nigeria by the 1800s that led to the establishment of a large Islamic empire involving much of the northern regions of Cameroon. Given the barriers imposed by religious differences, as much of the South of Cameroon practiced Christianity35 indicates that different haplotype structures may underlie the various ethnicities and consequently different linkage disequilibrium and recombination patterns. Given that genotype imputation, which is constantly employed to boost the power of association studies, relies upon linkage disequilibrium between SNPs66, it may be inferred from this analysis that the different populations would perform differently on imputation3. Therefore, to optimize the imputation on Cameroonian datasets, an imputation panel including individuals across Cameroon’s ethnic groups, or a panel—as that recently provided by the NHLBI’s Trans-Omics for Precision Medicine (TOPMed) program67—including individuals with close genetic ancestry to Cameroon’s ethnic populations would be required. In addition, considering that these genetic differences would characterize different axes of genetic variation in an association study, one would expect significant false positive results when all the ethnicities are analyzed together. Therefore, larger sample sizes would be required for association studies in such a highly structured population to be sufficiently powered to identify markers associated with specific phenotypes. Hence, association analysis performed on each ethnicity separately would be more profitable given that ethnic information is accurately captured.We, however, note that such a retrospective stochastic assessment of the genetic differences among Cameroonian ethnicities and other populations with respect to their demographic histories has several limitations; first, our analysis relied on self-reported ethnicity of the father and mother of each participant which may not have been accurate. Second, not only the SB of the Western Highlands cohabited with Nigerian populations during the British and French sovereignty, but the BA population of the South West as well. Perhaps, other factors may have been in play to account for the close genetic relatedness between the SB and the YRI of Nigeria. Nevertheless, these results highlight key differences in the genetic architecture of Cameroonian ethnicities that may have significant bearing on genetic association studies for this population. For instance, the fine-scale clustering of the three ethnicities based on information provided by patterns of ancestral haplotype sharing15, the sickle cell trait (HbAS), encoded by the rs334 locus, known to contribute the strongest protective effect against severe malaria in sSA, was found to have its weakest effect in Cameroon. One would have expected a high protective effect at this locus in the Cameroonian populations, owing to the country’s high sickle cell disease (SCD) burden68. Although the complex polygenic nature of malaria and environmental factors may be at play, it is possible that the prevalence of the HbAS trait differs significantly among Cameroonian ethnicities such that a joint analysis could have masked the real effect. More so, MalariaGEN’s analysis was based on the BA and SB ethnicities of Cameroon only. Interestingly, the FO ethnicity is more genetically close to Gambian populations  countries. In their recent study27. However, out-group ascertainment in African populations remains a challenge as the “most recent common ancestor” of African populations remains to be established. Although the roots of anatomically modern humans have recently been traced to Botswana69, the Mende population from Sierra Leone (MSL), shown to harbor the largest proportion of ancestry from a basal African lineage7 fitted well as an out-group in our analysis. A couple of test analyses supported this observation; (1) pairwise FST estimates with the 1000 Genomes populations without SNP ascertainment required either over a million SNPs or > 50,000 SNPs with MAF > 0.35 to observe estimates similar to those previous reported, (2) SNP ascertainment with all African populations except the MSL did not result in FST estimates as have been previously reported (3) Finally, SNP ascertainment in the MSL population resulted in similar FST estimates as have been previously reported using less than a million SNPs with MAF as low as 0.05. Moreover, the MSL population was estimated to have differentiated ~ 300 thousand years ago (ka)—200 ka7, about the same time modern humans are thought to have originated from Botswana (Southern Africa)69. Hence, in the absence of a publicly available and well-established out-group for African populations, the usage of the MSL population may serve such a purpose.Population genetic approaches that measure genetic distance and quantify shared ancestry are more robust when SNPs are ascertained to be polymorphic in an out-group7, although one would expect, in principle, the core locus affecting a particular trait to be under a selective sweep. Indeed, African populations have had to endure immense pressure from infectious diseases being the oldest populations of anatomically modern humans (formerly hunter-gatherers)20. The case of malaria and SCD is well established, and both conditions are thought to have emerged around the same time (4000–5000 years ago) coinciding with the adoption of agriculture in Central Africa70. Recent reports however suggest that the human Plasmodium falciparum malaria and SCD arose much earlier, and at different times; ~ 60,000 and ~ 22,000 years ago respectively71. We also observe weak signatures of selection in the hemoglobin-beta (HBB) gene cluster. It is expected to pick weak signatures of selection in this region by iHS (which mostly targets positive selection) in particular since the method looks at the homozygosity of clusters of SNPs72. Signatures of balancing selection would appear weak because, although alleles occur at intermediate frequencies, an extended homozygosity of alleles of the same type as the core/focal allele would signify selection.Finally, we observed signatures of selection in this study, suggesting that Cameroonian populations have come under strong disease pressure. The strong signatures targeted primarily immune response and food/drug metabolism genes, suggestive of polygenic adaptation of the population to diseases and changes in diet. Selection, therefore, acts on multiple loci across multiple genes to simultaneously drive phenotypic adaptationGYPA-B] rearrangement in the glycophorin gene cluster, a region of ancient balancing selection73 and the common alpha-thalassemia deletions74, both of which are prevalent in malarious areas77. Therefore, novel tools that incorporate such variants in selection scans would be highly beneficial.Meanwhile, as SNP-based approaches for selection scan continue to yield useful information regarding the adaptive evolution of specific genomic loci, valuable information may still be missed at loci with more complex genetic architectures. This is particularly true for regions with structural variants and weak linkage disequilibrium such as the Dantu [Population structure remains a major confounding factor in genetic association studies, diminishing the power of GWAS in Africa. Smarter sampling strategies and analysis designs are therefore needed to effectively detect and significantly minimize/correct population structure effects in African populations. We observe considerable substructure within our Cameroonian populations to warrant that GWAS is performed on an ethnicity basis. In addition, the analysis of a larger number of Fulani individuals in Cameroon with their Bantu and Semi-Bantu counterparts may shed further light on the contribution of their possibly non-Cameroonian ancestral proportions and the broader demographic history of this West-Central African region. The characterization of the genetic structure within other African populations will elucidate the real extent of their effects so as to inform genetic association studies on the continent.78. The samples were then contributed to the Malaria Genomic Epidemiology Network  Consortial Project 1 (CP1) where genotyping, genotype calling, and alignment to the human reference genome build 37 (hg19) were performed as previously described10. Data access, retrieval and analysis were performed according MalariaGEN policies79.Our analysis was conducted with samples from unrelated individuals belonging to the Bantu, Semi-Bantu and Foulbe ethnicities from the South West, Littoral, and Centre regions of Cameroon. The individuals were recruited (1471) as part of a malaria cross-sectional study in Cameroon between 2003/05 and 2007/08 and DNA extraction was performed as previously described80. We explicitly removed individuals whose reported nationality was “Non-Cameroonian” or “Missing”. Individuals with discordant sex information were identified and excluded using Plink1.9′s—heck-sex filter. Furthermore, individuals with more than 10% missing data rate (missingness), and individuals with outlying heterozygosity were excluded from further analysis. Related samples were excluded by computing an identity-by-descent (IBD) matrix with 88252 high quality independent SNPs , LD-pruned such that all pairs with correlation, r2 > 0.2 within any 5 kb region, using a step size of 50b, were excluded along with all those that failed the Hardy–Weinberg equilibrium (HWE) test at p-value 1 × 10−8. One individual from each pair with Pi_HAT (mean IBD) > 0.1875  were removed, removing only the individual with a higher missingness  and resulting in 1185 individuals. To further exclude individuals of outlying ancestry, we projected Cameroonian populations against African populations of the 1000 Genomes reference panel phase 3 (1KGP3)82 using smartpca version 16000  from the EIGENSOFTv7.2 package8. This saw the removal 111 individuals, resulting in a final set of 1073 individuals. SNP quality control and subsequent analyses were performed on autosomal SNPs . SNPs with minor allele frequency (MAF) < 1%, missingness > 4%, and SNPs that showed significant deviation from the HWE at p-value < 1 × 10−8 were excluded. A total of 1,588,393 autosomal SNPs passed all QC procedures and were retained for further analysis.Sample QC was performed on the autosomes and the X chromosome (of the 1471 samples) using Plink1.9https://www.well.ox.ac.uk/~wrayner/tools/) to check and validate the dataset against the 1KGP3 for ref/alt allele assignment, ID names, positions and alleles. VcfCooker v1.1.1 (https://github.com/statgen/gotcloud/blob/gotcloud.1.17.5/bin/vcfCooker) was used to convert the dataset from plink binary format to VCF format, while checking the validity of the dataset against the human reference genomic build 37. To further ascertain for ancestral/derived allele assignment, we used Plink280 to scrap ancestral allele assignments from ENSEMBL genome’s 1000 Genomes Phase 3 release 98 variation file downloaded from ftp://ftp.ensembl.org/pub/release-98/variation/vcf/homo_sapiens/ (Accessed 2019-09-28)83. We sorted the dataset according to base-pair position with bcftools v1.9 from the SAMtools package84 and phased each chromosome separately with EAGLE v2.4.185 against the 1KGP3 reference panel, using the combined HapMap recombination map in build 37 coordinates included in the software. We used a k parameter (–Kpbwt) of 50,000  conditioning haplotypes and 10 iterations (–pbwtIters) to increase the phasing accuracy. Phasing accuracy was assessed from the log file produced by the software.Prior to phasing, we excluded palindromic A/T, C/G SNPs which are usually problematic to the procedure. We then used the WRayner perl script v4.2.9 , while maintaining all other default parameters. PC plots and FST heatmaps showing the clustering of the populations into subgroups were generated using R 3.6.129. To further investigate the population structure in our data set, we performed model-based clustering as implemented in Admixture33. Admixture utilizes a maximum likelihood approach to estimate the underlying ancestral coefficients and allele frequencies and then a moving block bootstrap approach for estimating standard errors. This analysis was performed on the set of “ancestry informative markers” described above, which were LD-pruned for MAF ≥ 0.05. The analysis was done with 5 cross-validation runs (K = 1–5) and 300 bootstrap runs. Also, the samples were sorted using Plink1.9′s –indiv-sort flag in the order BA-FO-SB so as to allow for proper grouping in the ancestral coefficients (Q) bar plot. We further resolved the clusters by running Structure v2.3.434 in the admixture mode using 3 pre-defined clusters with 20,000 burnin and 100,000 main iterations. To explore the fine-scale genetic relatedness captured by haplotype structure, we ran ChromoPainter in the linked (LD) mode on a subset of 125 Cameroonian individuals   obtained by thinning the full data-set using Plink1.9′s –thin-indiv-count command which does a random sampling of the number of samples required. ChromoPainter summarizes the genomic proportions shared among each donor and recipient individual as a “Coancestry matrix” (Xij) of the expected number of genetic elements donated to individual i from individual j35. We then used FineStructure to assign individuals into clusters setting 100,000 burnin and sampling iterations each. Finally, we generated a tree file with FineStructure for a better assignment of the individuals into different clusters. Clusters were visualized and cluster plots produced using the FineStructure graphical user interphase (fineStructureGUI) and R.Axes of genetic variation  were computed using rehh)51. rehh estimates the probability that a locus, randomly shuffled from any chromosome is identical by descent to a core  locus . As this probability breaks down with increasing distance upstream and downstream from the focal locus, the integral of the area under the EHH plot when the focal allele is an ancestral allele to the integral when the focal allele is a derived allele yields the iHS score. Therefore, large negative iHS values would signify selection at the derived allele and vice versa87. In the absence of a focal locus, Rsb is computed using EHH scores for all loci. Replacing the focal and derived alleles with the two populations being compared as popA and popB respectively, Rsb yields selection signatures for pairwise population comparisons. Thus, as above, large negative Rsb values would signify selection in popB and vice versa. iHS and Rsb statistics were computed in our data set using phased haplotypes with MAF ≥ 0.05. First, iHS was computed on the pooled data set , then Rsb was computed with separate pairs of the different ethnicities . To assess the significance of selection signatures, rehh computes a two-sided p-value from the Gaussian cumulative distribution function of iHS estimates. The p-values were adjusted by the Benjamin–Hochberg (BH) and Bonferroni (BF) methods. Furthermore, rehh generates manhattan plots for visualization of iHS and Rsb results with their corresponding p-values. Genome-wide significance threshold for the manhattan plots was estimated by dividing 0.05 by the effective number of SNPs  (Bonferroni method).Signatures of selection were investigated by computing the integrated extended haplotype homozygosity (EHH) score (iHS) and cross-population locus-specific integrated (EHH) score (Rsb) using the R package REHHv3.01  > 0.1 within 5 kb genomic regions with a step size of 50 bp using hapFLKv1.4088. FLK accounts for unequal effective population sizes (Ne) and hierarchical population structure by estimating a kinship matrix which is used to model the covariance matrix of the population allele frequencies. Since the FLK test is not robust to very low levels of heterozygosity, we excluded SNPs with ancestral allele frequency (AAF) < 0.2 as recommended46. FLK test for haplotypes (hapFLK)88 was performed per chromosome using linked loci with MAF ≥ 0.05. hapFLK screens for positive selection signatures using the Sheet and Stephens model89 to summarize local haplotype diversity in a sample by clustering similar haplotypes together. The clusters are then considered as alleles to compute the FLK statistic. A K-parameter (number of clusters to model) of 30 was used while all other options were set to default. Although the genome-wide distribution of hapFLK appears to be bimodal with a large proportion of the values showing good fit to a normal distribution, Fareillo et al., implemented a means of estimating p-values via robust estimators of mean and variance from a standard normal distribution, and reducing the influence of outliers, using a rlm function in R. The p-values were adjusted following the procedure previously described. Genome-wide significance threshold was computed as previously described. hapFLK manhattan plots were generated using the qqman package in R45.We further computed the extended Lewontin–Krakauer Fst outlier test (FLK)All experiments in this study were conducted in adherence to the set of ethical principles of the Declaration of Helsinki. Ethical clearance for the study was obtained from the Institutional Review Board of the Faculty of Health Sciences, University of Buea  while administrative authorization was sought from the South West Regional Delegation of Public Health. Authorization to conduct the surveys in primary schools was obtained from the Regional Delegation of Basic Education or the Catholic Education Secretariat. Informed consent was obtained from each participant or their caregiver following a clear explanation of the content of the information sheet for the cases and blood bank donors used as controls. Authorization to enroll participants from health facilities or schools was obtained from the Director or Head teacher and only subjects/caregivers who volunteered to participate by signing a written informed consent were enrolled.Supplementary Information.Supplementary Dataset 1.Supplementary Dataset 2.


Koreans are thought to be an ethnic group of admixed northern and southern subgroups. However, the exact genetic origins of these two remain unclear. In addition, the past admixture is presumed to have taken place on the Korean peninsula, but there is no genomic scale analysis exploring the origin, composition, admixture, or the past migration of Koreans. Here, 88 Korean genomes compared with 91 other present-day populations showed two major genetic components of East Siberia and Southeast Asia. Additional paleogenomic analysis with 115 ancient genomes from Pleistocene hunter-gatherers to Iron Age farmers showed a gradual admixture of Tianyuan (40 ka) and Devil’s gate (8 ka) ancestries throughout East Asia and East Siberia up until the Neolithic era. Afterward, the current genetic foundation of Koreans may have been established through a rapid admixture with ancient Southern Chinese populations associated with Iron Age Cambodians. We speculate that this admixing trend initially occurred mostly outside the Korean peninsula followed by continuous spread and localization in Korea, corresponding to the general admixture trend of East Asia. Over 70% of extant Korean genetic diversity is explained to be derived from such a recent population expansion and admixture from the South. The 1000 Genome Project (1KGP) showed that East Asians displayed a common genetic bottleneck with non-African humans around the last glacial maximum . HoweverKoreans belong to the Altaic language group and are known to be homogeneous in Northeast Asia along with the Chinese and the Japanese. There are ∼85 million Koreans in total  unified by shared ethnic and linguistic traits. There are currently several hypotheses on the origins of the Korean. The Korean Y-chromosome haplogroup (O2b-SRY465) suggests the ancestors of the proto-Koreans are related to the people who inhabited northeastern China during the Neolithic  and Bronze  Ages . On the Paleogenomics is a powerful tool to reveal the exact genetic lineages and affinities that cannot be resolved with present-day populations alone because frequent and complex genetic exchanges occur with or without cultural and linguistic exchanges. Archeological data unearthed in Korea provide the proto-Korean chronology and prehistories of the Korean Peninsula. The oldest archaic relics, such as the Acheulean axes, that have been found in South Korea date back hundreds of thousands of years, however, human bone preservation is poor due to the acidic soils and cannot acquire any ancient genetic data . The earhttp://variome.net/Asian_Genome_Data_for_Korean_Origin, last accessed April 17, 2020).A total of 88 Korean samples were used that are available from the KoVariome database  supplem online ahttp://broadinstitute.github.io/picard/, last accessed April 17, 2020) and conducted IndelRealigner and BaseRecalibration using GATK (ver. 2.3.9) (Samples were subjected to WGS and genotyping  online. . 2.3.9) . We pred. 2.3.9)  with “–h. 2.3.9) . Finally. 2.3.9) .PRJEB14817, PRJEB24939, and PRJEB9021 and GenBank with accession ID of KC417443.1 for the Tianyuan mitochondrion. Consensus sequences of ancient and modern mitochondrial genomes were generated by SAM tools with minimal depth 5. Then, multiple sequence alignment of the consensus sequences was performed by MUSCLE. The phylogenetic tree was constructed by MEGA7 with a Gamma distribution model and pairwise deletion for gap treatment. Divergence time between nodes was calibrated by MEGA7 with the four previously suggested calibration points for A , B , C , and D   .K = 2–14 (K = 10 (corr. = 0.78). We used the admixture result of K = 10, which best represents the genetic cluster analyzed by fineSTRUCTURE. We performed a principal component analysis (PCA) analysis conducted with EIGENSOFT (ver. 6.0.1) smartpca , where X and Y are ancient and present-day populations, respectively. To better represent the genetic association of the present-day population against a focal ancient genome, we applied a scaled f3 statistic by f3scaled = (f3 − m)/(M − m), where m and M represent the minimum and maximum f3 statistic . In this analysis, both X and Y were ancient genomes.To investigate the genetic relationship between populations of interest, we used the MIXTOOLS . The genic fig. 2 online. D-statistics and f3 statistics in our study. We first set the skeleton for the admixture model as Tianyuan, Onge, and Ami by adapting a previous study (Z = 0.044). Then, we added Kinh which has a high admixture F3 score with Devil’s Gate to Koreans (worst-fitting Z=−3.887) and then to Devil’s Gate, Ulchi, Koryak, Mixe, and MA1 (worst-fitting Z = 3.317). Finally, Koreans, Han, and Japanese have been added to model the suggested admixture of East Siberians (Esi) and East Asians b (EAb) . We manually calibrated the final model with a time point which was estimated using the ALDER results.To construct an admixture model depicting the historical genetic makeup of Koreans and other Asians, we fitted the SNP panel to the admixture models with the qpgraph program  based onus study  (worst-fsi), East Siberians (Esi), and two groups of East Asians (EAa and EAb) . The dendrogram correlation analysis showed the greatest consensus between the fineSTRUCTURE clades and ADMIXTURE results at K = 10   online, and EAb) A. The grURE tree  online. ulations B, consisulations . We reanulations  with 6 rulations C. Consis and EAa C top. Weanalysis    shares more alleles with present-day Siberians (Esi and Wsi) and East Asian (EAb) populations than with other present-day populations such as European, West-, and South Asians (si and EAb populations had significant genetic affinities with ancient Southeast Asians (ancSEA), Devil’s Gate, and Bronze and Iron age ancients who lived in central steppe regions (ancCS) , where X and Y were ancient and present-day populations, respectively (si and EAa/b) showed a similar amount of Tianyuan’s genetic ancestry by showing D ≈ 0. It suggests Neolithic Devil’s gate (Northern part of Korea) is possible to be admixed with another genetic component. In addition, Tianyuan’s genetic ancestry had a significantly higher level of genetic affinity with Wsi, Esi, and EAb populations than with ancCS  . This an Yoruba)  online. h Asians  online.  (ancCS)  online. ectively  online. ectively B, suggesth ancCS B. It sugth ancCS  online. si and EAa/b using D-statistic in the form of D  populations showed similar levels of affinity with Tianyuan-derived alleles to the Esi populations and were equally distant to Tianyuan lineage. This suggests Devil’s Gate ancients and present-day Esi and several EAb populations were subject to similar genetic influences over time and are expected to be a single clade since they are all separated originally from the Tianyuan lineage. These lines of analysis reveal that the basal ancient of the Tianyuan genome was separated in the Neolithic or pre-Neolithic era and independently affected current Koreans.We examined Tianyuan’s genetic affinities for E, EAa/b)  online. D. It showed Devil’s Gate genomes shared more derived alleles with most of the present-day Esi and EAb populations than with Neolithic Man Bac in Vietnam , and Russian (Ulchi) ethnic group, still had dominant genetic contributions from Devil’s Gate compared with Oakaie and Nui Nap ancients. This suggests that local genetic differences observed in present-day EAa/b populations  ∼0  ∼0  and irAltai (Iron Age in Russia). Unlike the Devil’s Gate’s ancestry, the Neolithic Man Bac shares more derived alleles with most of the present-day Esi and EAb populations than either the Bronze Age ancSEAs  or ancCSs    populations from Man Bac , followed by Nui Nap (Bronze Age in Vietnam). In a previous study, Nui Nap was a new genetic component close to present-day Vietnamese and Dai but not the ancestors of Austroasiatic speakers   ∼0 A and D(Y EAb) ∼0  online. irAltai)  online. ulations B. We alslations)  online. ancients . It supp Man Bac  online.  Man Bac A. We obsatistics . Notablyspeakers . Meanwhi Yoruba)  online.  Yoruba) C. This s Yoruba) C. These a/b and Siberian (Esi and Wsi) mtDNAs  mtDNAs C. We inc) mtDNAs  online. ) mtDNAs . It also) mtDNAs , who wer) mtDNAs . We also) mtDNAs . The mtD lineage C. It sug lineage . Haplotyb genomes (70%). This is consistent with the EAb ancestry frequency in contemporary Koreans. This model generally describes well the gene flow among the three Northeast Asians; Korean, Chinese, and Japanese.We estimated the admixture time of Koreans using 286,222 SNPs and obtained significant prediction results from only three populations as references; Yakut, Han, and Japanese . The estsi) and East Asia (EAb). Ancient genome comparisons revealed that the genetic makeup of Koreans can be best described as an admixture of the Neolithic Devil’s Gate genome in Russia and the Iron Age Vat Komnou in Southeast Asia. Our analyses of ancient and present-day populations suggest a long and gradual admixture model of two Neolithic founders, the Devil’s Gate founder in Russia and the founder from Tianyuan Cave in China. These two major components were admixing throughout East Siberia and East Asia for an extended time up until the Neolithic period. Subpopulations of current East Asians, as well as modern Koreans, were probably established by a later regional genetic transition during the Bronze Age. The peopling of Korea is most likely a part of large population expansion and the subsequent admixture events which occurred in East Asia, rather than a unique isolated event or migration. We think that this kind of recent rapid expansion and admixture could be general models for other East Asian and Southeast Asian populations in which Bronze and Iron Age populations expanded and admixed with other peripheral region populations. We analyzed the haplotype distributions of 88 Koreans compared with ancient and modern whole genomes and suggested two major haplotype expansion events. A comprehensive genome comparison confirmed that Koreans possess dual ancestral genetic components originating broadly from East Siberia (Eevaa062_Supplementary_DataClick here for additional data file.


Genetic distances and relatedness were displayed through multidimensional plotting and phylogenetic tree using various populations in the region. Our study showed that the Bahraini population was clustered with neighboring countries such as Kuwait and Emirates which indicates that these closely geographical regions share similar allele frequencies and are more genetically related than other reference population studied.This paper evaluates the forensic utility of 30 insertion-deletion polymorphism (indel) markers in a sample from the Bahraini population using the Qiagen Investigator DIPplex Kit. Allele frequencies and forensic stats of the 30 indels were investigated in 293 unrelated individuals from different governorates of the Kingdom of Bahrain. None of the markers showed significant deviation from Hardy Weinberg equilibrium except for HLD88 locus and no linkage disequilibrium were detected between all possible pair of the indel loci, assuming that these markers are independent and their allele frequencies can be used to calculate the match probabilities in the Bahraini population. The high power of discrimination (CPD = 0.9999999999998110) and the low combined match probability (CPM = 1.89 × 10 Compared to short tandem repeat (STRs), indels are found to give promising results for population genetic studies and forensic identification2. In this study, we have used a commercially available indel kit known as the Investigator DIPplex  in a sample from the Bahraini population. This kit contains 30 indel markers, which are located on all chromosomes, and Amelogenin sex-informative marker3.Due to the benefits of their short amplicon lengths and the lack of stutter peaks, these markers can be successfully used to analyze degraded DNA samples in challenging forensic cases4. It is one of the most densely populated countries with estimates of Bahrain’s population stood at 1,314,562 persons. Of these, 568,399 are Bahraini citizens (46%) and 666,172 are expatriates (54%)5.This paper contains population data obtained from 293 unrelated Bahraini individuals from different governorates. Bahrain being in the Persian Gulf, connected to the Eastern Coast of the Arabian Peninsula, Iran, Iraq and Oman7.Because of the geographic location of Bahrain, the diversity of the population had been affected due to the prehistoric cultural events that took place and the migrations flow in this areaTwo hundred and ninety-three (293) buccal swabs were collected using cotton swabs  from healthy unrelated Bahraini males. The research study was publicized through different media platforms. Participants who wished to contribute their samples communicated with the corresponding author and presented at the General Directorate of Criminal investigation and Forensic Science—Kingdom of Bahrain to deliver their samples for the research after obtaining informed consent.The age of the participants was ranged from 20 to 70 years old. Ethical review for conducting tests was obtained and approved by the Research and Research Ethics Committee (RREC) (E007-PI-10/17) in the Arabian Gulf University, Manama, Kingdom of Bahrain. All participants agreed to the informed consent which were provided prior to their contribution. All research was performed in accordance with relevant guidelines/regulations. In each case, males declared their ancestry  from four different geographical subdivisions of the country  were sampled.Genomic DNA was extracted using QIAsymphony SP instrument  following magnetic beads principal. Subsequently, the extracted DNA was quantified using Quantifiler HP DNA Quantification kit  in the 7500 Real-Time PCR System  according to manufacturer’s recommendation.About 0.5 ng of the extracted DNA was amplified using Investigator DIPplex kit  with full-volume reactions (10.5 µl) following manufacturer’s protocol in 30 cycles conditions via MicroAmp Optical 96-Well Reaction Plate  along with the provided positive control (9948) and nuclease-free water as a negative control in a Veriti thermal cycler  following the PCR thermal cycles provided in the DIPplex manufacture protocol.The PCR products (1 µl) were separated by capillary electrophoresis in an ABI 3500xl Genetic Analyzer  with reference to the BTO size standard  in total of 12 µl master mix consisting of BTO size standard and Hi-Di formamide . GeneMapper ID-X Software v1.4  was used for genotype assignment in combination with the Investigator DIPplex Template Files and Qiagen DIPSorter software . Experiments were performed in the Biology and DNA Forensic Laboratory, Ministry of Interior, Kingdom of Bahrain which is accredited with Collaborative Testing Services (CTS).http://cmpg.unibe.ch/shiny/STRAF/)8.Forensic parameters such as match probability (MP), discrimination power (PD), probability of exclusion (PE), polymorphism information content (PIC), number of alleles  and observed heterozygosity (Ho) and the insertion allele frequencies (+ DIP) and the deletion allele frequencies (-DIP) of the 30 indels were calculated using the STRAF online software  and linkage disequilibrium (LD) tests between all pairs of the 30 indels, and p values were corrected by the Bonferroni10.Arlequin statistical software v3.511, UAE 12, Iraq13, Iran14, Turkey13, Slovenia13, Lithuania13, Bangladesh15, Indonesia15, and Japan15 using POPTREE2 software16 and represented by a nonmetric multidimensional scaling (NM-MDS) analysis using IBM SPSS Statistics v21.0 Software to investigate the populations structure between Bahraini population and the abovementioned populations based on Fst’s genetic distances.Interpopulation pairwise genetic distances based on Fst calculated from allele frequencies of the population of Bahrain and the rest of populations extracted from the literature which included Kuwait17 via MEGA X: Molecular Evolutionary Genetics Analysis18. The tree was constructed with allele frequency data of thirty indel markers  for all populations in corrected fixation index (Fst) using neighbor joining for phylogeny in 1000 permutations.In order to compare between different genetic structures of the populations, phylogenetic tree was constructed from allele frequency data using the neighbor-joining methodEthical review for conducting tests was obtained and approved by the Research and Research Ethics Committee (RREC) (E007-PI-10/17) in the Arabian Gulf University, Manama, Kingdom of Bahrain.All participants provided informed consent prior to contribution their buccal swab samples.All authors/participants provided consent for publication. All figures are generated from software indicated inthe materials and methods.Allele frequencies, and forensic efficiency parameters for the 30 indel loci in the Bahraini population are shown in Table P < 0.00017, except for HLD88 which was still deviated even with the correction. The expected heterozygosities (He) ranged from 0.413 to 0.501 with a mean value of 0.481.The observed heterozygosities (Hobs) ranged from 0.332 (HLD97) to 0.534 (HLD6 and HLD125) with a mean average of 0.450. Values for the polymorphic information contents (PIC) ranged between 0.328 and 0.375.There was no deviation from Hardy–Weinberg equilibrium (HWE) after applying the Bonferroni correction value of −13 for Bahrainis, allowing a reliable level of discrimination power in forensic cases. Regarding the allele frequency as indicated with deletion and insertion frequencies shown in Table P < 0.000115 after Bonferroni correction) revealed no allelic association between all possible pairwise combinations of 30 indels, indicating the independence of the 30 indel markers as shown in Table All markers were highly polymorphic and informative for forensic application using Bahraini population sample. To determine the forensic efficiency, we evaluated power of discrimination (PD), power of exclusion (PE) and matching probability (MP). The combined power of discrimination (CPD) and the combined power of exclusion (CPE) for 30 indel markers were 0.9999999999998110 and 0.99276, respectively. The combined MP was 1.89 × 1019. To reveal population genetic similarities and divergences between Bahraini population and other populations previously reported, we have constructed the phylogenetic tree  markers in a sample from the Bahraini population was successfully evaluated in this paper using the Qiagen Investigator DIPplex Kit. The deviation of HWE in HLD88 locus could be a result of high diversity of the studied population or due to the high polymorphism of HLD88 locus, which can also be supported by the PD and PM parameters. In earlier studies of autosomal STRAs for the LD and after applying the Bonferroni’s corrections, it was shown that for all possible combinations located on the same chromosome indicated minor findings for departures from the independence. Therefore, these studied indels in different loci can be counted as independent for calculation of matching probabilities. We have compared Bahraini population data with other populations according to the available data using the accessible loci. Regarding the Interpopulation diversity, the phylogenetic tree was constructed based upon the data from the 11 populations which were consistent with other population data from the region based upon the Fst values obtained.In order to measure the population differentiation due to genetic structure, Fst values are obtained for different populations. It is shown that the Bahraini population shares comparable results with its neighboring countries (Kuwait and UAE) based on the 30 indel markers which indicates that these population have more genetic flow than other distant population resulting in similar pattern of allele frequency distribution between them. Once more studies of Arab populations in the region become accessible, it may be more probable to develop a greater understanding of the genetic associations between the different populations for the Arabian Peninsula.This study increases the population database relevant for the application of genetic markers in forensic studies and can be complementary to STRs population genetic studies in many challenging forensic cases. To conclude, this is the first study to report the allele frequencies and forensic statistical parameters of Bahraini population using the 30 insertion and deletion polymorphisms included in the Investigator DIPplex Kit. Interpopulation comparisons showed that differences were high among populations worldwide, which revealed that DIPplex Kit might be performed well in intercontinental forensic population analysis. The 30 indels markers consisting of straightforward genotyping procedure with low mutation rate and high level of information indicates a great potential in forensic investigations especially in cases where degraded or low quality samples gave partial/null profile using the conventional STR markers, or in paternity cases where additional set of markers are needed to increase the power of the evidence.Supplementary information.


Disentangling the contribution of long‐term evolutionary processes and recent anthropogenic impacts to current genetic patterns of wildlife species is key to assessing genetic risks and designing conservation strategies. Here, we used 80 whole nuclear genomes and 96 mitogenomes from populations of the Eurasian lynx covering a range of conservation statuses, climatic zones and subspecies across Eurasia to infer the demographic history, reconstruct genetic patterns, and discuss the influence of long‐term isolation and/or more recent human‐driven changes. Our results show that Eurasian lynx populations shared a common history until 100,000 years ago, when Asian and European populations started to diverge and both entered a period of continuous and widespread decline, with western populations, except Kirov, maintaining lower effective sizes than eastern populations. Population declines and increased isolation in more recent times probably drove the genetic differentiation between geographically and ecologically close westernmost European populations. By contrast, and despite the wide range of habitats covered, populations are quite homogeneous genetically across the Asian range, showing a pattern of isolation by distance and providing little genetic support for the several proposed subspecies. Mitogenomic and nuclear divergences and population declines starting during the Late Pleistocene can be mostly attributed to climatic fluctuations and early human influence, but the widespread and sustained decline since the Holocene is more probably the consequence of anthropogenic impacts which intensified in recent centuries, especially in western Europe. Genetic erosion in isolated European populations and lack of evidence for long‐term isolation argue for the restoration of lost population connectivity. The species' range extends from Central Europe to the Asian Far East, encompasses a wide range of habitats  and climates , and includes populations with varied recent demography, some of which led to near extirpation in the last century by anthropogenic impacts and extermination policies, followed by varied rates of recovery. The fossil and historical records indicate that the Eurasian lynx was already present in Europe during the Pleistocene  from Jerez Zoo (Spain), and one Lynx canadensis from Ostrava Zoo (Czech Republic) were sampled to be used as an outgroup and to identify the ancestral state of detected variants  North‐Eastern (NE) Poland ; (2) Balkans; (3) Carpathian Mountains; (4) Latvia; (5) Norway; (6) Kirov region, Russia; (7) Ural Mountains, Russia; (8) Tuva (the Republic of Tyva), Russia; (9) Yakutia (Republic of Sakha), Russia; (10) Primorsky Krai, Russia; and (11) Mongolia , using bwa‐mem  project and a separate capture‐based study . Reads were mapped to the l et al.  using bw‐mem Li,  with defL. lynx individuals, 76 at low–medium depth (4–13×) and four at high depth (19–28×), and mitogenome data for 96 L. lynx individuals with an average depth of 137.7× , for most of the analyses  through time (3 × 106–104 years) on the basis of the nuclear genome, we used a PSMC model , two from Asia (Vladivostok and Yakutia) and two from Europe (Carpathians and Kirov). For each individual, we generated a diploid consensus file using samtools mpileup as suggested by Li and Durbin  to randomly sample one allele from each of the two fasta Files. PSMC analyses were then conducted on the pseudodiploids as described above for true diploids. We performed 100 bootstraps , although with limited power when sample sizes are below the hundreds  were inferred using the relationship between linkage disequilibrium and eN  into cat coordinates  using the synteny previously defined by Abascal et al.  workflow in gatk 3.4 , and on the one L. rufus sample as outgroup. Allele counts were extracted from each of the two VCF files using a custom script, and both resulting allele count files were merged under the assumption that any SNP absent in one of the species (but present in the other) would be fixed for the reference allele. We ran treemix version 1.12  and calculated the proportion of variance in relatedness between populations explained by each model. To assess the consistency of migration edges, we performed nine additional runs for m = 2 with different random seeds. Both the tree models and the residuals from the fit of the models to the data were visualized using the R script included in treemix.We used threepop program of treemix, to detect past admixture between populations. This test checks whether population X is related to populations A and B through a simple tree , or through an admixture of A and B . To assess the statistical significance of the test, threepop obtains a standard error from blocks (here set to a size of 100 SNPs) and then generates a Z score. Z score values below −2 indicate significant support for admixture. This test was conducted for all possible combinations of (a) three representative European populations , (b) one Asian test population (Tuva or Yakutia) and (c) the three remaining Asian populations .We also ran the three‐population test  –setMaxDepth (average [AVR] depth for the population + [0.95 * stdev depth for the population]) –setMinDepth (AVR depth for the population − [0.95 * stdev depth for the population]) − skipTriallelic 1); and we took the base observed in L. rufus as the ancestral state  was computed using angsd and ngsdist from ngstools  and Tajima's D) was computed as the number of substitutions between L. lynx and L. rufus divided by the number of covered sites, based on a genus‐wide variant‐calling performed following the GVCF workflow in gatk 3.4 . The ratio between diversity and divergence (θ/D) was used as a measure of diversity normalized by mutation rate. The average θ/D ratio was calculated for X chromosome (X) and autosomal (A) windows and these were used to obtain an X/A ratio for each population. Standard errors of X/A ratios were calculated by bootstrapping over windows. We used R to plot SFS and diversity values, as well as to plot our diversity estimates along with those reported for other mammals for comparison.To infer recent population size changes we compared X chromosome θ versus. autosomal θ, while controlling for divergence , haplotype diversity (Hd), nucleotide diversity (π) and mean number of pairwise nucleotide differences (k) were calculated using the popgenome .To estimate a substitution rate for the mitochondrial genome in felids, we used a set of 15 felid mitogenomes downloaded from GenBank and 33.1Lynx lynx individuals  using PSMC are concordant throughout most of the reconstructed period, indicating a long span of shared history between all populations  to 600 thousand years ago (kya), and then declining more steeply to around 200 kya. This steep decline is followed by an apparent recovery of the population until 70 kya, although this could instead indicate the emergence of population structure  as outgroup supported a model of population divergence with one or two migration events , or from the Urals to Tuva and to Yakutia population  resulting from f statistics using threepop confirmed the admixture in Tuva, but not in Yakutia .Regarding gene flow, the 2 Figure , and the2 Figure . Signifiia Table , and ide3.2Unlike the pattern inferred for the nuclear genome, the phylogenetic tree reconstructed from whole mitogenome sequences revealed several old mitochondrial lineages in Europe, which may have diverged during periods of isolation in separate glacial refugia Figure b. The ol3.3Our a priori populations vary in the extent of the area sampled, ans may have affected our estimations of genomic diversity and differentiation. Nevertheless, individual‐based clustering and PCAs confirmed the relative genetic homogeneity within and differentiation among our a priori defined populations .K = 2, supported as the uppermost level of structure by the Evanno et al. , separate bottlenecked populations in Europe   which ended up with the extirpation of many European populations, probably shaped the current genetic patterns of the species.4.1Phylogenetic and demographic analyses support a long common history for Eurasian lynx populations during most of the Pleistocene. The species suffered a massive demographic decline registered by PSMC between 3 Mya and 200 kya Figure . Low speWithin continents, European populations underwent periods of isolation during the Late Pleistocene, as revealed by the divergence of different mitochondrial lineages, whereas populations in Asia remained largely connected as shown by nuclear and mitochondrial data Figures b and 3b.4.1.1Climatic fluctuations during the Late Pleistocene could have contributed to range contraction and subsequent emergence of population structure and divergence as revealed by nuclear and mitochondrial data Figures  and 3. ADeep mitochondrial divergences contrast with the recent isolation within Europe inferred from nuclear data. This discrepancy could be attributed to male‐biased dispersal, which is characteristic of the species  have been found associated with hunting by prehistoric humans throughout Europe . In addition, we cannot discard the possibility that the morphological variation that sustained previous subspecific delimitations represents plastic responses to local environments mediated by epigenetic changes, as suggested by a recent study of K.S., J.A.G. and M.R. conceived the project and designed the study; A.P.S., G.N., I.O., I.V.S., J.A.G., K.S., M.G.D. and M.P. provided samples; E.M., K.W. and M.L.P. performed the laboratory work; B.M.C., D.K.R., E.M., K.W. and M.L.P. analysed the data; B.M.C., D.K.R., E.M., J.A.G., K.S., M.L.P. and M.R. interpreted the results; M.L.P. drafted the manuscript with support from B.M.C., D.K.R., E.M. and J.A.G. and critical input from A.P.S., K.S. and M.R; J.A.G. supervised the project. All authors approved the final version of the manuscript.https://github.com/mlucenaperez/contemporary_analysisBam files generated in this study have been deposited in the European Nucleotide Archive (study accession no. PRJEB28038). Mitogenomic consensus sequences for each sample have been deposited in GenBank (accession nos. MK229198–MK229293). Scripts used for bioinformatics analyses are available in:  Click here for additional data file. Click here for additional data file. Click here for additional data file.