Anopheles moucheti is a major malaria vector in forested areas of Africa. However, despite its important epidemiological role, it remains poorly known and insufficiently studied. Here, levels of genetic differentiation were estimated between different A. moucheti populations sampled throughout its distribution range in Central Africa.Polymorphism at ten microsatellite markers was compared in mosquitoes sampled in Cameroon, the Democratic Republic of Congo and an island on Lake Victoria in Uganda. Microsatellite data were used to estimate genetic diversity within populations, their relative long-term effective population size, and the level of genetic differentiation between them.A. m. bervoetsi while other samples consisted of A. m. moucheti. Successful amplification was obtained at all microsatellite loci within all A. m. moucheti samples while only six loci amplified in A. m. bervoetsi. Allelic richness and heterozygosity were high for all populations except the island population of Uganda and A. m. bervoetsi. High levels of genetic differentiation were recorded between A. m. bervoetsi and each A. m. moucheti sample as well as between the island population of A. m. moucheti and mainland populations. Significant isolation by distance was evidenced between mainland populations.All specimens collected in Tsakalakuku (Democratic Republic of Congo) were identified as A. m. bervoetsi which should henceforth be recognized as a full species and named A. bervoetsi. Isolation by distance is the main force driving differentiation between mainland populations of A. m. moucheti. Genetically and geographically isolated populations exist on Lake Victoria islands, which might serve as relevant field sites for evaluation of innovative vector control strategies.High levels of genetic differentiation supports complete speciation of  Anopheles moucheti mosquitoes can sustain malaria transmission intensities as high as 100–300 infected bites per man per year in villages located at the vicinity of large rivers and slow-moving streams where its larvae develop [Malaria remains one of the world's major health problems claiming at least one million deaths each year in Africa . In the  develop -5. Howev develop .Anopheles moucheti is a group of three morphological forms: A. moucheti moucheti, A. moucheti nigeriensis and A. moucheti bervoetsi, which can be distinguished by minor variations in the size and distribution of pale fringe spots and pale vein spots on the wings at the adult stage, and at the larval stage by the number of branches of the saddle hair (>5 branches for A. m. bervoetsi and <5 branches for the two others) [A. moucheti group in Central Africa and were therefore of poor diagnostic value [A. m. moucheti, A. m. nigeriensis and A. m. bervoetsi and a diagnostic PCR assay was subsequently developed allowing straightforward identification of the three taxonomic units within the A. moucheti group [A. m. moucheti is widespread throughout the forested areas of Central Africa, whereas A. m. nigeriensis and A. m. bervoetsi were found only in their type localities in Nigeria and the Democratic Republic of Congo (DRC), respectively. Microsatellite DNA markers have been isolated from A. moucheti [A. moucheti populations situated 65–400 km apart in Cameroon, suggesting high levels of gene flow at this geographical scale [ others) ,8. Howevic value . More reti group . This stmoucheti  and thesmoucheti . Very loal scale .A. moucheti group and to precise the taxonomic status of An. m. bervoetsi. Analytical methods, based on various aspects of the data, are used to provide insights into the role and relative importance of geographic distance, demographic parameters (eg effective population size and demographic instability) and natural barriers to gene flow such as habitat discontinuities and speciation in shaping the observed population structure.Previous findings from Cameroon  are expaA. m. bervoetsi, Tsakalakuku  and Kenge ; and from the island of Bufumira  on Lake Victoria in Uganda .Anopheles moucheti specimens were visually sorted from other anophelines according to morphological identification keys [Mosquito collections were conducted from July 2003 to April 2004 in Cameroon , in Deceion keys ,13. All Genomic DNA was extracted from wings or legs of each individual mosquito as described earlier . MorpholRs, an unbiased estimator of the number of alleles in each sample accounting for differences in sample sizes [He [Genetic diversity within samples and overall was measured at each locus by estimating allele richness le sizes , and He izes [He , the unbizes [He . Genotypizes [He . LinkageK) in the dataset without prior information on the sampling locations, using STRUCTURE 2.2 [K (K = 1 to 8), with a burn-in period of 100,000 iterations and 100,000 replications. The method of Evanno et al [ad hoc quantity, ΔK, based on the second order rate of change of the likelihood function between successive values of K.Genetic differentiation between populations was assessed by estimating Wright's F-statistics , calculaTURE 2.2 . A modelno et al  was usedHe) and from the number of alleles and sample sizes (Heq) are expected not to be significantly different in a population at MDE. Comparing He to Heq across loci, therefore, provides the basis for testing this hypothesis. If the population recently experienced a bottleneck, rare alleles will be rapidly lost and therefore Heq will decrease faster than He. Thus, observing a significant number of loci with He>Heq suggests the focal population recently experienced a bottleneck while the reverse trend  may suggest population expansion. Estimates of expected heterozygosity under MDE were calculated assuming a Stepwise Mutation Model (SMM) and a Two Phase Model (TPM) with 10–30% indels larger than the repeat unit. Statistical significance of the deviation from MDE was assessed for each sample across all loci by the Wilcoxon signed ranks tests and sign tests available in BOTTLENECK.Because demographic instability such as recent population bottleneck and/or expansion might bias genetic differentiation estimates to a significant extent ,25, hete2-1}/8 [Differences in effective population size (Ne) between samples might further increase estimates of genetic differentiation between populations because differences in Ne violates assumptions of the island model of population migration, assumed to hold true when devising F-statistics . Estimat2-1}/8 ,27, wher-5, [-5, [-4 proposed by Lehmann et al [Anopheles gambiae was conservatively adopted for estimating A. moucheti long-term Ne. Nevertheless, inferences were drawn on a relative scale, using the product of Neμ as a proxy of long-term Ne for each population, therefore alleviating any bias due to incorrect estimation of the mutation rate.Because the average mutation rate does not vary much even between well separated species such as pigs  and mic-5, [-5, ), the vann et al  for AnopIn all instances where multiple tests were conducted simultaneously, the sequential Bonferroni procedure  was applA. moucheti group were analysed in this study, including 223 mosquitoes from Cameroon that were previously genotyped [A. m. bervoetsi and the rest were A. m. moucheti. Genotypes at 10 microsatellites were determined. All loci amplified successfully in all A. m. moucheti populations and were highly polymorphic, showing between 11 (AM13) and 17 (AM5 and AM15) distinct alleles. By contrast, only six loci could be amplified in the A. m. bervoetsi sample, among which AM13 was not polymorphic (Table A. m. moucheti population collected from the island of Bufurima (Uganda). All mainland populations of A. m. moucheti showed similar average allele richness (range 5.53–6.66) and expected heterozygosity (range 0.771–0.833).A total of 355 mosquitoes of the enotyped . Of thesHardy-Weinberg expectations were significantly rejected (P < 0.001) for seven out of 10 loci when considering the pooled samples as belonging to a single panmictic population, with heterozygote deficits being evidenced at all loci, as expected when different gene pools are mixed. At the population level, 22 out of 65 tests did not conform to Hardy-Weinberg expectations after the multi-test analysis was taken into account. Significant deviation from HWE varied across loci in a population-dependent manner. The Uganda population from Bufumira island had the highest number of loci in departure from HWE (6 of 10) while the Kenge population had the fewest (1 of 10).-6).Exact tests for linkage disequilibrium within each of the seven populations resulted in three significant values out of 276 comparisons after correction by the Bonferroni procedure  and one in Simbock (AM9-AM10)). No pair of loci appeared in linkage disequilibrium in more than one population, suggesting genetic independence between loci. When the test was performed in the pooled populations, two pairs of loci (AM2-AM16 and AM2-AM20) out of 45 possible combinations showed highly significant P values . The island population of Bufurima (Uganda) showed higher levels of differentiation with this core group, with pairwise Fst estimates in the range 0.167–0.223 (P < 0.001). The highest levels of genetic differentiation were observed in all comparisons involving the A. m. bervoetsi sample collected in Tsakalakuku (DRC), with Fst estimates ranging 0.343–0.448 (P < 0.001).Table A. m. moucheti from Cameroon and DRC, (2) A. m. moucheti from Bufumira island in Uganda and (3) A. m. bervoetsi  mainland and DRC,  A. m. moA. m. moucheti from Cameroon and DRC  were used for the Mantel test. Positive and highly significant correlation  was found between genetic (Fst) and geographic distances. Using the equation of the regression line of Fst on the logarithm of distance between sampling sites , showed that the estimates were five to nine folds higher for mainland populations of A. m. moucheti, whereas they were at least five folds lower for the A. m. bervoetsi sample. This demonstrates significant heterogeneity in effective population size between the different genetic clusters identified above.Estimates of long-term Ne were similar for all mainland ns Table . These wA. m. moucheti, nor in the A. m. bervoetsi population. However, a consistent trend for lower-than-expected heterozygosity  was evidenced for the Bufurima island population, suggesting recent demographic expansion.Estimates of genetic differentiation and effective population size however are based on the assumption of MDE. Results of the heterozygosity tests Table  did not A. m. moucheti populations from different geographic locations and one A. m. bervoetsi population were compared for variation in polymorphism and allele distribution at 10 microsatellite loci. Successful amplification at each microsatellite locus was obtained for all A. m. moucheti specimens while only six loci could be amplified in the A. m. bervoetsi sample, one of which did not show any polymorphism, all specimens investigated showing the same single allele at a homozygous state. This result provides further support for speciation within the A. moucheti group of malaria vectors in Central Africa and reflects, for the first time, genome-wide differentiation between A. m. moucheti and A. m. bervoetsi. Indeed, although the exact cytological location of the microsatellite markers is not known yet, linkage disequilibrium analysis revealed no evidence for genetic linkage between loci, suggesting they provided independent replicates for genome-wide estimation of genetic differentiation between samples. Successful amplification of microsatellite alleles was demonstrated among closely related species such as humans and great apes [A. m. bervoetsi to full specific rank as a closely related sibling species of A. m. moucheti.In this study, six eat apes  as well eat apes  and membeat apes ,36. Howeeat apes ,38. Theseat apes ,8 and moLinkage disequilibrium analysis further ruled out the hypothesis that the fairly high number of loci which were found out of HWE in several collections was indicative of inbreeding and/or population subdivision (within samples). If this was the case, genome-wide signatures of departure from HWE and high linkage disequilibrium between loci should be evidenced, because members of the different sub-populations would have different probabilities to carry certain combinations of alleles . Such trA. m. bervoetsi and each of A. m. moucheti populations were very high and statistically significant , falling in the upper range of values reported between well separated anophelines sibling species using various molecular markers [A. m. bervoetsi as a genetically distinct entity within the A. moucheti group. Accordingly, it seems reasonable to consider this taxon as a full, independently evolving species within the A. moucheti group and, henceforth, to refer to this species as Anopheles bervoetsi. However, considering that A. bervoetsi has never been reported to occur in sympatry with A. moucheti s.s., nor outside of its type locality, this assertion should be validated through traditional crossing experiments, which are yet impossible to implement because members of the A. moucheti group have never been maintained successfully under insectary conditions. Preliminary analysis of 237 field-collected A. bervoetsi specimens after ELISA detected three females infected by Plasmodium falciparum . Although incrimination of this species as a malaria vector through dissection of its salivary gland still has to be processed, this points to a possible and previously unrecognized role of this mosquito in malaria transmission in Central Africa.Fst estimates recorded between  markers -44. ThisA. m. moucheti populations from Cameroon and DRC, separated by distances >1,000 km  showed that the observed level of differentiation was three to nine folds higher than expected. Such high Fst estimates probably reflect the contribution of large water bodies separating this island population from mainland ones, acting as a barrier to gene flow by restricting opportunities for migration between populations, as was demonstrated for A. gambiae in this area [A. gambiae compared to neighbouring mainland populations [A. moucheti group. As mentioned above however, estimates of Ne derived from He are sensitive to deviation from MDE. No significant deviation from MDE was evidenced within A. m. moucheti populations, although, to some extent, trends for recent population expansion were revealed for the island population of Bufurima. Colonization of Lake Victoria islands by anthropophilic malaria vectors probably followed initial human settlements on these islands in the early 1900s [Significant isolation by distance was revealed between mainland his area  and elsehis area ,45,46. Mhis area . Althougulations  and the ly 1900s ,48. Initly 1900s ,49. Infely 1900s .A. moucheti group of malaria vectors would have been obtained with the inclusion in this study of specimens of A. m. nigeriensis. However, as mentioned above, collections conducted in and around the type locality of this species were unsuccessful. Earlier investigations allowed collection of a few representative specimens [A. m. nigeriensis populations, as was observed for A. m. moucheti in areas of southern Cameroon [Clearly, a more comprehensive picture of the genetic structure and distribution of genetic diversity within and among natural populations of members of the pecimens  but samppecimens  and thisCameroon . As formCameroon , the avaA. m. bervoetsi as a full-rank, genetically independent, species within the A. moucheti group of malaria vectors. The species should henceforth be named Anopheles bervoetsi. However, its epidemiological role as a vector of human malaria parasites still deserves further investigation because roughly nothing is known to date on its biology and behaviour. Isolation by distance seems to be the major factor shaping A. moucheti s.s. populations' genetic structure throughout its distribution range across forested areas of Central Africa but significant geographical barriers to gene flow exist, as evidenced from reduced effective population size and high levels of genetic differentiation observed in a population collected from an island on Lake Victoria. Such genetically isolated populations in a geographically confined environment might be of considerable interest for a safe assessment of new and innovative vector control strategies aiming at population suppression and/or replacement, such as those based on the release of sterile or otherwise genetically altered mosquitoes.In conclusion, this study provides strong support for considering CAN was involved in the study design and implementation, conducted field sampling, microsatellite genotyping and data analysis,  prepared and drafted the manuscript. FS supervised the study conception and design, contributed to data analysis and drafted the manuscript. CN participated to field sampling in Cameroon and microsatellite genotyping. LM organized and greatly contributed to sampling in Uganda. PK was involved in molecular analysis and helped with markers selection resources. PAA and DF were involved in the conception of the study and revised the manuscript.


Human genetic diversity in the Pacific has not been adequately sampled, particularly in Melanesia. As a result, population relationships there have been open to debate. A genome scan of autosomal markers (687 microsatellites and 203 insertions/deletions) on 952 individuals from 41 Pacific populations now provides the basis for understanding the remarkable nature of Melanesian variation, and for a more accurate comparison of these Pacific populations with previously studied groups from other regions. It also shows how textured human population variation can be in particular circumstances. Genetic diversity within individual Pacific populations is shown to be very low, while differentiation among Melanesian groups is high. Melanesian differentiation varies not only between islands, but also by island size and topographical complexity. The greatest distinctions are among the isolated groups in large island interiors, which are also the most internally homogeneous. The pattern loosely tracks language distinctions. Papuan-speaking groups are the most differentiated, and Austronesian or Oceanic-speaking groups, which tend to live along the coastlines, are more intermixed. A small “Austronesian” genetic signature  was detected in less than half the Melanesian groups that speak Austronesian languages, and is entirely lacking in Papuan-speaking groups. Although the Polynesians are also distinctive, they tend to cluster with Micronesians, Taiwan Aborigines, and East Asians, and not Melanesians. These findings contribute to a resolution to the debates over Polynesian origins and their past interactions with Melanesians. With regard to genetics, the earlier studies had heavily relied on the evidence from single locus mitochondrial DNA or Y chromosome variation. Neither of these provided an unequivocal signal of phylogenetic relations or population intermixture proportions in the Pacific. Our analysis indicates the ancestors of Polynesians moved through Melanesia relatively rapidly and only intermixed to a very modest degree with the indigenous populations there. The origins and current genetic relationships of Pacific Islanders have been the subjects of interest and controversy for many decades. By analyzing the variation of a large number (687) of genetic markers in almost 1,000 individuals from 41 Pacific populations, and comparing these with East Asians and others, we contribute to the clarification and resolution of many of these issues. To judge by the populations in our survey, we find that Polynesians and Micronesians have almost no genetic relation to Melanesians, but instead are strongly related to East Asians, and particularly Taiwan Aborigines. A minority of Island Melanesian populations have indications of a small shared genetic ancestry with Polynesians and Micronesians . Inland groups who speak Papuan languages are particularly divergent and internally homogeneous. The genetic divergence among Island Melanesian populations, which is neatly organized by island, island size/topography, as well as their coastal or inland locations, is remarkable for such a small region, and enlarges our understanding of the texture of contemporary human variation. The populations in New Guinea and the islands immediately to the east (the Bismarck and Solomons archipelagos) are well-known for their great diversity in cultures, languages, and genetics, which by a number of measures is unsurpassed for a region of this size . This arBy ∼3,300 YBP , that leThe distribution and relations of Pacific language families reflect ancient settlement. Austronesian is a widespread and clearly defined linguistic family with more than 1,000 member languages, which has its greatest diversity, and likely origin, in Taiwan ∼4,000–5,000 years ago . Some baAlmost all the other indigenous languages of Oceania are referred to as non-Austronesian, or Papuan. Most Papuan languages are found in New Guinea, with the remainder in nearby islands. This is a residual category of ∼800 languages. Most of these can be assigned to more than 20 different language families, but these families cannot be shown to be related on present evidence. There remain a number of “Papuan” isolates that cannot be grouped at all . Trans NAnalyses of genetic variation at some informative loci, particularly the mitochondrial DNA (mtDNA) , However, a number of unresolved issues remain concerning the proper interpretation of these and other data that a comprehensive genomic sampling of neutral biparental markers across Pacific populations should clarify. A list of these includes: 1) to whom are these diverse Melanesian populations most closely related outside this region ? 2) how does the genetic diversity and differentiation of Near Oceanic populations compare with those in other regions? 3) is there a clear organization of the variation among groups in Near Oceania ? 4) is there a genetic signature of Aboriginal Taiwanese/Southeast Asian or Polynesian influence in Melanesian populations, especially in the Bismarcks, where the Lapita Cultural Complex developed? and 5) are Polynesians more closely related to Asian/Aboriginal Taiwanese populations or to Melanesians?Here we report the analysis of 687 microsatellite and 203 insertion/deletion (indel) polymorphisms in 952 individuals from 41 Pacific populations, primarily in the Bismarck Archipelago and Bougainville Island, and also including select sample sets from New Guinea, Aboriginal Taiwan, Micronesia, and Polynesia. The results show the reduced internal variation of Near Oceanic Melanesian populations and the remarkable divergence among them, and how this divergence is influenced by island size and topography, and is also correlated with language affiliation. We also detected a very small but clear genetic signature of “Asian/Polynesian” intermixture in certain Austronesian (Oceanic)-speaking populations in the region . For global context, these data were compared with data from the Centre d'Etude du Polymorphisme Humain human genome diversity panel (HGDP-CEPH), composed of cell lines –24, espeOur sampling strategy concentrated on Papuan-speaking populations and their immediate Oceanic-speaking neighbors from the islands immediately to the east of New Guinea, in what is called Northern Island Melanesia, consisting of the Bismarck and Solomon Archipelagos see B. The thθ (θ̂) calculated from expected heterozygosity (eH) arranged from highest to lowest values, combining our Pacific populations and the HGDP-CEPH global set  iswhich rearranges toθ is defined as θ = 4eNμ, where eN is defined as the effective population size and μ is the per generation mutation rate. Assuming the mutation rate is constant across populations and that the stepwise mutation model is appropriate, θ̂ provides an estimate that is linearly correlated with effective population size. In contrast, H asymptotically approaches a value of 1 as the effective population size increases. Therefore, the use of θ̂ is more appropriate to represent differences in effective population sizes among populations .For autosomal loci, θ̂ values (between 4.8 and 2.9). Within the Melanesian set, inland populations generally had lower values of θ̂ than shore-dwelling groups, as shown. The three non-Pacific groups in the range between 4.8 and 2.9 were the Maya, Columbia, and Lahu. The Maya are known to have some European ancestry, which would explain their relatively high θ̂ for a Native American group; and the Lahu are an Asian population that was subject to particularly strong random genetic drift [θ̂ than related groups. This combining of groups has caused inflated levels of diversity and effective population size estimates .The pattern of variation in ic drift . ColumbiFST in the global HGDP-CEPH dataset, and found a linear relationship existed, with major deviations from the fitted line they believed consistent with admixture or extreme isolation. We analyzed this correlation by major region, adding our expanded Pacific dataset. The results, shown in FST values was extremely large. Only the Native American groups had an equivalent range of FST values, but these were unreliable since there were only five American populations distributed across very large distances.Ramachandran et al.  investigθ̂ [To quantify the degree of variation within and among populations, an analysis of molecular variance (AMOVA) for the Pacific materials plus the HGDP-CEPH dataset was performed, with the results shown in θ̂ .As shown in Apportioning the molecular variance by language group (between Oceanic speaking and Papuan speaking populations) only accounted for 0.2 % of the total, which, as indicated by the very small 95% confidence interval, was still significant. Since the two language categories are scattered across the islands, geography and intermixture will confound possible language effects. While the microsatellite variation among the Oceanic-speaking populations was significant, it was much greater among the Papuan-speaking populations (many of which are located in the mountainous interiors of the larger islands).K clusters, where K is specified in advance and corresponds to the number of posited ancestral populations; K can be varied across independent runs. Individuals can be assigned admixture estimates from multiple ancestral populations, with the admixture estimates summing to 1 across these population clusters.To investigate individual and population similarities, we applied a Bayesian model-based clustering algorithm implemented in the STRUCTURE program  to our PK split a cluster that had been defined in an earlier run, and individuals from the same populations had very similar membership coefficients in the inferred clusters. Details of the STRUCTURE results are provided in the K = 6 mostly involved Near Oceanic populations, we stopped the combined global analysis there, and analyzed the Pacific subset separately thereafter.There was a small but consistent “Asian/Polynesian” admixture estimate in specific Melanesian groups. Because clustering after FST “coancestry” distances 2 [An unrooted neighbor-joining tree for the same HGDP-CEPH and Pacific samples, excluding the indels, was calculated from a matrix of pairwise nolds' D , see Tabdistance , (δμ)2 [nce [μ)2 , the pronce [μ)2 , and Cavnce [μ)2 ) also inK = 2 and K = 3, the Asia-Pacific clusterings mirrored the first five runs of the global comparison. Bougainville formed a cluster contrasting with central New Britain at K = 3; the New Guinea groups separate at K = 4; and a central New Britain cluster splits at K = 5. Then, at K = 6, a Polynesian cluster appeared, centered on the Mãori, with high ancestral proportions for the Samoan and Micronesian samples as well as the Taiwanese Aborigines. The former “East Asian” ancestral proportion in Melanesian populations converted almost entirely to “Polynesian” in this run. At K = 7, 8, and 9, more Melanesian clusters formed in New Britain and New Ireland. All but one of the Melanesian cluster foci are Papuan-speaking groups, primarily located in the interiors of the large islands , the East Asians, and French (the French were included to identify European admixture). The STRUCTURE results are shown in ands see  and 8. TK = 10 produced essentially the same group ancestry proportions as shown in The approximate percentage of “European” admixture is best seen in As in the global comparison, an “East Asian/Polynesian” estimated ancestry proportion for a number of Melanesian populations only occurred at frequencies of >5% in certain Oceanic-speaking (Austronesian) groups, and it is hereafter referred to as the “Austronesian” genetic signature. In As a check on these results, particularly to verify the relationships of the Polynesians and Micronesians within our dataset, we performed a separate “supervised” STRUCTURE analysis ,36, wherK = 10 in pie-chart form (some populations from the same language groups with very similar probability profiles were merged). Neighboring groups tended to share similar profiles. New Britain, the largest and most rugged island, had the greatest internal differentiation, with five different assigned clusters at >50% probabilities in different populations. Bougainville groups had two common cluster assignments, while there was only one common cluster in New Ireland.FST coancestry distance matrix for 687 microsatellites  are plotted against the heterozygosities of each population, and the resulting correlations approach 1.0.However it is measured, genetic variation is reduced within Melanesian populations , while tpecially  and alsopecially ,37). As datasets A and 10BOur Structure and tree analyses of the combined microsatellite datasets indicate that Melanesians are quite far removed from Africans, in spite of their superficial similarities in hair form and skin pigmentation . In the θ̂ and expected heterozygosity—The pattern of Near Oceanic diversity has been made clear. The AMOVA analysis of the microsatellites showed that the larger and more rugged the island, the greater the differentiation among populations. The most divergent populations were in large island interiors while these same populations were internally the most homogeneous  Polynesians came from has remained controversial, and the number of proposed explanatory models for their origin form a continuum ,52. At oModels at the other end of the continuum assume contemporary genetic  similarities can carry a clear signal of past population relationships. Primary among these is “The Express Train to Polynesia” model . It propWith regard to human genetics, the published mtDNA evidence has generally been interpreted as supporting the “Express Train.” This is because a younger mtDNA haplotype (B4a1a1) is assumed to have been closely linked to the development and expansion of Polynesian populations. At present, the state of the evidence for this association is as follows: a) the precursor haplotype to B4a1a1 has been identified in Taiwan aboriginal populations ; b) the The “Slow Boat to Polynesia” model which is supported by NRY variant distributions, also assumes current genetic patterns in Oceania directly reflect prehistoric migrations and interactions. These NRY haplogroup distributions have been taken to suggest a very minor “Asian” contribution to current Polynesian populations, suggesting instead that Polynesians derived primarily from Melanesian (Near Oceanic) populations ,57,58. “Because of their comprehensive nature, we believe the results of our autosomal microsatellite survey present a resolution to this issue with regard to human genetic relationships. The fact that the STRUCTURE cluster containing Micronesians, Samoans, and Maoris has a detectable signature only in Oceanic-speaking Melanesians and Taiwan Aborigines supports the position that an expansion of peoples from the general vicinity of Taiwan is primarily responsible for the ancestry of Remote Oceania, and that these people left a small but still identifiable signature in (some Oceanic-speaking) populations of Near Oceania. Scenarios for different male and female dispersals have been proposed to reconcile the divergent mtDNA and NRY patterns in Oceania ,59, but Although the Polynesians in our analysis were similar to Taiwan Aborigines and East Asians, they might be even closer to other populations not covered in our study, from Indonesia, the Philippines, or Southeast Asia. While there is a substantial body of evidence that indicates Taiwan is the primary point of Austronesian dispersal ,61, therTo revisit the questions posed at the beginning, we can provide answers as follows.To whom are these Melanesian populations most closely related outside the Pacific? Outside the Pacific, East Asian populations are apparently the closest (but still very distant) relatives of Melanesians. Africans and Europeans are the most distant.1) How does the genetic diversity of Near Oceanic populations compare with groups in other regions? The within-group diversity in Melanesian populations is consistently very low, which acts to exaggerate the considerable among-group distinctions there. This great diversity in such a small region makes comparisons of human population structure from continent to continent problematic.2) Is there a clear organization of the variation among Melanesian groups? The diversity among groups is primarily organized by island size and topographic complexity, with the inland Papuan-speaking groups the most isolated and differentiated. Shore-dwelling Oceanic-speaking groups are more intermixed .3) Is there an identifiable genetic signature of Taiwanese/Southeast Asian or Polynesian influence in Near Oceanic populations, especially in the Bismarcks, where the Lapita Cultural Complex developed? There is a weak “Austronesian” genetic signature in only a portion of Oceanic-speaking populations in Melanesia, and none at all in Papuan-speaking groups .4) Are Polynesians more closely related to Asian/Taiwanese populations or to Melanesians? Polynesians are closely related to Asian/Taiwanese Aboriginal populations, while they are very weakly associated with any Melanesian groups (the closest association there appears to be with New Ireland populations). This is in accord with mtDNA interpretations, but differs from the usual interpretation of the NRY results. The sailing capabilities of the ancestors of the Polynesians transformed the nature of their Diaspora and kept them relatively homogeneous.5) Our Asia–Pacific sample set came from a variety of sources. The objective was to include between 15 and 25 unrelated individuals  from locales where individuals and their parents had all lived. These criteria were achieved in most instances. All of the samples except the cell lines were Whole Genome Amplified (Qiagen RepliG). Details are given below.1. Samples from Northern Island Melanesia were collected in three field seasons  in collaboration with the Institute for Medical Research of Papua New Guinea. Besides a 10 ml blood sample, a simple genealogy and residency questionnaire was taken, including in most instances parent and grandparent names, residences, and native languages. All individuals gave their informed consent for participation, and the study was approved by the Institutional Review Boards of Papua New Guinea, Temple, Michigan, Yale, and Binghamton Universities. Among over 1,500 samples collected, 995 were chosen for submission to the Marshfield Clinic for microsatellite and indel analysis. As many Papuan-speaking groups as possible were included, along with neighboring Oceanic-speaking groups, focusing on New Britain, New Ireland, New Hanover, Mussau, and Bougainville. We included multiple locales in larger language groups where feasible; and picked samples from individuals whose family's residence histories suggested close identification with the sampling locale. People of mixed parentage  could not always be excluded if the minimum required sample size was to be achieved. A number of individuals who were born on the New Guinea mainland but had settled in Northern Island Melanesia were taken to constitute one additional sample—the “Sepik” —so that this sample is a conglomerate. DNA was extracted as previously described .2. DNA was obtained from the Kidd lab collection of cell lines for: a) the Eastern Highlands of Papua New Guinea, primarily from the Gimi, which were collected in collaboration with the Papua New Guinea Institute of Medical Research, and also from Goroka Town; b) Micronesians, primarily from Belau, who drew each other's blood samples during their training in the Pacific Basin Medical Officer Training Program; and c) Samoans, who were in a combined collection from the Pacific Basin Medical Officer Training Program and from American Samoa. All individuals gave their informed consent for participation.3. New Zealand DNA samples were collected from indigenous Mãori individuals residing in the North Island. Individuals were unrelated by first degree, had two Mãori parents by self-report, and belonged to one segment of the wider Mãori population. Ethical clearance was granted by the NZ National Ethics Committee. DNA was extracted from blood using Qiagen kits.4. Taiwan Aboriginal samples comprise the Northeastern Taroko tribe from Hsiulin, part of the Atayal language group, and the Amis tribe living on the east coast of Taiwan and speaking Amis. All individuals were unrelated and had both parents belonging to the same tribe. Each individual gave informed consent to participation in population genetics studies and the project was approved by the Ethics Committee of the Hospital and the Department of Health of Taiwan. Blood samples were collected in acid citrate dextrose tubes. Genomic DNA was extracted from 500 μl of buffy coat using the QIAmp DNA kit  by Loo Jun-Hun at the Transfusion Medicine and Molecular Anthropology Laboratory, Mackay Memorial Hospital, Taipei.Each individual was originally genotyped for 751 microsatellite and 481 insertion/deletion autosomal polymorphisms. The microsatellites were drawn from Marshfield Screening Sets #16 and #54, and the indel markers were drawn from Marshfield Screening Set #101.890 markers typed in our Pacific series (203 indels and 687 microsatellites) had been typed in the HGDP-CEPH Human Genome Diversity Cell Line as described in , althougFST was estimated on the microsatellites as in Equation 5.3 from [The expected heterozygosity and average number of alleles per locus were computed on the microsatellites with the GDA software , using t5.3 from  , using K in most cases, except as noted in K = 7) and K = 10). When multiple runs at the same values of K produced discrepant results, we relied on majority rule  to pick the optimal result. For the combined global analysis, we terminated the STRUCTURE runs at K = 6, as explained in the Results, and for the Pacific set we terminated the analysis when it became unstable at higher values of K . Details are provided in the Cluster analysis of genotypes utilized the Structure versions 2.1 and 2.2 software package ,36. ResuIndividual similarity coefficients for pairs of runs were calculated as in  and MethFST distance matrices obtained with GDA. The bootstrap values for the Asia–Pacific dataset Click here for additional data file.Figure S2(A) Cavalli-Sforza and Edwards' Chord Distance .μ)2 [(B) Goldstein's (δμ)2 .(C) Nei's chord distance .(D) Proportion of shared alleles (PSA) .(9.3 MB TIF)Click here for additional data file.Figure S3The Mãori, Samoan, and Micronesian individual profiles are compared with eight specified representative populations .K = 8 .(A) The distribution of the Mãori, Samoans, and Micronesians across the eight specified groups, at K = 9).(B) The same samples and restrictions with an extra, unspecified, cluster ((4.3 MB TIF)Click here for additional data file.Table S1eH), estimated θ (θ), and mean alleles per locus.Language assignments, sample sizes, expected heterozygosity ((34 KB XLS)Click here for additional data file.Table S2(16 KB XLS)Click here for additional data file.Table S3(15 KB XLS)Click here for additional data file.Table S4(92 KB XLS)Click here for additional data file.Table S5(15 KB XLS)Click here for additional data file.Table S6(30 KB XLS)Click here for additional data file.Table S7(45 KB XLS)Click here for additional data file.Table S8(39 KB XLS)Click here for additional data file.Table S9(29 KB XLS)Click here for additional data file.


Plasmodium falciparum populations are not yet fully understood. In this study, the genetic structure of P. falciparum populations in the Philippines was examined.In the Philippines, malaria morbidity and mortality have decreased since the 1990s by effective malaria control. Several epidemiological surveys have been performed in the country, but the characteristics of the Population genetic analyses based on polymorphisms of 10 microsatellite loci of the parasite were conducted on 92 isolates from three provinces  with different malaria endemicity.P. falciparum in the Philippines were similar to those reported in the mainland of Southeast Asia or South America. In the low malaria transmission area , there was a low level of genetic diversity and a strong linkage disequilibrium (LD) when the single-clone haplotype (SCH) was used in the multilocus LD analysis, while in the high malaria transmission areas , there was a high level of genetic diversity and a weak LD when SCH was used in the multilocus LD analysis. On the other hand, when the unique haplotypes were used in the multilocus LD analysis, no significant LD was observed in the Kalinga and the Palawan populations. The Kalinga and the Palawan populations were, therefore, estimated to have an epidemic population structure. The three populations were moderately differentiated from each other.The levels of genetic diversity and the effective population sizes of In each area, the level of genetic diversity correlates with the local malaria endemicity. These findings confirm that population genetic analyses using microsatellite loci are a useful tool for evaluating malaria endemicity. Malaria is still one of the major public health problems in the Philippines, although the morbidity and the mortality have decreased in the last decade ,2. The Pet al reported a spectrum of population structures in Plasmodium falciparum: in high transmission areas, such as sub-Saharan Africa, weak linkage disequilibrium, high genetic diversity and low levels of genetic differentiation between populations were observed, while in low transmission areas, such as the Brazilian Amazon, significant linkage disequilibrium, low genetic diversity and high levels of genetic differentiation between populations were observed  [1 - ∑pi2], where n corresponds to the number of isolates examined and pi is the frequency of the ith allele.Expected heterozygosity (Ne) was estimated based on H and the microsatellite mutation rate  for P. falciparum 2 - 1} was used [Effective population size  is greater than that of H . The mode shift in allele frequency distribution for the presence of rare alleles was also examined. A normal L-shaped distribution indicates evidence of a non-bottlenecked population, whereas a shifted mode of distribution indicates evidence of a bottlenecked population. The BOTTLENECK program, version 1.2.02, was used to search for evidence of heterozygosity excess and mode-shift [Each population was examined for evidence of a recent genetic bottleneck . Heterozygosity excess was used for evidence of genetic bottlenecks ,20. In ade-shift ,21. The IAS) [IAS was calculated using the formula IAS = (VD/Ve - 1)/(l - 1) with permutation testing of the null hypothesis of complete linkage equilibrium (IAS = 0), where VD is the observed mismatch variance, Ve is the expected mismatch variance, and l is the number of examined loci. Significances of the observed IAS values were calculated by Monte-Carlo simulation, using 10,000 random permutations of the data. This statistic is a variation of the method proposed by Maynard-Smith et al [Multilocus linkage disequilibrium was assessed using the standardized index of association (IAS) ,23. ThisIAS) . IAS wasth et al . The resth et al ,22. Thisth et al . Since tFST) [FST were calculated using the FSTAT program version 2.9.3.2 and tested for significant difference from 0, based on 1,000 random permutations of the data set [The extent of population subdivision between the populations in the Philippines was estimated using Weir and Cockerham's Ø estimator for determining F statistics (FST) . FST werdata set .Allele frequencies of each locus in the three populations are shown in Additional File H)  Table . The meaNe values of the three populations were calculated from the mean expected heterozygosity and mutation rates of P. falciparum microsatellite loci using the IAM and the SMM (Table MM Table . The sizH) excess and patterns of allele frequency distribution  [H excess and deficiency. Statistically high levels of H excess compared with H deficiency (P < 0.05) were observed in the Kalinga and the Palawan populations when the IAM was applied to the analyses, indicating evidence of genetic bottleneck events. In contrast, there was no significant difference between the levels of H excess and H deficiency in the Davao del Norte population when the IAM and the SMM were applied to the analyses, indicating the absence of a genetic bottleneck event.Evidence of genetic bottlenecks was assessed based on heterozygosity (e-shift) ,20. TablIn the Davao del Norte population, the mode-shift indicator test showed a normal L-shaped distribution, indicating evidence of a non-bottlenecked population. This test could not be applied to the other two populations due to small sample sizes (<30) .IAS values were calculated for the three populations, with permutation testing of the null hypothesis of IAS = 0 (equilibrium of multilocus frequencies)  when the single-clone haplotype was examined using the 10 loci in the analyses. However, no significant linkage disequilibrium was found in the Kalinga and Palawan populations when the unique haplotypes were examined in the analyses.s) Table . IAS valFST values using the Weir and Cockerham estimator [P < 0.001) , effective population size (Ne) and multilocus linkage disequilibrium (IAS), were corrected for sample size, which enabled an appropriate comparison of different data sets.The failure to obtain allelic data of the 18 isolates might be due to low parasite density and/or misdiagnosis of other malaria species such as P. falciparum populations in the Philippines were different from each other. The Kalinga population showed a relatively low number of alleles and low levels of expected heterozygosity (H), being similar to those of South American populations of P. falciparum (H: 0.30–0.40) [H, being similar to those of mainland Southeast Asian (H: 0.51), Papua New Guinean (0.62–0.65), and Malaysian Borneo (0.46–0.63) populations of P. falciparum [Levels of genetic diversity of the three 30–0.40) ,12. In cuinean 0.2–0.65, alciparum .P. falciparum.The levels of genetic diversity seemed to be correlated with the levels of malaria endemicity. The API in Kalinga province was lower than that of the other 2 provinces. According to the report from the Center for Health Development-CAR in Kalinga province, the mean of API ± SD was 4.8 ± 1.9 (1996–2005). According to the report from the Kilusan Ligtas Malaria in Palawan province, the mean of API ± SD was 15.6 ± 4.4 (1999–2005). This difference of malaria endemicity between the two provinces probably affected the levels of genetic diversity of the two populations of FST) between the populations of P. falciparum in the Philippines (0.096–0.144) were moderate, and were similar to those between the mainland Southeast Asian and Papua New Guinean populations of P. falciparum (0.121–0.140), and lower than those between Sabah and Sarawak populations of P. falciparum (0.217–376) in Malaysian Borneo, as previously reported [Levels of genetic differentiations  was observed only when the IAM was used for the analysis. This result is not robust because modes of microsatellite mutation theoretically fit the SMM. Luikart and Cornuet recommended that only the SMM should be used for microsatellite data to test for genetic bottlenecks, because the IAM may detect heterozygosity excess even in non-bottlenecked populations [In the present study, possibilities of genetic bottlenecks with the Kalinga and the Palawan populations of ulations .P. falciparum in the Philippines, when a single clone was used in the analysis [et al proposed simple methods to distinguish between "clonal" population structure and "epidemic" population structure of microbial pathogens [Significant multilocus linkage disequilibrium was obtained from all three populations of analysis -24. The athogens . They deP < 0.05) even when the multiply-represented haplotype was treated as single individuals. This result indicated that a recombination rate was estimated as low in the Davao del Norte population, and that the population has possibly not experienced the recent epidemic expansion of a particular haplotype.On the contrary, the Davao del Norte population persisted with significant linkage disequilibrium  and the frequencies of MGI was found. The H values of the three populations were elevated as follows: Kalinga (0.39) < Davao del Norte (0.51) < Palawan (0.60), and the frequencies of MGI were also elevated as follows: Kalinga (0.0%) < Davao del Norte (6.7%) < Palawan (10.7%). In contrast, a negative correlation between the levels of linkage disequilibrium and the frequencies of MGI of the parasite was found. When single clones were used in the data set for the linkage disequilibrium analysis  and the Davao del Norte (two of 30 isolates: 6.7%) populations, but not in the Kalinga population. MGI is important to the maintenance of genetic diversity of the is Table , the levlciparum .P. falciparum. In Brazil, over 40% of local isolates were MGI detected by a single-copy antigen-coding gene (merozoite surface protein-1: msp-1), however less than 20% of local isolates were MGI detected by microsatellite typing [msp-1 gene and block 3 of msp-2 [However, caution is needed for detecting MGI by microsatellite typing, because it is likely to underestimate the prevalence of MGI of e typing ,27. In fof msp-2  in the pFST values between the three populations in the Philippines will be detected in the near future, with the onset of effective malaria control.The levels of genetic differentiation between the three populations in the Philippines were moderate, and lower than those reported in Malaysian Borneo where the parasite populations were highly fragmented from each other . High leHowever, the migration rate of people to the endemic areas in the Philippines may be different from that in Malaysian Borneo. In Malaysian Borneo, malaria endemic areas are typically in remote, isolated mountainous villages. In contrast, in the Philippines, malaria is not only endemic in remote areas  but also in relatively big cities or ports . For example, in Palawan, malaria is highly endemic in villages near seashores where fishermen live, and they sail from island to island, freely catching and selling fish.P. falciparum would be the key factors to estimate a potential of the emergence of drug resistance. Ariey et al demonstrated that a metapopulation concept would be applicable to explain the emergence of drug resistant malaria [P. falciparum populations in Southeast Asia and South America, where chloroquine resistance emerged, were consistent with a typical metapopulation structure, ie, parasite populations were differentiated in several subpopulations but not completely isolated from each other. The levels of genetic diversity and the characteristics of population structure of P. falciparum in the Philippines were similar to those reported in mainland Southeast Asia or South America where chloroquine resistant malaria emerged [et al indicated that some chloroquine resistant mutations seemed to have emerged in the Philippines [Although mechanisms for the emergence of drug resistance have not been fully understood, levels of genetic diversity and characteristics of population structure of  malaria ,30. They emerged . Indeed,lippines . Moreovelippines ,9,31. ThP. falciparum in the Philippines were examined using 10 highly polymorphic microsatellite loci of the parasite. The levels of the genetic diversity and the frequencies of MGI correlated with the local malaria endemicity. On the other hand, the levels of linkage disequilibrium correlated negatively with the local malaria endemicity. Genetic population analyses of P. falciparum using the microsatellite loci provide insight not only into understanding the basic biology of this organism, but also the epidemiology and control of the disease.The genetic diversity and population structure of The authors declare that they have no competing interests.MI carried out the molecular genetic studies, performed the population genetic analyses and drafted the manuscript. PTR, EAV, ADE, THat and SIK collected the patients' blood samples as well as helping with the writing of the manuscript. THay and KT helped with the population genetic analyses and helped with the writing of the manuscript. SK participated in the design of the study, acquisition of funding, coordination and writing of the manuscript.Plasmodium falciparum populations in the PhilippinesAllele frequencies and number of isolates (n) of 10 microsatellite loci of the three . Allele names refer to the base pair sizes of the product of polymerase chain reaction.Click here for filemsp1 and msp2 of the three Plasmodium falciparum populations in the PhilippinesPercentages of multiple-genotype infection (MGI) of . No. indicates the number of isolates for each measure.msp1: block 2 of merozoite surface protein-1 gene, msp2: block 3 of merozoite surface protein-2 gene, msp1 or msp2 represents the percentage of isolates that showed MGI in either msp1 or msp2.Click here for file


Rhodnius prolixus is the main vector of Chagas disease in Venezuela. Here, domestic infestations of poor quality rural housing have persisted despite four decades of vector control. This is in contrast to the Southern Cone region of South America, where the main vector, Triatoma infestans, has been eliminated over large areas. The repeated colonisation of houses by silvatic populations of R. prolixus potentially explains the control difficulties. However, controversy surrounds the existence of silvatic R. prolixus: it has been suggested that all silvatic populations are in fact Rhodnius robustus, a related species of minor epidemiological importance. Here we investigate, by direct sequencing  and by microsatellite analysis, 1) the identity of silvatic Rhodnius and 2) whether silvatic populations of Rhodnius are isolated from domestic populations.R. prolixus in palms and that silvatic bugs can colonise houses, with house and palm specimens sharing seven cytb haplotypes. Additionally, mitochondrial introgression was detected between R. robustus and R. prolixus, indicating a previous hybridisation event. The use of ten polymorphic microsatellite loci revealed a lack of genetic structure between silvatic and domestic ecotopes , which is indicative of unrestricted gene flow.Direct sequencing confirmed the presence of R. prolixus presents an unquestionable threat to the control of Chagas disease in Venezuela. The design of improved control strategies is essential for successful long term control and could include modified spraying and surveillance practices, together with housing improvements.Our analyses demonstrate that silvatic  Rhodnius prolixus may also infest palm trees and might repeatedly recolonise houses from palms. A complication is that a morphologically similar species, R. robustus, also infests palms but is of minor medical importance. Therefore, confusion exists as to the true identity of palm bugs and their importance in disease transmission.Chagas disease is spread by blood-feeding insects (triatomine bugs) that colonise poor-quality houses. Disease control relies primarily on killing domestic bugs by spraying dwellings with residual insecticide. In Venezuela, sustained control has proved difficult despite four decades of campaigns. Considered the main vector in Venezuela, the bug cytochrome b gene, and analysing microsatellites) to triatomines collected in Venezuela so that we could identify unequivocally the species of palm-dwelling Rhodnius and establish their role in maintaining house infestations. We demonstrated that R. prolixus is indeed present in palms, and that such silvatic populations can colonise houses and are a threat to the successful control of Chagas disease in Venezuela. This finding resolves a longstanding controversy of fundamental epidemiological importance. It is also an example of the application of molecular epidemiology to correct vector identification and successful disease control.We applied two molecular methods (sequencing DNA of the  Trypanosoma cruzi. Rhodnius prolixus is the primary vector in Venezuela and Colombia and is one of the main targets of the Andean Pact and Central American initiatives, together with the secondary vectors Triatoma dimidiata in Central America, Rhodnius pallescens in Panama and Rhodnius ecuadoriensis in northern Peru R. prolixus occurs in all States, where it colonises poor quality housing and exhibits high infection rates with T. cruzi.Chagas disease is a chronic parasitic disease transmitted by triatomine bugs (Reduviidae: Triatominae) and limited in distribution to the Americas. The causative agent is the protozoan R. prolixus persist and recent data indicate that transmission of T. cruzi may be increasing Triatoma infestans, has been eliminated over large areas following control efforts Triatoma infestans is considered to be a primarily domestic species, with the exception of Bolivian Andes and Gran Chaco region (Bolivia and northern Argentina) where silvatic populations were found R. prolixus is reported to have a widespread silvatic distribution in Venezuela, found most commonly in palm trees and birds nests and more rarely in other sites such as dry trees R. prolixus, together with localised control failures could be maintaining disease transmission in Venezuela R. prolixus populations has been questioned due to the identification of the closely related species Rhodnius robustus in palm trees in Venezuela Rhodnius robustus poses a problem as it is virtually indistinguishable morphologically from R. prolixus but this species it is of minor epidemiological importance as it does not colonise houses, although flying adults may enter domestic areas attracted by light R. prolixus and R. robustus, with morphometric and isoenzyme studies failing to detect interspecific differences R. robustus but also indicated the existence of more than one cryptic species Rhodnius specimens collected in a palm in Guarico State Venezuela were identified as R. prolixusSignificant progress has been made in reducing the incidence of Chagas disease in Venezuela through four decades of triatomine control R. prolixus, including five adjacent populations, from silvatic, domestic and peridomestic ecotopes in six Venezuelan States. Our aim was to contribute to the control of Chagas disease in Venezuela, through the provision of information that might allow the design of improved control strategies. We finally resolve this controversy over the existence of silvatic R. prolixus and the interaction between silvatic and domestic populations. Our analyses demonstrate that silvatic R. prolixus presents an unquestionable threat to the control of Chagas disease in Venezuela and that successful long term control could benefit from modified spraying and surveillance practices, together with housing improvement.Here we investigated the genetic structure of 34 populations of For the purpose of this study field work was carried out in 2001–2004 in the Venezuelan States of Lara, Portuguesa, Guarico, Cojedes, Barinas, and Trujillo see , Table 2Silvatic collections were made with Noireau live bait traps Bugs were collected in 12 localities from houses, chicken huts and palms. Positive houses were primarily of the traditional ‘rancho’ type, constructed of wattle and daub with palm and corrugated iron roofs. A total of 287 specimens were analysed by direct sequencing and 243 by microsatellite analysis . All houses inspected were negative and samples were isolated from palms only. In these areas the traditional rancho was replaced by cement block structures as part of the National Programme for housing improvement in the 1960s. A total of 21 specimens were analysed by direct sequencing only   the morphology of late nymphal stages, as described by Lent and Wygodsinsky  and by the inclusion of R. prolixus specimens collected from houses in Central America , beyond the geographical distribution of silvatic R. prolixus or R. robustuscytb analysis as R. robustus or R. prolixus. Five D2 sequences were also available in GenBank (see In order to confirm which species of b (cytb) . Eight pmens see  [17]. Inky 1979,  (2) by tBank see .cytb gene and a 633 bp fragment of the D2 region with the following primers: Forward cytb7432F 5′-GGACG(AT)GG(AT)ATTTATTATGGATC; Reverse cytb7433R 5′-GC(AT)CCAATTCA(AG)GTTA(AG)TAA; Forward D2F 5′-GCGAGTCGTGTTGCTTGATAGTGCAG; Reverse D2R 5′-TTGGTCCGTGTTTCAAGACGGGcytb: 95°C 5 min; 35 cycles of 95°C 30 s, 50°C 45 s, and 72°C 45 s; final extension of 72°C for 5 min. D2: 25 cycles 94°C 1 min, 50°C 2 min, 72°C 2 min. Amplicons were purified using Qiaquick kit (Qiagen) or Quick-clean (Bioline), as specified by the manufacturers. Purified PCR products were sequenced by fluorescent dye terminator chemistry using ABI Prism Bigdye (Applied Biosystematics), on an ABI Prism 377 automated DNA sequencer (PE Applied Biosystematics) or on a 48 capillary ABI 3730 DNA analyser. Forward and reverse sequences were aligned using Sequence Navigator V1.01 (Perkin-Elmer) or BioEdit V7.0.4.1 We amplified a 682 bp fragment of the R. pallescens AF045720, R. neglectus AF045716 and T. infestans AF045721. All sites were equally weighted.The number of variable sites was determined using Mega v 2.1 software Cytb haplotype genBank accession numbers. EF043576, EF043577, EF043578, EF043579, EF043580, EF043581, EF043582, EF043583, EF043584, EF043585, EF043586, EF043587, EF043588.cytb haplotypes specimens were placed into 34 population groups as listed in ST (Weir & Cockerhams 1984 unbiased estimator) generated in Arlequin v3.1 ST null distribution is obtained by permuting the haplotypes between the compared populations , given a null hypothesis of no difference between the populations (FST = 0). The p-value generated is the fraction of these permutations with an FST larger than or equal to the original estimate, if the given p-value is smaller than the nominated significance level, then the compared populations are considered to be significantly different. The nominal significance level was adjusted for multiple comparisons using the sequential Bonferroni procedure cytb analysis k = 561, p1 = 0.05/561 and p≤0.0001. Population geneflow was evaluated at different geographic levels 1) comparison of adjacent ecotopes, 2) comparison of populations within localities, 3) comparison of populations within and between States.For population analysis using The genetic divergence of the populations was also estimated by an analysis of molecular variance  as listed in th locus for a subset of 20 populations  but in brief significant linkage disequilibrium was detected between three loci pairs after Bonferroni correction in three populations; LIST14-017 and LIST14-042 in pop 9a, LIST14-010 and LIST14-013 in pop 20, LIST14-010 and LIST14-025 in pop 29 (O) and expected heterozygosity (HE) were calculated for each locus using the program Arelquin V2.000 IS was also calculated at each locus following Weir and Cockerham (1984) , as previously detailed Intrapopulation comparisons were based on the indices of population homogeneity FST-like) and populations evaluated at different geographic levels as above  using Arlequin v3.1 as above . The totST/(1−FST) and log transformed (ln) geographic distances. Rousset (1997) showed that a linear relationship occurs between natural log of geographical distance and FST/(1−FST) in two dimensional habitats The relationship between geographical and genetic distance over the study area was assessed by testing the correlation between Fcytochrome b (cytb). This included 304 specimens from houses, 219 from palms and 28 from chicken huts  and R. robustus cytb sequences (4 haplotypes). Identity scores for haplotypes 1, 2, 4–15 were highest for a R. prolixus specimen from Honduras  whilst haplotype 3 and haplotypes 16–18 were most similar to R. robustus from the Amazon  and Venezuela (roVE1 AF421340). The most common haplotype in the study (haplotype 1) was identical to R. prolixus from Honduras (prHO AF421339).In comparison with published sequences in GenBank our 18 haplotypes shared greatest similarity with R. prolixus haplotypes were identified in palms, thus confirming the existence of silvatic R. prolixus. In addition five R. prolixus haplotypes were common to both palms and houses  (pop 34). In a previous study in this area adult Rhodnius specimens, thought to be R. robustus, were found to enter houses at night to feed, attracted by light but not to colonise R. robustus adult (haplotype 16) was found in an infested house in the Locality Loma de Amarillo (pop 33), however all of the other domestic specimens were R. prolixus (haplotype 5), including all nymphs, thus indicating that this R. robustus adult may also have arrived to feed but had not colonised the house.However, silvatic R. robustus haplotype 3 was the second most frequent haplotype (13%), although this was limited in distribution to Portuguesa State, with the exception of three specimens. As mentioned the species R. robustus is not known to colonise houses, we were therefore surprised to find 14 nymphs of R. robustus haplotype 3 in four houses in the localities Terronal, Casarena and Palmarito thus suggesting that this silvatic species is capable of domestic colonisation in this Venezuelan State  revealed three haplotypes, varying at four sites (R. robustus (haplotype 3) had a D2 haplotype that was identical to R. prolixus   , while AR. robustus haplotype 3 and haplotypes 16–18 are the most divergent  Venezuelan R. robustus haplotypes 16–18, roVE1, roVE2e and roVE3f (99% bootstrap support). While clade II is composed of Amazonian R. robustus haplotypes. Within this group, haplotype 3 was identical to roBR9 (R. robustus from the Brazilian Amazon). These results indicate a closer genetic relationship between Venezuelan R. robustus and R. prolixus than R. robustus from the Amazon region giving further support to the existence of cryptic species within R. robustusIn our phylogenetic tree the 18 haplotypes divided into two major clades with high bootstrap values . Pairwise FST values indicated a lack of population division between four adjacent house and palm populations; 1) pop 29 and pop 30 ; 2) pop 6 and pop 7 ; 3) pop 2 and pop 5  and 4) pop 2 and pop 3  and between adjacent palm and chicken hut ecotopes, pop 27 and pop 28 . Additionally the two palms adjacent to pop 2, but sampled in different years, were not genetically different . These cytb results indicated that bugs can move between silvatic, peridomestic and domestic habitats, thus indicating geneflow. The divergence of adjacent populations was also estimated by AMOVA (see ST range = −0.02 to 0.05).To test for possible geneflow between silvatic, domestic and peridomestic areas, comparisons were made initially between five population pairs in adjacent ecotopes. . Pairwise FST values indicated a lack of population division between a palm and house sampled within the locality El Guamito , between a house, chicken hut and palm in the locality Cascabel , also within the locality Laguna Hermosa  and between houses in the locality Terronal . The population divergence within localities was also estimated by AMOVA (see ST range = −0.04 to 0.03)  .ST indicated structure does exist between populations in this State . When populations in Portuguesa were further analysed in ecotope groupings variation was greatest within populations (64%) in comparison to among groups (−9.2) or among populations within groups (45%) .A total of 287 specimens from domestic, silvatic and peridomestic ecotopes in this State were analysed by direct sequencing. For population analysis specimens were divided into 13 populations . A hieraST indicated that structure does exist between populations in this State  detected heterogeneity was due to a peridomestic population (pop 26), which was different from the majority of populations in Barinas (FST range = 0.28 to 1.0). When populations in Barinas were further analysed in ecotope groupings variation was greatest within populations (85%) in comparison to among groups (1.2%) or among populations within groups (14%) .A total of 146 specimens from domestic, silvatic and peridomestic ecotopes in this State were analysed by direct sequencing. For population analysis specimens were divided into 15 groups . A hiera, p = 0, . In popuST = 0.91, p<0.0001).A total of 27 specimens of domestic (pop 33) and silvatic origin (pop 34) were analysed by direct sequencing. Gene flow not evident between these two ecotopes  and between populations (44%) , however variation was greater within individual States than between States (15.5%) .A hierarchical analysis of all 34 populations analysed by  (15.5%) . AdditioST values generated including and excluding List14-017 were significantly correlated  and this locus was therefore included in the analysis. Departures from HWE were also related to excess heterozygosity at locus LIST14-013 (1 population) and LIST14-056 (2 populations). Null alleles can be problematical in microsatellite analysis and can result in departures from HWE. Here 54 specimens consistently failed to amplify at single locus and 2 specimens at two loci in 23 populations . Pairwise FST comparisons indicated a lack of population structure between three of the adjacent ecotopes; 1) between a house and palm , 2) pop 6 and pop 7 (FST = 0.04 p = 0.007) and 3) between a palm and chicken hut . These results reaffirm that bugs move between silvatic, peridomestic and domestic ecotopes. However, further heterogeneity was uncovered by microsatellite analysis between the remaining adjacent populations, in contrast to cytb analysis; between a palm and house  and pop 2 and pop 3 (FST = 0.2 p<0.0001), also between two palm populations .Geneflow between our five adjacent ecotope pairs was re-examined using microsatellite analysis .The divergence of adjacent populations was also estimated by AMOVA see . The amoST range = 0.002 to 0.06, p = 0.0006 to 0.38), also within the locality Cascabel . In the locality El Guamito a single house and palm were homogenous . FST comparisons detected population homogeneity within the locality G. Paguey between two house and two palm populations . These results agreed with cytb analysis. Also panmixia was also evident within the locality Terronal; between houses , between a house and palm  and between populations collected from the same house in different years . In the locality San Bartolo, no genetic structure was detected between two houses .Geographically distant populations from non adjacent ecotopes within individual localities were also re-examined. For example using microsatellite data panmixia was detected between a house, palm and chicken hut population within the locality Laguna Hermosa  and Laguna Hermosa . FST comparisons significant in the absence of bonferroni correction.The population divergence within localities was also estimated by AMOVA see . Again tST value indicated structure does exist within the State , (ST) indicate that a number of populations contributed to the detected heterogeneity in this State. A domestic population in Santa Lucia (pop 10) was different from the many of populations in Portuguesa possibly due to genetic drift (FST range = 0.13 to 0.42). Three microsatellite loci were monomorphic in this population and the mean number of alleles and allele richness was the lowest in the study (1.9 and 1.7). Domestic populations in the locality San Bartolo  were also different from the majority of other populations in Portuguesa (FST range = 0.04 to 0.26). Both populations were monomorphic at the two loci. Pairwise population comparisons (FST) indicated that geneflow also occurred between localities for example between a house in the locality Terronal and a palm in Palo Gacho . These results were also supported by cytb analysis.A total of 243 specimens from Portuguesa State were divided into 13 populations and analysed at 9 or 10 microsatellite loci. These included 130 domestic, 92 silvatic and 21 peridomestic specimens. A hierarchical analysis of all populations within Portuguesa detected a greater within population diversity (89%) than between populations (11%), however the associated F p = 0), . PairwisST = 0.12 p = 0, FSC = 0.11 p = 0, FCT = 0.01 p = 2; AMOVA; 2 groups; house <pop 1,2,4,6,9a,9b,10,13>, palm/ chicken hut <3,5,7,8, 35>).When populations in Portuguesa were further analysed in ecotope groupings variation was greatest within populations (88%) in comparison to among groups (0.9%) or among populations within groups (11%) ,  than Portuguesa (2.3). Expected heterozygosity was higher and ranged from 0.5 to 0.6 . A hiera p = 0), . When geST = 0.03 p = 0.5, FSC = 0.03 p = 0, FCT = −0.003 p = 5; AMOVA house <17,19,21,24a,24b> palm <18,20,23,24c,24d,25,28> chicken hut <16,22,26,27>).When populations in Barinas were further analysed in ecotope groupings variation was greatest within populations (97%) in comparison to among groups (−0.03%) or among populations within groups (3.3%) (FST range = 0.07 to 0.33).A single domestic population was analysed in this State (pop 31). Mean allele number and richness were low . This population was different by pairwise comparisons from the majority of populations analysed (FST range = 0.08 to 0.35).The single domestic and silvatic population from the locality Las Quebralitas also differed from the majority other populations in the study (FST range = 0.09 to 0.42). Four private alleles were detected in this population, all in the single female adult identified as R. robustus by cytb analysis.The domestic population analysed from Trujillo (pop 33) wa distinct from the majority of populations   than between populations (11%), however population structure was detected (F7 p = 0) .ST = 0.13 p = 0, FSC = 0.09 p = 0, FCT = 0.04 p = 0).Additional hierarchal analysis between States was carried out . Again variation was greatest within populations (87%) in comparison to among populations within groups (8.3%) or among groups (4.3%) (FST /(1−FST) against log transformed (ln) distances were conducted at various hierarchical levels (1) between populations (2) between localities (3) within Portuguesa and within Barinas State and (4) between States. Patterns were weakly correlated but significant at population level , locality level  and non-significant at State level . Patterns were weakly correlated but significant within Portuguesa State , within Barinas . R2 values range from 0 to 1, with values close to 1 indicating a greater correlation between the compared variables.Tests for IBD . In this region the post-spray reinvasion of houses is therefore unlikely, and vector control may be more straightforward. Nevertheless, adult silvatic R. robustus have been implicated in the sporadic transmission of T. cruzi in western Venezuela Our data detected silvatic R. prolixus haplotypes. Three of these shared haplotypes were found in domestic nymphs, in addition to domestic adults, thus indicating these silvatic R. prolixus are capable of invading and importantly colonising houses.From sequence analysis it is clear that common haplotypes occur across all ecotopes, with palm and house populations sharing five cytb) analysis of haplotype 3 confirmed the introgression suspected after the discovery of domestic nymphs of “R. robustus”. Introgression has been recorded previously in triatomine species R. robustus” are R. prolixus with introgressed R. robustus mitochondrial DNA. Additional support for introgression is the absence of unique microsatellite alleles in these haplotype 3 specimens, in contrast to our single domestic Venezuelan R. robustus adult (haplotype 16), which revealed four unique alleles.The incongruence detected between nuclear (D2) and mitochondrial (mtST and AMOVA). This includes examples of geneflow between five adjacent ecotopes, also within localities in both Barinas and Portuguesa State. These results indicate that bugs are moving between houses and between palms, in addition to between palms and houses. Importantly, this is supported by recent data analysis from Sanchez-Martin et al., (2006) where infested palms  within 100m of a house were identified as risk factors for house and peridomestic infestation, in addition to palm roofs less than one year old R. prolixus with pre- and postspray peridomestic and domestic populations was unable to differentiate the silvatic specimens as a separate subpopulation R. prolixus are capable of invasion and colonisation and a threat to effective vector control.In addition to shared haplotypes, population homogeneity was also evident by pairwise comparisons between house, palm and peridomestic sites (pairwise FST = 0.44). Both pairwise FST and AMOVA analysis suggest that population heterogeneity was more pronounced within Portuguesa State (39% between population variation) than Barinas (15% between population variation) . Additionally detected within and among populations variance did not differ greatly between the comparisons all 33 population or populations in an ecotope group hierarchy. This suggests gene flow occurs between populations from different ecotopes. AMOVA analysis of cytb data also suggested that detected heterogeneity is not related to the State of origin of a population. Again detected within and among populations variance did not differ greatly between the comparison of all populations or populations in a State group hierarchy.When all 34 populations were compared structure was detected  . InteresR. prolixus in Venezuela a panel of microsatellite markers was developed R. prolixusFor higher resolution of relationships between silvatic and domestic populations of cytb analysis population homogeneity was evident with non-significant pairwise comparisons detected between house, palm and peridomestic sites including adjacent ecotopes. However, some additional genetic diversity was revealed by microsatellites analysis. In the locality Las Queseras populations from an adjacent house and palm  were significantly different by microsatellites but not by cytb analysis. Additionally in the locality Terronal, a house (pop 2) and an adjacent palm (pop 3), and adjacent palms  were different by microsatellite analysis but indistinguishable by cytb analysis. AMOVA analysis also detected further structure between an adjacent palm and house  not evident in pairwise FST comparisons which are corrected for multiple comparisons.As for ST analysis and AMOVA analysis detected population homogeneity between palm and houses e.g. in the locality Cascabel (pop 17 and pop 18) and in Laguna Hermosa , between houses e.g. pop 9a, 9b in the locality San Bartolo, and between palms in the locality G. Paguey . These results can be explained by the movement of bugs not only between palms and houses but also between houses and between palms.Population homogeneity was detected between populations within localities in Portuguesa and Barinas. Both pairwise FST). Population structure was detected between all 33 populations . Distinct populations (pairwise FST) exhibited monomorphic loci and low allele richness, suggesting isolation and possible genetic drift. Hierarchical analysis also indicated that population heterogeneity was more pronounced within Portuguesa State (11% between population variation) than Barinas State (3% between population variation) (ST). This population was situated at the extreme distribution of sampled sites in Barinas and in an area where T. maculata infestations were more common, factors which may have contributed to detected genetic isolation. The separation of the domestic R. prolixus population from Trujillo State (pop 34) from all other populations indicates that the Andes mountain range and the predominance of silvatic R. robustus may also act as barriers to gene flow.Comparisons over wider geographic areas revealed population structure  . In PortCT = 0.01, FCT = −0.003), thus suggesting geneflow occurs between populations from different ecotopes. Interestingly microsatellite analysis detected greater heterogeneity between populations from different State (FCT = 0.04) as compared to cytb analysis.Hierarchical analysis of microsatellite data also indicated that population ecotope is not a factor in determining population differentiation within both Portuguesa and Barinas State (FWe investigate the relationship between genetic isolation and increasing geographic distances (IBD). However, while the relationship was significant between populations, between localities and within States, distance was not a critical factor influencing genetic differentiation as the detected correlations were very weak.cytb sequences. Microsatellites are fast-evolving, neutral, noncoding loci, whereas the cytb is a protein-coding gene with important metabolic functions and thus may be subject to selective constraints cytb typing e.g. pop 29, pop 30. Both or pairwise FST and AMOVA data for both methods are consistent with movement between silvatic and domestic habitats with ecotope not determining population structure and with greater population heterogeneity in Portuguesa than Barinas State.As expected a higher degree of population heterogeneity was detected with microsatellites than with the analysis of T. infestans from domestic and peridomestic ecotopes in Argentina. The analysis indicated a strong population structure, with limited gene flow and genetic drift leading to genetic differentiation and suggested an important role for recrudescence in post control infestations rather than reinvasion from untreated areas Our results contrast a recent microsatellite study of 19 populations of R. prolixus glue their eggs to palm fronds suggesting passive transport of bugs into houses on these fronds Rhodnius prolixus in Venezuela is known to be light attracted Movement of bugs between silvatic, peridomestic and domestic ecotopes probably occurs both actively and passively. Risk factor analysis detected an association between new thatched palm roofs and infestation R. prolixus in Venezuela represent a definite threat to successful control of Chagas disease, as suspected but controversially debated since populations of R. prolixus were reported in palm trees T. infestans has been eliminated over large areas From our data it is clear that silvatic populations of Rhodnius populations in the context of disease epidemiology and vector control in Venezuela. An important follow-up to this project would be to define population interaction more extensively, particularly in regions of Colombia, where silvatic and domestic Rhodnius populations also occur and reinvasion may be maintaining large domestic colonies of R. prolixusThis study has made a fundamental contribution to the understanding of Table S1ST values below diagonal  (Arlequin v3.1). Values in bold remain significant following sequential Bonferroni correction . See The pairwise comparison of 34 populations from six Venezuelan States by cytb analysis; F(0.05 MB PDF)Click here for additional data file.Table S2A = number of alleles, HO, HE = Observed and Expected heterozygosity, P = exact probability for expected Hardy Weinberg equilibrium conditions for each locus/population combination (Arlequin v2.1), M = monomorphic. FIS = Weir & Cockerham (1984) (GENEPOP V3.4). Values in bold departures from HWE significant after Bonferroni correction . See A = number of alleles, HO, HE = Observed and Expected heterozygosity, P = exact probability for expected Hardy Weinberg equilibrium conditions for each locus/population combination (Arlequin v2.1). FIS = Weir & Cockerham (1984) (GENEPOP V3.4). Values in bold departures from HWE significant after Bonferroni correction, populations analysed . ∧LIST14-079 amplified in subset of populations. M = monomorphic, NA = not amplified. See Summary of population microsatellite data. (A) Summary of population microsatellite data per locus . N = number of specimens amplified, N(0.02 MB PDF)Click here for additional data file.Table S3The pairwise comparison of 33 populations from six Venezuelan States at 9 microsatellite loci, FST values below diagonal  (Arlequin v2.1). Values in bold significant after sequential Bonferroni correction k = 528, p1 = 0.05/528, p≤0.0001. See (0.05 MB PDF)Click here for additional data file.Table S4The pairwise comparison of a subset of 20 populations at 10 microsatellite loci FST values below diagonal  (Arlequin v2.1). Values in bold significant after sequential Bonferroni correction k = 190, p1 = 0.05/190, p≤0.0003.(0.04 MB PDF)Click here for additional data file.


The Mlabri are a group of nomadic hunter-gatherers inhabiting the rural highlands of Thailand. Little is known about the origins of the Mlabri and linguistic evidence suggests that the present-day Mlabri language most likely arose from Tin, a Khmuic language in the Austro-Asiatic language family. This study aims to examine whether the genetic affinity of the Mlabri is consistent with this linguistic relationship, and to further explore the origins of this enigmatic population.We conducted a genome-wide analysis of genetic variation using more than fifty thousand single nucleotide polymorphisms (SNPs) typed in thirteen population samples from Thailand, including the Mlabri, Htin and neighboring populations of the Northern Highlands, speaking Austro-Asiatic, Tai-Kadai and Hmong-Mien languages. The Mlabri population showed higher LD and lower haplotype diversity when compared with its neighboring populations. Both model-free and Bayesian model-based clustering analyses indicated a close genetic relationship between the Mlabri and the Htin, a group speaking a Tin language.Our results strongly suggested that the Mlabri share more recent common ancestry with the Htin. We thus provided, to our knowledge, the first genetic evidence that supports the linguistic affinity of Mlabri, and this association between linguistic and genetic classifications could reflect the same past population processes. The Mlabri are a hill tribe in northern Thailand, inhabiting a dispersed area along the border with Laos ,2. TodayLittle is known about the origins of the Mlabri and most evidence comes from linguistic studies. The Mlabri language is classified as a Khmuic language, a subgroup of the Mon-Khmer language in the Austro-Asiatic language family . The avaIn this study, we analyzed populations samples from throughout northern Thailand, including the Mlabri as well as several neighboring groups, including the Htin, Hmong, Yao, and other populations speaking Austro-Asiatic and Tai-Kadai languages. Four HapMap population samples, representing Altaic, Sino-Tibetan, Indo-European and Niger-Congo language speakers, were also included in this study. We conducted a genome-wide analysis on these samples using 50K SNPs, to investigate the genetic affinity of the Mlabri, examine the concordance of genetic and linguistic affinities, and further explore probable origin(s) of this enigmatic hunter-gatherer group.Since this is the first genome-wide genetic study of this enigmatic population, we calculated several population genetic parameters, including SNP diversity, haplotype diversity and linkage disequilibrium (LD).e) were calculated based on allele frequencies of 55,561 SNPs and the results were shown in Figure e in Mlabri (0.197) is lower than that of any of other populations in which HSe is at least 0.250 (HM). The expected heterozygosity for haplotypes (HHe) were calculated based on haplotypes in 500-kb genomic regions (Methods) and the results are shown in Figure e in Mlabri (0.666) is also much lower than that of any of other populations in which HHe is at least 0.820 (TN). The HHe comparison obtained from larger size of genomic regions (1 Mb) show the similar results .Expected heterozygosity for SNPs . The number of haplotypes was estimated for two different window sizes (500-kb or 1-Mb) respectively, with adjustment for sample size difference among populations (see Methods). Again, we found that the genetic diversity was significantly lower in Mlabri than in other populations for both 500-kb segments Figure  and 1-Mb2 ≥ 0.5 , we observed this fraction to be 1.6- to 12.3-fold higher in Mlabri than in all the other Asian populations for the distance range above 10-kb to 200-kb. For those marker pairs with strong LD (r2 ≥ 0.8), the fraction in Mlabri is 2.2- to 31.3-fold higher in Mlabri than in all the other Asian populations, and 6- to 259-fold higher than in YRI. Furthermore, LD of r2 ≥ 0.8 extended more than 1 Mb in Mlabri, whereas in all the other populations, such strong LD extended only up to 200 kb.The significantly reduced genetic diversity in Mlabri was also reflected by its extent of linkage disequilibrium (LD). We assessed the extent of LD among markers with minor allele frequency (MAF) ≥ 0.05 Figure  and ≥ 0.5 Figure  or r2 ≥ 8 Figure . For marThe genetic characteristics obtained from above analysis, such as significantly increased LD and extremely reduced haplotype diversity are both consistent with the view from a previous study  that theWe first studied the clustering relationships among 446 individuals representing 13 populations in Thailand and the CHB and JPT from the HapMap project . We used an allele sharing distance (ASD)  as the gThe above clustering relationships among individuals were also confirmed by principal components analysis (PCA) at the individual level . As showSince the closer relationship between Mlabri and Htin could be due to recent gene flow from Htin to Mlabri or vice versa, we further performed Bayesian cluster analysis as implemented in the STRUCTURE algorithm  to examiBecause the analyses discussed above were all consistent in showing that individuals from the same population cluster together, it is meaningful to evaluate the genetic relationships among populations. A maximum likelihood tree of populations , based oHowever, the Htin showed signs of admixture in both STRUCTURE and frappe analyses Figure . This raIn this study, we analyzed genome-wide SNP data on the Mlabri, as well as several neighboring populations and HapMap population samples. The Mlabri population shows several substantial differences from the other populations: significantly increased LD, extremely reduced haplotype diversity and small effective population size (29), all of which are consistent with the view that the Mlabri were recently founded from a very small number of individuals of an agricultural group but subsequently adopted their current hunting-gathering lifestyle, as proposed by a recent study based primarily on mtDNA and Y chromosome data . AlthougBoth model-free and model-based clustering analyses strongly suggest that the Mlabri share a degree of common ancestry with the Htin, a group speaking Tin language. In this case -- as is the general rule in many human populations -- the genetic affinity of these populations is consistent with its linguistic affinity. This result, to our knowledge, is the first genetic evidence supporting the linguistic affinity of the Mlabri and Tin languages. Cavalli-Sforza and colleagues showed an apparent congruence between linguistic phyla and genetic clusters, and they proposed that this congruence indicates "considerable parallelism between genetic and linguistic evolution" . SubsequHunting and gathering was presumably the subsistence strategy employed by human societies for more than two million years, until the end of the Mesolithic period. Contemporary hunter-gatherer groups are often thought to serve as models of an ancient lifestyle that was typical of human populations prior to the development of agriculture. However, there has been complex interaction between hunter-gatherers and non-hunter-gatherers for millennia. There are contemporary hunter-gatherer peoples who, after contact with other societies, continue their ways of life with very little external influence. There are also contemporary groups usually identified as hunter-gatherers do not have a continuous history of hunting and gathering, and in many cases their ancestors were agriculturalists and/or pastoralists who were pushed into marginal areas as a result of migrations, economic exploitation, and/or violent conflict . Our curIn summary, our results strongly suggested that the Mlabri share more recent common ancestry with the Htin, a group speaking a Tin language. This result, to our knowledge, is the first genetic evidence supporting the linguistic affinity of the Mlabri and Tin languages. We proposed that Htin is most likely the source population from which the Mlabri genetically originated in case cultural reversion occurred in the history of Mlabri.Samples from the Mlabri as well as other 12 populations were collected in Thailand. The sample information, including sample size, ethnic and linguistic information is shown in Table Genotype data of 13 Thailand population samples generated using Affymetrix Genechip Human Mapping 50K Xba array were obtained from the Pan-Asian SNP Initiative . DetaileHaplotypes of 22 autosomes were inferred for each individual from its genotypes with fastPHASE  version e) was calculated based on allele frequencies.Heterozygosity for each SNP (HSe), the genome was divided into 500-kb regions, with each region having roughly 14 SNPs. HHe were calculated for each region using haplotype frequencies [e were averaged over all windows.To calculate heterozygosity for haplotypes  without replacement in each population. Note that Mlabri has the smallest sample size in all the populations studied. For a population with sample size larger than 36 chromosomes, the sampling was repeated 100 times for each segment and the average of the number of haplotypes of all replications was taken as the number of haplotypes.The cumulative proportion given a number of haplotypes was obtained by estimating the proportion of the sliding-windows across the genome carrying equal or less haplotypes.2 following Hill and Weir [Linkage disequilibrium (LD) between SNPs were measured using rand Weir  and calcPrincipal component analysis (PCA) was performed at individual level using EIGENSOFT version 2.0 .We used an allele sharing distance (ASD) ,43 as a The tree of individuals was reconstructed based on ASD distance and using Neighbor-Joining algorithm  with theAncestry of each person was inferred using a Bayesian cluster analysis as implemented in the STRUCTURE program ,46. We rThe program frappe  implemenSX, DK, MS, and LJ conceived and designed the study. JK contributed to the Tai-Kadai sample collection. MS and MS performed the technical work on SNP genotyping and QC procedures for all population samples from Thailand. SX collected data and performed the analysis. SX and LJ wrote the paper, to which all authors contributed. All authors read and approved the final manuscript.e) in 17 populations.Contains Figure S1 - Haplotype heterozygosity  shown on the abscissa. A: showing maximal probability estimation of ten runs at each K (from K = 2 to K = 9); B: showing probability estimation at K = 2 to K = 18 in all ten runs.Click here for fileContains Figure S2 - Geographical distribution of Thailand population samples. Red dots on the map indicated sampling locations. Information of population IDs can be found in Table Click here for file