Ricinus communis) is an agricultural crop and garden ornamental that is widely cultivated and has been introduced worldwide. Understanding population structure and the distribution of castor bean cultivars has been challenging because of limited genetic variability. We analyzed the population genetics of R. communis in a worldwide collection of plants from germplasm and from naturalized populations in Florida, U.S. To assess genetic diversity we conducted survey sequencing of the genomes of seven diverse cultivars and compared the data to a reference genome assembly of a widespread cultivar . We determined the population genetic structure of 676 samples using single nucleotide polymorphisms (SNPs) at 48 loci.Castor bean .Bayesian clustering indicated five main groups worldwide and a repeated pattern of mixed genotypes in most countries. High levels of population differentiation occurred between most populations but this structure was not geographically based. Most molecular variance occurred within populations (74%) followed by 22% among populations, and 4% among continents. Samples from naturalized populations in Florida indicated significant population structuring consistent with local demes. There was significant population differentiation for 56 of 78 comparisons in Florida (pairwise population ϕLow levels of genetic diversity and mixing of genotypes have led to minimal geographic structuring of castor bean populations worldwide. Relatively few lineages occur and these are widely distributed. Our approach of determining population genetic structure using SNPs from genome-wide comparisons constitutes a framework for high-throughput analyses of genetic diversity in plants, particularly in species with limited genetic diversity. Determining the extent and distribution of genetic diversity is an essential component of plant breeding strategies. Assessing genetic diversity in plants has involved increasingly sophisticated approaches, from early allozyme work, to amplified fragment length polymorphisms (AFLPs), and microsatellites. Due to their multi-allelic states, development of simple sequence repeats (SSR) or microsatellites is often the best option for investigating population differentiation, but development and genotyping of large numbers of samples can be costly and size homoplasy is often a concern . RecentlRicinus communis L.) is an agro-economically important species from the Euphorbiaceae family and appears to have low genetic diversity and no geographically based patterns of genetic relatedness based on AFLP and SSR studies [R. communis has been relatively little studied. However, recent sequencing efforts have revealed a moderate sized genome (~350 Mb) organized within 10 chromosomes  so in depth studies of castor bean genetics will be able to rapidly advance.Low genetic variation is a key feature of some agro-economically important crops such as peanuts  and wate studies . CompareRicinus communis appears to have originated in eastern Africa as suggested by the high diversity of plants in Ethiopia [Castor bean has historically been cultivated as an agricultural crop for the oil derived from its seeds, which has numerous industrial and cosmetic uses. In fact, castor oil has a long documented history of use for ointments and medicines by the ancient Egyptians and Greeks. Worldwide production of seeds in 2007 was 1.2 million metric tones, with India, China, and Brazil leading global harvests . The plaEthiopia ,11, but Ethiopia ,13. The Ethiopia . Castor Ethiopia .R. communis samples. The objectives of this study were five-fold: 1) to test the utility of SNPs in determining population structure, 2) to assess worldwide genome diversity in castor bean using SNPs; 3) to determine large-scale patterns of introduction and relatedness among populations; 4) to examine geographical patterns of genetic variation based on country of origin; and 5) to investigate fine-scale population structure using a subset of naturalized populations distributed across 13 sites from 12 counties in Florida, U.S.We used high-throughput SNP genotyping to assess genome-wide diversity and population structure in a worldwide collection of Our genome-wide assessment of SNP variation in castor bean revealed relatively low levels of genetic variation. The 232 high quality SNPs were discovered in 171,003 aligned bases, for a total of 0.13% or 1 SNP every 737 bases. We emphasize, however, that this still represents a small fraction of the genome, as reads of 98% identity and 98% read coverage in the Hale genome revealed 15.2 Mb of total sequence before filtering the data set for SNP discovery. Given that reads with 100% identity among all 8 cultivars were excluded from this analysis (because they did not contain SNPs), it is likely that the number of SNPs per base is overestimated (at a genome wide level) and true nucleotide diversity across the genome is much lower. Nonetheless, these data constitute substantially more genome coverage than achieved with previous analyses based on AFLPs and SSRs [n = 488) were best described by 5 clusters, as determined by the best K value in Structure  of samples from different populations/countries were separated at p < 0.01 [Additional file Nuclear SNP genotypes of the worldwide collection of germplasm samples  throughout Florida consisted of two distinct population groupings , the different populations were separated at p < 0.01 (Table 2 = 0.006). Assessment of genetic distances of the 12 populations using Principal Coordinates Analysis indicated that samples from 11 of the 12 populations each clustered together in a plot containing the first two axes (data not shown).Inclusion of samples from Florida with the worldwide sample collection strongly influenced overall Sites in 1 countiesR. communis suggests that it has low genetic diversity and structure for all populations that we sampled. Even our upwardly biased estimate of nucleotide diversity is far less than the average number of SNPs found in plants such as maize [R. communis are consistent with comparable reduced variation in many cultivated plants [R. communis may have lost much of its diversity through cultivation but human-assisted introductions and seed mixtures from different sources appear to have maintained this limited diversity in most populations. Low genetic diversity is likely a consequence of a genetic bottleneck due to domestication, as seen in a range of other crops [Our assessment of genome wide diversity in as maize . Low ratas maize  and argud plants , such asd plants  and cottd plants . Conversd plants -22. As ber crops . Alternaer crops ), but motructure  [ST comparisons, with some caution due to issues with non-equilibrium dynamics often associated with recent introductions of species [One aspect of working with populations that contain a mix of diverse genotypes is that they are often difficult to partition into well-defined groups, even with computationally rigorous programs such as Spproach) ,25. For pproach)  found thpproach)  found th species .The power of SNP discovery using our methods should not be misconstrued as an indication of diversity in a species that shows low overall genetic diversity; our SNP discovery found relatively few SNPs despite extensive survey of several castor bean genomes . Measures of population structure such as Fst  are typically based upon these rare SNPs and are not directly comparable to unbiased SNP discovery methods in other species. Therefore, our results are not directly comparable with other species for which SNP markers have been developed .PT values) among most of these populations. It appears that once plants have been introduced, inbreeding occurs within local demes, as evidenced by the significantly higher values of expected vs. observed heterozygosity in the Florida populations (mean F = 0.719). Gene flow is not regional, and R. communis is not dispersed widely after its initial introduction. Therefore, dispersal appears to be dependent on human introduction, or by limited escape into nearby disturbed areas, owing to the fact that the capsules are heavy, and seeds are explosively and therefore gravity-dispersed only meters from the parent plant [R. communis provides alternate options for reproduction, which suggests that pollen flow, and hence gene flow could be extensive among geographically proximal populations. Indeed, our assessment of genetic variation in Florida populations indicates that most accessions are a mixture of genotypes. However, these patterns are again consistent with those observed in germplasm accessions, suggesting multiple introductions rather than extensive gene flow among established populations. The fact that castor bean is capable of self-pollination, together with the observed high coefficient of inbreeding also suggests that selfing may be a common reproductive strategy. However, a more extensive study of levels of inbreeding within natural populations needs to be conducted to determine the degree to which castor bean preferentially self-pollinates versus outcrosses.Comparison of genetic to geographic distances in naturalized Florida populations indicated spatial structuring of populations and no evidence of a sequential spread from a single introduction point. Rather, there also appears to have been multiple introductions in Florida. Local differentiation, however, was present . Nuclear SNPs, on the other hand, are more variable, amenable to high throughput genotyping and will likely be the marker of choice for population-level analyses of species with sequenced genomes [R. communis research.Our study represents one of the most extensive genomic studies of worldwide SNP variation in an agricultural plant. With rapidly increasing capabilities in genome sequencing, this work provides a template for assessing population structure in non-model organisms and applying them to plants that have escaped cultivation. Although chloroplast markers have been effectively used in studying plant distributions, low effective population size in chloroplast DNA and reduced genetic diversity as compared with nuclear DNA makes these markers less suitable for studying recently established populations. Despite sequencing of eight chloroplast genomes for castor bean, few clade-specific SNPs were identified and only five haplotypes occurred in our worldwide collection , we focused on the development of nuclear SNPs. To this end, we carried out survey sequencing of seven diverse castor bean genotypes and compared those data to the reference genome sequence of the common U.S. cultivar 'Hale' .Given the low levels of genetic diversity observed among cultivars using AFLPs and SSRs , we adophttp://www.ars-grin.gov).We obtained seeds primarily from 152 accessions in the germplasm collection of the USDA-Agricultural Research Center in Griffin, Georgia. Our primary goal was to maximize geographic distribution of samples without regard to phenotype. The plants selected however did represent a broad range of phenotypic variation including dwarf, common, and large sized varieties, leaf color range from dark green to crimson, seed sizes ranging from small to large, seed colors including brown, tan, and reddish-brown, maturation from early to late season, and raceme size variation. Differences in oil production and oil quality from seeds likely varied but these were not quantified. All plants are believed to come from either horticultural or agricultural sources but this source distinction is not discernable from the USDA Germplasm Resources Information Network database , leaf tissue was taken for separate DNA extractions from 7-27 individual plants per site from 12 counties throughout the state. Thus, a total of 676 individual samples were included in this study. For a full description of greenhouse and extraction methods, see Allan et al. [We germinated at least 5 seeds per accession and dried leaf tissue from plants with successful growth after approximately 30 days. We then extracted total genomic DNA using Qiagen mini plant kits  for each plant individually. DNA used in analyses varied in concentration (~1-10 ng/μl), with the majority of samples standardized to 10 ng/μl. DNA was also obtained from plants grown at Lawrence Livermore and Los Alamos National Laboratories and was extracted in a similar manner. Analysis of this worldwide collection included 488 samples. For samples from naturalized populations in Florida . Genomic reads from different accessions were obtained by shotgun Sanger reads from plasmid genomic libraries or methylation filtration libraries [E. coli strain GC10 for regular shotgun libraries or strain DH5α for methylation filtration libraries.The castor bean genome has been sequenced using whole genome shotgun Sanger reads from plasmid and fosmid libraries, and the paired-end reads were assembled using the Celera assembler, reaching a 4× coverage . First, we selected the best multiplex combination using all 232 SNPs. This created a multiplex assay containing 35 SNPs. SNPs from the Greece, India, Mexico, and Puerto Rico cultivars were underrepresented in this assay, so we then created a second multiplex of 30 SNP loci using these cultivars exclusively. Five SNPs were run in both assays, which provided replication between runs. This provided Sequenom assays for 60 SNPs [Additional file Briefly, the iPLEX reactions use PCR to amplify specific regions containing a SNP. The primers are mass-labeled so that each product has a unique mass. During the extension reaction, a second PCR step, a mass-labeled nucleotide is then added in the SNP position, with each nucleotide having a characteristic mass. The PCR product is placed on a silicon chip, with each sample affixed to a spot containing the multiplex for all SNPs. The chip is then run in a mass spectrometer where the primer mass plus the SNP nucleotide mass is determined. In our assay, nucleotide base calls for SNPs were exported and assessed in Sequenom Typer Analyzer version 3.3. Base calls were automatically determined and then all plots were manually verified. Ambiguous calls were given an N in the data to indicate that no SNP was reliably determined.To assess the accuracy and dependability of calls, we ran 3 intraplate controls and had 2 interplate controls on every plate for each 96-well plate. No discrepancies occurred with any controls.ST or ϕPT values and analyses of molecular variance (AMOVA) [enalex 6.1 [PT values in particular, we generated pairwise comparisons of population differences with 999 data permutations in Genalex, which allows for an estimate that is analogous to Wright's FST combined with a probability value for population differentiation. Samples were coded based on country of origin, including samples with different USDA accession numbers but originating in the same country. We recognize that this approach may lump samples from different populations but we are confident in doing so because our primary analysis method assumes no a priori knowledge of groupings (see program Structure below). Samples from the United States were coded by state. In our AMOVAs, we only considered samples from localities  with ≥ 5 records to maintain confidence in this test. We grouped populations by geographic region: North America, South America, Africa, Asia, and Europe. To make regional sampling more uniform, Iran, Israel, Jordan, Syria, and Turkey were grouped with Europe; grouping them with Asia did not affect the results. We also performed a Mantel test [Our worldwide data set comprised 488 samples from 45 countries, with a mean of 11 samples per country. Fewer than five samples per country occurred when either DNA extraction or SNP analysis failed. We compiled the samples and corresponding base calls for all SNPs, determined standard genetic statistics such as ϕ (AMOVA)  and expoalex 6.1 . For ϕPTtel test  on sampltel test .tructure[R. communis introductions throughout the world are unknown. This program employs a Bayesian approach to modeling genetic structure and assumes no a priori knowledge of the relationship of genotypes, or number of populations. A series of models are constructed with different amounts of population structure (K) and samples are given a probability of assignment to a particular population based on their genotype. Modeling parameters were as follows: 20,000 burn-in period, 50,000 repetitions per run, an admixture model for ancestry, and allele frequencies set as independent. Use of the correlated allele frequency model did not noticeably affect population assignment of individuals. All assessments of parameter convergence were satisfied with the burn-in and repetition settings.We used the program Structure to deterTo increase confidence in population assignments, we conducted 10 runs for each value of K from 1-35. Model log likelihood values within each run rapidly began to asymptote but failed to reach a definitive maximum value . Therefolumpp, which addresses multimodality and/or label-switching in run comparisons [istruct[tructure and used the Full Search algorithm in Clumpp. For assessment of genotype groupings for each country  or county , we set a threshold of 60% for assignment of individuals to a particular cluster as done by Twito et al. [We compiled assignment probabilities for multiple runs in the program Cparisons . We used [istruct. Methodoo et al. . This clSNP: Single nucleotide polymorphism; AFLP: Amplified fragment length polymorphism; SSR: Simple sequence repeat; AMOVA: Analysis of molecular variance.JTF, GJA, PDR, and PK analyzed the data and wrote the manuscript. PK and PDR designed the study. APC, PDR, and JR sequenced the cultivars, generated the methylation-filtration libraries and performed SNP discovery. PJJ contributed samples and helped draft the manuscript. All authors read and approved the final manuscript.Pairwise population Phi-PT values from a worldwide germplasm collection. Differentiation of populations based on country of origin. Countries with fewer than 5 samples were removed from comparisons. Phi-Pt values are below the diagonal, with pairwise comparisons where p < 0.01 in bold. Probability values above the diagonal are based on 999 permutations.Click here for fileSequenom PCR primers. List of all primers used for Sequenom reactions, given in 5'-3' orientation. Extension primers for mass spectrometer readings not shown but available upon request. Two multiplexes were run; five SNPs were run in both multiplexes to allow for an internal check on assay reliability. Not all assays worked above our designated threshold so selected SNPs were dropped from analyses.Click here for fileRicinus communisLocations of 48 SNPs in . SNP location is based on contigs from Hale genome assemblies and contig number matches the R. communis database at JCVI. Mean observed heterozygosity (Ho) and mean expected heterozygosity (He) based on dataset of 676 samples, including samples from Florida.Click here for file


Samia cynthia ricini (Lepidoptera:Saturniidae), the Indian eri silkworm, contributes significantly to the production of commercial silk and is widely distributed in the Brahmaputra river valley in North-Eastern India. Due to over exploitation coupled with rapid deforestation, most of the natural populations of S. cynthia ricini are dwindling rapidly and its preservation has become an important goal. Assessment of the genetic structure of each population is a prerequisite for a sustainable conservation program. DNA fingerprinting to detect genetic variation has been used in different insect species not only between populations, but also between individuals within a population. Since, information on the genetic basis of phenotypic variability and genetic diversity within the S. cynthia ricini populations is scanty, inter simple sequence repeat (ISSR) system was used to assess genetic diversity and differentiation among six commercially exploited S. cynthia ricini populations. Twenty ISSR primers produced 87% of inter population variability among the six populations. Genetic distance was lowest between the populations Khanapara (E5) and Mendipathar (E6) (0.0654) and highest between Dhanubhanga (E4) and Titabar (E3) (0.3811). Within population, heterozygosity was higher in Borduar (E2) (0.1093) and lowest in Titabar (E3) (0.0510). Highest gene flow (0.9035) was between E5 and E6 and the lowest (0.2172) was between E3 and E5. Regression analysis showed positive correlation between genetic distance and geographic distance among the populations. The high GST value (0.657) among the populations combined with low gene flow contributes significantly to the genetic differentiation among the S. cynthia ricini populations. Based on genetic diversity, these populations can be considered as different ecotypes and in situ conservation of them is recommended.  Samia cynthia ricini (Lepidoptera:Saturniidae), a commercial silk producing insect, is believed to have originated in the Brahmaputra valley Ricinus communis L.), but it also feeds on a wide range of food plants such as Heteropanax fragrans Seem, Manihot utilissima Phol, Evodia flaxinifolia Hook, Ailenthus gradulosa Roxb etc  (set # 9), were tested for their efficacy in amplification of DNA. Twenty primers that produced reproducible robust bands, which appeared consistently and distinctly across three different amplifications, were selected for the study. PCR amplification of the DNA was carried out as described earlier www.mjr.com/), using 20 μl reaction mixture containing 10x PCR buffer, 2 mM dNTPs, 2.5 mM MgCl2, 0.10 μl Taq DNA polymerase (recombinant) (5 U/μl) , 2.0 μl of 1.5 μM ISSR primer and 40 ng DNA.ISSR primers , procurewww.syngene.com). Binary scoring of the profiles was done on the basis of presence or absence of a band at a particular locus. The amplification of the DNA with each primer was repeated thrice and only the robust bands were scored for the analysis.The PCR products were resolved on 1.5% agarose gel, in Tris - Boric Acid–EDTA buffer, stained with ethidium bromide (0.5 μg/ml) and photographed under Bioimaging system /(2Nab + Na + Nb)], where Nab is the number of bands that are shared by the genotypes ‘a’ and ‘b’; Na is the number of bands present in ‘a’ and Nb is the number of bands present in ‘b’ (ST) in POPGENE version 1.3  /Ht’ where DST is the average gene diversity between subpopulations, including the comparisons of sub populations with themselves. The DST= (Ht-Hs). GST is an extension of t = (1–∑pi2), where pi is the frequency of ith allele at a locus in a population and ∑ is the summation of all alleles. Hence, the Hs in the equation were defined in terms of gene diversities. However, for random mating subpopulations, gene diversities can be defined as expected heterozygosities under Hardy-Weinberg equilibrium averaged among sub populations (Hs) and of the total population (Ht). The estimate of gene flow from GST was calculated as (Nm) = 0.5 (1 – GST)/GST. To analyze the relation between genetic distance and geographic distance between the populations, regression analysis was done using inter- population genetic distance against the geographic distance between them in kilometers. A scatter diagram was prepared and the best-fit linear regression line was applied.From the binary data, genetic dissimilarity coefficients among the individuals were estimated using Dice’s coefficient (D) = 1–[ among the populations was 0.657  and was lowest in E3 (0.051) . When alas 0.657 . The paias 0.657 . E3 showS. cynthia ricini was distributed in the Brahmaputra river valley of Assam and in the hilly area. Titabar, where the population E3 was collected from, is located in the Upper Assam area and is approximately 421 kilo meters away from all other places of collection. The distance between the other populations was comparatively smaller. Regression of the genetic distance between the populations against geographic distance between them showed a positive correlation (R2 = 0.502)  .Bombyx mori  within the individuals of a population points to the fact that the populations are homogenous in nature, whereas the higher genetic variability (ca. 87%) among the populations indicated that the populations have already differentiated into separate genetic pools. Hence, these different gene pools should be conserved separately and maintained without any inter-mixing. Comparatively higher polymorphism (31.52%), heterozygosity (0.1093) and high boots trap values (98%) at the subgroup level observed in the E2 population points to significantly higher genetic differentiation at Borduar. Since moths of ST (0.657) combined with low gene flow (Nm = 0.2762) that has contributed significantly to genetic differentiation between populations  between the populations E5 and E6 is quite understandable as they are similar in phenotypic traits, such as cocoon color (greenish blue). The high phenotypic and genetic similarity and gene flow observed within these two populations suggests its common origin and later succession into different populations by adapting to the varying climatic conditions. Since the E3 (Titabar) population is genetically and geographically distanced from all other populations and seems to be an isolated one, detailed molecular analysis of this population is required to decipher its origin. The E3 population exhibited comparatively higher Gulations . The genosophila . S. cynteciation . At the S. cynthia ricini is believed to have originated in the Brahmaputra river valley of north-eastern India and this is the region where maximum genetic diversity is expected, the populations identified as genetically divergent have to be conserved in situ. The high economic value of the silk industry further emphasizes the need for urgent measures in this direction.Thus from this study it is clear that the populations evaluated not only differ in their phenotypic traits but also in their genetic make up. Since


An increasing number of studies demonstrate that genetic differentiation and speciation in the sea occur over much smaller spatial scales than previously appreciated given the wide distribution range of many morphologically defined coral reef invertebrate species and the presumed dispersal-enhancing qualities of ocean currents. However, knowledge about the processes that lead to population divergence and speciation is often lacking despite being essential for the understanding, conservation, and management of marine biodiversity. Sponges, a highly diverse, ecologically and economically important reef-invertebrate taxon, exhibit spatial trends in the Indo-West Pacific that are not universally reflected in other marine phyla. So far, however, processes generating those unexpected patterns are not understood.Leucetta chagosensis across its known geographic range using two nuclear markers: the rDNA internal transcribed spacers (ITS 1&2) and a fragment of the 28S gene, as well as the second intron of the ATP synthetase beta subunit-gene (ATPSb-iII). This enabled the detection of several deeply divergent clades congruent over both loci, one containing specimens from the Indian Ocean , another one from the Philippines, and two other large and substructured NW Pacific and SW Pacific clades with an area of overlap in the Great Barrier Reef/Coral Sea. Reciprocally monophyletic populations were observed from the Philippines, Red Sea, Maldives, Japan, Samoa, and Polynesia, demonstrating long-standing isolation. Populations along the South Equatorial Current in the south-western Pacific showed isolation-by-distance effects. Overall, the results pointed towards stepping-stone dispersal with some putative long-distance exchange, consistent with expectations from low dispersal capabilities.We unraveled the phylogeographic structure of the widespread Indo-Pacific coral reef sponge L. chagosensis, resulting in high levels of regional isolation. Reciprocally monophyletic populations constitute putative sibling (cryptic) species, while population para- and polyphyly may indicate incipient speciation processes. The genetic diversity and biodiversity of tropical Indo-Pacific sponges appears to be substantially underestimated since the high level of genetic divergence is not necessarily manifested at the morphological level.We argue that both founder and vicariance events during the late Pliocene and Pleistocene were responsible to varying degrees for generating the deep phylogeographic structure. This structure was perpetuated largely as a result of the life history of  Knowledge about the processes that generate and maintain marine biodiversity, and the evolutionary relationships and genetic variation of regional populations, along with assessments of the amount of demographic connection between these populations, are essential for understanding and effectively conserving and managing marine resources  such as Because marine organisms frequently have high dispersal potential (e.g. ), their This study focuses on tropical marine sponges, a highly diverse, ecologically and economically important, but notoriously understudied, marine invertebrate taxon . BiodiveLeucetta chagosensis Dendy 1913  served here as a model to test biogeographic and phylogeographic hypotheses, and to determine the degree of connectivity vs. isolation among populations, with a focus on the SW Pacific. L. chagosensis has a wide Indo-Pacific distribution, ranging from the northern Red Sea to the central Pacific  and from Okinawa (Japan) to Brisbane , and was considered to be a single species throughout this range .Double-stranded sequences of novel sequences generated in this study were assembled with CodonCode Aligner , automat studies ,21 (Genbng IUPAC  ambiguoung IUPAC . IntrageWe attempted to resolve length variant heterozygotes (LVH), which often cause problems when analyzing intron sequences , using SIndels and minisatellite repeats were treated as missing data, and were not recoded for phylogenetic analyses because they represented autapomorphies for regional populations, and as such did not contribute to resolving phylogeographic structure.All sequences were assembled and aligned as described above. Uncorrected p-distances were calculated using Paup*4 . NucleotATPSb-iII alleles were used and concatenated with both rDNA fragments into one alignment. Alignments were collapsed to contain only unique sequence types/alleles in COLLAPSE 1.2  with 10,000 discarded trees per chain (burn-in). For MIGRATE analyses, each diploid sponge individual was represented by two alleles/sequence types in the input file, whether it was homozygous or heterozygous. rDNA sequences showing IGPs were resolved into two sequence types using the parsimony approach outlined above, since MIGRATE does not allow ambiguously (IUPAC) coded nucleotide sites.Because of the acknowledged difficulties of using ene flow , we alsoene flow , such asene flow . The parDue to the large geographic distances between some populations without intermediate sample localities,  and the low sample sizes of some, we focussed our attention on estimating the migration rates among populations along the East-Australian coast, Papua New Guinea, and the southern Pacific only.ATPSb-iII) and revised the manuscript. All authors read and approved the final manuscript.GW conceived and designed the study, analysed and interpreted the data, and drafted and revised the manuscript. LSE carried out part of the molecular genetic work (rDNA) and contributed to data analysis and interpretation as well the manuscript revision. LM carried out part of the molecular work ; 2) corresponding nucleotide alignment.1) Amino acid alignment of three sponge Click here for fileATPSb-iII alleles with all posterior probability values from Bayesian analysis and Maximum Likelihood non-parametric bootstrap proportions indicated at branches.Bayesian phylogeny of 89 Click here for fileATPSb-iII alignment with all posterior probability values from Bayesian analysis and Maximum Likelihood non-parametric bootstrap proportions indicated at branches.Bayesian phylogeny of 92 unique genotypes from the concatenated rDNA and Click here for fileATPSb-iII) and the concatenated alignment of 92 unique genotypes found among the 105 individuals from which sequences of both loci could be obtained.Neighbor-Net analysis of the different partitions (rDNA and Click here for file


At the southern entrance to East Asia, early population migration has affected most of the Y-chromosome variations of East Asians.To assess the isolated genetic structure of Hainan Island and the original genetic structure at the southern entrance, we studied the Y chromosome diversity of 405 Hainan Island aborigines from all the six populations, who have little influence of the recent mainland population relocations and admixtures. Here we report that haplogroups O1a* and O2a* are dominant among Hainan aborigines. In addition, the frequency of the mainland dominant haplogroup O3 is quite low among these aborigines, indicating that they have lived rather isolated. Clustering analyses suggests that the Hainan aborigines have been segregated since about 20 thousand years ago, after two dominant haplogroups entered East Asia (31 to 36 thousand years ago).Our results suggest that Hainan aborigines have been isolated at the entrance to East Asia for about 20 thousand years, whose distinctive genetic characteristics could be used as important controls in many population genetic studies. It is well-known that East Asians exhibit many uniquely derived characteristics in their genetic structure because the populations have been isolated from those of Western Eurasia for some time Unfortunately, many events of population relocation in the history of East Asian have deeply buried the original diversity pattern of populations around the south entrance to East Asia. The most notable of these events are the backward migrations of Han Chinese Here we report that we found several isolated aboriginal populations on the Hainan Island in the southern tip of East Asia  that repLuobidong Cave of Sanya County revealed that modern humans may have lived in Hainan Island already about 10,000 years ago in Paleolithic Age Dongfang Xinjie Shell-heap site found in the present area of Cun people Hainan Island is a big island in the Tonkin Bay between East Asia and Southeast Asia. During the last Ice Age when the sea level was much lower than it is today, it was connected to the continent In our study, we analyzed the Y chromosome diversity of all the six populations of Hainan aborigines. We examined 22 single nucleotide polymorphisms (SNPs) and seven short tandem repeat (STR) polymorphisms in 405 male Hainan aborigines, and determined the haplogroups based on the nomenclature of YCC To determine the genetic relationship between Hainan aborigines and other populations, we performed two methods of clustering analyses  using thPC analysis also shows that the studied populations can be divided into a southern group and a northern group . Based oAccording to the SNP analyses, Hainan aborigines have been isolated from the northern populations, especially the Han Chinese migrations into South China who have brought the high frequency of haplogroup O3. The southward migration of Hmong-Mien populations might also have contributed to the increase in the frequency of O3 in South China. Our recent study revealed that Hmong-Mien and Sino-Tibetan may have a most recent common ancestor in southwestern China, and the O3 haplogroup was dominant in their common ancestral population (Unpublished data of Li Jin). As we have mentioned, O3 is almost absent in Hainan aborigines. However, the dominant haplogroups of Hainan aborigines, O1 and O2, are also dominant in mainland Daic populations and Taiwan aborigines. The SNP haplogroup analyses cannot exclude the gene flow between Hainan aborigines and Daic-Taiwan populations. Thus, aside from the dendrogram and the PC plot of SNPs, we analyzed the STR networks  of the tIn these networks, reference data Luobidong Cave site, the oldest archaeological finding in Hainan. We propose this age to be that of the Hainan aborigines, who have been essentially isolated since. Earlier archaeological sites may be found in Hainan Island in the future.There might have been more than one route for early migrants of modern human into East Asia. Another possible route of entering East Asia might be on the west side of the Southeast Asia-East Asia border  by searching the ISO639-3 code [lic], [jio], and [cuq]. The healthy donors were from different villages and had different surnames, ensuring that the individual samples were unrelated. Written informed consents were signed by all the 405 volunteers. Our study of human blood was approved by the Ethics Committee of the Chinese National Human Genome Center at Shanghai.Population samples were collected by drawing blood from the upper arms of 405 male volunteers of Hainan aborigines. The sample size of each population was given in Fifteen single nucleotide polymorphisms (SNPs) in the Y chromosome non-recombining portion were typed in the collected samples by PCR-RFLP . Six SNPs  were typed by Taqman (Applied Biosystems Co.). Seven SNPs  and seven short tandem repeat (STR) polymorphisms  were typed by using fluorescently labeled primers for PCR amplification. Denatured products were separated by acrylamide gel electrophoresis through the use of an ABI 3100 genetic analyzer to distinguish the alleles. These SNP and STR markers are all highly informative for studies of East Asian populations −4 per 25 years) −4 per year, the sum of the seven STRs. We assumed an average of 25 years per generation, resulting in 5176 years per mutation in the networks. Time estimates were confirmed by BATWING Dendrogram clustering and principal component (PC) analyses were performed by software SPSS13.0. STR median-joining networks were drawn by Network 4.201 Table S1The Y-STR haplotype frequencies of the Hainan aborigines.(0.04 MB XLS)Click here for additional data file.Dataset S1The original networks of O1a* and O2a*.(0.01 MB ZIP)Click here for additional data file.


Detecting population substructure is a critical issue for association studies of health behaviors and other traits. Whether inherent in the population or an artifact of marker choice, determining aspects of a population's genetic history as potential sources of substructure can aid in design of future genetic studies. Jewish populations, among which association studies are often conducted, have a known history of migrations. As a necessary step in understanding population structure to conduct valid association studies of health behaviors among Israeli Jews, we investigated genetic signatures of this history and quantified substructure to facilitate future investigations of these phenotypes in this population.Using 32 autosomal STR markers and the program STRUCTURE, we differentiated between Ashkenazi  and non-Ashkenazi  Jewish populations in the form of Northern and Southern geographic genetic components . The ability to detect substructure within these closely related populations using a small STR panel was contingent on including additional samples representing major continental populations in the analyses.Although clustering programs such as STRUCTURE are designed to assign proportions of ancestry to individuals without reference population information, when Jewish samples were analyzed in the absence of proxy parental populations, substructure within Jews was not detected. Generally, for samples with a given grandparental country of birth, STRUCTURE assignment values to Northern, Southern, African and Asian clusters agreed with mitochondrial DNA and Y-chromosomal data from previous studies as well as historical records of migration and intermarriage. The genetics of Jewish populations, particularly that of Ashkenazi Jews, has been studied extensively to answer questions of human evolutionary, historical, and medical significance -11. HumaY-chromosomal and mtDNA studies of Jewish populations and their local host populations have, at times, provided conflicting results, but can be summarized as supporting the following: 1. Almost all Jewish populations are derived from Middle Eastern ancestral populations ,11,20-23Several large-scale studies using autosomal markers demonstrated substructure among European populations, specifically non-Jewish Northern European, non-Jewish Southern European, and Ashkenazi Jews -26. AddiUsing thousands of SNPs and principal components analysis (PCA), Seldin et al , Price erelative differences between populations, the context of a sample in a given analysis  can affect clustering patterns. This aspect of the process of population substructure detection may be overlooked in case control association studies and may affect results if not taken into consideration. Based on this, we hypothesized that the presence or absence of putative parental populations in a STRUCTURE analysis would affect the ability to detect substructure in Jewish populations and differences between Jewish populations.Middle Eastern ancestry may be a common factor among Jewish populations; however, the majority of Jewish populations have been located outside of the Middle East for up to 2000 years. As is the case with other highly mobile human populations there has been historically documented gene flow between Jewish populations and local host populations. In addition, because these are populations defined, in part, by religion, gene flow into Jewish populations is a product of conversion as well as marriage. Thus, there should be genetic admixture in Jewish subpopulations that reflects, in part, their migratory histories and may contribute to current genetic differences among Jewish populations. It is known that detecting and quantifying recent admixture is dependent on the time since divergence of the putative parental populations as well as the number and information content of markers. Because clustering algorithms are also dependent on the To address this question thoroughly prior to conducting association studies of health behaviors among Israeli Jews, we examined population structure in Jewish populations of European, African, Middle Eastern, Central and South Asian origin. We genotyped 526 subjects, recruited in Israel, with 32 genome-wide unlinked microsatellite markers (STRs). To identify potential population structure in the Jewish population being studied, we also genotyped 254 individuals from self-identified Chinese, Thai, Ethiopian Jewish, African American, and European American samples using the same markers. The Jewish populations sampled here are not comprised of various percentages of discreet ancestral populations. Our premise is that Jewish populations originated in the Middle East but, subsequent to and in the course of long-range migrations, accumulated input from local host populations, each with its own migratory history. We include in our analysis genotypic data from present-day populations whose ancestry serves as a proxy for those populations that might have contributed once Jewish populations migrated out of the Middle East. Our results are of interest both to infer unknown and correlate with known aspects of Jewish history and for their theoretical implications for detecting substructure in seemingly homogenous populations. They are also of important applied interest for studying health-related phenotypes in our sample of Israeli Jews. To our knowledge, this is the first study to incorporate proxy parental groups into analysis of structure of a Jewish sample, as well as the first to investigate variation among and ancestry of world-wide Jewish populations with autosomal markers.Each of the Israeli subjects provided self-reported country of birth, country of birth of parents and grandparents, world region of family origin (not necessarily the same as country of birth of grandparents), whether they considered themselves to be Ashkenazi (as defined by respondents), Sephardic (similarly self-defined), mixed, other or none, and whether they, their parents, and grandparents had been born Jewish . A common practice in the medical and non-medical literatures is to subsume Jews of Spanish, Balkan, Middle Eastern, African, and Asian descent under the term "Sephardic", but since this term implies Spanish origin, it is imprecise and unclear. Further, due to continuous changes in the acceptability and applicability of the term, "Sephardic" among Israelis ,30, mediWhen there is no detectable substructure in a sample, after using the program STRUCTURE 2.2 ,37 each Within the Jewish-Israeli sample, for sets of individuals reporting four grandparents from the same country of birth, we averaged percent ancestry from Northern, Southern, Asian, and African components to evaluate possible geographic influences on ancestry components. We found that, in many cases, evidence of admixture with host populations based on our autosomal data confirmed the results of previous mtDNA or Y-chromosome studies ,22,23,40No population showed significant deviation from HWE expectations over all loci. Following application of a Bonferroni correction to correct for multiple testing , no p-values for individual loci are significant.The tetranucleotide and dinucleotide markers had similar average non-Ashkenazi/Ashkenazi delta values . Overall, the delta values for this marker panel (0.131) would not indicate a robust ability to differentiate between these two populations but the results of this study (considering the consistency of the observed ancestry coefficients with known geography and previous studies) show their utility for this purpose nonetheless , Morocco (0.03), Tunisia (0.09), Egypt (0.03)) are consistent with findings from the Behar mtDNA study, which detected low rates of Sub-Saharan African, and no North African maternal contribution to Moroccan, Tunisian, and Libyan Jewish populations. Our findings of significant Sub-Saharan African ancestry in Ethiopian Jews (0.55) in contrast to Yemenite Jews (0.03) were also consistent with mtDNA  and Y-chOur results on population substructure reflect the influence of numerous factors, including the recent founding of Ashkenazi vs. non-Ashkenazi Jewry, gene flow between these groups and between Jewish and non-Jewish populations, a highly complex migration history, and the characteristics and limitations of the marker set used in this study. The documented history of the Eurasian and North African Jewish populations indicates that the Diaspora did not radiate outward geographically from the Middle East in a simple starburst pattern -- rather, Ashkenazi and non-Ashkenazi Jews migrated repeatedly in and out of Europe, Africa, the Middle East, and Central, South, and East Asia. Cultural differences, once established, may have promoted differentiation between the Ashkenazi and non-Ashkenazi Jews in spite of their repeated geographic overlap. High variance of Northern and Southern components within a subset of individuals with the same grandparental country of birth even for those with large sample size table  likely rAlthough within each group there is a high degree of variability among individual assignment values, geographic patterns are seen in the average North/South percent assignment values between groups as defined by AJ or NAJ, grandparental world region of birth, or grandparental country of birth. For AJ and NAJ these differences were found to be statistically significant table   show significant Asian admixture (0.36) while also showing the highest ratio of Southern/Northern contribution. Of all non-Ashkenazi Jewish populations, Jews of Yemenite, Egyptian, and Central Asian descent have the largest Southern component . These results concur with both molecular and historical evidence. Behar et al  show sig-48. Jewssamples as well as the number and informativeness of markers . It is this gene flow from host populations combined with genetic drift and possible local selection pressures, that have led to detectable substructure among Jewish populations, perhaps more so than would be expected based solely on time since population divergence.relative population genetic differences and total information from markers and individuals on clustering patterns. As we have described previously, our STR panel was chosen specifically for its high information content , D21S11 [GenBank:AJ550387], D7S820 [GenBank:NC_000007], CSF1PO [GenBank:AF076965], D3S1358 [UniSTS:148226], TH01 [UniSTS:240639], D13S317 [GenBank:G09017], D16S539 , D19S433 [GenBank:G08036 ], vWA [UniSTS:240641], TPOX [GenBank:M25706], D18S51 [GenBank:L18333 ], D5S818 [GenBank:G08446 and FGA [GenBank:G3347]) and an additional 17 dinucleotide repeats . The amelogenin locus, included in the AmpF/STR Identifiler PCR Amplification kit for sex identification, was not included in any analyses. All STR markers were analyzed on an ABI PRISM 3100 semiautomated capillary fluorescence sequencer. Data were scored using Genemapper (ABI). We previously used this marker panel  to determine and statistically correct for ancestry in case-control studies and genome-wide linkage studies [All samples were genotyped for thirty-two unlinked autosomal STR markers . The panel is comprised of the 15 tetranucleotide repeats in the AmpF/STR Identifiler PCR Amplification kit  K - lnP(D)K-1)| and variance of lnP(D)are minimized. This identifies the highest value of K, prior to the plateau of lnP(D) [The authors of STRUCTURE recommend using the maximal value for lnP(D) to determine the best value of K for the data. However, it has been observed that lnP(D) will plateau while continuing to increase slightly as assumed K increases past the correct K. Therefore, identifying the K for which lnP(D) is greatest may not be sufficient to identify the correct (underlying) K. We employed StructureSum, an R script that uses the output from STRUCTURE to identify the K for which lnP(D) is maximized while both |lnP(D)f lnP(D) .STRUCTURE runs were unsupervised, using the admixture model and correlated allele frequencies. Structure randomly assigns clusters in each run such that the correspondence between runs is non-obvious. CLUMPP software  takes thFor Jewish-Israelis with four grandparents from the same country, for K = 4, individual assignment values produced by CLUMPP were averaged to arrive at values for Northern, Southern, Asian, and African ancestral components for the Jewish population of that geographic location. This was also done for AJ, NAJ, and ANAJ.0  is true. The t-test value of the observed data was then compared to that of the simulated data to obtain a p-value for mean differences in assignment values between AJ and NAJ.A two Sample t-test via monte-carlo permutation was used to test for significance between the individual AJ and NAJ assignment values with 10,000 samples simulated when H1 = heterozygote excess based on a Markov chain method. The parameters used were 5000 dememorizations, 1000 batches, and 5000 iterations per batch. The parameter values were increased from defaults until the observed standard error for p-values was less than 0.01. For the mixed Jewish sample this was performed for the sample as a whole, as well as for the AJ, NAJ, and ANAJ subsets. We used an exact test for multi-allelic markers because Chi-squared tests are inappropriate for such analyses [Tests for deviation from Hardy-Weinberg equilibrium expectations were conducted using GENEPOP 4.0  globallyanalyses .ith allele in populations A and B. The more effective the marker is at differentiating between populations, the higher the value for δ [Markers were evaluated for delta (δ) , a measuue for δ .JBL designed the study, carried out statistical analyses and drafted the manuscript. DH participated in project coordination, sample collection, and the writing of the manuscript. AS and AM participated in sample collection in Thailand. HRK carried out sample collection in the United States. RTM participated in project coordination and sample collection. EA and BS participated in project coordination and sample collection in Israel. JG participated in study design and supervision, project coordination, sample collection, and the writing of the manuscript. All authors read and approved the final manuscript.


For Chagas disease, the most serious infectious disease in the Americas, effective disease control depends on elimination of vectors through spraying with insecticides. Molecular genetic research can help vector control programs by identifying and characterizing vector populations and then developing effective intervention strategies.Triatoma infestans (Hemiptera: Reduviidae), the main vector of Chagas disease in Bolivia, was investigated using a hierarchical sampling strategy. A total of 230 adults and nymphs from 23 localities throughout the department of Chuquisaca in Southern Bolivia were analyzed at ten microsatellite loci. Population structure, estimated using analysis of molecular variance (AMOVA) to estimate FST  and RST (stepwise mutation model), was significant between western and eastern regions within Chuquisaca and between insects collected in domestic and peri-domestic habitats. Genetic differentiation at three different hierarchical geographic levels was significant, even in the case of adjacent households within a single locality . On the largest geographic scale, among five communities up to 100 km apart, RST = 0.12 and FST = 0.06. Cluster analysis combined with assignment tests identified five clusters within the five communities.The population genetic structure of RST and FST, supports the hypothesis of poor dispersal ability and/or reduced migration of T. infestans. The high degree of genetic structure at small geographic scales, inferences from cluster analysis and assignment tests, and demographic data suggest reinfesting vectors are coming from nearby and from recrudescence (hatching of eggs that were laid before insecticide spraying). Suggestions for using these results in vector control strategies are made.Some houses are colonized by insects from several genetic clusters after spraying, whereas other households are colonized predominately by insects from a single cluster. Significant population structure, measured by both  Trypanosoma cruzi. Chagas is prevalent throughout Central and South America, and it remains a chief concern in Bolivia. A movement that began in 1991 called the Southern Cone Initiative has been successful in reducing the incidence of Chagas disease in the Southern Cone countries of Argentina, Brazil, Chile, and Uruguay; but due to socio-economic and other factors, incidence remains high in Bolivia. The most important mode of transmission of T. cruzi to humans and other mammals is through feces of triatomine bugs. Thus, disease control and transmission prevention focus on elimination of triatomine vectors, and more specifically in Bolivia, it focuses on the elimination of Triatoma infestans. This study focuses on T. infestans in the Department of Chuquisaca, Bolivia. Ten highly variable microsatellite markers were used to analyze the population structure of insects collected in different towns. Statistical analyses show that T. infestans are highly structured, which means that they colonize on a small geographic scale. The results also suggest little active dispersal. These findings should be implemented during control efforts so that insecticide spraying focuses on geographic areas of colonization and re-colonization.Chagas disease is a protozoan infection caused by the parasite  Trypanosoma cruzi is transmitted by hematophagous insects of the sub-family Triatominae. Triatoma infestans is the major vector in the Andean highlands where the disease is endemic and has infected humans for over 9000 years T. infestans is exclusively domestic or peri-domestic, thus eradication of the vector in these regions, followed by vigilance against re-infestation, has proven largely successful in reducing transmission of T. cruzi and thus the prevalence of Chagas disease T. infestans in towns and homesteads is confounded by the possible re-infestation from surrounding sylvatic areas.Chagas disease is a parasitic disease in which the pathogenic agent, T. infestansMolecular genetic research can help vector control programs by identifying and characterizing genetically distinct vector populations and then developing effective intervention strategies T. infestans.To reduce transmission of Chagas disease, estimates of population differentiation are crucial to understand vector dispersal, sources of reinfestation, and gene flow; this genetic information is an important tool for effective management of insect control programs. Here we aimed to investigate the population genetic structure and inferred the source of colonization of vectors in the department of Chuquisaca, Bolivia using ten highly polymorphic microsatellite markers. The geographic region has high levels of human infection and house infestation and is located in a region thought to be the evolutionary origin of Insects were collected from 23 localities including both peri-urban (inhabited areas in the immediate vicinity of a city) and rural sites (less than 2000 inhabitants) in the provinces of Oropeza, Zudañez, Azurduy, Yamparaez, Tomina, Belisario Boeto and Hernando Siles within the Department of Chuquisaca, in the Bolivian highlands ranging from 1079 to 3020 meters above the sea level . This arT. infestans included in the present study were a mixture of nymphs and adults, collected from inside as well as the immediate vicinity of homes. Collections were made in the months of the Southern hemisphere summer 2002, spring 2005 and fall 2005. Forty-four insects came from a single corral in the community of Jackota in the province of Zudañez, 78 insects were collected in the community of Zurima in the province of Oropeza, and 37 were collected in Sucre the capital and main city of Chuquisaca located in the province of Oropeza. The remaining 71 insects came from collections in 20 localities throughout Chuquisaca. All insects included in the study were identified as T. infestans using taxonomic keys Specimens of DNA was extracted from three legs or 25 mg of tissue obtained from the posterior part of the abdomen of a given specimen using the Qiagen DNeasy DNA extraction kit . Care was taken to avoid sampling from the mid-abdomen as the stomach may inhibit the PCR reaction CAA ATA TTT TGT GTG TGT GTG -3”, TiD09* ‘5 –GGT CTT GGA TTT TAT GGG TTA TTT T -3’, and TiF03* ‘5 –CAC ACA AAC ACT TAA ACA CAC ACA A -3’) were modified so that the PCR product size did not overlap with other products of the same size range and fluorescence label. Our PCR reactions used the Qiagen Multiplex PCR kit . Template DNA (50–100 ng), primers and molecular biology grade water were added to the 2× multiplex PCR master mix to a final volume of 25 µL. The concentration of each primer was adjusted to permit good readings of the fluorescent peaks, modified by the addition of a fluorescence label and produced PCR products with the number of base pairs as follows: TiA02, 0.138, HEX, 173–225; TiC02, 0.138, HEX, 157–211; TiC08*, 0.276, 6-FAM, 110–144; TiC09, 0.552, NED, 125–159; TiD09*, 0.276, NED, 294–342; TiE02, 0.138, HEX, 147–167; TiE12, 0.276, HEX, 303–321; TiF03*, 0.276, 6-FAM, 215–269; TiF11, 0.138, NED, 256–280; and TiG03, 0.552, HEX, 200–250. The amplification protocol consisted of an initial step of 15 min at 95°C to activate the DNA polymerase and denature the template DNA, followed by 30 cycles of 30 sec at 94°C, 90 sec at 55°C, 60 sec at 72°C, and a final extension step of 10 min at 72°C. All reactions were carried out in a Techne TC-512 thermocycler . PCR products were diluted 1/10 in distilled water then analyzed on an ABI Prism 3100 genetic analyzer using a ROX labeled size standard. Genotypes were read using GeneMapperTM version 4.0 software . The multiple PCR products were analyzed on an ABI Prism 3100 genetic analyzer using a ROX labeled size standard. Genotypes were read using GeneMapperTM version 4.0 .We used ten previously published microsatellite markers: TiA02, TiC02, TiC08, TiC09, TiD09, TiE02, TiE12, TiF03, TiF11 and TiG03 We investigated population genetic structure at both ecological and geographic levels . EcologiST  and RST  Genetic population structure was investigated using hierarchical analysis of molecular variance (AMOVA) FST, (FST/1−FST) onto the natural log of geographic distance. In addition, a Mantel test To test for isolation by distance we performed a regression analysis of Slatkin's Nm) based on private alleles RST for the sample sizes used in our study. The results were adjusted for diploid data, M = 2Nm. Based on the assignment test (see below) results from the software Structure Nm).Pairwise multilocus estimates of the effective number of migrants  among groups of insects within households from Zurima (12 households), Serrano (4) and Ingre (1) was determined using the software Relatedness version 5.0 The average relatedness ; both measures are also significant among the five communities <100 Km apart  and among houses in Zurima . We also observed significant differentiation between domestic and peri-domestic populations within the community of Zurima .There was significant genetic differentiation among populations based on analyzed . BetweenRST and FST among communities . The dendogram based on Nei's genetic distances showed a cluster comprising populations from Zurima, El Chaco and Sucre differentiated from a sister cluster with the Jackota population . These tmunities  support habitats .Five clusters were identified among the 5 communities . When asAt the household level, five genetic clusters were identified from the seven households . Insectsr<0 (P<0.05) indicating significant outcrossing. For seven houses r>0 (P<0.05). A value of r≈0.25  was obtained for four households, and although the relatedness was similar in these households, the composition of the insect collection varied (1 site only adults 1 site only nymphs and 2 sites a mix of adults and nymphs). For the sites with the highest relatedness values , in 2 houses a single adult and 2–4 nymphs were collected and for one household only nymphs were collected.Relatedness of insects in nine out of seventeen houses was not significantly different from 0 . From thNm, among towns <40 Km apart was higher (2.03) compared with those among more distant communities (1.42) and among houses within the town of Zurima (0.99). The Mantel test of isolation by distance revealed a non-significant correlation between Slatkin's linearized FST and Nm vs. the natural log of geographic distance . Non-significant results were also observed when applying the Mantel test for a correlation between Nei's genetic distances and geographic distances among populations , and altitude . The Mantel tests had low power because of the small samples within many of the communities.The estimates of the effective number of migrants per generation, T. cruzi infection Our study region is an ecologically diverse but geographically small valley–mountain environment in the department of Chuquisaca in Southern Bolivia. This region has high levels of house infestation and vector and human T. infestans from Bolivia have found geographical variation in patterns of population structure in this vector; therefore we examined distinct ecological and geographic hierarchical groups ranging from a single goat corral to comparing western and eastern regions of Chuquisaca.Previous studies on population genetics and morphometry of ST and RST showed differentiation even within a community. Previous study in the same area using a mitochondrial cyt b gene T. infestans from highlands (>1800 m) and lowlands (<500 m). We examined insects from eastern and western Chuquisaca that significantly differ in altitude, both groups are >2000 m, and we detected significant differentiation at this ecological level.Genetic analysis over twenty-three localities throughout the department of Chuquisaca have revealed moderate but highly significant levels of genetic variation among populations. Both FST values were larger than FST, suggesting polymorphism is high and rates of migration are low ST) indicate lower differentiation because they do not distinguish among shared alleles in different populations that are not identical by descent. Similar values of RST and FST are only to be expected when mutation rates are negligible in comparison to migration and drift. When the SMM contributes to population differentiation, RST values should be larger than FST values ST>FST suggesting that mutation contributes to differences at this geographic level. However, there is no such pattern for pairwaise RST and FST among households suggesting that mutation does not contributes much to differentiation at this level.In our study, RST>FST, T. infestans has a low capacity for active dispersal T. infestans in Chuquisaca. In our study, the results of the assignment of individuals to genetic clusters  were assigned to one cluster. The relatedness of insects in Z-6 was significantly greater than 0 Click here for additional data file.


It was demonstrated previously that the three-locus RFLP haplotype, TaqI B-TaqI D-TaqI A (B-D-A), at the DRD2 locus constitutes a powerful genetic marker and probably reflects the most ancient dispersal of anatomically modern humans.We investigated TaqI B, BclI, MboI, TaqI D, and TaqI A RFLPs in 17 contemporary populations of the East European Plain and Siberia. Most of these populations belong to the Indo-European or Uralic language families. We identified three common haplotypes, which occurred in more than 90% of chromosomes investigated. The frequencies of the haplotypes differed according to linguistic and geographical affiliation.Populations in the northwestern (Byelorussians from Mjadel'), northern (Russians from Mezen' and Oshevensk), and eastern (Russians from Puchezh) parts of the East European Plain had relatively high frequencies of haplotype B2-D2-A2, which may reflect admixture with Uralic-speaking populations that inhabited all of these regions in the Early Middle Ages. The DRD2 gene is located on chromosome 11 and encodes the neuronal dopamine receptor D2, which plays a role in movement, emotional memory, and appetitive behavior , and the], and centers of dispersal, which probably reflect the most ancient dispersal of anatomically modern humans, have been proposed for their three-locus haplotypes [TaqI B, TaqI D, and TaqI A polymorphisms have also been studied on a worldwide scale -11; the ; the 11;plotypes . It has plotypes -14. Kiddplotypes  proposedHere we provide data on the variability of DRD2 haplotypes in a previously uninvestigated region, the East European Plain. We investigated 14 contemporary populations of the East European Plain and Siberia that belong to the Indo-European and Uralic language families. In addition, two populations of the Altaic language family  and a population of the North Caucasian language family (Adygeis) were included as reference groups. We also performed an updated global analysis of B-D-A haplotype frequencies using our data and data from the ALFRED database.We studied five RFLP loci, TaqI B, BclI, MboI, TaqI D, and TaqI A. The locations of these polymorphisms are shown on the gene map Figure , and allAlleles 1 at the TaqI B, BclI, and TaqI A loci were more frequent in the populations of Russians 1 and 5, Veps, Komi 2, Khants, Nenets, Yakuts, and especially Kalmyks, than in the other populations (Table : TaqI A (rs1800497) and TaqI B (rs1079597). Linkage disequilibrium between these SNPs is high and significant in all 11 populations included in the HapMap project phase 3. The MboI and TaqI D loci are located between SNPs rs2587548 and rs2734836 investigated in the HapMap project. Linkage disequilibrium between SNP rs2587548 and the TaqI A locus is low in the Utah population of European ancestry and high but nonsignificant in the Chinese and Japanese populations (HapMap project phase 2), which is in agreement with our results.Pairwise linkage disequilibrium (D') was strong in most cases Table . HoweverST-based genetic distances. The resulting distance matrix was visualized using multidimensional scaling (MDS)  Figure . Two larIn the Asian cluster genetic distances were significant between Yakuts and Kalmyks (P < 0.00001), Khants and Yakuts (P < 0.00001), Khants and Kalmyks (P < 0.04661), Nenets and Yakuts (P < 0.00099), but not between Nenets and Kalmyks (P < 0.07438). The distance between Khants and Nenets was small and non-significant (P < 0.52033).All populations of the European cluster had significant genetic distances from the populations of the Asian cluster. The European-1 and European-2 clusters were quite homogeneous; most distances were non-significant within the former, and all were non-significant within the latter. Only Russians 1 occupied distinct position within the European-1 cluster. In contrast, most pairwise genetic distances between the two subclusters were significant. Exceptions included Veps, which had no significant distances from the populations of the European-2 cluster, and Byelorussians 2, which, had a small number of significant distances from the populations of the European-1 cluster.The matrix of haplotype-based genetic distances was also compared with matrices of great circle geographical distances (data not shown) to assess the possible effect of isolation by distance. The matrices including all 17 populations were highly correlated according to the Mantel test . However, correlation of geographical and genetic distances was not observed among the populations of the European cluster, i.e., after exclusion of Khants, Nenets, Kalmyks , and Yakuts . The use of more realistic distance measurements around geographical barriers, such as the Azov Sea, the Caucasus, and some parts of the Ural Mountains had little effect on results of the test . Thus, isolation by distance is not a likely cause of the genetic variation observed in the East European Plain.ST-based genetic distances ; Uralic-speaking Veps, Komi 1 and 2. The populations of this cluster had the following haplotype frequencies: 0.50-0.65 for the "European" haplotype B2-D1-A2, 0.21-0.31 for the "worldwide" haplotype B2-D2-A2, 0.08-0.25 for the "East Asian" haplotype B1-D2-A1. Some populations of the East European Plain  fell into the European-2 subcluster, which is characterized by the following haplotype frequencies: B2-D1-A2, 0.37-0.52; B2-D2-A2, 0.27-0.45; and B1-D2-A1, 0.04-0.25. Two European subclusters can be differentiated on the basis of their B2-D2-A2 and B2-D1-A2 frequencies . This is also evident from Figure CT 0.018, P < 0.00001, Table CT 0.027-0.045, Table A low but highly significant level of population differentiation was observed between the European-1 and -2 subclusters  and grouped into the Intermediate-1 subcluster with Sino-Tibetan-speaking Riang and Tripperah from India, with Melanesians and Micronesians . They had the following frequency pattern: B2-D1-A2, 0.04-0.14; B2-D2-A2, 0.44-0.53; and B1-D2-A1, 0.34-0.46.ST value, 0.11413  Plain is a region in which peoples of the Indo-European and Uralic language families have come into contact over an extended period. Uralic-speaking peoples have the longest validated archaeological record in this region . The mosST value for the European cluster (0.013). However, some populations were characterized by a large percentage of significant genetic distances from the other populations of the cluster. Most such populations fell into the so-called European-2 subcluster defined by cluster analysis; the 'core' subcluster was termed European-1, and 59% of genetic distances between populations of the two subclusters were significant. European-1 and European-2 subclusters  fell into a single large cluster termed European. Many populations within this cluster are indistinguishable with our genetic marker, i.e., genetic distances between them were not significant, which is in agreement with the low Fster 0.01. Howevers Figure , which iThe European-2 subcluster includes two Middle Eastern populations (Jews 2 from Yemen and Druze from Israel), two Uralic-speaking populations (Finns and Komi 3), also four Slavic-speaking populations , and the Altaic-speaking Chuvash. All these linguistically and geographically distant populations are differentiated to some extent from the core of the European cluster, the European-1 subcluster, because of a relatively high B2-D2-A2 frequency.The B2-D1-A2 and B1-D2-A1 haplotypes apparently have centers of dispersal in Europe/West Asia and East Asia, respectively . The B2-B2-D2-A2 is the predominant haplotype in contemporary populations of Middle Eastern origin . Populations of Levant began to disperse into Europe about 50,000 YBP ,22. PeopThus, on a global scale, the B2-D2-A2 frequency possibly reflects genetic features of the first Eurasian settlers, e.g. in Jews 2 from Yemen and Druze from Israel, and, on a local scale, genetic features of Uralic-speaking populations, e.g., in Finns and Komi 3, Khants and Nenets. Therefore, the other members of the European-2 subcluster, Byelorussians 2, Russians 4, 5, and 6, and Chuvash, probably have a certain level of Uralic admixture. Moreover, according to other genetic and historical data, these populations have Uralic substratum.Studies on mtDNA -34 and YRussians 5 (Oshevensk) were most closely associated with Finns and Chuvash according to the MDS results Figure . In the According to archaeological data, the Arkhangelsk region (including Mezen' and Oshevensk) was populated by Uralic tribes in the Middle Ages ; see Fig. RussianThe Uralic genetic substratum is appreciable not only in the Northeast of the East European Plain but also in its northwestern part, for example, in the Pskov  and NovgRussians 1 (Andreapol') and Uralic-speaking Veps are close to Uralic-speaking Komi 2 (Obyachevo) on the MDS plot Figure . All theKomi populations demonstrate remarkable heterogeneity according to various marker systems. For example, Komi-Permyaks and Komi-Zyrians have rather different mtDNA haplogroup frequencies but both have a relatively high U4 frequency . In our B2-D2-A2 haplotype frequencies in Khants (0.48-0.49) and Nenets (0.48) fell within the range typical of the Asian-1 subcluster, 0.44-0.53. Therefore, the high frequency of the B2-D2-A2 haplotype in Uralic-speaking Khants and Nenets cannot be explained only by admixture of Europeans with typical East Asians; a combination of admixture and other processes such as gene drift or selection is a more likely explanation. The Khants and Nenets may have received the B2-D2-A2 haplotype both from the East European refugium and from East Asia, e.g., from Siberian populations of East Asian origin. One of such Siberian populations, the Yakuts, has a very high frequency of haplotype B2-D2-A2, 0.6-0.7. However, DRD2 haplotype frequencies in other Siberian populations of East Asian origin are unknown, and it is not possible to draw definitive conclusions about B2-D2-A2 frequencies in Siberia based on one ethnic group alone. Moreover, because Yakuts 1 exhibited low linkage disequilibrium between RFLPs, haplotype frequencies for this sample should be interpreted with caution. Yakuts 1 and 2 apparently do not belong to the cluster of typical East Asians, East Asian-1 , northern , and eastern parts (Russians 4 from Puchezh and Chuvash) of the East European Plain have relatively high frequencies of haplotype B2-D2-A2, which may reflect admixture with Uralic-speaking populations. Uralic genetic substratum in these regions, which were inhabited by Uralic-speaking tribes as late as the Early Middle Ages, was also shown by studies in which other genetic markers were used . Thus, the analysis of DRD2 haplotypes supports results on Slavic-Uralic admixture obtained using other markers, mainly neutral and sex-specific markers.The linguistic affiliations and geographical locations of the studied populations are shown in Table ). The locations of these polymorphisms are shown on the gene map  were obtained by venipuncture and collected into EDTA-coated containers. Informed consent was obtained from each individual. The research protocols and forms of informed consent have been approved by the Ethic Commission of the Medico-Genetic Scientific Centre of the Russian Academy of Medical Sciences . All individuals belonged to the native ethnic group of the region, i.e., their lineage in the region extended for at least two previous generations, and were unrelated to each other. DNA was isolated from leucocytes by proteinase K treatment and extraction with phenol-chloroform . Each DNp Figure . All end. Linkage disequilibrium values (D') between polymorphic loci were calculated as suggested by Lewontin [ST values [The general strategy of statistical analysis was similar to that used in the work of Poloni et al. . Allele Lewontin . FrequenLewontin  implemenT values  calculatT values .. Statistical comparison of haplotype frequencies in population groups was performed using the Kolmogorov-Smirnov test implemented in XLSTAT. Differentiation between population groups defined by MDS and cluster analysis was estimated using the analysis-of-molecular-variance approach, AMOVA [ST distances between haplotypes were used, i.e., all haplotypes were considered equidistant (a conservative scenario of pure drift). Significance of the genetic-structure indexes obtained with the AMOVA method was tested using a permutational procedure (1 × 106 permutations).Correlation of geographical and genetic distances was assessed using the Mantel test and XLSTAT version 2008.6.04 software (Addinsoft). Great circle geographical distances were calculated using the haversine formula; latitudes and longitudes were determined using Google Earth software. Multidimensional scaling (MDS) and cluster analysis with the unweighted pair-group average (UPGA) method were performed using STATISTICA version 6.0 h, AMOVA , implemeOVF carried out the polymorphism typing, performed the statistical analysis and drafted the manuscript. OIL participated in the polymorphism typing. AVK participated in the study design and helped with the statistical analysis and manuscript drafting. LAT, VAS, and AIM carried out sample collection and participated in the study design. SAL conceived of the study, participated in its coordination and helped to draft the manuscript. All authors read and approved the final manuscript.Frequencies of TaqI B-TaqI D-TaqI A haplotypes in the study populations and in reference populations. This is an extended variant of Table Click here for file


The calcium assimilation hypothesis suggests that carriers of the lactase persistence allele(s) (LCT*P) are favoured in high-latitude regions, where sunshine is insufficient to allow accurate vitamin-D synthesis. In this work, we test the validity of these two hypotheses on a large worldwide dataset of lactase persistence frequencies by using several complementary approaches.The lactase enzyme allows lactose digestion in fresh milk. Its activity strongly decreases after the weaning phase in most humans, but persists at a high frequency in Europe and some nomadic populations. Two hypotheses are usually proposed to explain the particular distribution of the lactase persistence phenotype. The We first analyse the distribution of lactase persistence in various continents in relation to geographic variation, pastoralism levels, and the genetic patterns observed for other independent polymorphisms. Then we use computer simulations and a large database of archaeological dates for the introduction of domestication to explore the evolution of these frequencies in Europe according to different demographic scenarios and selection intensities.gene-culture coevolution is a likely hypothesis in Africa as high LCT*P frequencies are preferentially found in pastoral populations. In Europe, we show that population history played an important role in the diffusion of lactase persistence over the continent. Moreover, selection pressure on lactase persistence has been very high in the North-western part of the continent, by contrast to the South-eastern part where genetic drift alone can explain the observed frequencies. This selection pressure increasing with latitude is highly compatible with the calcium assimilation hypothesis while the gene-culture coevolution hypothesis cannot be ruled out if a positively selected lactase gene was carried at the front of the expansion wave during the Neolithic transition in Europe.Our results show that  Its activity strongly decreases after the weaning phase in most humans. However, in many European individuals and in people from various populations of other continents, lactase is still active throughout adult life gene-culture coevolution (gcc) calcium assimilation  gcc model, while no evidence for the other hypotheses was found. Compatible with this theory, Coelho et al. −13,910*T originated in Eurasia before the Neolithic Era, and observed a significant departure from neutrality of this variant in the few populations examined. On the other hand, these studies do not explain the significant correlation found between LP frequencies and latitude in Europe cal model.The particular distribution of lactase persistence throughout the world indicates that this trait evolved under strong positive selection gcc and cal, using a large database of LP frequencies . However, this result was merely due to a sharp contrast existing between LCT*P frequencies observed in Europe (high to very high) and those observed in sub-Saharan Africa (low to very low), rather than to a south to north continuum of frequencies supporting an isolation by distance model . The correlation between LCT*P frequencies and longitude was very low but negative and significant , which was reflecting the higher frequencies observed in Europe and Africa, compared to East Asia. Globally, while these results were not supporting any peculiar model of natural selection, they were indicating that the evolution of LCT*P was probably not purely neutral.When all 115 worldwide populations were considered, a weak but significant (at level 5%) correlation was found between genetic and geographic distances (gcc hypothesis as we did for Africa (see below) due to a lack of data on pastoralism levels in Asia. Note also that for Oceania and the Americas, the number of available samples was too low  to get a representative pattern of LCT*P frequencies.When looking at continental scales, we did not observe any significant correlation between genetic and geographic variation in Asia , but we have found a positive and highly significant correlation with latitude . These results indicated that higher LCT*P frequencies were found in the north of the continent. Interestingly, this was where most of the African pastoral populations were located. We also calculated a linear correlation coefficient between LCT*P frequencies and pastoralism levels (PL) for 25 African populations (out of 38) for which PL data were available , a second group composed by the Tutsi , various populations of Beja and the Twaregs , and a fourth group composed by all other populations . A significant CTF of 0.44 (p<0.00001) was associated to this structure and was much higher than the SCF value . This confirmed that the particular pattern of LCT*P frequencies in Africa might be explained (at least in part) by variation in the level of pastoralism.In Africa, the distribution of LCT*P frequencies revealed an interesting pattern. Their correlation with longitude was not significant at the 1% level  while the correlation with longitude was not. The correlation with latitude was even higher when looking only at Indo-European populations (n = 26), which are considered by some authors r = 0.84, p<0.0001). Therefore, selection on LCT*P would have occurred during the Neolithic Era, and mostly in populations from northern Europe. This fits the cal hypothesis whereby a vitamin-D deficiency due to insufficient sunlight in Northern Europe would have been compensated by high levels of lactose assimilation. However, we cannot exclude that the correlation found between LCT*P frequencies and latitude was due to population history rather than to natural selection, since human migrations created genetic clines along a southeast-to-northwest direction for many classical markers in Europe In Europe, a positive and significant correlation has been found between genetic and geographic distances with 33 populations considered  and highly significant correlation between genetic and geographic variation for both RH LCT (0.2<r<0.62). They suggested that LCT variation in Europe was shaped at least in part by the same evolutionary forces as for RH and HLA, e.g. those linked to population history. On the other hand, they did not allow us excluding the influence of natural selection. To further investigate this question, we used a simulation method.To tackle this last question, we first studied the correlation, in Europe, between s) for this allele, and we looked for the s value that explained at best the observed frequency  in each population. Four scenarios were simulated combining the two different demographic models described below (CD and DD) for the Neolithic transition with the two alternative hypotheses for selection at LCT*P . The goal was to evaluate which of these combinations fitted at best the observed data.We simulated independently for Indo-European and a few populations from the Near East the evolution of the frequency of a dominant allele associated to LP since the Neolithic transition in those populations . We repes) in all four scenarios. More precisely, the absence of selection was compatible with LCT*P frequencies in all populations located south of latitude 41°N, except Valencia . On the contrary, higher coefficients were required in north-western populations for all scenarios tested, except for Poland  had, by far, the highest probability of obtaining the observed data (99.1%), followed by CD/cal (0.9%). Both gcc scenarios (DD/gcc and CD/gcc) had an associated probability equal to 0%. The average selection coefficient for the 1,000 best simulations was 1.2% with a 95% percentile between 0.8% and 1.8%. Given the fact that all those estimations had been obtained with the cal selection model (which makes the assumption that selection increases with latitude), s thus varied across populations from 0% in the southern populations, until a maximum of 1.8% in the most Northern populations. From these observations, the interval [0.8%–1.8%] can be taken as a plausible range for LCT*P selection coefficient in Europe. Note that the estimated values of the selection coefficients were smaller when simulating the Neolithic demic diffusion (DD) compared to the cultural diffusion (CD). This may be explained by the “surfing phenomenon”, which describes how the frequency of a neutral or positively selected mutation may increase during a demographic expansion, if this mutation is carried by the pioneers Among the four tested scenarios, Demic Diffusion with calcium assimilation (lbk” selection hypothesis (DD/lbk and CD/lbk) to see if the resulting simulations better fit the data than the four previously tested scenarios. Under the lbk selection hypothesis, we assumed that the selection coefficient was higher in the populations belonging to the former LBK area lbk” populations from our database  and values between 0% and 0.8% in all other populations . 360,000 new simulations were performed under this new scheme. The Approximate Bayesian Computation approach (ABC) estimated a probability of 0% for both DD/lbk and CD/lbk scenarios compared to the four previously simulated ones. We can thus not conclude that the hypothesis of a higher selective pressure on LP in central and north Europe due to more intensive cattle exploitation is a better explanation of the current distribution of LP in Europe, compared to the cal and gcc hypotheses.Alternatively to a selection increasing with latitude, Gotherstrom et al proposed that dairy farming was more intensively developed in Central European populations belonging to the early Neolithic Linearbandkeramik culture (LBK) gene-culture coevolution (gcc) calcium assimilation  Analyses performed on the large dataset gathered in this work did not show any strong correlation between LCT*P genetic distances and geographic distances among populations at the worldwide scale. This suggests that the evolution of the lactase gene cannot be simply explained by a model of isolation by distance. Indeed, the significant correlation coefficients found between LCT*P frequencies, latitude and longitude are better explained by a pronounced genetic differentiation between a few geographically distant populations than by continuous patterns of frequency variation. We thus analysed our data regarding the two main selective hypotheses proposed to account for the special pattern of LCT*P distribution, gcc hypothesis is favoured by the high and significant correlation found between pastoralism levels and LCT*P frequencies in 25 African populations, as well as by the Samova analysis, which reveals a significant genetic boundary between pastoral populations and the others. This result agrees with Holden and Mace's conclusion LCT, RH and HLA lead to quite similar results, it was possible to conclude to a significant effect of population history, but not to exclude a significant selection pressure on the lactase gene. We thus simulated the evolution of LCT*P according to various parameters .The s (0.8%–1.8%) are compatible with previous studies (i.e. 1.4–1.5%) DD) scenario associated to the cal selection model is the most compatible with the observed data (>99%). It is important to note that the DD scenarios tested here cannot be assimilated to the classical wave-of-advance model The results of our simulation show that the current frequencies of LCT*P in the North-western part of Europe are too high to be only due to genetic drift. We estimated the 95% upper limit for LCT*P selection coefficients as large as 3% in some populations . Our avecal hypothesis is possible because simulations strongly suggest a signature of selection along the latitude, but one can imagine another cause of variation for the selection coefficient, such as nutritional selection due to specific diets or economies in different regions. Indeed, while North-western European populations were the last  to introduce cattle domestication, dairy farming was more intensively developed in those regions While a strong selective effect on LCT*P is suggested in Northern Europe, its cause remains to be clarified. Actually the Of course, it is important to keep in mind the limits of the model tested in this study. The choice of parameters was very difficult and we had to resort to some approximations. Using a starting value of 1%, we made the assumption that LCT*P was present but not frequent before the Neolithic transition in Europe, as suggested by the rare data available cal, gcc and lbk) are strongly linked to the Neolithic diffusion. Both a selection coefficient increasing with latitude and a demographic spread of LCT*P would lead to identical signatures into the northern European populations compared to the southern ones. Consequently it is very difficult to disentangle the effects of demography from those of selection. One key to differentiate with more confidence the alternative selective hypotheses would be to get more samples from the Iberian Peninsula because in this particular region, LP frequency is expected to be very low under cal or lbk hypotheses and certainly higher under gcc. Another key would be to get more samples from other regions of the world, especially from Asia because cross-continental analyses will bring a more precise understanding of LP diffusion.Our study highlights the difficulty to evaluate the probability of the alternative hypotheses of selection for LP in Europe because a correlation between LP frequency and latitude is expected under all of them and also because they are not mutually exclusive. Indeed, as the basic assumption is that the selective pressure on LP started at the same time than milk consumption, all three selection hypotheses tested here  are favoured in high-latitude regions but eventually by another reason that still needs to be determined. Moreover, we show that the European Neolithic transition played an important role in the diffusion of LP over the continent as revealed by the simulation results but also by the facts that: i) the correlation between LCT and other independent loci submitted to distinct evolutionary forces (RH and HLA) is significant; ii) the sample from Valencia, in the Western part of Europe, is located at low latitude (below 41°N) but has an estimated selection coefficient above 0 , 7 American and 3 Oceanian 2 after ingestion of lactose Depending on the sources consulted for this study Pastoralism levels (PL) are used in our analysis of lactase persistence variation in Africa. These values, taken from Murdock's Ethnographic Atlas http://www.getty.edu). If no locality was indicated in the literature, we used the central coordinates of the corresponding country. Those coordinates were also used to calculate geographic great-circle distances between populations using a homemade program The geographic coordinates of each population were used for the correlation analyses. We determined those coordinates from population localities by using the “Getty Thesaurus of Geographic Names” . The group structure is modified 10,000 times and the largest CTF value is retained as the best structure. The SCF value (proportion of genetic variation explained by differences between populations within each group) associated to the optimal structure, as well as p values for both indexes, are also given.The Samova analysis r) between allele frequencies and either latitudes or longitudes, and we assessed its significance by using a Student's t test t test due to a possible non-independence between the data points , we chose a significance level of 1% (rather than 5%) to reduce type I errors. We also performed Mantel tests We calculated a linear correlation coefficient . For all other populations, this mean was either kept constant or given different values according to the scenario simulated .The size of the populations: the demographic growth was logistically regulated within each population (see Supplementary Information). Moreover, results were also obtained for a constant population size  was tested in order to find which value(s) best explained the observed LCT*P frequencies. Depending on the selective model considered, the tested selection coefficient was either kept constant for all simulated populations (gcc model) or increased with L, a factor related to the latitude of a population , in which a combination of two selection coefficients were tested to represent the hypothesis that dairy farming was more intensively developed in Central European populations belonging to the early Neolithic Linearbandkeramik culture (LBK) (see The LBK) see . To thisnumber of iterations: the program was run 10,000 times for each selection coefficient tested to obtain an empirical distribution of frequencies expected under each scenario.The s giving the highest proportion of simulations “compatible” with the observed frequency  and its 95% CI were determined (See Supplementary Information for further details).Following Currat et al. In order to consider possible uncertainties about the exact impact of the Neolithic transition on the European genetic pool CD): this model makes the assumption that the Neolithic transition was a cultural transmission of technologies without large movement of populations (by opposition to the Demic Diffusion model below). It implies that the know-how of the early farmers travelled over Europe from the Near East but not their genes. Consequently, all populations are simulated without any connection between them (i.e. possible migrations). In this case, our computer simulation program draws an initial frequency of LCT*P from a normal distribution with a mean equal to 1% in every population. We thus assumed that LCT*P was rare all over Europe and the Near East (average frequency of 1%) before the Neolithic transition. According to this model, simulated populations only differ among each other by the duration of selection since the introduction of farming : this model makes the assumption that the Neolithic transition was mainly due to the spread of early farmers from the Near East to the North and Western edges of Europe. It implies that the genes of the first farmers travelled over Europe during the Neolithic transition. To simulate this spread of genes, populations are connected to each other along the likely route of the Neolithic diffusion .These two alternative demographic scenarios were simulated in combination with the three selection hypotheses Initially, four alternative extreme scenarios were simulated:DD/gcc: Demic diffusion with gene-culture coevolution hypothesis; the frequency of LCT*P is transmitted from one population to the next  for the LBK group and a smaller s (0–0.8%) for all other populations.esis see ; The freesis see  and a coCD/lbk: Cultural diffusion with dairy farming hypothesis; There is no migration between populations and a higher selection coefficient is simulated in populations belonging to the LBK culture than in other populations.lbk) using the Approximate Bayesian Computation approach (ABC). For each simulation (whatever scenario is considered), Euclidian distances between simulated and observed frequencies are calculated according to Beaumont et al. We formally compared the 4 simulated scenarios (then 6 including Electronic Supplementary Information S1(0.10 MB RTF)Click here for additional data file.Figure S1Schematic view of the connections between populations according to the Demic Diffusion (DD) model. Arrows represent links between Neolithic source populations and target populations. Numbers represent the generations elapsed since the start of the Neolithic transition in the Near-eastern populations. For each population, the red curve represents the logistic demographic growth, and the green curve represents the evolution of LCT*P frequency. Note that these are not the simulated curves but schematic curves, as the LCT*P frequency evolves in many different ways depending on the parameters. Population names are as in (1.59 MB TIF)Click here for additional data file.Figure S2Results for simulations run under constant population size of 10,000 individuals. Selection coefficients required to fit the observed estimates of lactase persistence frequencies, according to the four scenarios simulated: DD/gcc; DD/cal; CD/gcc; CD/cal see . Bars re(6.83 MB TIF)Click here for additional data file.Table S1List of samples tested for lactase persistence phenotype (LP) and used in this study.(0.58 MB RTF)Click here for additional data file.Table S2Maximum likelihood values and associated selection coefficients, according to the simulated scenarios.(0.25 MB RTF)Click here for additional data file.