MTHFR) in 9population groups from Northern Eurasia and populations of the internationalHapMap project was investigated in the present study. The data suggest that thearchitecture of LD in the human genome is largely determined by the evolutionaryhistory of populations; however, the results of phylogenetic and haplotypeanalyses seems to suggest that in fact there may be a common “old”mechanism for the formation of certain patterns of LD. Variability in thestructure of LD and the level of diversity of MTHFRhaplotypescause a certain set of tagSNPs with an established prognostic significance foreach population. In our opinion, the results obtained in the present study areof considerable interest for understanding multiple genetic phenomena: namely,the association of interpopulation differences in the patterns of LD withstructures possessing a genetic susceptibility to complex diseases, and thefunctional significance of the pleiotropicMTHFR gene effect.Summarizing the results of this study, a conclusion can be made that the geneticvariability analysis with emphasis on the structure of LD in human populationsis a powerful tool that can make a significant contribution to such areas ofbiomedical science as human evolutionary biology, functional genomics, geneticsof complex diseases, and pharmacogenomics.The structure of the haplotypes and linkage disequilibrium (LD) of themethylenetetrahydrofolate reductase gene ( Genetic variability underlies the human phenotypic variation and plays a significantrole in explaining the differences between individuals in their susceptibility tocomplex diseases (CD) and in determining the metabolic pathways involved in thedevelopment of pathological processes. Single nucleotide polymorphisms (SNPs)represent the most common type of genome variability. Thanks to the efforts of theInternational SNP Consortium, ~ 10 million SNPs with an approximate density of1 polymorphism per 300 bp have currently been identified [At the time of writing, the architecture of LD in the human genome is the subject ofactive discussions and research 2–.It has Along with the whole-genome patterns of LD that have been investigated in moderngenome-wide studies 11–, thestrMTHFR )gene was selected for use as a locus to investigate the LD structure in populationsof various ethnic origins. According to the results of numerous studies, thepolymorphic variants of this gene are associated with the development of a number ofcomplex conditions, such as cardiovascular and oncological diseases, neural tubedefects, abnormal pregnancy, as well as other pathological processes. The enzymemethylenetetrahydrofolate reductase catalyzes the only intracellular reaction offormation of 5-methyltetrahydrofolate, which is required for homocysteine (HC)conversion into methionine. A decrease in the activity of this enzyme is frequentlycaused by mutations in the MTHFR gene and results in theaccumulation of HC and the development of moderate hyperhomocysteinemia.In this study, the methylenetetrahydrofolate reductase . The total sample included837 individuals. The populations studied represent two Eurasian race types,Caucasian and Mongoloid, and speak languages of four linguistic families . Only individuals, nonmetisized in atleast three generations, participated in the study. Ethnicity, genealogy, andmembership of the individuals in sub-ethnic groups (in some cases) were ascertainedon the basis of questionnaires. The sample of Tuvinians was collected in the TuvaRepublic (settlement of Bai-Taiga). Two Buryat populations were examined in theBuryat Republic (the city of Ulan-Ude and settlement of Khuromsha). The Yakut samplewas collected in the settlement of Dyupsya located in the eastern Ust-Aldan ulus(district) of Sakha Republic (Yakutia). The Central Asian populations arerepresented by Kyrgyz. Two Kyrgyz samples were made up of northern  and southern (the city of Osh) populations of the Republicof Kyrgyzstan; they belong to different sub-ethnic groups. The Russian sample wasrepresented by residents of Tomsk. The Ket population was collected in thesettlement of Kellog, Turukhanskii district, Krasnoyarsk krai. The Khanty populationwas collected in the settlement of Russkinskii, Khanty-Mansi Autonomous Okrug.The data presented in this study were obtained via a molecular genetic DNA analysisof individuals from nine population groups inhabiting different regions of NorthernEurasia and belonging to seven ethnic groups , Chinese , Japanese ,and Yoruba  populations presented in the HapMapdatabase .PolymorphismsMTHFR gene were selected for use asmarkers to study LD patterns: rs3753588, rs2066470, rs17037397, rs7533315,rs4846052, rs1801133 (C677T), rs6541003, rs2066462, rs1801131 (A1298C), rs17375901,rs2274976 (G1793A), and rs1537516 . Table2 briefly characterizes the studied loci of the MTHFR gene. Ten of 12 SNPs resulted from the transitions (3 A→G and7 C→T), two SNPs resulted from the transversions (A→С). Theselected polymorphic variants are distributed in a relatively uniform manner overthe gene sites ; the minor allelefrequency in most loci is at least 5% (according to the data from the NCBIdatabase). Genotyping was carried out in accordance with the previously describedprotocols [18–The following 12 SNPs of the Methods for the statistical processing of the results2 . The block structure was determined using the Solid Spine of theLD algorithm [Statistical analysis was performed using conventional software packages: Statistica6.0, ARLEQUIN, and Haploview 4.0. The distribution pattern of the resulting data wasdetermined using the Kolmogorov–Smirnov test; haplotype frequencies weredetermined using the EM algorithm. The LD between SNP pairs was assessed using theLevontin’s D’ coefficient and Pearson’s correlation coefficient rlgorithm  providedlgorithm . The rollgorithm , 24.Genetic diversity and haplotype structure at the MTHFR locus in populationsThe gene pool of modern human populations was formed as a result of sequentialevolutionary demographic processes: continuous evolution of genetic diversity inAfrica and population variance as modern humans migrated, with partial isolation andreduction of the gene flow in inverse proportion to the migration distance. Indifferent geographic areas, the populations have both a common and uniqueevolutionary history; the “fingerprints” of these histories can beobserved in the modern human genome as LD patterns 3, 25–.MTHFR gene SNPs toHardy–Weinberg proportions are presented in Table 3 . All 12 loci appeared to be polymorphic inalmost all the populations analyzed (with the exception of rs2066470 in the Ketpopulation). The minor allele frequency varied from 0 to 39%; seven SNPs  wereidentified in all the populations with a frequency higher than 5%. The resultingdata lie within the range of variations of allele frequencies and genotypes ofMTHFR polymorphisms which had been previously published andlisted in the databases of the Caucasian and Mongoloid populations. In all thesamples, the distribution of the genotype frequencies of almost all markers fittedinto the Hardy–Weinberg proportions . Low and medium heterozygosity values were observed in themajority of cases, which was consistent with the world data. The highestheterozygosity for the loci combination was detected in the Yakut population (0.28);the lowest heterozygosity value was recorded in the residents of the settlement ofKhuromsha (0.18). It is obvious that these values do not represent the SNPheterozygosity level of the populations examined, since the number of the loci takeninto account was too small. These values are of some interest as they provideinformation pertaining to the degree of polymorphism in the MTHFRgene. As for the deviation from the Hardy–Weinberg proportions observed inthis study, it could be a result of the shift in the estimated frequency values dueto the small size of the sample. On the other hand, the cases of a reliabledeviation of the distribution from the expected one may represent the specificity ofpopulation-genetic processes in the population, which can be associated both withthe parameters of the genetic-demographic structure of the population and with thelinkage with a functionally significant locus. We consider the latter reason to bemore plausible.The distribution of genotypes and the allele frequency, the observed heterozygosity,and the significance of goodness-of-fit of the MTHFR gene which has been best studied. The missense mutationin С677Т (substitution of cytosine by thymine at position 677) resultsin alanine replacement for valine in the enzyme catalytic domain. In individualshomozygous and heterozygous for the polymorphic allele, the in vitro activity of the enzyme is reduced by 70% and 35%, respectively. The677Т mutant allele frequency in world populations varies from total absence inthe Dendi tribe to 55% in Spanish populations [28–The С677Т (rs1801133) polymorphic variant is one of 12 SNPs in thetions 28–. In Russtions 28–, 32.In MTHFR gene. The enzymatic activity isreduced in individuals carrying the 1298С allele, although this reduction isnot as significant as that in the ones carrying the 677T allele. According to somestudies, MTHFR activity falls by 40–50% and a biochemical profile similar tothat in homozygous carriers of the 677T allele is observed in compound heterozygousindividuals [The А1298С transition rs1801131) resulting in the replacement of aglutamic acid residue by alanine in the enzyme regulatory domain is the secondcommon polymorphism in the ividuals . The lowividuals , 35.In  resultinMTHFR  gene SNP,rs2274976 (G1793А), impacts the HC level. This locus has been subjected toless investigation in comparison with С677Т and А1298С. Itis a known fact that the frequency of missense mutation G1793А varies from1.3% in the Ashkenazi Jewish population to 26.6% in the Indonesian populations ofJava [It has recently been ascertained that another  ofJava . It has  ofJava , 38.In MTHFR  locus should have beenformed due to the recombination events (see below).A total of 160 haplotypes were detected in our samples; the theoretically possiblenumber was 4,096. The maximum number of haplotypes was revealed in the Tuvinianpopulation (47); the minimum number was revealed in the Ket population (11). A highlevel of haplotype diversity was observed in most samples, bar the Ket, Buryat, andsouthern Kyrgyz populations. If each mutation that resulted in the formation of afixed polymorphic variant is assumed to be a unique event, and the mutation rate isassumed to be negligibly low, it should be postulated that only 12 haplotypes couldbe formed as a result of the mutation process. In this case, a considerable part ofthe haplotype diversity even in such a physically small genome region as anapproximately 20-thousand-bp long Fig.2 . The haplotypes with a frequency of more than 5% are referredto as the major ones. Three major haplotypes, GCCCCCACACGC, GCCCCTACACGC, andGCCTTCGCCCGC, were detected in all the populations studied, bar the Russian sample;the sum of their frequencies is more than 83% of the chromosomes observed in the Ketand Buryat populations, and more than 61% in the other samples. The degrees ofhaplotype diversity detected in the populations studied were different;nevertheless, all the samples contained identical major haplotypes, attesting to thefact that there can indeed be a common mechanism for the formation of the LDpatterns.The distribution of haplotypes occurring in the populations studied with a frequencyof over 2% is presented in Architecture of linkage disequilibrium for the MTHFR gene in certain Eurasian populations MTHFR genein 13 population samples is shown in  The maximum linkage between the SNPs studied wasdemonstrated for the southern Kyrgyz, Ket, Chinese, and Japanese populations. Inthese samples, all the allelic variants of the MTHFR gene underanalysis belong to the same haplotype block. A single block was also observed in theCaucasian individuals from the HapMap project; however, it did not contain thers2274976 and rs1537516 markers. Two blocks were detected in the northern Kyrgyzpopulation: the first one comprising three SNPs , the second one encompassing a 10-thousand-bp-long region and containingeight SNPs. Two blocks were also revealed in the Khanty population: the first onecontaining rs3753588, rs2066470, and rs17037397; the second one, identical to block№ 2 in the northern Kyrgyz population. Strong linkage between the first nineSNPs belonging to the first block (9-thousand-bp long), as well as that betweenrs2274976 and rs1537516 forming the second small block were revealed in the Buryatpopulation of the settlement of Khuromsha. A significant linkage between manypolymorphic variants was also detected in the Buryat population of the city ofUlan-Ude; however, two different blocks (3- and 8-thousand-bp long) were representedin the LD structure of this population. Three blocks were observed for the Russianpopulation: the first block was made up of two closely located SNPs (rs3753588 andrs2066470), the second block comprised five polymorphisms , whereas the third block was made up of fourSNPs .The structure of LD between the investigated loci of the Four small haplotype blocks consisting of two or three neighbouring SNPs can bedetected in the Tuvinian and Yakut populations. Two blocks were observed in theYoruba populations: the first one consisted of 2 thousand bp and containedrs2066470, rs17037397, and rs7533315; the second block was appreciably long (7thousand bp) and comprised four loci . Thus, the haplotype blocks (with lengths varying from 847 bp to 16thousand bp) were represented by several (3–6) major haplotypes, the sum ofwhich provided more than 90% of the chromosomes observed. The composition and sizeof the blocks varied depending on the population structure. It is particularlyremarkable that the functionally significant С677Т andА1298С polymorphisms were not linked in all the populations, bar thesouthern Kyrgyz and Ket samples, as well as populations from the HapMap project. Itshould also be noted that close linkage between the SNPs that form the second blockin the Yoruba population was observed in many populations.At the time of writing, the degree of haplotype diversity and length of linkageblocks in various genome regions have been identified in many human populations, 40. A cThe population-specific nature of the formation of LD patterns was confirmed in thisstudy. The strongest linkage  between the lociunder study was detected in the Ket, southern Kyrgyz, Chinese, and Japanesepopulations. Close linkage was also observed in the Buryat populations. Twoexplanations to the observed retention of block structure and length can beproposed: either common ancestors or positive selection; the latter frequentlyresulting in an increase in the length of the block containing a useful allele. Since tIt has been demonstrated that the length of LD patterns in the human genomedetermines the potential and design of association studies that use SNPs for themapping of the genes underlying complex indices. According to the currentestimations, the number of markers required for a LD-based genome scan of differentpopulations varies from 120, 000 to several millions and is attestable to thefollowing facts: the cost of genotyping is tremendous, and there can be problemswith the validity of the statistical conclusion. An assumption was made that thenumber of markers required for CD mapping will be considerably lower in populationswith a high degree of LD .Selection of tagSNPs in the MTHFR geneAccording to the estimations made by different researchers, the human genome containsmore than 7.5 million common SNPs with a minor allele frequency (MAF) of at least 5%, 53, whiMTHFR gene are selected . This is presumably becausethe gene is small and the polymorphic sites are located physically close to eachother. However, to achieve a prognostic significance of 99–100%, the number oftagSNPs needs to be considerably high. In our opinion, this fact is a significantdrawback of this algorithm.Two methods  were used in this study for tagSNPidentification. The genotype data are used in the STAMPA method, whereas no data onthe haplotypes and block architecture of the genome region under study are required. This alMTHFR gene were also identified in this studyusing the Tagger algorithm provided by the HaploView software [The tagSNPs in the software . An aggrsoftware . The tag MTHFR genewithin the samples. This fact was confirmed by the statistically significantcorrelation between the haplotype diversity and the variability of the number oftagSNPs . The problem of thepossible transfer of tagSNPs from one population to another is rather significantdue to the considerable topicality of association analysis using tagSNPs identifiedon the basis of the HapMap project data. An appreciably high prognostic significanceof tagSNP sets in several genome regions selected for CEU, CHB, and JPT with regardto the Caucasian and Mongoloid populations has been recently shown in a series ofexperiments [58–The prognostic significance of this set of tags with respect to the entirepolymorphism array under study is equal to 100%. According to the results obtained,unequal tagSNP sets were observed in different populations at the specifiedthreshold of prognostic significance, which was presumably caused by the variationof the LD structure and the haplotype diversity of the ments 58–. Neverthments 58–, 61, 62.ments 58–.Table 5 that theminimum number of tagSNPs at a prognostic significance of 90–95% is determinedusing the STAMPA algorithm, whereas the Tagger method is more efficient at aprognostic significance of 98–100%.A comparative analysis of the efficiency of STAMPA and Tagger algorithms depending onthe level of prognostic significance was carried out in this study. It is clear fromTagSNPs are widely used in various genetic studies as a tool that efficientlyrepresents genetic diversity. Nevertheless, the quality of the selected tagSNPsdepends on the original array in which they were characterized. If the originalmarker density was low, the selected tagSNP will “capture” lessinformation than is required for the analysis. The required marker density in theinitial data array varies within different genome regions depending on a number offactors, such as the recombination level, LD structure, SNP frequencies, mutationcharacter, and the demographic history of a population .Phylogenetic analysis of the relationships between haplotypes at theMTHFR locus and the assessment of the selective neutrality of the polymorphismsunder studyFig. 5 ). This factsupports the hypothesis of the Recent African Origin of modern humans. The fact thathaplotypes № 4 and 7, which are the closest ones to the ancestral variant,occur in the Yoruba population with a significant frequency provides further potencyto this hypothesis.The phylogenetic analysis of the relationships between the haplotypes, which aredetermined on the basis of diallelic markers and characterized by a frequency ofoccurrence in the total sample of more than 0.1%, were carried out by constructingphylogenetic trees (networks) of haplotypes using the median network algorithmimplemented in the Network software. The haplotype consisting of ancestral alleleswas used as the ancestral haplotype (the data was taken from the NCBI database). Theresults obtained attest to the fact that all the haplotypes observed in the humanpopulations under analysis originated from a common ancestral variant, which occursin the Yoruba population (with a frequency of approximately 12%) and in the Russianand southern Kyrgyz populations (with a frequency of 1%) . Cluster A is represented by10 haplotypes, two of which  are the most widespread  and were revealed in all the populations studied. It should be noted thatthe tree structure in this cluster is of a strongly pronounced star-shapedcharacter, which obviously indicates a sudden population expansion in thedemographic history. Cluster B contains eight haplotypes, including haplotype№ 3, which is the third most frequently occurring haplotype and is representedin all the populations, bar the Yoruba sample. It is worthy of note that haplotypes№ 8, 12, 17, 18, 21, 14, 23, 27, 11, 24, 19 of clusters A and B located at thebranch tips of the phylogenetic tree occur only in northern Eurasian populations andpresumably emerged relatively recently. Haplotypes belonging to cluster C contain alarge number of mutant alleles; they are likely to have been formed as a result ofrecombination events.All the haplotypes observed lie within six mutation steps from their common ancestorand can be subdivided into three major clusters, A, B, and C, which are formed fromthe CC –8mutations per locus per generation) was set for all the polymorphic variants [MTHFR  gene was equal to 314,000±135,000years. The median haplotype trees obtained by dividing the MTHFRgene into two blocks  were analyzed in order to assess theaccuracy of the results. This analysis was performed because of the fact that thespecified regions of the MTHFR gene occur in different LD blocks inmost populations, including the Yoruba sample. Therefore, an independentphylogenetic analysis of two blocks of the MTHFR gene may be lessprone to the possible errors added because of recombination. Finally, thecoalescence time for the first block turned out to be equal to 350,000±188,000years; the age of the ancestral haplotype of the second block was assessed as306,000±188,000 years.Since the SNP mutation rate and their diversity observed in the modern populationscan be assessed, it is possible to calculate the time of origin of this haplotypelineage. It has been known that diversity assessments based on the phylogeny of DNAnucleotide sequences are independent of demographic processes  and suitvariants . The genAlthough the phylogenetic analysis that was performed is an appreciably powerful andefficient tool to characterize the evolutionary relationships between thehaplotypes, it should be noted that particular care needs to be taken wheninterpreting the absolute assessments of the coalescence time , since the key parameters underlying these assessments include themutation rate and the absence of recombination within the genome region underanalysis.According to the results of a number of studies , 66, a rMTHFR  gene polymorphismsusing the Ewens–Watterson test, neutrality deviation was detected only atrs4846052 and rs6541003 in the Russian samples and 0.84 ( р  = 0.041), respectively) and the Caucasianpopulations from the HapMap project ( F  = 0.79 (р  = 0.021) and F  = 0.98 (р  = 0.030), respectively). All three functionallysignificant SNPs causing an increase in the HC blood level turned out to beselectively neutral. This can presumably be attributed to the fact that even acertain variation in phenotype can be selectively neutral, provided that it does notaffect reproductive efficiency [MTHFR  gene in the Israeli, Japanese, andAfrican populations. According to these data, the 677T allele is found in thehaplotypes with a selective advantage [When analyzing the selective neutrality of ficiency .Neverthficiency . Furtherdvantage .It is a known fact that the 677T allele frequency in world populations is veryheterogeneous, varying from complete absence in the representatives of Africantribes to 55% in Spanish populations 28–. MoreoveF s testwas negative and statistically significant in the populations of Tuvinians ,northern Kyrgyz , Yakuts , and Khanty , attesting to either apossible effect of negative selection on a specific genome region in thesepopulations or population expansion. However, the mixing of these populations withneighboring ones could have also resulted in an increase in DNA diversity and theF s test.The role of selection pressure in the formation of LD patterns and the level ofgenetic diversity in populations was assessed using the standard statistical testsof neutrality proposed by Tajima and Fu ,24. TheMTHFR gene in the populations of Tuvinians, northern Kyrgyz,Yakuts, and Khanty. It should be noted that these four populations are characterizedby a higher level of haplotype diversity (more than 70%) and low LD level among allthe groups under analysis.In general, the data obtained attest to the fact that stabilizing selection has animpact on the rs4846052 and rs6541003 loci in the Caucasian populations from theHapMap project and that the negative selection possibly affects specific haplotypesof the Genetic differentiation and relationships between the populations understudyFst ) in the total sample with respect to each of the markers selected arepresented in Fig. 6 . Itshould be noted that all the polymorphic variants studied demonstrated a reliabledifferentiation. It has been shown that the differences in allele frequencies at thers4846052, rs1801133, rs6541003, rs2066462, rs1801131, and rs2274976 loci contributemost to interpopulation diversity. The lowest degree of interpopulation diversity istypical of rs17375901. The level of genetic differentiation of the populations understudy with respect to the allele frequencies of the 12 MTHFR geneSNPs under investigation was equal to 0.015, and equal to 0.017 with respect tohaplotype frequencies. The assessment was carried out using the Fst coefficient.The data relating to the degree of gene differentiation  and Mongoloid  populations, as well as the close geneticrelationship between the Yoruba and HapMap Caucasian populations, and the Chineseand Japanese populations. It is of interest that the northern and southern Kyrgyzare located in different clusters at a significant genetic distance. The resolutioncapacity of the tree constructed based on only 11 loci is insufficient to make anydefinite conclusions relating to the details of the genetic relationships betweenthe populations under study and only attests to the degree of genetic variationsbetween them with respect to the  MTHFR locus.The phylogenetic analysis of the genetic relationship between the populations wascarried out based on a data array for 13 populations. The resulting dendrogramshowing the genetic relationships between the populations is presented inMTHFR locus in nine populations inhabiting northern Eurasia was based on aconception assuming that the human genome has a block structure. The data relatingto the Caucasian, Chinese, Japanese, and Yoruba populations obtained in the HapMapproject were used as a basis for population comparisons.This investigation of the architecture of linkage disequilibrium of the MTHFR gene invarious ethnoterritorial groups inhabiting northern Eurasia was revealed in thisstudy. In addition, similarity in LD architecture was detected for certainpopulations, attesting to the role of evolutionary history in the organization ofthe block structure of LD.A population-specificity of the LD structure of the MTHFR gene. The phylogenetic analysis of haplotypes showed thatall the haplotypes observed in the populations under study originated from a commonancestral variant, thus attesting to the significant role of recombination in thegeneration of genetic diversity in the  MTHFR locus and thepossibility of a sudden population expansion. The age of generation of the diversitywith respect to 12 SNPs of the MTHFR  gene was314,000±135,000 years.Different degrees of haplotype diversity were established for the populations understudy; nevertheless, identical major haplotypes were identified in all the sampleswith the exception of the Yoruba population, attesting to the fact that there mayhave been a common mechanism of formation of LD patterns in theMTHFR gene in populations of Tuvinians, northernKyrgyz, Yakuts, and Khanty, which are characterized by the highest level ofhaplotype diversity (over 70%) and a low level of LD among all the groups understudy, were also obtained in this work. All the investigated SNPs of theМTHFR gene showed reliable differentiation. Data onseparate loci demonstrated that the variations with respect to allele frequencies atloci rs4846052, rs1801133, rs6541003, rs2066462, rs1801131, and rs2274976 contributemost to the interpopulation diversity. The lowest degree of interpopulationdiversity is typical of the rs17375901 marker, which is monomorphic in the Yorubapopulation.Data supporting the impact of stabilizing selection on the rs4846052 and rs6541003loci in HapMap Caucasian populations and that of negative selection on certainhaplotypes of the MTHFRlocus in particular, is of a population-specific character and is to a significantextent determined by the evolutionary history of the population. It is obvious thatthe ethno-specific variability of haplotype blocks should be taken intoconsideration when analyzing the structure of LD and selecting tagSNPs duringgenetic mapping of common diseases both at the whole-genome level and forassociation studies, when a disease-associated variant is detected on the basis ofits linkage with the nearest sites in a relatively narrow genome region. Subsequentinvestigation of the character of the genetic diversity and linkage disequilibriumin the genome of specific geographical, ethnic, or population groups will enable toreconstruct the genetic history of populations and detect the footprints of thenatural selection associated with the adaptive variability. Thus, the architecture of LD in the human genome, and in the


Previous studies that pooled Indian populations from a wide variety of geographical locations, have obtained contradictory conclusions about the processes of the establishment of the Varna caste system and its genetic impact on the origins and demographic histories of Indian populations. To further investigate these questions we took advantage that both Y chromosome and caste designation are paternally inherited, and genotyped 1,680 Y chromosomes representing 12 tribal and 19 non-tribal (caste) endogamous populations from the predominantly Dravidian-speaking Tamil Nadu state in the southernmost part of India. Tribes and castes were both characterized by an overwhelming proportion of putatively Indian autochthonous Y-chromosomal haplogroups  with a shared genetic heritage dating back to the late Pleistocene (10–30 Kya), suggesting that more recent Holocene migrations from western Eurasia contributed <20% of the male lineages. We found strong evidence for genetic structure, associated primarily with the current mode of subsistence. Coalescence analysis suggested that the social stratification was established 4–6 Kya and there was little admixture during the last 3 Kya, implying a minimal genetic impact of the Varna (caste) system from the historically-documented Brahmin migrations into the area. In contrast, the overall Y-chromosomal patterns, the time depth of population diversifications and the period of differentiation were best explained by the emergence of agricultural technology in South Asia. These results highlight the utility of detailed local genetic studies within India, without prior assumptions about the importance of Varna rank status for population grouping, to obtain new insights into the relative influences of past demographic events for the population structure of the whole of modern India. Contemporary Indian populations exhibit a high cultural, morphological, and linguistic diversity, as well as some of the highest genetic diversities among continental populations after Africa The distribution of deep-rooted Indian-specific Y-chromosomal and mitochondrial lineages suggests an initial settlement of modern humans in the subcontinent from the early out-of-Africa migration The lack of consensus among previous studies may reflect difficulties associated with the conflicting relationships between genetics and the socio-cultural factors used to pool truly endogamous groups into broader categories, sometimes grouping Indian populations sampled from a wide variety of geographical locations together, such as a tribe-caste dichotomy or caste-rank hierarchy th century CE, under the reign of the Sarabhapuriyas, Here, we attempted to apply this strategy to unravel the population structure and genetic history of the southernmost state of India, Tamil Nadu (TN), which is well known for its rigid caste system In the present study, we examined the Y-chromosomal lineages of 1,680 individuals sampled from 12 tribal and 19 non-tribal well-defined endogamous populations. We first investigated whether tribal and non-tribal groups shared a common genetic heritage and characterized the proportion of putatively autochthonous and non-autochthonous Indian Y-chromosomal haplogroups. It is important to note that the total sample size used here is higher than those in other studies covering the entire Indian subcontinent. Further, the detailed anthropological annotation of endogamous populations sampled from a restricted region within India, together with the paleoclimatic, archeological and historical regional-background were all important aspects needed to reduce the confounding relationships among socio-cultural factors. This general approach allowed us to infer important genetic signals and the finer details of the population demographic histories. Therefore, we sought to determine which of the classifications based either on the Varna system , or social-cultural factors , or geography better indicated true endogamous groups by exhibiting higher between-population differences and lower within-population variation. Since both Y chromosome and caste designation are paternally inherited, we further explored whether any of these genetic differences could be attributed to the historical evidences of the establishment of the Hindu Varna system. In contrast, we found the overall Y-chromosomal patterns, the time depth of population diversifications and the period of differentiation correlated better with archeological evidences and the demographic processes of Neolithic agricultural expansions into the region.2 with a population of 62,405,679 (2001 Indian Census: http://censusindia.gov.in), the majority living in 17,272 villages. We sampled a total of 1,680 men, avoiding relatives to the third degree, from 12 tribal and 19 non-tribal endogamous populations, which were selected for their cultural uniqueness, geographical spread, and ethnographic features. Samples from tribal participants were collected in their isolated native villages and settlements from the tropical forests of Western Ghats on the west side of TN. In contrast, non-tribal populations exhibit a larger census sizes and geographical spread and they were sampled in colleges and community gatherings, covering 8% of the total villages from TN , is the southeastern most province of India, measuring 130,058 kmWhile many previous Indian population studies aimed to elucidate the main processes involved in the genesis of the social stratification by pooling populations into broad classifications such as caste-tribe dichotomy and social hierarchy DNAs were extracted from blood or mouth-wash samples using standard methods http://www.r-project.org/). The same software was implemented to perform non-metric multidimensional scaling (MDS) ST distances generated from the 17 Y-STR data of the TN populations, using ARLEQUIN. The relative HG age estimates were based on the variance of 17 STRs of the most frequent HGs for the seven MPG as previously described The software ARLEQUIN 3.11 We considered the problem of how to quantify the significance of the difference between specific population group structures. AMOVA's resampling scheme compares individual group structures to the whole ensemble of randomly varied assignments of populations to groups, as well as of samples to populations. This tests the hypothesis that a specific group structure represents organization of the genetics among populations better than would be expected by chance. In our case, we had the different problem of testing whether one group structure was significantly better than another group structure. In this case, assignments were already determined, and likely are both already better than expected by chance. The question we tested was whether that variation in data randomly drawn from a population could have produced sufficient variation in the AMOVA results to account for the differences between the specific group assignments being compared by chance? Hence we resampled the STR haplotypes with replacement, modeled by a multinomial distribution, and computed the median and 95%CI's of the results using R, version 2.9.1. We tested resampling sizes up to 5,000 times, and found that 500 were sufficient to give reasonable accuracy on the median and confidence interval estimates. We therefore resampled each configuration only 500 times.http://www.dei.unipd.it/~ciompin/main/Sito/Ultranet.html), where the underlying distance (metric) between nodes is ultrametric. Since tree structures reflect an ultrametric structure, an algorithm that maps the compatibility of associations according to such a structure may be uniquely informative. This approach, which is orthogonal to other phylogenetic approaches, helped confirm the results observed in RM network analysis, thereby validating the consistency of the population associations with evolutionarily related haplotypes.The phylogenetic relationships among Y-STR haplotypes drawn from individual haplogroups were estimated with the reduced-median (RM) network algorithm in the program Network 4.5.0 Coalescence methods, as implemented in BATWING In addition, BATWING admixture validation tests Besides assuming no gene flow, BATWING presupposes that the population samples are random. As a result, using BATWING to analyze the histories of individual HGs drawn from populations yields dramatically different estimates of coalescence times, times of expansion, and other population parameters because, as mentioned in the admixture modeling, BATWING is more sensitive to admixture than in-migration. Thus, BATWING may be applied to individual HGs to extract information about specific in-migration events. Further, HGs that tend to correlate strongly with overall population estimates are likely to be more representative of their common ancestral gene pool. These results may be expected in that selection of the modal population trees will tend to preserve configurations where the most common of the shared lineages comprise the strongest signals contributing to the likelihood function. Therefore, selection of modal trees acts as a filter that tends to exclude immigrating contributions, although it will be heavily influenced by inter-population migration.In these BATWING estimates, mutation rate priors were those previously proposed A total of 21 Y chromosome HGs were identified in the study populations . The ovep<0.0001) among non-tribal populations (13.7±1.03%) than among the tribal populations (7.4±1.09%). In contrast, the proportion of likely autochthonous lineages among the tribal populations (87.7±1.37%) was significant higher (Fisher test: p<0.0001) than in non-tribal populations (78.1±1.24%).The geographical origins of many of these HGs are still debated. However, the associated high frequencies and haplotype variances of HGs H-M69, F*-M89, R1a1-M17, L1-M27, R2-M124 and C5-M356 within India, have been interpreted as evidence of an autochthonous origins of these lineages during late Pleistocene (10–30 Kya), while the lower frequency within the subcontinent of J2-M172, E-M96, G-M201 and L3-M357 are viewed as reflecting probable gene flow introduced from West Eurasian Holocene migrations in the last 10 Kya ST) was applied to several different models of population differentiation to assess the proportion of genetic variation explained by geography, tribe-caste dichotomy, caste-rank hierarchy, and other socio-cultural factors reflecting subsistence practices  and the lowest variation within groups  were observed when populations were classified into the seven MPGs based on subsistence. Further analyses considering only the four non-tribal groups revealed a four-fold decrease in genetic variation among groups  when compared to the three tribal groups alone . Moreover, the exclusion of HTF reduced the between-group variance by more than two-fold (6.5% to 2.7%), while exclusion of HTK and BRH had little impact. On the other hand, the exclusion of BRH from non-tribal groups reduced the between-group variation threefold (1.5% to 0.4%).AMOVA using both HGs and STR distances  that is used to estimate the CTF index. It is logical that the Va null distribution would vary with different groupings if the relative impact of groups is high. Contrary to this, we found that the mean and the standard deviations of the null distribution did not vary much among groupings . Likewise, significant results were obtained for under-representation of F*-M89 in all BRH populations (p = 0.043), L1-M27 in HTF populations (p = 0.02) and R1a1-M17 in HTF populations (p = 0.003). Together, these results argue for the distinctiveness of the ancestral gene pools for MPGs and the shared heritage of these paternal lineages among populations within MPGs, in spite of their non-homogenous distribution. Further, the over-represented HGs marking MPGs explains in part some of the organization observed in the PCA and MDS results, and also yields insight into the differentiations noted in the AMOVA results.In addition, Fisher exact tests were used to determine the probability of observing multiple populations within an MPG sharing the same over- or under-represented HGs by chance  or because of the systemic inheritance of ancestral lineages among the constituting populations of MPGs. Our results rejected the hypothesis that random processes could have caused the significant over-representation of F*-M89 in HTF+HTK populations (p<0.0001), L1-M27 in DLF populations (p<0.001), H1-M52 in HTK populations (p<0.0001), and R1a1-M17 in BRH populations  connecting constituent haplotypes would suggest strong genetic drift or possibly sporadic intrusion from a genetically distinct source.F*-M89 was the only HG showing clear population-specific clusters  suggesting long-term isolation . In contTribes are generally considered as the descendants of the early settlers of India and, therefore, better depict the autochthonous genetic composition of India than non-tribal populations We configured several BATWING runs using different subsets of data to estimate the dates of population differentiation and explore the different demographic processes and affinities among the MPGs and their constituent populations. The first set of BATWING runs analyzed haplotypes from all HGs among all of the MPGs to investigate whether tribal and non-tribal MPGs have an independent origin or instead descended from a common ancestral gene pool. If tribal and non-tribal groups have independent origins, then it would be expected that population tree bifurcations marking the differentiation of these two groupings would exhibit very old divergence time estimates and non-overlapping confidence intervals (CIs). The second set of BATWING runs included only haplotypes from one of the most common HGs among MPGs. In this regard, we would like to emphasize that BATWING results using haplotypes from only one HG cannot be interpreted as population divergence times, but rather reflect the demographic histories of the specific paternal lineage among populations. Also, deviations from population estimates among the different runs could reflect in-migrations (gene flow) involving a particular HG rather than multiple paternal lineages obtained from assimilation from a common ancestral gene pool. For these reasons, we explored whether the paternal lineages for each HG originated from the MPG that exhibits the highest frequency of this HG as a way to identify sources and recipients of these Y-chromosomes. In addition, similar splitting patterns obtained for the different HG trees could be interpreted as demonstrating that the paternal lineages entered into the general gene pool from the same demographic event. BATWING constructed clear modal trees for three HGs  but not for the others . The three modal trees –S3c exhiFinally, a third set of BATWING runs were performed using all HGs from individual populations within selected MPGs to test whether the grouping of these populations could have affected BATWING estimates of population divergence and phylogenetic relationships –S4c. AllThe study populations from Tamil Nadu were characterized by an overwhelming proportion of Y-chromosomal lineages that likely originated within India, suggesting a low genetic influence from western Eurasian migrations in the last 10 Kya. Although non-tribal groups exhibited a slightly higher proportion of non-autochthonous lineages than tribal populations, the common paternal lineages shared by TN populations are likely drawn from the same ancestral genetic pool that emerged in the late Pleistocene and early Holocene. We also noted that the current modes of subsistence have shaped the genetic structure of TN groups, with non-tribal populations being more genetically homogeneous than tribal populations likely due to differential levels of genetic isolation among them. Coalescence methods, employed to identify specific and distinctive periods when genetic differentiation among populations occurred, indicated a time scale of ∼6,000 years. We discuss below whether the timing of the male genetic differentiation of the populations fits better with archeological and historical records for the implementation of the Hindu Varna system or with agriculture expansions in the TN region.Previous studies of Indian populations have grouped and analyzed the genetic data in the light of the Hindu Varna system kudith through 12th centuries CE Literary works from the Sangam period (300 BCE to 300 CE) describes a heterogeneous society that predates incorporation of already established populations into the Hindu Varna system The present study shows that the MPG classification reflects the genetic structure of the TN populations slightly better than other models, and that both tribal and non-tribal populations possess predominantly autochthonous lineages derived from a common gene pool established during the Late Pleistocene and Early Holocene. The distribution of over- and under-represented HGs suggests that populations within MPGs tend to share common genetic backgrounds. Using BATWING analysis, we estimate that social stratification for both tribal and non-tribal MPGs began between 6 Kya and 4 Kya, and detectable admixture between them has not occurred over the past 3 Kya, thereby allowing them to retain their genetic identity through cultural endogamy.et al, for convergence of multiple E-M123 founders into Turkey prior to re-expansion into Europe in order to explain the high diversity for that haplogroup Both the overall Y-chromosomal HG distribution and the divergence estimates for tribal and non-tribal groups, are consistent with the archaeological dates and the demographic processes involved in the expansion of agriculture in South Asia. The South Deccan region near southern Karnataka and southwest Andhra Pradesh contains the earliest evidence for an integrated agro-pastoral system in South India, and likely acted as agricultural center and source of dispersion around 5 Kya In addition to this moving frontier, broader and more static agricultural frontier zones could also have arisen at later stages. In this area, stable and growing farming populations interacted with local foragers and created new cultural traditions, with some potential inter-marriage and assimilation through trade taking place. Southern Tamil Nadu and the Kerala zone represent one such agricultural frontier zone that has persisted to the present after local foragers began to adopt cultivation based on agricultural sedentism around 3 Kya The overall Y-chromosomal landscape of TN suggests a complex process of agricultural expansion, which can be explained in terms of the formation of moving and static frontiers since 6 Kya, followed by migrations structured by habitat and occupation. However, because gene flow and differential assimilation of incoming migrations could alter the estimated divergence dates, they should be treated with caution. Our BATWING simulations and others from a previous study Although previous genetic studies have already drawn some of the conclusions presented here Thus, the sampling and analytical approach employed here suggest that detailed local genetic studies within India could give us new insights about the relative influences of past demographic events in relation to other socio-cultural and economic factors that might have influenced the population structure of the whole of India that is observed today. Nevertheless, it cannot be assumed that the same demographic processes or socio-cultural factors affected Indian populations from different regions in a similar manner. Whether corresponding Y chromosome genetic patterns can be also detected in other tribal and non-tribal populations within the South Deccan or in other Indian regions that have already been identified as centers of agricultural expansions, are open questions that future studies could potentially address using the methods presented here. Finally, it would also be important to investigate the relative impact of the processes explained here on the diversity patterns in other genomic regions by studying mtDNA and autosomal variation.Figure S1PCA plot showing the first two principal components of haplogroup frequencies for 97 non-tribal (circles) and tribal (squares) populations of India and nearby regions from previous publications, compared to the non-tribal  and tribal (diamonds) populations from the present study. Symbols have been colored according to linguistic classification. Population codes and references are shown in (TIF)Click here for additional data file.Figure S2Reduced median network of 17 microsatellite haplotypes within haplogroup. (a) HG C-M130 using 74 chromosomes, (b) HG H1-M52 using 292 chromosomes (c) HG H- M69 using 79 chromosomes, (d) HG L1 – M27/M76 using 235 chromosomes, (e) HG R1a1-M17 using 214 chromosomes. Circles are colored based on the 7 Major Population Groups as shown in (TIFF)Click here for additional data file.Figure S3Modal tree obtained by BATWING indicating the coalescence time divergence estimates (in years) among Major Populations Groups (MPG) using 17 STRs from haplogroup (a) F-M89, (b) H1-M52, (c) L1-M26/M72.(TIFF)Click here for additional data file.Figure S4Modal tree obtained by BATWING indicating the coalescence time divergence estimates (in years) among endogamous populations within (a) HTF and HTK groups, (b) DLF, (c) BRH and HTC, using 17 STRs from all haplogroups.(TIFF)Click here for additional data file.Table S1List of Y chromosome SNPS and haplotype data for the 1680 individuals from 31 tribal and non-tribal populations presented in this study.(XLS)Click here for additional data file.Table S2AMOVA analysis of various population groupings based on the 17STR haplotype & 95%CI based on re-sampling of the samples across populations.(XLS)Click here for additional data file.Table S3List of population codes and their publication references used in (XLS)Click here for additional data file.Table S4Fishers exact test p-values for the NRY HG frequencies among the 7 Major Populations Groups and among the 31 sampled populations.(XLS)Click here for additional data file.


Plasmodium vivax is the most widely distributed human malaria parasite outside of Africa, and its range extends well into the temperate zones. Previous studies provided evidence for vivax population differentiation, but temperate vivax parasites were not well represented in these analyses. Here we address this deficit by using complete mitochondrial (mt) genome sequences to elucidate the broad genetic diversity and population structure of P. vivax from temperate regions in East and Southeast Asia.P. vivax, examined using neutrality tests and mismatch distribution analyses, revealed population expansion events across the entire P. vivax range and the Myanmar population. Bayesian skyline analysis further supported the occurrence of ancient P. vivax population expansion.From the complete mtDNA sequences of 99 clinical samples collected in China, Myanmar and Korea, a total of 30 different haplotypes were identified from 26 polymorphic sites. Significant differentiation between different East and Southeast Asian parasite populations was observed except for the comparison between populations from Korea and southern China. Haplotype patterns and structure diversity analysis showed coexistence of two different groups in East Asia, which were genetically related to the Southeast Asian population and Myanmar population, respectively. The demographic history of P. vivax, especially in temperate/warm-temperate endemic areas of Asia. The results revealed divergence of the P. vivax populations in temperate regions of China and Korea from other populations. Multiple analyses confirmed ancient population expansion of this parasite. The extensive genetic diversity of the P. vivax populations is consistent with phenotypic plasticity of the parasites, which has implications for malaria control.This study provided further resolution of the population structure and evolution of  Plasmodium vivax malaria threatens 40% of the world's population and causes over 132 million clinical cases every year [P. vivax has the widest global distribution and occurs in Asia, the Middle East, South and Central America, and parts of Africa [P. vivax is also associated with severe pathology [P. vivax research and control is receiving renewed attention.P. vivax malaria and the underlying genetic diversity of the parasite. Indeed, across its geographic range, P. vivax displays extraordinary phenotypic variation in disease pathology, vectorial preference, and the pattern and frequency of relapse in the host (the time period at which the parasite remains dormant in the liver) [P. vivax are still not well understood.The history of malaria control provides considerable evidence for the resilience of e liver) . To dateP. vivax have relied on molecular markers. In an early study, low genetic variability at 13 microsatellite loci suggested little differentiation and a recent  world expansion of this parasite [P. vivax microsatellites are more polymorphic than those of P. falciparum [P. vivax population, probably as a result of host switch from Asian monkeys [Most investigations of the evolutionary history and population structure of parasite . Howeverparasite  used a bparasite ,8. Two rlciparum ,10. Othe monkeys -14. Whil monkeys  and may  monkeys ,14,16. W monkeys -14, it iP. vivax extends well into the temperate zone. The ability to survive in cool climates is attributed to sporogony in mosquitoes at a much lower temperature and the production of long-term hibernating hypnozoites in human liver. The latter adaptation is especially important for temperate strains, where the primary infection occurs eight months or more following inoculation by an infected mosquito [The geographic range of mosquito . In recemosquito ,19. The mosquito ,13,20, sP. vivax in East and Southeast Asia, we sampled four P. vivax populations in a transect across temperate  and warm temperate (southwestern China and northeastern Myanmar) climates  DNA sequences have been used for population and phylogenetic studies in a wide range of organisms ,13,21-23P. vivax parasite samples were previously collected in four locations of East and Southeast Asia, including 36 samples from Bengbu city, Anhui Province of China in 2004, 26 samples from Luodian county, Guizhou Province of China in 2005, five samples from villages near the northern border of South Korea in 2005, and 32 samples from Laiza township of northeastern Myanmar in 2006-2008  3α and msp3β genes [P. vivax mt genome based on the sequence of the reference strain Sal-I (GenBank: AY598140). Long-range, high-fidelity PCR amplification was performed using Advantage HD DNA Polymerase Mix , which has efficient 3' → 5' exonuclease proof-reading activity. PCR reactions in 50 μl contained DNA template, 1 μM of each oligonucleotide primer, 10 × Advantage HD PCR Buffer, 1 mM deoxynucleosides (dNTPs), and 5 units of polymerase mix. PCR was performed at 94°C for 1 min, followed by 30 cycles of 94°C for 1 min, 60°C for 2 min, and 72°C for 3 min. A final extension was done at 72°C for 10 min. PCR products were purified with the PrepEase Gel Extraction Kit  and sequenced using the BigDye version 1.1 on an ABI 3730XL sequencer. Overlapping sequences were obtained with the primers listed in Additional file JQ240331-JQ240429).A total of 99 clinical 8 Figure  [20,24,23β genes . The higP. vivax mt genome sequences from GenBank . Nucleotide diversity (π) - the average number of nucleotide differences per site between two sequences, and haplotype diversity (h) - the number and frequency of different haplotypes in the samples were estimated using DnaSP version 5.10 [P. vivax mt genomes. Sequence alignments were performed using inGAP v 2.2 [http://www.mbio.ncsu.edu/BioEdit/bioedit.html. To detect selection acting on these coding sequences, the number of synonymous nucleotide substitutions per synonymous site (Sd) and number of nonsynonymous nucleotide substitutions per nonsynonymous site (Nd) were calculated using the SNAP program http://www.hiv.lanl.gov/content/sequence/SNAP/SNAP.html. Phylogenetic trees were constructed by using the maximum likelihood method implemented in BioNumerics v5.10 . In order to define the relationship among the isolates at a micro-evolutionary level, we performed allelic profile-based comparison using a minimum spanning tree analysis with the TCS1.21 [For phylogenetic analysis, we retrieved additional 291 complete ion 5.10 . The thrAP v 2.2  and ClusAP v 2.2 , and wer TCS1.21  as well K from 1 to 14. The number of population clusters was inferred according to Evanno et al. [ad hoc statistic ΔK was calculated for the presence of individuals with ancestry in two or more of the K populations. Graphs of the STRUCTURE results were made using DISTRUCT. Sequences were analyzed for maximum sequence diversity and visually inspected with highlighter tools http://www.hiv.lanl.gov.Analysis of population structure was performed using STRUCTURE , which uo et al.  and the FST and ΦST values were estimated by permutation analyses using 1,000 permutations with an assumption of no difference between populations. The P-value was considered as the proportion of permutations resulting in the higher FST or ΦST value or equal to the observed one. Analysis of molecular variance (AMOVA) was used to evaluate the extent to which sequence variation was partitioned among populations and areas.ARLEQUIN software package version 3.11 was used to estimate genetic diversity indices and to assess population differentiation . PairwisP. vivax. Tajima's D test [Fs test [Fs test compares the number of haplotypes observed with the number of haplotypes expected in a random sample under the assumption of an infinite-sites model without recombination, and Fs was sensitive to population demographic expansion [0, θ1 (θ before and after the population growth) and τ  [i = θi/(θ+1)i+1, where Fi is the expected frequency of pairwise comparisons showing i differences, and θ is estimated by the observed mean of pairwise differences. The distribution usually has a ragged profile at demographic equilibrium due to stochastic lineage loss, but it is usually unimodal in populations following a recent population demographic expansion and population range expansion [Neutrality statistics and pairwise nucleotide differences were calculated to examine the historical demographic expansions of s D test  and Fu's[Fs test  were use[Fs test . Fu's Fsxpansion . In addial time) ,42. The xpansion ,43. ThisP. vivax was estimated using the strict-clock Bayesian MCMC method as implemented in BEAST v1.5.4 [Plasmodium fragile and Plasmodium knowlesi, assuming that they diverged as part of the radiation of their hosts (the silenus group of macaques and the divergence of the sinica and fascicularis groups) between 3.5 and 4.7 million years ago [http://beast.bio.ed.ac.uk/Tracer.TMRCA of T v1.5.4  with nonears ago . AnalyseP. vivax samples representing four populations collected in temperate and warm temperate regions of East Asia and Myanmar . In general, the topology of this tree roughly mirrored that of the minimum spanning tree. Both analyses showed that East Asian populations exhibited relatively large divergence from the remaining populations, suggesting that the temperate parasite populations in East Asia had a different demographic history.To further illustrate the relationship of the mt genome haplotypes, a maximum likelihood phylogenetic tree based on the complete genome sequences was constructed . Significant genetic structure was found between parasite populations, and most parasites formed geographical clusters. At K = 2, only the East Asian cluster was separated from the rest of the populations, although this division was also supported from analysis of the distribution of ΔK using the method of Evanno et al. [Earlier studies using both mt genome and microsatellites have found significant differentiation of the extant ulations ,12. To fo et al. . PosteriFST (only based on haplotype frequency) and ΦST (based on both haplotype frequency and genetic distance) for each population using the mtDNA sequences. The majority of pairwise FST values were greater than 0.25 (ranging from 0.25 to 0.85), suggesting that strong population differentiation exists among the populations. Notable exceptions included the following pairwise comparisons: South/West Asia and Africa, South/West Asia and South America, South/West Asia and Southeast Asia, as well as Korea and southern China . For the temperate parasite populations from Asia, which include northeast Myanmar, Korean, and three Chinese P. vivax populations, between-population genetic differences were strong  and statistically significant after sequential Bonferroni correction (P < 0.0001). The only exception was between the Korean and southern Chinese population (exact origin of the samples unknown), which showed little genetic differentiation   from this model and therefore were suitable for the analysis of demographic patterns. The Fs test and Tajima's D test agreed well with the mismatch analyses. The Fs values of the entire P. vivax populations as well as the East Asian and Myanmar populations were negative and significant (P = 0.025 for East Asian populations and P = 0.005 for Myanmar population). Tajima's D test compares two mutation estimators θs and θπ, which are affected by the size of the present-day and original population, respectively [S relative to π and generate a negative value of D [D test was negative (-2.05) and statistically significant (P = 0.001) for the entire P. vivax populations analyzed. For the Myanmar and East Asian populations, the D statistic was negative and only significant for the Myanmar population (P = 0.021). The τ value, which reflects the location of the mismatch distribution crest, provides a rough estimate of the time when rapid population expansion started. The observed value of the age expansion parameter (τ) was 4.18 units (95% CI: 2.56-10.41) of mutational time in P. vivax. In comparison, the τ value of the Myanmar population was smaller . Yet, the East Asian populations had a τ value of 5.13 (95% CI: 2.01- 13.3), which was even greater than that for all vivax populations, suggesting that the East Asian parasite populations may be at demographic equilibrium in recent evolutionary history. The results of the skyline analysis further supported P. vivax population expansion showing a rapid increase in effective population size starting ~ 200,000 years before present (ybp), followed by a more recent increase in population numbers starting at ~50,000 ybp  years ago, whereas the East Asian P. vivax population diverged at 383,000  years ago. Our estimate also indicated that the TMRCA for the whole vivax populations was around 346,000 to 452,400 years ago (95% highest posterior density).We estimated the divergence time of Myanmar P. vivax provide a foundation for the development of effective malaria control measures. Phylogenetic analysis of P. vivax using sequences from the mt genome and nuclear genes has placed this parasite in a clade that includes the Asian monkey parasites P. cynomolgi, P. simiovale and P. knowlesi [P. vivax, suggesting this parasite is probably the descendent of an ancestor that switched from Asian monkeys to humans. Regardless of what calibration points were used, TMRCA estimates support an ancient demographic history of the extant P. vivax parasites [P. vivax strains. P. vivax parasites also display significant phenotypic diversity such as different relapse patterns, which serve to distinguish tropical from temperate strains. Temperate strains with primary infection occurring eight months or more following inoculation by an infected mosquito were proposed to be a subspecies of P. vivax [P. vivax parasites were postulated to represent a separate subspecies based on molecular polymorphism and difference in vector preference [P. vivax populations from its entire geographical range is necessary to better understand the evolutionary history of P. vivax.The demographic history and genetic diversity of knowlesi -13. Thisarasites -14,48, wP. vivax . Similareference , albeit eference . TherefoP. vivax populations may have been influenced by historical population expansions and more recent migrations. In addition, the evolutionary process, often estimated from the geographical pattern of genetic variation, can be influenced by colonization events such as range expansion or reduction [P. vivax. First, the existence of a phylogeny consisting of several distinct, but closely related lineages suggests a rapid population expansion in the recent past  and comparable to other highly endemic areas of the world [P. vivax malaria was highly prevalent in central China, but it was considerably curtailed during the global malaria eradication campaign in the 1950s and 1960s [P. vivax malaria has resurged and outbreaks occurred in several central provinces [P. vivax population was also found in southern Thailand, where P. vivax population displayed a high level of clonality [Despite generally low endemicity, the global nic loci ,15,55-57fections ,55,58. Ohe world . Howevernd 1960s . Howeverrovinces ,60. We tlonality .P. vivax populations. Within its geographic range, P. vivax exhibits substantial population differentiation, especially between different continents [P. vivax [P. falciparum [FST and ΦST statistics revealed significant population differentiation between Myanmar and East Asian P. vivax populations. Even within a short distance, genetic differentiation may be significant due to possible migration or ecological constraints [P. vivax populations despite their geographic proximity. Interestingly, two major genotypes in China were also observed in the South Korean population [P. vivax have developed a trait of long relapsing liver hypnozoites, an adaptation to the long winter period of temperate climate when transmission is interrupted [For a finite population, unless there is complete panmixia and random sampling, a pattern of genetic isolation by geographic distance is generally expected . This prntinents ,12. The P. vivax  and P. flciparum  failed tstraints . Substanpulation ,65. Popuerrupted . Becauseerrupted . It is uerrupted ,9,67.P. vivax populations has been suggested to be the result of ancient hominid geographical expansion [P. vivax, as African parasites shared the major mtDNA haplotype with the South/West Asian samples. Culleton and collegues proposed that the present-day African and American populations may be the closest extant relatives of the African ancestor [P. vivax. The relationship of the temperate Chinese and northeast Myanmar P. vivax populations points to a possibility of population expansion from South/West Asia to temperate China via northeast Myanmar, which seems to make sense from a geographic point of view. It is noteworthy that the P. vivax parasites from northeast Myanmar, China and Korea all have similar, long relapsing patterns characteristic of temperate P. vivax strains [P. vivax parasites may represent a unique lineage, which is important to elucidate the genetic structure and history of expansion of P. vivax.Since genetic diversity of the global xpansion , the relancestor . Since c strains . FurtherP. vivax population structure by focusing on temperate zone parasite populations in East and Southeast Asia. Analysis of the complete mt genomes from 99 clinical samples confirmed that P. vivax displays extensive genetic diversity and natural populations are clearly structured. While most mtDNA haplotypes from the four temperate populations were related, suggestive of descendent from the same lineage(s), local population subdivision was also apparent. Multiple tests further confirm the ancient expansion of the P. vivax population.The present study readdressed the issue of the extant Sd: the number of synonymous nucleotide substitutions per synonymous site; Nd: the number of nonsynonymous nucleotide substitutions per nonsynonymous site; Markov chain Monte Carlo (MCMC): TMRCA: the most recent common ancestor; SNP: single nucleotide polymorphism; ybp: years before present.Mt: mitochondrial; LC designed experiments, guided the work and helped to draft the manuscript. MM carried out the molecular studies, analyzed data and drafted the manuscript. ZY provided the samples and carried some molecular studies. YH provided the samples. PH and AE revised the manuscript. All authors read and approved the final manuscript.Primer sequences. Primers used for amplifying and sequencing the mitochondrial genome of Plasmodium vivax.Click here for filePlasmodium vivax mitochondrial genomes isolated from Myanmar, China and South KoreaDistribution of SNPs in the . The graph shows the positions of the SNPs in the 5900 bp mt genome for the 30 haplotypes (h1 - h30) of P. vivax populations.Click here for filePlasmodium vivax parasitesPhylogeny of . A maximum likelihood tree based on mitochondrial genomes of 390 Plasmodium vivax samples.Click here for file


Pinna nobilis is the largest endemic Mediterranean marine bivalve. During past centuries, various human activities have promoted the regression of its populations. As a consequence of stringent standards of protection, demographic expansions are currently reported in many sites. The aim of this study was to provide the first large broad-scale insight into the genetic variability of P. nobilis in the area that encompasses the western Mediterranean, Ionian Sea, and Adriatic Sea marine ecoregions. To accomplish this objective twenty-five populations from this area were surveyed using two mitochondrial DNA markers (COI and 16S). Our dataset was then merged with those obtained in other studies for the Aegean and Tunisian populations (eastern Mediterranean), and statistical analyses  were performed. The results revealed genetic divergence among three distinguishable areas: (1) western Mediterranean and Ionian Sea; (2) Adriatic Sea; and (3) Aegean Sea and Tunisian coastal areas. From a conservational point of view, populations from the three genetically divergent groups found may be considered as different management units. The assessment of genetic variation in marine species and the evaluation of connectivity among populations are crucial components for conservation purposes and resource management Pinna nobilis   is an endemic Mediterranean species of great conservation interest that can be regarded as a flag species sensu Walpole and Leader-Williams Pinna nobilis occurs at depths between 0.5 and 60 m in soft bottoms primarily characterised by seagrass meadows P. nobilis. Moreover, Peharda and Vilibić P. nobilis veliger might exhibit daily vertical migration, spending the daylight hours in darker, deeper waters and occupying superficial layers at night, as observed for other bivalves The fan mussel P. nobilis has been included in the list of Mediterranean endangered species (Annex IV of the Habitat Directive and Annex II of the Barcelona Convention) since 1995. Remarkably, it is likely that some of the known geographic and ecological barriers P. nobilis populations. In the Mediterranean region, several authors have documented population subdivision in relation to physical barriers in other marine species with a high dispersal potential ; location: Baia di Porto Conte;- Augusto Navone, Director of the Marine Protected Area of Tavolara-Punta Coda Cavallo (year 2010); locations: Molara, Monte Petrosu;- Mauro Gargiulo, Director of the National Park Arcipelago di La Maddalena (year 2011); location La Maddalena;- Lorenzo Mascia, Director of the Marine Protected Area of Penisola del Sinis-Isola di Mal di Ventre (year 2011); locations: Oristano, Isola di Mal di Ventre;- Maddy Cancemi, Director of the Réserve Naturelle des Bouches de Bonifacio (year 2011); location: Isola Piana.No specific permissions were required for the locations placed out of protected areas, since those locations are not privately-owned or protected in any way.P. nobilis from 25 locations , put in proximity (4–5 cm) of the hinge ligament, and a 20–50 mg sample of mantle tissue was excised using Hartman Alligator Forceps 3.5. The stick was then removed, and the tissue sample stored in a 5 ml tube. The tissue sample was then transferred to the laboratory in a refrigerated box and there preserved in 75% ethanol. This method ensures the survival of the sampled individuals.Overall, we sampled 236 specimens of ocations , sited i5′-TGCTCAATGCCCAAGGGGTAAAT-3′ and H: 5′-AACTCAGATCACGTAGGG-3′) designed by the authors, since those provided by Katsares et al. DNA was isolated using the Qiagen DNeasy tissue kit. Mitochondrial regions were amplified using specific primers for COI (L: 5′-GGTTGAACTATHTATCCNCC-3′ and H: 5′-GAAATCATYCCAAAAGC-3′) and 16S  and sequenced using an external sequencing core service (Macrogen Europe). Dual peaks of similar height, which could be interpreted as evidence of mitochondrial pseudogenes in the nucleus (Numts) or heteroplasmy, were not observed in any of the electropherograms. The PCR products did not show any occurrence of aspecificity, excluding the possibility of multiple nuclear mtDNA-like sequences.Each 25 µl PCR mixture contained approximately 100 ng of total genomic DNA, 0.32 µM of each primer, 2.5 U of EuroTaq DNA Polymerase (Euroclone), 1× reaction buffer and 200 µM of each dNTP. MgClh), nucleotide diversity (π), and the mean number of pairwise differences (d) were obtained using the software package DnaSP 5.10 Sequences were aligned using the program Clustal W P = 0.06) performed with PAUP* 4.0b10 After having added to our COI and 16S sequences the COI K-values = 1 to 22, each with six replicates. Haplotypes were organised into haplogroups following the partition of sequences into the distinct genetic clusters evidenced by Bayesian clustering. The Bayesian clustering algorithms probabilistically assign individuals to groups based on nucleotide frequencies of DNA sequence data without presuming pre-defined populations The presence of population genetic structure was assayed by the Bayesian model-based clustering algorithm implemented in BAPS 5.2 www.fluxus-engineering.com). Mutations were inversely weighted according to the number of times they originated. Therefore, different weights were assigned to the point mutations: sites involved in a single mutational event = 90; sites involved in repeated mutational events = 90/number of repetitions.Genetic relationships among haplotypes were investigated by a median-joining network ST values between sampling localities. Samples with less than five individuals were excluded from population level analysis due to a lack of statistical power. We next conducted a hierarchical analysis of molecular variance (AMOVA) a posteriori according to the pattern of genetic structure suggested by the pairwise ΦST values. Significance of both pairwise ΦST values and AMOVA was assessed by a permutation test . Then, we defined a priori regional groups that corresponded to the main biogeographic subdivisions of the Mediterranean Patterns of genetic differentiation at the population level were assessed by Arlequin 3.5.1.3 0, θ1) were assessed using Monte-Carlo simulations of 10,000 random samples as implemented in Arlequin 3.5.1.3. The fit of the observed mismatch distribution to the expected distribution under a model of demographic expansion was assessed by Monte-Carlo simulations of 10,000 random samples. The sum of squared deviations (SSD) between observed and expected mismatch distributions was used to test the probability of obtaining a simulated SSD that was larger than or equal to the observed one.Aspects of historical demography were inferred by examining the fit of the observed mismatch distribution of DNA pairwise differences to a model of demographic population expansion DFs D are expected to occur in cases of recent population expansion or after a selective sweep Fs values indicate an excess of rare haplotypes, which might be caused by recent population expansion Fs can better detect demographic expansions, whereas Tajima’s D can better detect bottlenecks and populations contractions Tajima’s Statistical treatments were conducted as with the COI dataset. Note that for this dataset, redefined population groupings were required for some analyses  due to the lack of 16S sequences from Tunisian coasts.t = τ/2u t is the time since expansion in generations and u is the mutation rate of the entire DNA fragment, equal to u = µs, where µ is the mutation rate per nucleotide per generation, and s is the sequence length. Uncertainty in the estimates of expansion time since expansion was accounted for computing the 95% confidence interval (CI) of τ from the parametric bootstrap approach with 10,000 pseudo-replicates The modal value (τ) of the mismatch distribution of pairwise genetic differences was also used to estimate time since expansion using the formula We also used the Bayesian coalescent approach implemented in BEAST 1.7.4 We used the Bayesian skyline plot model Pinna nobilis individuals. Sixty-two different haplotypes ; overall higher values of genetic diversity were found in the other sites. Estimates of COI genetic diversity for each site are reported in Specific primers amplified an internal portion of COI 338 base pair (bp) long. Therefore, after the addition to our data of those from Katsares et al. plotypes , definedThe Bayesian assignment analysis identified four distinct haplotype groups, hereafter denominated P1, P2, P3 and P4 . The groThe median-joining network analysis identified a low level of divergence among haplotypes, as, with a few exception, connections were characterised by one or two point mutations, . Three hST estimates indicated significant genetic differentiation between the Aegean and Tunisian samples and the other sites   was obtained when treating the Venetian Lagoon as a separate third group  , A. Howerd group , B. Whenvariance , C and DP>0.05). When assuming the occurrence of discrete groups of populations that, according to AMOVA, represent three geographic areas of (1) Sardinia, Corsica, Elba Island and Sicily, (2) the Venetian Lagoon (Adriatic Sea), and (3) Aegean Sea and Tunisian coasts, we found that the two former groups displayed a unimodal mismatch distribution fitting the demographic expansion model (P<0.05). Neutrality tests showed a significant departure from equilibrium, with the exception of Tajima’s D for the Venetian Lagoon (Adriatic) population . A similar estimate was calculated for the Venetian Lagoon population, with time since expansion dating back to about 1.3 Mya (95% CI: 0.643–1.956 Mya). A more recent demographic expansion was inferred for the Aegean-Tunisian group starting around 0.5 Mya (95% CI: 0.319–0.848 Mya).The time since expansion calculated according to Rogers and Harpending The demographic history inferred by the Bayesian skyline plot revealed a signature of demographic expansion starting approximately 0.270 Mya and ending around 0.100 Mya . Howeverh = 0.961 and π = 0.005, respectively. A large number of haplotypes (77%) was found only at a single locality. The Aegean populations showed the lowest average values of haplotype diversity and nucleotide diversity (h = 0.333 and π = 0.001), whereas high levels of genetic diversity were found for almost all other sites. Estimates of genetic diversity for the concatenated COI and 16S dataset are provided in The concatenated dataset comprised 338 bp for COI and 450 bp for 16S. The sequences of 219 specimens were determined here. Twenty-five COI and 16S sequences from Katsares et al. The Bayesian analysis identified four haplotype groups, hereafter denominated N1, N2, N3 and N4 . The mosThe haplogroups frequency distribution largely resembled that obtained by the COI analysis . In partThe median-joining network of haplotypes  showed aST estimates obtained for the concatenated dataset     , A. A siP<0.001) , B. AMOVvariance , C and D P>0.05) , C and DP>0.05), with a unimodal distribution of pairwise DNA differences .The results of demographic analyses of the concatenated dataset partly agreed with those of the COI dataset. The mismatch distribution carried out on the entire dataset fitted the model of demographic expansion  theferences ; and (3)ferences , displayferences  showed sgean Sea  were likPinna nobilis populations from a wide study area, roughly corresponding to the central part of the western Mediterranean, Ionian Sea and Adriatic Sea ecoregions  the role of Mediterranean biogeographic boundaries in shaping the species’ genetic structuring.This study provides the first analysis on mtDNA genetic variation of P. nobilis further supports the effectiveness of this marker in investigating the genetic architecture of marine species  the Sardinian-Corsican region, Elba Island and Sicily (western Mediterranean and Ionian Sea) and (2) the Aegean Sea and Tunisian coasts (eastern Mediterranean).Our study showed the occurrence of a moderate level of genetic structuring in We detected a primary, significant pattern of genetic structuring along the West-East direction, with samples from the Aegean Sea (excluding the Xios-XI sample for the concatenated COI and 16S dataset) and Tunisian coasts, which were genetically differentiated from the remaining samples. This pattern could be explained as a possible consequence of a reduced gene flow between the western and eastern Mediterranean. Several studies have shown that a hindrance to gene flow exists between the western and eastern Mediterranean, even for species with high dispersal capabilities and large population sizes e.g., . This reDendropoma petraeum in adjacent locations in Tunisia and Sicily Patella rusticaPosidonia oceanicaOther studies have also shown that the genetic break between the western and eastern Mediterranean corresponds to the Siculo-Tunisian Strait e.g., ; nonetheSepia officinalisParacentrotus lividusOur results also suggest the occurrence of a weak but significant genetic divergence of the Venetian Lagoon population from the above-mentioned two groups of populations. This finding is further evidence of the semi-enclosed nature of the Adriatic Sea, which represents a well-defined phylogeographic region within the Mediterranean Interestingly, samples from Cyprus, at the easternmost part of the Mediterranean, exhibited haplotypes typical of the westernmost populations. The human-mediated introduction of planktonic larvae passively transported in the ballast water of commercial ships is a common finding in marine molluscs invasion P. nobilis, with expansions/contractions of its populations due to sea-level fluctuations of this period, repeatedly leading to the isolation, or partial isolation, of the Mediterranean basins P. nobilis because we would expect a bimodal mismatch distribution of pairwise DNA differences for the entire dataset, which may occur when different genetic lineages are present P. nobilis and the time dependency of molecular rate estimates P. nobilis populations in the Mediterranean.The results of historical demographic analysis and the estimates of time since expansion lead to the hypothesis of a Pleistocene scenario for Holothuria mammata from the Aegean Sea All populations analysed in this study are likely to have shared a common origin. Our data suggest an eastward expansion across the Mediterranean, likely mediated by marine currents , and followed by one or more founder events, that lead to lower diversity and private haplotypes in the Aegean and Tunisian populations. A similar trend was also reported for populations of the sea cucumber P. nobilis should benefit from improvements in the sampling plan (with the inclusion of samples from the western Mediterranean). Although part of the range of the species remains unsampled, our study represents an effort in this direction, as it triples the existing genetic information on P. nobilis with new data from a wide area of the western Mediterranean.Rabaoui et al. P. nobilis spanning two Mediterranean marine ecoregions (the western Mediterranean and Ionian Sea), which genetically diverges from the Adriatic population and those from the Aegean Sea and Tunisian coasts. The semi-enclosed nature of the Adriatic Sea may explain the genetic divergence of the Venetian Lagoon population. Hindrances to gene flow, that are related to biogeographic boundaries among these Mediterranean sectors, and the Pleistocene changes in sea levels, can be invoked to explain the pattern of genetic structuring shown in this study. Our results further suggest the occurrence of a West-East genetic break located eastward of the Siculo-Tunisian Strait, and also provide evidence of a further genetic break in this area which is in the North-South direction. In this context, it would be worth noting that other samples from the western  and eastern Mediterranean (e.g. from Egypt and Israel) would be very informative for the understanding of the species’ evolutionary history.The general picture revealed by our study is the presence of a large, homogeneous group of populations of P. nobilis populations in the Mediterranean.Interestingly, although the data for the local or regional levels of genetic erosion due either to human disturbance or to bio-ecological changes are unavailable, the higher levels of mitochondrial variability found in the Sardinian-Corsican region, Elba Island, Sicily and the Venetian Lagoon relative to the populations from the Aegean Sea and Tunisian coasts support the occurrence of an eastward demographic expansion. The lack of strong genetic divergence among these three geographic areas  is evidence of a common origin of sensu Frey From a conservation point of view, the three genetically divergent groups, (1) the samples from Sardinia, Corsica, Elba Island and Sicily, (2) the samples from the Aegean Sea and Tunisian coasts and (3) the Venetian Lagoon sample, should be considered as different management units. Moreover, the samples from the Venetian Lagoon may be considered a “peripheral isolate” Figure S1COI dataset: network analysis. Median-joining network showing the haplotypes relationships among Pinna nobilis populations. Small red plots on the nodes, labelled as “mv”, show median vectors, representing hypothetic connecting sequences, calculated with a maximum parsimony method. Haplotypes diverge each other for a single mutation except where Arabic numbers on network branches indicate the occurrence of a higher number of point mutations. Populations are labelled as reported in (TIF)Click here for additional data file.Figure S2COI-16S dataset: network analysis. Median-joining network showing the haplotypes relationships among Pinna nobilis populations. Small red plots on the nodes, labelled as “mv”, show median vectors, representing hypothetic connecting sequences, calculated with a maximum parsimony method. Haplotypes diverge each other for a single mutation except where Arabic numbers on network branches indicate the occurrence of a higher number of point mutations. Populations are labelled as reported in (TIF)Click here for additional data file.Table S1COI dataset: haplotype frequencies. Frequency distribution of COI haplotypes in 311 individuals from 34 populations of Pinna nobilis. N: absolute frequency; %: relative frequency within Mediterranean populations. Populations are labelled as in (DOC)Click here for additional data file.Table S2COI, COI-16S, 16S datasets: genetic divergence estimates. Sample sizes and genetic diversity estimates obtained for the mitochondrial regions analysed in Pinna nobilis. N: sample sizes; S: number of polymorphic sites; H: number of haplotypes; h: haplotype diversity; π: nucleotide diversity; d: mean of pairwise nucleotide differences. Populations are labelled as in (DOC)Click here for additional data file.Table S3COI dataset: Bayesian COI haplogroup frequencies. N: absolute frequency; %: relative frequency within Mediterranean populations of Pinna nobilis. Populations are labelled as in (DOC)Click here for additional data file.Table S4ST values among sampling localities.COI dataset: pairwise Φ Pairwise ΦST values between Pinna nobilis populations with at least five individuals. Significance was assessed by permutation test. Significant values after correction for multiple testing are reported in bold. Populations are labelled as in (DOC)Click here for additional data file.Table S5COI-16S dataset: haplotype frequencies. Frequency distribution of mitochondrial haplotypes in 244 individuals from 29 populations of Pinna nobilis. N: absolute frequency; %: relative frequency within Mediterranean populations. Populations are labelled as in (DOC)Click here for additional data file.Table S616S dataset: haplotype frequencies. Frequency distribution of haplotypes in 251 individuals from 29 populations of Pinna nobilis. N: absolute frequency; %: relative frequency within Mediterranean populations. Populations are labelled as in (DOC)Click here for additional data file.Table S7COI-16S dataset: Bayesian COI-16S mitochondrial region haplogroup frequencies. N: absolute frequency; %: relative frequency within Mediterranean populations of Pinna nobilis. Populations are labelled as in (DOC)Click here for additional data file.Table S8ST values among samples localities.COI-16S dataset: pairwise Φ Pairwise ΦST values between Pinna nobilis populations with at least five individuals. Significance was assessed by permutation test. Significant values after correction for multiple testing are reported in bold. Populations are labelled as in (DOC)Click here for additional data file.