Tibeto-Burman populations of India provide an insight into the peopling of India and aid in understanding their genetic relationship with populations of East, South and Southeast Asia. The study investigates the genetic status of one such Tibeto-Burman group, Adi of Arunachal Pradesh based on 15 autosomal microsatellite markers. Further the study examines, based on 9 common microsatellite loci, the genetic relationship of Adi with 16 other Tibeto-Burman speakers of India and 28 neighboring populations of East and Southeast Asia. Overall, the results support the recent formation of the Adi sub-tribes from a putative ancestral group and reveal that geographic contiguity is a major influencing factor of the genetic affinity among the Tibeto-Burman populations of India. Northeast India has always been a hotspot for population geneticists due to its unique and strategic geographic location and the presence of linguistically, culturally and demographically diverse populations practicing varied occupations (from hunting-gathering to settled agriculture) This region exhibits linguistic diversity  which can be attributed to diverse socio-cultural influences, extensive population interactions and putative long history of migrations experienced by the region in the past th–7th century AD Arunachal Pradesh (situated between latitude 26°30′N and 29°30′N and longitude 91°30′E and 97°30′E) is the abode of 26 major Tibeto-Burman speaking tribes and 110 sub-tribes and minor tribes The present study, perhaps for the first time, investigates the genetic status of six sub-groups of Adi tribe  based onAdi tribe, a Tibeto-Burman speaking population of northeast India, is of importance in understanding the genetic affinity among the Tibeto-Burman speaking tribes of India and neighboring populations of East/Southeast Asia. The results of the genetic affinity and diversity among Adi sub-groups, their differentiation as well as sub structuring have been presented in this study. Also, the genetic relationship of Adi with neighboring Tibeto-Burman populations of north and northeast India as well as with the linguistically divergent populations of East/Southeast Asia have been discussed.h), probability of homozygosity (P), Exact test (ET) and Likelihood ratio test (LR) (performed to check for departure from Hardy-Weinberg equilibrium) values are shown in ST values show least differentiation for the vWA locus (0.9398%) and a high degree of differentiation in case of D3S1358 locus (3.8685%). The average GST value is observed to be 2.34% indicative of the lower degree of genetic differentiation among Adi populations.The allele frequency distribution of the 15 STR loci among the studied six Adi sub-groups has been previously reported The locus-wise results of the exact test of population differentiation are shown in ST value of 0.02379 indicates a low degree of genetic differentiation, among the studied groups, which might probably be attributed to the recent formation of the different factions from a common ancestral group. Two important factors that might possibly have played a key role in the genetic differentiation of Adi are: fission due to inter-tribal conflicts and relative geographic isolation of the formed contemporary Adi sub-tribes SC: 0.02885) as well as their ethno-history (FSC: 0.02743) did not reveal any significant differences among the groups. In both cases, the variation among the populations and within the groups was around 2.8% and among the individual within the populations was around 11.6% as in case of the single group analysis. The variation within individuals was found to be around 86% at different levels of analyses.AMOVA results, presented in K. Simulation summary for K = 2 and K = 3, including the logarithm of estimated probability of data (Ln Prob) values, values of proportion of membership of each pre-defined populations in each of the two or three clusters and the corresponding α values are given in K = 2 and K = 3, Adi Komkar show higher proportion of membership (0.613 and 0.471) to cluster 1. Adi Pasi-Lower shows higher values of membership proportion (0.451) in case of clusters 2, when K = 3. The extent of genetic differentiation do not differ in case of K = 3  and K = 2  categories. The pattern depicted in K = 2 and K = 3) shows no clear indication of sub-structuring among the six Adi sub-populations.To understand the extent of sub structuring among the Adi sub-tribes we have performed structure analysis with different values of A-NJ phylogenetic trees  consist exclusively of the six Adi sub-groups which retained their previous clustering pattern (Manipur-Garo cluster) includes three populations from Manipur  along with the distantly located Garo of West Bengal. Garo of Meghalaya stands out as an outlier to this cluster. The corresponding PCA plot . There were very few sporadic biological studies among some sub-tribes of Adi The results of the allele frequency variation at 15 STR loci reveal the underlying microsatellite diversity among the studied sub-tribes of Adi. The extent of deviation of the studied loci from HWE, among Adi, differs at the sub-structural level that is indicative of their unique population structure. For instance, Pasi-Upper and Panggi deviate from HWE at maximum number of loci (6 and 4 respectively) whereas Minyong show deviation at only one locus and Padam shows none. This scenario in case of Pasi-Upper and Panggi populations is probably due to their small size and relative isolation in remote Upper Siang hilly regions. In contrast, least deviation in Minyong (one locus) and absence of deviation in Padam might be the resultant of their comparative large population size distributed over plain East Siang regions, in proximity to the urban area.The least average heterozygosity value among Panggi (∼74%) might be explained by their small size, strategic location and preferential marriage practices among clans prohibiting external gene flow ST value (2.34%) among Adi which is an indication of the low degree of genetic differentiation among the sub-groups. This low degree of differentiation among the sub-groups is further substantiated by the clustering pattern obtained from the PCA plot and phylogeny, where all the sub-tribes form a single close cluster.According to the folklore tradition of Adi, formation of their sub-groups was guided by fission-fusion process as a result of inter tribal war fares in the recent past. The above ethno-historical information is supported by the low average GA close cluster of Komkar and Panggi sub-tribes, observed in the PCA plot and the phylogenetic tree, corroborates with their geographic proximity and also the clustering of Padam with this group yields further support to the ethno-historical account that Komkar and Panggi sub-groups were formed from the larger Padam group SC values (around 2.7%) irrespective of ethno-historical or geographical grouping of the populations suggest that the formation of the sub-populations was a recent phenomenon and that the ethno-history and geography had less influence on the overall genetic make up of the populations. So in spite of the socio-cultural, geographic and linguistic diversity, Adi sub-groups remain genetically less differentiated. However, this observation needs to be speculated as the increase in the number of samples and the microsatellite loci might contradict the above observation. The STRUCTURE analysis also support the findings of AMOVA, wherein no clear sub-structuring was observed among the Adi sub-populations (for both K = 2 and K = 3 runs). Overall, the low average GST values, close clustering in PCA plot and phylogeny, low FST and FSC values of AMOVA and absence of discrete sub-structuring among Adi sub-tribes support their recent formation from a common ancestral group.AMOVA results also show least genetic differentiation among the sub-tribes of Adi. The low FThe phylogenetic analyses of Adi and other Tibeto-Burman groups show geography based clustering. Tibeto-Burman tribes of India are supposed to have arrived in several waves of migrations during different time periods from eastern, southeastern and central Asian regions and have localized in various geographical regions of the subcontinent The inclusion of populations from East and South-east Asia in the phylogenetic analyses reveals the clustering of the Luoba ethnic group of Tibet with the Adi groups of Arunachal Pradesh. According to the ethnologue information, Luoba Tibetan (Boga'er Luoba), categorized under the North-Assam branch of the Tibeto-Burman sub linguistic family, is also alternatively referred to as Adi/Abor and is supposed to have been derived from the ‘Tani’ group, the putative ancestral population of Adi. They are located in southern fringes of central Tibetan region, which is adjacent to the Upper Siang district of Arunachal Pradesh. The clustering of Luoba with Adi further supports the ethno-historical accounts of their putative common origin.The fifty populations from East and Southeast Asian countries along with the Adi and other Indian Tibeto-Burman populations show an interesting pattern of clustering. Some Tibeto-Burman populations of India  get clustered with the Tibetan populations from Tibet and China whereas some others  cluster along with Southeast Asian populations. All the morphologically similar populations, irrespective of their linguistic affiliation, cluster together possibly with respect to their geography and ethno-historical account of migration.Overall, Adi and other Tibeto-Burman speaking populations of India are regionally well differentiated and exhibit genetic affinity with the neighboring populations of East/Southeast Asia, based on their shared ethno-history. However, a clearer picture will possibly emerge from the analysis of increased number of informative genetic markers and from the uniparental markers like mitochondrial DNA and Y chromosome.Gaon Burah’ (village head) of each village of the studied populations. Prior informed consent were obtained verbally from 836 healthy voluntary participants belonging to five sub-tribes  of the Adi tribe, distributed at different villages in East Siang  and the Upper Siang  districts of Arunachal Pradesh, north east India , East and Upper Siang districts, and approval of the ‘Further, to understand the genetic relationships between Adi sub-tribes and other neighboring Tibeto-Burman speaking populations, the generated autosomal STR data of Adi was compared with the published allele frequency data (for the nine common loci) of other sixteen Tibeto-Burman speaking populations from north (Ladakh) and northeast  India I STR® Identifiler kit  according to manufacturer's instructions. While the amplified products of Pasi and Minyong sub-tribes were separated on a 4% polyacrylamide gel using the ABI Prism 377 automated DNA sequencer (Applied Biosystems), the amplified fragments of Panggi, Komkar and Padam sub-tribes were separated and detected using the ABI Prism® 3100-Avant Genetic Analyzer . The resultant data was then analyzed using GeneScan™ Analysis Software (Version 3.7) and the allele designations were done with Genotyper™ DNA Fragment Analysis Software (Version 3.7) . The laboratory experiments were carried out following all the quality control measures.High molecular weight DNA was isolated, from the collected blood samples of Adi sub-tribes, using the standard phenol/chloroform method h) at each locus and the probability of homozygosity (P) were estimated to evaluate the extent and magnitude of genetic diversity among the sub-groups of Adi. Also, likelihood ratio test (LR) and the exact test (ET) were performed to test the possible divergence of each locus from the Hardy-Weinberg Expectations (HWE) ST) The allele frequencies of the 15 STR loci were calculated, from the obtained genotype data of the Adi sub-tribes, using the DNATYPE software A) and the standard genetic distance (DST) measures of Nei et al., A and DST distance measures by employing the software DISPAN. To check for the reliability and consistency of the clustering pattern of the obtained dendrograms, a total of 1000 and also 10,000 bootstrap replications were separately performed. In order to further explore the topology of the obtained phylogenetic trees including the positions and lengths of the branches, branching patterns as well as the cluster formation, the radiation form of the trees were also constructed using the phylogenetic software Mega v3.1 A distance measure is the most efficient for obtaining correct phylogenetic trees under various evolutionary conditions and also is least affected by small size A–NJ trees.To understand the genetic relatedness between the six studied Adi populations; pair-wise genetic distances using the modified Cavalli-Sfroza distance , Chicago, IL, USA. The PCA plot further substantiates the dendrogram clustering method, and especially when bootstrap values of the dendrogram are considerably low, the similar clustering in both the PCA plot and the dendrogram indicates the consistency of the results obtained.To characterize the clustering trends exhibited by these studied populations, the data dimensionality was reduced by performing a covariance analysis between factors [Principle Component Analysis (PCA)]. This analysis was performed based on the DIn order to investigate the genetic variation within and between the sub-populations of Adi, Analysis of Molecular Variance (AMOVA) was performed using Arlequin 3.01 Structure 2.1 program K (from 1 to 5) under the assumption of admixture model and correlated allele frequencies among populations. Each run was carried out several times to ensure consistency of the results.To obtain a vivid insight into the sub structuring among Adi sub-tribes, a model-based clustering method was employed, using genotype data consisting of unlinked markers, as implemented in In addition to the above analyses performed on Adi populations, we also conducted the comparative analyses of Adi sub-groups with sixteen Tibeto-Burman speaking populations of north and northeast India and also with other neighboring East and Southeast Asian populations that share similar physical features with that of Adi. Phylogenetic analysis (as described above) as well as Principle Component Analysis, were performed on these populations, based on the available allele frequency data, to understand their underlying genetic affinity and also to obtain a better clarity of the genetic status of Adi populations with the Tibeto-Burman speaking regional populations and linguistically diverse other global populations of East/Southeast Asia.Table S1Sample size, geographical distribution, linguistic affiliation and the subsistence pattern of the studied populations.(0.10 MB DOC)Click here for additional data file.Figure S1DA-NJ phylogenetic trees, based on 15 loci, depicting the genetic relationship between the Adi sub-groups.(0.05 MB TIF)Click here for additional data file.Figure S2DA- NJ phylogenies, based on 9 loci, depicting the genetic relationship between 22 Tibeto-Burman populations of India.(0.09 MB TIF)Click here for additional data file.Figure S3DA- NJ phylogenies, based on 9 loci, depicting the genetic relationship between 50 populations of India and East/Southeast Asia.(0.18 MB TIF)Click here for additional data file.


Whale sharks are a declining species for which little biological data is available. While these animals are protected in many parts of their range, they are fished legally and illegally in some countries. Baseline biological and ecological data are needed to allow the formulation of an effective conservation plan for whale sharks. It is not known, for example, whether the whale shark is represented by a single worldwide panmictic population or by numerous, reproductively isolated populations. Genetic analysis of population structure is one essential component of the baseline data required for whale shark conservation.o = 0.66 and He = 0.69. All but one of the eight loci meet the expectations of Hardy-Weinberg equilibrium. Analysis of these loci in whale sharks representing three major portions of their range, the Pacific (P), Caribbean (C), and Indian (I) Oceans, determined that there is little population differentiation between animals sampled in different geographic regions, indicating historical gene flow between populations. FST values for inter-ocean comparisons were low , and only C×I approached statistical significance (p = 0.0495).We have identified 8 polymorphic microsatellites in the whale shark and used these markers to assess genetic variation and population structure in a panel of whale sharks covering a broad geographic region. This is the first record of microsatellite loci in the whale shark, which displayed an average of 9 alleles per locus and mean HWe have shown only low levels of genetic differentiation between geographically distinct whale shark populations. Existing satellite tracking data have revealed both regional and long-range migration of whale sharks throughout their range, which supports the finding of gene flow between populations. Whale sharks traverse geographic and political boundaries during their life history and interbreed with animals from distant populations; conservation efforts must therefore target international protection for this species. Rhincodon typus , is the largest shark and the largest fish The whale shark, Whale sharks have been the target of widespread active fisheries in the past, and while they are currently protected in many waters, open fisheries remain in several countries. Whale sharks are listed as Vulnerable in the IUCN Red List of Threatened Species, and in 2002 the species was placed on CITES Appendix II. A slow growth rate and late time to sexual maturity make animals such as the whale shark particularly slow to recover from overfishing or habitat disruption, and current evidence indicates that whale sharks are declining in number. Aerial surveys, mark-recapture and photo identification have been used to track the abundance over time of whale sharks in various regions. While not all studies agree, most data suggest that whale shark aggregations have fewer sharks of smaller average size in recent years The use of microsatellites as a tool to understand the population genetics of a species has revolutionized the field of conservation biology A total of 68 whale shark samples were collected from 11 different sites: Veraval, India (8); Utila, Bay Islands, Honduras (6); Galapagos Islands, Ecuador (5); Dania Beach Florida, USA (1); Mossel Bay, South Africa (1); Mahe, Seychelles (3); Cocos Island, Costa Rica (1); Ningaloo Reef, Western Australia (5); Gulf of Tadjoura, Djibouti (15); Bahia de La Paz, Baja California, Mexico (5); and Male, Maldives (18) . These sHaeIII and RsaI, blunt-ended with Klenow and T4 DNA polymerase, dephosphorylated with calf intestinal phosphatase and purified using QIAquick (Qiagen). The forSNX (5′-CTAAGGCCTTGCTAGCAGAAGC-3′) and revSNX (5′-pGCTTCTGCTAGCAAGGCCTTAGAAAA-3′) linkers were annealed, then ligated to the blunt-ended DNA using T4 DNA ligase at room temperature overnight. The linkered DNA was purified using QIAquick and hybridized with 30-mer CA and GA biotinylated oligonucleotides in 12× SSC, 0.1% SDS hybridization buffer at 65 degrees for 2 hours. Repeat-containing DNA fragments were isolated using streptavidin coated magnetic beads  according to the manufacturer's protocol. The beads were washed once wash at room temperature in 2× SSC, 0.1% SDS and once at 50 degrees with 1× SSC, 0.1% SDS. Purified DNA was eluted in 10 mM Tris at 95 degrees, and amplified by PCR using the SNX primer. The amplified DNA was cloned using the TopoTA kit (Invitrogen), and individual clones were sequenced and analyzed for the presence of repeats  Following extraction, DNA samples were quantified using a nanodrop spectrophotometer, and 30 ng DNA per locus was amplified by PCR using primers against unique sequences flanking each microsatellite. Amplification employed 6-Fam labeled forward primers, and reverse primers tailed with the sequence Although a total of 68 whale shark samples were analyzed, some samples did not yield sufficient quantities of DNA to analyze all loci, and some DNAs did not yield useful data for all loci. Different numbers of samples are therefore reported for the different loci. In all cases, where fewer than 68 animals were used for analysis, this is indicated in the methodology. In individual tests for genetic differentiation (Structure and PCA), the single South African animal was treated individually. In analyses where animals were grouped into populations, this animal was included with the Indian Ocean group. Input files for various software were constructed using the program Create when possible A Bayesian approach using genotype data for individual animals was performed to detect any population structure across the entire data set using the program STRUCTURE 2.2 s) was produced using the program POPULATIONS 1.2.30 Traditional tests for population differentiation were performed by calculation of F-statistics using FSTAT and Microsatellite Analyzer (MSA) 4.05 e) was performed using ARLEQUIN 2.0 θH), was calculated from the expected homozygosity (HomE). Assuming that the population is in mutation-drift equilibrium, Theta(θH) is θH = (1−HomE)/HomE, where HomE = 1−HE, and HE is expected heterozygosity. To evaluate the utility of microsatellite genotypes as individual genetic tags, we estimated the probability of identity for individual loci and over all eight loci using Cervus 3.0.3 Calculation of effective population size (Na) per locus ranged from 3 for Rtyp8, to 34 for Rtyp6, with a mean of 9. The Rtyp6 locus was unusual in its degree of polymorphism, and Na excluding Rtyp6 was 3–8, with an average of 5.4. The range for observed heterozygosity (Ho) was 0.45–0.85, and for expected heterozygosity (He) 0.40–1.00. The loci were analyzed with the program MicroChecker, and with the exception of Rtyp6 all conformed to Hardy-Weinberg equilibrium, with no evidence for scoring error, null alleles or large allele dropout Whale sharks are the only genus within their family, Rhincodontidae, and microsatellites had not previously been described from this species. Microsatellites have been isolated from members of the sister families Stegostomatidae and Ginglymostomatidae, but microsatellite sequences are highly unlikely to be conserved across families S), a measure of genetic diversity that describes the number of alleles per locus independent of sample size, could not be calculated individually for several populations due to small sample size. This value was therefore examined after the animals had been pooled into ocean-specific populations, as described below. Values for RS ranged from 2.0 to 8.7 . Allelic richness . FST values for P×I and P×C were not statistically significant, while the value for C×I approached statistical significance (p = 0.0495). The Caribbean animals may therefore be more differentiated from Indian Ocean sharks than they are from Pacific, or than Pacific and Indian are from each other. Those animals for which sex is known were analyzed separately as male and female populations using FSTAT; this analysis did not detect any statistically significant genetic difference between males and females (data not shown).In this study, small sample numbers precluded analyzing individual sampling locations as populations of whale sharks. Since the Bayesian analysis programs did not discern inherent structure among the individual whale sharks in our data set, the animals were grouped for further analysis into three ocean-specific populations—Pacific , consisting of samples from La Paz, Galapagos and Cocos Island; Caribbean , consisting of samples from Utila and Florida; and Indian , consisting of samples from Djibouti, Seychelles, Maldives, Veraval and Ningaloo . The sinS), which describes the similarity between two groups of individuals based on allele frequencies using the Infinite Alleles Model, was calculated using the program POPULATIONS 1.2.30. Pairwise values for DS were P×C = 0.248, C×I = 0.169 and P×I = 0.078. These data indicate a lack of strong genetic differentiation between any of the analyzed whale shark populations.Nei's standard genetic distance (De) reflects the number of breeding adults and the potential for inbreeding and genetic drift in the species. Effective population size was estimated based on the θH value, which is θH = 4Neμ, where μ is the mutation rate. We used a mutation rate of 1×10−3 mutants/generation/locus The effective population size , while FST values for the Caribbean population were somewhat larger when compared to both the Pacific and Indian animals . Only the C×I population approached statistical significance for genetic differentiation (p = 0.0495). It is likely this value would be more highly significant if additional Caribbean animals were available for analysis. FST for C×I remains low, however, indicating subtle differentiation between Caribbean and Indian whale sharks. Despite this finding, the data show that there has been significant gene flow between geographically disparate populations. The individual-based analyses (STRUCTURE and PCA) also indicate no clear genetic clusters of whale sharks based on sampling location  were found to consist of a single continuous population displaying isolation by distance throughout the North Atlantic, with genetically distinct Iberian and Black Sea populations only Xiphias gladius) mitochondrial DNA showed that fish from the Pacific, Mediterranean, Northern Atlantic and Southern Atlantic comprise four genetically distinct populations Balaenoptera brydei) have a lifestyle seemingly similar to that of whale sharks, as they inhabit tropical and warm temperate waters, and have been shown to undertake equatorial migrations. Nonetheless, microsatellites and mitochondrial DNA analysis was used to show that these whales comprise distinct oceanic populations in the Northern Pacific, Southwestern Pacific, Southeastern Pacific and Indian oceans, with low levels of gene flow Thunnus thynnus) has yielded conflicting results for population structure between the Mediterranean and northwest Atlantic ST values were statistically significant between three of the four populations, the values were quite small, ranging from 0.005 to 0.034 Sphyrna lewini) showed significant genetic divergence between Atlantic and Indo-Pacific populations, and identified a cryptic species within the northwest Atlantic High rates of gene flow are typical in large, vagile marine species, which have few barriers to migration between populations. For some species, this migratory activity supports a single panmictic population that shows little genetic variability. For example, harbour porpoises . These data indicate significant gene flow between Indian and Pacific Ocean populations, with reduced levels of interaction with Atlantic animals, a result that parallels the data from our microsatellite analysis. It is possible that the somewhat greater degree of Caribbean/Atlantic population differentiation seen using mitochondrial DNA markers, in comparison to the microsatellites described here, indicates some level of female site fidelity. This hypothesis must be qualified, however, given the small number of animals analyzed in both studies.Two previous studies have employed mitochondrial DNA sequence analysis to compare whale shark populations. Analysis of the mitochondrial control region was used to study the relatedness of 41 whale sharks in the Gulf of California ST values of 0.81 between South Africa and Australia, and of 0.89 between South Africa and New Zealand ST = 0.063–0.067) of nuclear markers was found, particularly between Belize and the other nurseries. Genetic differentiation was far greater using analysis of mitochondrial DNA, however, providing evidence for female philopatry. Any demonstration of female philopatry in whale sharks must await the sampling and analysis of larger numbers of these animals.Female philopatry is found in numerous shark species. White sharks are highly migratory, for example, yet genetic analysis has shown strong population structure. Mitochondrial DNA analysis of sharks from South Africa found evidence for population differentiation in comparison to sharks from Australia/New Zealand, with FSatellite tracking studies of whale sharks have demonstrated both short and long range migratory movements, which support the gene flow inferred with microsatellite markers. Sharks tagged near Taiwan, at Gladden Spit off the coast of Belize, and at Ningaloo reef in Western Australia, all recorded short range movements within their ocean basins Ecological data also support frequent movements of whale sharks between populations. Our sample set displays a striking absence of females, with 2.8 males for every 1 female, reflecting the observation that most feeding aggregations are composed largely of immature males. For example, most sharks observed at Ningaloo Reef, at Gladden Spit, and off the coast of Djibouti are juvenile males −9. As has been demonstrated in other sharks, seals and rays, the ability to genetically identify any individual within a population can discern breeding structure, paternity and sibship While early estimates proposed that the world population of whale sharks might number no more than a few thousand, identification of larger aggregations of animals in previously unknown locations has revised that estimate upwards. Data from tagging and mark-recapture studies has shown that surprisingly few animals are resighted at most locations. Of 72 whale sharks tagged at Gladden Spit, only 17 individuals were resighted over 5 years The work presented here describes the first identification of microsatellite loci in the whale shark, and the use of these loci to analyze population structure across a panel of whale shark DNAs from three different ocean basins. This much-needed first look at whale shark population structure using nuclear markers showed little genetic differentiation between geographic populations. Rather, the data confirm a history of gene flow between populations, supporting migration and interbreeding between these seemingly disparate groups. Such data are supported by satellite tracking studies that show frequent mid-range and periodic long-range migrations. Though this level of gene flow is sufficient to genetically normalize populations, it is unlikely to be sufficient to reestablish depleted populations. As whale sharks cross geographic and political boundaries in their movements, international protection should be sought to ensure the continued survival of this species. In addition, it should be kept in mind that genetic methods of population study reflect only the history of the species. They cannot detect more recent changes in behavior that may be caused by overfishing, habitat disruption, tourism, or other anthropogenic activities currently impacting whale shark populations.


C. parvum samples using micro- and minisatellites. Treated as a single population, the population structure was epidemic. When regional populations were analysed, there was evidence of sub-population structure variations. This was dependent upon excluding sub-groups exhibiting significant genetic distance from the main population, implying genetic sub-structuring. We tested the hypothesis that these sub-groups originated outside the UK and demonstrated that one sub-group clustered with Peruvian samples. A geographically comprehensive panel of isolates would fully confirm this result. These data indicate limited sub-structuring within a small geographical area, but substantial sub-structuring over larger geographical distances. Host movement influences parasite diversity and population structure, evidenced by strong correlation (r2 = 0.9686) between cattle movements and parasite diversity. Thus, the population structure of C. parvum is complex, with sub-populations differing in structure and being influenced by host movements, including the introduction of novel multilocus genotypes from geographically distinct regions.We genotyped 297 Scottish  However, a subset of human C. parvum MLGs was genetically distinct and not found in cattle, indicating either cycling of these particular MLGs within the human population, or an unidentified source of infection. The analysis was expanded with 242 additional samples , 297 were included in the present study. The C. hominis samples from Aberdeenshire analysed previously  agarose gels, and visualised by staining with 0.2 μg/ml ethidium bromide under UV illumination.Seven of the markers, and associated PCR conditions, were those used previously; ML1 , GP15 S, MS1 Kh, TP14, M2.3® software. Each allele was assigned a number and an MLG designated by the combination of alleles at each locus. If a sample contained multiple MLGs, i.e. was a mixed infection, the predominant allele for each marker was scored, based on peak height from the Genescan® output.The size of each PCR product was determined by separation on a capillary-based sequencer . The inclusion of size standards  allowed sizing of alleles by Genescan2.4http://www2.biology.ualberta.ca/jbrzusto/cluster.php), which generated a Phylip DRAWTREE string and bootstrap values , which was converted into a dendrogram by TREEVIEW (http://taxonomy.zoology.gla.ac.uk/rod/treeview.html). Nei's genetic distance (D) and Wright's fixation index (FST) were calculated using the genetic distance analysis program . The standardised index of association The MLGs were analysed using CLUSTERING CALCULATOR . However, the population is in linkage equilibrium when analysis is undertaken with identical MLGs treated as single data points. Therefore, the C. parvum population in Scotland has an epidemic population structure. The program used to analyse the data (VD) and the 95% critical value of this (L), such that if VD is not greater than L, there is good fit to the null hypothesis of panmixia. Additionally, a probability (P) is calculated that the observed pairwise allele frequencies fit a simulation of these frequencies, based on random mating . Linkage disequilibrium was measured between alleles at all pairwise combinations of loci using the index of association (IAS) , which h(IAS) A. Analys(IAS) . Firstly(IAS) . Secondl(IAS) , negatin(IAS) . This la(IAS) . Maynardilibrium A. Possibilibrium  were excthe data  indicatem mating .C. parvum population structure is panmictic in Dumfriesshire and Aberdeenshire, but epidemic in Orkney and Thurso. The main difference is that the Dumfriesshire and Aberdeenshire sample sets include isolates from human infections. However, if only cattle isolates are analysed, the difference in population structure between Dumfriesshire/Aberdeenshire and Orkney/Thurso is maintained (data not shown). If the human sample data set is analysed in isolation, the results indicate an epidemic structure (data not shown). However, the human data set is small, and caution is attached to this interpretation. In conclusion, the C. parvum population in Scotland has an epidemic population structure. However, there are differences at a local geographic level, with Thurso and Orkney exhibiting an epidemic structure, and Aberdeenshire and Dumfriesshire showing evidence of panmixia.Analysis was also undertaken on the individual populations  was used to compare the different populations  was surprisingly the largest measured between the four populations. This is unexpected, as they are the closest geographically, but it must be stressed that D is still low. The FST values, a measure of population sub-structure, agree with this trend , but moderate differentiation between Orkney and the other populations. Interestingly, the data combine to suggest that the Orkney C. parvum population is more distinct than those in the other regions. The most likely explanation for this is the low level of allele polymorphism found in the Orkney samples, resulting in allele bias, perhaps resulting from the Orkneys being islands with limited movements of cattle.Nei's genetic distance  or group 3 (D = 1.52). However, all three groups are genetically distinct, further evidenced by the FST values  indicating a high level of genetic differentiation  than to groups 1 and 2  or the main Scottish C. parvum population .While the analysis of the Scottish samples provides no real evidence of geographic sub-structuring, it was important to test for this with more geographically distinct isolates, particularly as this might provide an explanation for the outlier groups described in Section  samples . However samples , the Per3.6www.bcms.gov.uk) and provided data on the number of cattle killed (on farm) in each area that were born outside of the respective study areas, for the years 1999–2003. Data for 2001 were not included due to the FMD outbreak. When this measure of host movement was plotted against the average number of C. parvum alleles per locus from each geographic area (r2 = 0.9686). Therefore, the data suggest that the movement of cattle into a region contributes to the genetic diversity of C. parvum.Cattle movements in the four regions were obtained from the British Cattle Movement Service . The over-representation of specific MLGs results from the inclusion of the Thurso and Orkney populations and implies greater gene flow within and between Aberdeenshire and Dumfriesshire. This is supported by the genetic distance and FST between populations, which show limited differentiation and low values between Aberdeenshire and Dumfriesshire. Thus, although there is random genetic exchange occurring across the whole population, the two northern-most populations demonstrate an epidemic structure with certain MLGs predominating. The data also provide evidence against any significant host or geographical sub-structuring.The previous studies did not examine the population structure of the Scottish samples treated as a single population. However, Nei's genetic distance and the inbreeding coefficient 4.2C. parvum infections in Europe, compared with the predominance of C. hominis in other regions, has led to the suggestion that intensive agriculture in European countries is linked to the higher prevalence of C. parvum human outbreaks  are comparable to those between the related species of the Drosophila melanogaster complex , indicating that they are a rare source of infection, or rarely cause disease. Whether this is because they are from an unidentified source of infection, or there is (for groups 2 and 3) a human–human transmission cycle, requires further study. The grouping of Peruvian C. parvum human samples with outlier group 3 raises the possibility that the minor group of samples represented by the outlier groups are brought into circulation either by animal import or human travel, and, at the time of sampling, had not undergone recombination with the indigenous Scottish population, explaining their significant genetic difference. Indeed, of the MLGs in outlier group 3, two (MLGs 94 and 95, C. parvum parasite, a large sample set from geographically diverse regions needs to be compared, in order to examine the degree of genetic differentiation and potential gene flow between regions. Whether the outlier samples can interbreed with the main population is uncertain as they have possibly been recently imported and thus not extensively transmitted. However, if the genetic differences between Peruvian and Scottish C. parvum are a feature of geographically well-separated populations, then it could be possible to identify the origin of imported strains. Longitudinal studies would be required to establish if these genetically distinct parasites could undergo genetic exchange with the local population.A small subset of human samples from Aberdeen and Dumfriesshire, genetically distinct from the main population, had previously been reported  and thes complex . The ide


Genome-wide data provide a powerful tool for inferring patterns of genetic variation and structure of human populations.ST = 0.0040, p<10−4), also between Eastern and Western Finland . The latter indicated the existence of a relatively strong autosomal substructure within the country, similar to that observed earlier with smaller numbers of markers. The Germans and British were less differentiated than the Swedes, Western Finns and especially the Eastern Finns who also showed other signs of genetic drift. This is likely caused by the later founding of the northern populations, together with subsequent founder and bottleneck effects, and a smaller population size. Furthermore, our data suggest a small eastern contribution among the Finns, consistent with the historical and linguistic background of the population.In this study, we analysed almost 250,000 SNPs from a total of 945 samples from Eastern and Western Finland, Sweden, Northern Germany and Great Britain complemented with HapMap data. Small but statistically significant differences were observed between the European populations (Fa priori assumptions of homogeneity among Finns and other seemingly isolated populations. Thus, in association studies in such populations, additional caution for population structure may be necessary. Our results illustrate that population history is often important for patterns of genetic variation, and that the analysis of hundreds of thousands of SNPs provides high resolution also for population genetics.Our results warn against  Emerging genome-wide data are a powerful resource for analysis of population genetic variation, including population history and structure. These studies are of importance not only for researchers with historical interests, but also as a baseline for population-based studies of human disease, most notably association analyses of complex diseases where unknown population structure may introduce bias We employed genome-wide SNP data to characterize genetic variation in Finland and Sweden in comparison with two reference populations from Germany and Great Britain, which have a Central European background and are larger, older and more admixed. Additionally, we also compared these data to the three HapMap populations from Europe, Africa and Asia th century onwards. Even then, the population size throughout the country remained small, causing extensive genetic drift which, together with local and regional founder and bottleneck effects, led to the characteristic features of historical settlement of Finland: heavily drifted and isolated small breeding units. The results of this process have been seen in both common and especially rare autosomal alleles The population history of Northern Europe has been reviewed earlier by several authors Genetic variation in Sweden, Germany and Great Britain has been characterized less extensively than in Finland, and there is little evidence of strong population structure. In Sweden, mitochondrial DNA and Y-chromosomal studies indicate some geographical gradients The aim of this study was to characterize the genetic variation of Finland, Sweden, Northern Germany and Great Britain together with the HapMap data  on a fin−14 for all population pairs except between Sweden v. Western Finland, Germany and Great Britain). The Structure analysis . The same pattern was also observed when comparing the allele frequencies in the study populations and in CEU and CHB+JPT: the Eastern Finns had the largest proportion of SNPs deviating towards the Asian frequencies (−5), also when markers with smallest differences were excluded (data not shown).When data from HapMap Han Chinese+Japanese and Yoruba individuals was included in the analysis, the MDS plot of IBS formed a triangle of the three continents in the first two dimensions, with the third dimension separating the European populations clinally from each other . In the quencies ; p<10−5,ST calculations (ST = 0.0072–0.0094) and the smallest between the British and Germans (FST = 0.0005). All the FSTs differed from zero (p<10−3), and most of them also differed from each other . The FST over all populations was 0.0040 (p<10−4). Notably, there was no indication of the closer relationship of the two Finnish populations that was observed in the IBS analysis of individuals . Differences in the extent of linkage disequilibrium were highly significant (p<6.2×10−10) for all population pairs except Germans and British .The information about the grandparental birthplaces of the Finnish samples enabled a more detailed analysis of population structure within Finland. In the multidimensional scaling plot of IBS within Finland , the firAnalysing large numbers of autosomal markers has advantages over the traditional tools of population genetic studies. Mitochondrial DNA and Y-chromosomal markers represent only two loci and thus do not fully capture the evolutionary history throughout the whole genome, and limited numbers of autosomal loci may lack the power to detect differences especially between closely related populations. In this study, we used 250,000 SNPs to elucidate the population structure and differentiation in Northern Europe by analyzing carefully ascertained samples from Eastern and Western Finland, Sweden, Germany and Great Britain. Our results revealed a relatively strong population structure within Finland, and a small but significant differentiation between all the populations, although especially the Germans and British appeared genetically very homogeneous.ST values showed a pattern of very small yet statistically significant differences between the populations. The overall FST (0.0040) was equal to the FST between European regions calculated from a similar set of markers ST = 0.0032) was similar to that between the Icelandic subpopulations (0.0034) ST and other measures accounted for such a small proportion of the total genetic variation that large numbers of SNPs are needed to observe them, once again illustrating how most of the human genetic variation is found between individuals instead of populations The FThe MDS plot of the European populations showed a pattern of population differences that was consistent with our other analyses and earlier observations of a greater degree of differentiation in the geographical extremes of Europe The MDS analysis of Finns showed a pattern resembling their geographic origins, although with some overlap of the provinces. A similar regional clustering of individuals has been seen in the Swiss The extreme features of Eastern Finland-high linkage disequilibrium, high similarity within the population, increased number of monomorphic markers and divergence from the other populations-are in accordance with earlier studies Another factor behind the outlier status of Finland could be admixture with other populations outside the studied region. Indeed, the comparison to the Asian HapMap samples revealed interesting differences between the studied populations, with the Nordic populations and especially Eastern Finns appearing to harbour a significantly stronger Asian affinity than Central Europeans. A similar eastern influence has been observed in Y-chromosomal, mitochondrial DNA and autosomal studies of the Finns ST = 0.0005, λ = 1.11) despite being genotyped in different laboratories, and thus these datasets seem comparable. Additionally, the bias in SNP ascertainment for the chips and in the LD-based formation of smaller datasets  . All the2<0.2, and 6369 SNPs with minor allele frequency >0.1 and r2<0.02. The former set was used for the IBS and inbreeding analyses and the latter for Structure and FST analyses. Many of the analyses were performed without the HapMap populations in order to avoid extensive sampling or possible bias due to their lower sample sizes. We performed most of the analyses in parallel in Plink version 1.00 (http://pngu.mgh.harvard.edu/purcell/plink/) www.R-project.org) The genotype calling was done by the BRLMM algorithm in the Affymetrix GeneChip Genotyping Analysis Software (GTYPE) version 4.1, and the quality control procedures followed for the most part the Wellcome Trust Case Control Consortium standards ST calculations we used Arlequin 3.11 We calculated pairwise identities by state (IBS) for all samples, and performed classical multidimensional scaling (MDS) on the identity matrices for the total data and for the European and Finnish datasets separately. The informativeness of the presented dimensions was assessed by calculating the proportion of their respective eigenvalues to the sum of absolute eigenvalues. Distributions of IBS in sample pairs within and between populations, as well as marker and sample heterozygosities and inbreeding coefficients were calculated in GenABEL, together with distributions of minor allele frequencies in the populations. Geographic coordinates for each Finnish individual were determined as the mean of grandparental birthplace coordinates, and the geographic distances between all the individuals were calculated as great-circle distances in R package fields To study the extent of eastern influence, we counted in each of the five European populations the number of markers where the population's allele frequency and the CHB+JPT allele frequency deviated from the CEU allele frequency to the same direction, and the number of markers where the allele frequencies deviated in opposite directions. We then compared the numbers to the null hypothesis that all the five populations stem from the same proto-European population (approximated by the CEU frequencies) from which they have subsequently diverged via genetic drift in the absence of admixture. In such a case, one would expect the number of markers drifting into a given direction (e.g. towards the Asian frequencies) to be similar across the populations, whereas a varying degree of eastern admixture in each population would result in disparate marker proportions. Using the number of deviating markers instead of the absolute size of the deviations should even out some of the effects of differing extent of drift in the populations.The statistical significancies of the differences between the distributions of each analysis were tested in R by first assessing their normality by a Shapiro-Wilk test. As all were strongly non-normal, the pairwise analyses  were done with a sign test; in the independent analyses , an overall significance of the difference was first calculated from a Kruskal-Wallis one-way analysis of variance, and if that was significant, the differences were further located by pairwise comparisons with a Mann-Whitney U test. The medians given in Table S1Quality control parameters and the different datasets used in analyses(0.04 MB XLS)Click here for additional data file.Table S2The number of SNPs per population that have a frequency deviation from CEU to the same or opposite direction as Asia (CHB+JPT). The markers with identical frequencies in either CEU and the studied population or CEU and CHB+JPT have been excluded. The proportions differ significantly (p<10-5).(0.02 MB XLS)Click here for additional data file.Table S3Summary table of population statistics(0.02 MB XLS)Click here for additional data file.Figure S1Animation of the three-dimensional multidimensional scaling plot of the identity by state matrix of the Europeans (a), and the Finnish samples (b), with the legend in (c). The file can be opened e.g. in most internet browsers. Abbreviations as in (20.95 MB GIF)Click here for additional data file.Figure S2Admixture proportions of the European individuals in a Structure analysis of K = 3 (a); and the probabilities of different numbers of clusters in the Structure analysis of the European dataset (b), and the Finnish dataset (c).(0.54 MB TIF)Click here for additional data file.Figure S3Multidimensional scaling plots of the identity by state matrices for the whole dataset. Plots in the 1st and 2nd dimensions (a), and the 1st and 3rd dimensions (b). The label of each axis shows the proportion of the dimension's eigenvalue to the sum of absolute eigenvalues of all the dimensions. Abbreviations as in (0.50 MB TIF)Click here for additional data file.Figure S4Median IBS and overdispersion factor (lambda) of the quantile-quantile plot for each population pair (a), and values of chi-square test statistic (b) and expected mean IBS (c) for combinations of allele frequencies in two populations. In the chi-square calculation, samples from both populations are assumed to be size n; the actual test statistic will be n times the plotted value. The IBS calculation assumes Hardy-Weinberg equilibrium. Obviously, the IBS is highest  in a marker whose allele frequency is either high or low in both populations, whereas the chi-square value is less dependent on the actual size of the allele frequencies and more directly related to their difference. Thus, a given set of genome-wide allele frequencies can lead to different results in different analyses. Note that low minor allele frequencies are most common in Eastern Finland.(0.71 MB TIF)Click here for additional data file.Figure S5Geographic versus genetic distance for all Finnish individual pairs. The p-value is based on 10 000 replications. Correlation coefficient is 0.31 (p<10-6).(0.15 MB TIF)Click here for additional data file.


Current information about the expansion of Bantu-speaking peoples is hampered by the scarcity of genetic data from well identified populations from southern Africa. Here, we fill an important gap in the analysis of the western edge of the Bantu migrations by studying for the first time the patterns of Y-chromosome, mtDNA and lactase persistence genetic variation in four representative groups living around the Namib Desert in southwestern Angola . We assessed the differentiation between these populations and their levels of admixture with Khoe-San groups, and examined their relationship with other sub-Saharan populations. We further combined our dataset with previously published data on Y-chromosome and mtDNA variation to explore a general isolation with migration model and infer the demographic parameters underlying current genetic diversity in Bantu populations.Correspondence analysis, lineage sharing patterns and admixture estimates indicate that the gene pool from southwestern Angola is predominantly derived from West-Central Africa. The pastoralist Herero-speaking Kuvale people were additionally characterized by relatively high frequencies of Y-chromosome (12%) and mtDNA (22%) Khoe-San lineages, as well as by the presence of the -14010C lactase persistence mutation (6%), which likely originated in non-Bantu pastoralists from East Africa. Inferred demographic parameters show that both male and female populations underwent significant size growth after the split between the western and eastern branches of Bantu expansions occurring 4000 years ago. However, males had lower population sizes and migration rates than females throughout the Bantu dispersals.Genetic variation in southwestern Angola essentially results from the encounter of an offshoot of West-Central Africa with autochthonous Khoisan-speaking peoples from the south. Interactions between the Bantus and the Khoe-San likely involved cattle herders from the two groups sharing common aspects of their social organization. The presence of the -14010C mutation in southwestern Angola provides a link between the East and Southwest African pastoral scenes that might have been established indirectly, through migrations of Khoe herders across southern Africa. Differences in patterns of mtDNA and Y-chromosome intrapopulation diversity and interpopulation differentiation may be explained by contrasting demographic histories underlying the current female and male genetic variation. Among the complex series of demographic events that shaped the patterns of human genetic variation in Africa, the massive dispersal of Bantu-speakers stands as one of the most impressive examples of human migration. Both linguistic and archeological evidences suggest that the spread of Bantu languages started about 4000 years ago in the adjacent grasslands of Cameroon-Nigeria and involved large movements of farmers carrying an agricultural tradition especially well-suited to the climate conditions prevailing in subequatorial Africa ,2. AccorGenetic data have great potential for unraveling the complex population history underlying Bantu expansions, but there are a number of difficulties related to sampling coverage and parameter estimation that need to be overcome. So far, most of the available genetic information has been gathered in phylogeographic studies of Y-chromosome and mitochondrial DNA (mtDNA) variation. These studies identified several mtDNA haplogroups likely to be associated with the Bantu migrations that trace their ancestries to different geographic regions of Africa . In contBeing exposed to the effects of the Benguela current, southern Angola provided a new environment characterized by increasing levels of aridity that challenged the progression of the agricultural lifestyle that had predominated in the well irrigated lands of the Congo basin ,2. FacedHere we present an analysis of the western edge of the Bantu expansions based in the study of Y-chromosome, mtDNA and lactase persistence genetic variation in West Savanna Bantu-speaking groups from southwestern Angola. We analyzed the data in the context of regional and continental genetic diversity by assessing the differentiation between these populations and their levels of admixture with Khoisan-speaking groups, and by examining the relationship between southwestern Angola and other areas of Africa. Furthermore, we combined our dataset with published data on Y-chromosome and mtDNA variation from Southeast Africa to explore a general isolation with migration (IM) model ,20 and iBuccal swabs were collected from 54 Kuvale, 153 Nyaneka-Nkhumbi, 21 Guanguela, 96 Ovimbundu, and 45 Bantu-speaking individuals with other ethnic affiliations. Individuals were grouped according to self-identified ethnicity, and only samples from unrelated individuals were included in the study. Samples were collected after informed consent in donors' hometowns and villages located in the administrative province of Namibe, southwestern Angola Figure . Besidesth century. The populations that remained in the western side of the Chokwe penetration progressively adopted cattle and became mixed farmers [The groups included in our sample represent West-Savanna Bantu-speaking populations  from the farmers .HpaI (absent in L3), 2349 MboI (present in L3e), 10084 TaqI (present in L3b) and 8616 MboI (absent in L3d). After this initial assignment step, we used the available information on HVS-I and HVS-II variation provided by published complete mtDNA sequences to refine and/or rename the classifications according to the most recently updated mtDNA phylogeny [We have sequenced both hypervariable segments I  and II  of the mtDNA control region. MtDNA sequencing was performed as described previously . All HVShylogeny  (see AddTo characterize the nonrecombining portion of the Y-chromosome (NRY) we genotyped 9 unique event polymorphisms  and 11 short tandem repeats . The DYS385 locus consists of a duplicated tetranucleotide STR region and was omitted from some analyses. Except for YAP, UEPs were typed by direct sequencing of PCR products. Primer sequences and protocols are provided upon request. Short tandem repeats were typed with the Promega Powerplex Y System. All Y-chromosome combined haplotypes, defined by UEP and STRs, are shown in Additional file Lactase persistence was screened by direct sequencing of a 359 bp PCR fragment located within intron 13 of the MCM6 gene, which contains all single nucleotide polymorphisms (SNPs) that have been so far associated with lactase persistence in human populations: G/C -14010; T/G -13915; C/T -13910; and C/G -13907 -32  and ancestral (NA) populations, time since divergence (t) and migration rates in both directions (m1 and m2). These demographic terms are obtained by conversion from estimated basic parameters that are scaled by the mutation rate (per locus per generation): θA = 4NA μ; θ1 = 4N1 μ; θ2 = 4N2 μ; t = tμ; m1 = m1/μ; m2 = m2/μ. The mtDNA dataset consisted of 724 HVS-I 276 bp-long sequences ranging from positions 16090 to 16365, including 358 sequences from southwestern Angola, collected in the present study, and 366 assorted sequences from different ethnic groups from Mozambique and Zimbabwe  are within the range found in other Sub-Saharan regions [k (= 14.61), and fail to reveal any significant signal of population expansion, as reflected by the nonsignificance of both Tajima's D and Fu's Fs tests  contribution of West-Central Africa to the southwestern Angola mtDNA pool Table . Contribk = 24.6) than in the Nyaneka-Humbi (θk = 60.3) and the Ovimbundu (θk = 61.1), revealing a consistent pattern of population size reduction and genetic drift in the Kuvale group.In accordance with the trend observed for mtDNA Table , NRY divFigure Although the majority ~80%) of Y-chromosome lineages in southwestern Angola belong to haplogroup E3a-M2 Figure . The sma0% of Y-cTo study the patterns of lineage sharing in the Y-chromosome, we used the populations from the comparative NRY African dataset with reported haplotype data for a common set of seven STR loci  under reasonable computation times using the mtDNA dataset  are 4 to 8 fold higher than ancestral population sizes (NA) showing that the dispersal of Bantu speaking groups involved both significant size growth and geographic expansions. Population growth could actually have been even more marked, if bottlenecks occurring at the formation of daughter populations caused initial size reductions [1~10000, N2~5000; Table 1 and N2 ~30000; Table A~1200) and mtDNA (NA~7100) are also different.Estimated current population sizes  were found to be consistently higher than 2N1m1 (migration from Southeast into Southwest), but this observation must be regarded with caution, since 2N2m2 estimates from independent runs failed to converge to a single maximum , in other cases the pattern reversed and the major peak corresponded to unusually high 22Nm2values (~180)  Table .1and N2 ~7000; NA~1300; Table In order to overcome the limitations of single locus estimates, we have also generated inferences based on the combined mtDNA and NRY datasets Table . Joint eIt is generally accepted that the mtDNA pool of Bantu speaking populations comprises a diverse set of lineages that trace their phylogeographical ancestry into three major sub-continental regions: West Africa, East Africa and West-Central Africa ,11,24. IIt is, therefore, likely that the occupation of Southwest Africa has been preceded by a period of assemblage of diverse mtDNA contributions up north, in West-Central Africa, followed by subsequent migrations from specific dispersal centers into the southwest. According to linguistic and archeological evidences, a likely dispersal center to Angola would have been located in savanna areas just south of the equatorial forest, around the Tshikapa site, where premetallurgical Bantu speakers originating in Cameroon/Gabon might have acquired iron technology and livestock from eastern Bantu peoples, before proceeding to the southwest ,50. The In contrast with the collection of diverse haplogroups that is generally found in the maternal pool, the NRY haplogroup composition is highly homogeneous in most potential source areas of Bantu dispersions, due to the predominance of haplogroup E3a-M2 in West and West-Central Africa ,9,51. HoDespite the substantial differences between their levels of haplogroup variation, both NRY and mtDNA data concurred in showing that the populations sampled in Namibe are clustered together with other Bantu groups from elsewhere. Within the local context of southwestern Angola, the divergence of the Herero-speaking Kuvale from other population groups was found to be associated with the lack of signals of demographic expansions Table , suggestA further feature of the genetic composition of the Kuvale that is not paralleled by the Herero from Namibia is their substantial levels of assimilation of Khoe-San lineages Table . In factThe presence in Namibe of the -14010C lactase persistence mutation, which had only been previously found in Kenya and Tanzania , raises We have attempted to infer basic demographic properties of Bantu expansions using the framework of the IM model by assuming that populations located in the southwestern and southeastern edges of sub-equatorial Africa encompass the deepest branches of Bantu divergence after a common origin in West-Central Africa.A major advantage of the IM class of models is the ability to disentangle the effects of evolutionary factors that are typically confounded in summary statistics based in equilibrium models ,20,48. HIn spite of these caveats, several consistent results could be found, showing that the analyses presented here do provide informative parameter estimates that may be contrasted in the future with other inferential frameworks and empirical datasets. Our joint estimation of the time of split between the two edges of Bantu migrations .Click here for fileProbability densities for the basic demographic parameters of the IM model. The figure provides marginal posterior probability densities for independent runs of the program IMa using the mtDNA HVS-I sequence dataset (L = likelihood).Click here for fileProbability densities for the basic demographic parameters of the IM model. The figure provides marginal posterior probability densities for independent runs of the program IMa using the joint mtDNA and Y-chromosome datasets (L = likelihood).Click here for file


Pararge aegeria  which has recently colonized agricultural landscapes in NW-Europe. Butterflies from woodland and agricultural landscapes differ in several phenotypic traits . We investigated whether phenotypic divergence is accompanied by genetic divergence between populations of different landscapes along a 700 km latitudinal gradient.Past and current range or spatial expansions have important consequences on population genetic structure. Habitat-use expansion, i.e. changing habitat associations, may also influence genetic population parameters, but has been less studied. Here we examined the genetic population structure of a Palaeartic woodland butterfly Populations (23) along the latitudinal gradient in both landscape types were analyzed using microsatellite and allozyme markers. A general decrease in genetic diversity with latitude was detected, likely due to post-glacial colonization effects. Contrary to expectations, agricultural landscapes were not less diverse and no significant bottlenecks were detected. Nonetheless, a genetic signature of recent colonization is reflected in the absence of clinal genetic differentiation within the agricultural landscape, significantly lower gene flow between agricultural populations (3.494) than between woodland populations (4.183), and significantly higher genetic differentiation between agricultural (0.050) than woodland (0.034) pairwise comparisons, likely due to multiple founder events. Globally, the genetic data suggest multiple long distance dispersal/colonization events and subsequent high intra- and inter-landscape gene flow in this species. Phosphoglucomutase deviated from other enzymes and microsatellite markers, and hence may be under selection along the latitudinal gradient but not between landscape types. Phenotypic divergence was greater than genetic divergence, indicating directional selection on some flight morphology traits.Clinal differentiation characterizes the population structure within the original woodland habitat. Genetic signatures of recent habitat expansion remain, notwithstanding high gene flow. After differentiation through drift was excluded, both latitude and landscape were significant factors inducing spatially variable phenotypic variation. Range expansions are recurrent events that have important genetic consequences Pararge aegeria. The range of P. aegeria has shifted to the north with recent climate change P. aegeria has recently increased both in distribution and abundance within the core part of its European range (including The Netherlands and Belgium)In this study, we used microsatellite loci to study the genetic consequences of range and habitat expansion in the speckled wood butterfly, P. aegeria in permanent woodland populations and recently colonized populations in agricultural landscapes (i) how are woodland populations structured in space along the latitudinal cline, i.e. is there a genetic signature of post-glacial recolonization?; (ii) is effective connectivity between P. aegeria populations high?; (iii) how were the agricultural populations colonized, i.e. from one southern agricultural source population, or several independent colonization events from agricultural and/or woodland populations?; (iv) is there directed gene flow through matching habitat choice  P. aegeria relative to latitude and landscape.The overall aim of the present study is to analyze the population genetic structure in relation to expansion in habitat use. Specifically, we aimed for analyzing and comparing the genetic population structure of ndscapes  to addrePae3 and a maximum of 46 alleles for Pae7. Allozyme loci were less polymorphic, with a total of 32 alleles for 4 loci.All microsatellite loci were polymorphic with a total of 195 alleles over 6 loci and a minimum of 23 alleles for Pae3, Pae4 and Pae11 showed significant Hardy-Weinberg disequilibrium due to heterozygote deficit in global tests (Pae3 (0.233) and population F1 – Pae11 (0.2192)). However, null allele frequency estimates were relatively low , suggesting some signature of selection. No other loci showed signatures of selection as a rerun of the same analysis without PGM ensured. Therefore, the PGM locus was excluded from all subsequent analyses.Population differentiation for PGM was much higher than expected  latitude x landscape interaction effects were detected, suggesting that the decrease in diversity with latitude was not completely equivalent for the two landscapes  for all populations was 0.041 (CI:0.0359–0.0451), and was larger for agricultural populations  than for woodland populations . While genetic differentiation was significantly higher between agricultural than woodland pairwise comparisons , the degree of inter-landscape differentiation was significantly different neither from agricultural - agricultural  nor woodland – woodland population comparisons . Population genetic differentiation increased  with latitude. The latitude x landscape interaction effect was not significant, i.e. population differentiation varied in a similar way along the latitudinal cline for both landscapes.Population differentiation was statistically significant but very small , with Fs2 = 0.377, P = 0.003), as well as for agriculture-agriculture population pairs  and woodland-woodland population pairs . Genetic similarity fell to zero at a smaller distance interval in the agricultural landscape (61–137 km class ) than in the woodland landscape (137–195 km), indicating a larger neighborhood size for woodlands , while the local structure was not significant in woodlands, i.e. neighbors were not genetically more different than randomly chosen pairs. Neither global nor local structures were significant in the agricultural landscape.No clear population structure was detected by genetic clustering of populations by Bayesian inference. The highest likelihood, with or without prior geographic information, was for K = 1 in all runs. However, using spatial multivariate analyses, a significant clinal pattern was detected within the woodland landscape only . Only thP<0.001) was detected for agricultural populations  compared to woodland populations . Low Fst values (see above) indicate high dispersal, with higher dispersal in the woodland compared to the agricultural landscape . .Genetic analyses suggest lower dispersal within the woodland compared to the agricultural landscape. Using the private allele method, a significant lower number of migrants per generation Nm  in mass (PC1), relative thorax (PC4) and melanization was higher in the agricultural versus the woodland population comparisons  for P. aegeria in this study. This type of variation was observed in the same species in a preliminary study by our group with low He (0.056) and predominance of one allele (P95%: 0.300) for 10 polymorphic loci . In their study on P. aegeria, Hill et al.et al.e of 0.198 for this locus alone). The low level of allozyme polymorphism observed in P. aegeria can be interpreted within an evolutionary context. It is a phenotypically plastic species both for morphology and life-history traits We observed high genetic diversity and low differentiation which is typical of high density and/or highly mobile species P. aegeria. However, increase in environmental heterogeneity with latitude should not be ruled out as Excoffier et al.P. aegeria tircis.Both microsatellite and allozyme genetic diversity decreased with latitude. This is most likely due to successive founder effects and typical of post-glacial recolonization events Multivariate spatial analyses showed a clinal population genetic structure for the woodland landscape. This is likely the result of step-by-step dispersal movements. Significant isolation by distance also confirmed the genetic proximity of geographically closely located populations, and genetic distinctiveness of remote populations.st) for both microsatellite  and allozyme  data was extremely low and highly similar between markers when the “selected” enzyme (PGM) was excluded from the analysis. Compared with other butterfly species  between agricultural populations also supports this hypothesis. First of all, during the colonization period, Fst increases because the number of demes is increasing and because the migrants founding the new demes have less and less variability, so new demes differ more from the average deme et al.st), i.e. higher differentiation towards the north as observed in this study. Subsequent gene flow will probably homogenize population structure, resulting in decreased Fst values, similar to those observed within the woodland landscape. The absence of significant differences in population differentiation between agricultural – woodland population pairs with both within-landscape pairs (agriculture-agriculture and woodland-woodland) suggest that colonizers originated from both agricultural and woodland populations.Neither a decrease in genetic diversity in the recently colonized landscape nor a disproportional decrease in allelic richness compared to heterozygosity was observed in Centaurea diffusaFinally, weaker isolation by distance and statistically significant negative correlations at shorter distance in the agricultural landscape  also suggest colonization through long distance dispersal in the agricultural landscape. Although isolation by distance is generally interpreted as equilibrium between drift and migration, it may also be the consequence of serial founder effects accompanying range expansion from one or several agricultural populations However, greater extinction-recolonization dynamics within the less buffered agricultural landscape may also contribute to higher population differentiation. Nevertheless, numerous extinctions and recolonizations should also result in decreased genetic diversity within the agricultural landscape, and no significant landscape differences were detected.P. aegeria have shown landscape related variation for both dispersal propensity and immigration. In an experimental landscape with lab-reared individuals, Merckx et al. P. aegeriast) and the private allele based dispersal estimates confirm more migrants between woodland compared to between agricultural populations. On the other hand, speckled woods of agricultural populations are able to target habitat from a wider distance than woodland individuals Dispersal is a function of dispersal propensity during emigration, displacement during transfer and settlement during immigration P. aegeria.The absence of habitat specific dispersal suggests that the more northern agricultural populations may have been established through dispersal from both woodland and other agricultural populations. The results also suggest that there is no evidence for habitat-directed dispersal in st) for forewing size, forewing aspect ratio and basal wing melanization, was significantly larger than genetic differentiation. This suggests that the degree of differentiation in quantitative traits exceeds the differentiation by genetic drift alone. Directional selection favoring different phenotypes in different populations is plausible Phenotypic differentiation , as habitat structure related thermal conditions influence the ratio of alternative mate-locating strategies  primarily is a woodland butterfly, but it also occurs in fragmented, agricultural landscape with hedgerows in NW-Europe The speckled wood  and stored at −80°C. They represent a cohort of directly developed butterflies. At least 20 individuals were sampled in both agricultural (N = 11) and woodland (N = 12) populations along a latitudinal gradient of more than 700 km . Frozen Allozymes were studied following the methods described in Pae2, Pae3, Pae4, Pae7, Pae11 and Pae16DNeasy Tissue Kits (QIAGEN) were used to extract genomic DNA from butterfly legs. The six polymorphic microsatellite loci used were: Pae3 (0.0843). Simulations Deviations from Hardy-Weinberg equilibrium and occurrence of linkage disequilibrium were tested by ARLEQUIN st values to neutral distribution of Fst as a function of expected heterozygosity, generated by a coalescent-based simulation model based on Loci that show unusually low or high levels of genetic differentiation are often assumed to be subject to natural selection. We tested for evidence of selection by comparing observed Fe), allelic richness (A) number of (private) alleles, and locally common alleles  were calculated by GENALEX 6.2 Observed and expected (unbiased) heterozygosity (Hst<0.05) and loci were characterized by >2 alleles, Fst values were calculated following st RH' was used. However, Fstsensu Weir and Cockerham agricolae package in R 2.8.1, R Development Core Team 2009) was used.Since genetic structure was very weak  using the private allele method st) was calculated as in st, st after taking variation in Fst into account. Residual variation was regressed against latitude, landscape and latitude x landscape to test for landscape and/or latitude dependent directional selection.The proportion of among population phenotypic variance in morphological traits Click here for additional data file.Table S2Microsatellite and allozyme genetic diversity. A: Agricultural landscape, W: woodland landscape; Ho : observed heterozygosity UHe : unbiased expected heterozygosity, A: allelic richness, PrivA: private alleles, LCA25: locally common alleles .(0.03 MB DOC)Click here for additional data file.Table S3Variation in genetic diversity (based on microsatellites) in relation to latitude, landscape and latitude x landscape. He : unbiased expected heterozygosity, A: allelic richness, Private A: private alleles, LCA25: locally common alleles . Bold values: p<0.05, italic values: p<0.10.(0.05 MB DOC)Click here for additional data file.Table S4Regression of allozyme diversity against latitude, landscape and latitude x landscape. (A) all enzymes and (B) all enzymes without PGM.(0.03 MB DOC)Click here for additional data file.Table S5Likelihood ratio test (LRT) between several maximum likelihood based dispersal models. Full model , woodland source model , landscape-selective dispersal model . AIC values for each model are shown. Model with the lowest AIC is the most likely model. For latitudinal zone correspondence, consult code in (0.03 MB DOC)Click here for additional data file.Table S6Allele frequency data for allozyme and microsatellite markers used in this study. Please refer to (0.12 MB DOC)Click here for additional data file.Figure S1Allozyme based observed heterozygosity in relation to latitude and landscape. Full line: linear regression for agricultural populations (•), dotted line: woodland populations (Δ). Significance levels are available in (0.02 MB TIF)Click here for additional data file.Figure S2Genetic and phenotypic differentiation. Fst and Pst values for agricultural-agricultural (•) and woodland-woodland (Δ) population pairs of size and dispersal relevant morphological variation . All values are with 95% confidence intervals.(0.16 MB TIF)Click here for additional data file.Figure S3Spatial genetic autocorrelation correlogram of populations. Population pairs within A) agricultural and B) woodland landscapes. Dotted lines represent upper and lower 95% CI around the null hypothesis . Filled dots represent significant r values (p<0.05), empty dots non-significant values. Error bars indicate 95% CI of r estimated by bootstrapping (n = 1000).(0.15 MB TIF)Click here for additional data file.


Dopamine receptor D2 (DRD2) is an important gene having functional significance in the fields of neuropsychiatry and pharmacology and also has importance in evolutionary studies.TaqI sites  in the DRD2 gene in 232 unrelated individuals from five ethno-linguistically distinct endogamous tribal populations; Siddis and Gonds of Uttara Kannada district, Karnataka; Varli and Kolgha of Valsad district, Gujarat; and Dangi Konkana of Dang district, Gujarat. The genotype data obtained after molecular analysis of the three DRD2 sites was subjected to statistical analysis such as calculation of allele frequencies, haplotype frequencies among others. Subsequently, a neighbor-joining tree was also constructed from the data obtained.This study was undertaken to find out the haplotype distribution and linkage disequilibrium (LD) pattern for the three TaqI ‘A’ and TaqI ‘B’ sites in both the populations.The three DRD2 sites were found to be polymorphic in all the populations. All the populations showed high levels of heterozygosities. Out of the eight possible haplotypes, most populations shared seven haplotypes. Of all the populations, Siddis showed the highest frequency of the ancestral haplotype B2D2A1 (11.4%). Significant LD was found to exist for The findings are in concurrence with those from other Indian studies, especially from Dravidian-speaking South Indian populations. Similar pattern of diversity observed for ethnically and linguistically diverse populations in the present study is indicative of complex structure of Indian populations. DRD2) system, located on chromosome 11,[DRD2 locus has been used to understand the biologic consequences in terms of the population structure, among the five endogamous population groups with distinct ethnic and linguistic backgrounds, chosen from South Indian state of Karnataka and the West Indian state of Gujarat.Over the years, population genetic studies have seen massive progression from the use of unlinked markers to linked markers. This has enabled haplotype construction to study population dynamics, which further provides meaningful insights both in the evolutionary and disease association studies. The global survey of variation in the dopamine receptor D2  inhabiting Uttara Kannada district is primarily distributed in six talukas, namely, Sirsi, Mundagoda, Joida, Haliyala, Yellapura, and Ankola.[ancestry,–5 majorlGonds: Gonds is the largest tribal group of India, concentrated in Madhya Pradesh, eastern Maharashtra, Chhatisgarh, northern Andhra Pradesh, western Orissa and northern Karnataka. The study samples were collected from Bhatkal taluk of Uttara Kannada district of Karnataka. They are considered to have migrated here from Adilabad district of Andhra Pradesh in 18th century. Gonds in Karnataka number about 8000[bout 8000 and contVarli: The term Varli has been derived from the Sanskrit word ‘Varal’ meaning uplander. They are generally found to inhabit hilly terrains. Varlis are famous for their ancient Indian folk art tradition of painting. Historians believe that the Varli tradition can be traced back to the Neolithic period between 2500 BC and 3000 BC, indicating the antiquity of the tribe. Varlis have migrated to South Gujarat from Konkan area of Maharashtra state. They are physically tall, dark, slim and well built, with features described as proto-Australoid. They were traditionally hunters and gatherers but now majority of the tribesmen own small land holdings. They have four endogamous divisions, namely, Shuddha, Murdes, Davars and Nihirs. Each of the four divisions has exogamous clans. Their population size in Gujarat is 255, 271.[Dangi Konkana: The Konkanas in Gujarat are immigrants from western coastal strip of Maharashtra, western India. They are dark, short-statured people and show ethnic affinities with Varlis. Konkanas largely depend upon agriculture, agriculture labor, fishing and collection of minor forest products for their subsistence. The community is divided into a number of exogamous units like Mahala, Gavid, Gavit, Gaikwad, etc. They practice group endogamy and clan exogamy. The Konkanas of district Dang are known as the Dangi Konkana. The total population of Dangi Konkana is 50,201.[s 50,201.Kolgha: The Kolghas are classified as a primitive tribal group (PTG) in Gujarat state of India and their population size is 48,000.[s 48,000. Spoken dDRD2, spans over 270 kb and has been mapped to locus 11q22.3-q23.1.[DRD2 gene is of special interest as it is a target site of many neuropsychiatric drugs, and is thus of prime concern in the fields of neurology, psychiatry, endocrinology, among others. It is a strong candidate gene implicated in alcoholism and other substance use disorders.[TaqI ‘A’ site,[TaqI ‘A’ site is the one most frequently studied in association studies.[DRD2 locus are TaqI ‘A’, TaqI ‘B’ and TaqI ‘D’. These three TaqI restriction fragment length polymorphisms (RFLPs) are used to construct haplotypes in the order TaqI ‘B’, TaqI ‘D’ and TaqI ‘A’. The site-present alleles at the three sites are represented as ‘B2’, ‘D2’ and ‘A2,’ respectively, and the site-absent alleles are represented as ‘B1’, ‘D1’ and ‘A1,’ respectively, where B2, D2 and A1 alleles are the ancestral alleles.[215The gene under study, .3-q23.1. It encod.3-q23.1. DRD2 genisorders.–12 Begin‘A’ site, several ‘A’ site, of which studies.15 The geleles.215DRD2 sites – TaqI ‘A’, TaqI ‘B’ and TaqI ‘D’, but the reported data are for lesser number of chromosomes because of technical errors. Distribution of the samples by birth place and tribes is given in A total of 232 chromosomes were typed in the five populations for the three autosomal co-dominant biallelic TaqI ‘A’,[TaqI ‘B’[TaqI ‘A,’[DRD2 sites were amplified using the standard primers and protocols.[TaqI as per the manufacturer’s recommended conditions. Electrophoresis was subsequently carried out in 2% agarose gel stained with ethidium bromide for visualization. Following this, data obtained were subjected to statistical analysis.The three sites, TaqI ‘A’, TaqI ‘B’[TaqI ‘B’ TaqI ‘A,TaqI ‘A,’ have beeTaqI ‘A,’ The threrotocols.21 The poAll the procedures of data collection and analysis were in accordance with ethical standards of the Helsinki Declaration (1975).DRD2 sites in the five populations were made by direct gene counting and the assumption of Hardy-Weinberg Equilibrium was tested using χ2 goodness-of-fit test. The estimates are presented in Allele frequency estimates for the three TaqI sites are found to be polymorphic in all the populations and none is found to show significant departure from Hardy-Weinberg Equilibrium at 5% level of significance. B2 and D2 alleles have frequencies greater than 60% in all the populations, the only exception being Kolgha which shows a frequency of 0.53 for the B2 allele. Kolgha shows highest frequency values for the ancestral alleles D2 (0.79) and A1 (0.459), whereas highest frequency for ancestral allele B2 (0.706) is seen in Siddis. Of the three sites, greater variation is seen in frequency distribution of the A1 allele.All the three Average heterozygosity was computed according to Nei and the All the study populations show high level of diversity with respect to the three sites and the heterozygosity values range from 0.4341 among Gonds to 0.4482 among Dangi Konkana.Within each population, haplotype frequencies were estimated by maximum likelihood method from the multi-site marker typing data, using the program HAPLOPOP. The haplotype frequencies for the five populations are presented in When haplotype diversity is taken into account, it is found that of the eight possible three-site haplotypes, no population shows all the eight haplotypes. Gonds and Dangi Konkana each show seven haplotypes; Varli and Kolgha each exhibit six haplotypes; and Siddis have only five haplotypes. All the populations lack B1D1A1 haplotype in common. The next most frequently absent haplotype is B1D1A2. The three haplotypes, namely, B2D2A2, B2D1A2 and B1D2A1, together occur with a frequency greater than 80% in all the study populations. The ancestral haplotype B2D2A1 is found to be present in highest frequency in Siddis (11.4%) and lowest in Dangi Konkana (2.3%).DRD2 sites are shown in The standardized pairwise linkage disequilibrium (LD) value D’ was computed for each pair of markers Data on TaqI ‘B’ and TaqI ‘A’ sites. Except for Gonds, all other populations show significant LD between sites TaqI ‘B’ and TaqI ‘D’; three of five populations show significant values for LD between TaqI ‘A’ and TaqI ‘D’ sites.The values are generally low (<0.2) in all the comparisons. All the populations show significant values in the comparison between A dendrogram was constructed using the neighbor-joining (NJ) method to identSiddi population is grouping with Gonds on one hand and Kolgha is lying on the other extreme of the NJ tree and is closer to Dangi Konkana and Varli than the two South Indian populations.TaqI sites define evolutionary relationships of the DRD2 haplotypes. Alleles at the three sites show a variable pattern of distribution across different populations of the world or more so across different geographic areas. Ancestral allele B2 is observed at higher frequencies than B1 allele in African (>0.72) and European populations (>0.68) as compared to other world populations.[2TaqI ‘B’ site varies from 36.67% in Onge tribe[The three lations.226 Ancestnge tribe to 91% inge tribe It is obnge tribe27–29 butnge tribe Similar nge tribe27–29TaqI ‘A’ and TaqI ‘B’ Single Nucleotide Polymorphism (SNPs) in all the study populations, although not as high.As also seen in other studies,2728 sign27et al.[Haplotype distribution pattern reveals haplotype sharing between the study populations and a similar pattern of distribution. Most of the study populations are showing ancestral haplotype B2D2A1 at a frequency >5%. This is in contrast to the observation made by Kidd et al.28 but loet al. and Thotet al. The valuet al.27–29 Theth and 17th centuries.[th century,[DRD2 locus as evident from allelic and haplotype distribution pattern. These similarities could either be because of their common ancestor stock and/or a result of long ethno-historical contacts that might have shaped their biologic structure also. It is clear from the NJ tree that the Siddi group, having African ancestry, is separated from other groups but closer to its geographic neighbor. Also, Kolgha, a PTG, is positioned separately from other groups but is closer to its geographic neighbors. Although only one locus has been considered in the present study, haplotype analysis is known to be a robust method for studying population structure. Thus, the results obtained in the present study indicate that the structure of Indian populations is complex and is the by product of cultural, temporal and spatial changes over a period of time.Structure of any population is affected by various factors such as demographic history, size, ethnicity, drift, differential selection, among others. The population groups in the present study are ethnically, geographically and linguistically distinct. Except for Kolgha (which is a PTG), all other study groups are immigrants to their current geographic location. Siddis are African immigrants, who were brought to west coast of India by Portuguese in the 16 century, and sinc