Rastrelliger kanagurta were examined in 23 populations collected from the Indonesian-Malaysian Archipelago (IMA) and the West Indian Ocean (WIO). Despite the vast expanse of the IMA and neighbouring seas, no evidence for geographical structure was evident. An indication that R. kanagurta populations across this region are essentially panmictic. This study also revealed that historical isolation was insufficient for R. kanagurta to attain migration drift equilibrium. Two distinct subpopulations were detected between the WIO and the IMA (and adjacent populations); interpopulation genetic variation was high. A plausible explanation for the genetic differentiation observed between the IMA and WIO regions suggest historical isolation as a result of fluctuations in sea levels during the late Pleistocene. This occurrence resulted in the evolution of a phylogeographic break for this species to the north of the Andaman Sea.Phylogeographic patterns and population structure of the pelagic Indian mackerel,  In the marine realm, fish are abundant and ubiquitous and observed levels of genetic differentiation among populations are often low . This paPlectorhinchus flavomaculatus, a coral reef fish sampled from the South China Sea in the Xisha, Zhongsha and Nansha archipelagos examined variation in the mtDNA control region and identified two discrete lineages [Thallasoma hardwicki were examined from six localities in the South China Sea and three localities from the Solomon Islands in the South Pacific Ocean [Lutjanus erythropterus [Pampus argenteus, with three distinct clades  across the Indo-West Pacific region [However, despite an apparent lack of any physical barriers to gene flow, many marine taxa have often been reported to show population structure –11. Wheric Ocean . Three mropterus  reportedc region . Late PlRastrelliger kanagurta is an epipelagic Scombrid that occurs widely across the Indo-West Pacific from South Africa, the Seychelles and the Red Sea, east to Indonesia and northern Australia to Melanesia, Micronesia, Samoa, China and the Ryukyu Islands of southern Japan. It is also believed to have entered the eastern Mediterranean Sea via the Suez Canal [Rastrelliger that also includes R. brachysoma and R. faughni. In the seas surrounding Malaysia, R. kanagurta is widespread across the northwest and east peninsular Malaysia, Sabah and Sarawak [The Indian mackerel, ez Canal . It is o Sarawak  where it Sarawak . This sp Sarawak .R. kanagurta. These have included investigations of west peninsular populations employing RAPD markers [R. kanagurta from the Indian Peninsular [R. kanagurta in the Strait of Malacca off the coast of Perak [To date, there has only been limited study of the population genetics of  markers  and mtDN markers . A similninsular  while inninsular . All stuninsular –22 reporof Perak .b (Cyt b) was utilised to assess population structure in R. kanagurta. This marker was chosen because Cyt b contains both slowly evolving codon positions, which are useful in phylogenetic studies and rapidly evolving codon positions that are useful for population studies [b a suitable marker for studies of both phylogenetic and population genetic studies of fish [Genetic markers including; allozymes, mtDNA and microsatellites have been employed widely as markers to investigate the population structures of many marine fish taxa –25. Diff studies , 26–27.  of fish .R. kanagurta populations in waters that form part of a major marine biodiversity hotspot in the Indonesian—Malay Archipelago (IMA). These include the Strait of Malacca (an extension of the Indian Ocean), the South China Sea, the Sulu Sea and the Celebes Sea. In addition, four populations outlying this area were included as reference populations and to provide a wider coverage of the species natural distribution. These were from Can Tho, Vietnam, represented the northern South China Sea and two populations from Trang, Thailand and Banda Acheh, Indonesia represented the East Indian Ocean (Andaman Sea). The fourth population from Bandar Abbas, Iran represented the West Indian Ocean and served as the outgroup. Variation in the complete Cyt b gene was utilized to examine genetic structure in R. kanagurta across this vast expanse of waters.The current study focused on examining genetic variation in R. kanagurta individuals were collected from 19 locations within the IMA; Strait of Malacca (SOM), South China Sea (SCS), Sulu Sea and Celebes Sea between 2010 to 2011  and H15630 (5’-TTA ATT TAG AAT CCT AGC TTT GG-3’) . The PCR reaction mixture consisted of 2.5μL 10x PCR buffer (iNtRON), 3.3μL 25mM MgCl2 (iNtRON), 0.5μL 2.5mM of dNTP (iNtRON), 10 pM of each primer, 5U of Taq polymerase (iNtRON) and ∼50ng of genomic DNA. Amplification conditions were; initial denaturation at 94°C (2 min); 35 cycles of 94°C (20 sec), 50°C (20 sec), 72°C (1 min 10 sec) and a final extension at 72°C (5 min) before termination of the reaction at 4°C. Amplification of the 25 μL reaction volume was conducted in a G-Storm Thermal Cycler . PCR products were purified using Promega Wizard SV Gel and PCR Clean-Up System  based on the manufacturer’s specifications. All purified products were sent for DNA sequencing  in both the forward and reverse directions using Applied Biosystem ABI3730x1 capillary-based DNA sequencer.Extraction of DNA template was done using the Aqua Genomic DNA isolation kit  as according to the manufacturer’s protocol. Isolated DNA template was PCR amplified for the complete mitochondrial Cyt b sequences were edited in MEGA 5 [2 [2 were calculated using coalescent simulations with 5000 replicate runs for each simulation. To detect whether sampled populations were demographically stable or expanding or decreasing over time, the demographic history of each sampled population was assessed applying Harpending’s raggedness index, Hri [ri value (P < 0.05) is evidence for rejecting this model [0 and θ1(i.e. θ before and after the population growth) and τ  [8 iterations with a burn-in of 107 under the TN93 model, a strict molecular clock and a stepwise skyline model. Genealogies and model parameters were sampled every 1000 iterations. All operators were optimized automatically. Result of skyline plots were generated by Tracer1.5 [Forward and reverse Cyt n MEGA 5 . Sequencn MEGA 5  and summn MEGA 5 . Phylogen MEGA 5 . The comn MEGA 5 . Historin MEGA 5  statistin MEGA 5  in additEGA 5 [2  in DnaSPEGA 5 [2 . Signifidex, Hri  implemendex, Hri  and mismdex, Hri –39 impledex, Hri . Both Hapansion) , 40. Valpansion) . BEAST vpansion)  was usedracer1.5 .ST) and sequence-based statistics (NST) [FST describes variation between populations within total, FSC describes variation among populations within region and FCT describes variation among regions within total [Further analyses of genetic differentiation among populations were conducted for haplotype-based statistics (Hcs (NST)  with sigcs (NST)  in DnaSPcs (NST) . Estimatcs (NST) –46. Genecs (NST) . AMOVA pin total . Spatialin total  to identin total .b gene and a final sequence length of 1140 bp was obtained after alignment and editing of ambiguous sequences revealing 241 unique haplotypes. Sequences were deposited in Genbank with accession numbers JQ681542-JQ681735. A total of 220 variable sites were identified at the first and third base positions in the codon with a ratio of 1:8. Of these, 122 (10.7%) were parsimony informative. The ratio of transversion to transition substitutions of the entire data set was 1:11. Haplotype diversity was very high, ranging from 0.952 to 1.000, while nucleotide diversity was very low, ranging from 0.008 to 0.011  corroborated results from Fu’s Fs analysis indicating that some populations had expanded recently.Overall, 14 out of 23 population tested were significant with negative values for Fu’s Fs. This suggests historical population expansions at these sites  Table 3Table 3 aR. kanagurta matched the bimodal mismatch distribution for all four clades. Comparison between observed frequencies of pairwise differences with those expected under various demographic models [b of 1–2% per million years [R. kanagurta population expansion was estimated to have taken place 171,403 to 342,807 years bp.Past population demographic of c models  Fig. 4)R. kanaguc models –51. Bayeon years –53, the FCT = 0.0023, p<0.05) but significant genetic differentiation was present between at least two groups. Genetic differentiation among populations within groups was 0.41% and significant . Presumably this results from the divergence of the BA population from all other sampled sites when site BA was included in the analysis. Genetic variation within populations was high at 99.36% revealing that most of the variation was present within populations, while FST were low and not significant . The analysis was repeated with the Iranian (BA) population (WIO) excluded to test. The test is to discover whether genetic differentiation was evident at finer spatial scales across the IMA and the surrounding seas . The samples were divided into 6 groups; group 1: SOM; group 2: SCS-1; group 3: SCS-2; group 4: SS; group 5: CS, group 6:AS. The FCT value for this additional analysis was not significant (p>0.05).AMOVA analysis performed on the 23 populations, subdivided into seven geographical groups according to the seas . The South China Sea (SCS1 and SCS 2) was further subdivided due to its extensive wide geographical area. AMOVA analysis showed that among group variation was less than 1%  and geographical distance  among the sampled populations  (data not shown) and were not significant (p>0.05) after FDR correction (α = 0.05). An indication of minimal or no genetic structure present. Further analyses of genetic differentiations estimate based on HST (0.0024), NST (0.1581) and KST* (0.0246) were very low and all were not significant, which also inferred a high level of gene flow among sampled R. kanagurta populations (Nm = 136.45 for haplotype-based statistic and Nm = 7.39 for sequence-based statistic).Results of a Mantel test  supporteulations . The graFCT value , a clear signal that there were two genetically different R. kanagurta stocks among the sampled populations, namely the IMA and adjacent regions and a western Indian Ocean population at Bandar Abbas.Based on estimates of k = 2 to 8 in the SAMOVA analysis each group represented a statistical discrete phylogeographic grouping. However, k = 2 yielded the highest Scomber australasicus  [Acanthocybium solandri  [Thunnus albacares  and skipjack tuna, Katsuwonus pelamis  [R. kanagurta, high Hd and low π values suggest a recent population expansion from a low effective population size, i.e., rapid population growth after a bottleneck event with associated accumulation of many new mutations [In this study, high haplotype diversity, Hd values (ranging from 0.952 to 1.000) coupled with very low nucleotide diversity, π (ranging from 0.008 to 0.011) observed in most populations concord with reports from earlier studies of several pelagic marine fishes. Previous studies include the spotted mackerel, = 0.007) ; wahoo, = 0.006) ; yellow = 0.084) . Populat= 0.084) . In the utations .b data for R. kanagurta. There were, however, no distinct geographical lineages present among the IMA and neighbouring marine populations in this study. All of the IMA sites are grouped in Clades 1 and 2 with no obvious pattern of geographic structuring. Clade 3 and 4 were almost exclusively composed of individuals from the western Indian Ocean at Bandar Abbas. Several Iranian individuals were also members of Clade 1  and Clade 2 (hap 228). Two haplotypes present in the Strait of Malacca were members of Clade 3 (hap 45) and Clade 4 (hap 14), respectively. These indicate only very limited genetic exchange between the two regions. The divergence of these clades, however, may have happened in the past during the lowering of sea level during the Pleistocene period. Past geological and climatic events have undoubtedly played a major role in this inferred population expansion. The paleogeography of the IMA changed dramatically across the Quaternary period [Chelon haematocheilus [Lateolabrax japonicas [Four phylogenetic groups were evident in the Cyt y period . This ary period –64. Thisocheilus  and Japaaponicas .R. kanagurta where previously they would have been isolated. Estimation of the time since expansion of R. kanagurta suggests that the timing of this event ranged from 171,403 to 342,807 years bp. The timing of the event is consistent with the occurrence of sea level rises cyclically during the late Pleistocene between 1,600,000 to 10,000 years ago [R. kanagurta and led to smaller isolated populations. During that time, no barriers to dispersal by sea were present, at least between Peninsular Malaysia and the islands of the Riau Archipelago and Sumatra. Thus consequently permitting free migration of R. kanagurta populations across the region. The last glacial maximum (LGM), 30,000 to 19,000 years ago led to the final decline in sea levels to modern times [During the last Interglacial Period, there were marine connections between the Indian Ocean and the South China Sea via the Straits of Singapore . These cears ago . Land brrn times –68. A surn times . This prrn times . During rn times  producinR. kanagurta. They experienced almost complete panmixia evident in modern populations in SE Asia over a vast expanse of two oceans. It can be conjectured therefore, that the brief duration of LGM, followed by a rise of sea level approximately 18000 years ago until the present modern sea levels were reached, was insufficient for R. kanagurta to attain migration drift equilibrium. This would be manifested in low genetic differentiation and low genetic structuring among extant populations until the present time.Isolated marine populations at this time would have been subjected to increased levels of inbreeding. Potentially leading to declines in population levels of genetic variation and genetic structuring of populations during this short period of the LGM. There are also possibilities of individuals that may have dispersed to the east or west of the adjacent flooded basins. This pattern is not concordant however, with the pattern observed in Decapterus macrosoma and D. macarellus [Euthynnus affinis [R. kanagurta populations were in the South China Sea from which populations could have radiated both eastwards and westwards.Similar scenarios have been hypothesized for many marine species present across this region. Examples include the Indo-Pacific butterfly fishes , pelagiccarellus  and East affinis . Based oFST values reported here among the 23 R. kanagurta populations are concordant with a pattern of Cyt b homogeneity and high gene flow (Nm = 136.45). The level of genetic differentiation among populations has a predictable relationship with rates of important evolutionary processes  [Marine fishes with high dispersal potential typically display low levels of population genetic structure and associated high levels of ongoing gene flow . This wo, drift) . Thus, a, drift) .Decapterus macrosoma and D. macarellus [Euthynnus affinis [FST and FCT outcomes. An indication of shallow genetic structure among populations of R. kanagurta. This was confirmed by spatial genetic heterogeneity (k = 2 in SAMOVA) between groups comprising populations in the IMA and surrounding seas vs a second group in the western Indian Ocean . The Mantel test results did not support an ‘isolation by distance’ model. Thus a more plausible explanation for population differentiation between the two distinct groups of R. kanagurta was a result compounded by co-effects of various factors, including historical physical isolation during the Pleistocene epoch, larval dispersal factors and ocean currents.Several other investigations have also reported relative panmixia in marine species across this region. For example, an apparent lack of genetic structure in marine species present in the Coral Triangle region of Southeast Asia has also been reported in several marine taxa including; pelagic scads, carellus  and the  affinis . Slight  affinis –77. ThisEpinephelus coiodes from China (South China Sea) and Malaysia through Indonesia (Southeast Asia) [Historical isolation would suggest the genetic differentiation evident between the IMA and WIO regions may have resulted from fluctuations in sea levels during the late Pleistocene. This led to a phylogeographic break for this species to the north of the Andaman Sea. Studies of other marine taxa across this region have reported co-effects on population differentiation of various factors. This was observed in the orange-spotted grouper, st Asia) .R. kanagurta.Ocean surface currents can also play an important role in marine larval dispersal. The surface current of the Indian Ocean is mainly under the influence of monsoon currents, also referred to as monsoon drift. It flows between the Bay of Bengal and the Arabian Sea . In contb analysis of R. Kanagurta populations in the current study provide strong evidence for two discrete R. kanagurta stocks comprising western Indian Ocean and Southeast Asian populations. The two clades should therefore be considered different management units. While the patterns are clear, results need to be validated because they are based on evidence from a single maternal lineage gene that may not necessarily reflect the complete story.The Cyt R. kanagurta populations in Malaysian waters showed only weak spatial differentiation with high genetic variation as inferred from mtDNA Cyt b. The study did reveal however, two discrete populations of R. kanagurta, the Southeast Asian populations  and an Iranian population (western Indian Ocean). In general, however, the data here suggest that R. kanagurta populations in the IMA and adjacent waters can be regarded as a single stock unit for management purposes. Their inferred demographic history also suggests that R. kanagurta populations potentially expanded in the late Pleistocene and that gene flow appears to be ongoing among extant populations today.The sampled S1 Table(DOCX)Click here for additional data file.


Understanding the population structure and patterns of gene flow within species is of fundamental importance to the study of evolution. In the fields of population and evolutionary genetics, measures of genetic differentiation are commonly used to gather this information. One potential caveat is that these measures assume gene flow to be symmetric. However, asymmetric gene flow is common in nature, especially in systems driven by physical processes such as wind or water currents. As information about levels of asymmetric gene flow among populations is essential for the correct interpretation of the distribution of contemporary genetic diversity within species, this should not be overlooked. To obtain information on asymmetric migration patterns from genetic data, complex models based on maximum‐likelihood or Bayesian approaches generally need to be employed, often at great computational cost. Here, a new simpler and more efficient approach for understanding gene flow patterns is presented. This approach allows the estimation of directional components of genetic divergence between pairs of populations at low computational effort, using any of the classical or modern measures of genetic differentiation. These directional measures of genetic differentiation can further be used to calculate directional relative migration and to detect asymmetries in gene flow patterns. This can be done in a user‐friendly web application called divMigrate‐online introduced in this study. Using simulated data sets with known gene flow regimes, we demonstrate that the method is capable of resolving complex migration patterns under a range of study designs. Fst . Where this occurs, parameters such as genetic differentiation can be significantly skewed Dias . This, iIn asymmetric systems, it becomes important to estimate directional migration to fully understand the processes leading to genetic structuring of populations, as well as to allow for effective conservation and management decisions to be derived from such information , while the smaller of the two estimates indicates the population most likely to behave as a sink. By testing whether these two genetic distances are significantly different from one another, it is also possible to determine whether migration occurs at a significantly higher rate in one direction over the other. The concept of a hypothetical pool of migrants makes it possible to gain new information about direction of gene flow using regular symmetric measures of genetic differentiation.In contrast, this study introduces a new, relatively simple and tangible method for the detection of asymmetric migration from allele frequency data. This approach provides robust information on the direction of migration and is intended to fill the gap between existing complex methods for measuring asymmetric migration and symmetric measures of genetic differentiation. The method is based on defining a This new approach opens up a new dimension of applications where directional measures of genetic differentiation can be used both to explore and to detect asymmetric migration patterns. It is argued that this tool will be of major utility in molecular ecological studies where information about the presence/absence of genetic connectivity and its dynamics among populations is of interest. Consider, for example, the benefit of being able to explicitly identify source and sink populations in metapopulations, having the ability to test whether physical barriers precluded gene flow in a particular direction, or the ability to understand the influence of ecosystem processes on gene flow . Accordingly, we present this method as a new tool to researchers interested in a better understanding of the demographic and evolutionary dynamics of populations and species and more specifically on the use of this information for conservation and management.Ht) and the mean heterozygosity within the individual populations (Hs)  can be estimated from the vector of allele frequencies a∈N as follows:a∈N and b∈N, within‐ population heterozygosity (Hs) and total‐population heterozygosity (Ht) can be estimated from equation For a pairwise comparison involving two populations with allele frequencies a and b can be defined using vector algebra:From these expressions, measures for genetic differentiation between populations with allele frequencies D is calculated as described by  are equivalent to the (biased) estimators used for calculation of genetic differences as compiled for example in Jost . MultiloCrawford , and muld in Nei .To introduce the new analytic approach, the following hypothetical scenario is considered. Assuming two populations A and B with strong directional gene flow in the direction A → B, but no gene flow in the opposite direction . In addition, population B may exchange genes with other populations that are genetically distinct from A and thus containing alleles not present in A. How would such a situation be reflected in the allelic frequencies? Generally, it can be expected that most alleles present in population A are also present in B, whereas alleles present in B may or may not be present in A due to the absence of gene flow from B to A.In the case of neutral loci, the relative allele frequencies of A are expected to be reflected in the migration, and therefore, the proportions of the allele frequencies is assumed to be equal in population B. An idealized example of an allelic matrix (A) is listed in Table f. The allelic frequencies represented in the pool are calculated as the  geometrical means of the allelic frequencies in the corresponding two populations of interest: fi=γaibi with γ=∑iaibi−1 if ∑iaibi>0 and γ = 0 otherwise.This concept can be formalized as follows: For each mutual pair of populations to be investigated (populations A and B in this example), a hypothetical pool of migrants with an allelic composition inferred from the two populations surveyed is introduced. That is, the hypothetical population has an allelic distribution f are defined:f should be symmetric in its arguments, as the order of populations is arbitrary.f should be nonzero only for alleles present at nonzero frequencies in both populations.Alleles not represented in one of the populations can be assumed not to be relevant for gene flow. As a consequence, Let us present a general motivation of this definition of the hypothetical pool of migrants. From the hypothetical scenario, a number of requirements for the hypothetical pool f consistent with these two conditions would beαk,βk>0 for all k, an arbitrary K defining the number of summands involved and arbitrary weighting and normalization constants γ and γk. Here, arbitrary means that, at this stage, any function defined through any arbitrary set of the parameters above would be consistent with considerations 1 and 2. In this vein equation A general form for a1/a2=b1/b2, which mirrors the fact that the proportions of alleles 1 and 2 in population A is reflected in the gene flow and transferred to population B. In order to be consistent with the hypothetical scenario, the pool of migrants is required to reproduce this proportion of alleles as well:fi/fj=ai/aj=bi/bj is required for all combination of alleles i and j present in both populations.In case of strong gene flow only occurring in one direction between populations, initially with mutually different alleles, f needs to be normalized, that is it needs to fulfill ∑ifi=1.As a vector of allele frequencies, In the optimal example data discussed in connection with the hypothetical scenario Table , a1/a2=bf, it can be defined that αk+βk=1 for any k, making an equation of the form0<αk<1 for all k, the most general functional approach for the allelic frequencies in the pooled populations. In cases where populations do not share any alleles, f will not contain any alleles shared with either of these populations. In such cases, genetic differentiation can be set to 1 in both directions.From the 3rd requirement on α1=γ1=1/2 and K = 1, resulting inγ=∑iaibi−1. Hence, the vector of allelic frequencies of the hypothetical population, f, is composed of the normalized geometrical means of the respective components of a and b.Equation f. For this purpose, any standard, nondirectional measures for genetic differentiation can be applied. Here, we choose to use Gst and D as introduced in the section As a measure for directional differentiation, both A and B can now be compared to their shared hypothetical pool of migrants P populations, a P × P‐matrix B can be defined, containing the directional measures for genetic differentiation, asFor the general case of a number of E is a placeholder for estimates of genetic differentiation, such as Gst and D. A is the matrix of allele frequencies containing the population‐specific allele frequency vectors a. The value of a particular matrix element bij can now be taken as an indicator of the potential for gene flow from population i to population j. Low values indicate a high potential for migration, while high values indicate low potential for migration in the direction of interest.Here, f = a, implying that the genetic constitution of A is equal to the hypothetical pool of migrants, whereas the genetic differentiation between the pool and population B is nonzero )=0.15 and D) = 0.42). This result indicates that there is a lower potential for gene flow from B to A than from A to B, which is consistent with the initial hypothetical scenario.With the idealized example data listed in Table It is important to emphasize that, in this study, we only consider idealized conditions. Thus, the impact of more complex scenarios, including, for instance, recent common ancestry, unequal population sizes, or populations not being at equilibrium, is not considered at this point. The motivation is kept as general and clear as possible to encourage further developments, which allow for adapting the method to more specific and complex conditions. Notwithstanding its simplicity, however, we argue that our model seems to be robust enough to deal with a wide range of conditions.Fst and by extension Gst can directly be related to migration Nem≈Here, we use equation D can be used to calculate migration using the finite island model. If we assume that the mutation rate does not differ between populations, μ can be eliminated from the Equation (22) in Jost  provide an estimate for the directional relative migration from population i to population j. To assess whether gene flow is significantly asymmetric, the directional genetic distances presented in the previous section can be assessed by means of a bootstrap procedure.The matrix N = 1000), under three distinct gene flow patterns . While we acknowledge that these three patterns do not cover all scenarios likely to take place in nature, they are good starting points to begin to assess the potential usefulness of the new method.To assess the performance of the method described, we evaluated it under variable gene flow patterns and sample designs. This was performed by simulating multiple microsatellite data sets from two populations (m = 0.00025), medium (m = 0.005), and high (m = 0.05), which correspond to population divergence levels of Fst=0.5,0.05,0.005 using the equation Fst≈14Nem+1 (Wright The ability of the proposed new approach to resolve each of these migration patterns was tested for three levels of gene flow as follows: low (1 Wright .m/2). For the asymmetric bidirectional pattern, gene flow was simulated in both directions, but at 1/4 from population A to population B and 3/4 from population B to population A (m*1/4 and m*3/4).In cases where the pattern was unidirectional, gene flow was simulated in the direction from population B to population A. For the symmetric bidirectional pattern, gene flow was simulated in both directions at an equal rate . This process was repeated for the number of loci, which were also increased from 10 to 100. Three gene flow patterns, under three levels of migration, tested for 10 different sample sizes and 10 different numbers of loci resulted in 180 unique simulation scenarios, for each of which 1000 simulation replicates were generated. When increasing sample size was evaluated, the number of loci was fixed at 50, and when the number of loci was evaluated, the sample size was fixed at 50.For each combination of gene flow pattern and gene flow rate, 1000 microsatellite data sets were simulated for each sample size starting with For each combination of parameters, the number of times out of the 1000 replicates that the method detected higher migration from population B to population A was calculated in percent. For the unidirectional and the asymmetric bidirectional gene flow patterns, the method was expected to detect higher rate in this direction and the result was expected to approach 100%.In the scenario with symmetric bidirectional gene flow, the method was expected to estimate equally high migration rates in both directions, and therefore, we did not expect to see a signal. In this scenario, due to random chance, half of the simulations where expected to be higher in one direction and the other half to be higher in the other direction. Thus, a value of 50% was the expected outcome.5×10−4, a value that is thought to be common at microsatellite loci in vertebrates .In all simulations, mutation rate was fixed at m = 0.005). When the sample size reached 20, the correct direction was found in 95% and more of the simulations for both D and Gst , the least number of correct directions was estimated coming close and up to 75% only for the highest sample size and number of loci tested , is illustrated in Figures D when the sample size was 20 and over , the number of correct directions for estimates calculated from , the numst, Fig.  and 80% When the migration rate was high 0.05), the method did not estimate the correct direction at a sufficiently high percentage. However, the percentage did increase with sample size and the number of loci .divMigrate‐online is designed within the shiny framework  Estimate allele frequencies from resampled data for both populations as well as their hypothetical pool of migrantsD or Gst) between the hypothetical pool of migrants and both populations for all x resamplesCalculate a user‐specified relevant parameter .Test for overlap of the estimated 95% confidence intervals. Where there is no overlap, the directional gene flow components are said to be significantly different (asymmetric).The divMigrate‐online application provides an integrated statistical testing facility, where the asymmetry between migration rates is tested. The statistical significance of differences between directional components of gene flow for a population pair is estimated as follows:This procedure can be coupled with network visualizations, such that only statistically significant asymmetric links between population pairs are plotted.While the web application will be sufficient for most users, there may be those who require more flexibility when conducting specialized analyses . For this purpose, the methods provided in divMigrate‐online are also implemented in the divMigrate function from the diveRsity R package ).Using fastsimcoal2 https://github.com/kkeenan02/diveRsity#development-versionInstall diveRsity from github (instructions can be found at Launch divMigrate‐online: shiny::runGitHub(kkeenan02/divMigrateonline)divMigrate‐online is a web‐based software application and can be accessed on any operating system through a web browser at https://github.com/kkeenan02/divMigrate-online. Pull requests can also be made through this repository.The source code for the software is freely available at m = 0.005). In situations where gene flow was high (m = 0.05), the method failed to detect underlying patterns of migration at a sufficiently high percentage for any of the sample sizes or number of loci combinations tested. The low number of times that the simulated direction was found when migration was high is likely linked to the homogenizing effect of high gene flow, resulting in very low differentiation between populations . The directional components of gene flow were thus obscured . However, the upward trend for both increasing sample size and number of loci suggest that under suitable conditions, the method may be capable of resolving such patterns even for cases where genetic structuring is weak.Here, we have introduced a novel alternative approach that estimates directional genetic differentiation and relative migration from any relevant measure of genetic differentiation, thus allowing for the detection of asymmetric gene flow patterns. Tests using simulated data demonstrate that the new approach can detect underlying gene flow patterns with high confidence when migration is present in either one or two directions. The method performed best when gene flow was intermediate  and n is the number of populations), as is the case in the simulations used here, the use of Gst is recommended . This statistic incorporates complementary information from both Gst and D, suggesting it may be a more generally suitable measure of migration. When using NmAlcala for calculating the percent of correct directions in the different simulation scenarios, the result is very similar to the result obtained when using Gst. The simulation results for NmAlcala is shown in NmAlcala as well as from D and Gst.Based on the simulation results, relative migration calculated from The network plots in divMigrate‐online has the very useful property of representing similar population samples as local clusters within the network space. Because the directional migration calculations require that populations are predetermined, this feature is likely to be useful only as a confirmatory complement to more quantitative methods such as those implemented in STRUCTURE .Only simulated and already published data is used in this manuscript, computer code is freely available at github repository (


Detecting population structure and estimating individual biogeographical ancestry are very important in population genetics studies, biomedical research and forensics. Single-nucleotide polymorphism (SNP) has long been considered to be a primary ancestry-informative marker (AIM), but it is constrained by complex and time-consuming genotyping protocols. Following up on our previous study, we propose that a multi-insertion-deletion polymorphism (Multi-InDel) with multiple haplotypes can be useful in ancestry inference and hierarchical genetic population structures. A validation study for the X chromosome Multi-InDel marker (X-Multi-InDel) as a novel AIM was conducted. Genetic polymorphisms and genetic distances among three Chinese populations and 14 worldwide populations obtained from the 1000 Genomes database were analyzed. A Bayesian clustering method (STRUCTURE) was used to discern the continental origins of Europe, East Asia, and Africa. A minimal panel of ten X-Multi-InDels was verified to be sufficient to distinguish human ancestries from three major continental regions with nearly the same efficiency of the earlier panel with 21 insertion-deletion AIMs. Along with the development of more X-Multi-InDels, an approach using this novel marker has the potential for broad applicability as a cost-effective tool toward more accurate determinations of individual biogeographical ancestry and population stratification. Ancestry-informative markers (AIMs) have been applied to infer the ancestral origin of an individual or to estimate the apportionment of ancestry components in admixed individuals or populations123478Recently, multi-allelic haplotype loci (minihaps), which consist of multiple SNPs that are closely linked, have attracted increasing attention15The two main ethnic minorities in China, the Tibetan and Uygur groups, are subject to different branches of language. Both have unique features in their customary, history and hereditary characteristics. As migration is the basic source of ethnic formation and differentiationIn this study, we detected the hierarchical genetic population structure via an established panel of X-Multi-InDels, which can be successfully amplified in a single multiplex PCR and analyzed via one-capillary electrophoresisNa) of three in the populations studied. Haplotype frequencies  were applied to the full-loci dataset. The population data of most loci were in HWE, as summarized in HWE at two or three loci was observed in two of the three populations (Han and Tibetan), all loci were in HWE after Bonferroni’s correction.The panel of 13 X-Multi-InDels had retained fine genotype resolvability. For evaluating the overall genetic diversity of the loci in different Chinese populations, genotypes were obtained from 462 unrelated subjects (data not shown). As expected, all loci were polymorphic with the number of different alleles . For these reasons, each X-Multi-InDel was treated as an independent marker. However, an in-depth study of the overwhelming LD patterns in the different populations is still needed, and a 3-generation pedigree study will play an important role in the future.As examined, markers separated by different molecular distances (>0.93 Mb) located on the same chromosome may not segregate independently in families. Thus, it is necessary to perform LD analyses. In this study, no significant LD was found after Bonferroni corrections in any of the populations . This obStudies have shown that population history, such as genetic drift and admixture, has an important consequence on the degree of population LDDA, DS and DC matrices were utilized to reveal the genetic relationships of the studied populations (DC matrices for the full-population datasets (Neighbor-joining (N-J) trees built from ulations . The phyulations  construculations . Howeverulations . Similardatasets . All 17 ∆K and mean log likelihood LnPr (X|K)STRUCTURE was used to evaluate how effectively the X-Multi-InDel panel distinguishes among population groups. The full-population datasets were analyzed under different K values ranging from 2 to 17 (K = 2 to K = 17). The consequences under different K settings for the full-population dataset are shown in Although the results provide evidence that a minimal panel of 10 X-Multi-InDels can be used to distinguish human ancestries from three major continental regions with the same efficiency as a panel of 21 InDel AIMs in an earlier studyThe aim of this study was to validate the potential feasibility and utility of X-Multi-InDel markers as a new AIM for ancestry inference. In this study, substantial support for the validity of this panel was provided. The 13 X-Multi-InDels have multiple haplotypes and high levels of heterozygosity in the population samples that we have studied. The panel has demonstrated its capacity to detect hierarchical genetic structures for populations derived from the major continents. Although it was still ineffective to infer ancestral origin and admixture proportions for the groups within a continent using only 10 X-Multi-InDels, the hierarchical effect achieved using this pilot panel is already astonishing, commensurate with the findings from the 21 InDel AIMs published in an earlier studyhttp://www.ncbi.nlm.nih.gov/variation/tools/): 246 individuals of African ancestry , 379 of European ancestry , 286 of East Asian ancestry  and 181 of American ancestry . In addition, each of the subject populations is culturally homogeneous; the subjects can be considered to be generally representative of their own ethnic groups. Abbreviations and inhabiting regions of these populations are detailed on the website https://catalog.coriell.org/1/NHGRI/Collections/1000-Genomes-Collections. The full-population dataset is considered to consist of genotyping results for 10 X-Multi-InDels on each individual (n = 1092) from 14 distinct worldwide geographical locations along with three Chinese subject populations. In particular, under the influence of the sequencing coverage rate, data were obtained from 10 out of the 13 X-Multi-InDels analyzed in this work .The study protocol was approved by the Institutional Review Board of West China School of Basic Science and Forensic Medicine of Sichuan University, and all participants gave written informed consent. The methods were carried out in accordance with the approved guidelines. Blood samples were randomly collected from 462 unrelated healthy subjects  from three collection sites in China . SampledAll blood samples were collected in tubes containing EDTA. Genomic DNA was extracted using a Chelex 100 methodHE) was considered to be a superior variability estimator in population geneticsHE). The X-Multi-InDel panel was optimized to be genotyped in one multiplex PCR reaction containing 13 primer pairs. The amplification of the 13 X-Multi-InDels was performed in a single PCR multiplex reaction using 1× Qiagen multiplex PCR master mix (Qiagen), 1× primer mix and 0.5 ng of genomic DNA in a 10 μL final reaction volume. Thermal cycling conditions consisted of an initial step at 95 °C for 15 min; 30 cycles at 94 °C for 30 s, 62 °C for 90 s, and 72 °C for 60 s; and a final extension at 60 °C for 60 min.The size (molecular extent) range of the 13 X-Multi-InDels was 110 to 298 bp, with an average of 228 bp and a median value of 204 bp. The expected heterozygosity (®ID-X software (Applied Biosystems). Fragment sizes of each reaction were automatically determined with established panels and then manually checked and adjusted. Unsuccessful reactions were retried either until they were successful or they failed twice.Electrophoresis of amplified products was conducted on an ABI PRISM 310 Genetic Analyzer. The electropherograms were analyzed, and the genotypes were assigned using GeneMapperHO) and HEHWEPIC)ChrX-STR.org online calculating toolsThe haplotype frequencies, observed heterozygosity (DsDADs (Nei’s standard distances) is precisely linear with time, it was more appropriate for estimating evolutionary timeDs, DA distances (revised Nei’s genetic distance) were superior for clarifying the evolutionary relationship of closely related populationsDC DC is drift based, relatively independent from a specific mode of mutation of alleles and fluctuations in effective population sizeFor the construction of dendrograms, Genetic population structure analysis was performed by examining the haplotypes by means of the Bayesian clustering procedure implemented in STRUCTURE 2.3.4535455Two ancestry models, “no-admixture” and “admixture”, run STRUCTURE57All of the statistical analyses, including genetic diversity, genetic differentiation, genetic distances and population genetic structuring, were built on the full-loci dataset. As mentioned above, precise haplotype data of three loci, Y3, R1 and G1, could not be obtained from the 1000 Genomes dataset. In the following calculations, almost the same statistical analysis approaches and settings of the full-loci dataset were adopted for the full-population dataset from the 17 global populations (14 from 1000 Genomes and three from Chinese subject populations).How to cite this article: Fan, G. Y. et al. Detecting a hierarchical genetic population structure via Multi-InDel markers on the X chromosome. Sci. Rep.6, 32178; doi: 10.1038/srep32178 (2016).


Bubo blakistoni) was previously widespread on Hokkaido Island, Japan, but is now distributed only in limited forest areas. The population size on Hokkaido decreased during the 20th century due to reduction and fragmentation of the owl’s habitat. To elucidate temporal and spatial changes in population structure and genetic diversity, we analyzed 439 individuals collected over the last 100 years.Blakiston’s fish owl  haplotype and microsatellite analyses. The lowest value for effective population size, which was estimated by moment and temporal methods from microsatellite data, occurred in the 1980s. Five haplotypes were found in the mtDNA control region; most haplotypes were previously widespread across Hokkaido, but have become fixed in separate areas after the bottleneck period. Genetic differentiation among local populations, as indicated by both mtDNA and microsatellite data, likely arose through population fragmentation.The owl population may have been divided into limited areas due to loss of habitats via human activities, and have lost genetic variability within the local populations through inbreeding. Our mtDNA and microsatellite data show that genetic diversity decreased in local populations, indicating the importance of individuals moving between areas for conservation of this species on Hokkaido. Bubo blakistoni), the largest owl endemic to northeastern Asia, comprises two subspecies: B. b. doerriesi in continental Eurasia and B. b. blakistoni on Hokkaido and the southern Kuril islands  and museum and archaeological samples (n = 34). DNA was amplified by polymerase chain reaction (PCR) with primers reported by Omote et al. [From among samples, for which familial information was available, one sib from each family was selected per sampling interval for analyses of mtDNA, which is maternally inherited. Part of the first domain of mtDNA control region  was amplified and sequenced from modern samples excluding sibs  and nucleotide diversity (π) were calculated using ARLEQUIN 3.5.1.2 software [Fst) among local populations  on Hokkaido Island  from Omote et al. ,16; 13D8Ho) and expected (He) heterozygosities were calculated using ARLEQUIN. Allelic richness (Ar) and inbreeding coefficient (Fis) were calculated with FSTAT 2.9.3 software [Ho, He, Ar and Fis values were tested by pairwise non-parametric Wilcoxon signed-rank test by using STATISTICA 10 software (StatSoft). Because the sample size in older periods were small, Fst values among local populations were calculated for each locus and combined by jackknifing, providing confidence intervals via bootstrapping, using FSTAT, and tested with 10,000 permutations by using ARLEQUIN. Effective population size (Ne) was estimated by moment method, the bias-corrected version of the method based on LD [Ne was estimated on each sampling period with monogamous mating model. In the temporal method, Ne was estimated between continuous two sampling periods, assuming that generation time was five to ten years and that census size was 100 to 150. Populations that have undergone a bottleneck often exhibit a reduction in allele number and Ho, with allele number decreasing faster than Ho [Ho is larger than He estimated from allele frequencies assuming mutation-drift equilibrium [Departures from Hardy-Weinberg equilibrium (HWE) and linkage disequilibrium (LD) were tested for each microsatellite locus by using ARLEQUIN. The observed  or completely isolated five local populations in equal sizes (Ne = 8). The Ne values were based on estimation by moment and temporal methods in the latest interval. For estimates, 1000 iterations were performed with the following constant parameters: mean life span = 16 years; age at maturity = 2 years; completely overlapping generations; each female mates with a single male each year; and sex ratio = 1:1.To determine the number of genetic clusters that best fit the microsatellite data, a Bayesian assignment analysis was performed with STRUCTURE 2.3 software ,27. The lculated . Individlculated . Changessoftware . We assuFst values among the populations were significantly high (Table h and π showed similar trends (Table Part of the mtDNA control region (590 bp) was sequenced for 150 individuals, including the old museum and archaeological samples; sequences were obtained for 31 stuffed specimens and three archeological bones. Based on five substitution sites, five haplotypes were detected in the Hokkaido Blakiston’s fish owl population and designated BFH01–BFH05 Table . After 1gh Table . Changesds Table . In the Ne values by moment method on each period and temporal method assuming that the generation time was eight years and that census population size was 100. Both methods indicated that Ne values were 20–50 in the last 20 years, Ne was lowest in 1980s about 10 at most 20, and moment method suggested a remarkably large value in the oldest period 1965–1980.Most genotypes 99.6%) for the seven microsatellite loci examined were determined from blood, frozen tissue, or fibroblast samples, for a total of 407 samples including 120 samples from Omote et al. [9.6% for K = 4 to have the highest ΔK value, which was more than 4× higher than ΔK values of the other numbers of K. The four clusters corresponded mostly to the local populations on Hokkaido , was significantly decreased from the 1965–1980 interval to the 1997–2012 interval in overall Hokkaido population. Although He declined slightly, Ho decreased significantly more rapidly than He from the 1981–1996 to the 1997–2012 interval  in two of four local populations (Akan and Konsen) in the 1981–1996 interval and in two of five (Daisetsu and Akan) in the 1997–2012 interval. Fis value was significantly increased from the pre-1980 to the 1997–2012 interval .The STRUCTURE analysis showed o Figure . Before nt Table . The meaA previous microsatellite analysis of Blakiston’s fish owls on Hokkaido in the intermittent intervals  suggesteFst values from both the mtDNA haplotype and microsatellite data markedly increased after the 1980s , which apparently experienced a bottleneck early in the 20th century, showed lower genetic diversity and increased differentiation in recent (2005–2008) samples than in historical (1899–1915) samples [Tympanuchus cupido) in Wisconsin, which decreased in number by 91% via loss and fragmentation of the habitat, lost genetic diversity for both mtDNA and microsatellite markers over a period of 50 years [Haliaeetus albicilla) populations in Europe experienced dramatic declines during the 20th century, but have retained high levels of genetic diversity, probably because of long generation time (average life span is around 17 years) [Aquila adalberti), the population of which fragmented into small patches, exhibits reduced mtDNA h and π, but a microsatellite analysis showed undiminished level of nuclear diversity [Populations that undergo bottlenecks often lose genetic diversity . Based o samples . The gre50 years . Other s7 years) . The Spaiversity .Ar and Ho (Table Ne value of the Hokkaido population around 1980 (Figure Ne values reported by Athrey et al. [He smaller than Ho [Ho was decreased more rapidly than He in the overall Hokkaido population. Population fragmentation could explain the results of the present study, as it increases biased mating and cuts population into tinier local patches. The Fis values increasing after 1981 (Table Ne of the mitochondrial genome is 4× lower, mtDNA haplotype diversity may be more sensitive for demographic events than nuclear diversity. The present study showed the level of genetic diversity similar to the two eagle species, but Ho and Fis were significantly changed. Therefore, it indicates that the Blakiston’s fish owl population on Hokkaido would have undergone more serious fragmentation and inbreeding.Blakiston’s fish owl also has long generation time, and the present study indicates that the Hokkaido population experienced bottleneck and fragmentation event. Although species with long generation times show resistance to loss of nuclear diversity, as reported by Hailer et al.  and MartHo Table . This in0 Figure  was actuy et al.  and Johny et al.  in popul than Ho . Heteroz81 Table  indicate81 Table . Our resHo for the next 50 years indicated a more rapid decrease in genetic diversity when the local populations remained isolated. Therefore, conserving appropriate habitats for reproduction and promoting movements among local populations will be critical for the survival of populations and the species. Because this species has the ability to move among areas on Hokkaido, as shown in the present study, their movement should be supported and promoted through conservation projects such as corridors connecting isolated habitats.Inbreeding often significantly affects birth weight, survival, reproduction, and resistance to factors such as disease, predation and environmental stress . For conHokkaido population of endangered Blakiston’s fish owls has been fragmented into local areas. In the present study, we clarified that most mtDNA haplotypes of old museum samples were distributed over large areas, and that the local populations have been isolated since 1980s. Microsatellite analyses and field observations showed the smallest population size on Hokkaido in around 1980s when forest and river environments were severely damaged by human activities. These indicate that the Blakiston’s fish owl population has recently decreased and fragmented due to loss and division of habitats on Hokkaido. The genetic diversity has been decreased for the last 30 years due to genetic drift and/or inbreeding in the tiny local populations. Our study demonstrates that movement of a small number of individuals between local areas could restore genetic diversity, and that promoting movements among local populations will be effective for conservation of the species. For long-term conservation management of Blakiston’s fish owls on Hokkaido, it is necessary to monitor changes in genetic diversity and study the movement of individual owls in the field.


Apis mellifera subspecies in the Middle-East, many uniquely adapted and survived to local environmental conditions. In this study we explore the genetic diversity of A. mellifera from Syria (n = 1258), Lebanon (n = 169) and Iraq (n = 35) based on 14 short tandem repeat (STR) loci in the context of reference populations from throughout the Old World (n = 732).Apiculture has been practiced in North Africa and the Middle-East from antiquity. Several thousand years of selective breeding have left a mosaic of Apis mellifera syriaca occurs in both Syrian and Lebanese territories, with no significant genetic variability between respective populations from Syria and Lebanon. All studied populations clustered within a new fifth independent nuclear cluster, congruent with an mtDNA Z haplotype identified in a previous study. Syrian honeybee populations are not associated with Oriental lineage O, except for sporadic introgression into some populations close to the Turkish and Iraqi borders. Southern Syrian and Lebanese populations demonstrated high levels of genetic diversity compared to the northern populations.Our data suggest that the Syrian honeybee A. m. anatolica and/or A. m. caucasica alleles were identified. The policy of creating genetic conservation centers for the Syrian subspecies should take into consideration the influence of the oriental lineage O from the northern Syrian border and the large population of genetically divergent indigenous honeybees located in southern Syria.This study revealed the effects of foreign queen importations on Syrian bee populations, especially for the region of Tartus, where extensive introgression of  Apis mellifera L. into four different evolutionary lineages: West Mediterranean (M), which includes the Western European subspecies Apis mellifera mellifera; North Mediterranean (C); African lineage (A) which groups the African honeybee subspecies; and finally the Oriental lineage (O) located geographically in the Middle East and represented mainly by Caucasian and Turkish honeybees A. m. caucasica and A. m. anatoliaca respectively [Morphometric approaches subdivide the honeybee ectively . Severalectively . Geneticectively -7., A. m. syriaca, occurs throughout most of Syria, Lebanon and the North Jordanian territories [A. m. syriaca belongs to the African lineage A [A. m. syriaca in fact belong to the African lineage A , consistent with the morphometrical data [A. m. syriaca from northern Syria are spatially indistinct from A. m. anatoliaca in Turkey, and a question remains concerning the level of introgression between them. Indeed, based on nuclear single-nucleotide polymorphism (SNP) markers [A. m. anatoliaca and A. m. caucasica.The Syrian honeybeeritories  and sitsineage A ,2 but haineage A . Howeverineage A ,10. As scal data . Nonethe markers , a numbeA. m. meda (Iran) than to A. m. syriaca. The link between Iraqi, Iranian and Eastern Syrian individuals is consistent with mtDNA results reported by [Ftayeh, 1994  reportedorted by , wherebyA. m. syriaca), is known to be the most genetically diverse among the species [A. m. lamarckii, colonies from Egypt and Northern Sudan, on the southerly extent of range of A. m. syriaca. Franck, 2000 [A. m. lamarckii to a newly defined mitochondrial lineage called ‘O’. Not only did such a designation create confusion with Ruttner’s morphometric lineage (O), but [A. m. lamarckii and A. m. syriaca samples, that the former grouped among African populations [The African mitochondrial lineage A, to which most African bees belong , but  demonstrulations ,16.A. m. anatoliaca and A. m. meda) and (A) (A. m. syriaca) from the south. The respective north–south gradient of the (C / O) and (A) mtDNA among honeybee populations from Syria in congruent with this pattern of diversification [Thus the Syrian honeybee population is located in a spatial contact zone between morphometrical lineages O from the north and north-east  to 30 (A7). The highest averages of detected alleles overall loci were 12.36), (12.50) and (13.71) for populations of Rif-Dimashq, As-Suwayda and Quneitra, respectively  and the African (A) lineages whilst the second cluster grouped all our studied populations with the populations belonging to the North Mediterranean (C) and Oriental (O) lineages. When five clusters are assumed (K = 5), referenced populations from each of the four evolutionary lineages  are grouped together and the populations from our study are then grouped in a fifth cluster. Within this fifth group, two populations (Al-Hasakah and Tartus) show clearly heterogeneity in their composition, combining individuals classified as belonging to the fifth cluster with a strong allelic introgression coming from the O lineage , , . Between Syrian/ Lebanese populations and the O lineage the distinction is more subtle  by comparison with values within Syria and Lebanon (FST = 0.01- 0.14)  and are intermediate between those of the A lineage (0.697-0.891) and those of the O lineage (0.615-0.678). Genetic diversity within populations was marginally higher for southern populations, whether from Syria or Lebanon  of populations is well separated from the C lineage with a long branch and a high bootstrap value (100%). Nevertheless, the separation of the Syrian populations from the O lineages seems to be less supported with lower bootstrap values. This difficulty of discrimination from the O lineage is probably because the studied region lies in a contact zone between the African continent and the Caucasus, whereby physical proximity drives introgression between the two lineages.Apis mellifera have already been observed on the other side of the Mediterranean sea between M and A lineages for Spanish populations. Gene flow through Gibraltar has allowed allelic exchanges to take place between North African (Algeria and Morocco) and West European honeybee populations over many generations, a phenomenon previously confirmed at molecular level by a north–south gradient of M and A lineages in Spain and Morocco respectively [Contact zones in ectively .On the NJ tree, Al-Hasakah and Al-Quamishli populations located in north of Syria are grouped together on a branch clearly separated from the other Syrian populations with high bootstrap value Figure . Such seA. m. meda, a subspecies from the O morphological lineage expected to spread to the North of Iraq [A. m. meda can be differentiated from the other O lineages subspecies.Population of Al-Mawsil from Iraq is supposed  to belong to  of Iraq ,2. This The situation is different for the population of Tartus, which is not geographically close to Al-Mawsil. This population is probably influenced by a gene flow coming from the O lineage through the Turkish border and via Cyprus. Anecdotal information from local beekeepers suggests that multiple colonies have recently been imported via this route. Indeed this population contained 10% of haplotypes C1 and C3 which are characteristic to central European subspecies belonging to the C lineage .Apis mellifera syriaca is, geographically speaking, an oriental honeybee and takes, based on classical morphometry, a central position among the four other lineages in Ruttner’s phenogram [A. m. syriaca was supported by [A. m. anatoliaca. The phylogeographic origin of Syrian/Lebanese A. m. syriaca is open for debate. In contrast to the nuclear data presented here in which suggest the sister group status between these bees and group O; mtDNA analyses in fact suggest a north-east African origin [A. m. syriaca fits completely within the mtDNA haplotype Z, a new subgroup of the African evolutionary lineage A described by [A. m. syriaca, remains to be seen, as no North-East African nuclear data is present within the current study (samples Morocco and Algeria only were included).The Syrian honeybee subspecies henogram , closestorted by  who grouorted by ,7. Howevn origin . As suchribed by . WhetherA. m. syriaca and North African honeybees could suggest an independent African origin for this group. However, A. m. syriaca also has affiliations with oriental lineage ‘O’ , and further samples from North Africa are required to clarify the relationship. From a conservation genetic point of view, the presence of an indigenous clade in Syria is encouraging. However, queen importations and allelic introgression imposed by modern beekeeping activities are nonetheless detectable, and our study demonstrates the value of rigorous microsatellite analysis in demarcating population boundaries. For future preservation of the genetic diversity of this locally adapted subspecies we strongly recommend the establishment of genetic conservation centers for the Syrian honeybee Apis mellifera syriaca.In summary, our data suggest that Syrian, Lebanese and Iraqi honeybee populations form a separate fifth major honeybee lineage. Affiliations between ‘A. m. syriaca’ (N: 1462) were collected in 2007 and 2008 from sixteen Syrian, four Lebanese and one Iraqi study site, sampling one worker per managed hive. Sample numbers were as follows: Syria , Lebanon  and Al-Mawsil: 35 from Iraq. The geographical locality of each region and the number of samples is shown in Figure Honeybee workers Apis genome [DNA samples were amplified using multiplex PCR reaction with 14 microsatellite loci. Ten of these microsatellite loci were already known  ,23,31-33s genome  and testAp33: Ap33-1 5′-TTTCTTTTTGTGGACAGCG-3′;Ap33-2 5′-AAATATGGCGAAACGTGTG -3′,A8: A8-1 5′-CGAAGGTAAGGTAAATGGAAC-3′;A8-2 5′-GGCGGTTAAAGTTCTGG-3′,Ap36: Ap36-1 5′-CTACGCGCTTACAGGGCA-3′;Ap36-2 5′-GCCGAAATTCAACGCTCA-3′,Ap66: Ap66-1 5′-TTGCATTCGGTCTCCAGC-3′;Ap66-2 5′-ACTTGCCGCGGTATCTGA-3′.PCR amplifications were carried out in a total volume of 10 μL, containing 1 μL of Taq 5× buffer (Promega), 1.2 mM MgCl2, 25 pmoles of each primers, 1 μL bovine serum albumin (BSA), 25 nmoles of each dNTP, 0.6 unit of Promega Taq polymerase and 1.0 μL of DNA extract. Annealing temperature was set at 54°C for each plex. PCR products were visualized by capillary electrophoresis (Applied Biosystems 3130) and sized with the internal size-standard ROX from Applied Biosystems. GeneMapper v.4.0 software (Applied Biosystems) was subsequently used for allelic scoring.A. m. mellifera from France (n = 50) and Belgium (n = 48), as well as A. m. iberiensis from Portugal (n = 24) for the (M) lineage; A. m. intermissa from Morocco (n = 28) and Algeria (n = 165) for the (A) lineage; A. m. carnica from Hungaria (n = 24), Slovenia (n = 91) and Croatia (n = 50) as well as A. m. cecropia from Greece (n = 49) for (C) lineage; and finally A. m. pomonella from Uzbeckistan (n = 37), Kazakhstan (n = 39), A. m. caucasica from Georgia (n = 25), Armenia (n = 17) and A. m. anatoliaca from Turkey  for the Oriental lineage (O).Our population samples were compared to fourteen additional populations representing the Ruttner’s evolutionary lineages. Subspecies names used in the current study were given according to the geographical origin of the sampled population and from . ReferenK) was estimated using the value for ∆K [K-means clustering algorithm. As described in [Fst values provided by GENEPOP package version 3.1 [Identification of genetically similar groups of individuals was obtained with the software STRUCTURE v2.3.3 . The rese for ∆K . Non-parribed in ; the ‘trribed in . Expecteribed in . To evalribed in . Bootstrribed in  re-samplsion 3.1 . Finallysion 3.1  and betwsion 3.1 .http://www.labarchives.com/The data sets supporting the results of this article are available at https://mynotebook.labarchives.com/share/Alburaki/My45fDIzMzUzLzMvVHJlZU5vZGUvMjYyMjU1MDg2OXw5Ljk=The authors declare that they have no competing interests.MA carried out data collection and analysis. BB and HL carried out data analysis. SM participated in data collection. LG participated in the design of the study and coordination. SWS participated in discussion of the study. AA participated in the design of the study and sample collection. LG participated in data discussion and manuscript preparation. MA participated in the design of the study, data analysis and manuscript preparation. All authors read and approved the final manuscript.Figure S2. Output of the discriminant analysis of principal components (DAPC) for the studied populations form Syria, Lebanon and Iraq along with the four populations of the reference lineages . Figure S3. Pairwise multilocus unbiased estimate of Fst calculated by ARLEQUIN software in each studied and reference populations. Reference populations are representing the four evolutionary lineages M, A, C and O. Table S1. Expected heterozygosity (HE) for each locus in each studied populations as well as overall loci for each studied populations. Refer to Table Table S2. Number of detected alleles for each microsatellite locus in each studied population. Mean values and Standard deviations (s.d.) are calculated for each population. Refer to Table Genetic clustering of few Syrian, Lebanese and one Iraqi honeybee populations and five honeybee subspecies using 14 microsatellite loci. The number of cluster K was set at five populations for the Syrian and Lebanese populations. Click here for file


The initial contact of European populations with indigenous populations of the Americas produced diverse admixture processes across North, Central, and South America. Recent studies have examined the genetic structure of indigenous populations of Latin America and the Caribbean and their admixed descendants, reporting on the genomic impact of the history of admixture with colonizing populations of European and African ancestry. However, relatively little genomic research has been conducted on admixture in indigenous North American populations. In this study, we analyze genomic data at 475,109 single-nucleotide polymorphisms sampled in indigenous peoples of the Pacific Northwest in British Columbia and Southeast Alaska, populations with a well-documented history of contact with European and Asian traders, fishermen, and contract laborers. We find that the indigenous populations of the Pacific Northwest have higher gene diversity than Latin American indigenous populations. Among the Pacific Northwest populations, interior groups provide more evidence for East Asian admixture, whereas coastal groups have higher levels of European admixture. In contrast with many Latin American indigenous populations, the variance of admixture is high in each of the Pacific Northwest indigenous populations, as expected for recent and ongoing admixture processes. The results reveal some similarities but notable differences between admixture patterns in the Pacific Northwest and those in Latin America, contributing to a more detailed understanding of the genomic consequences of European colonization events throughout the Americas. We collaborated with six indigenous communities in British Columbia and Southeast Alaska to generate and analyze genome-wide data for over 100 individuals. We then combined this dataset with existing data from populations worldwide, performing an investigation of the genetic structure of indigenous populations of the Pacific Northwest both locally and in relation to continental and worldwide geographic scales. On a regional scale, we identified differences between coastal and interior populations that are likely due to differences both in pre- and post-European contact histories. On a continental scale, we identified differences in genetic structure between populations in the Pacific Northwest and Central and South America, reflecting both differences prior to European contact as well as different post-contact histories of admixture. This study is among the first to analyze genome-wide diversity among indigenous North American populations, and it provides a comparative framework for understanding the effects of European colonization on indigenous communities throughout the Americas. The population history of indigenous peoples of the Americas is of perennial interest to scholars studying human migrations. The Americas were the last continents historically peopled by modern humans, with recent evidence supporting an initial human entry via Beringia after the last glacial maximum Population-genetic approaches applied to dense genome-wide datasets have recently expanded the forms of evidence available for studies of human migration Most of this genomic work has focused on populations in Latin America and the Caribbean, evaluating the demographic impact of colonizing individuals of European and African descent on local indigenous groups, and relatively few genome-wide investigations have been performed specifically on indigenous North American populations. Owing, in part, to differences in colonization practices between the British and French in North America and the Spanish and Portuguese in Central and South America, North America experienced a substantially different history of admixture To obtain a detailed picture of the genetic landscape of the Pacific Northwest of North America, we generated data on over 600,000 genome-wide single-nucleotide polymorphisms (SNPs) in 104 indigenous individuals from four coastal communities in Southeastern Alaska and British Columbia and two communities living in interior British Columbia. We combined these data with existing data from other geographic regions that together encompass 64 worldwide populations. This worldwide dataset allowed us to investigate the genetic structure of indigenous populations of the Pacific Northwest both locally and in relation to continental and worldwide geographic scales, and to further analyze the admixture landscape in the region. The results uncover both differences in the admixture patterns seen among indigenous Pacific Northwest populations as well as notable differences from comparable patterns observed in admixture studies of Latin America, illuminating differences in the histories of admixture experienced by Native American populations from across the American landmass.We genotyped 104 individuals from six native Pacific Northwest populations and one native population of Mexico at 616,794 SNPs. To assess the new samples in relation to relevant populations from other geographic regions, we integrated our new data with previously published standard data sets on 64 worldwide populations  and S1. P = 0.194; P = 0.012) and South  American groups.Genome-wide mean haplotype heterozygosities in the indigenous populations from British Columbia and Alaska  in the Surui to 0.790 (SD = 0.006) in the African Americans. African populations had the highest heterozygosities , and South American populations had the lowest (mean = 0.485 with SD = 0.046). The admixed African American (ASW) and Mexican American (MXL) populations had relatively high heterozygosities, a likely result of admixture between differentiated ancestral sources.−5, 2R = 0.803, P = 5.3×10−25). If we exclude the six Indigenous Northwest populations, however, the correlation increases , suggesting that while heterozygosity in Indigenous Northwest groups is not incompatible with expectations from the serial-founder model, regional demographic mechanisms, such as peculiarities in migration routes, population-size fluctuations, and recent admixture, have likely had sizeable influences on genetic diversity in the Pacific Northwest. Note that although alternative haplotype block definitions change the scale of heterozygosity values, they have little effect on population patterns , and these populations cluster far apart in MDS and neighbor-joining. In the MDS plot (FST = 0.022 with SD = 0.014 across 276 pairs), separate from a tight cluster containing the 21 East Asian populations (mean pairwise FST = 0.013 with SD = 0.010 across 210 pairs). Considering all comparisons between East Asian populations and Middle Eastern, European, and Central and South Asian populations, mean pairwise FST is 0.091 . The African American and Mexican American populations cluster at intermediate distances between the European cluster and most African and Central American populations, respectively, reflecting the expectation of intermediate placement for admixed groups FST value (0.094), as shown by the long branches separating these populations on the neighbor-joining tree.In general, the shared ancestry identified in the population-level MDS and neighbor-joining plots accords with geography, in agreement with previous observations MDS plot , Middle ρ = 0.655, P = 0.002). This apparent discrepancy potentially arises from uneven sample sizes that can mask differences between geographic regions represented by fewer individuals. Thus, for example, while Oceanians and South Americans have high genetic differentiation , these populations have less influence on the plot than more numerous Europeans and East Asians, who have slightly lower ASD  but are placed much farther apart in The individual-level MDS plot in ρ = 0.938, P<2.2×10−16).To reduce the effect of uneven sampling on the MDS analysis, we randomly subsampled individuals based on their worldwide geographic distribution see . In an MThe subsampled plot further clarifies relationships in the Americas. Unlike in We performed additional MDS analyses to further investigate genomic clustering patterns for the Pacific Northwest, restricting our attention to randomly sampled subsets of individuals from Eurasia and the Americas see . These at0 = 0.958). Exclusion of Central and South Asians from the analysis leaves the locations of European, East Asian, and American individuals largely unchanged (t0 = 0.999); because many of the Pacific Northwest individuals continue to lie on axes oriented toward the Europeans and East Asians, this result suggests that the Europeans and East Asians, and not the Central and South Asian populations, are more likely to be sources of recent admixture signals in the Pacific Northwest.Considering 641 individuals from Europe, Central and South Asia, East Asia, and the Americas, clustering patterns remain largely unchanged from those at the worldwide scale, with the Pacific Northwest individuals placed largely on paths toward the Europeans and East Asians (t0 = 0.997), with some Pacific Northwest individuals oriented toward East Asians. This result provides support for recent East Asian admixture in some Pacific Northwest individuals, but not in other Native Americans.If we consider only individuals of European and American origin , then thL0 = 0.028, with SD = 0.009, between Splatsin and Stswecem'c individuals, and all other Pacific Northwest individuals; P<0.001). These two populations originate from interior British Columbia, whereas the other four populations are coastal, suggesting a difference in demographic history for interior and coastal Pacific Northwest groups. The pattern is consistent with coastal and interior populations being placed in different cultural regions as defined by the Smithsonian Handbook of North American Indians At a local geographic scale in the Pacific Northwest, in a plot excluding all other populations , the disdmixtureK from 2 to 7, which identified clusters specific to the Native American populations . Unlike most Central and South Americans, at K = 3, most Pacific Northwest individuals resemble admixed Mexican Americans in their membership profiles (mean membership in the blue cluster = 0.344 with SD = 0.226 and mean = 0.514with SD = 0.150 respectively). One exception is that the Haida individual located near Africans in The A cluster . At K = K = 4, all 30 runs of Admixture produce a new green cluster for Oceanians, who have ∼100% membership in this cluster, and for East Asians, who have majority membership. A membership signal for this cluster is visible in the Pacific Northwest individuals, but not in Central and South Americans. At K = 5, the new pink cluster is most pronounced in the East Asians; in a similar manner to the pattern at K = 4, while Pacific Northwest individuals have substantial membership in the pink cluster at K = 5 (mean = 0.140 with SD = 0.113), Central and South American populations do not (mean = 0.023 with SD = 0.012). At K = 6, the new yellow cluster is centered on the Pacific Northwest (mean = 0.627 with SD = 0.214) and the Seri, Pima and Mayan (mean = 0.615 with SD = 0.115) populations. Pacific Northwest individuals no longer have appreciable membership in the purple cluster, whereas Central American and Colombian individuals retain substantial membership in this cluster . Because the yellow cluster subsumes mostly the formerly purple membership in the Pacific Northwest populations and, to a lesser extent, some of the pink but not the blue component from K = 5, this cluster likely represents Native American ancestry components distinct from those of Central and South Americans, tracing to genetic differentiation that originated prior to admixture events that followed European contact. Further support for this hypothesis is provided at K = 7 and higher, for which clustering solutions for Pacific Northwest populations do not change appreciably from those at K = 6  for coastal Pacific Northwest populations  and 63 YBP for the interior Splatsin and Stswecem'c populations . Furthermore, we obtained older estimates for the Central American Mayan and Mexican American populations . Finally, we find evidence for a slightly older East Asian admixture event in coastal compared with interior Pacific Northwest populations . However, this latter result should be regarded with caution given the larger variance among estimates across values of K, likely due to difficulties in estimation of small absolute levels of East Asian admixture in our sample set  and Southeastern Alaska , and for three Seri individuals from northwestern Mexico  workshop, convened at the Institute for Genomic Biology at the University of Illinois Urbana-Champaign. This workshop provides training for Native Americans in genomic and bioinformatics techniques as well as opportunities to discuss ethical, legal and social implications of genomic research specific to indigenous communities.In each of the 104 individuals, we obtained genotypes at 616,794 SNP loci included on the Illumina 610-Quad genotyping array, using a minimum of 500 ng of DNA per individual and following the manufacturer's recommended protocol. Raw data generation used an Illumina BeadArray Reader with BeadScan Software, and genotypes were called using the Genotyping (GT) Module (v1.7.4) of Illumina GenomeStudio (v2010.2). We restrict our analyses to autosomal SNPs; consequently, the 26,507 CNV and 15,270 non-autosomal markers present on the array were excluded. We further excluded 992 SNPs with ambiguous genomic position and 1,981 SNPs that failed genotyping in the sample set Stage 1, . We therWe applied a three-stage quality control procedure . First, elpair (v2.0.1) elpair was applied to five non-overlapping sets of 9,999 SNPs  in which all SNPs were separated by at least 100 kb. In these analyses, we considered only the 210,639 autosomal SNPs that were polymorphic in all seven indigenous populations, using genetic map positions obtained by interpolation on the Rutgers combined physical–linkage map elpair using count estimates of allele frequencies in that population. To exclude inter-population relative pairs, we applied Relpair to the whole dataset using count estimates of allele frequencies in the dataset.The presence of related individuals in a dataset can influence genetic diversity patterns We identified 24 intra-population first- and second-degree relative pairs that involved 36 distinct individuals: 11 in the Tsimshian population , six in the Splatsin population , three in the Stswecem'c population , and two each in the Haida  and Tlingit  populations. No inter-population pairs of close relatives were identified.To minimize the number of individuals excluded, we first removed from the dataset 11 individuals that appeared in more than one pair. Next, we removed five individuals appearing in only a single pair, selected on the basis of higher levels of missing data. Following the removal of the 16 related individuals from the preliminary dataset containing 565,635 autosomal SNPs, we repeated the population-genetic quality control procedure Stage 3,  and exclTo investigate the indigenous populations in relation to genetic variation in other populations, we merged the indigenous Northwest dataset with similar publically available data for the 11 populations in release 3 of HapMap project We considered 938 individuals from the H952 subset The HapMap Phase III data set We assembled data on 2,055 individuals—938 HGDP-CEPH, 1,117 HapMap—from 64 populations and 590,461 SNPs shared by these two datasets. At 106,591 SNPs, the datasets had genotypes given for opposite strands, and we converted HGDP-CEPH genotypes to match the HapMap genotypes.We merged the combined HapMap and HGDP-CEPH dataset with the indigenous Northwest dataset at the 475,109 autosomal SNPs the datasets shared in common Stage 4, . At 90,8Use of haplotype statistics rather than per-SNP values provides a way of comparing heterozygosities with SNPs ascertained in geographically limited samples, and has been an informative approach for assessing population differences in heterozygosity for standard SNP panels such as those on Illumina arrays Using this approach, we defined a total of 11,408 high-LD SNP blocks, with a mean of 518.6 blocks per chromosome (SD = 282.2), ranging from 111 blocks on chromosome 22  to 1,040 on chromosome 2 . The 11,408 blocks contained 87,246 of the 475,109 total SNPs, with a mean of 3,965 per chromosome .linkNext, we calculated genome-wide mean haplotype heterozygosities in each population. Considering all individuals in a population, we estimated haplotype sample frequencies in each of the 11,408 haplotype blocks using the –hap-freq option in Pexceeded 0.5 cM/Mb, and (ii) 11,408 random non-overlapping blocks of 5 to 15 contiguous SNPs in the combined dataset. For both analyses, blocks were chosen such that their distribution of lengths exactly matched that of the blocks in the previous analysis with all inter-SNP mean recombination rates below 0.5 cM/Mb.To evaluate how different block definitions might influence haplotype heterozygosities, we repeated the analysis using two alternative block definitions: (i) nonoverlapping blocks of 5 to 15 contiguous SNPs in which all inter-SNP mean recombination rates rdist.earth from the fields package in R 2R), and the Pearson product of moment correlation test P value obtained using cor.test in R. We excluded from this calculation three non-indigenous populations: African American (ASW), Caucasian (CEU), and Mexican American (MXL).To evaluate the relationship between haplotype heterozygosity and distance from Africa, we used the geographic coordinates of the seven new indigenous populations , taking FSTFST matrix, we first performed metric multidimensional scaling (MDS) using the cmdscale function in R. To evaluate how accurately the first two dimensions of the MDS analysis reflect the full 71×71 FST matrix, we calculated the Spearman rank-sum correlation ρ between the Euclidean distances separating all pairs of populations in the MDS plot and the corresponding FST values.We investigated genetic differentiation among the populations in the combined dataset using pairwise multilocus FST matrix using the method of Gascuel bionj function from the package ape in R. To evaluate the robustness of the tree topology, we performed 1,000 bootstraps of the pairwise FST matrix and constructed 1,000 corresponding bootstrap trees. For each edge in the tree constructed from the true data, we counted how many of the trees constructed from the 1,000 bootstraps supported that edge.Second, we constructed a neighbor-joining tree (NJT) from the 71×71 pairwise asdcmdscale in R. To evaluate the representation of the ASD matrix by the first two MDS dimensions, we calculated the Spearman rank-sum correlation ρ between Euclidean distances separating all pairs of individuals in the MDS plot and their corresponding ASD values.We constructed a 2,140×2,140 allele-sharing dissimilarity (ASD) matrix between individuals with the program The combined dataset is heterogeneous both in terms of population sample sizes and in geographic representation . Such heP, containing all I remaining populations  in the combined dataset ordered by increasing geographic distance from Addis Ababa, and constructed a corresponding vector, D, containing the differences in geographic distance from Addis Ababa of contiguous populations in P, where Dj is the difference in geographic distance of populations Pj and Pj+1 . Considering all values in vector D, if Dj was less than a threshold distance d, we assigned populations Pj and Pj+1 to the same population group; otherwise, they were assigned to different groups. Note that geographic groups defined this way can contain populations that are far from one another  only British Columbian and Alaskan individuals, (ii) only African Americans (ASW), and (iii) only Mexican Americans (MXL). Next, we created a vector,  another -c. We thρ between the Euclidean distances separating all pairs of individuals in the MDS plot and their corresponding ASD values. For each of the five analyses, we report the results corresponding to the replicate with highest ρ among the 1,000 replicates.We performed five different analyses. For each analysis, the sampling and MDS were repeated 1,000 times, and for each replicate, we computed the Spearman rank-sum correlation d = 1,000 km to place the 62 populations into eight groups in addition to the Indigenous Northwest populations, the African Americans, and the Mexican Americans  only the 46 populations originating from Europe, Central-South Asia, East Asia, and the Americas, (ii) only the 36 populations from Europe, East Asia, and the Americas, (iii) only the 15 European and American populations, and (iv) only the 27 East Asian and American populations. In addition to the Indigenous Northwest and Mexican American groups, these strategies produced 12 .To formally compare different MDS analyses, we used a Procrustes approach In the MDS plots of 82 individuals sampled from each population group, we performed four analyses that compared the two-dimensional positions of: (i) the 243 individuals shared in the worldwide MDS plot and the MDS plot restricted to Eurasian and American populations, (ii) the 450 individuals shared in the MDS plots restricted to Eurasian and American populations and restricted to European, East Asian, and American populations, (iii) the 287 individuals shared in the MDS plots restricted to European, East Asian, and American populations and restricted to European and American populations, and (iv) the 348 individuals shared in the MDS plots restricted to European, East Asian, and American populations and to East Asian and American populations.L0L0 using 1,000 permutations of the “coastal” and “interior” labels among individuals in both groups. We report a P-value corresponding to the fraction of permuted L0 values greater than or equal to the observed L0 between coastal and interior groups.Finally, we performed an MDS analysis based on pairwise ASD computed using all SNPs in the combined dataset and restricted to the six Pacific Northwest populations. We present the MDS plot after Procrustes transformation using as a reference the MDS plot restricted to European and American populations. We computed the average linkage distance between coastal and interior groups of individuals, dmixture v1.22 dmixture runs identify K genetic clusters, where K is specified by the user, without considering population affiliations of individuals. The method assigns each individual's genotype membership proportions in each cluster. Following author recommendations, prior to running Admixture, we LD-pruned the SNPs in each sample set, using the –indep-pairwise option in Plinkr2 threshold of 0.1.To investigate genetic structure in the combined dataset, we used the model-based clustering algorithm in Admixture analyses, considering values of K from 2 to 12. First, we performed 30 independent runs of Admixture for each K using 67,158 LD-pruned SNPs in the set of 528 worldwide individuals that maximized the Spearman rank-sum correlation ρ between the Euclidean and ASD distances in the 1,000 replicate worldwide MDS analyses (see above). Second, we performed 30 independent runs of Admixture for each K, using 61,131 LD-pruned SNPs in the set of 641 individuals from 41 European, East Asian, and American populations that maximized the Spearman ρ between the Euclidean and ASD distances among the 1,000 replicates at the appropriate scale (see above). The geographically imprecise CEU population was excluded from the Admixture analyses.We performed two series of Admixture run, the termination criterion was set to default following author recommendations: a run was terminated when log-likelihood increased by less than 10−4, regardless of the total number of iterations performed.For each Admixture across the 30 replicates for each value of K using the software Clumppdmixture runs at a given value of K. We defined runs with SSC>0.9 as belonging to the same Admixture clustering solution, or “mode,” and individual genotype membership proportion results were averaged across runs within modes prior to plotting using DistructSeparately for both series of analyses, we explored the clustering solutions inferred by ATo further investigate differential genomic admixture patterns observed between coastal and interior Pacific Northwest populations, we computed an estimated time since admixture with populations of European and East Asian origin. We used eq. 26 from Verdu and Rosenberg dmixture cluster obtained using individuals of European, East Asian, and American origin. We considered European or East Asian admixture fractions as membership proportions in the blue or pink cluster, respectively, for K from 2 to 6 or 3 to 6 (at K = 2 the “pink” East Asian cluster does not exist), respectively, in all individuals from coastal Pacific Northwest populations , and separately in all interior individuals (Splatsin and Stswecem'c). For comparison, we applied the same approach to separately estimate the time since European admixture in the Mexican American (MXL) and Maya populations.We calculated the moments of admixture fractions from a source population as the mean and variance of membership proportions in the corresponding ANote that eq. 26 in ALDERALDER estimates the time of admixture onset from the decay in admixture LD observed in the admixed population, fitted to the admixture LD decay theoretically expected under a model with a single pulse of admixture We investigated the time of onset of European or East Asian admixture separately by population, using the admixture linkage-disequilibrium approach implemented in ALDER one-reference population model, separately using each Pacific Northwest population, the Mexican American population, and the Maya population as the admixed group. We considered only populations of European and East Asian origin with 20 or more individuals in our sample set as possible reference populations for the admixture events. We considered all 475,109 autosomal SNPs in our final data set for this analysis.We lack non-admixed North and Central Native American source populations, and therefore considered the ALDER to test, based on a weighted LD-based test analogous to the admixture f-tests from Reich et al. We then used ALDER to estimate, separately for each admixed and reference pair, the time of onset of admixture and the mean admixture level in the admixed population, using default parameters recommended by the authors For all pairs of populations with significant levels of admixture identified, we used Figure S1above 0.5 cM/Mb. (A) Mean expected haplotype heterozygosity in each population, with standard deviations across the 22 autosomes. (B) The correlation between mean haplotype heterozygosity and geographic distance from Addis Ababa.Genome-wide haplotype heterozygosities using haplotype blocks constructed from low-LD SNPs only. We define genome-wide haplotypes as nonoverlapping blocks of 5 to 15 contiguous SNPs in which all inter-SNP mean recombination rates are (TIF)Click here for additional data file.Figure S2below 0.5 cM/Mb (with a one-to-one correspondence between blocks). (A) Mean expected haplotype heterozygosity in each population, with standard deviations across the 22 autosomes. (B) The correlation between mean haplotype heterozygosity and geographic distance from Addis Ababa.Genome-wide haplotype heterozygosities using haplotype blocks constructed from random SNPs. We define genome-wide haplotypes as random nonoverlapping blocks of 5 to 15 contiguous SNPs in the combined dataset, each block containing the same number of SNPs as the blocks defined previously with all inter-SNP mean recombination rates (TIF)Click here for additional data file.Figure S3dmixture structure among worldwide populations for values of K from 2 to 12. Plots are described in Alternative A(TIF)Click here for additional data file.Figure S4dmixture structure among European, East Asian, and American populations for values of K from 2 to 12. Plots are described in Alternative A(TIF)Click here for additional data file.Figure S5Summary of quality control procedures.(TIF)Click here for additional data file.Figure S6Map of the population groups for analysis worldwide, used in (TIF)Click here for additional data file.Figure S7Map of the population groups for analysis with the Eurasian and American populations, used in (TIF)Click here for additional data file.Figure S8Map of the population groups for analysis with the European, East Asian, and American populations, used in (TIF)Click here for additional data file.Figure S9Map of the population groups for analysis with the European and American populations, used in (TIF)Click here for additional data file.Figure S10Map of the population groups for analysis with the East Asian and American populations, used in (TIF)Click here for additional data file.Table S1aHapMap Phase III. bHGDP-CEPH. cThis study. dThe distance to Addis Ababa along waypoint routes. eGenome-wide mean haplotype heterozygosity and standard deviation across 22 chromosomes. fThe fraction of missing genotype data among the 475,109 total SNPs in the combined dataset, with the standard deviation taken across individuals within the population.Populations in the combined dataset. (DOCX)Click here for additional data file.Table S2FSTMatrix of pairwise genetic dissimilarities among populations estimated using pairwise (TXT)Click here for additional data file.