Russula virescens (Schaeff.) Fr. are among the most popular wild edible mushrooms in Yunnan province, southwestern China. However, almost nothing is known about their biology. This study investigated the diversity and population genetics of a R. virescens ally from Yunnan. A total of 210 samples were collected from 13 geographical locations throughout the main distribution range in Yunnan. The patterns of genetic variation within and among these geographic populations were analyzed using sequences from three nuclear and two mitochondrial DNA fragments. Analysis of the ITS sequences revealed that samples from Yunnan showed 3–6% sequence difference from R. virescens samples from North America and Europe and formed a distinct clade. Our multilocus population genetic analyses suggested frequent gene flow among geographic populations of the R. virescens ally in Yunnan. Interestingly, the nuclear and mitochondrial genes exhibited different levels of gene flow and recombination. We discuss the implications of our results for understanding speciation, reproduction and conservation of this important biological resource.Mushrooms that are morphologically indistinguishable from  Tricholoma matsutake, Cantharellus cibarius, Boletus edulis, and Russula virescens.Ectomycorrhizae (EcM), symbiotic associations between fungi and plants’ roots, are commonly found in natural ecosystems and are indispensable for healthy forests and grasslands. In the complex EcM network, carbohydrates, minerals and water are frequently exchanged between host plants and fungal mycelia Russula are among the most important wild edible mushrooms, contributing an estimated thirty percent of the overall edible mushroom trade in Yunnan R. virescens found in Europe and North America. Indeed, most Chinese researchers have traditionally referred to “Qingtoujun” from southwest China as belonging to R. virescens or this species complex Yunnan province in southwestern China is a famous biodiversity hotspot At present, Qingtoujun cannot be artificially cultivated. Like many other wild gourmet mushrooms from this region, the increasing consumer demands can lead to overharvesting. For example, over 70% of the Qingtoujun in wild mushroom markets are small immature fruiting bodies with their caps closed and have relatively little commercial value . Furthermore, there are increasing disturbances to the forest ecosystem and loss of habitats associated with this mushroom in this region. At present, the impact of these changes on natural populations of this mushroom remains virtually unknown. Indeed, very little is known about the biology of Qingtoujun, including its ecology, evolution and population genetics. Understanding these fundamental issues could be important for developing sustainable conservation and utilization strategies.Russula genus have been conducted in China Russula in China (and elsewhere) has been the dearth of morphological features to reliably distinguish the over 700 published species in this genus. In addition, only a small number of Russula species have their DNA sequences represented in databases. The recent application of DNA sequences (including DNA barcodes) for strain and species identifications are beginning to shed light on the taxonomy, systematics, and phylogenetic relationships within this genus. For example, a recent molecular phylogenetic and population genetic study revealed that “Dahongjun” (or the “Big Red Mushroom”) from southern China contained at least three phylogenetically divergent lineages and that none of these lineages belonged to Russula vinosa, a morphologically similar species distributed in Europe and North America but has been used to describe Dahongjun in China. Interestingly, the three lineages are largely geographically structured and there is a high level of genetic diversity within each of the lineages in southern China Despite their important ecological roles and economic significance, relatively few studies of the R. virescens from Europe and North America? Second, how is genetic variation structured within and among geographic populations of Qingtoujun from Yunnan? Are these populations genetically differentiated as was shown in the “Big Red Mushroom”? And third, are population structures inferred from the nuclear and mitochondrial markers different for geographic populations of Qingtoujun from Yunnan?In this study, we collected and analyzed 210 mushroom specimens from 13 geographical locations in Yunnan. The patterns of genetic variation within and among these geographic populations were analyzed using sequences from five DNA fragments including three from the nuclear genome , and two from the mitochondrial genome [the ATPase subunit 6 gene (ATP6) and the cytochrome oxidase subunit III gene (COX3)]. The data are used to address the following specific questions. First, how much DNA sequence divergence is there between Qingtoujun from Yunnan and A total of 210 isolates of Qingtoujun were collected from 13 geographical locations in Yunnan province in southwest China during the rainy season (June-August) in 2010 and 2011. Since Qingtoujun is not on the endangered or protected species list, no specific permit is required for picking and trading this mushroom. Our samples were obtained from a combination of sources: local farmers’ markets, roadside mushroom sellers, and our own collection efforts in public lands that required no specific permits to access. Our sampling locations spanned about 600 km from north to south and 500 km from east to west. These 13 local populations belonged to nine Prefectures . The sample size and geographical coordinates for each population are presented in CAACAATTAATACAATGGTAAG 3′) and ATP6-4A (5′ CCAAAGGAATTAAAGTGAAT 3′) for the mitochondrial ATP6 gene; CHSI-5S (5′ CGATTTCACCGCAAGCAC 3′) and CHSI-5A (5′ CGAACCCTCAGCGTAGTT 3′) for the nuclear CHSI gene; COX3-1S (5′ ACTTTAGGTGCAGTTATG 3′) and COX3-1A (5′ TAGCTCCTCTTCTATCTCCTTG 3′) for the mitochondrial COX3 gene; and RPB2-2S (5′ CAACGGTGTCTGGATGGG 3′) and RPB2-2A (5′ CAGGGAAAGGAATGATACTGG 3′) for the nuclear RPB2 gene. Sequencing was performed by BGI Co. Ltd . Sequences resulting from this study are deposited in Genbank with the following accession numbers: KC598287∼KC598496 (ITS); KC598849∼KC599024 (RPB2); KC599025∼KC599200 (CHSI); KC598497∼KC598672 (ATP6); and KC598673∼KC598848 (COX3).Genomic DNAs of all samples were individually extracted using the CTAB method slightly modified for fungi The complete ITS sequences were assembled using DNAStar v. 5.0. These sequences were then used as queries to retrieve closely related sequences with comparable lengths from GenBank . The retRussula genus analyzed so far have shown genotypes to be consistent with each cell being a diploid and/or dikaryotic, sequences at the two single copy nuclear genes that we obtained directly through PCR from each Qingtoujun fruiting body likely contained information from both alleles.Since the heterozygous sites observed for the ITS gene fragments could be due to its multi-copy nature within individual ribosomal RNA gene clusters and not between homologous chromosomes, our analyses didn’t rely solely on information from the ITS gene. Instead, the population genetic analyses were based on four other DNA fragments that were either single copy nuclear gene (CHSI and RPB2) or mitochondrial genes (ATP6 and COX3). Because fertile mycelia and fruiting bodies in the STF values and determine the potential correlation between genetic and geographical distances (Mantel test). The analysis of molecular variance (AMOVA) was performed to estimate the relative contributions of geographic separation to the overall genetic variation. As mitochondrial genomes are typically homogeneous within individual cells and among cells within a fruiting body In order to analyze the two nuclear gene sequence information fully, we first inferred the putative allelic sequences for both the CHSI and the RPB2 loci respectively from each individual mushroom using the Bayesian method by PHASE 2.1 AI) AI values were adjusted based on the numbers of variable nucleotide sites to obtain the standardized RbarD values The relationships between alleles at these four genes were analyzed using the program Multilocus version 1.3b KC598287 ∼ KC598496 [the internal transcribed spacer (ITS) regions of the nuclear ribosomal RNA gene cluster]; KC598849∼KC599024 [the second largest subunit of the nuclear RNA polymerase gene (RPB2)]; KC599025 ∼ KC599200 [the chitin synthase gene (CHSI)]; KC598497∼KC598672 [ATPase subunit 6 gene (ATP6)]: and KC598673∼KC598848 [the cytochrome oxidase subunit III gene (COX3)].We successfully obtained ITS sequences from all 210 Qingtoujun specimens. Analysis of the 601 aligned nucleotides identified a total of 13 ITS sequence types for the 210 specimens. The distributions of these genotypes among local populations are shown in R. virescens from America and Europe  contained 4, 72 and 100 strains respectively. The four strains in haplotype 1 were distributed in four different populations while haplotypes 2 and 3 were found in every geographic population .Different from the above two mitochondrial genes, the two nuclear genes sequenced here were found to contain heterozygous nucleotide sites within many of the individual mushroom samples. Because the samples we used were fruiting bodies which, in most basidiomycete mushrooms, represented the diploid or dikaryotic phase of their life cycle, we hypothesized that the heterozygous sites in our strains were most likely due to sequence divergence between the two alleles within each individual at the specific loci. The differences were unlikely to be due to paralogous copies of the genes because many individuals were completely homozygous at either or both the CHSI and the RPB2 loci. Therefore, to analyze the allelic relationships among our samples, we first inferred the allelic sequences within each individual at each of the two loci. This was accomplished using the program PHASE 2.1 At the CHSI gene fragment, 354 nucleotide sites were aligned and analyzed. A total of 32 variable sites were found among the 176 strains. PHASE analyses inferred a total of 82 haplotypes. The two most frequent haplotypes were haplotype 1 and 13. Haplotype 1 was found 101 times  distributed in 10 of the 13 populations. Haplotype 13 was found 93 times and distributed in all 13 populations. Among the 82 inferred unique haplotypes of the CHSI gene, 58 were found only in one population each. The relationships among the haplotypes are presented in At the RPB2 gene fragment, 429 nucleotide sites were aligned. A total of 25 variable sites were found among the 176 strains. PHASE analyses inferred a total of 76 haplotypes at the RPB2 gene fragment. The most frequent haplotype in our sample was haplotype 8, with a total count of 71 and distributed in 12 populations. Thirty-five of the 76 haplotypes were found only in one population each. The relationships among the haplotypes at this locus are presented in R. virescens from Yunnan province. To identify the overall population structure as well as to compare the potential differences in population structure between the nuclear and mitochondrial genomes, we analyzed three datasets. The first included information from all four loci; the second from only the two nuclear loci; and the third from only the two mitochondrial loci. The results of our analyses are shown in The haplotype information obtained above was used to understand the population structure of STF values ranged from 0.012 (between GeJiu and DaLi separated by about 227 kilometers) to 0.123 (between ShiZong and BaoShan which are about 484 kilometers apart)  . In totas apart) . Though ar genes . Howeverar genes , the paiThe overall analysis of molecular variance (AMOVA) based on the three types of data revealed that over 90% of the genetic variation was found within local populations . While AR. virescens populations from Yunnan was correlated to their geographical distances, we conducted a Mantel test. As shown in STF values of four genes combination  regions within the nuclear ribosomal RNA gene cluster have been broadly used to identify fungal species and are the recommended DNA barcode for fungi R. virescens, consistent with their morphological similarity. However, our samples showed 3∼6% ITS sequence divergence with R. virescens samples from Europe and America. The sequences from GenBank  that showed >99% sequence identities to our sequences were all from unidentified samples from Japan or China. The significant ITS sequence divergence between ours and those from Europe and North America suggest that our samples likely belong to a novel species different from but closely related to the European and American R. virescens, similar to those found in many other fungi R. virescens ally, belonging to the same R. virescens species complex as those from Europe and North America.Significant divergence in ITS sequences has been found between geographically distinct populations in a variety of fungi, including several basidiomycete mushrooms R. virescens species complex in Yunnan. First, we found broad ITS genotype sharing among geographic populations, with the most frequent ITS genotype shared by 193 strains from all 13 local populations. Second and more importantly, our analyses based on data from the four gene fragments through several tests  all showed consistent evidences for gene flow. Specifically, the haplotype inferences identified that three of the loci  each had at least one haplotype distributed in all 13 local populations analyzed here. The fourth loci RPB2 had one haplotype (haplotype 8) that was found in 12 of the 13 local populations. Indeed, AMOVA results showed that over 90% of the observed genetic variation were found within individual local populations. The Mantel test also showed no statistically significant correlation between the STF values and geographical distances, suggesting that the observed gene flow is frequent and unlikely to be impeded by the hundreds of kilometers between populations examined here. There are many valleys and mountains in Yunnan and several are located in our sampled regions. While these geographic barriers may temporally facilitate divergence, they did not seem to have prevented gene flow among these populations.Though evidence for statistically significant population differentiation was found, our study revealed strong evidence of gene flow among populations of the Laccaria amethystina separated by over 450 km apart showed limited genetic differentiation with the STF values showing no correlation with geographic distance, similar to what we found here. The ectomycorrhizal mushroom Tricholoma matsutake from southwestern China also showed limited  genetic differentiation. The limited genetic differentiation between geographic populations in R. virescens could be due to several factors including natural long-distance dispersal of basidiospores At present, large-scale population genetic studies of ectomycorrhizal mushrooms are still relatively limited. However, previous studies have revealed examples of both frequent gene flow among geographic populations for some species and significant differentiation for others. For example, Roy et al. R. virescens. Frequent sexual reproduction would generate random associations among alleles while extended asexual reproduction would generate significant non-random associations among alleles. Our analyses showed that alleles between the two nuclear genes as well as between the nuclear and mitochondrial genes were associated with each other not significantly different from random, consistent with prevalent sexual reproduction in natural populations of R. virescens species complex in Yunnan. Our results thus emphasize the importance of maintaining mature fruiting bodies in natural environments to conserve the genetic diversity and ensure future reproduction of this mushroom.Associations among alleles from within the same or different loci are commonly used as indicators for sexual (i.e. recombination) or asexual reproduction in natural populations of microorganisms. Testing allelic associations is typically accomplished through the analyses of multilocus linkage disequilibrium R. virescens species complex from Yunnan. First, more polymorphic sites and higher numbers of haplotypes were observed for nuclear genes than for mitochondrial genes. For example, four and three haplotypes were observed for the mitochondrial ATP6 and COX3 genes respectively. In contrast, 82 and 76 haplotypes were inferred from the nuclear CHSI and RPB2 genes respectively. This large difference was mainly due to the limited number of polymorphic nucleotide sites for the two mitochondrial genes: 6 polymorphic sites total for the combined 633 nucleotides of the ATP6 and COX3 gene fragments vs. 57 polymorphic sites for the 783 bp of the CHSI and RPB2 genes. Our results contrast with those found in human fungal pathogens Candida albicansCryptococcus gattiiOur analyses revealed several differences between the two mitochondrial and two nuclear genes in our samples of The second difference between mitochondrial and nuclear genes in our samples was in the patterns of allelic association and linkage equilibrium. As expected, the nuclear genes showed random associations among their alleles while alleles at the two mitochondrial genes showed significant non-random associations. However, limited but clear evidence of phylogenetic incompatibility was found between the two mitochondrial genes, consistent with low level of mitochondrial recombination. Our results add to the growing literature on mitochondrial recombination in natural populations of fungi R. virescens species complex in Yunnan. At present, the mechanism for the observed difference in gene flow between the two genomes is not known. However, one possibility is that after basidiospores disperse to new environments and germinate to form homokaryons, existing resident dikaryons instead of self-sterile homokaryons might be involved in fertilizing the immigrant homokaryon. As has been shown in laboratory experiments, this type of mating would result in a new dikaryon with a recombinant nuclear genome containing the mitochondria of the immigrant Aside from the levels of polymorphism, geographic distributions of the observed genetic variations also showed differences between mitochondrial and nuclear genes. Specifically, evidence of genetic differentiation was very limited among populations for mitochondrial genes while statistically significant genetic differentiation was observed for nuclear genes. Such a pattern suggests differential gene flow between the nuclear and mitochondrial genes with the mitochondrial genome showing a greater rate of gene flow than nuclear genes among populations of the R. virescens species complex from southwest China. This mushroom is one of the most economically and ecologically important in that region. Our analyses identified that the Yunnan populations of this species had ITS sequences distinctly different from those from Europe and the Americas. Abundant genetic variations were identified within the Yunnan samples at the sequenced loci, especially at the two nuclear gene loci. We found limited geographic structuring, frequent gene flows, and little evidence for significant correlation between geographic and genetic distances among these populations. In addition, our analyses revealed differences in the patterns of nuclear and mitochondrial genetic variation. The mechanisms for such differences and the potential speciation of the Yunnan populations of the R. virescens species complex from those in other regions require further analyses.In conclusion, our study is the first to examine the population biology of the Figure S1A maximum parsimony tree of the mitochondrial ATP6 sequences of the R. virescens species complex in Yunnan, southwest China. A total of four ATP6 haplotypes were found in our samples. For each ATP6 haplotype (HAP), the first number represents the haplotype assignment; the characters after represent the county/community from where the strains were sampled; the last number represents the total number of strains belonging to the specific haplotype. Only representative sequences of unique haplotypes from each location are shown.(TIF)Click here for additional data file.Figure S2A maximum parsimony tree of the mitochondrial COX3 sequences of the R. virescens species complex in Yunnan, southwest China. A total of three COX3 haplotypes were found in our samples. For each COX3 haplotype (HAP), the first number represents the haplotype assignment; the characters after represent the county/community from where the strains were sampled; the last number represents the total number of strains belonging to the specific haplotype. Only representative sequences of unique haplotypes from each location are shown.(TIF)Click here for additional data file.Figure S3A maximum parsimony tree of the nuclear CHSI gene sequences of the R. virescens species complex in Yunnan, southwest China. A total of 82 unique CHSI haplotypes were found in our samples. For each CHSI haplotype (HAP), the first number represents the haplotype assignment; the characters after represent the county/community from where the strains were sampled; the last number represents the total number of strains belonging to the specific haplotype. Only representative sequences of unique haplotypes from each location are shown.(TIF)Click here for additional data file.Figure S4A maximum parsimony tree of the nuclear RPB2 gene sequences of the R. virescens species complex in Yunnan, southwest China. A total of 76 unique RPB2 haplotypes were found in our samples. For each RPB2 haplotype (HAP), the first number represents the haplotype assignment; the characters after represent the county/community from where the strains were sampled; the last number represents the total number of strains belonging to the specific haplotype. Only representative sequences of unique haplotypes from each location are shown.(TIF)Click here for additional data file.Figure S5Relationship between the levels of genetic differentiation  estimated based on all four gene fragments  and geographical distances between pairs of local populations of the R. virescens species complex from Yunnan, southwestern China. The X-axis represents geographic distances (in kilometers) between pairs of local populations and the Y-axis represents STF values between pairs of local populations.(PDF)Click here for additional data file.Figure S6Relationship between the levels of genetic differentiation  estimated based on the two nuclear genes (CHSI and RPB2) and geographical distances between pairs of local populations of the R. virescens species complex from Yunnan, southwestern China. The X-axis represents geographic distances (in kilometers) between pairs of local populations and the Y-axis represents STF values between pairs of local populations.(PDF)Click here for additional data file.Figure S7Relationship between the levels of genetic differentiation  estimated based on the two mitochondrial genes (ATP6 and COX3) and geographical distances between pairs of local populations of the R. virescens species complex from Yunnan, southwestern China. The X-axis represents geographic distances (in kilometers) between pairs of local populations and the Y-axis represents STF values between pairs of local populations.(PDF)Click here for additional data file.Table S1Genetic differentiation  estimated based on information from all four gene fragments  between all pairs of geographic populations of the R. virescens species complex in Yunnan, southwest China.(DOCX)Click here for additional data file.Table S2Genetic differentiation  estimated based on information from the two nuclear gene fragments (CHSI and RPB2) between all pairs of geographic populations of the R. virescens species complex in Yunnan, southwest China.(DOCX)Click here for additional data file.Table S3Genetic differentiation  estimated based on information from the two mitochondrial gene fragments (ATP6 and COX3) between all pairs of geographic populations of the R. virescens species complex in Yunnan, southwest China.(DOCX)Click here for additional data file.


Tetranychus evansi is an emerging pest of solanaceous crops worldwide. Like many other emerging pests, its small size, confusing taxonomy, complex history of associations with humans, and propensity to start new populations from small inocula, make the study of its invasion biology difficult. Here, we use recent developments in Approximate Bayesian Computation (ABC) and variation in multi-locus genetic markers to reconstruct the complex historical demography of this cryptic invasive pest. By distinguishing among multiple pathways and timing of introductions, we find evidence for the “bridgehead effect”, in which one invasion serves as source for subsequent invasions. Tetranychus evansi populations in Europe and Africa resulted from at least three independent introductions from South America and involved mites from two distinct sources in Brazil, corresponding to highly divergent mitochondrial DNA lineages. Mites from southwest Brazil (BR-SW) colonized the African continent, and from there Europe through two pathways in a “bridgehead” type pattern. One pathway resulted in a widespread invasion, not only to Europe, but also to other regions in Africa, southern Europe and eastern Asia. The second pathway involved the mixture with a second introduction from BR-SW leading to an admixed population in southern Spain. Admixture was also detected between invasive populations in Portugal. A third introduction from the Brazilian Atlantic region resulted in only a limited invasion in Europe. This study illustrates that ABC methods can provide insights into, and distinguish among, complex invasion scenarios. These processes are critical not only in understanding the biology of invasions, but also in refining management strategies for invasive species. For example, while reported observations of the mite and outbreaks in the invaded areas were largely consistent with estimates of geographical expansion from the ABC approach, historical observations failed to recognize the complex pathways involved and the corresponding effects on genetic diversity.The spider mite  Specimen collection records are typically the only source of information concerning the historical distribution of a species. However, geographical range is not a static attribute of a species, a point particularly well illustrated in the case of invasive species, for which rapid range expansion can occur when individuals move from their native areas. Such dispersal events at large spatial scales and multiple colonisations, may occur within short time frames, making it difficult to reconstruct the history of accidentally introduced populations. In addition, introductions are typically characterized, at least initially, by small population sizes, often with no immediately recognizable environmental or agricultural impacts. Thus, initial stages of invasions often go undetected, further complicating historical reconstructions. Detecting minute arthropod pests, including spider mites, is particularly challenging. As a result high-impact invaders can reach unmanageable population numbers before major agricultural damage appears.Most spider mites are small  and cryptic and easily transported hidden within commodities Tetranychus evansi, is native to South America T. evansi was misidentified and confused with the closely related species T. urticae, until molecular markers were first applied to distinguish between the two species The red tomato spider mite, T. evansi based on mitochondrial (mt) Cytochrome Oxidase I (COI) and nuclear ribosomal Internal Transcribed Spacer (ITS) sequences we showed that multiple cryptic introduction events occurred in the Mediterranean basin corresponding to at least two main distinct lineages (I and II), each originating from distant geographical regions in South America T. evansi the pathways and modes of colonization remain unresolved. Like many cryptic invaders, the colonization of new continents by T. evansi is possibly the result of several alternative invasive scenarios, each of which requires formal testing to provide an adequate spatial and temporal picture of the global invasion.In a previous study of T. evansi samples into clusters based on genetic variation, and then use these clusters in the subsequent analysis to infer colonization history. We argue that the ability to understand the invasion history of this species, and others like it, is critical to defining strategies for quarantine, control, and management.The development of multi-locus genetic markers (e.g. microsatellites), and the application of novel ABC methods can provide detailed estimates of demographic and historical parameters allowing quantitative comparisons of alternative invasion scenarios. Here, we ask which historical scenario best explains the colonization of Europe and Africa from South America, based on genetic data and historical modeling. In particular, we examine the number of colonization events in these regions and their timing. We also ask whether the two distinct mite lineages now in Europe coexist separately or if the current populations reflect an admixture event following colonization. To answer these questions, we first group T. evansi contained low levels of genetic diversity. Individuals in eight of 31 populations showed no variation across all loci  with divergent evolutionary histories. Here, we first tested whether variation in nuclear loci also supported these clusters, and then we used the results in the subsequent ABC analyses. A Bayesian clustering analysis of microsatellite data implemented in structure confirmed the presence of two genetic clusters, I and II (COI lineages >90%), a few individuals originating from two Portuguese populations  were assigned to cluster I with a high posterior probability (>98%), while the majority of Portuguese mites fell into cluster II. In addition, some individuals from these two populations  and from Ameal  were identified as possessing apparent hybrid ancestry between the two clusters  were identified .When entified  correspoentified . An addientified , essentientified . The samFST = 0.13; data not shown), hence further tests of alternative introduction scenarios were not performed with this cluster. For populations in cluster I, alternative invasion pathways were exhaustively compared in a subsequent ABC analysis.Most of the introduced populations in cluster II displayed low genetic polymorphism  and weakT. evansi invasion emerged. First, the colonization of the African continent (AF) by the Brazilian population (BR-SW) preceded the arrival of the mite in Europe (EU). This hypothesis is supported by five scenarios, which result in a cumulative posterior probability of 0.66. By contrast, the cumulative probability of the four scenarios supporting the colonization of Europe before that of Africa is 0.31 and the scenario of the invasion of the MED population before the others was very poorly supported (posterior probability 0.03). Second, the MED population resulted from an admixture event having occurred between the African (AF) population, and a second independent introduction of the same Brazilian (BR-SW) origin. The choice of this admixture event is supported (p<5%) in six scenarios (cumulative posterior probability 0.71). Among the four best supported scenarios of an admixed MED population . The admixture between the same populations also appears in scenario 8 (posterior probability 0.05), except that the admixture event is estimated to have occurred later, posterior to the mite entry in Europe. Likewise, two other scenarios  were well marked, with clearly peaked posteriors (t4), which roughly corresponds to 72 years, assuming 10 (87 years) to 15 (58 years) generations of T. evansi per year in the Mediterranean-invaded areas  between AF and EU populations , in particular considering the small upper confidence limit. Estimates of the introduction time of the mite in Africa remains however poorly defined (t2) with a median of 3200 generations ago (90% CI: [1570–4750]). According to scenario 7 (the most likely one), the divergence between the Brazilian (BR-SW) and the African (AF) populations was dated (t1) at 133 [106–160] years ago, assuming 20 to 30 generations per year in tropical Africa . However, because of the scenario choice, the time period involved in the t2 simulation is much longer than for t1; the probability to estimate accurately short time periods is superior. Differences between the posterior and prior density curves for population size associated to the introduction events suggest that the invasion resulted in a reduced number of mites having arrived in AF (N2 = 61), in MED (N4 = 202) and in EU (N3 = 231)  . While ttd and ta: divergence and admixture times, respectively) and the admixture rate (r). The divergence between the two parental populations was dated at a median of 56,188 generations ago , whereas admixture time (ta) was estimated at only 13 generations. The admixed Portuguese population results from mites from Lagos and Luz de Tavira in estimated proportions of 0.87 and 0.13 respectively.The hypothesis of a hybrid origin of part of the Portuguese population was also clearly supported by the ABC analysis . DemograT. evansi to two well-supported clusters, which matched the COI lineages I and II found previously by Boubou et alCOI sequences) and the old divergence time between them , it is clear from the admixture event detected in Portugal and also by previously identified heterozygote ITS sequences et alT. evansi in Europe. Rather, this invasion resulted from at least three independent introductions. Two originated from highly divergent sources in Brazil, lineage I and II. A third introduction involved lineage I also from Brazil, but arriving at Europe through Africa.Genetic signatures from multi-locus microsatellites clearly assigned invasive populations of Tetranychus evansi persists at very low densities in Brazil, where it is not considered a pest, as in most of South America T. evansi densities in these regions, as expected in co-evolving prey-predator systems As predicted by the hypothesis of South America being the native area for this mite, higher genetic diversity was found in Brazil compared to the introduced range, except the admixed populations from Portugal. However, a precise source of the invasion could not be assigned. This lack of resolution might result from under-sampling source regions which can lead to underestimating native diversity T. evansi was first reported in Africa, with records from Mauritius in mid-1950's, La Réunion, 1976, Zimbabwe, 1979, South Africa, 1985, Kenya, 2001 and Tanzania, 2003 , so that a time lag might have occurred before the mite was first reported from southern Spain in mid-1990. This discrepancy could be due to the fact that, although high agricultural activity in southern Spain has been accompanied by intensive pest detection programs , it is likely that spider mites can remain hidden in surrounding vegetation until they switch to crops and reach pest levels. For example, the palm mite, Oligonychus afrasiaticus, was first reported in Israel early 1980's on weeds, but only started to cause commercial damage on palm trees almost two decades later An additional level of complexity of the invasion as inferred from ABC, could be detected in southern Spain (MED) where the mites resulted from an admixture of mites from Africa (AF) and an independent introduction from southwest Brazil (BS-SW). Consistently, the MED group includes the most polymorphic populations  in the worldwide invaded areas . Genetic data suggest the arrival of T. evansi from Brazil, Bahia, where it was mistakenly under the name of Tetranychus marianaeIn the case of eastern Africa, estimates of the mite introduction in the continent remains poorly defined. According to ABC analysis, the Brazilian (BR-SW) and the African populations diverged about 130 years ago. Yet, according to historical records, the mite arrived much more recently in Africa  to eastern Africa (AF), which was the source of both the admixed MED population from southern Spain and other European populations (EU), the later displaying an expanded distribution, having been recently detected in remote areas in east Asia, as in Japan and China. This colonization scenario is consistent with an invasive “bridgehead” effect, in which a particular invasive population serves as the source of colonists for other areas The analysis supports a second independent source of introduction from the Brazilian atlantic coast (BR-AT). These populations (cluster II) exhibited low genetic diversity limiting our ability to test alternative invasion pathways. However in the invaded region, mites of cluster II were found widely in Portugal, but very restricted in Catalonia, despite intensive surveys done in Southern Europe. We did not have the statistical power to confirm the scenario of mites arriving directly from Brazil to Portugal; however this pathway was better supported than the alternative scenario of mites arriving from Brazil to Catalonia and from there to Portugal.T. evansi thus provides an excellent opportunity to evaluate what biological traits differ among invasive or non-invasive populations.The geographical distributions of the two distinct invading genotypes, lineages I and II, suggest that the two mite lineages differ in invasive potential. The occurrence of two invasion biologies or syndromes in The introduction of species in new environments is often associated with founder events resulting in low genetic variability and consequently low evolutionary potential. Nevertheless, numerous invasions have successfully occurred despite these genetic bottlenecks T. evansi, introduced populations were found to exhibit low genetic polymorphism and in some cases they were invariable, raising the apparent paradox of how they become successful invaders despite experiencing significant genetic bottlenecks during colonization T. evansi. It remains to be tested if new recombinant invasive genotypes via intraspecific hybridization resulting from admixture of the two lineages possess novel phenotypes that could facilitate invasion success. Such genetic changes can be substantial and might have unexpected behavioural or physiological consequences In the case of T. evansi is thought to be constrained in its range by the absence of diapause in colder climates T. evansi in more temperate climates is sorely needed. For an agricultural pest such as T. evansi, dissecting biological traits favoring the invasion potential will also provide relevant information to management strategies in the face of global change Research has also suggested that adaptive evolution may play a crucial role in invasions of some species This study confirms that recent advances in approximate ABC approaches to reconstructing historical demography can be particularly informative for the study of invasive species, even when invasion histories are complicated. A great number of alternate scenarios can often be constructed to explain current invasions. Here we challenged the genetic data with 24 possible invasion histories , about 10 ng of total DNA and DNase-free water. Multiplex PCRs consisted of an initial denaturation at 95°C for 15 min, 40 cycles at 94°C for 30 sec, annealing at 57°C for 90 sec, extension at 72°C for 60 sec and a final extension of 60°C for 30 min. PCR products were detected using an ABI PRISM 3130xl (Applied Biosystems), 1 µL of the PCR product diluted at 1/300 was mixed with 18.9 µL of Hi-Di™ formamide (Applied Biosystems) and 0.1 µL of GeneScan™ −500 LIZ® Size Standard (Applied Biosystems). Alleles were scored using GeneMapper® V4.0 (Applied Biosystems).Individual mites were collected from a total of 31 localities representing both native (Brazil) and introduced (Africa and Europe) areas . A totalCOI gene (868 bp) previously obtained for most of the genotyped mite individuals was used to assign samples to either lineage I or II previously described in Sequences of the ITS region  and a portion of the mt dnasp v.4.20.2 FST computed under the Kimura 2 parameter (K2P) model arlequin version 3.1 arlequin. Two sets of groups were compared: 1) populations within two clusters ; and 2) populations within native and introduced areas. Permutations to determine significance were set at 10,000 and the distance method used to determine variation in the dataset was the number of different alleles present in each group.Indices of genetic diversity  were calstructure v.2.3.3 a priori information about collection sites. In all simulations performed in structure, we used a “burn-in” period of 5×104 followed by a run length of 5×105 MCMC iterations, which were sufficient to give a stable α and estimate of the log probability of the data. We first ran separate models including the whole data with K ranging from 1 to 12. We selected the upper K value as 12, based on the number of groups of populations identified by FST values (data not shown). Because two main clusters were identified (I and II), these were submitted separately to a second run of analyses, using K = 1–5 and K = 1–7, respectively. Finally, a third analysis was performed on all introduced populations (K = 1–5) from Europe and Africa. For each value of K, 10 independent runs were computed. To estimate the most likely K value, we used the ΔK statistics structure. distruct program The program structure, e.g. as Hardy-Weinberg (HW) equilibrium within populations and linkage equilibrium, might be violated, in particular HW for arrhenotokous species, as often occurs for spider mites pca-gen version 2.9.3 Because some of the assumptions of structure, further refined based on diversity indices information (Approximate Bayesian Computation (ABC) analysis was performed using samples in the cluster I identified by ormation . The natormation , they weormation , only th6 simulated data sets (on average 106 per scenario), each scenario being given a uniform prior probability (1/24). The prior distributions of parameters are reported logit transformation to parameter values. All ABC computations were performed using a beta-version of the program diyabcv.2 Tentatively all possible evolutionary scenarios leading to the following four populations were included in the analysis with the only two conditions: i) the BR-SW population was considered as ancestral to all other populations and ii) the MED population was the only population potentially issued from an admixture of BR-SW and either one of the two remaining populations. A total of 24 scenarios  were foustructure analysis (see r) of alleles of the parental population Lagos in the admixed population and the time of the divergence (td) and the admixture events (ta) were estimated according to the evolutionary scenario shown in 6 simulated data sets was generated and posterior distributions of parameters were estimated using the 15,000 (1%) simulated datasets closest to the observed dataset for the local linear regression, after application of a logit transformation to parameter values. diyabc was used to estimate all ABC computations.The occurrence of an admixture event in Portugal, suggested by microsatellite data in mites collected from this region, was tested using ABC methods. One admixed population, formed from pooling the two Portuguese populations (Castanheira do Ribatejo and Aljustrel) and two parental populations (Luz de Tavira and Lagos), was created based on information obtained from the ysis see . The proFigure S1Schematic representation of the 24 competing introduction scenarios considered for the inference of the introduction routes of Tetranychus evansi in Africa and Europe tested by ABC analysis. Four populations were considered in each analysis as identified by the clustering structure analysis . N2, N3, N4 correspond to number of founder individuals and were assumed to be different in all introduced populations. NB, NA, NM, NS correspond to stable effective population size in Pop1 – Br-SW, Pop2 – AF, Pop3 – EU and Pop4 – MED, respectively. The time of event (ti), in number of generations, corresponds to the time at which an introduced population has diverged from its source population; the duration of the initial bottleneck (db) was assumed to be the same in all the introduced populations. Time 0 is the sampling date. Admixture rate r relative to population Pop1 – BR-SW and 1-r to either population Pop 2 – AF or Pop3 – EU. We assumed that all populations evolved as isolated demes and no exchange of migrants occurred after the introduction. All parameters with associated prior distributions are described in (TIF)Click here for additional data file.Table S1Microsatellite loci assembled in two multiplex PCR sets, used to genotype Tetranychus evansi mites (details on microsatellite isolation and characterization are described in ). The fluorescent dye  used for each locus to 5′end label either the forward or reverse primer is indicated.(XLS)Click here for additional data file.Table S2Prior distribution of demographical and historical parameters describing: a) the 24 introduction scenarios of Tetranychus evansi in the Europe and Africa and b) in the admixture model in Portugal. Prior distributions for marker parameters were the same in the two ABC analyses.(XLS)Click here for additional data file.


Akodon cursor occurs in dense rainforest from northern (8° S) to southern (26° S) states along the Atlantic coast of Brazil. Previous karyological and molecular data revealed two major clades, one including northern (8-15° S) and the other southern (19-26° S) populations. The center of geographic distribution (15-20° S), which included the state of Espírito Santo, was identified as a potential vicariance region. Since river barriers are among the most discussed models in the study of Neotropical diversification, we examined whether the Rio Doce (19° S) plays an important role in shaping the population genetic divergence of A. cursor by including samples from Espírito Santo in the analysis. Our results showed that the northern-southern division region in Atlantic forest was no coincidence with the presence of the Rio Doce by refuting the hypothesis that this river is an effective barrier to gene flow between populations. Instead, we found evidence that isolation by geographical distance shaped the phylogeographical structure in the southern lineage. However, there is uncertainty about effectiveness of the processes involved and further studies based on wider sampling are needed. This habitat loss has been closely linked to fragmentation but the region still harbors one of the highest percentages of endemic species in the world, with many species and even genera of vertebrates still being described  and the influence of geographical barriers  is widely distributed in rainforest along the Atlantic coast of Brazil  and Bahia (northern lineage) were used to represent the two major lineages previously observed and to help polarize the analysis.The aim of the present work was therefore to examine the partitioning of genetic variability in A. cursor collected at 10 spawning sites in three states with Atlantic rainforest: Bahia (n = 10), Espírito Santo (n = 48) and São Paulo (n = 5)  and 5-50 ng of template DNA. The thermal cycling conditions were: 94 °C for 3 min followed by 40 cycles of 94 °C for 30 s, 48 °C for 40 s and 72 °C for 90 s, with a final extension step of 72 °C for 5 min.DNA was extracted from samples of frozen muscle or liver obtained from 64 specimens of  (n = 5) , as descThe amplicons were purified with either PureLink PCR purification kits (Invitrogen) or ExoSAP-IT® (USB Corporation), according to the manufacturers instructions, and then quantified in a NanoDrop spectrophotometer prior to cycle-sequencing. Sequencing used forward and reverse primers and was done directly in a capillary ABI3700 genetic analyser with the Big Dye Terminator protocol (Applied Biosystems). Electropherograms were inspected visually using BioEdit v. 7.09  and aligh) and nucleotide (π) diversity indices were computed  and low nucleotide diversity, such as is frequently encountered in populations undergoing a population bottleneck followed by demographic expansion, with the accumulation of mutations . The phylogenetic tree topology and haplotype network agreed with the clustering of the northern and southern lineages observed in the maximum parsimony analysis presented by The MP tree topology (data not shown) and median-joining network  showed tAkodoncursor had a significant population genetic structure of ΦST = 0.51, and AMOVA, which was used to assess whether the Rio Doce influenced this group division  and Águia Branca or Governador Lindenberg (ES) populations showed high divergence  grouped as a monophyletic clade . Despitevergence .Although this was no evidence of previous fragmentation in populations from São Paulo, this apparent “monophyly” may reflect either the lack of sampling of intermediate populations  or the fact that the SP and ES populations are geographically far (~1000 km) from each other in the southern lineage. These observations suggest that isolation by geographical distance and the history of vicariance  could play an important role in shaping the population genetic structure of the southern lineage, including populations from São Paulo and northern Espírito Santo.et al., 2010). However, testing the isolation by distance mechanism requires further investigation.It is well-known that sampling the extremes of a population distributed as cline can yield spurious evidence of vicariance. The data for Águia Branca and Governador Lindenberg provide further evidence of this potential isolation by distance and support the conclusion that in the geographically complex Atlantic Forest it is unlikely that forest refugia or barrier hypotheses alone will account for general patterns of lineage diversification (Thomé A. cursor (Specimens with karyotypes 2n = 14 and 15 were distributed across all haplotype network branches , thus in. cursor .A. cursor in the southeastern Atlantic forest. Rather, the data raise questions about the influence of isolation by geographical distance in modeling the phylogeographical structure of the southern lineage of A. cursor. However, there is uncertainty about effectiveness of the processes involved, and sampling on a wider scale is necessary to assess the importance of distinct isolation mechanisms.In conclusion, our data do not support a primary influence of the Rio Doce as a putative barrier in shaping the divergence between the two main mitochondrial lineages of


Plasmodium falciparum on the coast and in the low-lying inland areas. Local heterogeneity in the epidemiology of malaria suggests that parasites from multiple locations will need to be surveyed to define the population biology of P. falciparum in the region. This study describes the population genetics of P. falciparum in thirteen villages spread over four distinct catchment areas of Papua New Guinea.The South West Pacific nation of Papua New Guinea has intense year round transmission of P. falciparum isolates from the parasite populations of two inland catchment areas, namely Wosera (number of villages (n) = 7) and Utu (n = 1) and; and two coastal catchments, Malala (n = 3) and Mugil (n = 3). Analysis of the resultant multilocus haplotypes was done at different spatial scales (2-336 km) to define the genetic diversity , linkage disequilibrium and population structure throughout the study area.Ten microsatellite loci were genotyped in 318 STF) did not correlate with geographic distance suggesting that parasite populations are fragmented. Population structure was also identified between villages within the Malala area, with the haplotypes of one parasite population clustering with the neighbouring catchment of Mugil.Although genetic diversity was high in all parasite populations, it was also variable with a lower allelic richness and expected heterozygosity for inland populations compared to those from the more accessible coast. This variability was not correlated with two proxy measures of transmission intensity, the infection prevalence and the proportion multiple infections. Random associations among the microsatellite loci were observed in all four catchments showing that a substantial degree of out-crossing occurs in the region. Moderate to very high levels of population structure were found but the amount of genetic differentiation (P. falciparum in this region is likely to be a consequence of the high transmission intensity combined with the isolation of human and vector populations, especially those located inland and migration of parasites via human movement into coastal populations. The variable genetic diversity and population structure of P. falciparum has important implications for malaria control strategies and warrants further fine scale sampling throughout Papua New Guinea.The observed population genetics of  Plasmodium falciparum is a major cause of morbidity and mortality in tropical and sub-tropical regions of the world [P. falciparum, allowing the rapid evolution and dissemination of advantageous traits such as drug resistance and antigenic variability. Malaria control would be more effective if the target parasite populations could be surveyed before an intervention to determine the extent of (i) genetic diversity, as a predictor of the populations' resilience to interventions; (ii) linkage disequilibrium, to understand the potential for multilocus haplotypes to spread through the region; and (iii) population structure, to map the distribution of diversity over geographic space and thus infer patterns of parasite migration. Population genetic surveys are therefore an essential preliminary step in designing the most appropriate and effective malaria control measures and as a baseline upon which to monitor their impact.Malaria arising from infection with he world . The difP. falciparum, as defined by multilocus genotyping, shows a general pattern of increasing genetic diversity, but decreasing linkage disequilibrium (LD) and population differentiation in association with the parasite transmission intensity (Americas < Asia Pacific <Africa) [The worldwide population genetic structure of <Africa) ,3. This <Africa) . In Asia<Africa) . Moderat<Africa) , and loc<Africa) ,8 have b<Africa) . Signifi<Africa) ,11. The <Africa) ,12 and l<Africa) . This emP. falciparum, Plasmodium vivax, Plasmodium malariae and Plasmodium ovale) occurs. Whereas, in the highlands, Plasmodium spp. infection is present but is mostly due to sporadic epidemic transmission [P. falciparum transmission is high but variable among different regions, villages and even clusters of houses within villages [Anopheles punctulatus complex and several minor species contributing to the transmission of malaria [Anopheles farauti s.l. is known to fly only short distances (< 2 km). For these reasons, it is possible that gene flow among different parasite subpopulations of Papua New Guinea is limited. The presence of local parasite population structure has been suggested by differing seroprevalence to a P. falciparum antigen (S-antigen), even among closely spaced villages [P. falciparum in Papua New Guinea. To date, there has only been one other study investigating the population biology of P. falciparum in Papua New Guinea by multilocus genotyping [The epidemiology of malaria in the South West Pacific nation of Papua New Guinea is highly variable. Malaria transmission is confined to the coastal and lowland zones where intense perennial transmission of the four major human malaria species  and Tatemba) . Resultant chromatograms were analysed with Peak Scanner V1.0 software (Applied Biosystems) to count the number of 3D7- and FC27-specific peaks  and thus estimate the total number of P. falciparum clones. From this we calculated two molecular epidemiological correlates of P. falciparum transmission intensity for each village: (i) the infection prevalence, calculated as the proportion (%) of samples with a positive PCR result and (ii) the proportion of infections with multiple clones, defined by the presence of more than one peak  on the chromatogram. Only the P. falciparum isolates shown to contain single msp2 alleles were used for microsatellite genotyping.Genomic DNA was extracted from whole blood samples using the 96 well QiaQuick DNA extraction kit (Qiagen). To identify escribed . This apP. falciparum isolates was performed using the Illustra Genomiphi V2 DNA amplification kit  according the manufacturer's instructions. Each of these isolates were then genotyped using ten putatively neutral microsatellite markers developed by Anderson and colleagues , the number of alleles per locus (A) and the expected heterozygosity, calculated as p is the frequency of the ith allele and n is the number of alleles in the sample. Because A is strongly influenced by sample size it is only reliable for large sample sizes (e.g. catchments) therefore we also calculated the allelic richness (sR) which is normalized on the basis of the smallest sample size and based on the rarefaction method developed by Hurlbert [SAI) was calculated using the program LIAN version 3.5 [ST F between haplotypes [Allele frequencies for the 13 villages and overall were determined using CONVERT version 1.31 software. This software was then used to generate input files for the various population genetic software used . GeneticHurlbert  and implHurlbert . Associasion 3.5  for the 1−∑i=1npi, where pplotypes ; and RSTplotypes  using onplotypes ).ST Fand ST Rwas tested by comparison with 95% confidence intervals from 1023 permutations. As ST Rconsiders the distances between alleles it is the more sensitive of the two statistics. Correlations between genetic differentiation and geographic distance  were measured using the Mantel test [Structure v. 2.3 software [Structure assigns individual multilocus haplotypes probabilistically to one of a number of clusters (K) or jointly to multiple clusters (admixture) based on the allele frequencies at each locus [K = 1-20 for 10,000 Monte Carlo Markov Chain (MCMC) iterations after a burn-in period of 10,000 using the admixture model and correlated allele frequencies for the analysis. The most likely K was defined by calculating the rate of change of K, ΔK, according to the method of Evanno et al [Significance for both tel test  in FSTATtel test . As smalsoftware  was alsoch locus ,47. The no et al  and geogno et al . Each nono et al , the algmsp2 genotyping identified 765 P. falciparum isolates. Throughout the study area there was considerable variation in the infection prevalence  and the proportion of isolates that contained multiple clones   contained single msp2 alleles and were thus selected for genotyping at ten microsatellite loci. Microsatellite genotyping confirmed 213 single clone infections  while revealing a further 219 multiple clone infections including 106 with only two clones . Haplotypes were reconstructed from the single clone infections (single haplotypes) and two clone infections (dominant haplotypes) as described in detail in the Materials and Methods. The remaining 113 samples were found to contain more than two clones and were excluded from the dataset. There was no significant genetic differentiation between single and dominant haplotypes within each catchment  so the two datasets were combined, giving a total of 318 multilocus haplotypes for population genetic analysis  as isolates successfully genotyped (n) in the dataset showing that all haplotypes were unique (Table sR) and expected heterozygosity (eH). Because it had a larger sample size, Wosera had the highest A but the normalized statistic s, Rwas similar to that of Mugil and Malala and it had the second lowest e Hafter Utu. For the villages, Utu and several of the Wosera villages had the lowest values for all diversity parameters compared to the majority of coastal villages  and the correlates of transmission intensity . In addition, allele frequencies varied among sites . The latter analysis was used to confirm LD in the absence of haplotypes predicted from multiple infections, which can result in higher estimates of recombination and thus bias against the detection of LD. To check whether associations may have arisen from clonal propagation, LD can be measured among unique haplotypes [Non-random associations among loci (multilocus LD) were measured for all complete haplotypes n = 159) and also those from single infections (n = 111) by calculating the Index of Association  distributed over two inland and two coastal catchment areas in the north of the country where malaria research and control efforts are focused. A previous analysis of the same set of microsatellite loci in two coastal villages (Buksak and Mebat) approximately 80 km apart in nearby areas of Madang Province reported a high degree of genetic diversity (He = 0.62 - 0.65), a lack of significant LD  and minimal differentiation between the two populations (FST = 0.015) [He = 0.64-0.77) and that parasite populations are heterogeneous with moderate to very high population structure detected throughout the study area . Differences between the study of Anderson et al [P. falciparum population structures throughout Papua New Guinea.This is the most extensive study to date investigating the genetic structure of  He = 0.6 - 0.65,  He = 0.6 - 0.65, P. falciparum in the region, these are discussed below. Given the potential difficulty in controlling diverse parasites, such knowledge has important practical implications for malaria control across the country.Levels of diversity are an indication of the fitness of the parasite population and thus how difficult it may be to target with drugs or vaccines. The diversity among catchments was high but also variable, with the inland populations of Wosera and Utu having lower levels of allelic richness and heterozygosity than the coastal populations of Malala and Mugil. The lack of association between the molecular epidemiological correlates of transmission and diversity in Papua New Guinea suggest that a number of factors influence the population genetics of As Papua New Guinean parasite populations had high levels of genetic diversity it was not surprising to find a lack of significant LD in all four catchments studied, while the LD found for the total dataset can be explained by the Wahlund effect due to the observed population structure . Within ST Fvalues can be explained by small sample sizes for all except for that between Matukar/Bunu and Karkum (Mugil), and Nindigo and Gwinyingi (Wosera). However, the cluster and network analyses did not suggest any geographic population structure within catchments other than Malala. An isolation-by-distance model did not explain the observed geographic population structure, suggesting that there is a non-continuous distribution of diversity in Papua New Guinea. This fragmented population structure may be explained by movement of the human host with a lack of transport between catchments combined with higher rates of migration into coastal catchments as described above.The population structure between the inland (Wosera and Utu) and coastal populations  indicated the existence of barriers to gene flow and thus parasite migration, and other possible influences on population structure such as natural selection and genetic drift within each catchment. Although only ~50 km from the provincial capital of Madang town, it takes several hours to travel to the remote Utu village with only one road entering and leaving and there is no direct route of travel (road or air) between Madang and the East Sepik Provinces. Whereas, the lower extent of population structure between Malala and Mugil, and small amount of mixing between these two populations indicated in the cluster and network analyses probably reflects their direct connection via the Pacific Highway. Despite this direct route of possible gene flow between Malala and Mugil, the population structure was significant. Migration of diverse parasites into these locations via human movement may partially explain this observation. For Malala, there is a boarding school with students attending from across Madang and East Sepik Provinces. Lower levels of differentiation among catchments occurred between Wosera and Malala so it is plausible that some gene flow occurs between these locations via movement of students and their guardians. In Mugil, there is constant movement of people ferrying to and from the well-populated Karkar Island (17 km of the coast), however the lack of samples from this location makes this speculation difficult to confirm. The genetic differentiation and cluster analyses also indicated that population structure occurs on a local scale (< 20 km). In particular, in the Malala area the village of Amiten/Susure which is located ~6 km inland was found to be genetically distinct from Malala/Suraten and Wakorma which flank the school. In fact, these analyses suggested that the Amiten/Susure population was more similar to parasites from the Mugil villages, suggesting that they may represent the "true Madang coast" population. In Mugil and Wosera, some villages were also differentiated. Here, the low but significant P. falciparum in Papua New Guinea cannot be ignored. At least six distinct anophelene species transmit malaria in the region. In Madang Province, Anopheles farauti 4 is predominant in the inland villages such as Utu and Amiten/Susure whereas A. faurauti 1 is more common in villages that are proximal to the coast [Anopheles koliensis and Anopheles punctulatis are the predominant vectors [P. falciparum population structure between two co-existing vectors, Anopheles gambiae and Anopheles funestus [P. falciparum in Papua New Guinea is likely the result of a combination of factors, including the limited movement of both human and mosquito hosts, in addition to the greater accessibility of the coast in comparison to the inland populations. The public health implication for these findings is that parasite populations that might be assumed to be similar for development of malaria control strategies, such as vaccines, in fact are genetically distinct and thus may respond differently to such interventions. However, some populations may be easier to control if they are isolated from external sources of parasites. Utu, having the least diverse and most genetically differentiated parasite population appears to be the most isolated catchment, and thus a location where malaria control strategies may be the most efficient.There are other possible explanations for the patterns of population structure within Papua New Guinea. People from different catchments belong to different language groups , indicathe coast ,32. In t vectors . The popfunestus  suggestifunestus ,31,32 sofunestus  may alsoP. falciparum can help guide malaria control efforts. Such knowledge is becoming paramount as the Papua New Guinean government prepares to intensify malaria control, not only for guiding these control efforts but also monitoring whether they are having an impact on parasite populations. This broad spatial survey of the population genetics of P. falciparum in Papua New Guinea has identified high but variable levels of genetic diversity, random associations among loci and population structure found at different spatial scales. The results have significant implications for malaria control in the Pacific region and show that countrywide population surveillance is needed throughout Papua New Guinea.A detailed understanding of the population genetics of The authors declare that they have no competing interests.P. falciparum samples and helped interpret the results. N.S., J.N., B.K. and L.T. co-ordinated the field studies and collected samples. C.O.B. performed the network analysis. P.M.S. provided logistical support. J.C.R. helped co-ordinate the study and write the paper. A.E.B. conceived the study design, co-ordinated the study, performed experiments and data analysis and wrote the paper. All authors read and approved the final manuscript.L.S. performed experiments, data analysis and helped write the paper. J.W. and P.O.N. performed experiments and data analysis. I.M. co-ordinated the field studies, provided P. falciparum transmission in Papua New GuineaIntensity of . Two correlates of parasite transmission, the infection prevalence and the proportion of infected people carrying multiple P. falciparum clones, were estimated by msp2 genotyping.Click here for fileAssociations between transmission intensity and genetic diversity in Papua New Guinea. Matrix of Spearmans rank correlation coefficients (ρ) and in brackets, associated p-values.Click here for filePlasmodium falciparum populations from Papua New GuineaMicrosatellite allele frequencies for . n/a.Click here for filePlasmodium falciparum populations of Papua New GuineaGenetic differentiation between . Matrix of pairwise FST  and RST values  between villages.Click here for fileAssociations between geographic distance (km) and pairwise genetic differentiation in Papua New Guinea. Matrix of Mantel correlation results and in brackets, p-values.Click here for filePlasmodium falciparum microsatellite haplotypes from Papua New GuineaDefinition of the most probable number of clusters for 318 . Structure analysis (ΔK plots) for A) all haplotypes (B) inland and coastal datasets and (C) and each of the catchments.Click here for filePlasmodium falciparum haplotypes within four catchment areas of Papua New GuineaNetwork analysis of 318 . Weighted network of haplotypes showing relationships among individuals for each catchment (A) Wosera (B) Utu (C) Malala and (D) Mugil. Each coloured circle represents a haplotype (node), and black lines indicate shared alleles among individual haplotypes. The threshold for a connection was set at three matching loci between two haplotypes.Click here for file


Accurate determination of genetic ancestry is of high interest for many areas such as biomedical research, personal genomics and forensics. It remains an important topic in genetic association studies, as it has been shown that population stratification, if not appropriately considered, can lead to false-positive and -negative results. While large association studies typically extract ancestry information from available genome-wide SNP genotypes, many important clinical data sets on rare phenotypes and historical collections assembled before the GWAS area are in need of a feasible method  to infer the geographic origin and potential admixture of the study subjects. Here we report on the development, application and limitations of a small, multiplexable ancestry informative marker (AIM) panel of SNPs (or AISNP) developed specifically for this purpose.Based on worldwide populations from the HGDP, a 41-AIM AISNP panel for multiplex application with the ABI SNPlex and a subset with 31 AIMs for the Sequenome iPLEX system were selected and found to be highly informative for inferring ancestry among the seven continental regions Africa, the Middle East, Europe, Central/South Asia, East Asia, the Americas and Oceania. The panel was found to be least informative for Eurasian populations, and additional AIMs for a higher resolution are suggested. A large reference set including over 4,000 subjects collected from 120 global populations was assembled to facilitate accurate ancestry determination. We show practical applications of this AIM panel, discuss its limitations for admixed individuals and suggest ways to incorporate ancestry information into genetic association studies.We demonstrated the utility of a small AISNP panel specifically developed to discern global ancestry. We believe that it will find wide application because of its feasibility and potential for a wide range of applications. Characterization of human ancestry has been of interest for decades as information about population structure can provide novel insight into the human past and remains an important topic in the rapidly evolving biomedical field. For example, because genetic variants conferring risk to a particular disease may be geographically restricted because of evolutionary forces such as mutation, genetic drift, migration and natural selection, the assessment of the genetic background in individuals chosen for a study is crucial in genetic epidemiology .While still a topic of controversy , there iEspecially in the context of mapping disease genes, more objective and accurate methods of defining homogenous populations for the investigation of specific population-disease associations are required. This is not only paramount for specific mapping approaches such as admixture mapping , but hasIn order to assess genetic background diversity, a large number of ancestry informative marker (AIM) panels have been developed for particular applications. Genome-wide panels for admixture mapping have been developed for Hispanic populations , AfricanHowever, determining ancestry and controlling for population structure is just as important in smaller genetic association studies. These include for example candidate gene studies involving only a few genetic markers, replication of GWAS findings, or consist of smaller, highly valuable collections of rare pathological phenotypes and historical collections with limited amounts of DNA. Genotyping these samples on large AIM panels or leveraging ancestry information from preexisting genotyping is often not practical or possible.To address this specific need, we set out to develop a highly informative AIM panel that would allow us to infer a subject’s ancestral origin at the continental level and estimate admixture proportions among at least seven main geographic regions Africa, the Middle East, Europe, Central and South Asia, East Asia, Oceania and the Americas. The selection of such AIMs has to focus on SNPs with the largest allele frequency differences between the continental regions of interest to achieve the desired resolution at the continental level. Such high resolution is required because genetic diversity of human populations follows gradients or geographic clines within and among continents rather than specific clusters or clades ,23,24.We further aimed for the development of a feasible method to determine ancestry, as resources such as funding and available DNA are often limited for these applications. We therefore developed panels of AISNPs suitable for multiplex application on two commonly used platforms, the ABI SNPlex  and SequLastly, we specifically focused on the applicability of our panel to determine the ancestry of subjects from any of the worldwide geographic origins. To date, most research involving genetic association studies has focused on populations of European descent, where longer LD blocks require fewer genetic markers to be genotyped . HoweverHere we describe the development of AIM panels based on the well-studied global reference populations from the HGDP-CEPH , which in = 131), the Middle East , Europe (n = 158), Central/South Asia , East Asia , the Americas (n = 64) and Oceania (n = 28) . We used Infocalc 1.1 [n = 547,458). Next, the top 5,000 markers with the highest I_n were chosen (I_n > 0.077) and, to reduce the correlation of markers, were subjected to LD pruning using PLINK [Genotypes of HGDP subjects from the Illumina 650Y SNP array are publicly available  between each of the 21 pairwise continental region comparisons. This set of 210 markers was then further reduced in an iterative way by considering multiplex genotyping requirements for the ABI SNPlex genotyping system  and Sequhttp://hapmap.ncbi.nlm.nih.gov/; phase III release 2 and 3) standard set HAP1161 [http://alfred.med.yale.edu/) [To validate the AIM panels and increase the global coverage of the reference population set for downstream applications, we included two additional, very large data sets with worldwide populations: the International HapMap Project  . GenotypFinally, to illustrate a practical application of the 41-AIM panel with our complete set of global reference populations, a contemporary population sample of 2,392 subjects ascertained in Southern California  was genoPopulation structure and individual ancestry estimates were obtained using STRUCTURE v2.3.2.1. ,37. To aTo further validate the 41-AIM panel, ancestry estimates of 3,077 independent subjects of known ancestry from 68 global populations (reference set 2) were determined at k = 7 using the above STRUCTURE parameters, but now including prior population information of the HGDP reference set. Allele frequencies were updated using only individuals with population information at a migration prior of 0.05. Graphs were plotted using DISTRUCT v1.1 .CLUMPP v1.1.2  was usedPopulation structure was further analyzed using principal component analysis (PCA) implemented in the EIGENSTRAT software  and multST, a genetic distance measure for inter-population differentiation compared to intra-population variation. Significance of pairwise FSTs was established using 10,000 permutations. A Mantel test was used to correlate the FST matrices based on the 41-AIM and 31-AIM panels. Calculations were performed in ARLEQUIN 3.5 [As a measure of informativeness of the different AIM panels at the population level, we calculated FTo investigate the informativeness of the AIM panels in detecting admixture at the individual level, subjects from two admixed populations of the Southern California test population (self-reported African Americans and self-reported Hispanic White and Native Americans) were selected. These subjects were subjected to the Illumina HumanOmniExpressExome array, and individual ancestry estimates were determined with a second, independent approach . In addition, a comparison of all pairwise FST values among the 52 populations showed a highly significant correlation among the FST values calculated based on the 41-AIM panel and the 31-AIM panel , further indicating no significant loss of power to discern global ancestry in the smaller panel.In general, we found high FLastly, the population structure of the HGDP was analyzed using STRUCTURE. To facilitate a comparison with previous studies . We calculated the percentage of subjects that clustered correctly, using criteria of >85% and >50% cluster membership (MS), respectively.We further tested the performance of the 41-AIM panel in a realistic setting and estimated the ancestry of 3,077 test subjects from 68 regionally collected populations from the HapMap III and Yale collections. These test populations have been extensively characterized by us and others  and We found that African populations had very high cluster membership in the African cluster, but East African populations  showed slightly lower values. As expected, admixed African Americans as well as a population of Ethiopian Jews showed some cluster membership in Europe and the Middle East, and less than 50% of the subjects were included in the African group at the 85% MS criteria.The ethnoreligious Samaritans, Yemenite Jews and Druze clustered with the Kuwaiti predominantly in the Middle East, but also showed a significant European contribution. As expected, most European populations clustered predominantly with Europe. However, there was a significant Middle Eastern component, even for the Northern European populations such as the Finns and Irish, demonstrating the somewhat reduced specificity of the 41-AIM panel to distinguish between Europe and the Middle East compared to the resolution between other continents. When applying the less stringent 50% MS criterion, most populations had over 90% of their subjects placed in Europe. Not surprisingly, the Russian populations Adygei, Chuvash, Komi Zyriane and Russian Vologda were found to have a significant Central/South Asian component.The Central/South Asian cluster included the majority of the Gujarati, Keralite and Thoti Indians at the 50% MS criterion. As expected, the Kachari Assam, located in the East, also showed a significant East Asian contribution. However, there was no predominant placing in any of the seven continental groupings for the Khanty, a population from western Siberia. This is expected since the current continental grouping at K = 7 does not include a specific Siberian/North Asian cluster. The Khanty are currently our only representatives of this large geographic area.The East Asian test subjects from 15 diverse populations clustered in East Asia with almost no exception. Most Southern Malaysians also showed some Central/South Asian contribution. Most Native American populations clustered predominantly in the Americas. Exceptions were the admixed Muscogee and HapMap Mexicans, which were not placed in this cluster, but showed a strong European component. The Oceanic cluster included all Papua-New Guinean and Nasioi Melanesian subjects. However, the Micronesian and Samoan subjects from this broad geographic area were not assigned to Oceania at the 50% MS criterion, but were found to be admixed with a strong East Asian component.Finally, we combined the HapMap III and Yale collections with the HGDP, and further analyses were conducted with our complete reference population set including 4,018 subjects genotyped on the 41-AIM panel. A principal component analysis (PCA) including all 4,018 subjects and averaged for each of the 120 populations is shown in Figure We performed an analysis of the eigenvalues of the first 15 PCs and found that over 56% of the genetic variation among the seven continental regions was accounted for by the first five PCs Figure .To highlight a practical application of the 41-AIM panel and our large collection of reference populations, we considered the case of a genetic association study with subjects collected in Southern California. In order to minimize spurious results due to population stratification , a PCA is often applied. PCs can be used as an easy tool to visualize large amounts of data or can be included as covariates in association analyses to adjust for population stratification.n = 13, open symbols) in specific applications.PC plots of the first three PCs generated based on genotype data of the 41 AIMs are shown in Figure p < 0.001). An often-applied alternative to the PCA is the multidimensional scaling (MDS) approach implemented in the genetic association software PLINK. MDS analyses lead to essentially the same results  prior to association analysis, using clustering methods such as implemented in STRUCTURE. Additional population stratification and varying degrees of individual admixture are then accounted for within these more homogeneous groups.n = 484, Pearson’s r = 0.81 for the proportion of Native American ancestry in panel A, r = 0.81 for the proportion of European ancestry in panel C) and the African Americans . Slightly reduced correlations of individual admixture estimates were achieved between the GWAS-derived and smaller 31-AIM panels . Importantly, a detailed inspection of the scatter plots indicates that the AIM panels lack sensitivity in detecting admixture in individuals with low proportions of admixture (in the range of < 20-25%) when compared to the GWAS-derived panel.To assess the informativeness of the AIM panels in detecting admixture at the individual level, we compared STRUCTURE admixture estimates based on the 41 and 31 AIMs with independently derived estimates based on a large, GWAS-derived panel (see Methods). Subjects were selected based on self-report from the admixed African American and Hispanic White and Native American populations Figure . IndividOur motivation was to develop a feasible method to discern continental ancestry that would enable a safeguard against the impact of population stratification in small genetic association studies where limited resources preclude large genotyping efforts. We achieved this by choosing a very small set of highly discriminative AIMs suitable for multiplexing applications, thus enabling lower cost and higher throughput. To ensure a wide application potential, we optimized our panel for two commonly used multiplex platforms, the ABI SNPlex  and SequOur panel was able to accurately discern the global ancestry of a large majority of subjects originating from one of the seven specific ancestral clusters. This was the case for both the full 41-AIM panel and the subset of 31 AIMs, indicating that a balanced reduction of markers in these small panels did not significantly impact the robustness of the results. A direct comparison of our findings with a previously published small panel of 93 AIMs published by Seldin’s group  showed 8ST values of 0.06 - 0.09 among Europe, the Middle East and Central/South Asia indicated little genetic diversity. The clinal distributions of allele frequencies between Europe and East Asia pose a challenge for the identification of highly discriminative markers, a limitation also impacting other small AIM panels [Not surprisingly, the biggest limitation to imputing global ancestry was found for subjects from Eurasia, where low FM panels ,46. We tIndependent of the statistical method used to determine ancestry and admixture proportions, the results of these analyses depend not only on the informativeness of the genetic markers, but also strongly on the set of reference populations included. An omission of reference subjects from an ancestral group likely leads to misclassification of test subjects with similar ancestries. For example, we previously found that African Americans clustered strongly with Central Asians in a three-way admixture analysis (erroneously) including only reference subjects from Europe, Central Asia and the Americas.We considered this crucial issue during panel development and leveraged the publicly available 52 HGDP populations collected across the globe . We thenWhereas ancestry assignment of subjects from a specific geographic area represented by a cluster in the reference population set is a quantifiable and relatively straightforward task, admixed subjects resulting from ancient or recent contact of populations with distinct ancestries pose challenges.If such a cohort consists of admixed subjects with known ancestry contributions, such as two-way admixed Mexican Americans collected from a distinct area in Southern California, the varying degree of European and Native American ancestries can easily be estimated in admixture analyses implemented in Statistical packages such as STRUCTURE, ADMIXTURE  or BAPS However, in a clinical sample including subjects of unknown ancestral origin and complex population structure, as is often the case in our studies e.g., ,49,50), ,50, 49,5Adding to the complexities of accurately differentiating ancestral groups and estimating admixture proportions is the appropriate incorporation of this information into the design of genetic association studies. While the negative impact of population structure on association studies is well known , and metDepending on the composition and relative numbers of subjects from different ancestral backgrounds, common questions in such studies include the genetic definition of African Americans, which typically show degrees of European admixture that vary among individuals . There iFor practical purposes, we often employ a multi-tier approach: we first group subjects into continental clusters using a majority criterion with statistical methods such as STRUCTURE and then confirm the plausibility of the grouping with demographic data, where available. Next, we aim to place most subjects into a very small number of clusters including genetically similar subjects, for example, by combining similar continental groups such as from Eurasia, and excluding outliers of minority ancestries. Lastly, we control for additional population stratification within clusters by incorporating MDS components into association studies and ultimately combine results in meta-analyses, where appropriate. Such a method was, for example, employed for the Southern Californian population sample presented here, which encompassed a wide array of self-declared ethnic groups. Our approach resulted in a four-cluster analysis with 61% European Americans, 18% subjects with Native American admixture, 7% subjects with African admixture, and 15% subjects of other ancestry and/or complex admixtures.In conclusion, we demonstrated the utility and limitations of a small AIM panel specifically developed to discern global ancestry. We believe that it will find wide application because of its feasibility and potential for a wide range of applications. To allow this reference set to be readily accessible for others to use, we are entering the allele frequencies for these 41 SNPs into ALFRED   as an “SALFRED: allele frequency database; AIM: ancestry informative marker; AISNP: ancestry informative single nucleotide polymorphism; δ: allele frequency differences; MS: cluster membership; GWAS: genome-wide association study; I_n: marker informativeness; MDS: multi-dimensional scaling; PC: principal component; PCA: principal component analysis.The authors declare that they have no competing interests.All authors read and approved the final manuscript. CMN conceived of the study, participated in its design and coordination, and wrote the manuscript. TS carried out genotyping. AXM performed the statistical analyses. KKK and JRK participated in the coordination of the study and in the writing of the manuscript. XW carried out genotyping and assisted in preliminary analyses at Yale. All authors read and approved the final manuscript.Geographic sampling location, population name, number of subjects and source of genotype data of 120 reference populations.Click here for fileChromosomal position (GRCh37.p5), alleles and informativeness (I_n) of 1,442 continental AIMs and sequence information for the multiplex 41-AIM and 31-AIM panels.Click here for fileblack). Subjects from 107 typical reference populations are color coded.MDS plots of the first five MDS components for a visual inspection of a large population sample collected in Southern California (Click here for file