E. arundinaceus (Retz.) Jeswiet is a warm-season, tall-growing perennial species native to much southern portion in China. The grass has been extensively used in sugarcane breeding and is recently targeted as a bioenergy feedstock crop. However, information on the genetic structure of the Chinese wild germplasm is limited. Knowledge of genetic variation within and among populations is essential for breeding new cultivars in the species. The major objective of this study was to quantify the magnitude of genetic variation among and within natural populations in China.PPB) was 80.00% and 27.07%, genetic diversity (EH) was 0.245 and 0.099, effective number of alleles (EN) was 1.350 and 1.170, and Shannon's information index (I) was 0.340 and 0.147, respectively. The populations were clustered into six groups exhibiting a high level of genetic differentiation, which was highly associated with geographic origins of respective germplasm populations, but was not significantly associated with geographic distances between the populations.In this experiment, we analyzed genetic variation of 164 individuals of 18 populations collected from natural habitats in six Chinese provinces using 20 sequence-related amplified polymorphism (SRAP) primer pairs generating 277 polymorphic bands. Among and within the populations, the percentage of polymorphic bands (E. arundinaceus germplasm based on the SRAP molecular marker analysis of native populations. The genetic structure of populations in the species has been substantially affected by geographic landforms and environments. The diverse collection will be highly valuable in genetic improvement in the species per se and likely in sugarcane.This is the first report indicating that large genetic variation exists in the Chinese  E. arundinaceus (synonym of Saccharum arundinaceum Retz.) is a warm-season, tall-growing, caespitose perennial species native to China and certain other south and southeast Asian nations of temperate climates to tropical environments Saccharum officinarum L.), the species has contributed to the genetic improvement in sugarcane breeding Miscanthus, Narenga, Saccharum, and Sclerostachya, so is considered to be a member of the “sugarcane complex” Saccharum × Erianthus hybrids, which are further crossed to sugarcane clones in the production of wide intergeneric hybrids E. arundinaceus has been well documented. Using morphological traits, a high level of variation was reported in E. arundinaceus accessions from China, while the variation from Indonesia was relatively low E. arundinaceus had 2n = 4x = 40 and 6x = 60 somatic chromosomes while 2n  = 2x = 20 was rare E. arundinaceus from Indonesia appeared to have a low level of molecular variability E. arundinaceus from India was more polymorphic than from Indonesia Genetic variation in E. arundinaceusIn the last two decades, amplified fragment length polymorphism (AFLP), inter simple sequence repeat (ISSR), random amplified polymorphic DNA (RAPD), restriction fragment length polymorphism (RFLP) but sequence-related amplified polymorphism (SRAP) have been used in characterizing genetic diversity in E. arundinaceus in six provinces of China. Therefore, the major objective of this study was to quantify the magnitude of genetic variation among and within the natural populations.Information on genetic variation among and within populations could help better understand the natural variation in the species on a large geographic scale, which is useful in sampling and deploying the germplasm in breeding programs. We collected 18 indigenous populations of Erianthus arundinaceus samples at the locations in China, because the research was funded by the Ministry of Science and Technology and the earmarked fund for China Agriculture Research System of the People's Republic of China, and the species is not an endangered or protected species.This study was approved by the Department of Grassland Science, Animal Science and Technology College, Sichuan Agricultural University; Sichuan Academy of Grassland Science; Guizhou Grassland Science Institute; and Grassland Institute, China Agricultural University. No specific permissions were required for collecting E. arundinaceus individual leaf samples in 18 populations were collected in Sichuan, Yunnan, Guizhou, Guangxi, Guangdong and Hainan provinces, China (see Following the population sampling method by Jing and Lu hina see . Sampledhina see . The lea3 solution. Gel images were then photographed by Gel Doc(TM) XR System .Sequence related amplified polymorphism analysis was conducted according to a previously established protocol Clearly amplified PCR bands were visually scored for presence (1) or absence (0), and then were assembled into an Excel matrix for the following analyses. Use of dominant marker data in genetic diversity analysis can lead to estimation bias with overestimating parameters by as much as 5%, especially with small sample sizes Np), percentage of polymorphic bands (PPB), Shannon's information index (I), observed number of alleles(ON), effective number of alleles (EN), Nei's gene diversity(EH), genetic diversity within populations (Hs), total genetic diversity (Ht), genetic differentiation coefficient (Gst), gene flow estimates (Nm), and Nei's genetic distance were calculated using POPGENE The number of polymorphic loci (PPB) within each population ranged from 16.94% (Pop3) to 33.33% (Pop4) with an average of 27.07% while PPB was 80.00% at the species level. Among these 18 populations, Pop4 and Pop14 exhibited the greatest level of variability . By contrast, genetic diversity was the least in Pop3, with ON = 1.17, EN = 1.11, I = 0.092, and EH = 0.063. The average of ON, EN, I and EH was 1.27, 1.17, 0.147 and 0.099 within populations, and was 1.80, 1.35, 0.340 and 0.245 among the populations, respectively  among populations are given in STG) and gene flow estimate (Nm) indicated a high level of population differentiation .A highly significant (P<0.001) genetic difference was found among groups, among populations, and within populations . The resE. arundinaceus was studied using individual clones, which were collected from Southeast Asia and Chinese tropical and subtropical regions. These studies showed the variation level of E. arundinaceus was different in different regions. The genetic diversity of E. arundinaceus clones in Indonesian was studied using morphological traits, demonstrating those clones had low genetic variation E. arundinaceus clones from Indonesia with rDNA, RAPD and RFLP markers PPB over 18 natural populations of E. arundinaceus in China was 80.0%, lower than the PPB value  in the study of Cai et al. E. arundinaceus.In previous reports, the genetic diversity of E. arundinaceus from the Philippines, Indonesia, India, Vietnam and China, show that E. arundinaceus from pacific Island countries (the Philippines and Indonesia) has lower genetic variation. In contrast, E. arundinaceus collections from continental countries  have larger genetic variation. We speculated that the low genetic variation of E. arundinaceus from island countries was generated by the effect of ocean isolation and relatively homogenous environments in the countries. The pacific island countries are isolated by the ocean, which may have effectively blocked or minimize gene flow from germplasm outside the islands, consequently reducing genetic diversity E. arundinaceus populations  from Hainan island (Hainan province) which had lower genetic variation  than the mean of all populations  from China. Possibly the germplasm on Hainan Island was isolated from receiving pollen from the germplasm on Chinese mainland by the Qiongzhou Strait. Similarly, mountains, especially the high mountains in the southwestern Chinese provinces, could form physical isolations limiting pollen facilitated gene flow among E. arundinaceus populations. It appears that the populations from Sichuan Basin  and those from Sichuan Daliangshan region (Pop4 and Pop5) presented a geographical differentiation separated by southern mountains of the Tibetan Plateau. Similarly, the populations from Guizhou (Pop8 and Pop9) and the populations from Guangxi  were separated by mountains of the Yunnan-Guizhou Plateau. Those mountains might also isolate the populations in Yunnan (Pop6 and Pop7) from those in other regions. However, the populations (except Pop2 and Pop3) from these isolated regions had higher genetic variation than the mean of all populations from China, suggesting that the effect of isolation by mountains was less than from the ocean.Comparisons of the genetic variation levels of E. arundinaceus through examining Chinese native populations and revealing new biological characteristics of the species. In this study, the average of within population diversity in E. arundinaceus (EH = 0.245) is higher than short-lived perennial (EH = 0.20), mixed-mating species (EH = 0.18) and selfers (EH = 0.12), but similar to outcrossers (EH = 0.27) reported by Nybom EH value of Miscanthus floridulus (EH = 0.30) E. arundinaceus in this report, while the EH value of Saccharum spontaneum (EH = 0.23) E. arundinaceus. The high He value of E. arundinaceus revealed in this experiment suggests that E. arundinaceus be an outcrossing species.This is the first report characterizing genetic variation in E. arundinaceus population substructure (STG) was 0.55, indicating more than a half of genetic variation occurred among populations. The results of STG was similar to the results from AMOVA, which showed that 51.44% genetic variation existed among populations (P<0.001) and the remaining occurred within populations (P<0.001). Chang et al. (2012) reported genetic variation among populations was lower than that within populations in S. spontaneumM. floridulus populations S. spontaneum and M. floridulus populations. Hamrick and Godt E. arundinaceus populations is affected by the natural landforms and geographical conditions.In this study, the Nei's estimate of Nm) would be able to resist the effect of genetic drift within populations and prevent the differentiation of populations as the value of Nm >1, and when the value of Nm <1 the genetic drift could lead to genetic differentiation among populations Nm value of E. arundinaceus (an outcrossing species) populations in this study was only 0.41, indicating that there was a lower level of gene flow and significant genetic differentiation among the 18 populations. The natural landforms in the sampling areas of E. arundinaceus forming the geographic isolation and heterogeneity of the ecological environment affect gene flow, the genetic and geographical divergence among the populations E. arundinaceus populations in this study were isolated by ocean or mountain. It appears that the isolation affected not only gene flow but also the genetic diversity of E. arundinaceus through natural selection within local environments. In our study 18 E. arundinaceus populations were clustered into six groups, which belonged to different isolated regions. The Mantel tests indicated that there was no significant associated relationship between genetic distance and geographic distances between populations. The result was similar to that in S. spontaneumGene flow (E. arundinaceus plants reported previously are tetraploids (2n = 4x = 40) and hexaploids (2n = 6x = 60) E. arundinaceus germplasm are elusive. Further investigation efforts on the association between ploidy forms and genetic variation of the native germplasm in Asian countries, especially China may shed light on the evolution and formation of genetic variability within the species.In addition to diploids (2n = 2x = 20), most Chinese Appendix S1SRAP data for 18 populations of Erianthus arundinaceus amplified using 20 primer pairs, coded as presence (1) and absence (0). Note: data rows in red color were excluded in data analysis due to more than 161 of “0” and data rows in blue color were changed to monomorphic loci due to more than 161 of “1” according to Lynch and Milligan (XLS)Click here for additional data file.


Leptopilina boulardi, a parasitoid of Drosophila of African origin and widely distributed over temperate and (sub) tropical climates.The genetic structure of populations can be influenced by geographic isolation  and ecology. We examined these effects in L. boulardi from five climatic zones in Iran and measured genetic differentiation at nuclear  and mitochondrial  loci. An Analysis of Molecular Variance (AMOVA) for the AFLP data revealed that 67.45% of variation resided between populations. No significant variation was observed between climatic zones. However, a significant difference was detected between populations from the central (dry) regions and those from the wetter north, which are separated by desert. A similarly clear cut genetic differentiation between populations from the central part of Iran and those from the north was observed by UPGMA cluster analysis and Principal Coordinates Analysis (PCO). Both UPGMA and PCO further separated two populations from the very humid western Caspian Sea coast (zone 3) from other northern populations from the temperate Caspian Sea coastal plain (zone 2), which are connected by forest. One population (Nour) was genetically intermediate between these two zones, indicating some gene flow between these two groups of populations. In all analyses a mountain population, Sorkhabad was found to be genetically identical to those from the nearby coastal plain (zone 2), which indicates high gene flow between these populations over a short geographical distance. One population from the Caspian coast (Astaneh) was genetically highly diverged from all other populations. A partial Mantel test showed a highly significant positive correlation between genetic and geographic distances, as well as separation by the deserts of central Iran. The COI sequences were highly conserved among all populations.We sampled 11 populations of L. boulardi showed clear genetic structure in AFLP profiles, but not in COI sequence data. The transfer of fruits containing Drosophila larvae parasitized by L. boulardi appears to have caused some unexpected gene flow and changed the genetic composition of populations, particularly in urban areas. Nevertheless, our results suggest that climate, geographic distance and physical barriers may all have contributed to the formation of genetically distinct populations of L. boulardi. Inevitably, there will be overlap between the portions of variance explained by these variables. Disentangling the relative contributions of climate and geography to the genetic structure of this species will require additional sampling.The Iranian populations of  Changing climates are expected to have profound effects on the genetics of insect populations. One way to study such effects is to compare populations of a widely distributed organism across contrasting climates.One of the main constraints on local genetic differentiation and adaptation is extensive gene flow between populations. Reduced dispersal between populations can lead to genetic subdivision of populations  and may Leptothorax rugatulus [DNA markers provide powerful and efficient tools to study genetic diversity in insect populations -5 at botugatulus  a mitochDrosophila parasitoids have played a major role in these studies, because of their ease of maintenance in the laboratory, the enormous biological and genetic information on their Drosophila host, which has been used as model organism for almost a century [Leptopilina boulardi  populations. L. boulardi is a larval parasitoid of Drosophila of African origin [L. boulardi collected from five contrasting climate zones in Iran . The Sorkhabad population, collected from a mountainous region on the slope of the Damavand mountain near Mazandaran clustered together with the wet Caspian Sea coastal populations. Consistent with UPGMA tree and PCO plots, low Fst values in pair-wise comparison were observed when we compared Sorkhabad with other northern populations .The 9 primer combinations yielded 670 scorable bands of which 147 21.94%) were polymorphic. AMOVA of the AFLP results revealed that the main proportion of genetic variation resided among populations . Pair-wise differences of 1.94% werPrincipal Coordinates Analysis (PCO) revealed informative separation of populations Figure . The firL. boulardi populations . The result of this test showed that a considerable part of the genetic variation was explained by geographic distance and supported the UPGMA and Principal Coordinate analyses since all geographically-close populations resembled each other. The only exception was the population from Astaneh, which was highly distinct from all others populations, even from those collected from a distance of less than 15 km. A partial Mantel test including all populations found no significant effect for isolation by the deserts of central Iran . However, by excluding the Astaneh population from the partial Mantel test, we found a significant effect for isolation by dry desert . After correcting for the effect of isolation by desert, the partial correlation between geographic and genetic distances decreased somewhat, but still remained highly significant , the genetic distances between populations correlated closely with geographic distances. A partial Mantel test revealed a significant correlation between habitat isolation and genetic diversification. AMOVA also indicated diversification of populations originating from distinct dry or wet regions. Populations collected within short proximity tended to resemble each other genetically , as expected because of the high likelihood of gene flow. This was true even for populations that were ecologically very different: the mountain population of Sorkhabad was genetically indistinguishable from populations from the nearby coastal plain. The partial Mantel test showed significant effects of geographical barriers as well as the distance on genetic divergence. The positive correlation between genetic and geographic distances in our study is consistent with several other genetic structure studies [Drosophila parasitoid populations are rare. A comparison of L. clavipes populations in western Europe found distinct genetic differentiation between sexual and asexual populations [The AFLP analysis provided enough information to allow a clear distinction among  studies ,13,21 onulations , but no The different climatic environments we sampled  undoubtedly impose different selection pressures on the life history of parasitoids. The genetic differences we observed between geographically isolated populations imply low gene flow. This will facilitate genetic response to such selection.L. boulardi populations. Parasitized fruit fly larvae may be transported with fruits and give rise to populations dissimilar from the neighbouring ones, in particular in urban areas. We found unexpected genetic divergence in two northern populations Astaneh (which was highly divergent from the neighbouring) and Nour (which showed intermediate structure between two Caspian Coast zones). Both populations originated from urban areas and could have arrived with fruit transports.In addition to geography and climate, human activity also appears to affect the genetic structure of In addition to geography and climate, variation in hosts may result in adaptation and genetic differentiation of parasitoids , due to Populations in the central part of Iran were sampled from isolated locations surrounded by dry desert. The relatively high genetic divergence over relatively short geographic distance between these central populations compared to northern populations implies that the barrier formed by unsuitable habitat may contribute to genetic diversification independently of geographic distance . In the case of low quantity or sheared DNA the DNA extraction was repeated using five new females from the same strain.Five female wasps in four replications were pooled per strain for DNA extraction using an adapted CTAB protocol for insects , includiL. boulardi populations, we employed the Amplified Fragment Length Polymorphism (AFLP) technique [http://www.neb.com). EcoRI/MseI adaptors were ligated to the restriction fragments. Preamplification was conducted using EcoRI+A and Mse+C primers  using specific peak filters and were manually checked for correct alignment of the size standard. The positions of polymorphic markers between 80 and 500 bp were scored and exported as a binary matrix in an excel sheet.PCR products from the selective amplification were purified run on a DNA sequencher MegaBACE™ 1000 system  was used as out-group. PCR amplification was performed using a forward primer designed by Scheffer and Grissell ['-CGAATAAATAATATAAGATTTTG-3' and a reverse primer designed for Leptopilina clavipes , COI-2413-R, 5'-TCATCTAAAAATTTTAATCCCAGT-3'. The amplification was carried out on a Thermocycler PTC-2000 using the following thermal cycles: 3 min at 92°C followed by a touchdown with one degree drop in annealing temperature per cycle from 53-40°C , then 25 cycles of 10 sec at 92°C,10 sec at 40°C and 2 min at 72°C, ending with 5 min extension at 72°C. Each PCR reaction contained 1.2 μl of 2.5 mM DNTPs, 0.5 μl of 10 μM of each primer, 0.15 μl of Taq DNA polymerase(5 U), 1.5 μl of 10X Buffer and 0.45 μl of 15 mM MgCl2 μl; all Qiagen products and was adjusted to 15 μl with autoclaved nanopure water. The PCR products were purified using Wizard SV Gel and PCR Clean-Up System  following the manufacturer's protocol. The amplified bands were sequenced both forward and reverse on MegaBACE™ 1000 sequencher. The Sequencher software Version 4.2 (Gene Codes Corp.) was used to assemble the contigs and obtain consensus sequences. The sequences were aligned using pairwise-alignment in MacClade 4.08 [A 638 bp portion of the cytochrome oxidase subunit I (COI) gene was amplified for three individuals per strain to evaluate the mitochondrial variation among the 11 Grissell , COI-177ade 4.08  and editFst statistics were estimated using ARLEQUIN software [Population structure was investigated using a molecular variance software package (AMOVA) and software ,31, ver.software  in Treecsoftware  for windsoftware  with 100software  using thsoftware  version software  using Nesoftware  for calcMAS designed the project, collected samples in the field collected and analysed the data and wrote the manuscript. JJMvA participated in the field work, supervised project and improved the manuscript. KK helped in improving methodology and data analysis, supervised the project and improved the manuscript. All authors read and approved the final version.Fst and P value for pair wise comparison of populations. Below diagonal Fst value and above P value of pair wise comparison of populations derived from AMOVA of AFLP results of eleven populations of Leptopilina boulardi from Iran.Click here for fileSampling points. Sampling points and their GPS coordinates, climate zone and elevation above sea level.Click here for file


Euptelea pleiospermum. Young leaves from 1078 individuals belonging to 36 populations at elevations of 900–2000 m along upper reaches of four rivers were genotyped using eight nuclear microsatellite markers. We found no evidence for the unidirectional dispersal hypothesis in E. pleiospermum within each river. The linear dispersal pattern along each river valley is mostly consistent with the “classical metapopulaton” model. Mountain ridges separating rivers were genetic barriers for this wind-pollinated tree species with anemochorous seeds, whereas river valleys provided important corridors for dispersal. Gene flow among populations along elevational gradients within each river prevails over gene flow among populations at similar elevations but from different rivers. This pattern of gene flow is likely to promote elevational range shifts of plant populations and to hinder local adaptation along elevational gradients. This study provides a paradigm to determine which of the two strategies (migration or adaptation) will be adopted by mountain riparian plants under climate warming.We used landscape genetics and statistical models to test how landscape features influence connectivity or create barriers to dispersal for a mountain riparian tree species,  Poa hiemataCastanopsis eyreiAinsliaea faurieanaSilene ciliataMigration and adaptation are two main responses of mountain plants to climate warming For mountain riparian plants, gene flow is influenced by mountain landscape characteristics, such as elevation, stream flow, river valley and mountain ridge River valleys are long-recognized corridors for riparian or aquatic plants, which are characterized by linear distribution patterns Mountain ridge is the predominant factor causing limited gene flow and genetic differentiation in mountain plants Euptelea pleiospermum Hook. f. et Thoms (Eupteleaceae) is a diploid (2n = 28) E. pleiospermum is restricted to linear suitable habitats. It is wind-pollinated and flowers in early spring before leaf-out. Adult trees produce numerous indehiscent fruits. The small and light samaras are dispersed first by gravity and secondarily by wind and/or water Euptelea pleiospermum populations located along four main rivers (elevation gradients) within one mountain. Specially, the following questions were addressed: (1) does the recognized water-mediated seed dispersal result in genetic diversity accumulation in downstream populations? If not, which of the other linear models is suitable for populations within rivers? (2) Do river valleys and mountain ridges act as migration corridors and genetic barriers, respectively?The purpose of the present study is to examine the landscape genetic structure of All necessary permits were obtained for the described field studies. Mr. Jingyuan Yang from the Administration Bureau of the Shennongjia National Nature Reserve issued the permission for each location.Euptelea pleiospermum is a common tree species in riparian forests at elevations between ∼1200 and ∼1900 m in this area The study area is located in mountain riparian forests of the Shennongjia National Nature Reserve (SNNR) , central China . Its sumE. pleiospermum along each river. In this study, a total of 36 populations were sampled with six to 12 populations for each river . PCR reaction was conducted in a PTC-100 thermal cycler . PCR reactions were performed with the following cycle profile: an initial denaturation at 94°C for 5 min; then 35 cycles of denaturation at 94°C for 50 s, annealing at 56°C for 50 s and extension at 72°C for 1 min; and final extension at 72°C for 10 min. After amplification, identification of the alleles was based on the position of the fragments in relation to a 25-bp marker ladder on a polyacrylamide gel of high resolution with silver stain.The microsatellite genotyping procedure was performed as described by Wei and Jiang A), expected (HE) and observed (HO) heterozygosity, and inbreeding coefficient (FIS) across all loci and locus-population combinations. FSTAT 2.9.3.2 AR) for each population and to test for linkage disequilibrium (LD). Departure from Hardy-Weinberg equilibrium (HWE) was tested by Fisher’s exact test in GENEPOP version 4.0 GENETIX 4.05 HE and AR) and the position of populations along the course of the river were conducted using the SPSS statistics program .To reveal the pattern of genetic diversity along rivers, regression analyses between genetic diversity parameters . MIGRATE uses Bayesian inference with long chains  and 1,000 burn-in per chain. We then compared mean values of downstream and upstream migration rates along each river valley, and significance was tested with a paired-samples t-test.To provide direct evidence for the dispersal pattern along each river, contemporary and historical migration rates between populations along each river were calculated by using the programs BayesAss version 1.3 FSTDESTFST were based on 1000 randomizations. We compare genetic differentiation among populations from different rivers and that among populations within the same river , and significance was tested by performing an independent-samples t-test.Pairwise population differentiation was estimated at two scales: among populations and among rivers. To analyze the level and partitioning of genetic variation among geographical groups, among populations within geographical groups, and within populations, analysis of molecular variance (AMOVA) K panmictic groups without a priori assignment of individuals to sampling locations and by minimizing deviations from Hardy-Weinberg equilibrium and linkage equilibrium. The LOCPRIOR model, recently developed by Hubisz et al. K = 1 to K = NP +3 , with a burn-in period of 100,000, and MCMC iterations of 1,000,000, based on the LOCPRIOR model and the admixture model with correlated allele frequencies. We applied Evanno et al.’s ad hoc statistic, ΔK, to calculate the uppermost hierarchical level of structure (K). For the spatially sensitive TESS analysis , we set 10 runs per Kmax , for Kmax ranging from two to six, with 300,000 total sweeps and a burn-in of 100,000.We used the program DISTRUCT version 1.1 To test whether the individuals from the same river valley or from similar altitudes can be clustered into genetically homogeneous groups, nonspatial and spatially sensitive Bayesian clustering were implemented by using STRUCTURE version 2.3.3 FST/(1–FST)) and altitudinal distances  among populations was executed using the Mantel test implemented in GenAlEx 6.41 To test for isolation by distance (IBD) E. pleiospermum. At the species level, all eight nuclear microsatellite markers were highly polymorphic, having 6–29 alleles per locus with an average of 12.8. At the intra-population level, mean number of alleles per locus (A) ranged from 3.88 to 6.88, and allelic richness (AR) varied from 3.64 to 4.58 (HE) ranged from 0.555 to 0.687 (HO) ranged from 0.386 to 0.633 (FIS) were detected in 34 of 36 populations  and biological (e.g. inbreeding) reasons. The test for genotypic disequilibrium found that only one (EP036×EP294) of 28 locus pairs showed significant genotypic disequilibrium in 12 populations. Because no consistent genotypic disequilibrium was found between any locus pair across all populations, all loci were used for further analyses.Linear regression analyses did not reveal any significant negative relationship between genetic diversity and the position of populations along the course of the rivers , suggestFST and DEST) were found between populations within each river valley. Significance test revealed that most values of FST were significantly different from zero , and DEST values showed the same trend  (See We found moderate levels of genetic differentiation among most pair of populations from different rivers . FurtherP<0.001). However, no significant differentiation was found among populations located at different altitudinal zones .The results of the AMOVA were shown at ad hoc statistic ΔK at K = 4. The bar plot assuming K = 4 showed that individuals from different rivers were clustered into distinct groups  and X8 (1389 m) populations from Xiangxi River were clustered into the Nan River group, as revealed by STRUCTURE and TESS analyses . This caFST and DEST values for inter-river comparisons were higher as compared to intra-river comparisons. Third, the Bayesian clustering approach divided individuals from the four rivers into four distinct groups Click here for additional data file.Table S2Historical migration rates between Euptelea pleiospermum populations along each river.(DOC)Click here for additional data file.Table S3Pair-wise population DEST  and FST .(XLS)Click here for additional data file.


Oncorhynchus keta) in Prince William Sound (PWS), Alaska, with 135 single nucleotide polymorphism (SNP) markers. Historical population structure was inferred from the analysis of DNA from fish scales, which had been archived since the late 1960’s for several populations in PWS. Parallel analyses with microsatellites and a test based on Hardy-Weinberg proportions showed that about 50% of the fish-scale DNA was cross-contaminated with DNA from other fish. These samples were removed from the analysis. We used a novel application of the classical source-sink model to compare SNP allele frequencies in these archived fish-scales (1964–1982) with frequencies in contemporary samples (2008–2010) and found a temporal shift toward hatchery allele frequencies in some wild populations. Other populations showed markedly less introgression, despite moderate amounts of hatchery straying. The extent of introgression may reflect similarities in spawning time and life-history traits between hatchery and wild fish, or the degree that hybrids return to a natal spawning area. The source-sink model is a powerful means of detecting low levels of introgression over several generations.The extent to which stray, hatchery-reared salmon affect wild populations is much debated. Although experiments show that artificial breeding and culture influence the genetics of hatchery salmon, little is known about the interaction between hatchery and wild salmon in a natural setting. Here, we estimated historical and contemporary genetic population structures of chum salmon ( Interactions between hatchery-reared and wild Pacific salmon can be a source of genetic change within and among wild populations Oncorhynchus kisutch) shifted to earlier times as a result of the genetic influence of strays from a hatchery in which eggs were taken from the early portion of the run The effects of stray hatchery fish on wild populations can be measured in several ways. Fin clips, physical tags, and thermally marked otoliths allow for the direct detection of hatchery fish in natural spawning areas One approach to estimating genetic introgression in salmonids is based on individual-based methods that attempt to identify hatchery-wild hybrid and backcross fish Oncorhynchus keta) populations in Prince William Sound (PWS), Alaska  genotypes. This QC was critical because the analysis relied on DNA extracted from archived scales originally sampled decades ago to age fish. The handling of fish during sampling promotes cross-contamination between scales, which can affect genotyping. Accurate genotypes are especially important for detecting low levels of introgression. Second, we compare the historical genetic population structure estimated from archived scale samples to contemporary population structure. If introgression from hatchery populations into wild populations is occurring, wild populations are expected to gradually resemble hatchery populations. Third, we develop a novel application of the classic source-sink model and use SNP frequencies in historical and contemporary samples to quantify introgression rates of hatchery chum salmon in four naturally spawning populations.All work was conducted in accordance with animal welfare guidelines stipulated in field collection permit CF-2009-0019, issued by the State of Alaska Department of Fish and Game (ADF&G), Juneau, AK. This study did not involve endangered or protected species and the sampling locations were not privately-owned or protected in any way.We sampled chum salmon from WNH and from four streams in PWS . Each fish was screened for 185 nuclear DNA and 3 mitochondrial DNA SNP markers  [20]–[25P<0.05) linkage disequilibrium in more than half of the collections, or if the markers were known to be linked. When we found linkage between a pair of loci, the locus with the lower heterozygosity, or the locus with the larger percentage of missing data in the historical samples was discarded.Several steps were taken to ensure genotype accuracy. First, 8% of the samples in each collection were reanalyzed for each marker to ensure reproducibility and to identify possible laboratory handling errors. We assumed that any inconsistency was due equally to genotyping errors in the initial analysis and to errors during quality control. Second, we excluded samples with poor quality DNA, in which individuals lacked scores for more than 20% of the SNP markers We anticipated that DNA extracted from the archived scales might be contaminated with DNA from other scales, because the archived scales had been used for aging and had not been collected with protocols specifically for DNA analysis. DNA is located chiefly on the surface of a scale in epithelial tissue and mucous. We devised two methods to detect fish-to-fish contamination. First, we analyzed the archived fish DNA for 7 microsatellites Second, we used model selection based on genotypic frequency expectations. This method makes three key assumptions: 1) loci are in HWE and linkage equilibrium, 2) contaminated individuals are contaminated by one, and only one, other fish from the same population, and 3) if a fish is contaminated at one locus, it is contaminated for all other loci. Genotypes were subscripted by collection AA, Aa, and aa. We modeled This data vector takes three possible states corresponding to the genotypes l, and are simple functions of the allele frequencies, k is contaminated, given only two possible models. Individuals with posterior probabilities greater than 75% corresponded to Bayes factors greater than 3 Here, encies,  , while  OpenBUGS  for 100,Oke_U1016-154 departed from HWE in the historical collections and was removed from subsequent analysis. Additionally, the three mitochondrial DNA  and two nuclear  loci were removed, because they were invariant in our samples. Fourteen pairs and one triplet of loci showed significant linkage disequilibrium in more than 50% of the collections  to subdivide and test for differences among the three collection years, 2008–2010. Fisher’s exact test for 12 comparisons detected a significant difference (P = 0.017) between Wells River 2008 and 2009, but not between collection years at the other sites. The historical samples were too small to test for temporal differences. Otolith markings indicated that 18 fish from contemporary field collections were hatchery-origin strays and were removed from the analysis.The four contemporary samples were large enough (Ho) and expected (He) heterozygosities averaged over loci with ARLEQUIN 3.5.1.2 FST (differentiation among spawning sites), FSC , and FIS . Second, pairwise exact tests FST values were calculated with the R package ‘hierfstat’ FST values and a consensus tree. Fourth, we used STRUCTURE 2.3.3 K = 1–9 populations.For the SNP data, we estimated observed  of the total variability was due to differences among the four spawning sites, and 0.15%  was due to temporal differences between samples at the same site. Fisher’s pairwise exact tests for genetic differentiation echoed these results, showing significant differences (P<0.001) between historical and contemporary collections from the same sites (FIS). The amount of differentiation among the four historical samples was slightly larger  than among the four contemporary samples .Overall, 1.45%  best fit a four-population model . Generaln = 6 generations) from the source-sink equation indicated that m = 0.257, 95% PD: 0.209–0.328), and Siwash and Beartrap creeks showed intermediate levels of introgression . Constantine Creek showed the lowest, but still significant, level of introgression .In our source-sink model, evidence for introgression appeared as a convergence with time between allele frequencies in a wild population with allele frequencies in the hatchery. This convergence produced a positive deviation from the expected one-to-one relationship between the slope of the difference between source and sink allele frequencies ls River . Less inls River . Bayesiaulations . Wells R1 and F2 hybrids and backcrosses in a population, but may not detect the effects of introgression over several generations. Additionally, our application of the sink-source model to analyze DNA in archived scales and contemporary samples allowed us not only to detect allele-frequency shifts, but also to estimate introgression rates over 6–7 generations.Our analysis of archived scales and contemporary collections of chum salmon in Prince William Sound demonstrates genetic introgression from hatchery strays into the four natural spawning populations examined in this study. The use of the source-sink model appears to have provided more power for detecting introgression than the use of individual assignments with STRUCTURE, which has commonly been used in other studies to detect introgression. Both STRUCTURE and NEWHYBRID We expended considerable effort on quality control to increase the accuracy of genotypes so that our analyses had the power to detect small hatchery influences on wild populations. First, we implemented methods to minimize the potential for genotyping bias in these procedures. For example, because some loci may be inherently more scorable than other loci in samples with poor quality DNA, we excluded the latter samples to reduce the inclusion of suspect genotypes. Poor quality DNA can yield valid genotypes for some loci, but not others.Second, we devised two methods to screen for DNA contamination between scale samples, including the supplementary use of microsatellites, and model selection based on Hardy-Weinberg Expectations (HWE) for SNP loci. The estimation of genotype and allele frequencies critically depends on excluding contaminated samples. Inferences of introgression, in turn, depend on accurate estimates of these frequencies. Therefore, we excluded any archival or contemporary samples that appeared to be contaminated with DNA from other fish. About 1.2% (0–2%), on average, of the DNA extractions from the five contemporary samples tested positive for contamination with the HWE-based test, but not with microsatellites . These pFST = 0.0158), relative to the historical samples (FST = 0.0161) may indicate convergence among these wild populations because of a common source of migrants. A similar temporal shift was detected among populations of Atlantic salmon  in France; divergence at 17 microsatellite loci among historical samples was larger  than among contemporary samples  Persistent straying from a single-source population over several decades can potentially erode genetic diversity among populations. Previous studies of genetic population structure in chum salmon in PWS showed a considerable amount of genetic diversity among populations in the 1990s FST = 0.001 to 0.010), the exact tests of differentiation were significant for each hatchery-sample comparison because of the power provided by a large number of markers. Hence, the individual assignments made with STRUCTURE did not indicate a substantial amount of recent hybridization between hatchery brood stock and wild populations, even though our source-sink model showed significant allele-frequency shifts after six generations of hatchery supplementation.Our genetic analysis also indicates that the most recent contributions of brood stock to the WNH from Wells River swamped the genetic signatures of previous brood stock from other localities. Hence, we used genotypes in the WNH sample as the ‘source’ in our source-sink estimations of introgression. Even though the absolute amount of differentiation was small  and the lowest level of genetic introgression . This isA high level of genetic introgression was expected in this small population (mean 3000 adults), because it is located close to the WNH and because it receives a large number of hatchery strays Oncorhynchus nerka) could exchange large numbers of migrants each generation, yet remain genetically distinctive because of reduced reproductive success. In addition to straying intensity, introgression may be influenced by local population size Unexpectedly low levels of introgression, despite persistent straying, have been found in other salmonids. Hendry et al. This is the second largest chum salmon run in PWS (ADF&G unpublished data). Wild populations in this creek showed intermediate levels of genetic introgression from WNH fish. A small proportion of fish from Beartrap Creek was used as brood stock at the WNH until 1986, but a genetic signature of this source is absent in WNH fish. If a genetic legacy from the early use of Beartrap Creek fish as brood stock were present in contemporary WNH brood stock, straying from WNH into this creek would tend to steepen the source-sink curve and lessen a signal of introgression . Even thWells River chum salmon showed the highest level of genetic introgression from hatchery salmon. This river supports the largest chum salmon population in PWS with an estimated mean run-size of 23,100 fish in the past few years . This poAn alternative, but not exclusive, explanation for these results is that low abundances of wild fish in a stream lead to high levels of introgression for the same intensity of straying from the hatchery. Wells River chum salmon populations were depressed during 1991–1994 and 1997–1998 with an average estimated size of only 7200 spawners. These population sizes were less than 30% of the average size of 23,089 fish (1979–2010). A proportionately larger amount of interbreeding with hatchery fish may have occurred during these years. A single successful introgression event could spread through subsequent generations, because of overlapping generations of spawning fish combined with the incorporation of hatchery genotypes into the population. As for Siwash Creek, published percentages of hatchery strays in Wells River were calculated from simple averages unweighted for overall escapement and may therefore not reflect the proportion of hatchery fish in this population. For example, although hatchery strays at Wells River averaged 7.4% over the entire spawning season , samplesThe results of our study yield three important insights into detecting genetic introgression of hatchery-reared chum salmon into wild populations. First, DNA extractions from archived scales collected without using protocols for genetic sampling showed high levels of contamination with DNA from other fish. About half of the samples we attempted to use showed signs of contamination. Questionable genotypes from contaminated DNA extractions would have compromised the accuracy and power of our analyses. Rigorous quality control is an essential step in the genetic analysis of archived fish scales.Second, the use of the source-sink model to search for temporal allele-frequency shifts appears to be a powerful method for documenting small amounts of genetic introgression over several generations. Unlike other models used for estimating genetic introgression, the source-sink model in a Bayesian context yields a confidence interval around the introgression rate. Assignments by the program STRUCTURE, which attempts to resolve population structure with the fit to Hardy-Weinberg expectations and linkage disequilibrium may not provide enough statistical power to detect small levels of introgression beyond one or two generations. Random mating is expected to lead to Hardy-Weinberg proportions in a single generation, and pseudo-linkages are expected to decay in a few generations. Small probabilities of assignment to the wrong population in our analysis likely represent type I error because of the close absolute similarity among populations. These errors were of the same order of magnitude as the probabilities of assignment to the WNH brood stock or to Wells River, the origin of the brood stock. Hence, these individual assignments may not provide evidence of introgression.Third, the results of our study yield insights into the extent that hatchery strays have influenced wild populations of chum salmon in PWS after more than 30 years of large-scale hatchery production. These results show that some populations are more susceptible to genetic introgression by hatchery strays than other populations. Both proximity to a hatchery and the intensity of straying were less important, under some circumstances, than similarity in spawning time. Mismatches in other life-history traits may also be important in retarding genetic introgression into wild populations. Nevertheless, our results show a general convergence of allele frequencies in wild populations toward hatchery allele frequencies. While this convergence demonstrates introgression at neutral genes, the fundamental concern is over the effect of introgression on adaptive variation. Introgression from hatchery strays applies equal pressure on neutral and adaptive genes in wild populations, but it is uncertain to what extent genes underlying adaptation are resilient to introgression Table S1Expected (HE) and observed (HO) heterozygosity for 188 single nucleotide polymorphisms in populations of chum salmon in Prince William Sound, Alaska.(DOCX)Click here for additional data file.Table S2Microsatellites used to test for contamination of chum salmon samples.(DOCX)Click here for additional data file.Table S3OpenBugs code to test for contamination between samples.(DOCX)Click here for additional data file.Table S4Pairs of single nucleotide polymorphisms (SNPs) exhibiting significant linkage disequilibrium in at least 4 of 9 collections in Prince William Sound chum salmon.(DOCX)Click here for additional data file.Table S5OpenBugs code to implement source-sink model of introgression.(DOCX)Click here for additional data file.


ABCA1*Arg230Cys polymorphism were correlated. Further, we examined whether the evolutionary consequences of the agriculturally constructed niche in Mesoamerica could be considered as a gene-culture coevolution model. For this purpose, we genotyped 229 individuals affiliated with 19 Native American populations and added data for 41 other Native American groups (n = 1905) to the analysis. In combination with the SNP cluster of a neutral region, this dataset was then used to unravel the scenario involved in 230Cys evolutionary history. The estimated age of 230Cys is compatible with its origin occurring in the American continent. The correlation of its frequencies with the archeological data on Zea pollen in Mesoamerica/Central America, the neutral coalescent simulations, and the FST-based natural selection analysis suggest that maize domestication was the driving force in the increase in the frequencies of 230Cys in this region. These results may represent the first example of a gene-culture coevolution involving an autochthonous American allele.Culture and genetics rely on two distinct but not isolated transmission systems. Cultural processes may change the human selective environment and thereby affect which individuals survive and reproduce. Here, we evaluated whether the modes of subsistence in Native American populations and the frequencies of the  Homo sapiens sapiens, a particularly important event that triggered a new and striking gene-culture-coevolution cycle was the development of agriculture and animal domestication during the Neolithic period . Further, the human gene-culture coevolution mediated by the domestication of plants and animals has been argued to provide some of the clearest and most spectacular examples of niche construction. The Niche Construction Theory can be defined as a branch of evolutionary biology that emphasizes on the ability of organisms to modify the pressure of natural selection in their environment and thereby act as co-directors of their own evolution, as well as that of other directly associated species Human cultural practices have drastically modified environmental conditions and behaviors, promoting rapid and substantial genomic changes often associated with positive selection and adaptation , these first migrants had to adapt to an immensely wide variety of environments. In some regions during this evolutionary trajectory, as in Mesoamerica and the Andes, hunter-gatherer/forager societies gave rise to agriculturalist and urban communities, while others remained with a hunter-gatherer/forager subsistence system until the time of contact with Europeans or even until the present day. Thus, studies with Native American populations can provide useful information for better understanding gene-culture coevolution and the niche construction processes.et al.DRD4) alleles in several South Amerindian populations and found a significant difference in the allelic distributions between hunter-gatherers and agriculturalists, with an increase of the 7R allele among the former; and (2) Acuña-Alonzo et al. 230Cys allele  of the ATP-binding cassette transporter A1 (ABCA1) gene, which was previously associated with low HDL-cholesterol levels and obesity-related comorbidities, was exclusively present in Native American and mestizo individuals. These authors verified that cells expressing the ABCA1*230Cys allele showed a 27% cholesterol efflux reduction, confirming that this Native American autochthonous variant has a functional effect in vitro. Other investigations have shown that the presence of ABCA1*230Cys explains almost 4% of the variation in plasma HDL-C concentrations in Mexican admixed populations Based on studies with blood groups and other classical genetic polymorphisms, J. V. Neel and F. M. Salzano were pioneers in identifying complex population processes highly dependent on cultural factors in Native Americans  from Meso/Central America and South America , and a part of this information was used in some of our analyses. The populations were clustered according to their geographical location and ancient mode of subsistence as follows: (1) Mesoamerican agriculturalists, (2) Andean agriculturalists, and (3) South American hunter-gatherers/foragers. Of course, caution is needed regarding this classification, since subsistence modes are not stable over time and may not be unique. However, the two categories adopted here  represent general pre-Colombian subsistence conditions, providing a starting point for research related to gene-culture dynamics in Native Americans.New data for the  America . AdditioEthical approval for the present study was provided by the Brazilian National Ethics Commission (CONEP Resolution no. 123/98) for the Brazilian samples, as well as by ethics committee of: (a) Universidad de Antioquia, Medellin, Colombia (Colombian samples); (b) Universidad Nacional Autónoma de México, Ciudad de México, México (Mexican samples); (c) Universidad de Costa Rica, San José, Costa Rica (Costa Rican and Panamanian samples); (d) Universidad of Chile, Santiago, Chile (Chilean samples); (e) Université Paul Sabatier Toulouse 3, Toulouse, France (Bolivian and French Guianian samples). Individual and tribal informed oral consent was obtained from all participants, since they were illiterate, and they were obtained according to the Helsinki Declaration. The ethics committees approved the oral consent procedure as well as the use of these samples in population and evolutionary studies.Arg230Cys polymorphism was genotyped using TaqMan assays . Allele frequencies were obtained by direct counting. The level of the population structure observed within and between Mesoamerican agriculturalist, Andean agriculturalist, and South American hunter-gatherer/forager groups was estimated using F statistics and the Arlequin 3.5.1 software t-test  using the R Stats package .The et al., unpublished data). Based on this additional information   =  [−2p/(1−p)]ln(p), where E(t1)  =  expected age, time is measured in units of 2N generations, and p  =  population frequency ABCA1*230Cys, we considered the average of frequencies of all populations and only those from Mesoamerica/Central America . This program allows a Bayesian inference of the mutation age using an intra-allelic coalescent model to assess LD across the nineteen SNPs that occur around ABCA1*230 . Taken into account the historical information about our sampled populations, three parameters were introduced: (a) Generation time of 25 years; (b) Proportion of population growth of 0.005; and (c) Proportion of population sampled of 0.0002. The program was run in the haplotype mode using two million of iterations.Slatkin and Rannala Based on the long-range haplotype test STF) with that generated for a null simulated distribution under a hierarchical island model using a coalescent approach. In this model, demes exchange more migrants within groups than between groups to generate the joint distribution of genetic diversity within and between populations p value can be estimated from the joint distribution for the population heterozygosity (eH) and STF using a kernel density estimation procedure ABCA1*230 locus were known. Data from 20 other autosomal SNPs  from these same individuals were compiled from a major SNP panel  Mesoamerican agriculturalists (n = 68) vs. Andean agriculturalists (n = 35), (2) Mesoamerican agriculturalists vs. South American hunter-gatherers/foragers (n = 23), (3) Mesoamerican agriculturalists vs. South Americans , and (4) Andean agriculturalists vs. South American hunter-gatherer/foragers.To detect loci under selection, we used a method that contrasted the observed population differentiation  statistics–was compared to the observed genetic diversity of two genetic datasets: (1) that of the ABCA1*230 locus plus the same 19 flanking SNPs listed in item c.1; (The genetic diversity obtained in the simulations–summarized by intra- (heterozygosity) and interpopulation tem c.1; . The sumZea mays ssp. mays) had a single domestication origin from the wild grass teosinte (Zea mays ssp. parviglumis) in the Río Balsas region, southwestern Mexico, approximately 6,300–10,000 calendar years before present Zea (maize and/or teosinte) in the Americas and have been used to estimate the age of maize domestication and dispersion Zea pollen dates from several American archeological sites, and we selected this data set to perform our analysis. To test the connection between maize culture and the ABCA1*230Cys variant, we used allele frequencies from Mesoamerica/Central America populations  as well as Zea pollen dates obtained in archeological sites located geographically near these populations  were obtained using the Statistica 7.0 software .Genetic, archeological, botanical, and paleoecological data furnished evidence that maize . On the other hand, the highest STF among populations within subdivisions was observed in the South American hunter-gatherers/foragers , and a value 5 times lower was found among Mesoamerican agriculturalists ; no sign of structuration was found in the Andes area (p = 0.36).vs. agriculturalists) were compared using the student’s t-test . A comparison of South American hunter-gatherers/foragers and Andean agriculturalists revealed no significant differences (p = 0.351).No significant differences in allelic frequencies were found when subsistence modes , no significant departure from the expected under neutrality was found (data not shown). These results suggest that the ABCA1*230 allele frequencies in Mesoamerica are incompatible with a simple neutral model.Patterns of genetic diversity between populations can be used to detect loci under selection ABCA1*230 polymorphism and 19 flanking SNPs presented a slightly lower average heterozygosity than the putative neutral region dataset (0.32 vs. 0.34 respectively); but global and pairwise STF were higher for the ABCA1 region . Considering that both genomic regions were studied using the same quantity of markers and the same sampling strategy, in populations that were subjected to the same demographic history, the observed differences may occur due to diverse factors, one of them being natural selection. To test this hypothesis each dataset, summarized by the above-mentioned statistics, was also compared to each of 100,000 neutral simulations by means of Euclidian distances. The empirical dataset containing the ABCA1*230 polymorphism presented a poorer fit to neutrality than the putative neutral region dataset, showing Euclidian distances that were twofold higher than those of the neutral simulations (70.64 vs. 35.12). Interestingly, when the Mesoamerican agriculturalist subdivision was excluded from the analysis, this difference dramatically decreased (45.80 vs. 35.12). Thus, the poor fit to neutrality observed at the ABCA1*230 site and its flanking regions may be associated with the genetic pattern found in Mesoamerica.Our neutral demographic simulation analysis showed results in the same direction. The region containing the ABCA1*230Cys allele frequencies and the distribution of the Zea pollen relics in Mesoamerica (p = 0.002). It is important to note that the populations used in the correlation analysis performed here  were investigated for microsatellites and other genetic markers in previous studies conducted by our and other groups A significant correlation was observed between the ABCA1* Arg230Cys polymorphism.We can now examine some hypotheses in an attempt to explain the results and to draw the evolutionary scenario associated with the pattern of diversity of the Maize is considered the most important native crop of the Americas Other crops were also present in the pre-Columbian Mesoamerican civilizations , it is reasonable to suppose that ABCA1 alleles, since cholesterol plays an important role in various infectious processes, such as the entry and replication of Dengue virus type 2 and flaviviral infection ABCA1 gene deletions confer complete resistance against cerebral malaria in mice ABCA1*230Cys selective sweep associated with agricultural development. A sedentary village lifestyle with a corresponding growth in the density of the local population can promote an increase in the mortality rate, particularly in children under 5 years of age Rotavirus and Coronavirus , Streptococcus, Staphylococcus, Plasmodium , and HerpesvirusABCA1*230Cys variant in these infectious processes will require additional functional studies.Noteworthy is that other environmental factors may also have been involved in the distribution of the Arg230Cys presented a worse fit to neutrality than loci known to be neutral, indicating that selective mechanisms are necessary to explain the genetic diversity of Arg230Cys, especially when the Mesoamerican agriculturalist subdivision is considered in the analysis. This result also supports our hypothesis that maize domestication in Mesoamerica lead to changes in the gene pool of the natives from that region.In agreement with this historical scenario, the genetic variation in Arg230Cys polymorphism in South American groups, a situation different from that in Mesoamerica.South America presents much more diversity in relation to habitats, people, and culture than Mesoamerica. For instance, maize arrived in South America, but apparently the level of consumption seen in Mesoamerica/Central America was rarely found there. Archaeological data indicate that only during the implantation and expansion of the Inca Empire (800–500 YBP) was the level of maize consumption important, but the level of consumption was not comparable to that of Mesoamerica/Central America 230Cys allele, with a probable origin in America continent, seems have been the target for an ongoing directional selective sweep as a result of the origin and spread of the maize culture in ancient Mesoamerica.In conclusion, our analyses demonstrate for the first time a robust correlation between a constructed niche and a selected Native American autochthonous allele. The Figure S1Twenty SNPs selected based on their location (chromosome 9: from position 85252250 to 85317359) inside the putative neutral region, defined by Schroeder and colleagues, which comprises ∼76,000 bp around the D9S1120 locus.(JPG)Click here for additional data file.Table S1Populations included in the selection analyses.(DOC)Click here for additional data file.Table S2Allele frequency by region of 20 SNPs located around the D9S1120 locus.(DOCX)Click here for additional data file.Table S3Allele frequency by region of 19 SNPs located around the ABCA1*230 locus.(DOCX)Click here for additional data file.


Anemone multifida. Using peak height and dominant AFLP data, we quantified population differentiation at non-outlier  and outlier loci to determine the potential contribution of natural selection and selectively neutral evolution to population divergence. We found 13 candidate loci, corresponding to 2.7% of loci, with signatures of divergent natural selection between alpine and lowland populations and between alpine populations (Fst  = 0.074–0.445 at outlier loci), but neutral population differentiation was also evident between alpine populations . By examining population structure at both neutral and outlier loci, we determined that the combined effects of selection and neutral evolution are associated with the divergence of alpine populations, which may be linked to extreme abiotic conditions and isolation between alpine sites. The presence of outlier levels of genetic variation in structured populations underscores the importance of separately analyzing neutral and outlier loci to infer the relative role of divergent natural selection and neutral evolution in population divergence.Population differentiation can be driven in large part by natural selection, but selectively neutral evolution can play a prominent role in shaping patters of population divergence. The decomposition of the evolutionary history of populations into the relative effects of natural selection and selectively neutral evolution enables an understanding of the causes of population divergence and adaptation. In this study, we examined heterogeneous genomic divergence between alpine and lowland ecotypes of the allopolyploid plant,  Population divergence and speciation have been driven in large part by the joint effects of neutral evolution and natural selection While natural selection is often the cause of speciation and ecological divergence The primary focus of most population genomic investigations is on detection and patterns of variation of outlier loci Anemone multifida. Alpine and lowland habitats differ extensively in abiotic and biotic conditions In this study, we conduct a genome scan for putative signatures of natural selection between alpine and lowland ecotypes of the allopolyploid plant, A. multifida includes outlier loci that may have been the target of natural selection, 2) whether populations in the same environment possess similar alleles that would indicate that natural selection (vs. genetic drift) has led to alpine and lowland adaptation, and 3) if selectively neutral evolution has driven population divergence. To accomplish these goals, we conducted a genome scan for outlier loci amongst multiple alpine and lowland populations. Incorporating both binary and peak height AFLP data The goals of this study were to determine 1) whether the genome of A. multifida, outlier loci should show limited differentiation between populations in the case of balancing selection, or extensive differentiation if divergent selection has had an effect. Alternatively, if natural selection has not affected specific sites throughout the genome, there should be no evidence of outlying loci. Analysis across multiple populations from each environment allows separation of demographic and environmental factors in determining genetic population structure, and identifies whether populations diverged primarily due to selectively neutral evolution. If so, neutral evolutionary processes, such as gene flow or genetic drift, have affected population evolution, in which case populations should be differentiated at the neutral loci. In contrast, high gene flow and/or negligible genetic drift should generate limited genomic differentiation between populations.If natural selection has affected the genome of Anemone multifida is a widespread species that occupies habitats from sea level to high alpine. A. multifida is hypothesized to be an allotetraploid based on observations of two distinct chromosome sets, one of which is similar to chromosomes from a clade of alpine specialist species, whereas the other set is more similar to chromosomes from a lowland clade A. multifida may possess alternate copies of alleles that are advantageous in alpine environments  and lowland environments (from the “lowland” chromosome set), which may explain its wide habitat range A. multifida exhibit extensive morphological variation A. multifida individuals during flowering from each of two alpine and three lowland sites Alberta, Canada, during June and July, 2011  were determined by reference to the internal sizing standard (GS-500 LIZ) in the software GENEMAPPER v4.0 . Fragments between 100–500 bp were scored using automatic allele binning in Genemapper, with a cut-off intensity of 100 fluorescent units to minimize false-allele calling. Polymorphic peaks identified in Genemapper were then manually checked for quality and consistent scoring. AFLP alleles with multiple peaks were discarded due to the unreliable sizing of the fragments. AFLP alleles with amplification at or near the 100 fluorescent unit cut off were manually checked for consistent scoring, as peaks with amplification just below the threshold can be a source of allele drop out Genemapper provides the option of exporting both the dominant  and peak height data from each allele . Most analyses have chosen to use the dominant data as the basis of genotyping individuals, but we also incorporated peak height data for comparison A. multifida. STRUCTURE analyses assume diploid inheritance, which is potentially violated for many of the AFLP loci in this study of an allopolyploid species. Simulations using a burn-in of 10000 iterations and 10000 MCMC replicates after burn-in were used to determine the probability of the model assuming 1 to 7 populations. These simulations were replicated 10 times at each level of K, allowing for a correction of the STRUCTURE results following The number of genetic clusters was first identified with a principal components analysis of AFLP data using R statistical software We analyzed the distribution of genetic variation among and within populations with an analysis of molecular variance (AMOVA) analysis using GenAlEx ST), we used AFLPsurv v1.0 ST from the frequency of the null AFLP allele using a number of options. We chose a Bayesian method with non-uniform prior distribution of allele frequencies assuming Hardy-Weinberg conditions were met To estimate and test genetic population structure between sampled sites , leaving 479 markers in the final dataset . AmongstThe first and second principal components of the neutral locus dataset explained approximately 13.0% and 10.3% of the overall variance, respectively. Additional principal components explained less than 6% of the variance. At the neutral loci, most individuals clustered together, regardless of population of origin, although there was variation along the PC1 axis, and a number of individuals deviated from the major cluster . Five inSTRUCTURE analysis suggested a most likely K of 4 or 5 distinct populations. After correction for the variance in probability estimates, the model of K  = 4 populations had the highest support, whereas K  = 5 had lower support than other models . The numST of 0.041 and a 99% upper limit FST of 0.021 (i.e. p<0.01). The HWP alpine population was significantly subdivided from all populations .AFLPsurv identified significant population structure amongst sampled sites at the neutral loci, with a global Fulations . The lowrom zero . ApproxiUsing the 13 outlier loci identified in with the dominant AFLP data type, the first and second principal components of the outlier locus dataset explained approximately 29.5% and 16.8% of the overall variance, respectively. Further principal components explained less than 9% of the variance individually. Most individuals from the lowland sites grouped together . Alpine ST of 0.255 and a 99% upper limit FST of 0.022 (i.e. p<0.01). FST differed significantly from zero between all population pairs, but was highest between lowland and alpine environments as well as alpine sites .All analyses of the outlier loci consistently identified three groups of individuals, corresponding to each alpine site and a lowland group. AFLPsurv identified significant population structure amongst sampled sites at the outlier loci, with a global Fne sites . ApproxiThe frequencies of alleles appeared to vary the most between alpine and lowland populations , but the2 = 30.78, df  = 8, p<0.001; 4,118 = 8.83, p<0.0001; Phenotypic variation was present between populations. Floral colour frequencies differed significantly between sites  represented possible signatures of natural selection, within the 1–4% range reported from other studies of species that occupy contrasting environments The existence of population structure at both neutral and outlier loci suggests natural selection and neutral evolution have related effects in alpine and lowland population divergence. Neutral population divergence between alpine sites suggests the processes such as genetic drift, restricted gene flow or other demographic parameters may have affected the evolution of outlier loci in alpine environments in addition to the putative effects of natural selection. However, outlier levels of genetic differentiation at a number of loci, despite the lack of complete population differentiation in some alpine populations, suggests natural selection can affect ecotypic divergence between lowland and alpine populations in the presence of gene flow. The isolated and extreme nature of alpine habitats may generally mean that drift, mutation and other neutral evolutionary processes are necessarily linked to the effects of natural selection in alpine environments, although the degree to which this occurs may depend on levels of gene flow between populations and environments.Alpine environments generally exert extreme abiotic selection for survival and reproduction, and successful organisms must function at lower temperatures, shorter growing seasons, and exposure to wind, intense radiation, and falling debris Our findings are consistent with other studies in underscoring the importance of considering neutral evolutionary factors when inferring cause of evolution at outlier loci or traits under selection While both natural selection and neutral evolution appear to have affected population divergence, the relatively small sample of populations limits the implications of these results to the regional level. Further sampling of populations from both lowland and alpine environments across the range of the species will add to these results and aid in the determination of how population divergence between environments and amongst alpine populations has occurred in this species. The utilization of AFLP for genome scans has limitations. AFLP loci may be associated with a particular region of the genome, possibly missing section of the genome that are under natural selection The high frequency of white-flowered individuals in alpine environments, and the shorter plants at an alpine site suggests these phenotypes reflect differences between alpine and lowland environmental conditions. Low shoots can reduce damage from exposure to wind and falling debris, and closer proximity to the ground can also limit freezing and frost formation The higher false discovery rate and fewer detected loci based on peak height data than dominant data suggests peak height is more conservative in the identification of outlier loci. This contrasts with another investigation that found band intensity had a higher power to detect outlier loci than did dominant data Population divergence is in many cases caused by divergent natural selection in species that span different environments, but can also be linked to or caused primarily by neutral evolution. A detailed analysis of population structure at both neutral and outlier loci as we conducted in this study can elucidate the relative contribution of selection and neutral evolution to population divergence, and is an important component of the determination of the causes of outlier levels of genetic variation. The interaction of selection and neutral evolution may be a prominent mode of population divergence amongst alpine populations. Further sampling of alpine populations in multiple species will determine if this is generally true for plants, while the incorporation of high-throughput sequencing may reveal the identity and putative function of loci associated with alpine and lowland adaptation. An accurate determination of the extent of neutral population differentiation in conjunction with the potential effects of natural selection will remain an important component of inferring the relative contribution of these evolutionary processes to variation across the genome in natural populations.Figure S1Variation in plant height and flower colour within and among lowland sites, BHS, BL and WC, and alpine sites, HSB and HWP.(TIFF)Click here for additional data file.


Populations of the Arabian Peninsula have a complex genetic structure that reflects waves of migrations including the earliest human migrations from Africa and eastern Asia, migrations along ancient civilization trading routes and colonization history of recent centuries.Here, we present a study of genome-wide admixture in this region, using 156 genotyped individuals from Qatar, a country located at the crossroads of these migration patterns. Since haplotypes of these individuals could have originated from many different populations across the world, we have developed a machine learning method "SupportMix" to infer loci-specific genomic ancestry when simultaneously analyzing many possible ancestral populations. Simulations show that SupportMix is not only more accurate than other popular admixture discovery tools but is the first admixture inference method that can efficiently scale for simultaneous analysis of 50-100 putative ancestral populations while being independent of prior demographic information.By simultaneously using the 55 world populations from the Human Genome Diversity Panel, SupportMix was able to extract the fine-scale ancestry of the Qatar population, providing many new observations concerning the ancestry of the region. For example, as well as recapitulating the three major sub-populations in Qatar, composed of mainly Arabic, Persian, and African ancestry, SupportMix additionally identifies the specific ancestry of the Persian group to populations sampled in Greater Persia rather than from China and the ancestry of the African group to sub-Saharan origin and not Southern African Bantu origin as previously thought. The ancestry of people currently populating the Arabian Peninsula is complex. Centrally located among three continents, the geography of the region has contributed to migration influx during different epochs of human history. Qatar, located on the eastern edge of the Arabian Peninsula, is at the center of these patterns. The firAdmixture in modern humans can reveal individual and population demographic histories and origins-7 and hoin silico generated admixed populations from the HGDP panel. By varying population genetic parameters we are able to infer the accuracy for many different admixture scenarios and show that not only is SupportMix more accurate but is also robust to perturbations in parameters.To address the challenge of inferring admixture for individuals from Qatar, we have developed "SupportMix," a machine learning method for admixture analysis. SupportMix is a two-level method where the first level makes use of support vector machines (SVM), a class of supervised machine learning algorithms known to be among the most accurate techniques for general classification problems, to identify the putative ancestral origin of a genomic segment. The second level is a smoothing application that makes use of a hidden Markov model (HMM) to detect transitions between ancestral origins in the admixed genomes . We applied SupportMix to analyze admixture in 156 unrelated individuals from Qatar genotyped by microarray. Since wk genetically distinct populations. This is accomplished by training classifiers on k′ ≥ k putative ancestral populations where these k′ population must contain either the true k ancestral populations or good proxy populations for them. For each population multiple individuals have to be sampled at the same N polymorphic sites as the admixed individuals. Every ancestral and admixed haploid genome, i, we represent as a vectorN sampled polymorphic sites. In addition every ancestral haploid genome is associated with a scalar yi ∈ 1..k′ identifying the population of the haploid genome.We consider the problem of inferring the ancestral-population origin of the two phased haploid genomes of sampled individuals who can trace their recent ancestry to xi are divided into windows,w consecutive loci where j indexes the N/wwindows in individual haploid genome i. The inference problem can therefore be formalized as finding the appropriate labeli. We use support vector machines (SVM)[To find region-, or locus-specific ancestry assignments in admixed individuals, the vectors of genotypes, es (SVM) for thisk′ ancestral populations. For an excellent tutorial to SVMs we refer the reader to the article by Asa Ben-Hur[j, consisting of w markers. For the case k′ = 2 (more on k′  > 2 below), the labels yi  become -1 and 1 and the discriminant function is found by the following constrained optimization problem: For each window of the genome we train a unique and independent SVM to optimize the classification of the  Ben-Hur. Specifiwis a vector and b a scalar that together define a w-1 dimensional hyperplane. This hyperplane optimally separates the two populations, in the w dimensional genotype space, subject to a penalty of misclassified individual samples proportional to the "slack" variable C and the distance, εi, on the wrong side of the plane of sample i. The determined discriminant functionk′  > 2 the standard one-against-one trick for binary classifiers is used, that is all k′(k′ − 1)/2 pairs of populations are trained against each other using the above standard binary SVM[k′ (k′ − 1)/2 classifiers and the final ancestry is assigned the most common ancestry assignment of all these classifiers.where nary SVM. Then thk′ populations. This success rate is used heuristically to determine w, the size of the window necessary to discriminate the different ancestral populations while keeping w as small as possible to preclude recombination in the admixed genomes within each window and is also used as a parameter for the HMM.In addition to training the SVMs on the ancestral data, accuracy of the SVM classification is estimated with a three-way cross-validation. This was accomplished by subdividing the individuals in the ancestral populations into three independent sets, where SVMs were trained on two of the subsets and tested on the remaining third subset three different times. The accuracies of these three sub-samplings were averaged and taken as a measure of the success rate for each window across the k′ hidden states and k′ output states. Assuming recombination occurs at every generation, recombination points are modeled along the chromosome as a Poisson process dependent on the recombination rate and number of generations, g, since admixture[ gd−)/(k′ − 1) where d is the genetic distance between windows, measured in Morgans. The emission probabilities are p for the corresponding hidden state and (1−p)/(k′ − 1) for the other possible states, where p is the success rate of the SVM at the corresponding window as determined by the cross-validation (described in previous section). This HMM, in addition to accounting for LD, has the advantage of smoothing out the classifications returned by the SVM, so as to limit the effect of regions with poor information content. The posterior probability of the ancestry at each window location is determined using the forward-backward algorithm and the most probable ancestry is used as the estimated ancestry.The output classification of each admixed individual is used as the input to an HMM to extract the ancestral origins of each window. Each SVM is independent from every other SVM and as such, there is no assumed correlation between ancestral states across window boundaries. Biologically this correlation generally exists within genomic blocks as a consequence of linkage disequilibrium (LD). By introducing a Markov condition across windows, we reintroduce this correlation. Specifically the HMM has dmixture. The trad between neighboring windows was estimated from the combined HapMap genetic map[d across the entire genome, where d was chosen to match the mean across the genetic map.The genetic distance etic map. To testThe 156 unrelated Qataris were processed on the Affymetrix 5.0 genotype array according to the protocols described by Hunter-Zinck et al.. The 440The remaining SNPs were phased using Beagle. For theTo assign segments of the genomes of each Qatar individual to possible ancestral populations, the HGDP sample data was usedin silico analysis. The other HGDP populations were excluded as there were not enough individuals to simulate admixture and also exclude the ancestral indiviudals when training SupportMix. Admixed populations were generated by in silico mating of individuals from the different populations using a method similar to the one proposed by Price et al.[α :(1 − α) was achieved by sampling l haploid chromosomes from each of the ancestral populations and mating each chromosome i ∈ (1..l) with the corresponding chromosome in the other ancestral population using a recombination model. The origin of the admixed genomic regions was determined using two steps: 1. the initial origin was determined by probability α to be from population one, 2. the recombination spots along the genome were determined using a Poisson process such that the expected run lengths follow 1 −egd−, where g is the number of generations since admixture and d is the distance along the genome measured in Morgans as determined by HapMap[α to (1 − α). For three or more populations, the admixture was determined by choosing the origin populations in ratios of α1: α2 : α3 : .... For each admixed population, four haploid genomes were generated and the "ancestral" haploid genomes were discarded before the training of SupportMix and LAMP-ANC.A total of 651 unrelated individuals from 31 global populations in the HGDP-CEPH dataset that each have at least 10 sampled individuals (20 phased haploid genomes) were used for e et al. but extey HapMap. At eachα = 0.5, g = 5. To explore the effects of α, g, window size and genetic map used, seven simulated admixed populations were used that spanned the range of population structure in these 465 populations. For three way admixture, mixture between Yoruban, French, and each of the other HGDP populations, as well as Yoruba, Bedouin and each of the other HGDP populations were simulated separately, with equal proportions for the populations  and g = 5.For admixture between two populations, 465 admixed populations were generated using all 31 ancestral populations with in silico data and to PCA and STRUCTURE for the Qatar sample. The PCA was carried out on the 71,982 genotype SNPs common to both Qatar and HGDP using the singular value decomposition and STRUCTURE, using the built in admixture model[k = 3 using 20,000 burn-in iterations and 10,000 iterations after burn-in. LAMP is a very accurate method for ancestral deconvolution of genotype data that has consistently been shown to do better than most previously published methods, while being able to handle more than two ancestral populations[SupportMix was compared to LAMP-ANC for re model, was runulations. We did ulations.in silico admixed populations was carried out where accuracy was measured by percent of correctly assigned loci. The accuracy was very high, especially for two populations with a high degree of population structure  the accuracy of SupportMix was diminished slightly especially when two of the three populations were very similar, as determined by low Fst  as putative ancestral populations, SupportMix roughly divided the Qataris into three sub-populations: Arab-Qataris, Persian-Qataris and African-Qataris, named after the origin of the majority of ancestral alleles. The sub-populations had different degrees of admixture between Middle-Eastern, Greater Persian and sub-Saharan African populations out of the 55 HGDP populations. This result was consistent, though more detailed than both STRUCTURE and principal component analysis (PCA) results Figure repeatedThe Arab-Qataris showed very little admixture with non-Middle Eastern populations. Summarizing the assignments of loci by taking the mean number of loci per individual and the standard deviation across individuals, 88.5 ± 8.3% (expressed as mean ± standard deviation) of the loci were of Middle Eastern origin of which 63.2 ± 12.0% were most similar to Bedouins, 12.4 ± 5.3% to Palestinians and 10.2 ± 4.4% similar to Druze  and little admixture with the Persian populations (6.8 ± 7.8%) as well as fewer Middle-Eastern alleles (30.5 ± 20.7%) compared to the other two sub-populations. The African alleles were mainly assigned to populations in sub-Saharan Africa in the equatorial region, specifically, Mandenka (20.0 ± 9.0%), Yoruba 18.4 ± 10.5%) and Bantus sampled in Tanzania (16.8 ± 9.0%). There were very few (0.0 ± 0.3%) loci assigned to southern African populations such as the Bantu, Pygmy and San sampled in South Africa and Namibia  contribute at least 2% of the ancestry to the Qatari population while the vast majority of the 55 world populations contribute less than 0.1% of the ancestry. Several regions, including those of African ancestry  and Persian ancestry , are likely being assigned multiple ancestral origins because each acts as a proxy for a true, unsampled ancestral population.The Arabian peninsula is at the intersection of the ancient and historical migration patterns of three continents and the genomes of the people in the modern country of Qatar reflect this rich history. Our analysis of Qatari genomes has allowed us to infer the fine details of these migrations and to fill gaps in our understanding of the human migration history of this region. This analysis was made possible by SupportMix, which can make accurate assignments where prior specification of ancestral populations or a specific population genetic model is problematic to formulate. In a broader view, SupportMix provides a tool for accurate and robust ancestral assignment by simultaneous analysis of a worldwide selection of ancestral populations. Such analyses will be critical for accurate assignment in the many world-wide admixed populations that are likely to have unexpected ancestry that reflects a richer history than known from anthropological or historical studies.LO drafted the manuscript, participated in the design of the study and concieved and performed the statistical analysis. JS, NH, RM and LC obtained samples and processed the genotyping data. JLRF aided in the researching the hisory of Qatar. CB conceived the initial idea of using machine learning to aid in ancestry assignments. JGM participated in the design of the study and together with RGC coordinated the project and helped draft the manuscript. All authors read and approved the final manuscript.Table comparing accuracy of SupportMix using a genetic map versus a fixed recombination rate.Click here for fileAdditional Figure 1 - Ancestry assignment for Qatari individuals for entire genome. Ancestry assignments for Qatari individuals. (top) Locus-specific ancestry assignment from SupportMix colored by most probable ancestral population as in Figurek = 3 clusters.Click here for fileTable of average ancestry assignments for the three Qatari sub-populations.Click here for file