Pleurotus eryngii var. tuoliensis, which were collected from nine geographical locations in Yumin, Tuoli, and Qinghe counties in the Xinjiang Autonomous Region of China, was analysed using two molecular marker systems (inter-simple sequence repeat and start codon targeted). At the variety level, the percentage of polymorphic loci and Nei’s gene diversity index for P. eryngii var. tuoliensis was 96.32% and 0.238, respectively. At the population level, Nei’s gene diversity index ranged from 0.149 to 0.218 with an average of 0.186, and Shannon's information index ranged from 0.213 to 0.339 with an average of 0.284. These results revealed the abundant genetic variability in the wild resources of P. eryngii var. tuoliensis. Nei’s gene diversity analysis indicated that the genetic variance was mainly found within individual geographical populations, and the analysis of molecular variance revealed low but significant genetic differentiation among local and regional populations. The limited gene flow (Nm = 1.794) was inferred as a major reason for the extent of genetic differentiation of P. eryngii var. tuoliensis. The results of Mantel tests showed that the genetic distance among geographical populations of P. eryngii var. tuoliensis was positively correlated with the geographical distance and the longitudinal distances , which indicates that geographical isolation is an important factor for the observed genetic differentiation. Nine geographical populations of P. eryngii var. tuoliensis were divided into three groups according to their geographical origins, which revealed that the genetic diversity was closely related to the geographical distribution of this wild fungus.The genetic diversity of 123 wild strains of  Pleurotus mushroom, which is commercially called Bai Ling Gu, is a precious edible fungus of high nutrient and medicinal value [Ferula plants in the Umbelliferae family [Pleurotus mushroom found specifically in China is a branch of Pleurotus eryngii, which evolved independently in China [Pleurotus eryngii var. tuoliensis through morphological identification and internal transcribed spacer (ITS) analysis [The al value ,2. In the family . It has e family . Accordie family -6. The rin China . The scianalysis .P. eryngii species complex has the most abundant population diversity in the genus Pleurotus. It consists of at least six varieties [P. eryngii var. eryngii, var. ferulae, and var. nebrodensis [P. eryngii var. eryngii and var. ferulae was more abundant than that of var. nebrodensis, var. Elaeoselinum, and var. Thapsia [P. eryngii var. eryngii and var. ferulae revealed a greater variation within geographical populations than that between geographical populations [arieties . Becausearieties -13. Analrodensis . The gen Thapsia . The anaulations .Inter-simple sequence repeats (ISSR) markers have been widely applied to analyses of genetic variance and population structure in many types of organisms -21. BecaP. eryngii var. tuoliensis was studied using ISSR and SCoT markers. The goals of this study were to investigate the genetic structure characteristic and the level of genetic variation of P. eryngii var. tuoliensis, and to thereby infer the factors that influence the genetic differentiation in the populations.In this study, the genetic variation of 123 samples of P. eryngii var. tuoliensis is not considered an endangered species, commercial collecting nowadays is forbidden for protecting the local ecological environment, but collecting for research purposes is allowed. Anyone who collects the wild mushroom of P. eryngii var. tuoliensis for commercial purpose will be fined by the local Agricultural Bureaus .Although the wild mushroom of Based on the natural occurrence of fruiting bodies, we confirmed the number of sampling sites in Yumin, Tuoli, and Qinghe. A total of 123 wild samples were collected from nine sites in three regions of Xinjiang from March to May of 2009 . These sPure culture of each strain was obtained by isolating tissue culture from the fruiting bodies. A small piece of tissue was removed aseptically and transferred into a culture tube containing potato dextrose agar (PDA), and incubated in the dark at 25°C for 7-9 days. These samples were next stored at 4°C until needed.Mycelia for DNA extraction were cultured on PDA Petri dishes with cellophane at 25°C for 7 days. The total DNA was extracted using a DP305-Plant Genome Extraction Kit . The purity and quality of the genomic DNA were determined through spectrophotometry and electrophoresis on 1.0% agarose gel. The DNA solution was stored at -20°C.Taq buffer, 0.2 mM dNTPs, 0.5 mM of each primer, and 1 U of Ex Taq DNA polymerase (TaKaRa). The PCR reaction conditions were the following: initial denaturation step at 94°C for 4 min, 35 cycles of 50 s at 94°C, annealing at 55°C for 50 s, and extension at 72°C for 2 min, and final extension at 72°C for 7 min. The amplified products were resolved on a 1.0% agarose gel and stained with ethidium bromide. The results were observed through an ultraviolet gel imaging system. The molecular weights of DNA bands on the agarose gel were estimated using a DNA ladder, i.e., 2-log (TaKaRa).Seven ISSR primers were selected in a pre-experiment . The ampTaq buffer, 0.25 mM dNTPs, 0.5 mM of each primer, 1 U of Ex Taq DNA polymerase (TaKaRa), and 20 ng of the template DNA. The PCR reaction was performed using the following thermal cycling protocol: 94°C for 5 min, 35 cycles of 94°C for 1 min, 55°C for 1 min, and 72°C for 2 min. The reaction was completed through incubation at 72°C for 10 min. The SCoT products were resolved through electrophoresis on 1.5% agarose gels.Eight out of 36 SCoT primers were selected, which produced clear and reproducible profiles . SequencThe gels were scored for the presence or absence of reproducible bands. Each band was regarded as a locus with two alternative alleles. The data from the ISSR and the SCoT analyses were converted into a binary data matrix as discrete variables (1 = presence and 0 = absence). To expand the range of DNA polymorphisms assayed in a genome and to increase the reliability of the results, the diversity analyses were performed in this study using combined data of ISSR and SCoT.The genetic variability at the variety or population level was analysed under the assumption of Hardy-Weinberg equilibrium. The percentage of polymorphic bands (PPB), the numbers of effective alleles (Ne), Shannon’s information index (I), and Nei’s gene diversity index (H) were calculated using the GenAIEx 6.41 software .According to the method developed by Nei, the gene diversity statistics, including the total allelic diversity (Ht), the mean allelic diversity within populations (Hs), the proportion of the total allelic diversity found among populations (Gst), and the gene flow among populations (Nm), were obtained using the POPGENE (v 1.31) . The totThe pairwise Nei’s genetic distances between populations were calculated using the POPGENE software. In order to examine whether local populations that are geographically close are genetically similar, correlation of genetic distances with geographical, latitudinal and longitudinal distances respectively was analyzed through three Mantel tests using the GENALEX program .The cluster analysis for the populations was performed with the NTSYSpc-2.1e program  based onP. eryngii var. tuoliensis showed that the percentage of polymorphic bands, the number of effective alleles, Nei’s gene diversity index, and Shannon’s information index were 96.32%, 1.381, 0.238, and 0.377, respectively. The results from these analyses suggested that there are high levels of genetic variations within this variety. The analysis of the genetic diversity among 123 samples of The percentage of polymorphic bands among the nine geographical populations ranged from a low of 33.44% for the HJ population in Tuoli to a high of 77.59% for the WT population in Yumin with an average of 57.75%. In addition, Nei’s gene diversity index ranged from a minimum of 0.149 to a maximum of 0.218 with an average of 0.186, whereas Shannon’s Information index ranged from 0.213 to 0.339 with an average of 0.284 . The twoP. eryngii var. tuoliensis and that the genetic diversity within populations (Hs) was 0.161. The relative degree of gene differentiation among the nine populations (Gst) was 0.218. In other words, 21.8% and 78.2% of the genetic diversity was between populations and within populations, respectively, which reveals that a large amount of the genetic diversity was found within populations.The results from Nei’s genetic diversity analysis showed that the average total genetic diversity (Ht) over all loci was 0.206 for The results of AMOVA suggested significant genetic differences within populations. The results obtained from the Nei’s genetic diversity analysis were consistent. Specifically, 13% of the total genetic variance (PhiRT) was found between populations from different regions. The next level, i.e., among populations within regions, contributed 8% of the total genetic variance (PhiPR), and the remaining 79% genetic variance was obtained from within individual populations (PhiPT). All of the three levels contributed significantly to the overall genetic variation, as determined through the permutation analyses .P. eryngii var. tuoliensis. Compared with those of other basidiomycetes [The gene flow (Nm) estimate obtained by Gst was 1.794 for omycetes ,37, the P. eryngii var. tuoliensis varied from 0.021 to 0.134 . Moreover, we found that the longitudinal differences, rather than the latitudinal differences, were associated with the genetic distance .The genetic distances between populations of to 0.134 . The smaP. eryngii var. tuoliensis were divided into three groups according to their geographical origins, which indicate that the genetic diversity is closely related to the geographical distribution. The populations from six locations in Yumin were group together in the first cluster, and the second cluster consisted of the two populations from Tuoli. The AR population located in Qinghe was first clustered with the Yumin group, whereas the Tuoli group was far from the above two groups and clustered last  were used to evaluate the genetic diversity of the wild mushroom of rameters -40. Thesrameters . Despiterameters ,42.Lentinula edodes (99.6%) [Auricularia polytricha (99.8%) [P. eryngii var. tuoliensis, which independently evolved in China, is a major branch of the P. eryngii species complex and is only distributed in the north area of Xinjiang. The geographical distribution of this variety was not as wide as that observed for P. eryngii var. eryngii or var. ferulae [P. eryngii var. tuoliensis is higher than those of the other varieties  in the species complex, with the exception of P. eryngii var. eryngii [In fungi, high levels of genetic variability are usually observed in wild populations that reproduce sexually, have broad ecological niches, and/or have a wide geographical distribution . The per (99.6%)  and Auri (99.8%) . The mus ferulae . In gene ferulae . However eryngii . This re eryngii . The dry eryngii . Stressf eryngii ,50.P. eryngii var. tuoliensis (Gst = 0.218), and this result is similar to the findings obtained previously in several basidiomycete species. For example, the intercontinental populations of the model basidiomycete Schizophyllum commune were differentiated significantly with a Gst value of 0.214 [Phlebia centrifuga, eight populations across northern Europe showed a Gst value of 0.072 [Tricholoma matsutake respectively from southwestern, and northeastern China was found to be approximately 0.10 [The genetic variance was partitioned within populations rather than among populations of of 0.214 . For theof 0.072 . The Gstely 0.10 ,52. The ely 0.10 .Pleurotus ostreatus [Mycena rosea [Armillaria bulbosa [Lycoperdon pyriforme [P. eryngii var. tuoliensis, which corresponds to those of the Italian populations of P. eryngii and P. ferulae, as shown by the fact that most of the variations are contained within local populations [P. eryngii var. tuoliensis. Efficient patterns of dispersal, establishment and spread of the fungi appear to be other factors that influence the local genetic structure of populations. Fungal populations are established by two different dispersal mechanisms. The structure of local populations that are mainly maintained through basidiospore dispersal is characterised by a large number of genetically different individuals within a bit substrate, as has been observed with streatus  and Mycena rosea . The pop bulbosa  and Lycoyriforme , respectulations , would iP. eryngii var. tuoliensis , the northwest winds prevail in these regions. The dry windy climate might contribute to the long-distance transmission of spores. The gene flow mediated by spore dispersal might weaken the effect of the geographical distance isolation to some extent. The microclimate in Yumin is similar to that in Tuoli, but the populations within these two regions are effectively separated by hills. Studies of biogeography have indicated that certain particular ecological factors can cause population divergence at the microscale. Natural geographical barriers, including mountains and/or rivers, are one of the most important factors [The results from the Mantel tests and the cluster analysis supported the hypothesis that the genetic relationship among populations was closely associated with their geographic distributions  and 3. C factors ,60.


The greater Himalayan region demarcates two of the most prominent linguistic phyla in Asia: Tibeto-Burman and Indo-European. Previous genetic surveys, mainly using Y-chromosome polymorphisms and/or mitochondrial DNA polymorphisms suggested a substantially reduced geneflow between populations belonging to these two phyla. These studies, however, have mainly focussed on populations residing far to the north and/or south of this mountain range, and have not been able to study geneflow patterns within the greater Himalayan region itself. We now report a detailed, linguistically informed, genetic survey of Tibeto-Burman and Indo-European speakers from the Himalayan countries Nepal and Bhutan based on autosomal microsatellite markers and compare these populations with surrounding regions. The genetic differentiation between populations within the Himalayas seems to be much higher than between populations in the neighbouring countries. We also observe a remarkable genetic differentiation between the Tibeto-Burman speaking populations on the one hand and Indo-European speaking populations on the other, suggesting that language and geography have played an equally large role in defining the genetic composition of present-day populations within the Himalayas. Until relatively recently, the reconstruction of human population prehistory was predominantly based on archaeology, linguistics, ethnography and somatology. In recent decades, DNA research has provided a new and powerful tool for studying mankind's prehistory. A geographical area of particular interest for human population prehistory is the Himalayas, stretching from Pakistan to Myanmar and forming the highest land boundary on our planet. There are several competing theories about the origin of the people currently residing in Asia in general and the Himalayan region in particular in situ, a possible consequence of subdivision and extreme isolation over long periods of time. In addition to being one of the linguistically most complex regions, the Himalayas were sometimes thought to form the boundary between the Indo-European and Tibeto-Burman language phyla. In fact the real linguistic boundary, at least in present times, runs roughly parallel to and well south of the highest mountain peaks, through the foothills and lowlands Linguistically, the Greater Himalayan region is one of the most complex areas of the world. It contains populations speaking languages belonging to six different linguistic phyla  and two confirmed linguistic isolates (Burushaski and Kusunda). This complex linguistic patchwork may be an indication of the Himalayas being an ancient source of genetically differentiated populations and languages which evolved Regardless which settlement theory will prove to be correct, there can be no doubt that the amount of (pre)historic geneflow between people residing in and around the Greater Himalayan Region must have been influenced substantially both by the large linguistic diversity and by the rugged terrain which, even with modern means, still renders large parts of the region very difficult to traverse. Previous genetic studies, mostly focussing on the Y-chromosome or the mtDNA, have indeed indicated the possible presence of a genetic boundary in this area. But so far, these studies have mainly included population samples from countries surrounding the Himalayas or at most, a small, poorly defined, Nepalese population sample For the purpose of the EUROCORES collaborative project 'Language and Genes of the Greater Himalayan Region' we have collected blood samples from Nepalese and Bhutanese volunteers over the age of 18. These DNAs are anonymous and identified only by code. Collection of these blood samples took place in Nepal and Bhutan. All subsequent research using these samples was done in the UK, Italy, and the Netherlands. Prior to sample collection, the entire project was submitted to the LUMC Medical Ethics Committee. Since this project was strictly non-commercial, non-medical and a non-intervention study, samples would be obtained under full consent, and identified by code only, and of adult (>18) individuals, the LUMC MEC did not further process this project, which was in line with the relevant version of the Dutch Guidelines of Medical Research. Hence no approval was necessary, and not obtained.www.le.ac.uk/ge/maj4/Himalayan_OMLLreport.pdf.A detailed description of the entire project, including sample collection, study design and consent structure and acknowledgements is available via In short, sampling took place according to the general guidelines of the human global diversity project, and full written informed consent was obtained from each individual as was agreed by bilateral agreements between Leiden University Medical Center and the responsible authorities in Nepal and Bhutan. Individuals were approached via representatives of the respective language communities and the purpose of the project was explained in local languages. Consent forms to be signed included a condensed text of the sampling guidelines. When a donor was unable to read or write, the consent text was read to the donor in his/her local language, after which one of the project's co-workers filled in the donor's data. For some communities, detailed explanation in the local language was given and video-recorded for archival purposes. All consent forms are archived at Leiden University Medical Center.Our aim was to collect samples from approximately 50 unrelated representatives of each of the major ethnolinguistic groups present in Nepal and Bhutan Between December 2002 and February 2003, blood samples were collected from 947 unrelated Nepalese volunteers .In order to be able to perform detailed comparative statistics, reference data from countries surrounding the Greater Himalayan Region were necessary. Our aim was to collect as many representatives as possible of all linguistic phyla present in this region.DNA samples from two Dravidian-speaking Indian populations In this way, we collected genetic reference data from a total of 6,899 people, representing 6 Austroasiatic populations, 23 Altaic populations, 4 Daic populations, 33 Dravidian populations, 5 Hmong-Mien populations, 37 Indo-European populations and 34 Tibeto-Burman populations, including 6 Tibeto-Burman populations from India (supporting information (SI) DNA from the Himalayan blood samples was isolated using Autopure LS® from Gentra Systems, according to the manufacturer's specifications.lSTR® Identifiler® (Applied Biosystems) and FFFL® (Promega). PCR amplification was performed according to the manufacturers' specifications, but using a total reaction volume per sample of 12.5 µl instead of 25 µl. PCR products were analysed using an ABI3100 automated DNA sequencer and the Genemapper®ID software.After DNA isolation, all Himalayan samples were genotyped for 21 forensic autosomal STRs, contained in three commercially available multiplex PCR kits: Powerplex®16 (Promega), AMPFThe autosomal genotypes of 66 The overall allele frequency data for Nepal and Bhutan have been published previously Allele frequencies and summary statistics were calculated using a combination of the Excel add-ins Microsatellite Toolkit Since only a small number of reference populations could be collected for the phyla Austroasiatic, Daic and Hmong-Mien , we excluded these phyla from all statistical analyses in order to prevent sampling bias influencing the results. Furthermore, we decided to treat the 6 Tibeto-Burman speaking Indian populations as Himalayan test populations rather than reference populations, in order to investigate the possibility of  admixture with Indo-European and/or Dravidian speakers from India.The presence or absence of population (sub)structure within the Himalayas was examined using the following analysis methods:FST matrix for all population pairs was generated using GenAlEx 6.5 and analysed in the program Barrier vs 2.2 1. In order to test for the possible presence of a genetic boundary, a pairwise 2. Admixture estimations for the Himalayan populations were generated using a weighted least squares approach in the program Admix95 3. The dataset was further examined in the program STRUCTURE 2.3.2 4. In order to verify the STRUCTURE results, the dataset was subsequently examined using the Excel-based program FLOCK 3.0 FST matrix as generated by GenAlEx 6.5 was used both in a principal coordinates analysis (PCoA) within GenAlEx 6.5 and in a multi-dimensional scaling analysis (MDS) using the program NCSS97 5. The 6. Using the R script CHROMA, kindly provided by Jeff Long 7. Subsequently, the genotype data were analysed in Bottleneck 1.2.02 8. Pairwise matrices for linguistic, geographical and genetic distance, both on the population level (165*165) and on the individual level (7415*7415), were analysed using the software tool zt A simple Mantel test, which determines the classical Pearson correlation coefficient (r) and corresponding p-value for pairs of distance matrices.A partial Mantel test, which determines the correlation between two distance matrices while controlling the effect of a third matrix, in order to remove spurious correlations.In both types of Mantel tests, the null hypothesis is that the distances in the different matrices are independent. On the population level, both the simple and partial Mantel tests were performed with 10,000, 100,000 and 1 million randomizations, however, this did not affect the resulting correlation coefficients. On the individual level, only 10,000 randomizations could be tested, due to limitations of computational capacity. In order to get an indication of the correlations both within and across linguistic phyla, all Mantel tests were performed on the dataset as a whole and on 4 subsets; one for each phylum.The distance matrices used in the Mantel tests were constructed as follows:FST/(1- FST) matrix FST matrix previously generated in GenAlEx 6.5. For individuals, a pairwise dissimilarity matrix was constructed. For this purpose we first determined pairwise similarity by counting the number of shared alleles and dividing this number by the total number of alleles compared. Subsequently, genetic dissimilarity was calculated as 1-similarity.For genetic distance between populations, we constructed an The matrices for geographical distance, measured 'as the crow flies', were constructed using the Geographic Distance Matrix Generator (GDMG) version 1.2.3 For the linguistic distance matrices, the information as given by van Driem For the Tibeto-Burman, Altaic and Dravidian populations, the construction of these linguistic networks was straightforward, since these linguistic phyla have been extensively studied and the subclassifications within them are mostly undisputed. However, we would like to register the following proviso regarding these networks: The ethnolinguistic identification of our Himalayan populations has been rigorous and for most of the reference populations in this study, the ethnolinguistic identification of the populations as given by the original researchers is also unproblematic. However, for some of the reference populations, adequately specific ethnolinguistic information was not provided, leaving us with no other option than to assume the most likely linguistic background for these populations based on their region of origin.Regarding the Indo-European network, in addition to the proviso given for Tibeto-Burman, Altaic and Dravidian, we here clarify some of our choices:a. Indo-Aryan phylogeny is a relatively neglected field. Whilst methodologically-rigorous historical linguistic comparisons have been devoted to Eastern Indo-Aryan phylogeny, e.g. Chattterji b. Within the network, there are two nodes classified as Indo-European with suspected Tibeto-Burman substrate. This classification is based on the widely perceived phenotypical similarity of these populations with Tibeto-Burman speaking populations of Nepal. Furthermore, their languages contain features such as biactantial verbal agreement or otherwise peculiar morphology in the Indo-European context. A widely held but so far untested explanation for these traits has been to attribute a Tibeto-Burman origin to these populations who must have, in that case, adopted their current Indo-European language long ago.Subsequently, these four networks were combined into one network of Asian languages . Based o9. In order to verify the zt results and to test for possible isolation by distance effects on the genetic composition of our dataset, the genetic, geographic and linguistic distance matrices constructed for analysis in zt were further analysed using the Isolation By Distance Web Service (IBDWS) version 3.23 In total, we collected DNA samples from 947 unrelated Nepalese volunteers, 920 unrelated Bhutanese volunteers and 109 unrelated North-Indian volunteers, belonging to a total of 60 ethnolinguistic groups from the Tibeto-Burman phylum and 15 ethnolinguistic groups from the Indo-European phylum . For manGeneral allele frequency data for the Nepalese and Bhutanese populations were published previously ulations , indicatIn order to explain the current genetic situation in the Greater Himalayan Region, we assumed that one the following three theories must be true:1. Geographical distance has played a more important role than linguistic differences in the occurrence or absence of (pre)historical population interactions.2. Linguistic differences played a more important role than geographical distance in the occurrence or absence of (pre)historical population interactions.3. Geography and linguistics played similar roles in the occurrence or absence of (pre)historical population interactions.These theories lead to the following predictions for the genetics of the Himalayan populations:If theory 1 is correct, the genetic composition of the Himalayan populations, all residing to the south of the geographical boundary, should show the closest resemblance to the genetic composition of the Southern reference populations, independent of the language they speak.If theory 2 is correct, we would expect the genetic composition of the Indo-European Himalayan populations to resemble that of the Indo-European reference populations most closely, with a corresponding resemblance between the Tibeto-Burman Himalayan populations and the Tibeto-Burman reference populations. The latter despite the fact that they reside on opposite sides of the geographical boundary.If theory 3 is correct, the genetic composition of the Himalayan populations can resemble that of any or none of the reference populations.Since the aim of the current study was to try to pinpoint a possible genetic boundary assumed to be present in the Himalayan populations, we first analysed our data in Barrier vs2.2. However, other than drawing two circular barriers isolating the Lhokpu and the Dora from all other populations, no barriers were indicated by the program. From this we concluded that if genetic differences exist between the Himalayan populations of different linguistic affinities, which is still very likely in view of the results of previous studies, they are, at least with the current (limited) autosomal STR dataset, too subtle to be translated into a clear boundary.Admixture proportions were determined for the populations Nepal Tibeto-Burman, Nepal Indo-European, Bhutan and India Tibeto-Burman in order to investigate the genetic contribution by each of the parental populations Indo-European, Tibeto-Burman, Dravidian and Altaic . For theFor the Indian Tibeto-Burman speakers the Admixture analysis resulted in negative admixture values for the Indo-European and Altaic parental populations, contrasted by an admixture value of >100% for the Tibeto-Burman parental population, suggesting that the model of 4 parental populations is not applicable to this population. Unfortunately, several alternative scenarios with only two parental populations also resulted in negative values for one of the two parental populations (not shown). Therefore, no useful admixture estimates could be obtained for the Indian Tibeto-Burman speakers.In order to investigate the possibility of more subtle genetic differences, which might still give some indication toward the likelihood of the three theories stated above, we subsequently analysed the data with STRUCTURE 2.3.2 , containing the gene identity values for all pairwise comparisons between the Altaic and Tibeto-Burman reference populations and the Tibeto-Burman speakers from Nepal and Bhutan.2. A transition zone with approximately equal amounts of blue and green, containing the values for the comparisons of the Tibeto-Burman speakers from India and the Indo-European speakers from Nepal to each other and the populations mentioned under 1.3. A mostly blue area , containing the values for the comparisons of the Indo-European and Dravidian reference populations to all other populations.Since in all these observations the Himalayan Tibeto-Burman speakers show higher degrees of similarity to the Northern reference populations than to the Southern reference populations, the results seem to support a linguistic rather than a geographical subdivision for the Himalayan populations. However, it is not possible to unambiguously rule out one or the other option, since the observed differences between the Himalayan Tibeto-Burman speakers and the Himalayan Indo-European speakers are relatively small.In addition, some observations cannot easily be explained simply by linguistic or geographical subdivision:When running STRCTURE for K>2 , althougIn the MDS plot , six TibFurthermore, in the MDS plot, the Himalayan populations are not as tightly clustered together as the reference populations.In CHROMA , the colSince, due to time-honoured traditions, many of the populations in and around the Greater Himalayan Region tend to marry within their own population or social class, we expected the within population gene identity values to be generally higher than the among population gene identity values. However, as can be seen in The results seen for Tharu and Danuwar & Kachadiya Danuwar may be an indication that these populations, as suspected, do not represent original Indo-European speakers, but rather a group of populations with Tibeto-Burman roots in which the original Tibeto-Burman language has been completely replaced by an Indo-European language.A possible explanation for the seemingly aberrant behaviour of the other populations mentioned above is that these populations may have undergone recent bottlenecks leading to reduced genetic variation. The assumption that bottlenecks may indeed have occurred seems to be confirmed by the reduced heterozygosity values, reduced average numbers of alleles and higher average relatedness values observed for most of these outlying populations .A likely cause for such a bottleneck, at least for Lhokpu, Black Mountain Mönpa and Toto is that these populations are known to have been almost completely isolated from their neighbouring populations until relatively recently, due to both geographical and cultural boundaries. Geographical isolation and subsequent genetic drift could also explain the results observed for the Kham, who live in a very rugged and difficult to negotiate part of Nepal and for the Layap, who live mostly in the relatively inaccessible northernmost part of Bhutan. And for some of the other populations, such as Barām and Thangmi, a certain degree of isolation due to their low social status may have played a part.However, analysis of the data in Bottleneck 1.2.02 did not indicate a significant recent bottleneck in these populations (data not shown). Furthermore, geographical and/or social isolation cannot be the only explanation for the seemingly aberrant observations, since a certain degree of geographical isolation can be assumed for all Himalayan populations, simply because they live in an area which, historically and even with modern means, is relatively inaccessible. This relative geographical isolation may have resulted in smaller effective population sizes for the Himalayan populations as compared to the populations from surrounding countries, which may explain the less dense clustering pattern the Himalayan populations display in the MDS plot. Alternatively, the observed clustering pattern for the Himalayan populations may lend support to the theory that the Himalayan region has been populated during the early ages of human settlement in Asia, giving the Himalayan populations more opportunity to drift than the populations in the surrounding countries.Therefore, at present, we do not have a satisfactory explanation for the seemingly aberrant observations, although of course, we cannot rule out the possibility that either the test-populations investigated in this study may not have been adequate representations of the actual populations, e.g. due to too few test-samples or that the set of 15 autosomal STRs chosen in this study does not provide sufficient historical depth to provide a straightforward answer to our problem.In addition to the analyses discussed above, we performed several Mantel tests in zt, both on the population level and on the individual level. The results of the tests performed across linguistic phyla are summarized in For the comparison of genetic distance with both geographical and linguistic distance, the correlation coefficients obtained within linguistic phyla were, on average, approximately 10 times lower than those obtained for the complete dataset and not or only marginally significant. Correlation coefficients for geographical vs linguistic distance were comparable to those obtained for the complete dataset (not shown).Previous studies, mainly based on the Y-chromosome and/or mtDNA, have indicated the presence of a genetic boundary in Asia, roughly corresponding with the Himalayan mountain range; more recently, some autosomal surveys of Asian populations have also been performed with similar results Another explanation for our autosomal results being less straightforward could be the dampening effects that recombination and mutation are known to have on the use of autosomal data for deep-rooting analyses. Therefore it is very well possible that the genetic distance based on these 15 autosomal STRs represents a different, probably more recent, historical timeframe than the linguistic distance and the geographical distance. In order to  overcome the effect of high mutation rates, markers with a slower mutation rate than that of autosomal STRs can be used. Additionally, the inclusion of Y-chromosomal and mitochondrial data may compensate for the effects of recombination. For that purpose, we decided to initiate a detailed autosomal SNP survey in addition to screening mtDNA and Y-chromosome polymorphisms. This research is still on-going.Figure S1Network of Asian languages. Fitting all populations into this network was not possible with equal branch lengths between all nodes. Therefore, the variation in branch lengths within this network is merely a spatial necessity and does not indicate smaller or larger linguistic distances. In the construction of the linguistic distance matrix, each branch was counted as 1 step, unless indicated differently. Nepalese populations are shown in red, Bhutanese populations in blue, Indian Tibeto-Burman populations in purple, Southern reference populations in green and Northern reference populations in orange, MRCA: Most Recent Common Ancestor, AP: Andhra Pradesh, BR: Bihar, KA: Karnataka, MH: Maharashtra, OR: Orissa, UP: Uttar Pradesh.(PDF)Click here for additional data file.Figure S2Results of FLOCK 3.0 analyses. S2A: Results for K = 2. As with the STRUCTURE analyses a clear difference in the clustering patterns for Himalayan Indo-European speakers vs that for Himalayan Tibeto-Burman speakers is visible. S2B: Results for K = 4. In contrast with STRUCTURE, FLOCK does not cluster the Lhokpu, Black Mountain Mönpa and Toto into separate clusters for higher numbers of K. In accordance with STRUCTURE, no further (sub)structuring is observed for the Himalayan populations with higher numbers of K.(PDF)Click here for additional data file.Figure S3PCoA analysis results. The colour coding is identical to the colour coding used in the MDS plot. The population-codes used are explained in (PDF)Click here for additional data file.Figure S4Results of Chroma analysis. This figure represents a colour-coded square (pairwise) gene identity matrix (Nei's minimum genetic distance). In general it can be assumed that population pairs showing higher among populations gene identity values (yellow/light orange) are likely to be more closely related than population pairs showing lower among population gene identity values (blue). The diagonal from the lower left corner to the upper right corner represents the within population gene identity values. Populations showing relatively high (>0.25) within population gene identity values are: Orochen-1 (HOR: 0.268), Orochen-2 (ORO: 0.253), Trung (TRU: 0.303), Lisu (LIS: 0.258), Nù (NUB: 0.253), Barām (BAR: 0.261), Thangmi (THG: 0.275), Lakha (LAK: 0.258), Layap (LAY: 0.252), Lhokpu (LHP: 0.288), Black Mountain Mönpa (MON: 0.253) and Toto (TOT: 0.328). Other abbreviations in this figure: DKD: Danuwar & Kachadiya Danuwar, THR: Tahru, AL: Altaic reference populations, TB1: Tibeto-Burman reference populations, TB2: Tibeto-Burman populations from Nepal, TB3: Tibeto-Burman populations from Bhutan, TB4: Tibeto-Burman populations from India, IE1: Indo-European populations from Nepal, IE2: Indo-European reference populations, DR: Dravidian reference populations.(PDF)Click here for additional data file.Table S1Overview of all reference populations supplied for this study.(XLS)Click here for additional data file.Table S2Overview of Nepalese populations pooled for or excluded from statistical analyses.(XLS)Click here for additional data file.Table S3Summary statistics.(XLS)Click here for additional data file.


Since their arrival in the Tibetan Plateau during the Neolithic Age, Tibetans have been well-adapted to extreme environmental conditions and possess genetic variation that reflect their living environment and migratory history. To investigate the origin of Tibetans and the genetic basis of adaptation in a rigorous environment, we genotyped 30 Tibetan individuals with more than one million SNP markers. Our findings suggested that Tibetans, together with the Yi people, were descendants of Tibeto-Burmans who diverged from ancient settlers of East Asia. The valleys of the Hengduan Mountain range may be a major migration route. We also identified a set of positively-selected genes that belong to functional classes of the embryonic, female gonad, and blood vessel developments, as well as response to hypoxia. Most of these genes were highly correlated with population-specific and beneficial phenotypes, such as high infant survival rate and the absence of chronic mountain sickness. Humans first reached the Tibetan Plateau during the Last Glacial Maximum (22–8 kya) The Tibetan Plateau is unique in its high absolute elevation and low temperature. However, Tibetans have lived on the plateau for tens of thousands of years and adapted to the high-altitude environment better than other populations. Tibetans exhibit many biological features in common with other high-altitude mammalian species (such as antelopes and pigs), including absence of chronic mountain sickness (CMS), thin-walled pulmonary vascular structure, and high blood flow HBB (Hemoglobin-B), which causes resistance to malaria, and LCT (lactase), which is essential for the digestion of dairy products Human adaptation to high-altitude environment is believed to a result of advantageous genetic mutation and selective pressure. Many well-characterized human genes that play important roles in environmental adaptation have been identified, such as EGLN1, PPARA, and EPAS1Three recent studies have identified several genes that play important roles in high-altitude adaptation, including 2<0.5) from the dataset to perform individual ancestry and admixture proportions analysis, assuming a range of ancestral components from 2 to 6 (K = 2 to 6) without prior knowledge concerning population identity  and HGDP (Human Genome Diversity Project). Nineteen world-wide populations  and ten East Asian (EA) populations  were included in the metadata. 509,491 autosomal SNPs overlapped within this dataset . We usedidentity .K = 2, we had two ancestral components: the Tibetan and Japanese ancestries, while from K = 3 to 6, the Southern (Cambodian and Lahu), Northern , CHB, and Lahu populations appeared accordingly. At K = 6, we had the Tibetan, Cambodian, Lahu, Daur, and JPT (Japanese from HapMap) populations, and each exhibited only one major component in its ancestry. In sharp contrast, there were the Yi, Mongolian, and Han populations (represented by CHB); all had multiple ancestries  on both world-wide and EA populations at the individual level based on genotypic information from 509,491 SNPs. The PCA plot for world-wide samples showed that populations within a continental/regional group were clearly separated from one another . The disWe constructed an unrooted neighbor-joining phylogenetic tree based on the distance matrix of nucleotide information from 165,073 poorly-linked SNPs . The popTo uncover the genetic imprints of harsh environmental factors over thousands of years, we investigated genetic differentiation between Tibetans and their geographic relatives—the EA populations. Since genetic loci with unusual degrees of differentiation often provide indications of selection FST between the Tibetan population and those from HapMap and HGDP (EA) under various SNP densities. To reduce locus-to-locus stochastic variation, we generated a test statistics from a 200-kb window as the average FST above a cutoff value FST is very limited SLC24A5, SLC45A2, EDAR, and PAWR, were positively selected (FST among EA populations in EPAS1 (HIF-2α) .We calculated the locus-by-locus pairwise selected . The genFSTP<0.008 in all tests) was located on chromosome 2 from 46.4 Mb to 46.6 Mb; both of its adjacent 200-kb regions also showed significant values , no peaks were observed at EPAS1. This suggests that EPAS1 is potentially under positive selection only in Tibetans. The second significant region (P<0.02 in 6 tests) also show near-complete sweep to the surrounding 600-kb area. The EGLN1 gene within this region is also involved in the response to hypoxia and potentially be the target of positive selection. EPAS1 and EGLN1 play central roles in the activation of hypoxia-inducible genes and homeostasis of HIF under hypoxia and normoxia CDH13, ANGPT1, RUNX1, FOXO1, JMJD2C, GLIS3, MAT2B, A2M, RYR1, and NPAS3  of FST, iHS, and XP-EHH for further functional analysis on GO terms (P = 0.0064), response to hypoxia (P = 0.0096), embryonic development (P = 0.029), and female gonad development (P = 0.0196). The candidate genes, which include EPAS1, ANGPT1, EGLN1, FOXO1, RUNX1, RYR1 and CDH13, may play essential roles in adaptation to high altitudes.Adaptation to high altitudes is a complex biological process and requires coordination among many genes and pathways. We took genes from the regions with pre-set cutoffs  had undergone positive selection in Tibetans, but not HIF-1α despite its involvement with most of hypoxia-inducible genes HIF-1α is highly conserved HIF-1α, which is universally expressed EPAS1 is unique to vertebrates, neofunctionalized HIF-1α, which responds to acute hypoxia, EPAS1 plays an important role in prolonged hypoxia Tibetans possess biological characteristics or phenotypes unique to people who live at high altitudes. These characteristics include adaptation to hypoxia, the absence of CMS, and high offspring survival rate. Adaptation to hypoxia is mediated by the hypoxia inducible factor (HIF) complexes which consist of α and β subunits (HIF-1β) EGLN1/PHD2, which is a member of the 2-oxoglutarate-dependent dioxygenase superfamily and a sensor for low oxygen levels HIF1α-dependent, but HIF2α-independent, manner HIF-2α and its degradation regulator EGLN1 had undergone positive selection. However, HIF-1α, as the up-regulator of PHD2, had not. How exactly PHD2 reciprocally regulates HIFα requires more in-depth research.Another candidate gene under positive selection was EPAS1 and ELGN1, which are involved in the response to hypoxia, may have protected Tibetans from hypoxic damage and CMS. Genes involved in blood vessel development also played important roles in high-altitude adaptation. Well-developed blood vessels can increase the efficiency of oxygen utilization. High blood flow and high infant survival rates have been two vital phenotypes that have allowed Tibetans to adapt to high altitudes VEGFA, ANGPT1, and ANG2, sequentially regulate the placental vascular network from generation to maturation ANGPT1 was under positive selection, while VEGFA and ANG2 were not. Interestingly, expression of VEGFA and ANG2 can be up-regulated by hypoxia, whereas no evidence has been found that ANGPT1 can be as well ECE1, TGFBR3, CELSR1, and ACVR2A) have shown significant signs of positive selection in Tibetans. In addition, four genes  that are involved in female gonad development were also candidates for positive selection in Tibetans. Therefore, we can assume that positive selection acting on genes involved in the development of blood vessels, the placenta, embryos, and female gonads may have all contributed to the Tibetans' high infant survival rate.Our gene ontology analysis showed that positively selected genes were enriched in categories related to the response to hypoxia and the development of blood vessels, embryos, and female gonads. Genes like The quality of population-based genetic studies depends strongly on unbiased sampling. This is especially important in our case, since Tibetans from different geographic regions have displayed strong heterogeneity in their genetic background  EGLN1 and EPAS1, we also found other potentially selected genes, including ANGPT1, ECE1, and LEPR, in high-altitude adaptation. These genes associate with various biological functions, such as the development of blood vessels, the placenta, embryos, and female gonads. The altered functions may certainly affect infant survival and result in adaptive phenotypes of Tibetans. More in-depth sampling and functional analyses of these genes in the future may reveal more molecular details concerning the adaptation of the Tibetan population to high-altitudes.Our data have demonstrated that the Proto-Tibeto-Burman people form a major clade within East Asian populations, and that the major migration route into the Tibetan Plateau was via the Hengduan Mountain valleys. We also found that the Yi population, and not the Han populations used in previous studies, was a more appropriate reference for exploring the adaptation of Tibetans to high-altitudes All donors signed the informed consent for cell line establishment and subsequent biological investigations. This project was reviewed and approved by the Ethics Committee at the Beijing Institute of Genomics, Chinese Academy of Sciences.Thirty unrelated Tibetans  were collected. Sample cell lines were derived from immortalization of peripheral lymphocytes by the Epstein Barr virus.. The derived cell lines were deposited at Immortalized Cell Bank of Beijing Institute of Genomics, Chinese Academy of Sciences supported by the Knowledge Innovation Program of the Chinese Academy of Sciences. All DNA samples were obtained with DNA-extraction kits  and genotyped on the Human 1M-Duo v3 chip  according to the manufacturer's specifications. Genotyping module of Genomestudio  was employed to call the raw data. The clustering position of genotypes was determined by standard cluster file provided by Illumina. All individuals were successfully genotyped at call rate >98.1% with genotype call threshold of 0.15. We removed all CNV markers and SNPs  that cannot be accurately clustered, leaving 1,157,616 SNPs, of which 41,873 are on the sex chromosomes and 138 on mitochondrial DNA. Only autosomal SNPs were used in the study.2 test) of autosomal SNPs was tested in our Tibetan samples. Due to multiple testing, we set the threshold of the HWE test at P = 0.001. Of 1,115,605 autosomal SNPs, 4,066 (0.36%) failed the test and were excluded from our analyses.HWE (χftp.illumina.com). Except for haplotype phasing, our HapMap-related analyses included 45 CHB, 45 JPT, 59 CEU, and 60 YRI. The HGDP data was downloaded from Laboratory of Neurogenetics (http://neurogenetics.nia.nih.gov/paperdata/public/) with 258 individuals from 14 populations: Mozabite, Palestinian, Bedouin, Druze, Balochi, Kalash, Burusho, Uygur, Yakut, Cambodian, Lahu, Yi, Daur, and Mongolian.The Human-1M chips data of HapMap samples was downloaded from Illumina , we removed all transversion SNPs and aligned the genotype of transition SNPs among the three datasets. In total, 509,491 overlapping SNPs between data from Human-1M chips and Human-550 chips were used for the HGDP-related analyses.The ancestral allele of a SNP was obtained from NCBI  run, either 10,000 iterations or a likelihood increase between consecutive iterations of less than 0.0001 was used for a pre-specified ancestry population number (K). We run at least three different seeds for each K from 2 to 6. Fine structures of EA populations were constructed using 8 populations. We removed two outlier populations of EA, Uygur and Yakut, for Uygur is a mixture population between EA and South/Central Asian and Yakut is a mixture population between EA and European populations We inferred individual ancestry proportions with the Principle component analyses were performed with the smartPCA software Phylip software We constructed a phylogenic tree using the FSTFST from SNPs with FST value larger than 0.2  We scored zero for the test statistics of a window, if the window has no more than 3 SNPs with pairwise FST larger than 0.2. The empirical P-value of a window was obtained as the percentage of statistics greater than its window average. The window regions with P<0.05 were considered as candidate under positive selection. Other cutoff values from 0.2 to 0.9 had also been tested with little variation at the significant level in the windows with extremely high FST. Larger cutoff values tend to remove the background noise of the test statistics by dropping more loci from a window. We therefore gradually raised the cutoff values to obtain the most significant (P<0.001) windows with extremely high FST in each population pair, and then assigned the empirical P-value to these windows. The overall windows with P<0.05 at the FST cutoff value of 0.2 for each Tibetan pair are showed in We estimated site-independent pairwise P-value by calculating the percentage of statistics larger than that of each window. For the XP-EHH test, we used regional populations (CHB and JPT) as references for Tibetans. The overall windows with P<0.05 of iHS and XP-EHH for Tibetans are showed in The HapMap and Tibetan datasets were used for haplotype-based selection tests. The test scripts of iHS and XP-EHH used here were provided by FST) on Tibetans. We selected the window regions significant (P<0.05) for at least 5 out of the 10 tests and extracted the genes from the regions as a gene list for pathway enrichment analysis. Genes with no functional annotations provided by RefSeq were removed. The final gene list  was used to rank the overall enrichment of the annotation groups. The value is defined as minus log transformation on the average P-values of each annotation term and was set at 1.3  for significance. Additionally, a classification stringency parameter was used in the functional annotation clustering to control the fuzzy clustering of DAVID, and we used the high stringency for tight, clean and smaller numbers of clusters.We generated a dataset with ten selection tests  for 497 individuals from 19 world-wide populations and (B) for 167 individuals from 8 EA populations.(EPS)Click here for additional data file.Figure S2Principal component analyses of population structure on the 8 populations from East Asia, using 509491 autosomal SNPs. (A) Eigenvector 2 vs. eigenvector 3. (B) Eigenvector 2 vs. eigenvector 4.(EPS)Click here for additional data file.Figure S3The regions with extreme test statistics  indentified from HapMap + Tibetans sample pairs and (B) HGDP (EA population) + Tibetans sample pairs. Each row is a 200-kb genomic window; each column is a population pair. The figure shows windows with extreme test statistics (P<0.001) from each population pair. The physical position of each window on the human genome was shown on the left of the figure. Gene within or near each window is showed on the right. Genes without functional summary provided by RefSeq were removed. The windows with no functional gene are not shown. Genes colored in red are well-characterized genes under positive selection in different populations.(EPS)Click here for additional data file.Figure S4The regions with extreme test statistics  indentified from HGDP (EA population) + Tibetans sample pairs. Each row is a 200-kb genomic window; each column is a population pair. The figure shows windows with extreme test statistics (P<0.001) from each population pair. The physical position of each window on the human genome was shown on the left of the figure. Gene within or near each window is showed on the right. Genes without functional summary provided by RefSeq were removed. The windows with no functional gene are not shown.(EPS)Click here for additional data file.Figure S5The Haplogroup distribution of Tibetans with different geographic locations. We used our genotype information from 138 mitochondrial SNP markers (included in the Human-1M chip) for haplogroup analysis, and compared our findings to that of Tibetan samples from Zhao et al  (EPS)Click here for additional data file.Table S1Basic information of populations under study and the numbers of markers used in different analyses.(DOC)Click here for additional data file.Table S2200-kb genomic regions with extreme high values of pairwise FST identified in the top five percent of empirical distributions from all Tibetan pairs.(XLS)Click here for additional data file.Table S3200-kb genomic regions identified in the top five percent of the XP-EHH and iHS tests.(XLS)Click here for additional data file.Table S4Candidate genes used for gene ontology analysis.(DOC)Click here for additional data file.


Coffea canephora coffee tree for approximately thirty years in the Ivory Coast. Association genetics would help to speed up this program by more rapidly selecting zones of interest in the genome. However, prior to any such studies, the linkage disequilibrium (LD) needs to be assessed between the markers on the genome. These data are essential for guiding association studies.A reciprocal recurrent selection program has been under way for the C. canephora have been used for this work, with the goal of identifying ways of using these populations in association genetics. We identified changes in the LD along the genome within the different C. canephora diversity groups. In the different diversity groups studied, the LD was variable. Some diversity groups displayed disequilibria over long distances (up to 25 cM), whereas others had disequilibria not exceeding 1 cM. We also discovered a fine structure within the Guinean group.This article describes the first results of an LD assessment in a coffee tree species. Guinean and Congolese breeding populations of C. canephora. The coffee recurrent selection scheme being implemented in the Ivory Coast can thus be optimized. Lastly, our results could be used to improve C. arabica because one of its parents is closely related to C. canephora.Given these results, association studies can be used within the species  Plant breeding is making increasing use of molecular markers to speed up selection cycles and thereby more rapidly introgress interesting zones of the genome in varieties of agronomic interest. In addition to QTL analyses, which only focus on a single progeny, association genetics can be used to focus on a set of populations to identify zones of interest of the genome in breeding. However, for association studies to be successful it is necessary to know the degree of linkage disequilibrium (LD) beforehand, along with its extent and its distribution within the species studied. This disequilibrium, which measures the intensity of linkage between markers, is used to determine the molecular marker density of the genome, which is necessary for association studies to be effective-3.Association studies are particularly relevant in perennial species, as they can be carried out on pre-existing populations, in collections or in selection trials. They do not require the creation of specific populations by controlled crossing, as is the case with conventional genetic mapping approaches. AssociaCoffea canephora Pierre ex A. Froehner (2n = 2X = 22) is a strictly outcrossing diploid species with a genetic self-incompatibility system[No large-scale LD study has been undertaken yet on the coffee tree. y system,8. It pry system. One of y system,11. ThisC. canephora are relatively small in size. The strictly outcrossing reproduction system of this species and the different levels of existing kinship create complex genetic structures at the population scale. This structure in populations is superimposed on the larger diversity group (DG) scale[However, natural populations of G) scale.C. canephora as identified using molecular markers[Our work, therefore, consisted in assessing the LD within the Guinean and Congolese DGs of  markers-14.The expected results were as follows: i) knowledge of the LD dynamics at the genome level for a certain number of coffee diversity groups or populations, ii) enhanced knowledge of the genetic structure of the Guinean diversity group, and iii) identification of populations that can be used in association genetics.C. canephora divided into five DGs based on the diversity analyses carried out in previous studies[We studied 356 genotypes of  studies,13 DG SG2: genotypes cultivated in the Ivory Coast and Uganda or in collections in Brazil, most likely originating from the Congo basinDG SG1: genotypes cultivated in Togo and Benin (Niaouli population) or from surveys in the Luki collection , originating from the Atlantic seaboard, from Gabon to the Democratic Republic of CongoC. canephora[All individuals were genotyped using 108 microsatellite markers mapped to eleven linkage groups  on a canephora linkage All LGs were studied with a larger number of markers on LGs A, B, D, F, G and H. The average distance between markers was 13 cM, ranging from 0 cM for the closest marker pairs to 243.3 cM for the most distant marker pairs.The genotyping was performed according to the protocol described in. Size coA model-based Bayesian analysis implemented in STRUCTURE,18 was pThe Guinean diversity group is composed of a large number of natural populations from the forests of the Ivory Coast, including the Pélézi population , which exhibits some original characteristics. The wide variety of these populations, as well as the existence of Guinean genotypes taken from smallholder plantations in the living collections in the Ivory Coast, further justify an in-depth study of this diversity group.Earlier diversity studies did not offer sufficient resolution to analyze the genetic structure of this group in a satisfactory manner (lack of markers). The genetic structure of this group was analyzed using DARwin software based on the calculation of genetic dissimilarities between individuals followed by a factorial analysis from a dissimilarity matrix (FADM) and the construction of a neighbor-joining tree (NJ),22. For C. canephora is a highly heterozygous species. Therefore, it is difficult to distinguish the allele phase of the double heterozygotes Aa/Bb, i.e., whether A is associated with B or with b at the haplotype level[2) rely on an estimation of the haplotypic or gametic linkage disequilibrium[The species pe level. Howeverilibrium, i.e., uilibrium-27 to reWe used the entire set of 356 genotypes, as the algorithm functions better with genetically structured data. NeverthTo enable the use of PHASE while maintaining the Stepwise Mutation hypothesis for the microsatellite markers, our data that were expressed in allele sizes were converted into repeat numbers using CREATE software.Five algorithm repeats based on 1000 iterations, 100 thinning intervals and 1000 burn-ins were performed, and the repeat displaying the greatest maximum likelihood was used for the remaining analyses.The resulting tables adopted for each LG were then merged and formatted for incorporation into PowerMarker to analyze the LD, declaring genotype data of the known phase as the data type.D’ and r2 values for the set of possible combinations of markers, two-by-two, using PowerMarker software[The LD was analyzed for the five DGs, together and separately. We calculated the software. These msoftware,33.2 values as a function of distance for each of the groups studied.Exact Fisher tests were carried out for all the possible combinations to test whether the haplotype frequencies between two loci were the product of the allele frequency corresponding to the two loci. Allele counting was organized in a contingency table, and permutations, following an algorithm using a Markov-Monte-Carlo chain, were used to calculate the unbiased p-values associated with the test. We corrFst between DG significant at a 5% level.The ad-hoc statistics Δ(K) indicate an uppermost level of structure in five populations. The Δ(K) and bar-plot from the STRUCTURE run showing the highest lnP(D) for K=5 are presented in FigureThe ad-hoc statistics Δ(K) based on the STRUCTURE analysis gave an uppermost level of structure within DG G in three populations. The Δ(K) and bar-plot from the STRUCTURE run showing the highest lnP(D) for K=3 are presented in FigureTable2 and D’ values (after Bonferroni’s correction) as a function of distance for the set of genotypes. In general, the D’ values were much higher than the r2 values. In addition, it seems that these values were much more stochastic with large proportions of very high LD that corresponded to non-significant associations. When the different DGs were considered (data not shown), D’ seemed to be very sensitive to the structure effects and was also sensitive to the variations in allele frequencies between the different markers. Consequently, although the values of the two measurements appeared to decrease with distance, it seems that D’ was less sensitive than r2 to that decrease, with values remaining high at very long distances.FigureD’ measures only the recombination history, whereas r2 measures both the recombination and mutation[D’ is globally higher than r2 and can potentially reveal more associations. The calculated values of D’ are lower when population sizes are small, which is why this parameter is preferred when studying the evolutionary history of large populations. However, with association studies, the most indicative value of potential power is the r2 measurement, as it provides an indication of the way the markers and phenotypic traits being studied will be correlated. We cite the D’ for a general comparison on the set of individuals before discussing in detail some differences in r2 between the different populations.As these two parameters have different properties, the information provided is not redundant. Indeed, mutation. These dThe results of this analysis revealed a general decrease in the LD with distance. Nevertheless, we found as many different cases as there were DGs studied.2 values compared to the other populations, with a large proportion of between-LG LD. The major genetic structure in two populations (Niaouli and Luki) helps to explain such results. In light of these results, it is difficult to choose an r2 limit that can be used in association genetics for this DG and that will avoid the risks of false detections.For DG SG1 Figure, we foun2 values was found between unlinked markers. The r2 values were extremely low, even for some very close markers. This observation may be explained by the origin of this DG, which must have undergone greater genetic mixing than the other origins. Indeed, this group originated from a major center of Coffea canephora diversity and is constituted mainly by genotypes resulting from the cultivation and selection processes. It can also be linked to the high number of alleles found in this DG, resulting in a more effective recombination process and diluting the LD signal. For this DG, it is possible to hypothesize that obtaining comparable values between linked and unlinked markers is not due to a structural phenomenon, but rather to very low or non-existent LD in this population. Indeed, structure analyses indicated that this diversity group is not structured in sub-populations and that most of the genotypes within this group are of a cultivated origin. Consequently, more than one microsatellite per cM would be needed in this DG to have any hope of satisfactorily covering the entire genome.In DG SG2 Figure, a large2 values between linked markers than between unlinked markers were observed, with a clear decrease in those values with distance. Although some values over 0.1 could be seen for the DG as a whole, this was not the case for subgroups Gsub1 and Gsub2. Indeed, in these two subgroups, we found significant and high r2 values between close markers. By choosing an empirical r2 limit of 0.2, we arrived at an LD covering approximately 5 cM for subgroup Gsub1 and 20 cM for subgroup Gsub2 , significant p-values were preferentially located close to the diagonal, and hence, between linked markers. This preferential localization was particularly apparent for DGs GP and C. In contrast, DGs SG2 and G displayed a large share of significant p-values outside the LGs. DG C only had a few rCoffea. We identified numerous different cases of LD evolution along the genome within the different DGs. We were also able to identify a cryptic structure within DG G. This very fine genetic structure had not been discovered in earlier studies either due to a lack of resolution or because too few markers were used.This LD study is the first of its kind in the genus 2) is expected to depend on both the effective size of the population and the recombination rate between the loci considered[As in all species, we found a decrease in the LD with distance. For a population in equilibrium between mutation and genetic drift, the LD  corresponding more or less to natural populations.Indeed, the graph showing the set of rshown by,35. This2 values obtained suggest that the LD is significant at very short distances. Indeed, natural populations of coffee trees are usually small, isolated populations with a small number of mother trees and a few juveniles, involving major relations of kinship, despite the strict outcrossing of the studied species.We were thus able to reveal a high variability in LD within the different DGs, with a large share of residual between-linkage group disequilibrium in DG SG2 and DG SG1. These results may potentially lead to the detection of false positives in association studies, even at low levels of genetic structure. The importance of this “genomic” LD  was variable depending on the groups, and by taking into account structure and kinship in association studies, it will be possible to overcome this variability. The residual genomic LD values for the less-structured DGs may be explained by different kinship levels within the natural populations of our species. Nevertheless, for DG SG2, the low rWe used Bonferroni’s correction to consider only truly significant values. Nevertheless, this correction is very conservative and may lead to a substantial loss of power in association studies. Many other corrections have been proposed in the literature in recent years, but none seems to be satisfactory. Moreover, we have shown that, in our case, this correction mainly made it possible to eliminate a certain number of disequilibrium values between unlinked or very weak markers. Normally, in association studies, such a correction will not be necessary because the main source of error (genetic structure) will be controlled. These questions should be given due consideration along with updates in the proposed models. Models that take into account structure and kinship in association studies appear to be a major advance in these approaches, helping to increase both the power and the resolution of such studies.Our study enabled us to more effectively determine a fine genetic structure for DG G. Structure seems to exist in these populations, but there are indications of major gene exchanges between them. We found a structure in three subgroups  with both model-based and distance-based analyses. This very fine genetic structure can only be studied with a large number of markers. DG G was initially described by Berthaud using isOur results show, particularly for the Guineans, that a large number of “control” markers  are needed to separate the fine structure into populations. Therefore, our case seems to be quite similar to the case of maize, where a set of eighty-nine microsatellite markers was used by Flint-Garcia et al. to studyD’ and r2) were found both between unlinked and linked markers, preventing any distinction between associations based on a physical link between markers and those created by the structure. Therefore, the genomic control approach (adaptation of the significance limit to the number of associations detected between unlinked markers) appears to be less efficient and may lead to a large number of false negatives. This observation is one of the greatest criticisms of this model advanced by Yu et al.[After correction of the p-values by the Bonferroni method, some large and significant values of the two LD measurements  can be easily applied to populations used in breeding processes, such as populations involved in the RRS in the Ivory Coast. In countries such as Uganda, GWAS should be applied to the entire germplasm used for breeding for tolerance to Coffee wilt disease (CWD). Therefore, the candidate gene approach should only be used in very low LD populations or for specific purposes.We were able to demonstrate a cryptic structure within the Guinean diversity group (DG G). This very fine genetic structure was not detected in earlier studies either due to a lack of resolution or because too few markers were used. Nevertheless, complex dynamics seem to exist within the coffee populations of the Guinean region that are substantially different from those found in the forests of Uganda, as described by Musoli et al.. A more Coffea, has enabled us to consider association studies in the species C. canephora. In the different DGs, we identified numerous different cases of LD evolution along the genome. This study provided us with a basis for carrying out association studies and thereby optimizing the reciprocal recurrent selection scheme for the genetic improvement of C. canephora. This selection scheme is based on the complementary traits of diversity groups and the agronomic value of intergroup hybrids. Association studies within groups will allow us to significantly improve each of the complementary populations for their traits of interest. An initial application of this approach can be employed in Guinean populations (exhibiting high LD) with a limited number of markers throughout the genome. A selection process should be efficient and quick using association studies for the early selection of heritable traits (such as vigor or bean size) in intergroup hybrids.The LD study, the first of its kind in the genus The major genetic structure of our species, which may have limited the feasibility of association studies a few years ago, can now be taken into account in the latest models. Nevertheless, it will be important to carefully consider all the models and, in particular, to avoid over-correcting the structure effect, as this action may lead to numerous false negatives and a major loss of detection power.C. canephora could be extrapolated to C. arabica. Although C. arabica has a very restricted genetic base, as shown by Lashermes et al.[C. canephora.Lastly, the results of our study on s et al. and Anths et al., one of LD: Linkage Disequilibrium; DG: Diversity Group; LG: Linkage Group; cM: Centimorgan.The authors declare that they have no competing interests.PC, MD, DP, KA, SB and TL designed the study. HL provided the plant material. PC, FDB and KA carried out the genotyping experiments. PC and DP analyzed the data. PC, FDB and TL wrote the manuscript. All authors read and approved the final manuscript. Detailed information on the 108 SSR markers used. Click here for file Number of alleles, expected heterozigosity(He), observed heterozigosity and Hardy-Weinberg equilibrium exact test associated p-values and standard deviation (s.d.) for each locus and for the whole population and the Diversity Groups SG2, SG1, C, G and GP. NA stands for not available due to monomorphic locus. Click here for file Number of alelles and Hardy-Weinberg equilibrium statistics for the Diversity Groups G, GP, Gsub1, Gsub2 and Gsub3. NA stands for not available due to monomorphic locus. Click here for file Amova and F-statistics computation over the 7 DG . * : significant value at 5%, ns: non significant value. Click here for file


The genetic diversity and structure of horses raised in France were investigated using 11 microsatellite markers and 1679 animals belonging to 34 breeds. Between-breed differences explained about ten per cent of the total genetic diversity (Fst = 0.099). Values of expected heterozygosity ranged from 0.43 to 0.79 depending on the breed. According to genetic relationships, multivariate and structure analyses, breeds could be classified into four genetic differentiated groups: warm-blooded, draught, Nordic and pony breeds. Using complementary maximisation of diversity and aggregate diversity approaches, we conclude that particular efforts should be made to conserve five local breeds, namely the Boulonnais, Landais, Merens, Poitevin and Pottok breeds. Therefore, it would be useful to identify priorities among conservation purposes and this requires characterising diversity and genetic relations between breeds [During the twentieth century, horse breeding has undergone large changes in Europe. Previously considered as an agricultural, industrial and war tool, horse is now essentially bred for hobby riding. Draught horses, in particular, have been less and less used as utility horses, and many draught breeds have undergone a dramatic decrease in population size: according to the Haras Nationaux, out of the nine French draught breeds, six have annual births below 1000. Measures for During the last fifteen years, microsatellite markers have frequently been used to evaluate genetic distances and to characterise local breeds, -10. SomeWith about 800 000 animals belonging to 50 different breeds (source: Haras Nationaux), France shows a large diversity of horse populations. Among these breeds, 21 have a French origin or have been bred in France for at least a century. According to the FAO, at least 15 populations have disappeared during the last 50 years, and eight indigenous breeds are still considered as endangered or endangered-maintained. Among those breeds, the majority are draught breeds, namely the Ardennais, Auxois, Boulonnais, Poitevin and Trait du Nord breeds, the other ones being the Merens warm-blooded breed and the Landais and Pottock pony breeds. Information on the genetic diversity of French endangered breeds could help breeders and providers, decide where they should place more emphasis.In the present study, we first analysed the genetic diversity of 39 horse populations reared in France: within-breed diversity, breed relationship and population structure were investigated, using microsatellite data. Then, we focussed on 19 breeds of French origin or having been raised in France for at least a century, and evaluated the conservation priorities between these populations, using different approaches to evaluate within, between and total diversity.French nomenclature divides horse breeds into three groups: warm-blooded, draught horses and ponies. In this study, 39 populations were considered Table . These 3For each of the 39 populations, 23 to 50 animals born between 1996 and 2005, were sampled amounting to 1679 animals. Except for the Przewalski horse, where no pedigree data was available, the sampled animals were known to have no common parents. For the conservation approach, the study focussed on 19 populations, either of French origin, or having been bred in France for at least 100 years . In this approach, 50 animals were randomly sampled among the four and three AA and SF subpopulations, respectively, to constitute two populations.AHT4, AHT5, ASB2, HMS1, HMS3, HMS6, HMS7, HTG4, HTG6, HTG10, VHL20), with all but two (HMS1 and HTG6) being recommended by the International Society of Animal Genetics for parentage testing and used in similar studies (except HMS1) [Eleven microsatellite markers were used to perform the analysis (pt HMS1) ,10. For (MNA), observed (Ho) and non-biased expected heterozygosity (He), were calculated using GENETIX [Allele frequencies, mean number of alleles  GENETIX . Wright  GENETIX  was used GENETIX  and depa GENETIX  on loci. GENETIX .DR [http://bioinformatics.org/~tryphon/populations/). Regarding the DR distance, a NeighborNet tree was drawn using SPLITSTREE 4.8 [K) between 1 and 22, with a burn-in period of 20 000 followed by 100 000 MCMC repetitions. Pairwise similarities (G) between runs were computed using CLUMPP [The matrix of Reynolds unweighted distances DR  was compTREE 4.8 . A factoTREE 4.8 ): using g CLUMPP .CB) is evaluated, based on the Weitzman [Vk of diversity when the population k is removed from the whole set of breeds (in this study we used DR distance). Then, the within-breed contribution (CW) is defined as:To evaluate the conservation priorities in a set of populations, taking into account contributions to within-population and between-population genetic diversity, Ollivier and Foulley  have proWeitzman  loss Vk H(S) is the average internal heterozygosity of the whole set S and H(S/k) the average internal heterozygosity of the set when k is removed. Finally, the aggregate diversity D of a population is defined as:where CP) could be computed as the product between the breed contribution (CB) and the probability of extinction (Pex) of the breed, assumed to be directly proportional to the inbreeding rate (ΔF). Following Simianer et al. [Pex can be approximated asThe cryopreservation potential .where ort term , we consGDT can be considered as the exact sum of the gene diversity within population GDWS and the gene diversity between populations GDBS considering the following equations:Caballero and Toro  have devn is the number of populations, fij is the average coancestry between populations I and j, and Dij is the Nei minimum distance between populations I and j. The contribution of a population to the diversity is evaluated by computing the loss or gain of diversity ΔGD when the population is removed.where ci) of each population, which can maximise the total diversity at the next generation, using the following equation:The authors have also proposed to evaluate the contributions (GDTN in equation (7), with the following restrictions: for each population i, ci ≥ 0 and Σi ci = 1.The contributions can be computed by maximising HTG4 and HMS1) to 15 (locus ASB2). Some rare alleles in the whole data set were found with a high frequency in the PRW population: for instance, with the HTG6 loci, the two most frequent alleles in the PRW population (70%) were seldom found in other breeds (less than 1%). Heterozygosities, mean number of alleles (MNA) and allelic richness (AR) are presented in Table MNA and AR were highly correlated, . He ranged from 0.43 in the FRI breed to 0.79 in the PFS breed, while Fis per breed ranged from -0.08 (TDN breed) to 0.11 (PRE breed).One hundred and nine alleles were found over all populations and all markers. The average number of alleles per locus was 9.8 ranging from seven (locus locus ASB. Some raHMS1). Using global tests, five populations  and two markers (HMS3 and HTG10) showed significant deficit in heterozygotes (P < 0.01). Other studies have shown similar results for these two markers [Some significant heterozygote deficits after corrections were found, for different loci and populations , showing similar results to previous figures  or 17 (0.81) than for K = 15 (0.83), the results are shown for this last value. Figure K, using runs having the highest pair-wise similarity coefficients.Breed assignment to clusters provides complementary information on genetic relationships between populations. As K = 2, there was a clear separation between draught and warm-blooded horses, with other populations showing intermediate results. When K reached 3, Nordic/primitive breeds, ponies, and some warm-blooded horses segregated more or less clearly from the two other clusters. As K increases to 4 and 5, the five clusters were constituted of Nordic/primitive breeds, draught horses, ponies, warm-blooded populations close to the AR breed and warm-blooded populations close to the PS breed. Some breeds were shared among the last three clusters, such as LAND between ponies and AR groups, and APPAL among the three clusters. When K reached 6, depending on the runs, FRI or PRW populations were alternately isolated, which led to a decrease of similarity across runs and explains the low similarity coefficient (0.562) in comparison with other K. When K = 7, these two populations were isolated. The different runs highlight some differences among sub-populations of AA and SF breeds, underlining a more important proportion of AR genes in AA6, AA5 and respectively SFA97 and SF98 groups. Some warm-blooded  and pony breeds (HAF) were classified with draught horses, while the CAM warm-blooded breed was clustered with ponies. As K reached 15, most breeds were shared among different clusters. The ARD, AUX and TDN breed constituted a single cluster while FJ/IS and LUS/PRE constituted two others. In a few cases, a single cluster was essentially associated to a single breed .For GDWS of 0.685, a gene diversity between populations GDBS of 0.073, and a total gene diversity GDT of 0.758. Table CW and ΔGDWS ranged from -0.48 to 0.50 and from -0.0055 to 0.0069 respectively. In both cases, the POIT breed showed a particularly low within-breed diversity. CW and ΔGDWS were negatively correlated . For between-breed diversity, CB and ΔGDBS ranged from 0.85 to 12.60 and from -0.0041 to 0.0024, respectively. Here, the POIT breed showed a particularly high contribution to the between-breed diversity. The correlation between CB and ΔGDBS was not significant. D and ΔGDT, accounting for total diversity, were negatively correlated . They ranged from -0.32 to 1.25 and from -0.0042 to 0.0039, respectively. In both cases, the ARD and PS breeds showed a particularly low and high diversity, respectively.In the set of the 19 French breeds, we found a gene diversity within population Considering contributions to the between-breed diversity and probabilities of extinction, the BOUL, LAND and POIT breeds showed the highest cryopreservation potentials .GDT are given in Table GDT of 0.79. Besides, to maximise the total gene diversity, seven of the 19 breeds should be maintained, namely the BOUL, COBND, LAND, PFS, POT, PS and SF breeds.Contributions of each population for an optimal  i.e. close to the value found by Luis et al. [et al. [He were slightly lower  for the eight breeds shared between their study and the present one. Differences on the within-breed diversity among studies using microsatellites can be explained, on the one hand, by the loci used and, on the other hand, by the populations analysed, incidentally belonging to similar breeds but having different recent histories. In the AR breed, we found a He value of (0.72) with a significant deficit of heterozygotes, which can be explained by the fact that this is an international breed in which mating between close relatives is common [et al. [et al. [et al. [He = 0.78), in comparison with the French PER population (He = 0.68). Because PER populations have been bred in America since the end of the 19th century, such results should be interpreted bearing in mind that the French PER population has probably suffered from recent bottlenecks due to several modifications of the selection aims.Differences between breeds explained 10% of the total genetic variation, which is quite similar to other analyses, where values ranged from 8% to 15% -4,9. Accs et al. . Plante s common . Plante  [et al.  have fouue of 0.7 with a sThe three approaches based on genetic relationships  gave similar results. The populations considered in the present study can be classified into four more or less differentiated clusters: warm-blooded, draught, Nordic and pony breeds. Similar patterns of clustering have been found in other studies ,3,9,10. K reached the number of breeds, indicate that the most relevant division is situated at a level superior to that of the breeds [Population differentiation tests and Bayesian approaches indicate clear differences between sub-populations of AA and SF. Such results may be largely explained by differences in the proportion of thoroughbred (PS) origins in the gene pool of these sub-populations. Within the AA breed, AA5 and AA6 populations appeared distinct from AA9 and AA10 populations and close to the PS breed. This was in agreement with the studbook rules: on the basis of pedigree data, AA5, AA6, AA9 and AA10 populations were indeed found to have respectively 94%, 89%, 44% and 59% of genes from PS origin . Within the SF breed, the SF8 (not differentiated from the PS breed) was distinct from SFA97 and SFB98 populations. This result was in agreement with previous results from pedigree data : the SF8e breeds . Such a In the present study, an almost comprehensive sampling of French breeds was achieved. The different approaches used gave an estimation of the contribution of each breed to the whole French horse stock. Petit  has propThe results given by the aggregate diversity and gene diversity approaches were slightly correlated. By definition, breeds with low contributions to aggregate and total diversities should have related breeds in the data set. Thus, ARD, TDN, and AUX breeds, which were genetically highly related, illustrate quite well such a hypothesis.D of BOUL, MER and POIT breeds were quite high, and taking into account population size, CP was the highest for BOUL, LAND and POIT breeds. Using the approach of Caballero and Toro [GDT decreased only when LAND and POT breeds were removed, and those two breeds plus the BOUL breed should have been kept to optimise GDT. The differences can be explained by the methods used in the two approaches, particularly considering the evaluation of the contributions to between-diversity. Using the approach of Caballero and Toro [GDBS, which will not be the case when using the Weitzman approach. However, one advantage of the approach of Caballero and Toro [GDT is the sum of GDWS and GDBS. In fact, our results outline that both approaches should be considered as complementary to identify which breeds have to be taken into account in a context of genetic resource management. Therefore, conservation priorities should concern particularly BOUL, LAND, MER, POIT and POT breeds.According to the approaches of Ollivier and Foulley  and Cabaand Toro , GDT decand Toro , some Weand Toro , were noand Toro  is the fAnother advantage of the method of Caballero and Toro  is the pFinally, several considerations have to be taken into account before taking final conservation decisions , such asBased on this study, horse breeds raised in France can be clustered into four groups. These groups were found to be meaningful according to the use of breeds, morphological characteristics and/or geographical origins. The combined use of different methods allowed us to identify breeds for which conservation efforts should be a priority, in order to preserve the maximum genetic variability. Since several horse studies have used similar panels of markers ,9,10, itThe authors declare that they have no competing interests.JCM carried out the genotyping. AR contributed to the description of the populations and carried out the sampling collection. LC performed the preliminary analysis. GL carried out the computational analysis and prepared the manuscript. XR participated in the computational analysis and preparation of the manuscript. CDB participated in the preparation and the revision of the manuscript. EV participated in the design of the study and the revision of the manuscript. All authors read and approved the final manuscript.Supplementary Figure 1. Neighbour-Net for the 34 horse breeds, based on Reynolds DR distanceClick here for fileSupplementary Figure 2. Correspondence analysis of allele frequencies for 33 of the populations studied (PRW is not included). The projection is shown on the first two axes.Click here for fileSupplementary Figure 3. Evolution of mean ln of likelihood according to K on twenty runs (standard deviation indicated)Click here for file