Sorghum halepense) is a striking example of a post-Columbian founder event. This natural experiment within ecological time-scales provides a unique opportunity for understanding patterns of continent-wide genetic diversity following range expansion. Microsatellite markers were used for population genetic analyses including leaf-optimized Neighbor-Joining tree, pairwise FST, mismatch analysis, principle coordinate analysis, Tajima’s D, Fu’s F and Bayesian clusterings of population structure. Evidence indicates two geographically distant introductions of divergent genotypes, which spread across much of the US in <200 years. Based on geophylogeny, gene flow patterns can be inferred to have involved five phases. Centers of genetic diversity have shifted from two introduction sites separated by ~2000 miles toward the middle of the range, consistent with admixture between genotypes from the respective introductions. Genotyping provides evidence for a ‘habitat switch’ from agricultural to non-agricultural systems and may contribute to both Johnsongrass ubiquity and aggressiveness. Despite lower and more structured diversity at the invasion front, Johnsongrass continues to advance northward into cooler and drier habitats. Association genetic approaches may permit identification of alleles contributing to the habitat switch or other traits important to weed/invasive management and/or crop improvement.Johnsongrass ( Sorghum halepense) has become one of the most noxious agricultural weeds globally [From noble beginnings as a promising forage, Johnsongrass  an annual native of Africa, and S. propinquum (2n = 20), a wild perennial native to wet subtropical habitats in southeast Asia that diverged from S. bicolor ~1–2 million years ago [S. bicolor) [Johnsongrass 2n = 40) formed by natural hybridization between ears ago . Johnson0 formed bicolor) , virtualbicolor)  that hasUnlike most other agricultural weeds, Johnsongrass is also an aggressive invader of natural or minimally managed habitats. Johnsongrass is a state-listed noxious weed in 20 states and an invasive species in 16 states . This trA compelling question is whether the spread of Johnsongrass is because it is a remarkably effective ‘general purpose genotype’  or refleArabidopsis populations have demonstrated reduced genetic diversity and increased linkage disequilibrium patterns as populations migrate away from their centers of origin. Range expansions from a limited genetic stock have significant levels of background relatedness, but have rarely been explored in invasive plants, which are modern rapid globetrotters [Rapidly spreading populations contain demographic signatures due to serial founder effects. Global and large continent-wide genetic data from human and trotters .As a foundation for investigating principles underlying the post-Colombian spread of Johnsongrass across the USA, we study Simple Sequence Repeat (SSR)-based genotypes to elucidate colonization and diversity patterns of geo-referenced populations sampling most of its continental United States range. This natural experiment provides a unique opportunity to track ‘blank slate’ colonization occurring within ecological time-scales, and provides a new perspective by broadening the timescale of invasion occurring over two centuries. A blank slate colonization may follow three trajectories: Founders may go extinct, maintain a restricted local population, or spread to all available habitats. These trajectories are shaped by the resultant vector of major co-acting evolutionary forces such as genetic drift, gene flow and natural selection. As a tetraploid with genomes inherited from two grasses respectively adapted to dry and tropical moist conditions, Johnsongrass is an outstanding experimental system in which to investigate aggressive invasion conforming to the third population trajectory.DNA genotyping also provides a means to investigate whether patterns of genetic variation are related to the habitat or climate from which samples were collected. Findings complement and enhance historical information about the origins and spread of Johnsongrass, in particular indicating two independent invasions in very different climates separated by about 2000 miles. Finally, DNA fingerprint data provides the basis for selection and early characterization of a ‘diversity panel’ of US Johnsongrass, potentially suitable for use by association genetic approaches [S. propinquum as the out group were calculated by PowerMarker v3.25 [A total of 599 genotypes were studied, with 1–32 from each of 70 different collection sites in 12 states  funneled the 599 genotypes into 231 groupings  tree with optimized leaf ordering visualized phylogeographic relations along the east–west axis . The treThe N-J tree provides evidence for a second introduction, in Arizona (AZ). The founder cluster from AZ shows a very high correlation with the geographical axis. There are at least two distinct episodes of colonization from AZ into TX and then reaching to Florida (FL) and GA. , TX, Nebraska (NE) and Kansas (KS)  were found in agricultural habitats. Genotypes from disturbed habitats were mostly found in the more recent Phase 4 and 5 clusters, and genotypes from roadside habitats were largely restricted to the Phase 5 clusters . In thesP > 0.213; multinomial logistic regression, Z-test), although agricultural habitat was less common in areas with high mean annual temperatures (P < 0.031). However, such sampling bias would have countered the trends we observed, as it would have caused us to be more, and not less, likely to sample non-agricultural habitat in the warmer ancestral populations.These results suggest that initial expansion of Johnsongrass occurred primarily in agricultural habitats, with a subsequent transition of a cluster of closely related genotypes to non-agricultural habitat throughout the entire US range. This pattern was not influenced by sampling bias across geographic clines. Multinomial logistic regressions show that the three habitat types were equally represented along latitudinal, longitudinal, and rainfall clines  into Oceania [Cocos nucifera) around the World has been reconstructed [Historical demography of a population is imprinted on present day genetic structure, and genetic trees can be quite informative to understand the past. Researchers have successfully reconstructed pre-Columbian dispersal of sweet potato  to non-agricultural  habitats in a multi-phase colonization process . AncestrThe finding that DNA genotyping is diagnostic of a habitat switch suggests that specific alleles or allele combinations may be responsible for the switch, and that their identification might lead to novel means of mitigating the spread of Johnsongrass, at least to non-agricultural habitats. The limited sampling of SSRs that was sufficient for this study is inadequate to scan the entire genome for associations with important Johnsongrass traits, but reduced-representation resequencing of a diverse subset of the accessions studied here may provide the required resolution. For example, extensive spread and admixture may have seeded diverse geographies with identical-by-descent alleles for herbicide resistance, that appear to comprise independent evolutions when subsequent conditions such as increased herbicide usage favor their increase in frequency . Such a Despite lower and more structured diversity at the invasion front, Johnsongrass continues to advance northward , with prS. halepense, provides for association genetic approaches to identify alleles responsible for specific traits of importance to weed/invasive management, and/or to crop improvement [The richness of alleles in Johnsongrass and its spread to environments beyond the reach of its progenitors could offer novel and valuable variants for improvement of sorghum for resistance to drought, cold, disease and other stresses. Our improved understanding of population structure, together with an incipient ‘diversity panel’ of US rovement .S1 FigFigure A. Map of sampling sites and Principle Coordinate Analysis (PCoA) distribution of samples. Figure B. Principle Coordinate Analysis and locations of five groups at two extremes shown with number of genotypes in each group. Figure C. Mismatch distribution profiles (demographic expansion) from 12. Figure D. Mismatch distribution profiles  from 12 states. Figure E. STRUCTURE analysis of genotypes at K = 80. Figure F. Neutrality test results for genetic markers employed in the study after 10000 simulations.(DOCX)Click here for additional data file.S1 File(NWK)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(CSV)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.


Tabebuia serratifolia (Bignoniaceae) to understand the disjunct geographical distribution of South American seasonally dry tropical forests (SDTFs). We specifically tested if the multiple and isolated patches of SDTFs are current climatic relicts of a widespread and continuously distributed dry forest during the last glacial maximum (LGM), the so called South American dry forest refugia hypothesis, using ecological niche modelling (ENM) and statistical phylogeography. We sampled 235 individuals of T. serratifolia in 17 populations in Brazil and analysed the polymorphisms at three intergenic chloroplast regions and ITS nuclear ribosomal DNA.We studied the phylogeography and demographical history of c. 130 ka (thousand years before present). Simulations and ENM also showed that the current spatial pattern of genetic diversity is most likely due to a scenario of range expansion and range shift towards the Amazon Basin during the colder and arid climatic conditions associated with the LGM, matching the expected for the South American dry forest refugia hypothesis, although contrasting to the Pleistocene Arc hypothesis. Populations in more stable areas or with higher suitability through time showed higher genetic diversity. Postglacial range shift towards the Southeast and Atlantic coast may have led to spatial genome assortment due to leading edge colonization as the species tracks suitable environments, leading to lower genetic diversity in populations at higher distance from the distribution centroid at 21 ka.Coalescent analyses showed a demographical expansion at the last Haplotype sharing or common ancestry among populations from Caatinga in Northeast Brazil, Atlantic Forest in Southeast and Cerrado biome and ENM evince the past connection among these biomes.The online version of this article (doi:10.1186/s12862-016-0779-9) contains supplementary material, which is available to authorized users. Tabebuia impetiginosa }, where N0 = 10,000 was the same for all scenarios, and N1 shifted among them according to our theoretical expectation  for model choice. The log-likelihood, ln(L), was estimated as the product of the height of the empirical frequency distribution at the observed value of diversity by the maximum height of the distribution. AIC (-2Ln(L) + 2 K, where K is the number of free parameters, 2 for all models) was transformed into AIC weight of evidence (AICw), given by exp[-0.5(AIC – AICmin)]  , from wh pattern . AICw waels (see ).T. serratifolia, we used spatially explicit analyses to detect spatial patterns in observed genetic diversity in response to late Quaternary climate oscillations, for both cpDNA and ITS. Spatial expansion may lead to gradients in genetic diversity because of allele surfing during the colonization of new areas and “lead trail” expansion  among pairs of populations were estimated for both cpDNA and ITS and correlated with a geographical distance matrix (logarithm of geodesic distance) by a Mantel test using ArlequinVer 3.11 [We first tested if differentiation is an effect of isolation by distance . PairwisVer 3.11  and stath) and nucleotide (π) diversities, Ne and θ due to expansion of climatically suitable conditions. For this, we obtained the distance between each sampled population and the centroid of historical refugium and of the predicted distribution at 21 ka, 6 ka and present-day, and then we performed quantile regressions of genetic parameters against these spatial distances and against climatic stability.We used quantile regressions to analyse the relationships of climatic suitability and stability through time with genetic diversity . For thi


Plasmopara viticola is one of the most devastating diseases of grapevines worldwide. So far, the genetic diversity and origin of the Chinese P. viticola population are unclear. In the present study, 103 P. viticola isolates were sequenced at four gene regions: internal transcribed spacer one (ITS), large subunit of ribosomal RNA (LSU), actin gene (ACT) and beta-tubulin (TUB). The sequences were analyzed to obtain polymorphism and diversity information of the Chinese population as well as to infer the relationships between Chinese and American isolates. High genetic diversity was observed for the Chinese population, with evidence of sub-structuring based on climate. Phylogenetic analysis and haplotype networks showed evidence of close relationships between some American and Chinese isolates, consistent with recent introduction from America to China via planting materials. However, there is also evidence for endemic Chinese P. viticola isolates. Our results suggest that the current Chinese Plasmopara viticola population is an admixture of endemic and introduced isolates.Downy mildew caused by  Plasmopara viticola (Berk & Curt.) Berl. &. De Toni., is one of the most troublesome diseases of grapevines worldwide1. This disease was first identified in North America2. It is hypothesised that the disease was accidentally introduced in to European grape vineyards in 1870s4 via grapevine cuttings, which were used to replace French vineyards destroyed by Phylloxera5. Today, grape downy mildew is found in all grape-growing regions, causing major economic losses6. In China, grapevine downy mildew was first reported in 18997. At present, this disease can be observed in almost all Chinese grape vineyards, causing significant yield losses. Many studies have been conducted to understand disease epidemiology and resistance development against P. lasmopara viticola in China. However, it is less known about the population structure of this pathogen.Grape downy mildew caused by 8. Previous studies have shown that the causal agent of grape downy mildew, P. viticola, exhibits four cryptic lineages, which are structured according to the host species; Vitis aestivlis, V. labrusca, V. riparia and V. vinifera4. To date, studies of cryptic species have mainly focused on their complex phylogeography, gene flow and hybridization processes9. For plant pathogens, it is also important to understand fitness differences within or between cryptic species to determine whether a strain may be more or less virulent than other strains, including parental strains (for hybrids)11. For some instance, hybridization has resulted in “superspecies,” which may exhibit an expanded host range12, surpassing parental strains and attaining the ability to exploit a new host13. Therefore, it is necessary to understand how pathogen populations vary with geography and host.Using the multiple gene genealogical approaches it has shown that the morphological simplicity and similarity among closely related fungal species often contained cryptic lineages17. Yin et al.7, observed high genetic diversity in Chinese P. viticola populations using SSR markers. However, the origin of the Chinese P. viticola population and its relationship with the American population are currently unknown. The Chinese P. viticola population might have originated from an exotic introduction via introduced plant materials from its native country. Fungal pathogens have the ability to spread with planting materials worldwide18. To develop control measures, it is necessary to identify the routes of introduction and study the evolutionary potential of pathogen populations19.Several studies have been conducted to elucidate the genetic diversity and population structure of grape downy mildew pathogens using various molecular markersP. viticola isolates in China and their evolutionary relationships with American P. viticola isolates. We used multilocus sequence data to determine the genetic diversity of P. viticola isolates obtained in nine grape-growing regions in China from two Vitis cultivars; V. vinifera and V. vinifera × V. labrusca. We sequenced and analysed the internal transcribed spacer (ITS), the large subunit of ribosomal RNA (LSU), a fragment of the gene encoding beta-tubulin (TUB) and a fragment of the actin gene (ACT) in 103 P. viticola strains. We hypothesized that if P. viticola was introduced to China via planting materials, there might be a close phylogenetic relationship between Chinese isolates and American isolates.Hence, the objectives of the present study were to understand the genetic diversity of P. viticola isolates were isolated via single-sporangium isolation . The population “B” included isolates obtained from temperate monsoon climate region . PCR amplification for ACT, ITS, TUB and LSU gene regions using total genomic DNA as the template was successful for all isolates. The GenBank accession numbers for the American sequences used in this study are as follows: ACT: hap1–32 (JF897783–JF897815), ITS: hap1–4 (JF897779–JF897782), LSU: hap1–8 (JF897848–JF897855) and TUB: hap1–32 (JF897816–JF897847).Sample collection sites and the respective number of isolates obtained from each of the nine provinces in China are given in Fig. P. viticola, analyses were conducted using DnaSp (V5)20. A summary of the genetic diversity and polymorphism indices for each gene region is given in Table 21 values as 0.48 and 1.0 respectively. To understand the gene flow and genetic differentiation, the Chinese P.viticola population was divided in to two climatological populations, subtropical population (A) and temperate monsoon population (B). Table h = 0.68) and nucleotide diversity (pi = 2.80) was observed for the temperate population from TUB. The highest Fst between two populations was given by TUB as 0.28. To examine the evidence for recombination between the American and Chinese isolates, alleles from ACT and TUB were analyzed (ITS and LSU were excluded because of lack of polymorphism). For these regions, Hudson22 recombination parameter (R), overall genetic association by Kelly23 (Zns) and level of linkage disequilibrium (Za and ZZ)23 were calculated using equations available in DnaSP. In the ACT region, estimated R for gene was 0.001 and R between adjacent sites were 0.00. In this analysis, 990 pairwise comparisons were observed with 151 gametic types. Minimum numbers of recombination events24 were observed as 14 (Rm). We observed ZnS for ACT as 0.25. For ACT, Rozas et al.25, Za and ZZ values on recombination were 0.24 and -0.0097 respectively. In the TUB region, estimated R was 0.001 and R between adjacent sites were 0.00. In this analysis, 2,701 pairwise comparisons were observed with 464 gametic types. Minimum numbers of recombination events were observed as 25 (Rm). The overall genetic association (ZnS) was 0.2056 for the TUB region. Calculated Za and ZZ values were 0.2413 and 0.0357.To understand the genetic diversity of Chinese P. viticola and the American isolates, four phylogenetic trees were constructed based on each of the four gene regions . In this analysis, we excluded identical sequences from the Chinese isolates and used representative sequences from each haplotype given by polymorphism analysis. According to phylogenetic analysis, there are no distinct cladding patterns in Chinese P. viticola population based on geographical origin  clustered in a different sub-clade with 68 bootstrap values from MP analysis.The phylogenetic tree constructed for ITS region is shown in Fig. P. viticola fo.s. riparia lineage) belonged to a different cluster with five Chinese isolates belongs to Chinese haplotype one. The TUB phylogenetic analysis  worldwide. The disease symptoms include oil spots, leaves with the multiple lesions, and abscission, and severe infection resulted in defoliation16. Sporangia appear under the leaves and on other infected parts of the plants2. Bunches of young fruit turn brown and die rapidly when infected16. Older berries can be infected if the pathogen invades through the pedicel, causing symptoms referred to as brown rot or leather berry2. The disease epidemiology of this pathogen involves primary infection by oospores of the sexual stage of the pathogen16. This primary infection is followed by several asexual cycles of sporangium production, which results in colony infection and successful disease progression16.27. According to world statistical data from 2014, the total area under grape vineyard cultivation is 7,573 Kha, for both table and wine grapes. China has become the second largest grape grower worldwide based on vineyard area 27. However, in certain areas, grape production has decreased because of the continental climate, which allows disease epidemics of powdery and downy mildew to occur28. Therefore, it is important to understand the diversity and virulence factors of these pathogens for resistance breeding and the selection of fungicide-resistant strains. According to the literature, China is one of the centres reported for the origin of Vitis species30. It is predicted that 70 of the known species in this genus originated in China. Among them, the Chinese traditional cultivars are highly resistant to Elsinoe ampelina Shear29, Coniella diplodiella (Speg.) Petr. & Syd. 1927 and Colletotrichum Corda species30. In addition, characteristics such as flavour and breeding ability have garnered more attention for these traditional cultivars32.China is one of the main grapevine-growing countries in the world. Chinese grape production has dramatically increased in the past 20 yearsP. viticola reached China has not been studied. In addition to the origin of the pathogen, it is important to understand the genetic diversity of P. viticola in Chinese vineyards to achieve sustainable management. To understand the genetic diversity and the origin of this pathogen, we collected P. viticola samples from nine grape-growing regions in China. However, majority of our samples were from Beijing since it was difficult to collect samples from long distances. Majority of single sporangia isolated from the traditional cultivars were failed. This might be due to the host specificity of the P. viticola isolated from Chinese traditional cultivars. Due to the host specificity and obligate biotrophic nature of the pathogen, we were unable to obtain samples outside of China. Thus, this study was only depended on the sequence data available in the NCBI GenBank and the sequences generated in the current study.Grapevine downy mildew was first reported in North America. However, the pathway through which 33. Therefore, we included TUB and ACT gene regions into the analysis. Previous studies have also confirmed that these gene regions are useful and informative to understand the population structure and genetic diversity of the P. viticola4.In population genetic studies, DNA sequencing approach facilitates data for both high level phylogenetic analyses among species and for the analyses of genetic variations among isolates within populations and species. We performed sequencing of the ITS, LSU, ACT and TUB gene regions for the Chinese populations. We also obtained sequences from North American isolates from NCBI GenBank. There were no considerable amounts of sequence data available for the European isolates in the NCBI. Therefore, we excluded the European isolates from the current study. We selected both conserved and non-functional DNA fragments for this analysis. Non-functional DNA regions are more informative than the conserved regions34. In the population investigated in the present study, we observed low haplotype and nucleotide diversity for ITS and LSU gene regions. However, ACT and TUB gave high haplotype diversity for Chinese P. viticola population. The sub population belongs to temperate monsoon region (A) gave higher haplotype diversity. The estimated nucleotide diversity also reflected similar results as above. Thus, it indicted high genetic diversity for Chinese P. viticola population. The average nucleotide diversity and estimated Theta (θ) per site were also high for TUB gene region. Yin et al.7 have studied Plasmopara viticola population using SSR markers to understand the population structure of grape downy mildew in China7. In that study, they observed a high level of genetic diversity in the P. viticola population. The current study confirms the high genetic diversity of Chinese grape downy mildew population using multilocus sequence data. In addition to Chinese studies, several studies have been conducted to understand the genetic diversity and population structure of the Plasmopara viticola35. A study conducted by Rumbou and Gessler35 using microsatellite markers revealed that the P. viticola population from the Mediterranean islands exhibits limited variation and that disease epidemics in this region have been driven by multiple clonal expansions. The authors suggested that these findings might have been due to the isolation of Mediterranean islands from the main continent. A study conducted by Roxual et al.4, based on multiple genealogies and sporangia morphology showed that American P. viticola consists of a complex of four cryptic species, each of which is associated with different host plants. However, in the current study, we did not observe any host specificity but our major finding provide evidence for the climatological subpopulations of Plasmopara viticola.Genetic diversity of Chinese downy mildew population was calculated via haplotype diversity analysis. Haplotype diversity is a combined result of mutation, recombination, marker ascertainment and demography36. Such increments may enhance the potential of pathogens to invade plants36. Since we observed high genetic diversity from the isolates obtained from northern China (temperate climatic region), the pathogen may have shown the potential to evolve with the changing environmental factors. Therefore it is necessary to alter the current management strategies with time11. To understand the gene flow and genetic differentiation between the two Chinese P. viticola subpopulations, we calculated nearest neighbour statistics (Snn). The Snn measures how often the most similar sequence or another sequence (nearest neighbour) is from the same distinct population37. This measurement is particularly powerful for small population sizes. If two populations exhibit a value near to one, this indicates that these two populations are highly differentiated, as almost every sequence would be similar to those from the same population39. A value of 0.5 is expected if populations are not genetically structured. In this case, the closest sequences would have an equal probability of being most similar to those from either population39. From our sequence data, we obtained Snn value higher than 0.5 for ACT and TUB . This suggested that the Chinese P. viticola population is genetically structured according to climatological origin. Other than that, Fst also can be used to measure genetic differentiation of a particular population.Over the last few years, northern China has experienced increased temperatures38. It demonstrated a high potential for inbreeding within the groups examined in the present study38. The highest Fst was given by the TUB gene region as 0.277. Similar results were found in Yin et al.7, in which Chinese P. viticola population separated into three subpopulations according to geographical origin. Together, both studies suggest the existence of climatological subpopulations in P. viticola in China. However, the main objective of the present study was to understand the possible introduction pathways of P. viticola to the Chinese grape vineyards.Fst calculates the degree of genetic differentiation within a population based on allele frequencies21. The positive D value reflects the selection maintaining variation, which might be resulted due to recent population contraction3. In the present study, we observed positive value for Tajima’s D for ITS and TUB (1.00 and 0.478). Within a population, controlled selection could result in population contraction21. This may be caused by the application of fungicides over the years. Many types of chemicals have been used to control downy mildew5. This might have led to develop resistant strains in the population, and many genetic types were lost from the main population40. Therefore, in the current population, we might only observe the individuals resulted from the resistant selection. In addition to the diversity analysis, use of multilocus sequence data gave us further information to track the origin of the Chinese P. viticola in China33.Tajima’s D indicates how much population variation can be sustained over timePlasmopara viticola from North America, we analyzed Hudson and Kaplan24 index for the recombination. In here, we calculated the number of recombination events in the history of a sample of sequences (R) and the number of recombination events that can be parsimoniously inferred from a sample of sequences (Rm). Once the rate of recombination equals to the zero, R gives zero. We obtain R equals to zero for both ACT and TUB regions, which reflect zero recombination events, occurs in history for the current samples. Since the R is given the value on history of the sample, Rm, denote the minimum number of recombination events implied by the data using the four-gamete test. We observed 14 and 28 recombination events within 133 P. viticola isolates (From China and America). The positive ZZ value, which reflects intragenic recombination, has played an importance role in nucleotide variation23. We observed positive value on TUB region, as well as higher number of recombination events. Therefore, a recent recombination event might have happened between Chinese and American isolates, when they introduced to China with planting materials. To ensure the possible recombination between native isolates and Chinese isolates we performed phylogenetic analysis and haplotype network construction.To ensure the hypothesis of the possible introduction of 41. In here, we used the most parsimonious network development in which the number of mutations separating each haplotype, with a parsimony probability of 95%42. In each network, ancestral haplotype was predicted based on rooting probability43. In the present study, we developed four haplotype network for ITS, LSU, ACT and TUB gene regions. In each network, we observed that there was an ancestral relationship between Chinese and American isolates. In phylogenetic analysis, similar cladding patterns were observed. This result supports our hypothesis on the introduction of grape downy mildew pathogens to China from Northern America. In addition to that, we observed several isolates from China, which showed independent clustering in phylogenetic analysis. Those isolates were obtained from Chinese traditional grapevine cultivars. These results suggested that current P. viticola population in China might have an admixture population, where the population is made up with both endemic and introduced isolates. The endemic evolution is possible with the cross breeding over many generations. Chinese traditional grape cultivars are frequently used in breeding programs to develop high quality and pathogen resistant cultivars27. To overcome this selection pressure, pathogen might have evolved from generation to generation. In addition to that, the planting materials exchange between countries might be lead to introduce some foreign strains to the China.Haplotype networks allow to understand the coexistence of ancestral and derived haplotypes and it is an account for recombination. Therefore, haplotypes networks are preferable for intraspecific analysesPlasmopara viticola population in China. We used DNA sequencing at multiple loci as the marker of choice in the present study. Using multiple genealogical approaches, we observed high genetic diversity in Chinese P. viticola population. Our results demonstrate that the P. viticola population in China sub divided according to the climatological regions. Since most samples were collected from Vitis vinifera, we cannot provide conclusion on host specificity of Chinese P. viticola population. Therefore, to understand the host specificity related pathogenicity, further studies are required. The Plasmopara viticola population in China was observed to have selection maintaining variation by giving positive Tajima’s D values. It is predicted that, this is due to resistance development over recent years with the excess use of chemical fungicides or the resistance breeding. Observing possible recombination between American and Chinese isolates supported our hypothesis on possible introduction from Native American population. Both phylogenetic and haplotype networks showed possible ancestral relationship with each other supporting the hypothesis of possible pathogen introduction form native population. However, Plasmopara viticola isolates obtained from traditional Chinese grape cultivars were observed to be genetically distant in above the analyses. Therefore, we concluded that the current Plasmopara viticola population in China is a combination of endemic and introduced strains.The present study was conducted to understand the genetic diversity and evolutionary pathways of V. vinifera and V. vinifera × V. labrusca hybrids. Diseased samples were collected into plastic bags with sterilized tissues, which were dipped in distilled water to maintain humid conditions.The sample collection sites were located in nine provinces in China: Beijing (BJ), Guangxi (GX), Hebei (HEB), Hubei (HUB), Hunan (HUN), Liaoning (LN), Jilin (JL), Ningxia (NX) and Shanxi (SX). In Beijing, samples were collected from seven districts: Changping (BJC), Daxing (BJD), Fangshan (BJF), Haidian (BJH), Pinggu (BJP), Tongzhou (BJT) and Yanquing (BJY). Symptomatic tissues were collected from both Infected leaves were incubated for 2–4 days at 18 °C under a photoperiod of 16 h day/8 h night. To isolate the pathogen, the “leaf disc incubation” method was used. A humid container was constructed using two 90 diameter sterilized Petri dishes, in which the petri dishes were stacked upon one another. Filter paper soaked in 8 ml of sterile water was added to the bottom dish. To inoculate the pathogen, leaves from susceptible cultivars (Red Globe and Pinger) were used. Summer Black leaves used as the control. Selected young leaves were examined under a dissecting microscope to ensure that no downy mildew had fallen on the foliage or mycelia. Selected leaves were surface sterilized with a 1% (mass percent concentration) sodium hypochlorite solution for 30 sec, followed by three washes with sterilized distilled water. Excess water on the leaf blades was absorbed with sterile filter paper. Once disinfection was completed, 2 cm diameter cork borer was used to obtain leaf discs from the middle of the leaf, avoiding the main vein and the larger lateral veins. Finally, the flat leaf discs were placed in the prepared humidifiers, with five leaf discs per dish. Under a dissecting microscope, an acupuncture needle (size Ф 0.25 × 40 mm) was used to transfer newly generated sporangiophores from the incubated leaves onto leaf discs. For each grape cultivar, 50 leaf discs were inoculated. To obtain pure cultures, single-sporangium isolation was repeated several times. After leaf disc cultures obtained from a single spore were established, they were used for colony formation. For DNA extraction, approximately 100 mg of the mycelium was collected. The DNeasy Plant Mini Kit  was used to extract total genomic DNA.2, 5 μM of each deoxynucleoside triphosphate (dNTP), 1 μl of each primer and 0.3 U of Taq Silver star DNA polymerase in 1x reaction buffer. The thermo cycling conditions were as follows: 95 °C for 4 min, followed by 40 cycles of 95 °C for 40 s, 52–58 °C for 45 s, and 72 °C for 90 s, with a final step at 72 °C for 10 min. Following the PCR amplification, products were visualised on 1% agarose gel under UV light using a Gel DocTM XR + Molecular Imager . Sequencing of the positive amplicons was carried on a Sun-biotech Company Sequencer .PCR amplification of total genomic DNA was conducted for four gene regions: internal transcribed spacer one (ITS), the large subunit of ribosomal RNA (LSU), a fragment of the gene encoding beta-tubulin (TUB) and a fragment of the actin gene (ACT). The primer sequences and annealing temperatures employed for PCR are given in Table 44 to ensure quality sequences. Consensus sequences were generated using DNAStar (v5.1). Sequences were analysed using the GenBank BLASTn search engine of the National Center for Biotechnology Information (NCBI). The sequence data have been deposited in GenBank . The sequences for American isolates were obtained from NCBI GenBank4.The chromatograms of the sequences were checked using BioEdit (v5)20 was used to calculate the following parameters: number of haplotypes, haplotype diversity and pairwise nucleotide diversity (Л). To overcome population size effects parameters were obtained after 1,000 repetitions, and the median estimate for each parameter was recorded. Tajima’s D21 was calculated to test for departure from an equilibrium natural model of evolution. DnaSP (v5) was used to measure the Tajima’s D value, with a permutation test of 1,000 replicates. To understand the population structure of P. viticola, nearest statistics (Snn) and F statistics values were calculated. To calculate the recombination between alleles, Hudson22 calculation and to identify the linkage disequilibrium Kelly23 calculation were performed.The combined sequence dataset including the Chinese isolates was evaluated to understand genetic diversity. DnaSP (V5)http://mafft.cbrc.jp/alignment/server/)45. If necessary, manually aligned the files using BioEdit44. Phylogenetic relationships among individuals were inferred via maximum parsimony (MP) implemented in PAUP (v4.0)46, maximum likelihood (ML) in RAXM47 and Bayesian analyses in MrBayes (v3.0b4)48. MP genealogies were constructed using the heuristic search function with 1000 random addition replicates, performing tree bisection and reconstruction (TBR) using a branch-swapping algorithm49. Branches of zero length were collapsed, and all multiple parsimonious trees were saved. Descriptive statistics including tree length (TL), consistency index (CI), retention index (RI), relative consistency index (RC) and homoplasy index (HI) were calculated. Differences between the trees inferred under different optimality criteria were evaluated using Kishino-Hasegawa tests (KHT)50. Maximum likelihood analysis was conducted in raxmlGUI (v0.9b2)47 with 1000 non-parametric bootstrapping iterations, using the general time reversible model (GTR) with a discrete gamma distribution. Tree with the final likelihood value of −12360.77 was selected as the best scoring tree and the replicates were plotted relative to that. Suitable models for Bayesian analysis were obtained from JModel Test (v2.1.4)51. In the Bayesian analyses, Markov Chain Monte Carlo (MCMC) chains were run from random trees for 10,000,000 generations and sampled every 1,000 generation. The temperature value was lowered to 0.15, burn-in was set to 0.25, and the run was automatically stopped as soon as the average standard deviation of split frequencies was less than 0.01. Resulted phylogenetic trees were viewed using Treeview (v1.6.6)52.The individual sequence sets belongs to ITS, LSU, ACT and TUB were aligned using the default settings of MAFFT (v7) Supplementary table S1


Prostate Cancer (PCa) is a complex and heterogeneous disease. The androgen receptor (AR) and the signal transducer and activator of transcription 3 (STAT3) could be effective targets for PCa therapy. STAT3, a cytoplasmatic latent transcription factor, is a hub protein for several oncogenic signalling pathways and up-regulates the expression of numerous genes involved in tumor cell proliferation, angiogenesis, metastasis and cell survival. STAT3 activity can be modulated by several Post-Translational Modifications (PTMs) which reflect particular cell conditions and may be implicated in PCa development and progression. The aim of this work was to analyze STAT3 PTMs at different tumor stages and their relationship with STAT3 cellular functions. For this purpose, sixty-five prostatectomy, Formalin-fixed paraffin-embedded (FFPE) specimens, classified with different Gleason Scores, were subjected to immunoblotting, immunofluorescence staining and RT-PCR analysis. All experiments were carried out in matched non-neoplastic and neoplastic tissues. Data obtained showed different STAT3 PTMs profiles among the analyzed tumor grades which correlate with differences in the amount and distribution of specific STAT3 interactors as well as the expression of STAT3 target genes. These results highlight the importance of PTMs as an additional biomarker for the exactly evaluation of the PCa stage and the optimal treatment of this disease. Prostate Cancer (PCa) is worldwide one of the most common male tumors. About 95% of prostate cancers are adenocarcinoma. PCa is characterized by an evident clinical, histological and biological heterogeneity. Clinically PCa goes from “latent” to an “aggressive” form. The “latent” form, a slow-growing tumor, appears histologically differentiated and indolent, without any symptoms. On the contrary, the aggressive form is a fast-growing tumor with a lethal progression , 2. The STAT3 is ubiquitously expressed and plays multiple functions in normal cell . In the 727-STAT3) increases STAT3 transcriptional activity . Se. Se31]. Normal and tumor FFPE sections (10 μm) were weighed  , 35. The™ paraffin kit (TrimGen) according to the manufacturer's instructions. RNA was quantified spectrophotometrically, and its quality was assessed by 1.5% agarose gel electrophoresis and staining with ethidium bromide. To rule out the possibility of DNA contamination, the extracted RNA was incubated with 10 μm/ml RNase-free DNase at 37°C for 30 min.Total RNA was extracted from three 10 μm thick FFPE sections (corresponding about 30 mg of tissue). RNA was purified with WAXFREE™ RT reagent Kit (Takara). Gene expression was evaluated with specific primers for PSA, BIRC5, SRD5A2, MMP2, CRP and SOD2 (Quanti-Tect QIAGEN) by Real-Time PCR using a MJ MiniOpticon Detection System (BioRad Laboratories) with SYBR green fluorophore using Brilliant SYBR Green QPCR Master Mix (Stratagene).The reverse transcription was carried out with PrimeScript™ mod. MP System (Bio-Rad Laboratories), acquired by ImageLab Software ver. 4.1, and quantified using ImageJ analysis software (http://rsbweb.nih.gov/ij/). Measured protein abundances were normalized to actin amounts and the tumor/control abundance ratio for each protein was used for analysis. The ratios of tumor to control tissue values were used to correct for possible inter-individual differences in protein abundance not related to PCa. Immunodetection was carried out using polyclonal antibodies  against STAT3 (cat. n. 9132), pY705-STAT3 (cat. n. 9131), pS727-STAT3 (cat. n. 9134) and acK685-STAT3 (cat. n. 2523). At least three experimental replicates were performed for each biological sample.Western blot analysis was carried out on proteins extracted from tumor and control regions of FFPE tissues (15–20 μg protein) resolved by 10% SDS-PAGE and transferred to nitrocellulose membranes . The mempost-hoc test to analyze differences between Gleason groups.The repeatability of results was confirmed by performing all experiments at least three times, and values are presented as the means, standard deviations (boxes) and min/max values (bars). Statistical analyses were performed with GraphPad software using a one-way ANOVA test followed by Bonferroni


Oryza nivara. The present study was conducted on populations of Oryza rufipogon and Oryza nivara from Eastern India employing morphological and molecular characteristics. The cluster analysis of the data on morphological traits could clearly classify the two wild forms into two separate discrete groups without any overlaps i.e. lack of intermediate forms, suggesting the non-sympatric existence of the wild forms. Amplification of hyper variable regions of the genome could reveal 144 alleles suggesting high genetic diversity values (average He = 0.566). Moreover, with 42.37% of uncommon alleles between the two wild relatives, the molecular variance analysis (AMOVA) could detect only 21% of total variation (p < 0.001) among them and rest 59% was within them. The population structure analysis clearly classified these two wild populations into two distinct sub-populations (K = 2) without any overlaps i.e. lack of intermediate forms, suggesting the non-sympatric existence of the wild forms. Clear differentiation into two distinct groups indicates that O. rufipogon and O. nivara could be treated as two different species.The inter relationships between the two progenitors is interesting as both wild relatives are known to be the great untapped gene reservoirs. The debate continues on granting a separate species status to  The crop improvement programs are dependent on continuous infusions of genes/QTLs from landraces and wild relatives to address the genetic enhancement programs and this path requires an assessment of diversity in the base populations to select desirable genotypes for use. Though utilization of natural variation available in the landraces is a continuous ongoing process of the crop improvement programs, it is important to explore the wild germplasm forms with novel traits as wild relatives of cultivated crop species are increasingly recognized as a valuable repository of useful variation. Wild species of rice are reservoirs of many useful genes but a vast majority of these genes remained untapped.Rice, the world’s most important food crop and primary food source for more than one third of the world’s population, accounts for around half of the calories consumed by three billion Asians. To meet the future demands of food needs of the population, there is an urgent need to enhance the productivity of rice that has reached a plateau due to a narrow genetic base of the modern ricecultivarsOryza rufipogon and Oryza nivara, the two closest relatives of Asian cultivated rice (O. sativa L.) are considered to be its progenitors3. As the wild relatives of rice can be an important source of beneficial genes in rice breeding, O. rufipogon and O. nivara have long been the subject of extensive taxonomic, phylogenetic and population studies4.O. rufipogon is a perennial, photoperiod sensitive, largely cross-pollinated, grows in habitats that include swamps and lakes, areas with year round assured water availability while O. nivara is an annual, photoperiod insensitive and predominantly self-pollinated species and adapted to dry habitats that lack of ponded water. While the distribution of O. rufipogon is over a wide area comprising South and South east Asia, Papua New Guinea and Australia, O. nivara is restricted to South and Southeast Asia6.Oryza nivara is considered as a distinct species10 or as an ecotype of O. rufipogon11 and its taxonomic status is not clear due to the inconsistent reports. The data derived from isozymes12, RAPIDs13, allozymes and RFLPs14, transposon display markers15, tourist sequences16, miniature inverted-repeat transposable elements inAFLPs17, SSRs13, STSs18 and various gene sequences21 did not detect any significant divergence between O. nivara and O. rufipogon, while the data derived from AFLPs22, microsatellites24, combined sequence data from chloroplast, mitochondrial and nuclear DNA25 and SNPs26 did indicate differences to grant a species status to O. nivara.O. rufipogon27 and some of the other important beneficial traits from O. rufipogon include yield QTLs, resistance to rice tungro virus and elongation ability are of great importance for rice breeding29 while the widely used resistance to grassy stunt virus in cultivated rice is unique to O. nivara and not observed in any other species or cultivated rice germplasm.Till date, the contribution of two wild progenitors to rice improvement is significant. The hybrid rice era in rice has started with the discovery of the WA (wild abortive) cytoplasm in 30, the advent and advances in molecular techniques has provided us opportunities to assess the variation at molecular level. The molecular techniques are well known for their use in analysis of specific genes, understanding gene action, genetic map generation, phylogenetic studies and species evolution that can also offer help in understanding the extent and distribution of genetic variation within and between species.However, populations of these species are rapidly declining due to changes in farming systems, economic development, urbanization, industrial exploitation and other human disturbances in the past few decades.The understanding of the extent and type of genetic variation of wild rice populations can help in identification of valuable accessions and conservation and utilization strategies can be formulated. Though morphological data is known to be of great value for assessment and comparison of diversity patterns within and among populationsO. nivara and O. rufipogon populations with an objective to understand their inter relationship and evolutionary aspects.The present study reports the results based on detailed investigations on morphological characteristics and several loci at molecular level in O. nivara and O. rufipogon populations, both showed high level of variation for their qualitative morphological traits. Of the 22 characters, 19 and 17 showed variation in the O. nivara and O. rufipogon populations, respectively , leaf: shape of ligule (split) and leaf: auricles (present), all being monomorphic. Similarly, the characters like panicle: awns (present), panicle: curvature of main axis (straight), panicle: secondary branching (weak), leaf: shape of ligule (split) and leaf: auricles (present) could not identify any difference among the O. rufipogon accessions. Among the O. nivara accessions, highest variation was detected for basal leaf: sheath colour , leaf: angle , culm: attitude , internodes: colour  and flag leaf: attitude of blade , but some short bold and short medium grain types  were detected in the O. nivara accessions. Some of the accessions of O. rufipogon were observed to be having broader leaf as compared to O. nivara. However, no significant variation for leaf length, ligule length, culm diameter, 100 grain weight and panicle length was detected between O. rufipogon and O. nivara groups. Thin or medium thick stem types were detected in most of the O. rufipogon and O. nivara accessions. Further, only one O. rufipogon accession was identified to have broad leaf (>2 cm). Highest variation was detected for panicle length within O. rufipogon and O. nivara collections.High level of variation was observed in O. rufipogon and O. nivara collections. The markers amplified a total of 144 alleles with an average of 4 per locus  for markers having multi-nucleotide repeat motif followed by tri-nucleotide (4.077), di-nucleotide (3.882) and tetra-nucleotide (3.000)  rare alleles (Supplementary Table O. rufipogon accessions (cumulative n = 116) than in O. nivara accessions (cumulative n = 71). Of these, some of the accessions were detected with more than one rare allele for more than one loci. For instance, AC100141 (O. rufipogon) showed two rare alleles of 120 bp for RM336 and 200 bp for RM8020.One O. nivara accessions i.e. AC100467 amplified highest number of rare alleles at 4 loci . In case of O. rufipogon population, rare alleles were detected in the accessions like AC100474, AC100323, AC100438, AC100439, AC100346 and AC100430 for a highest of 3 loci in different combinations. Further, some of the O. rufipogon and O. nivara accessions shared similar rare alleles. For example, O. rufipogon accessions like AC100279 and AC100323 shared the rare allele of 170 bp of RM495 with the O. nivara accessions like AC100450, 100448, AC100477, AC100471, AC100468, AC100297 and AC100338. Similar types of allele sharing between O. rufipogon and O. nivara were detected for RM16649 (160 bp and 180 bp), RM510 (130 bp), RM336 (120 bp) and RM72 (200 bp).The alleles which were amplified in less than 5% of the entire population for any given loci were considered as rare allelesO. nivara accession, AC100313, was detected with the maximum of 3 unique alleles of 250 bp (RM423), 280 bp (RM422) and 500 bp (RM3392) (Supplementary Table O. nivara accession (AC100016) showed uniqueness for RM510 with an amplicon of 400 bp and O. rufipogon accession (AC100485) was detected to amplify a unique allele for RM3866 (450 bp).Unique allele addresses the potentiality of any given loci to detect an accessions with any distinct amplicon that is different from the entire population. In our study, a total of 5 unique alleles were identified with the tested set of loci. Interestingly, one O. rufipogon and O. nivara accessions. The locus, RM72 was detected with a highest of 5  common alleles between both the populations  were amplified in accessions collected from Odisha and 129  in those collected from West Bengal  was marginally higher in the Odisha collection (0.488) than in West Bengal collection (0.479). However, the genetic diversity value was higher in the accessions collected from West Bengal (0.516) as compared to Odisha (0.507). A polymorphism percentage (%P) of 97.220 was observed both in case of Odisha and West Bengal accessions.Separate analysis was carried out to understand the SSR level variation between O. nivara accessions collected from Odisha and West Bengal. A total of 114 alleles and 127 were amplified in the O. nivara accessions collected from Odisha  and West Bengal (average of 3.568), respectively. In the O. nivara accessions of Odisha the expected homozygosity (Ho) was higher (0.524) than of West Bengal (0.498). In contrast, the genetic diversity (He) present in the West Bengal accessions was higher (0.497) than the accessions collected from Odisha (0.470). Similarly, the West Bengal collection showed higher (94.440) polymorphism percentage (%P) as compared to the counterpart i.e. Odisha (91.670).A similar trend of variation was observed for O. rufipogon accessions were more diverse (He = 0.512) than the O. nivara accessions (He = 0.484). Similar trend was observed for number of effective alleles , Shannon’s diversity index , and polymorphism percentage . The gene flow (Nm) was also higher in O. rufipogon (0.068) as compared to O. nivara (0.052).Cumulative analysis, combining the accessions of both the states, revealed that O. rufipogon population and total O. nivaraO. nivara population separately, regardless their collection states, 21% of the total variation was present among population while 79% was within population  was done to reveal the total existing variation within and between populations . When we grouped the total on Table . SecondlO. nivara accessions collected from 24 Parganas of West Bengal)] to 2.551 [between AC100030  and AC100313 ] with an average of 0.852.Pair-wise genetic distance is the measure to identify the underlying genetic relationship among the genotypes. The pair-wise genetic distance calculated for all the accessions with 36 SSR markers ranged from 0.148 [between AC100424 and AC100425  was 0.520 with a FST value of 0.264. The distinctiveness of O. rufipogon and O. nivara in separate sub-population was similar to the observations in neighbour-joining tree.The population structure analysis revealed highest value of 959.28 at K = 2, suggesting that the entire collection could be divided into two distinct sub-populations i.e. SP1 and SP2 Fig. . SP1 conO. rufipogon sub-population was further divided into two separate groups i.e. sub-population 1–1 and sub-population 1–2 with a membership percentage of 37.8 and 16.8, respectively. Further increase of K to 4 divided the O. nivara accessions (SP2) to sub-population 2–1 (membership percentage of 30.7) and sub-population 2–2 (membership percentage of 14.7). This observations were in line with the neighbour-joining tree where the separated group in population structure analysis could clearly be identified.Moreover, at K of 3 the The genetic diversity measures were calculated based on the accessions collected from a particular district. The district from which < 5 accessions were available were not included in the analysis. The average number of allele for any district ranged from 3.917 (Bardhhaman) to 1.729 (Mayurbhanj) , the characterization of the diversity in them could lead to understand their dynamic relationship.The aim of the present study was to harness the potential of morphological and molecular variation available within and between two wild rice populations i.e. O. rufipogon and O. nivara accessions. Awn, a specificfeature of wild forms, wasdetected in all the accessions. Interestingly, high variation  was detected for traits like basal leaf sheath colour, leaf angle, culm attitude, internode colour and flag leaf attitude in both wild forms but the frequencies varied between the species. For example, in O. nivara, the green basal leaf sheath was predominant (52%), while majority (47%) of the O. rufipogon accessions were having purple basal leaf sheath. The dendrogram constructed based on the morphological traits clearly demarcates O. rufipogon and O. nivara into two distinct classes.At morphological level, as expected, a high level of variation was detected in both O. rufipogon and O. nivara for different agronomic traits provide us signs related to their ecological adaptation. While majority of the O. rufipogon and O. nivara accessions are of late duration, the medium duration accessions are of interest as they can lead us to understand the mechanisms involved in tolerance to drought and photo period sensitivity and the underlying genes involved. Earlier studies reported, fine type of grain in O. rufipogon and bold type in O. nivara32. In the accessions studied, variation for grain type was observed in both wild forms with medium slender and long slender grains observed in O. rufipogon while majority of the accessions of O. nivara are either short bold or short medium type. All the accessions under study are with weak culm and one of the major drawbacks in wild rices is culm thickness and for this reason, wild rices are of spreading type.The wide variation observed in 33 where a total of 106 alleles were detected. Further, Singh and his colleagues24 could detect an average of 2.4 alleles per locus with 25 SSR markers in wild rice collection of Indo-Gangetic Plains of India whereas, we could detect an average of 4 alleles per locus. Instead of using random SSRs, targeting the hyper variable regions of rice chromosome to exploit their genetic diversity would have an edge over the random SSRs. Except one locus i.e. RM261, high allelic variationwas seen with 35 markers used and from the results it can be suggested that the present set of markers used in this study can serve as a reference set for genetic diversity studies in rice. This observation was further justified as these markers showed high resolving power with PIC values ranging from 0.428 for RM261 to 0.982 for RM16649 and 17 out of 36 loci had a PIC value of more than 0.900. The average PIC value in our study (0.852) was higher than the value reported earlier in wild rice of Indo-Gangetic plains of India (0.790)24, wild rice collection of UP and Bihar of India (0.5247)32 and Indian rice mini-core collection (0.671)34.Marker technologies can dissect variation efficiently at molecular level and the 36 highly variable SSR markers employed in the study could amplify a total of 144 alleles, a value much higher than the number of alleles detected in wild rice population of Uttar Pradesh and Bihar of IndiaO. rufipogon and O. nivara accessions of eastern India (average He = 0.577) was higher than the diversity reported in Indian rice hybrids (He = 0.432)35, Thai rice lines (He = 0.436)36 and rice germplasm of DPRKorea (He = 0.340)37. Further, this set of wild accessions were more diverse than the wild rice collections of eastern Indo-Gangetic plains24, while it was quite comparable with the wild rice collection from UP and Bihar of India33. As the Sundarbans delta of West Bengal and western part of Odisha are considered as two major biodiversity hot spotsfor different flora and fauna, the wild rice accessions of these regions and nearby areas are expected to display higher amount of genetic diversity39. Further, predominance of tribal communities who still prefer traditional land races and traditional farming practices in these two states  as compared to other states could be a reason for low level of genetic erosion in these areas.As expected, the diversity detected in O. nivara accession i.e. AC100313. Since O. nivara it the closer to O.sativa, genome sequencing of this particular O. nivara accession could unravel its genetic constitution. Different QTLs associated with drought tolerance were mapped on rice chromosome 2 and 3 and since O. nivara is adapted to upland conditions, the unique alleles identified on these chromosomes are of great interest.Interestingly, more than 20% of the total amplified alleles were identified as rare alleles. This observation confirms the earlier reports that wild rices could be preferred over cultivated varieties/landraces for mining valuable alleles for improvement of rice. The rare alleles may be associated with special traits which could be highly valuable in breeding programmes after their validation. In our study, of the five unique alleles detected, three were detected in a single O. rufipogon from O. nivara effectively with distinct allelic variation between the two populations. The allele sharing between O. rufipogon and O. nivara was detected to be high (57.64%), revealing close genetic association between them. Close relationship between these wild forms of eastern India might be due to a common ancestry and/or regular gene flow between them. This observation is similar to the reports of Banaticla-Hilarioand colleagues40 who also reported more than 50% of allele sharing between O. rufipogon and O. nivara. As expected, O. rufipogon accessions were more diverse (He = 0.512) as compared to O. nivara (He = 0.484)40. The higher level of gene flow (Nm = 0.068) detected in O. rufipogon population can be attributed to its higher out pollination rates.Of the thirty six locitested, two loci could differentiate O. rufipogon and O. nivara) detected in the study is similar to the results on the collections from Indo-Gangetic plains of India24 and Vientiane region of Laos23. However, we could not detect any regional differences between the collections of West Bengal and Odisha which could be due to their close geographical location or due to diversification at a place and subsequent spread to the other region. The Bayesian model based clustering showed clear molecular divergence of O. rufipogon and O. nivara into two separate and distinct populations at K = 240. Similar to nuclear genome variation41, organelle level variation i.e. chloroplast, mitochondria, between O. rufipogon and O. nivara was reported earlier43. Another interesting observation in our study was the absence of admixtures in the entire population which is in contrast to the report of Vaughan and his group44 who reported abundant presence of introgressed/intermediate forms in the Asian gene pool. The availability of true to type O. rufipogon and O. nivara accessions will be of immense value to the future rice improvement programs. However, further studies are needed to ascertain the allelic similarity/diversity between the wild and cultivated forms.Though there was extensive allele sharing, species differentiation  separately, manually increasing the K value of the previous analysis was preferred45. Interestingly, at K = 3, the O. rufipogon accessions collected mostly from North-western regions of Odisha, formed an independent group and at K = 4, the O. nivara accessions from 24 Parganas and its adjacent districts of West Bengal formed a separate group. From the neighbour-joining dendrogram of different districts, it can be observed that the collections from adjoining districts that a share a common boundary, were grouped together. Moreover, a close association was observed between a group of districts of West Bengal comprising 24 Parganas and its nearby districts and the North-western districts of Odisha suggesting similarity over a broad geographical area. In Odisha and West Bengal, rice is generally grown under rainfed conditions and the region has both uplands and low lands, the habitats suitable for the growth of O. nivara and O. rufipogon respectively. Zheng and Ge21 suggested that domestication, natural selection and gene flow events must have led to the fixation of different genetic constitutions of O. rufipogon and O. nivara accessions.One of the basic questions is how to treat 40 studied the differentiation of O. rufipogon and O. nivara and compared the sympatric and non-sympatric populations. In their analysis, when O. meridionalis was added to the other two groups (O. rufipogon and O. nivara) of Southeast Asia, only 10% variation was detected among the sympatric populations. But, in our case, where the populationsare non-sympatric, we could detect 21% variation among species, thus clearly demonstrating the level of differentiation was non-sympatric populations than in their sympatric counterparts. This result provides some substance to the first domestication events and suggest thepossibility of a common ancestor for these two wild ancestors of rice.Banaticla-Hilarioand colleaguesO. nivara has always been a debatable issue since 1960s and it was considered as a distinct species10 while it was treated as a habitat shift related speciation from O. rufipogon4. The observation of highly diverse accessions in both O. nivara and O. rufipogon, with little sharing of common alleles among them indicates that these two forms can be treated as diverse forms. Normally, description and differentiation between O. rufipogon and O. nivara has been focused with sympatric population of different locations in earlier studies. However, population genetic structure of these two wild relatives in a non-sympatric population has not been exploited much. From the results obtained in the present study where genetic variation between O. rufipogon and O. nivara, a non-sympatric population of eastern India was focused, it is interesting to differentiate the two groups in separate cluster at minimal model based analysis. Though there is evidence for conservation of some of the morphological traits and sharing of allelic uniqueness between the two groups, existence of them in a non-sympatric manner and their clear differentiation in alternative groups provides insights to the idea that O. rufipogon and O. nivara could be treated as different species. This is possible either through (i) accessions of both could have been evolved separately or (ii) one could have evolved from the other and fixation of genetic constituent thereafter according to agro-ecological condition leading to some of the intermediate and alternative forms due to the ecological shift to form an annual that lack in photosensitivity trait and with a curtailed duration.Origin of O. rufipogon and 108 O. nivara wild rice accessions were collected from various areas of Odisha and West Bengal  following a modified cetyltrimethylammonium bromide extraction protocol48. Thirty-six SSR loci amplifying the hyper variable regions49, three from each of 12 chromosomes of rice were selected for the genetic diversity analyses. Details of the SSR loci used in the present study are given in Supplementary Table Taq DNA polymerase. The PCR condition was an initial denaturation at 94 °C for 5 min followed by 35 cycles of denaturation at 94 °C for 30 sec, annealing  at 50–60 °C for 45 sec, extension at 72 °C for 1 min and a final extension of 7 min at 72 °C. The final PCR products were resolved in 2.5% ethidium bromide stained (1 µg/ml) agarose gel. The separated PCR products were visualized under UV light and photographed using Typhoon FLA 7000 fluorescent image analyzer . The size of each intense amplified fragment for all SSR loci was determined by comparison with the size standard (100 bp DNA ladder) and scored by incremental numbering from the lowest molecular weight band to the higher molecular weight bands and the genotype matrix was prepared.Fresh leaf samples were harvested from one-month young seedlings and genomic DNA was extracted from each wild rice accessions , effective number of alleles (Ne), expected homozygosity (Ho), Nei’s genetic diversity index/expected heterozygosity (He)51 and Shannon Index (I) were evaluated using POPGENE v 1.32  with 1,000 permutations. The dendrogram based on unbiased genetic distances among genotypes was constructed by un-weighted neighbour joining tree employing MEGA 652. Principal coordinate analyses (PCoA) of the accessions with 36 SSR loci were performed based on the simple matching coefficient using the dcenter and eigenvector matrices in the software GeneALEx653 with 1,000 random permutations. The Bayesian model-based clustering analysis of the genotypes was used for determining the optimal number of genetic clusters found among rice varieties using the STRUCTURE software54 with 1,00,000 burn-in (iteration) periods and 1,00,000 Markov Chain Monte Carlo (MCMC) replicates with ten independent runs (K) ranging from 1 to 10. The ΔK based on the change in the log probability of the data between successive K values55 was estimated using the software program Structure Harvester v6.056 and population clusters were produced by the software Structure Plot57 (http://btismysore.in/strplot). Moreover, genotypes were further grouped based on their collection by geographical location (districts) and the genetic diversity parameters of the genotypes within each district were determined using POPGENE v 1.32. The genetic variation within and among the populations was calculated by the procedure of AMOVA  using the software GeneALEx653.The amplified bands/alleles were scored as present (1) or absent (0) for each genotype and primer combination. The data were entered into a binary matrix and subsequently analyzed using different computer software packages. An allele that was observed in <5% was considered to be rare allele.The polymorphism information content (PIC) for each SSR marker locus was calculated using the formula: Supplementary Table S1-S9


Information generated in this research will be useful for optimizing molecular breeding values predictions and managing the available genetic resources.Knowledge about the genetic diversity of a population is a crucial parameter for the implementation of successful genomic selection and conservation of genetic resources. The aim of this research was to establish the scientific basis for the implementation of genomic selection in a composite Terminal sheep breeding scheme by providing consolidated linkage disequilibrium (LD) measures across SNP markers, estimating consistency of gametic phase between breed-groups, and assessing genetic diversity measures, such as effective population size  and separately per breed-groups: Primera (974), Lamb Supreme (380), Texel (227) and Dual-Purpose (125) was quite variable. The genotyped animals were outbred or had an average low level of inbreeding. Consistency of gametic phase was higher than 0.94 for all breed pairs at the average distance between SNP on the chip (~4.74 kb). Moreover, there was not a clear separation between the breed-groups based on principal component analysis, suggesting that a mixed-breed training population for calculation of molecular breeding values would be beneficial.Overall, as expected, levels of pairwise LD decreased with increasing distance between SNP pairs. The mean LD re highlights the need to genotype and phenotype a large training population in order to capture most of the haplotype diversity and increase accuracies of genomic predictions. The results reported herein are a first step toward understanding the genomic architecture of a Terminal Sire composite sheep population and for the optimal implementation of genomic selection and genome-wide association studies in this sheep population.This study reports, for the first time, estimates of linkage disequilibrium, genetic diversity and population structure parameters from a genome-wide perspective in New Zealand Terminal Sire composite sheep breeds. The levels of linkage disequilibrium indicate that genomic selection could be implemented with the high density SNP panel. The moderate to high consistency of gametic phase between breed-groups and overlapping population structure support the pooling of the animals in a mixed training population for genomic predictions. In addition, the moderate to high NThe online version of this article (doi:10.1186/s12863-017-0492-8) contains supplementary material, which is available to authorized users. Sheep farming is of significant economic importance to New Zealand and is represented throughout the country. The variable climates and landscapes have favoured the adoption of a wide diversity of sheep breeds that have adapted and performed well for different breeding objectives  under a range of production systems (e.g. intensive vs extensive). Although there are a significant number of purebred sheep farms, over time the New Zealand sheep industry has been characterized by a high and increasing proportion of composite breeds and crossbreed animals , 2. As dGenomic selection (GS)  has playe, pedigree and genomic inbreeding, and population structure. This investigation will also provide fundamental information related to the genomic architecture of this sheep population.The main objectives of this study were: 1) to collate and present the breeding history of new composite breeds widely raised in New Zealand and overseas; and 2) to establish the scientific basis for the implementation of genomic selection in a composite Terminal breeding scheme by: providing consolidated LD measures across SNP markers; estimating consistency of gametic phase between breed-groups; and, estimating other genetic diversity measures relevant for the successful predictions of molecular breeding values (mBVs), such as Nn = 208); 2010 ; 2011 , 2012 , 2013  and 2014 . DNA was extracted mostly from ear punch tissue ) – 1)*(1/4c) [c is the average genetic distance in Morgans estimated for each chromosome in the LD analysis (estimated using snp1101 package) and E[r2] is the expected r2 at distance c calculated as T = 1/2c [e was determined from current to 1,000 generations ago.To estimate N)*(1/4c) , where cT = 1/2c . Ne was G) calculated using all the genotyped animals and all SNPs that passed the quality control process. The G matrix was calculated using the method described by VanRaden [M is a matrix of counts of the alleles “A” , pi is the frequency of allele “A” of the ith SNP, and P is a matrix  with each row containing the pi values. Principal components were calculated using the prcomp function of R [To investigate the genomic composition of the population, the principal components were derived from the genomic relationship matrix (VanRaden : \documeion of R .PED) and genomic inbreeding coefficients in this population were estimated and compared. Pedigree information was available from 243,486 individuals born from 1990 to 2014 and FPED was calculated using the Meuwissen and Luo [Inbreeding coefficient based on excess of homozygosity :m is the number of SNP, pi is the minor allele frequency at loci i and ci is the genotype call .software , FEH):Diagonal of VanRaden’ G-matrix minus 1 (FVR): Genomic relationship matrix was calculated as in VanRaden [VR was calculated as the diagonal element minus 1 for each individual.VanRaden  and the Both pedigree  for all the genotyped animals was 0.346 (±0.009) and ranged from 0.249 to 0.383. He (± SD) was 0.350 (±0.006), 0.346 (±0.011), 0.340 (±0.007) and 0.332 (±0.010) for Primera, Texel, Lamb Supreme and Dual-Purpose, respectively.The 517,902 SNP markers that passed quality control spanned about 2.45 Gb of the genome, with an average distance of 4.74 kb between adjacent SNPs, which varied between chromosomes (ranging from 4.50 kb in OAR11 to 4.84 kb in OAR10). Figure The sheep population under investigation is predominantly focused on breeding for faster growth, higher carcass yield, survival and improved meat quality. The majority of the genotyped animals were progeny of Terminal Sire composites and Texel mated to a variety of maternal/dual-purpose breeds. The main breeds involved were Lamb Supreme, Primera, Texel, Romney, Coopworth, Landmark and Highlander. Due to the lack of literature for some of the composite breeds, we collate a brief history of them, presented in Additional file PED) and two genomic  inbreeding coefficients by year of birth were calculated  was 0.029. The genomic inbreeding coefficients based on excess of homozygosity (FEH) or G matrix (FVR) were −0.008 ± 0.031 (range: −0.079 – 0.301) and −0.009 ± 0.027 (range: −0.093 – 0.328), respectively. Correlation between FPED and genomic inbreeding was 0.27 (FEH) and 0.36 (FVR). The correlation between FEH and FVR was 0.51. There were individuals with high genomic inbreeding, but zero pedigree inbreeding (incomplete pedigree information). This highlights another advantage of genomic information for breeding programs.Pedigree (Fed Table . Pedigre2) between adjacent markers obtained for each chromosome are shown in Table 2 between adjacent SNPs was 0.263 ± 0.10 and chromosomal mean ranged from 0.244 (OAR26) to 0.282 (OAR13). The LD levels between adjacent markers were also evaluated by breed-group and are presented in Additional file 2-values were also averaged over all autosomes and plotted as a function of genomic distance between markers  was 0.24. Overall, levels of pairwise LD decreased with increasing distance between SNP. For distances between SNPs greater than 8 kb, the LD levels were less than 0.20 and decreased constantly, with exception of two points (up to 14 and 17 kb) where there was a small increase in LD. For SNP located more than 40 kb apart, the LD levels were less than 0.10.The results of descriptive analysis of SNP markers and LD r between e was evaluated for all animals together  and separately by breed-group  from the most recent generation to 1,000 generations ago  and separately per breed-group: Primera (974), Lamb Supreme (380), Texel (227) and Dual-Purpose (125) was quite variable. For all breed-groups, Ne decreased over time, except for Primera and Lamb Supreme breed-groups, which increased over the last five generations.The NAs presented in Fig. G matrix  on the rix Fig. . The ploThe short distance between adjacent SNPs is an advantage of the HD compared to lower density SNP chips, as in theory the markers would be closer to the QTL for the traits of interest and potentially in higher LD, allowing the markers to capture the QTL/causal mutations effects better and consequently increase the accuracies of mBVs predictions across breeds. The moderate MAF levels demonstrate the great genetic diversity of this population. However, these values can even be underestimated, because in the development of the HD SNP chip, a proportion of SNP with low MAF were included . From the was high, revealing the great genetic diversity of this population. Similar estimates were reported by Beynon et al. [Heterozygosity measures the level of genetic variation within a population with higher values indicating greater genetic variability. The mean Hn et al.  studyingn et al.  reportedn et al.  in a varn = 14,845) were outbred or had a low level of inbreeding on average (depending on the measure of inbreeding). However, there was a big range, indicating that there are inbred animals and this should be taken into account when planning matings in order to avoid high levels of inbreeding in the progeny. This can be implemented using a mating planning software to optimize the genetic contribution of each individual and control inbreeding at a target level.Another aspect of interest while studying a commercial population under selection pressure is to study the level of inbreeding. The inbreeding coefficient of an individual is the probability that, at a given locus, an individual has received the same ancestral-allele from both parents . It is kAs expected, some outbreeding (low inbreeding coefficients) was observed when estimating genomic inbreeding coefficients. The negative values correspond to animals with lower homozygosity than expected from the population MAFs. The low levels of inbreeding can be attributed to the high gene flow between different flocks by using outside sires (mainly Primera and Lamb Supreme flocks), recent composite breed formation, crossbreeding and reduced overlapping of generations. The majority of animals in this population are progeny from Primera and Lamb Supreme rams . Both composites were recently developed based on a screening of a large number of animals from various flocks regardless of breed, which means that several breeds  contributed to the formation of these composites. Even though there was not a clear trend of increased inbreeding levels over years, it is important to continue monitoring this parameter. Genomic data could actually be used as an important tool to establish the genetic difference among rams in order to plan mating. As shown in Fig. 2 level of 0.2. At the average marker spacing in the HD SNP chip (~5 kb) the average pairwise LD (r2) was 0.24. The results observed in this composite population indicate that genomic selection can be successfully implemented.The levels of LD influences the power of QTL detection and accuracy of genomic predictions . LD leveThere is little knowledge about the degree of genome-wide LD in the sheep breeds included in this investigation. In a LD study including a collection of 74 sheep breeds and 49,034 SNP, Kijas et al.  observed2 tend to underestimate LD [e. However, the authors evaluated the effect of low-frequency loci (MAF < 0.1) and observed that the removal of these SNPs caused a small inflation of r2 estimates. There are studies in dairy cattle showing that ascertainment bias in the estimation of LD using half-sib data might occur [n = 877), which represented well the populations. To investigate potential overestimation of LD estimates, we also performed the analysis using a balanced dataset (removing extra progeny data per sire), in which the average (range) number of progeny per sire was 12 (1 – 17) and the total number of genotypes was reduced from 14,845 to 10,300 animals. The estimates from both analysis were statistically equal (P > 0.05), and therefore, only the results using the full dataset were presented.The MAF distribution of the SNP influences estimates of LD . Betweenimate LD . As mentimate LD , the SNPht occur . One altht occur . HoweverThe low levels of LD observed in the population investigated could be due to the fact that sheep domestication is likely to have involved a genetically broad sampling of their wild ancestors, and subsequent bottlenecks associated with breed formation were less severe than in other species as noted by Kijas et al. . The lowEven though there is a variation in LD levels per chromosome, the differences were small. The reason for that may be because most traits where an intense selection pressure was applied were polygenic traits and the breeding programs are still recent . DiffereThe low levels of LD observed in this study have practical applications for the implementation of genomic selection. It highlights the need to use a HD SNP chip for genomic predictions in a multi-breed population as the level of LD is relatively small even at short distances. A low-density panel could not capture enough LD to successfully predict mBVs in a multi-breed population as the one under investigation. Our results support the need for a HD SNP chip (i.e. 600 K) for genomic selection in this population. An alternative to reduce genotyping costs is to genotype lambs with low-density and impute to HD SNP chip, which has already been proven to be feasible in New Zealand multi-breed sheep populations .The improvement in accuracy of mBVs for a specific breed based on using data from other breeds (or breed-groups/crossbreds) depends on the consistency of gametic phase between the SNP and QTL across breeds and on the similarity of QTL effects between breeds. The more distant the relationship between individuals, the shorter the genomic distance over which the phase will be consistent. As presented in Fig. Principal Component Analysis were used to visualize and explore the genetic relationships among individuals and breed-groups. Basically, PCA absorbs the information of allele frequencies into a small number of synthetic variables, facilitating the interpretation of population structure. PCA analysis showed that most breed-groups formed overlapping clusters and they are not clearly separated populations. The genetic closeness between these animals is probably due to crossbreeding and exchange of genetic material  and separately per breed-groups: Primera (974), Lamb Supreme (380), Texel (227) and Dual-Purpose (125) was quite variable. The Ne observed for this population is quite high indicating the genetic variability of this population. Kijas et al. [e estimate for New Zealand Texel of 282. For the other composite breeds, we are reporting Ne estimates for the first time. However, Table e based on literature estimates) involved in the formation of the composites Primera, Lamb Supreme and Dual-Purpose.The Nime Fig. , which is et al.  reportede for several sheep breeds from 100 (Wiltshire breed) to 1,317 (Qezel breed). The authors revealed that 25 breeds have Ne exceeding 500 and only two showed evidence of a narrow genetic base (Ne < 150), which is consistent with our findings. In general, sheep breeds have a higher level of genetic diversity compared to other species such as dairy cattle (e.g. Ne for Holstein = 99), suggesting a highly diverse population prior to domestication and that genetic bottlenecks were not as intensive as in other species [Kijas et al.  reported species .e. Therefore, the Ne estimates observed in this study also has implications for genomic selection, as genetic diversity is a key indicator of the required size of training population that is needed to achieve accurate genomic predictions. To ensure an animal population is long-term viable, a threshold of Ne = 100 has been given [The high genetic diversity and effective population size observed in this population implies that selection response for growth, carcass and meat quality traits may be expected to continue in the long term and higher genetic responses may be achieved compared to more homogeneous populations. Goddard and Hayes  showed ten given . Our resThis study reports, for the first time, estimates of linkage disequilibrium, genetic diversity, and population structure parameters from a genome-wide perspective in New Zealand Terminal Sire composite sheep breeds. Even though high genetic diversity was observed in this population, the observed levels of LD indicate that genomic selection could still be successfully implemented. The moderate to high consistency of gametic phase between breed-groups support the pooling of the animals in a mixed training population for genomic predictions. Effective population size seems to have been decreasing over time, however it is still high, highlighting the need for genotypes and phenotypes from a large number of animals in order to capture the haplotype diversity and increase accuracies of genomic predictions. Even though the average inbreeding levels were low, it is important to consider this information when planning matings, as there are some highly inbred animals. The results reported herein are a first step toward understanding the genomic architecture of a Terminal Sire composite sheep population and for the optimal implementation of genomic selection and genome-wide association studies in these sheep populations.