Cercospora beticola, is a major disease of Beta vulgaris worldwide. No sexual stage is known for C. beticola but in its asexual form it overwinters on infected plant debris as pseudostromata, and travels short distances by rain splash-dispersed conidiospores. Cercospora beticola infects a broad range of host species and may be seedborne. The relative contribution of these inoculum sources to CLS epidemics on table beet is not well understood. Pathogen isolates collected from table beet, Swiss chard and common lambsquarters in mixed-cropping farms and monoculture fields in New York and Hawaii, USA, were genotyped (n = 600) using 12 microsatellite markers. All isolates from CLS symptoms on lambsquarters were identified as C. chenopodii. Sympatric populations of C. beticola derived from Swiss chard and table beet were not genetically differentiated. Results suggested that local (within field) inoculum sources may be responsible for the initiation of CLS epidemics in mixed-cropping farms, whereas external sources of inoculum may be contributing to CLS epidemics in the monoculture fields in New York. New multiplex PCR assays were developed for mating-type determination for C. beticola. Implications of these findings for disease management are discussed.Cercospora leaf spot (CLS), caused by  These studies apply knowledge of the genetic composition and patterns of genetic diversity in pathogen populations to test hypotheses regarding their biology and plant disease epidemiology5. Population genetics has been used to make inferences about the origin9, sources of inoculum11, routes of introduction13, migration pathways17 and reproductive biology19 of plant pathogens. Knowledge of the reproductive modes of plant pathogens is critical to disease management, due to its significant impacts on the evolutionary trajectory and epidemiology of pathogen populations. Recombination as a result of sexual reproduction is an influential evolutionary force that generates potentially adaptive genotypic diversity, purges deleterious mutations, and produces sexual propagules with enhanced survival and dispersal20. In the absence of a known sexual stage for a plant pathogen, population genetic studies can provide indirect evidence to confirm the asexuality of pathogen populations23, or reveal historical or potentially cryptic sex in presumably asexual fungi24. In pathogen populations with frequent sexual reproduction, high genotypic diversity, random association of selectively neutral genetic loci and equal distribution of the opposite mating-types  are expected. Alternatively, a strictly asexual population is characterized by low genotypic diversity, lack of recombinant genotypes, widespread occurrence of a few clonal lineages, non-random association of unlinked loci, and skewed distribution of alternate mating-types in the population20.Population genetic studies of plant pathogens using selectively neutral genetic markers have made significant contributions to plant disease management in the past few decadesCercospora beticola, the cause of Cercospora leaf spot (CLS) of Beta vulgaris L. 25. Despite the lack of a known sexual form, C. beticola is considered to be a heterothallic, ascomycete fungus due to the discovery of two alternate mating-type genes (MAT1-1-1 and MAT1-2-1) at equal frequency in parts of Europe27. Population genetic studies have also suggested the potential presence of a sexual cycle for C. beticola in sugar beet production areas in North Dakota28. In contrast, no evidence for sexual reproduction has been found in other parts of the USA29 or the Middle East30.The presence of a sexual form remains unknown for the globally and economically important plant pathogen, Beta vulgaris ssp. vulgaris) industries25. Symptoms include light brown to grey leaf spots, usually with a red to purple margin in red beet varieties, which expand into necrotic lesions that result in premature defoliation31. Conidiospores are mainly disseminated through water splash over short distances, resulting in multiple infection cycles within a season and polycyclic epidemics25.In New York, CLS epidemics impose significant losses to both the fresh market and processing table beet  are often grown in close proximity along with other vegetables, on an annual basis. It is, therefore, plausible that infested plant debris from previous years may be a major source of inoculum contributing to the annual CLS epidemics on susceptible hosts in these farms. In contrast, large-scale monoculture table beet fields in New York involve rotations with non-host crops in the same field for a recommended minimum duration of three years, which aims to reduce the persistence of inoculum in soil and on infested plant debris. Nevertheless, CLS epidemics are prevalent and result in substantial losses in these fields, even with rotations lasting up to five years37, and even in the fields that have never grown table beet . The major sources of initial inoculum for CLS epidemics in monoculture table beet fields in New York are therefore largely unknown.Infested plant debris is considered to be a major source of inoculum initiating CLS epidemicsC. beticola populations in New York are capable of sexual reproduction, overwintering sexual structures may serve as primary inoculum for newly established table beet fields within several kilometres. Cercospora beticola is also known to infect a broad range of plants from various families38. Therefore, infected weeds  are believed to serve as green bridges for C. beticola inocula during crop rotations. For example, common lambsquarters  is a susceptible weed species that is frequently found in New York table beet fields and often has CLS symptoms on the leaves. Other potential sources of inoculum include asexual spores carried by wind39 or insects39 from neighbouring table beet or Swiss chard productions. Seedborne inoculum32 has also been suggested to contribute to CLS epidemics in some European countries41 but its contribution to CLS epidemics in New York has not been investigated.Understanding the relative contribution of inoculum sources to CLS epidemics is essential for the design of effective management strategies. If 43. Alternatively, if a local source of inoculum, e.g., locally-developed sexual or asexual structures or infected weeds, are the major source of primary inoculum, genetic structuring of C. beticola populations would be present.If the primary inoculum is introduced from a source outside a field of interest, such as infested seeds or windborne ascospores, we would expect to see a panmictic population with low population structuring due to frequent migration among fields. In fungal populations with long-distance dispersal of ascospores, a pattern of isolation by distance (IBD) is usually detected, resulting from the gradual spread of the disease from an original source of inoculum, hence, resulting in an increase in genetic differentiation among populations as distance increasesC. beticola on weeds or other crops could have profound impacts on CLS epidemiology as it would promote genetic diversity by reducing the impact of genetic drift through increasing the effective pathogen population size. Moreover, this would reduce the amount of disruptive selection pressure that the pathogen would have undergone by switching from a pathogenic to saprophytic mode.Potential survival of C. beticola inoculum surviving on local weeds or other susceptible crops (e.g. Swiss chard) is a major contributor to CLS epidemics on table beet, we would expect sympatric populations isolated from these alternate hosts to be genetically undifferentiated from C. beticola populations on table beet. To test this hypothesis, we examined Cercospora spp. populations from symptomatic lambsquarters growing sympatrically with table beet. Also, we genotyped C. beticola populations from Swiss chard and table beet plants growing in sympatry in mixed-cropping farms and gardens in New York and Hawaii, to investigate whether diseased Swiss chard may be a source of primary inoculum initiating CLS epidemics on table beet.If C. beticola populations in New York; ii) test the hypothesis that inoculum from lambsquarters and Swiss chard may contribute to CLS epidemics on table beet; and iii) determine the distribution of genetic diversity in C. beticola populations within and among mixed-cropping and monoculture table beet production to understand the relative contributions of local or external sources of inoculum to CLS epidemics. Herein, the term ‘population’ is used to refer to a group of isolates defined under certain geographical or host criteria .The objectives of this study were to i) investigate the potential for sexual reproduction in C. beticola isolates were obtained from 426 table beet and 14 Swiss chard plants collected from two mixed-cropping farms (Farms 1 and 2) and three monoculture table beet fields , in 2015  were slow growing (~10 mm diameter after 14 days). Further multi locus sequence typing  showed that these isolates belonged to C. chenopodii (data not shown), hence, were excluded from subsequent analyses. Cercospora beticola-specific primers  resulted in a ~170 bp product in all C. beticola isolates from table beet and Swiss chard as well as C. chenopodii isolates from lambsquarters. This demonstrated that the primers CercoCal-beta and CercoCal-R were not specific to C. beticola. On the other hand, none of the microsatellite loci amplified a band in C. chenopodii (data not shown). Re-genotyping 25% of isolates detected no genotyping error except for one primer pair (CbSSR3), which had an error rate of 0.01.In New York, 649 Two to four distinct MLGs were obtained from six of the 12 intensively sampled plants. Isolations from multiple lesions on single leaves detected two to four distinct MLGs in 12 of 21 leaves (57%), which, in 50% of the leaves, also belonged to different mating-types. Up to three distinct MLGs, showing variation at two to 10 loci, were associated with single CLS lesions on table beet and Swiss chard plants. In one lesion, distinct MLGs belonged to different mating-types.C. beticola populations collected from two varieties of table beet (‘Detroit’ and ‘Touchstone Gold’) were not genetically differentiated  or genotypic composition (P = 0.19). From 20 recurrent MLGs at Farm 2, 13 were shared between these two table beet varieties. Likewise, sympatric populations from table beet and Swiss chard did not show significant population differentiation  to 24 (CbSSR3) with an average of 6.75 alleles per locus. Nei’s unbiased gene diversity (He) per locus ranged from 0.034 (CbSSR1) to 0.87 (CbSSR3). In New York, locus CbSSR24 was monomorphic in Farm 1, while CbSSR1 was monomorphic in the other four locations. All loci were polymorphic for C. beticola populations from Hawaii  at all locations except for Farm 1, which had a moderate genotypic diversity (λ = 0.57). Each field contained a different dominant MLG , confirming the clonal origin of the majority of the overrepresented MLGs.P = 0.001), 65% among individuals within fields (P = 0.001), and 10% between the two states (P = 0.15). Similar results were obtained through AMOVA analysis using RST, where 20 and 80% of the total diversity existed among and within fields, respectively (P < 0.001), and none between New York and Hawaii (P = 1.00).AMOVA analysis of the entire population found 25% of the total observed genetic diversity was partitioned among the fields within states (PT showed significant differentiation among the five fields within New York (P < 0.013) except for Fields 3 and 4, which were not significantly differentiated (P = 0.715)  while the two mixed-cropping farms (Farm 1 and 2) showed greater differentiation from each other  and from the monoculture fields   but not for the clone-corrected data set   using fields as pre-defined populations also demonstrated that populations from the three monoculture table beet fields overlapped while those from mixed-cropping farms clustered separately Fig. . The Mana priori population assignment through Bayesian clustering analysis of both the clone-corrected and non-clone-corrected datasets resulted in detection of two distinct clusters in the population . The novel three-primer and four-primer mating-type assays developed based on non-exonic regions of the mating-type genes .The proportion of compatible loci (PrCP) was less than one in four sampling locations in New York and in two locations in Hawaii Table . The obsC. beticola populations in contrasting table beet production systems in New York. Sympatric Cercospora spp. populations collected from table beet, Swiss chard and lambsquarters in New York and Hawaii were also studied to establish whether inoculum from alternative hosts contribute to CLS epidemics on table beet.To enhance our understanding of the epidemiology and sources of inoculum contributing to CLS epidemics on table beet, 12 microsatellite markers were used to characterise the genetic structure of Cercospora beticola is known to infect a wide range of plants from various families48. Thus, infected weeds, especially from the family Amaranthaceae, are believed to serve as C. beticola inoculum reservoirs. One of these species is lambsquarters, which was found to be susceptible to C. beticola in glasshouse pathogenicity trials (data not shown). However, all Cercospora spp. collected from symptomatic lambsquarters plants growing in sympatry with table beet were identified as C. chenopodii. This is congruent with reports that studies in the Netherlands in 2003 also failed to isolate C. beticola from weeds49; and emphasises the need for more comprehensive surveys on a range of weeds from Amaranthaceae and other families to better understand the prevalence and incidence of C. beticola on hosts other than Beta vulgaris.C. beticola populations from different table beet varieties were not genetically differentiated based on microsatellite allele frequencies, nor did they differ in genotypic diversity or composition. Likewise, sympatric populations from Swiss chard and table beet in New York and Hawaii were not genetically differentiated. However, populations from Swiss chard and table beet from a mixed-cropping farm (Farm 2) in New York were significantly different in genotypic composition. Of the 20 recurrent MLGs in this farm, 13 were shared between the two table beet varieties while only five were shared between Swiss chard and table beet. This may be attributed to differences in virulence and/or aggressiveness of these MLGs on different hosts. For example, Melampsora larici-populina populations from cultivated and wild poplars showed little population differentiation based on neutral genetic markers while displaying different virulence profiles42. The lack of genetic differentiation of C. beticola populations from Swiss chard and table beet based on microsatellite allele frequencies is suggestive of lack of reproductive isolation between these populations. Thus, infected Swiss chard from nearby organic productions or home gardens may potentially serve as a source of inoculum for CLS epidemics in table beet fields.Sympatric 51, including C. beticola on sugar beet in Europe41. Moretti et al. used Random Amplified Polymorphic DNA (RAPD) and Direct Amplification of Minisatellite region DNA (DAMD) to characterize 10 C. beticola isolates obtained from a single lesion on sugar beet, all of which showed distinct banding patterns41. As the arrangement of the mating-type genes in C. beticola indicates heterothallism27, the co-occurrence of multiple MLGs in the same lesion may have profound impacts on population structure of this pathogen by enabling MLGs with alternate mating-types to sexually reproduce. Detection of two alternate mating-types in a single lesion in this study provides further evidence of the potential of C. beticola to undergo sexual reproduction.In 50% of intensively sampled plants and leaves, multiple unique MLGs were detected on the same plant or leaf. Single lesions were also occasionally found to be associated with up to three unique MLGs. Co-infection of a single lesion with multiple MLGs has been reported in other pathosystemsC. beticola population. Although the potential of rare parasexual events, somatic recombination or mitotic crossing over53 cannot be disregarded, the presence of mating-types in equal ratios is some table beet fields suggests that some C. beticola populations may be sexually reproducing.High genotypic diversity, detection of admixed genotypes by Bayesian clustering and DAPC analyses Figs  and S3, 20, tests of random mating were conducted on the two major clusters detected using Bayesian analyses , unequal frequency of mating-types in some fields, and linkage disequilibrium of microsatellite loci in all populations, even after clone correction, rejected the null hypothesis of random mating. Since other phenomena such as population structure/admixture can also result in linkage disequilibrium of locies Table , which a54. Hence, the fact that 47, 65, 20 and 74% of pairs of loci were incompatible in Fields 3, 4, 5, and Farm 2 may be interpreted as presence of recombination in these populations. Although it is plausible that high levels of homoplasy, which is a known drawback of microsatellite loci57, contributed to the low rate of phylogenetic compatibility, the possibility of recombination cannot be disregarded.To test the hypothesis of strict clonality, rather than random mating, we tested the presence of phylogenetic incompatibility between pairs of loci. Under the assumption that recurrent, parallel and reverse mutations are highly improbable, phylogenetic incompatibility of loci may only arise by recombination58. Survival of C. beticola inoculum on plant debris within soil has been reported in many studies to be at least 10 months35 to a maximum of three years36, depending on the climatic conditions. Planting sugar beet seed in infested soil was reported to result in infection of cotyledons and young seedlings33. Infection through the roots of mature plants has also been reported in sugar beet59 but could not be reproduced in subsequent studies35. Wilting of beet leaves under hot conditions in the field occurs frequently, bringing the leaves in contact with the soil33. The moisture trapped under the lower surface of the leaves may contribute to leaf infection from C. beticola inoculum in the surface of the soil33. However, the monoculture table beet fields in New York follow rotation guidelines with three to five37 years of non-host crops between table beet. This infers that after five years, infected plant debris must be present with sporulation close to the soil surface. Further studies are required to quantify the long-term survival of C. beticola on plant residues, or in soil33 in New York.Multiple studies have suggested that introduction of inoculum to initiate CLS epidemics occurs through rain splash dispersal of conidiospores from infected plant residues from previous yearsPT ≤ 0.042), and lack of differentiation between Fields 3 and 4 was surprising. Fields 3 and 4 are more than six kilometres apart, and separated from Field 5 by at least 50 km. Considering the long rotation periods between table beet crops in processing, monoculture table beet fields, significant founder events leading to high genetic drift and stochastic differentiation of the C. beticola population may be expected60, unless external sources of inoculum dominate and homogenise these populations. In addition to the lack of genetic differentiation, Fields 3 and 4 also had eight MLGs in common, and each shared four MLGs with Field 5. Although this could be caused by the low power of the microsatellite loci to resolve these clones into distinct MLGs, distribution of these clones over processing fields can be indicative of genotype flow. CLS epidemics in fields where sugar beet had not been planted for 20 years in the Netherlands61 and table beet fields that had never been planted to table beet , also suggest a means of long-distance dispersal for this pathogen.Low genetic differentiation among the monoculture table beet fields  by wind62. Moreover, long-distance dispersal of ascospores, if present, cannot explain the dissemination of clones as C. beticola is heterothallic and potential sexual reproduction would not likely result in the production of clones. Plausible sources of long distance dispersal of the MLGs among the monoculture table beet fields are insects32, human movement of agricultural machinery or seed, the role of which in C. beticola dispersal requires further investigation. In this study, the seed for the three monoculture fields all originated from Mount Vernon, WA, USA.A pattern of IBD is usually interpreted as the natural process of dispersal of windborne inoculum, resulting from the gradual spread of the disease from an original source of inoculumC. beticola populations from mixed-cropping farms in New York may be attributed to the dominant role of local sources of inoculum in initiating CLS epidemics in these fields. Populations from the two mixed-cropping farms in New York were highly differentiated from each other and also from the monoculture fields. Each of the two mixed-cropping farms contained a different dominant genotype  and three monoculture table beet fields   table beet fields with at least 3 years of a non-host crop prior to table beet cultivation.Sampling of n = 100 in each field). In addition, 28 symptomatic lambsquarters plants were collected from Field 5 to obtain a sympatric population of C. beticola. The mixed-cropping farms, each included only a few rows of table beet, which were intensively sampled at 5–10-m intervals along the rows. Farm 1 included three rows of table beet variety Detroit, from which 36 symptomatic plants were sampled. Farm 2 included three rows of table beet variety Touchstone Gold and one row of table beet variety Detroit as well as two rows of Swiss chard varieties Oriole and Ruby Red planted intermixed with each other. The incidence of CLS on the Swiss chard was substantially lower than on table beet at this location. Therefore, symptomatic leaves from 90 table beet and 14 Swiss chard plants were collected from Farm 2.Hierarchical sampling was conducted involving the arbitrary selection of sampling points at defined intervals along transects/rows, and sampling of multiple symptomatic plants within each location. The distance between the sampling points and number of plants sampled per location varied between the processing and fresh market production systems due to differences in the size of the fields. In the three monoculture fields, five sampling locations were selected at 20-m intervals along each of the two transects, which were separated by 50 m. Ten plants where arbitrarily sampled within a 5-m radius of each sampling location  using 12 microsatellite markers  as described previously66. Alleles were scored using the Geneious Microsatellite Plugin v. 1.467. To reduce genotyping error, genotyping was replicated for 25% of the samples, including all isolates showing null or rare alleles, and error rates68 (el) were quantified for each locus. Genotyping was conducted a third time for samples that showed allelic variation in locus CbSSR3 between the two replications.Genomic DNA was extracted from all fungal isolates as described previouslyC. beticola strains from single leaves or lesions may result in re-isolating the same individual multiple times, which may skew the result of population genetic analyses. Therefore, the multi-locus genotypes (MLGs) for all isolates were determined in the R package poppr v.2.2.070 to assess the occurrence of MLGs in single lesions and leaves. For subsequent genetic diversity and structure analyses, the data set was clone-corrected to the leaf level, i.e., identical MLGs isolated from a single lesion/leaf were identified and only one representative isolate from each leaf was retained for subsequent analyses. This resulted in 359 and 145 isolates from New York and Hawaii, respectively. In order to reduce the effect of genotyping error and missing data on genetic diversity, the data set was filtered in poppr using the Bruvo’s71 distance “farthest neighbour” algorithm, and filtering threshold estimated by the function cutoff_predict (0.02083333). All subsequent analyses were conducted on the filtered dataset.Intensive isolation of multiple 72 measure of allelic diversity and number of private alleles were estimated in GenAlEx74. Allelic richness (Ra) and private allelic richness (Pa) were estimated with rarefaction in ADZE v. 1.075. The number of multi-locus genotypes (MLGs), expected number of MLGs after rarefaction (eMLG), and evenness (E5) were estimated in poppr. Simpson’s76 complement index of genotypic diversity  was calculated in poppr, and was corrected for sample size by multiplying by N/(N-1). Statistical significance of the differences in genotypic diversities among populations was tested in GenoDive77 using 999 bootstraps. In order to investigate the sufficiency of the microsatellite loci to capture the genotypic diversity of the C. beticola populations, mean genotypic diversity was plotted against the number of loci using MultiLocus v.1.378. Recurrent genotypes (MLGs that occurred more than once) and their frequencies across populations were obtained using poppr. The probability that recurring MLGs could have arisen independently through sexual reproduction was investigated by estimating Psex in GenClone 2.079, and statistical significance was computed by 999 randomisations.Nei’sa priori populations  using both clone-corrected and non-clone-corrected datasets. Pairwise ΦPT80, an analog of FST assuming a stepwise mutation model, was calculated with 999 randomisations in GenAlEx74. Jost’s81 measure of population differentiation (D) and Hedrick’s82mmod83 in R, with 95% confidence intervals estimated after 1,000 bootstrap simulations. IBD for the five fields within New York was investigated through regression analysis of standardised genetic distance (ΦPT/(1 − ΦPT)) plotted against the logarithm of geographical distance. The Mantel84 test was conducted using ade4 implemented in mmod for both clone-corrected and non-clone-corrected data sets. Discriminant Analysis of Principal Components (DAPC)85 among pre-defined populations from fields were conducted in the R package adegenet v.2.0.186. The optimal number of PCs to retain was estimated through cross-validation using the function xvalDapc. A dendrogram of genetic distance among populations was produced through the unweighted pair group method with arithmetic mean (UPGMA) method in poppr based on Nei’s87 distance. Analysis of molecular variance (AMOVA)88 was conducted using ade4 implemented in poppr (999 permutations), and also by computing a distance matrix (RST) in Arlequin80 . For AMOVA analysis in poppr, the option of filtering with a threshold of 0.05 was used to correct for genotypes that had equivalent distance due to missing data.All analyses of population differentiation were conducted on a priori assumption of populations was tested using three methods for both clone-corrected and non-clone-corrected datasets: the model-based Bayesian clustering method implemented in STRUCTURE v.2.3.489, DAPC conducted in the R package adegenet, which does not require assumptions of linkage equilibrium of loci, and a distance-based dendrogram through UPGMA using Bruvo’s71 distance in Poppr. Using STRUCTURE, assignment of MLGs to clusters was inferred for one to 15 clusters. Each model was simulated 10 times with a burn-in period of 100,000 Monte Carlo Markov Chains, and a run length of 1,000,000 iterations. The optimal K was chosen by computing ΔK90 using STRUCTURE HARVESTER v.0.6.9491. The 15 replicated runs for the optimal K were combined and a single graphical output was generated using CLUMPAK92. DAPC analysis was conducted using adegenet, with the optimal number of clusters determined using the function find.clusters. DAPC was then used to assign individuals into clusters, retaining the number of principal components encompassing 89% of the cumulative variance. A distance-based dendrogram using Bruvo’s distance was plotted using poppr with 1,000 bootstrap replicates, and branch support values greater than 50% were retained.The existence of an underlying structure without 26 and 356/35828 primers. Isolates carrying the MAT1-1 and MAT1-2 alleles were expected to produce amplicons of 805 and 442 bp, respectively. However, in 30% of the isolates, multiple bands were amplified, which confused mating-type determination (data not shown). This could be due to the fact that C. beticola isolates, in addition to a complete MAT1 idiomorph, contain exonic fragments of both MAT1-1 and MAT1-2 alleles in their genome93. Thus, to improve specificity of the assay, new PCR primers were designed to the non-exonic regions of the MAT1 alleles  and a primer specific to MAT1-2-1 (CbMAT2-F2), which was paired with a primer developed by Bolton et al.28 to amplify a product specific to MAT1-2 isolates.For mating-type determination, all isolates were initially screened in a multiplex PCR using CercosporaMat1F/Rles Fig. ; Table 5les Fig. , and two2, 0.1 mM dNTPs (New England Biolabs), 0.1 μM of each primer, 0.8 U Taq polymerase (New England Biolabs), and 10 ng of template DNA. PCR conditions included an initial denaturation for 5 min at 95 °C; followed by 34 cycles of denaturation at 95 °C for 30 s; annealing at 62 °C and 58 °C for the three-primer and four-primer assays, respectively, for 30 s; and extension at 68 °C for 30 s; and a final extension at 68 °C for 5 min.The PCR mix for both the three-primer and four-primer assays  contained 1× PCR buffer  including 1.5 mM MgCl2) goodness-of-fit test, for both clone-corrected and non-clone-corrected datasets. To investigate random association of microsatellite loci, the index of association (Ia)94 and standardized index of association , which limits the power of LD analysis95.For each field, significant departures of mating-type frequencies from a 1:1 ratio were tested with a chi-square 96 was estimated using MultiLocus. Methods based on the principle of compatibility among sites/loci are among the most powerful to detect recombination97. Two loci are considered compatible (PrCP = 1), if it is possible to account for all the observed genotypes by mutations only; without having to infer homoplasy  or recombination. Under the assumption that recurrent, parallel and reverse mutations are very rare, phylogenetic incompatibility (PrCP < 1) provides evidence of genetic exchange, and may be interpreted as presence of recombination54. PrCP was estimated for C. beticola populations in each field as well as the clusters detected by Bayesian analysis. The statistical significance for the PrCP test was inferred by comparing the number of compatible pairs of loci in the observed data set to those from a randomly recombined data set in MultiLocus. The null hypothesis of random mating was rejected if more compatible loci than expected in a randomised population were observed (P < 0.05)99.To investigate evidence for recombination in Supplementary information


Novel genomics-based approaches such as genome-wide association studies (GWAS) and genomic selection (GS) are expected to be useful in fruit tree breeding, which requires much time from the cross to the release of a cultivar because of the long generation time. In this study, a citrus parental population (111 varieties) and a breeding population  were genotyped for 1,841 single nucleotide polymorphisms (SNPs) and phenotyped for 17 fruit quality traits. GWAS power and prediction accuracy were increased by combining the parental and breeding populations. A multi-kernel model considering both additive and dominance effects improved prediction accuracy for acidity and juiciness, implying that the effects of both types are important for these traits. Genomic best linear unbiased prediction (GBLUP) with linear ridge kernel regression (RR) was more robust and accurate than GBLUP with non-linear Gaussian kernel regression (GAUSS) in the tails of the phenotypic distribution. The results of this study suggest that both GWAS and GS are effective for genetic improvement of citrus fruit traits. Furthermore, the data collected from breeding populations are beneficial for increasing the detection power of GWAS and the prediction accuracy of GS. Total citrus fruit production in 2012 was estimated at 131.3 million tons , of which 52.6% were sweet oranges (Citrus sinensis (L.) Osbeck), 21.1% mandarins (C. reticulata Blanco), 11.2% lemons (C. limon (L.) Burm. f.) and limes (C. aurantifolia (Christm.) Swingle), and 6.2% grapefruit (C. paradisi Macfad.) and pummelos (C. maxima Merr.)3. Widely distributed commercial cultivars have been selected from various indigenous cultivars as somatic mutants (bud sports or nucellar seedlings)4. Although mutant selection is a simple approach to improve a trait, cross-breeding is beneficial for developing novel cultivars with unprecedented trait combinations. Swingle and Webber first initiated a systematic cross-breeding program of citrus at the U.S. Department of Agriculture in Florida, USA in 1893, and they selected some important cultivars by cross-breeding5. The available combinations of crosses and the number of individuals in the population limit the efficiency of selection. Large plant size limits the number of fruit trees in orchards, and their long juvenile period lengthens the time required for breeding, thus increasing the total cost of cross-breeding. During implementation of the citrus breeding program at the National Agriculture and Food Research Organization Institute of Fruit Tree and Tea Science (NIFTS) in Japan, various techniques to promote flowering and fruiting of the seedlings to shorten the breeding cycles and grafting and training methods were established7; however, the average breeding period from the cross to the release of a cultivar is still 25 years. The number of seedlings to be grafted at the orchard is the key step limiting breeding efficiency. Therefore, selecting promising seedlings at an early stage before grafting would greatly increase the probability of obtaining promising candidates.Citrus are among the most produced fruits in the world8. Genome-wide association studies (GWAS) and genomic selection (GS), both performed by using genome-wide markers, are becoming important and effective tools for plant breeding10. GWAS enable the detection of quantitative trait loci (QTLs) or causal genes from association between genome-wide markers and trait phenotypes, and outperform bi-parental QTL mapping because GWAS do not require the development of segregating populations12. GS enables the selection of superior genotypes based on genomic estimated breeding values (GEBV) estimated from information on genome-wide markers, and is more effective than marker-assisted selection (MAS), especially for traits controlled by a large number of genes14. In general, the power of GWAS and the accuracy of GS are boosted when sample size is large15. However, accumulating large data sets sufficient for these analyses is difficult for fruit trees because of their long juvenile period, large plant size, and difficulties in phenotyping. Poland suggested the idea of “breeding-assisted genomics”, in which the data collected from actual breeding populations is used for functional genomics in plants16. In fruit tree breeding, phenotypic data accumulated every year in conventional breeding programs could be useful for GWAS and GS. Although a number of GWAS and GS studies have been reported in animals and plants14, the potential for using multiple actual breeding populations in combination with the parental population has not yet been explored.In recent years, next-generation sequencing technologies have decreased the cost of single nucleotide polymorphism (SNP) genotyping and expanded the availability of numerous markersThe objective of this study was to assess the potential of GWAS and GS for the genetic improvement of citrus and to evaluate the potential of using multiple actual breeding populations in addition to the parental population for GWAS and GS.r2) between each pair of SNPs on the same chromosome. The r2 values were plotted against the physical distance for the parental population and the combined population; curves fitted for the relationships between r2 and physical map distance indicated that LD extended over 1 Mb (r2 > 0.25) and decayed to relatively low levels (r2 < 0.10) within 20 Mb in both populations  in both populations, although the decay of LD was slightly faster in the combined population. The patterns of LD decay were similar in all chromosomes  in the parental population  was estimated as 4, because cross-validation (CV) error rapidly decreased until K = 4 and changed little thereafter, although K = 6 corresponded to the lowest CV error . Hierarchical clustering divided the parental population into two major clusters Fig. . The smaion Fig. . Similarion Fig. . Whereasion Fig. .Figure 210(p) values and/or more significant SNPs  were detected for 15 traits (except for aroma intensity (Aroma) and bitterness (Bitter)) by using the combined population rather than the parental population. In the combined population, GWAS detected significant SNPs associated with 11 traits: fruit weight (Weight), fruit hardness (FruH), color of pericarp (ColorP), easiness of peeling (Peeling), color of flesh (ColorF), flesh hardness (FleH), juiciness (Juicy), firmness of locule membrane (FirmLM), number of seeds (Seed), Bitter, and acidity (Acid)  and three on Chr. 3 for FruH and Peeling (r = 0.76). Single common significant SNPs were detected for FleH and Juicy ; FirmLM and Seed ; and ColorP, ColorF, and Bitter (Chr. 6). A single common significant SNP for Weight, ColorP, Bitter, and Acid (Chr. 4) is consistent with the largest  peak SNP for sugar content (Brix).GWAS was carried out for 17 fruit quality traits Table  and S6. phytoene desaturation 1 (pds1) and carotenoid cleavage dioxygenase 4 (ccd4), respectively. A significant SNP on Chr. 3 for FruH and Peeling resided in the gene Ciclev10018456 m.g, which is annotated as callose synthase. This gene was also located in the region flanking a significant SNP for FleH. The common significant SNP on Chr. 4 for Weight, ColorP, Bitter, and Acid resided in the gene Ciclev10031681 m.g, which is annotated as glutamate dehydrogenase.Two genes, Ciclev10031587 m.g and Ciclev10031003 m.g, were found between the significant SNPs on Chr. 4 for ColorP and ColorF; these genes were located 490 kb and 17 kb apart from the nearest significant SNPs, respectively, within a high-LD region , intermediate for fruit shape (Shape), smoothness of pericarp (SmoothP), Aroma, FleH, Juicy, Seed, Bitter, taste (Taste), Brix, and Acid (0.3 ≤ r < 0.7), and low for appearance (Appear) (r < 0.3)  Fig. . GBLUP (.3) Fig. , the latr < 0.6; red points in Supplementary Fig. r > 0.7) for ColorP, ColorF, and FirmLM , GBLUP (RR) that treats all SNPs as random effects was more accurate than multiple linear regression (MLR) that treats the three most significant SNPs  than by GBLUP (GAUSS) (r = 0.74), whereas the accuracy of GBLUP (RR) was slightly lower than that of GBLUP (GAUSS) for all ranges of fruit weight  was higher than the above value, suggesting that marker densities in the population would be sufficient for GS.The LD pattern is a key factor influencing the power of GWAS and the accuracy of GS, because these two approaches are based on LD between markers and causal polymorphisms24 and can cause spurious associations in GWAS11. Hierarchical clustering, ADMIXTURE clustering, and PCA showed that some varieties in the parental population formed clusters related to fruit weight, but most varieties showed no clear separation. The wide range of LD observed in the parental population was probably due to its small size rather than to the population structure. Given the wide range and high level of LD and the weak population structure, GWAS and GS would be effective in citrus populations.Population structure extends the range of LD10(p) values of peaks detected in GWAS were greater with the combined  than with the parental population (111 varieties), which would increase the chance of finding good markers for MAS. These results indicate that GWAS using the combined population has higher statistical power for detecting QTLs than GWAS using only the parental population, and that the QTLs detected were shared between the parental and breeding populations. For some traits, the significant SNPs differ between the combined and parental populations, possibly because of the differences between the populations in detection power, allele frequency, or population structure.For most traits, the number of significant SNPs and the –log25. This difference was explained by narrower QTL regions because of the short conserved regions of LD across breeds26. In the present study, the LD decay was rapid in the combined population, although the mean LD between adjacent SNPs was similar (r2 = 0.45) in both populations, implying that the LD in the combined population is conserved over shorter distances than that in the parental population. The short conserved regions of LD could improve the precision of QTL detection in the combined population by narrowing down the QTL regions.In addition to the sample size, the difference in the LD pattern between the combined and parental populations can influence the resolution of GWAS. For example, multi-breed GWAS had a higher resolution than within-breed GWAS in dairy cattleFW8 for fruit weight has been identified in a bi-parental QTL mapping study that used a mandarin F1 population derived from ‘Fortune’ × ‘Murcott’27. The most significant SNP detected for Weight resided in the same region as FW8, confirming a single major QTL for fruit weight on Chr. 8.GWAS in the combined population detected SNPs significantly associated  with 11 traits. The most significant association was detected for Weight on Chr. 8 where one QTL named 27. Two genes, Ciclev10031587 m.g and Ciclev10031003 m.g, annotated as pds1 and ccd4 respectively, are colocalized with the QTLs on MUR4.127. The pds1 gene is involved in the carotenoid biosynthetic pathway in citrus29 and Arabidopsis30, whereas ccd4 is involved in carotenoid accumulation in peach32. The pericarp and flesh color of citrus fruits is determined by carotenoid accumulation3.Identification of seven common significant SNPs for ColorP and ColorF suggests a close genetic linkage between QTLs controlling these traits, resulting in high phenotypic correlation. Common significant SNPs for other traits could be explained similarly. Significant SNPs for ColorP and ColorF on Chr. 4 were located in multiple QTLs for fruit flavedo and juice color identified on the linkage group MUR4.1callose synthase gene (Ciclev10018456 m.g). The polysaccharide callose is a component of plant cell wall33. Cell wall polysaccharides are associated with cell wall elasticity and viscosity, which influence fruit firmness. Softening of fruit tissues in tomato and avocado is caused by the degradation of cell wall polysaccharides35.A significant SNP for FruH and Peeling on Chr. 3 resides in the glutamate dehydrogenase. In avocado, the activity and protein content of GDH remain constant during fruit development, but increase ~4-fold during fruit ripening36. Generally, the acidity decreases while the sugar content increases during fruit maturation3. GDH could have pleiotropic effects on fruit quality traits involved in citrus fruit maturation. If so, common significant SNPs could be used as common markers for such traits in MAS programs.A single significant SNP for Acid detected on Chr. 4 is consistent with those for Weight, ColorP, and Bitter, and with the largest  peak SNP for Brix. This SNP resides in the gene Ciclev10031681 m.g, which is annotated as r > 0.7) of MLR for ColorP, ColorF, and FirmLM imply that MAS performed by using a few markers enables the selection at low-cost genotyping for these traits.The prediction accuracy of GBLUP (RR) that treats all SNPs as random effects, was highest for all the traits except Appear and Bitter, implying that all the traits except Appear and Bitter are controlled by many genes and/or QTLs. For Shape, SmoothP, Aroma, Taste, and Brix, for which the levels of prediction accuracy were intermediate, even though no significant association was detected. These results suggest that GS is more accurate for these traits than MAS based on significant SNPs. On the other hand, high prediction accuracies  for Acid supports this idea. Whereas GBLUP (RR) captures additive genetic effects, GBLUP (GAUSS) captures non-additive genetic effects  by modeling non-linear relationships between markers and the phenotypeC. maxima, whilst some of the cultivated mandarins were developed by introgression from C. maxima to the ancestral mandarin species C. reticulata42; sweet oranges (C. sinensis) originated from pummelo and mandarin43. Thus, mandarins and sweet oranges are related to pummelos, which may have contributed to the high prediction accuracy for the extremely large fruit weight of pummelos. For other traits, RR also outperformed GAUSS in the prediction of the distribution tails, suggesting that RR is much more robust and accurate than GAUSS when used for this purpose. This is an important implication for the use of GS in breeding programs, because the prediction of distribution tails is important for increasing or decreasing genotypic values of target traits.The prediction for the tails of fruit weight distribution was accurate for large-fruit varieties. The extremely large fruit weight of pummelos was properly predicted, especially with GBLUP (RR), even though the accuracy of GBLUP (RR) for the whole range of distribution was slightly lower than that of GBLUP (GAUSS). This implies that the genetic architecture of the extremely large fruit weight of pummelos can be explained by the additive accumulation of fruit weight–related genes of “non-large-fruit” varieties, which include a number of mandarins and sweet oranges. Cultivated pummelos were originally selected from a single progenitor species, 44. Lower prediction accuracies across populations than within populations were also observed in apple46, grapevine47 and maize48. On the other hand, across-population evaluation is reportedly preferable to that within populations when the populations are closely related to each other, marker density is high, or the number of phenotypic records is small50. The advantage of using data from multiple populations should be discussed from the viewpoint of the costs of genotyping and phenotyping, as pointed out by Muranty et al.46. In the present study, the prediction accuracy was improved by combining the parental and breeding populations. The shorter conserved region of LD in the combined population than in the parental population could improve not only GWAS resolution but also the prediction accuracy by narrowing down the QTL regions. Our results suggest that multiple populations closely related to each other are useful to predict a small variation in the breeding population.For the breeding population, the prediction accuracy was lower in the model built using only the parental population than in the model built using the combined population. This result suggests that the high genetic diversity in the parental population is not always useful to predict a small variation in the breeding population. Generally, the prediction accuracy across populations that are of low relevance is lower than that within a population51. In the present study, the GWAS power was increased and the prediction accuracy was improved by combining the breeding and parental populations. The results support the idea of breeding-assisted genomics, in which the data collected from actual breeding programs will be beneficial for plant functional genomics16. Because the phenotypic data are accumulated every year, combining marker genotypic data in a breeding program and gathering them will increase the detection power of GWAS and the prediction accuracy of GS. The combination of genomics-based approaches, GWAS and GS, and the data from actual breeding programs will facilitate both functional genomics and fruit tree breeding.The results of this study suggest that GWAS and GS have a good potential for genetic improvement of fruit quality traits in citrus. Declining sequencing cost enables genomics-assisted breeding with very large numbers of DNA markers, and it has been anticipated that this approach would improve fruit tree breeding by avoiding constraints imposed by the long juvenile period and large plant size1 individuals, called the breeding population , called the parental population  software  with known hybrid trios. Finally, 1,841 verified SNPs were obtained  was 0.97.SNP genotyping for 768 or 1,536 arrays was performed as described by Shimizu r2) of SNP genotypes and plotted them against physical distance between the corresponding markers in Mb. Local polynomial regression with kernel weight was conducted using the “locpoly” function in the R package KernSmooth ver. 2.23–1355 to test the relation between the r2 values and physical map distances. Physical distances between adjacent markers were 0.056–2,243 kb .To estimate LD between each pair of SNPs within the same chromosome, we calculated the squared correlation coefficients  in the parental population varying from 1 to 10 was assumed when using ADMIXTURE. To choose the optimal K value, 5-fold CV was performed. PCA was conducted using the R function “prcomp”. The optimal number of principal components (PCs) in the parental population was determined by estimating the variances of PC scores. To locate the breeding population in the PCA space of the parental population, the PC scores of the breeding population were estimated based on PCA of the parental population. A heat map was generated to estimate the phenotypic population structure of fruit weight (log-converted Weight) by using R package ggplot2 ver. 1.0.058.The genetic structure of the parental population was estimated using hierarchical clustering, ADMIXTURE clustering, and PCA. Hierarchical clustering based on Ward’s method59 implemented in the “GWAS” function of the R package rrBLUP ver. 4.337. To avoid spurious associations due to population structure, a kinship matrix and four PCs were included in the MLM as fixed effects. The kinship matrix was computed by using the “A.mat” function of the R package rrBLUP ver. 4.337. The number of PCs was selected based on the variances of PC scores. The variance of PC score rapidly decreased until PC4, and gradually thereafter  and CitrusCyc Pathways version 3.0 databases (http://pathways.citrusgenomedb.org/). The LD heatmaps of the regions surrounding the peaks on Chr. 4 for ColorP and ColorF were constructed by using the R package LDheatmap ver. 0.99–160.Association analysis was conducted by using an MLM37 with linear ridge kernel regression (RR) and non-linear Gaussian kernel regression (GAUSS). We also used the R package glmnet ver. 1.9–861 for three different linear regression methods: Ridge Regression , Lasso , and Elastic Net . The latter is a combination of ridge regression and lasso. A non-linear decision tree-based ensemble learning method, Random Forest, was run in the R package randomForest ver. 4.6–1062. The Bayesian linear regression models Bayesian Ridge Regression (BRR), Bayesian Lasso (BL), BayesA, BayesB, and BayesC were also used for the modeling. These models were implemented in the R package BGLR ver. 1.0.363.We used 12 methods to evaluate prediction accuracy. A MLM-based method, genomic best linear unbiased prediction (GBLUP), was performed by using the “kinship.BLUP” function of the R package rrBLUP ver. 4.365, and the R package BGLR for BRR, BL, BayesA, BayesB, and BayesC. The BGLR package supports models for categorical traits.Because 14 of the 17 traits analyzed in this study were scored as ordinal categories (Table 10(p) values detected in GWAS, the accuracies of three models were compared. The first model was MLR implemented in the R function “lm”67. The top three peak SNPs with high –log10(p) values of each trait were entered in MLR unless the correlation coefficient between the SNPs was ≥0.6 to prevent multicollinearity. The proportions of the variance explained by the top three peak SNPs for each trait were also estimated by the MLR model. The second model was GBLUP (RR) that treats the effects of the top three SNPs as fixed effects and the effects of other SNPs as random effects. The last model was the commonly used version of GBLUP (RR), which treats all SNPs as random effects.To evaluate the effect of peak SNPs with high –logHo) of the parental population (>0.50)21, a multi-kernel model that considers both additive and dominance genetic effects was compared with a single-kernel model that considers only additive effects. Both models were implemented in the R package of BGLR ver. 1.0.363. The additive relationship matrix (kinship matrix) was computed by the “A.mat” function of the R package rrBLUP, whereas the dominance relationship matrix was calculated as D in equation (6) in Vitezica et al.68 using a modified “A.mat” function. The additive (h2) of each trait was computed as the ratio of To take into account the high observed heterozygosity (r) between observed and predicted genotypic values and regarded as r = 0 at r < 0.To verify the models, we evaluated the prediction accuracy by 10-fold CV using the parental population randomly partitioned into each fold. The CV was repeated 5 times, and the same partition patterns were adapted to all prediction models in each CV. The prediction accuracy was defined as Pearson’s correlation coefficients  and the non-linear Gaussian kernel regression model GBLUP (GAUSS). All varieties except the large- and small-weight groups were used as a training set, whereas the large- and small-weight groups were used as a test set to assess the difficulty of the prediction of the distribution tails. The training set was also used for leave-one-out CV as a control. The prediction accuracy for the distribution tails was also evaluated for other traits. Rank-ordered 4-fold CV of the parental population was performed. The prediction accuracy was calculated in each partition. To show the tails of the distribution, phenotypic variations of the traits were visualized as jitter plots superimposed onto boxplots by using the R packages ggplot2 ver. 1.0.1To evaluate the prediction accuracy in the breeding population, all 12 methods were applied. Three different training populations for modeling were compared: only the parental population, only the breeding population (with one family excluded), and both the parental and breeding populations combined (with one family excluded); the phenotype of the excluded family was predicted to mimic actual breeding in which the phenotypic information of a family targeted by GS is generally not available. The prediction accuracies for each family or combined all families were calculated.Supplementary informationDataset 1


We compare these mitochondrial DNA hypervariable segment sequences and haplogroup results with published ancient and modern Eurasian data. Whereas the analyzed Avars represents a certain group of the Avar society that shows East and South European genetic characteristics, the Hungarian conquerors’ maternal gene pool is a mixture of West Eurasian and Central and North Eurasian elements. Comprehensively analyzing the results, both the linguistically recorded Finno-Ugric roots and historically documented Turkic and Central Asian influxes had possible genetic imprints in the conquerors’ genetic composition. Our data allows a complex series of historic and population genetic events before the formation of the medieval population of the Carpathian Basin, and the maternal genetic continuity between 10th–12th century and modern Hungarians.The ancient Hungarians originated from the Ural region in today’s central Russia and migrated across the Eastern European steppe, according to historical sources. The Hungarians conquered the Carpathian Basin 895–907 AD, and admixed with the indigenous communities. Here we present mitochondrial DNA results from three datasets: one from the Avar period (7 The exact route and chronology of the Hungarian migration between the Ural region and the Carpathian Basin is continually debated among archaeologists, linguists and historians.The Finno-Ugric origin of the Hungarian language is well recorded by linguistic research, which lead to an assumption that there was a Uralic substrate of the ancient Hungarian populationet al. have described the mitochondrial DNA (mtDNA) of a small group of ancient Hungarians from the 10th–12th century Carpathian Basin, where the ancient Hungarians’ affinity to modern day Central Asia has been demonstrated. Tömöry et al. concluded, without simulation tests, that there was no genetic continuity between the classical conquerors and modern day Hungariansth–12th century population from the northwestern Carpathian Basin has been reported with heterogeneous maternal genetic characteristics similar to modern Europeans141516The genetic origin of ancient Hungarians is still in question, although some modern and ancient DNA studies have focused on this issue. For example, Tömöry et al. and Egyed et al. built the mtDNA control region and Y chromosomal microsatellite databases from different groups of modern Hungarians, including an “average” Hungarian group from Budapest and two groups of Hungarian minorities–Ghimes Csango and Szekler–living in modern Romania. Both Szeklers and Csangos were found to harbor some Asian genetic components, and the Csango population showed genetic signs of long term isolation, which differentiated them from the Szeklers and the population of Budapest1920et al. to the conclusion that the Hungarian conquerors were in small number and that the Hungarian language could be an example of cultural dominanceGenetic research of modern Hungarians has been a subject of four further mtDNA and Y chromosomal studies. Brandstätter th–10th centuries) in the Carpathian Basin is still sparse. Our research approaches the questions of maternal genetic composition and the origin of the ancient Hungarians, analyzing a dataset four times larger than previous work has attempted. The connections of the conquerors to the previous Avar and contemporaneous Slavic-Hungarian contact zone population will be determined, as well as connections to other ancient populations of Eurasia that have previously been published. We also compare our dataset with modern day data from the Carpathian Basin and Eurasia, in order to better understand the maternal genetic origin and legacy of the 7th–11th century population of the Carpathian Basin.The archaeogenetic contribution to the historical era of the Avar and conquest-periods  sequences were obtained from mtDNA of 111 individuals from the medieval Carpathian Basin: 31 mtDNA profiles from Avars, 76 from Hungarian conquerors and four from the southern Hungarian-Slavic contact zone see . The mtDth–10th centuries from the west Hungarian Vörs-Papkert site were excluded from any statistical analysis because of their offside geographical location and cultural differences from the Avar and Hungarian sites. Their mtDNA belonged to the common European J and H haplogroups, but with rare haplotype variants in ancient and modern mtDNA databases  had a mixed European-Asian haplogroup composition with four Asian haplogroups  at 15.3%, but a predominantly European , haplogroup composition . In the The principal component analyses (PCAs) of ancient and modern-day populations were computed based on haplogroup frequencies . PCA of The PCA of the investigated ancient and modern Eurasian populations demonstrated the clustering of most modern European populations by PC1, PC2, and PC3. Furthermore, their affinities to modern Near Eastern populations are represented by PC1 and PC3, whereas the modern Asian populations are dispersed along PC1. The conqueror population has a similar haplogroup composition to modern Central Asians and Finno-Ugric populations, which is also supported by Ward type clustering. While Avars rather showed modern European connections, the contact zone population had a Near Eastern type haplogroup composition .The distance calculations based on high subhaplogroup resolution also showed that modern Central Asian populations were highly similar to the conqueror population. The maternal genetic connections of the Avar group concentrated on modern Eastern European populations, and the contact zone group showed Southwest Asian affinities on genetic distance maps (GDM) llennium .ST values of Avars indicated non-significant differences among nearly all medieval European populations, and from Central Asia, as well as from many modern-day Europeans. The Hungarian conqueror population showed the lowest distances from modern-day Uzbeks and Turkmens (FST = 0.00335 and 0.00489 respectively) and from six ancient populations: medieval Poles (FST = −0.00018), Bronze and Iron Age in present-day Kazakhstan (FST = −0.00164), Bronze Age along the south central Siberian flow of Yenisey River (Minusinsk Hollow) (FST = −0.00208), Siberian Baraba population (FST = −0.01003), Avars (FST = 0.00233), and 6th century Lombards from Hungary , these values were non-significant (p > 0.05). The distances from the ancient populations were visualized on an FST level plot (ST = 0.00817), Italy (FST = 0.00923), Czechs (FST = 0.01023) and Avars (FST = 0.01094). For the genetic FST values and their corresponding p-values, see Pairwise genetic distances were calculated between 21 ancient and 52 modern populations. Interestingly, pairwise Fvel plot . The mixST values were displayed on multi-dimensional scaling (MDS) plots  plots . The plo plots 14.The sequence-based genetic distance maps, encompassing 141 modern populations, show congruently the Central Asian affinity to the conquerors, the European/Near Eastern characteristic populations to the Avar sequences, and predominant Near Eastern affinities to the contact zone group (th-century Lombards in Hungary (39.3%). The SHA analysis was strongly influenced by altering haplotype diversity and the high number of Cambridge Reference Sequence (rCRS) H lineages in medieval Spanish, Italian and Norwegian Viking groups, which caused high proportion of lineage sharing with only a small number (n = 4–5) of shared lineage types. Medieval populations from Italy and Spain shared many of their haplotypes (40–48%) with the Avar and contact zone populations as well. On the other hand, many lineages of the Bronze Age Andronovo, Baraba, and Bronze Age population of the region of today’s Kazakhstan were shared with the conquerors (37.5–29.4%), with some identical Asian lineages among them.The 101 ancient Hungarian samples belong to 75 HVS-I haplotypes (haplotype diversity Hd = 0.987). The haplotype diversity is highest in the Avar group, and lowest in the contact zone dataset . The shard BC to 2nd AD century, in the late medieval Yakuts, and in medieval Scandinavia. Two B haplotypes were present in today’s China, Kazakhstan and spread as far as Thailand. The detected conqueror C-C4, F1b, and G2a haplotypes were widespread in modern Eurasia, and had parallels even in China and Korea. Six of these C, F, and G2a haplotypes had parallels in ancient populations of Asia. Among the five Asian D lineages, two were unique in the database and two were common in Central and East Asia. One D haplotype however (Der4.522) showed rare occurrence in Kazakhs, Uzbeks and Altaians, and Siberian populations. Among the Avars, three Asian haplotypes  were found. One C haplotype had only one match in modern Kazakh population, the other M lineage was common in Central and East Asia, but also occurred in Southwest Asia and Europe. The third Asian haplotype was D4c1, which also occurred at low frequency in Central, North, and East Asia  and “high status” (n = 12) groups according to the excavated grave goods2614C dating is not accurate enough for dating 9th–10th centuries AD.In a previous study, Tömöry tus” n =  groups ath century cemeteries with small numbers of graves (7–18 graves), and identical lineages were found among cemeteries, rather than within them. This is especially interesting in light of the fact that seven analyzed cemeteries have been completely excavated . This phenomenon suggests that the conquerors had a mobile way of life or can be explained by the strong marriage connections of the Hungarian communities. The lack of, or small number of intra-cemetery maternal relations is striking at the sites Kiskundorozsma and Levice-Géňa , Szeged-Öthalom (eight unrelated people) and Harta. At the Harta site, fifteen women, three men and two children were excavated. We found only one pair of females with identical HVS-I sequences (a common rCRS H type), but in other cases the maternal kinship relation among the 16 typed individuals could be excluded by HVS-I analyses. Many academic archaeologists explain that the small conqueror graveyards were small family graveyards, and use the grave goods of the assumed generations in these graveyards as chronological horizonsth century Carpathian Basin. Nevertheless, other classic 10th-century graveyards, such as Balatonújlak, contained more signs of possible maternal relations within the cemetery  Eastern European, Central and North Asian data, which are currently still lacking, might reveal further signs of origin and admixture of the populations discussed here. Moreover, this may shed light on a complex population genetic structure of the first millennium BC of West, North, and Central Eurasia.th centuries (with n = 4). These together with the previously published 10th–12th century results were compared with published ancient and modern Eurasian mtDNA data. The results comprehensively demonstrate the conqueror maternal gene pool as a mixture of West Eurasian and Central/North Eurasian elements. Both the linguistically recorded Finno-Ugric roots and the Turkic, Central Asian influxes had possible genetic imprints in the conquerors’ mixed genetic composition. The small number of potential intra-site maternal relations compared to the number of detected inter-sites relations suggests that conqueror communities were mobile within the Carpathian Basin. Our data support the complex series of population genetic events before and during the formation of the 10th-century population of the Carpathian Basin. These processes might be defined by future ancient DNA studies focusing in the Ural region and in the Eastern European steppe using genome-wide sequencing techniques.This study contributes ancient mtDNA data to the research on Hungarian ethnogenesis and the conquest-period. We present the first described Avar-period ancient DNA dataset (n = 31), an almost four-fold enlargement of the existing Hungarian conquest-period dataset (with n = 76), and a magnified dataset from the Hungarian-Slavic contact zone of the 10–12th–10th century cemeteries excavated in the Carpathian Basin. The sampling was performed by co-workers of the Institute of Archaeology, considering various aspects: (1) geographical location; (2) chronology; (3) archaeological characteristics; (4) grave goodsThe human skeletal remains (bones and teeth) used in this study were collected from 6th–10th century Vörs-Papkert , and six samples from one site in the medieval Hungarian-Slavic contact zone, Zvonimirovo (located in present-day northern Croatia). Nine samples from these sites were already part of G. Tömöry’s PhD dissertationth century. They were probably not the first generation of conquerors, which is very problematic to distinguish at the current state of research. One further point to note is that the Avar samples belong to a single micro region of the Avar Khaganate, and therefore do not represent the whole Avar population of the Carpathian Basin.We investigated 144 medieval samples: from the Hungarian conquest-period 88 samples were analyzed from the cemeteries of Harta-Freifelt, Balatonújlak-Erdődűlő, Kiskundorozsma-Hosszúhát, Baks-Iskola, Szeged-Öthalom, Makó-Igási járandó, Szentes-Derekegyháza, Nyíregyháza, Kiszombor, Szentes-Borbásföld , and Levice-Géňa (Slovakia). Furthermore, four Avar-period cemeteries were studied with 50 samples collected from Szegvár-Oromdűlő, Pitvaros-Víztározó, Székkutas-Kápolnadűlő, the 9Sampling was carried out using gloves, facemasks, and body suits, in order to minimize the risk of contamination by contributors. Two bone fragments, usually two compact bone tissues from different parts of long bones, or one tooth and one compact bone fragment of a femur were collected from each individual. All stages of work were performed under clean conditions in a dedicated ancient DNA laboratory at the Institute of Archaeology, Research Centre for the Humanities, Hungarian Academy of Sciences, Budapest, following published ancient DNA workflow protocols and authentication criteria1213et al.et al.2, 25 min). The surfaces of teeth samples were cleaned by sandblasting , while the surfaces of bone samples were removed with a fresh drilling bit at slow speed, followed by UV exposure for 30 min on each side. Bone and tooth pieces were mechanically ground into fine powder in a sterile mixer mill (Retsch MM301).The specimens were prepared following the protocols described by Kalmár −1 primer; 1.5 mM MgCl2; 4mgml−1 BSA. The HVS-I region of mtDNA was amplified in two overlapping fragments with two sets of primers, and an additional 16 primer pairs were used to amplify haplogroup-diagnostic nucleotide positions in coding regions (see ® PCR Purification Kit (Qiagen) following the manufacturer’s protocol, or purified from 2% agarose gel with Bioline Isolate PCR & Gel Kit in a final volume of 15 μl. Sequencing reactions were performed using the ABI PRISM BigDye Terminator v3.1 Cycle Sequencing Ready Reaction Kit (Applied Biosystems) and sequencing products were purified by ethanol precipitation. The sequences were determined on ABI PRISM 3100 (PE Applied Biosystems) in cooperation with BIOMI Ltd . The sequences were evaluated with Chromas Lite 2.4.1 and GeneDoc softwareDifferent DNA extraction methods were used, repeatedly validating the results per sample1236ions see . Cyclinghttp://empop.online/).The sequence polymorphisms in the nucleotide position range 16040–16400 were compared with the rCRSWe could not determine the haplogroup classification of one sample (HAR1.56B), due to detection failure of U haplogroup-diagnostic at coding region position 12308. Therefore we included it only into shared haplotype analyses of HVS-I sequences, and excluded it from other statistical analyses.th–10th-century late Avar-Slavic mixed population in Transdanubia (western-Hungary). On the other hand, we included 26 samples from medieval Hungary described by Tömöry et al.et al., 2007); (2) Avars in the southeastern part of today’s Hungary (26 samples) (3) “contact zone”  described by Csákyová et al.Of the typed 111 mtDNA profiles, we excluded the site Vörs-Papkert from the population genetic analyses, because it represents a 9rd century BC–14th century AD) from Mongolia and Inner Mongolia (China), and late medieval Yakuts from Russia. In addition, in order to have a proxy for the genetically uncharacterized first millennium AD populations of Central and North Asia, we used prehistoric (Bronze Age and Iron Age) datasets from modern-day Russia, Kazakhstan and Mongolia. Their characteristics, abbreviations and references are described in The ancient datasets were compared with 57,098 published modern HVS-I sequences as well as 614 medieval sequences of European, Near Eastern and Asian populations: Lombards from Hungary and Italy, medieval population from northern Italy, medieval Basques from Spain, medieval populations from Poland, Iceland and Denmark, Vikings from Norway and Denmark, three ancient populations  and a further 18 ancient and 53–157 modern populations. Diversity indices were calculated in DNASP v5PCAs were carried out based on mtDNA haplogroup frequencies. We considered 31 mtDNA haplogroups in PCA of 21 ancient populations, while in PCA with the 3 medieval populations and 53 modern-day populations, 36 mtDNA haplogroups were considered . All PCAHierarchical clustering was performed using Ward type algorithmST values were calculated based on 35,203 modern and 764 ancient HVS-I sequences (np 16050–16383) of 73 populations: 21 ancient and 52 modern-day populations from Eurasia. Tamura & Nei substitution modelST values were analyzed using MDS and applied on the matrix of linearized Slatkin FST valuesPopulation comparisons were estimated using Arlequin 3.5.1et al.th–12th century and modern-day Hungarians and the culturally isolated minority populations, Szeklers and Csangos, who live in Romania , differentiating 211 sub-haplogroups. We calculated genetic distances of these modern populations from the three Carpathian Basin medieval populations . Second,68–16365 . The anaHow to cite this article: Csősz, A. et al. Maternal Genetic Ancestry and Legacy of 10th Century AD Hungarians. Sci. Rep.6, 33446; doi: 10.1038/srep33446 (2016).


Helianthus annuus L.) sampled along a latitudinal gradient in central North America. We analyzed geographic patterns of phenotypic diversity, quantified levels of within-population SNP diversity, and also determined the extent of population structure across the range of this species. We then used these data to identify significantly over-differentiated loci as indicators of genomic regions that likely contribute to local adaptation.Divergent phenotypes and genotypes are key signals for identifying the targets of natural selection in locally adapted populations. Here, we used a combination of common garden phenotyping for a variety of growth, plant architecture, and seed traits, along with single-nucleotide polymorphism (SNP) genotyping to characterize range-wide patterns of diversity in 15 populations of wild sunflower  were significantly correlated with latitude, and thus differentiated northern vs. southern populations. Average pairwise FLatitudinal differentiation in both phenotypic traits and SNP allele frequencies is observed across wild sunflower populations in central North America. Such differentiation may play an important adaptive role across the range of this species, and could facilitate adaptation to a changing climate.The online version of this article (doi:10.1186/s12870-016-0937-7) contains supplementary material, which is available to authorized users. Local adaptation, wherein populations have higher fitness in their ‘home’ environments than in non-native locales, is a topic of great interest in the field of evolutionary biology . The geIn the case of divergent selection, which would be expected to play an important role in the production of locally adapted populations, the focus is typically on measures of population genetic differentiation. More specifically, divergent selective pressures would be expected to produce elevated population structure in the vicinity of targeted genes relative to the genome-wide average e.g., –14). In . In 14])Arabidopsis thaliana for the flowering time genes FRIGIDA ). FRIGIDA  and PHYTOCROME C , in PopuHROME B2 , in DrosRECEPTOR , and in e AGOUTI . While tH. annuus), which is native to east-central North America [Sunflower is a member of the Compositae , which is one of the largest and most diverse families of flowering plants. The native range of common sunflower spans much of North America, and wild populations occur in habitats that are characterized by variation in a wide range of environmental variables, including: photoperiod, growing season, minimum/maximum temperatures, and precipitation. Common sunflower is also the wild progenitor of cultivated sunflower  array targeting 384 loci distributed throughout the sunflower genome. We used these data to investigate geographic patterns of phenotypic differentiation, describe overall patterns of population genetic variation, and identify loci that harbor the population genetic signature of local adaptation. We also placed our population genetic results in the context of prior quantitative trait locus (QTL) mapping studies in sunflower to determine whether highly differentiated loci co-localize with known QTL regions.H. annuus were obtained from the USDA’s North Central Regional Plant Introduction Station . These populations, which were sampled from a range of latitudes across central North America , as that could inflate genetic structure and/or phenotypic differentiation. Prior to germination, all seeds were cleaned with 3 % hydrogen peroxide, rinsed with deionized water, and placed on moist filter paper in a petri dish. To break dormancy, petri dishes were placed at 4 C in a dark cold room for 14 days. After the cold treatment, they were moved into a growth room where they were maintained under 16 h days at 23 C. Following germination, seedlings were planted in soil trays. Once established, these seedlings were transplanted into soil pots  and moved to the greenhouse, where supplemental lighting provided a consistent cycle of 16 h days and 8 h nights.Seeds from 15 wild-collected populations of Plants were arranged in the greenhouse in four blocks, each of which contained five individuals from each of the 15 populations . All plants were phenotyped for a variety of traits, including: days to four pairs of true leaves, days to flowering, plant height at senescence, branching architecture, seed size, and seed oil content/composition. Because wild sunflower is self-incompatible, manual crosses were performed to produce seeds. This involved intercrossing individuals within populations , with inflorescences being bagged to prevent cross-contamination. Seeds were then collected at physiological maturity and phenotyped. Oil traits were assessed following established protocols . BrieflyAll traits were tested for deviations from normality by determining whether a frequency histogram of trait values across all 286 full grown individuals .Leaf tissue was harvested from the 286 fully grown Table  individuo), and Nei’s unbiased expected heterozygosity . Results were imported into STRUCTURE Harvester [K was determined using the DeltaK method [Measures of genetic diversity, including the percentage of polymorphic loci, observed heterozygosity  were caty . We alsty . In gen3.5.1.2; , 16, or 3.5.1.2; . BayeSca3.5.1.2; . Followi3.5.1.2; ), loci w crosses , 41–44.We identified numerous traits that exhibited differentiation amongst the five sampled regions, with latitude being a significant factor in the partitioning of phenotypic diversity for traits such as flowering time, plant height, branching, and a number of seed oil traits Table . IndividK method for determining the most likely number of population genetic clusters [K = 2 clusters  in the northern portion of the range was separated from the other five populations at K = 2 . These loci included: one SNP on LG4 with no annotation; two SNPs located near the distal end of LG 6, one in HaFT2 [A. thaliana; one SNP on LG10 in the GRAS/DELLA transcription factor GAI; two SNPs on LG 12, one corresponding to an EF-hand-like domain-containing gene, and the other corresponding to a protein of unknown function; and one SNP located on LG 14 in a gene with high similarity to Defective Cuticle Ridges (DCR) in A. thaliana. BayeScan provided complementary outlier results by identifying three highly differentiated loci  already highlighted by Arlequin. Four loci had evidence of being significantly under-differentiated from both Arlequin and BayeScan. There were two under-differentiated loci on LG 13, including one SNP in a gene with an alpha-beta plait nucleotide binding role and another SNP in a gene with homology to 5′-AMP-activated protein kinase. SNPs in a glycoside hydrolase and a guanylate binding gene also had exceptionally low FST, and were found on LGs 8 and 17, respectively (data not shown).Multiple outlier identification programs highlighted the existence of an overlapping set of loci that exhibit the signature of local adaptation Table . Arlequiin HaFT2 , 47 and ST outliers: HaFT2 and a gene with a putative kinase function. Both of these co-localize with QTL related to flowering time in two sunflower mapping populations, ANN1238 × CMS 89 [HaFT paralogs, including HaFT1, which has been shown to be important with respect to cultivated sunflower’s photoperiod response [DCR homolog, which map to LGs 10 and 14, respectively), did not co-localize with any known QTL. One of the two outliers on LG12, an unannotated gene, co-localized with leaf shape and number of heads [The locations of our eight over-differentiated loci were compared to the locations of previously mapped sunflower QTL to identify traits potentially involved in local adaptation. On LG 4, an unannotated gene co-localized with a QTL for leaf number . As note× CMS 89  and ANN1× CMS 89 . This geresponse , 47. In response , 44. Intof heads . FinallyST outliers located near each other on LG 13 that co-localized with a shared set of QTL that included: number of branches, number of heads, head and leaf herbivory, stem diameter, achene length, leaf area, and stem height [Under-differentiated loci co-localized with QTL for a variety of different traits. Of particular interest were two low Fm height , 42–44.Populations across the range of wild sunflower harbor an exceptional amount of phenotypic diversity. The extent to which those traits contribute to local adaptation is an important question that can be addressed in a number of ways including reciprocal transplants, common garden measurements, and population genome scans. In our analyses, many traits  were differentiated in conjunction with latitude. As sunflower is a seed oil crop, there has been a considerable of research done to describe and uncover the genetic mechanism behind seed oil variation. In breeding lines, strong artificial selection has created divergent germplasm groups with vastly different oil profiles. In the wild, natural selection may act as a strong force in affecting what relative amounts of saturated and unsaturated fatty acids are most beneficial for populations living in certain environments.Seed oil composition exhibited significant latitudinal differentiation across the range. Previous studies of seed oil composition in a variety of species have revealed a negative correlation between saturated fatty acid content and latitude and degree of saturation at a relatively coarse geographic scale . By quanObserved differences in flowering time can be interpreted in a similar framework. Growing seasons tend to be shorter in higher latitudes; thus, there is a premium on flowering early to allow seed set before the end of the growing season. Alternatively, in lower latitudes, there is typically a longer growing season that may select for later flowering plants that may grow to a larger size and produce more and/or higher quality seeds. It must, however, be noted that plant height and flowering time are developmentally correlated; as such, they form a suite of inter-related traits , 49. TheH. annuus genetic diversity had hinted at a similar north/south division [The STRUCTURE analysis of the full dataset revealed an overall north/south division in the natural range of wild sunflower, with a transitional zone occurring in the vicinity Nebraska and Wyoming. Previous sampling of division , and ourdivision  showed tHaFT2. Both loci co-localize with previously identified QTL for flowering time, [FT2 is a gene whose Arabidopsis homolog has been shown to play a major role in promoting flowering [HaFT2 is an exceptional candidate for local adaptation due to previous gene expression work across the range of wild H. annuus [HaFT2 in which northern individuals exhibited higher expression than southern individuals, consistent with this gene playing a role in adaptive differentiation [HaFT2 exhibits a latitudinal cline in gene expression that is consistent with the effects of selection by providing population genetic evidence of selection on this gene, as well.In terms of population genetic differentiation, we identified interesting possible candidates for conferring local adaptation with respect to flowering time. We found two outlier loci on chromosome 6 with SNPs that co-localize with a gene with putative kinase activity and ng time, , 42 in alowering . Moreovelowering , 42, 47.lowering . Studieslowering . In addi. annuus . In shorntiation . Our resDefective in Cuticle Ridges (DCR). In A. thaliana, mutants of DCR have altered trichome development during leaf growth [We uncovered SNPs with significantly elevated population differentiation values on other chromosomes. A strongly differentiated SNP on LG 14 resides in the sunflower homolog of f growth , 57. Trif growth , retentif growth , and deff growth . As manyST outliers form a list of possible candidate genes for future experiments. Importantly, the extent of linkage disequilibrium needs to be assessed in these genomic regions in order to determine the size of the region of elevated population structure. A possible explanation for the absence of co-localizing QTL for some SNPs is that no wild × wild mapping populations currently exist for sunflower. Alternatively, many subtle (trichome density or morphology) and biochemical phenotypes have not been measured and thus could not have co-localized with population differentiation. Marker density has become the main limitation in genome scan studies of local adaptation in natural populations [These Fulations . The advulations –64.In this study we used 246 loci to characterize the range-wide genetic diversity and structure of the wild progenitor of an economically important crop species. These markers clearly indicated a genetic disjunction between northern and southern populations that occurs around the 40° north latitude, with populations in Nebraska appearing to be admixed (Fig.