R) genes, such as LepR1, against Leptosphaeria maculans, the causal agent of blackleg in canola (Brassica napus), could help manage the disease in the field and increase crop yield. Here we present a genome wide association study (GWAS) in B. napus to identify LepR1 candidate genes. Disease phenotyping of 104 B. napus genotypes revealed 30 resistant and 74 susceptible lines. Whole genome re-sequencing of these cultivars yielded over 3 million high quality single nucleotide polymorphisms (SNPs). GWAS in mixed linear model (MLM) revealed a total of 2,166 significant SNPs associated with LepR1 resistance. Of these SNPs, 2108 (97%) were found on chromosome A02 of B. napus cv. Darmor bzh v9 with a delineated LepR1_mlm1 QTL at 15.11-26.08 Mb. In LepR1_mlm1, there are 30 resistance gene analogs (RGAs) (13 nucleotide-binding site-leucine rich repeats (NLRs), 12 receptor-like kinases (RLKs), and 5 transmembrane-coiled-coil (TM-CCs)). Sequence analysis of alleles in resistant and susceptible lines was undertaken to identify candidate genes. This research provides insights into blackleg resistance in B. napus and assists identification of the functional LepR1 blackleg resistance gene.Utilising resistance ( Brassica napus L.  is one of the most important crops for edible oil, food and industrial purposes  gene and an avirulence (Avr) gene  gene . These Rlt stage . On the lt stage . Both tyR gene in the genome, genetic mapping of genotypic variants is frequently undertaken. Two of the most common approaches for genetic mapping are association mapping, or genome wide association studies (GWAS), and biparental linkage mapping. GWAS is an alternative approach to traditional mapping of biparental populations and has been widely used in B. napus  encoding a receptor-like protein (RLP), while Rlm4, Rlm7 and Rlm9 are also allelic variants of an RGA encoding wall-associated kinases (WAKs) -leucine rich repeats (LRR)  . Other bts (LRR) . One of B. napus . This stL. maculans isolates were used to genotype B. napus genotypes for the presence or absence of resistance gene LepR1. All isolates and their genotypes (-5) of each L. maculans isolate were dropped onto cotyledons of wounded ten-day-old seedlings grown in the glasshouse at 22°C. Disease symptoms were scored at 14 days post-inoculation on a 0-9 scale, as previously described  were used in this study  according to the manufacturer’s instructions. Total DNA was quantified using a Qubit 3.0 Fluorometer and the Qubit dsDNA BR Assay kit , while DNA quality was assessed using the LabChip GX Touch 24 . WGRS of the cultivars was carried out using an Illumina NovaSeq™ 6000 Sequencing System by GeneWiz , which generated approximately ~6 Gb of 150 bp paired-end sequencing data per cultivar.A total of 104 Australian is study Table S2From the WGRS data, raw FASTQ files were trimmed of adapter sequences and low quality bases using Trimmomatic v0.39  with a mB. napus genome cv. Darmor bzh v9 (https://github.com/samtools/bcftools). Duplicate reads were removed using samtools markdup. SNPs for each individual were called using bcftools v.1.10  plot in mixed linear model (MLM) with PCA  + Kinship matrix for family relatedness estimates (K) , were co markers . The phe markers . Figure  markers .LepR1 introgressed lines and structural variants, derived from the results of L. maculans containing AvrLep1 screened at the cotyledon stage  were visualised in Geneious R10  nearest to the LepR1 QTL identified in this study. The marker development was done in two stages. First, markers flanking each candidate were designed using the Primer3 function in Geneious R10   (The WGRS data of 104 ty SNPs) Table S3markers) .LepR1 resistance were identified across 10 chromosomes  at the 1.58 E-08 cut-off value, implemented with Bonferroni correction and FDR , however only 9 of them contained RGAs S6 and wLepR1 blackleg resistance were found in chromosome A02 in B. napus cv. Darmor bzh v9 (LepR1_mlm1) was 11.07 Mb in length (15.11-26.18 Mb)  of the total significant SNPs associated with r bzh v9 Table S3LepR1 introgression line (cv. Topas), mapped in genome cv. Darmor bzh v4.1 , LepR1_TLepR1, LepR1_SV, and LepR1_meta contained 1915 genes (46 RGAs), 344 genes (8 RGAs), and 1434 genes (20 RGAs), respectively  were also within the intervals of the LepR1_TLepR1 and LepR1_SV , with 17 having SNPs in the CDS region (CDS-SNPs)  (LepR1 candidate gene were 12 in BnaA02g25110D3 (RLK), 11 in BnaA02g31910D3 (RLK), BnaA02g33310D3 (RLK) and BnaA02g34160D3 (TM-CC), 10 in BnaA02g33320D3 (NLR) and 8 in BnaA02g26040D3 (RLK) (Eighteen (60%) out of the 30 RGAs underlying n total) Table 2.LepR1 candidates  had at least 4 SNPs in their CDS regions between resistant and susceptible lines  of Darmor bzh genomes (v4.1 or v8.1). For instance, Darmor bzh v9 has a total size of 1,043.4 Mb compared to the 850.3 Mb of Darmor bzh v4.1  .LepR1_meta was based on adult plant survival rate and average internal infection of the stem at maturity stage paired with imputed SNPs, a methodology that would identify quantitative instead of qualitative blackleg resistance in B. napus  kinase 9 (BnMPK9), BnaCnng11720D or C05p48150.1_BnaDar on C05 (R gene Rlm1 on A07 and pathogen effector AvrLm1 (Suppresor of BAK1-interacting receptor like kinase 1 (SOBIR1) genes on A03 and C03 (BnaA03g14760D and BnaC03g17800D) are also required for the blackleg resistance of the alleles LepR3 and Rlm2 (BnaA10g20720D) of A10 to effectors AvrLm1 and AvrLm2, respectively (The few significant SNP associations on C01 and C02 (having RGAs in the QTL) could possibly be due the incorrect mapping of DNA sequences containing notable SNPs that occurred during the genome assembly process in B. napus . Another and C02 , indicat thought . Functior on C05 , is an er AvrLm1 . Furtherectively .LepR1 gene. The CDS-SNPs of these RGAs could alter amino acids (aa) of the predicted proteins explaining the difference between susceptible and resistant phenotypes. Among the SNP types, a nonsense SNP leads to a premature stoppage of the amino acids, potentially changing the gene expression among cultivars with different genotypes. Although this study did not examine gene expression between cultivars with different LepR1 phenotypes, previous studies have shown that RLKs and NLRs were significantly expressed in LepR1 resistant cultivars  genes RPM1 against Pseudomonas syringae  can be found below: NCBI accession PRJNA885991.B. napus materials including DNA extraction. AV provided the phenotype data. AS-E, NS and RA provided bioinformatics support by calling SNPs on the reference B. napus cv. Darmor-bzh v9 genome and statistical codes. AC designed the primers/markers and prepared the materials for sequencing analysis. JB supervised, reviewed, and suggested revisions to the paper. All authors contributed to the article and approved the submitted version.AC and JB conceptualized the paper. AC wrote the original draft along with formal analyses. WT, PB, DE, and AV helped improve the paper by suggesting additional ideas and by thorough revision/editing. AC and NS planted the


Archaeological consideration of maritime connectivity has ranged from a biogeographical perspective that considers the sea as a barrier to a view of seaways as ancient highways that facilitate exchange. Our results illustrate the former. We report three Late Neolithic human genomes from the Mediterranean island of Malta that are markedly enriched for runs of homozygosity, indicating inbreeding in their ancestry and an effective population size of only hundreds, a striking illustration of maritime isolation in this agricultural society. In the Late Neolithic, communities across mainland Europe experienced a resurgence of hunter-gatherer ancestry, pointing toward the persistence of different ancestral strands that subsequently admixed. This is absent in the Maltese genomes, giving a further indication of their genomic insularity. Imputation of genome-wide genotypes in our new and 258 published ancient individuals allowed shared identity-by-descent segment analysis, giving a fine-grained genetic geography of Neolithic Europe. This highlights the differentiating effects of seafaring Mediterranean expansion and also island colonization, including that of Ireland, Britain, and Orkney. These maritime effects contrast profoundly with a lack of migratory barriers in the establishment of Central European farming populations from Anatolia and the Balkans.  •Three inbred genomes from Malta, dated around 2500 BC•As in moderns, the genetic structure of Neolithic genomes is shaped by geography•Genomic insularity of island Neolithic populations•A marked distinction between the Danubian and Mediterranean farming expansion routes Using high-coverage genomes from ancient Maltese, Ariano et al. show that they had signals of both recent and more distant inbreeding, indicating small population size. Using diploid genome-wide data, the authors explore the genetic geography of Neolithic Europe which, echoing modern patterns, is shaped by physical topography, especially its seascapes. These developed through a series of cultural phases, with some material indications of external connectivity,To examine the demography of Late Neolithic Malta, we sequenced genomes from Xagħra circle. The elucidation of fine structure among closely related groups such as European Neolithic populations is challenging and requires the resolution afforded by genealogical methods.The retrieval of ancient genomes from warm climates is highly challenging, and the island of Gozo in the Maltese archipelago is one of the southernmost contexts in Europe . Howeverf3-statistics . Interestingly, the Xagħra Circle site shows the lowest amount of HG ancestry (6.8% ± 2.5%) among other groups from the Later Neolithic  yet reported in prehistory: an assertion secured by its high genome coverage  and a coROH) were plotted and compared with ancient individuals  derive from a turning point in Maltese prehistory c. 2450 BC, with a reducing density of radiocarbon dates,Archaeological evidence for overseas communication with Malta in this period is mixed. Some products, such as obsidian, types of chert, and polished stone were definitely imported.The carving and circulation of apparently obese human figurines was a marked feature of the late Maltese Neolithic,,Shared IBD is sensitive to recent common ancestry and, because it is a genealogical rather than a frequency-based method,This geographical difference also manifests in patterns of between-site sharing C, with tTo explore the potential impact of maritime colonization and continental topography on Neolithic genetic structure, we carried out principal component analysis (PCA) on a matrix of pairwise IBD sharing between individual imputed ancient individuals , as wellResults from each show a convergence on the existence of three clusters: first, the Western Mediterranean, including Iberian, French, and Sardinian individuals; second, the Eastern Mediterranean, featuring Greek, Balkan, and Anatolian individuals as well as Central Europeans; and third, the British and Irish archipelago. These are visible as blocks in the IBD heatmap of Neolithic populations migrated through Europe via two major routes, an overland transfer into Central Europe and a maritime dissemination along the Mediterranean coast.This supports a model of agricultural expansion into Central Europe from the Balkans that involved substantial numbers of migrants and strong backward communication during the dissemination of the Linearbandkeramik (LBK) complex, with populations remaining relatively well connected throughout the Neolithic period.To explore further, we also EEMSs using a stepping-stone model and a distance matrix computed from allele frequencies,,The rapid Neolithic colonization of the Western Mediterranean from the east was associated with the impressed cardial complex and likely took place through iterative coastal placements along the northern maritime littoral.,,,British and Irish populations form a sister grouping to the Mediterranean Neolithic in the second fineSTRUCTURE branching  and visiBasque, Orcadian, and Irish distinctiveness emerged in pioneering studies of modern human genetic variation,dbradley@tcd.ie).Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact: Daniel G. Bradley  derives from a deeper stratified area of stacked burials that also contained rich ceremonial objects higher in the stratigraphy. The two later samples were found in shallower deposits to the west. Xaghra5 was part of a display area of initially articulated human remains placed with portable figurines that was intentionally dismembered, most probably, at least in part to the slightly deeper location of Xaghra9 slightly to the north. Xaghra6 was placed as the use of the site began to intensify whereas the other two samples date to the period of most intensive activity some four hundred years later (c. 2500 BC).For this project, 5 petrous bones and 4 teeth from the Xagħra Circle archaeological site in Malta have been processed in the clean room facilities of the Smurfit Institute, Trinity College, Dublin . Full boDate ranges have been estimated using the 95% confidence interval of Bayesian chronological models of 117 radiocarbon dates from the site and their stratigraphic relationships.The initial screening of each sample and blank controls was performed by constructing a double-stranded DNA NGS library, priorly treated with Uracil-DNA-glycosylase (UDG), using the method outlined in Meyer and KircherThe initial screening to assess the endogenous DNA was performed by sequencing all the libraries with the Illumina HiSeq 2500 platform (100bp SE) at Macrogen (Republic of Korea). Subsequently, 3 samples with high endogenous DNA were further sequenced to high coverage using the HiSeq 2500 Illumina platform (100bp SE) at Macrogen (Republic of Korea). One sample was further sequenced using NovaSeq (50bp PE) Illumina platforms at TrinSeq (Ireland).http://broadinstitute.github.io/picard/). The coverage of each completed aligned file was calculated using the tool Qualimap v.2.1.1.For samples sequenced in single-end mode, reads were trimmed of their adapters and filtered based on their length using the software cutadapt v.1.9.1To determine the sex of each sample we applied two methods, one outlined in,,,,Pseudohaploid genotypes were called at approximately 600,000 autosomal sites from the Human Origins panel,,outgroup-f3 statistics,f3 where X represents different populations tested  where X represents Neolithic populations that fall close in the PCA to the ancient Maltese . In this analysis the CHG population is represented by two individuals published in Jones et al.Similarly to test for admixture between the Maltese and Caucasus hunter-gatherer (CHG) or Steppe populations we built our . Each group was furthermore subdivided in bins of 1000 years and only sub-groups with at least 2 individuals were considered for this analysis. The reference group was comprised of the following genomes: . The source population are Anatolian_Neolithic represented by individuals from Barcin and WHG individuals represented by Loschbour and KO1 . The imputed VCF files were filtered for SNPs only and genotype probability of 0.95 using bcftools v.1.3From samples that had been screened using an in-solution target capture method we selected 231 published genomes for imputation with a reported coverage on target regions of at least 0.6X and 650K SNPs called from the 1240K panel. To increase the number of samples from Neolithic Sardinia we also included 4 samples with a coverage higher than 0.6X and at least 460K SNPs safely called from the 1240K panel. Before imputation we selected a set of approximately 6.2 million SNPs to be called on our target dataset using the 1000 Genomes Project (1000G) resource as reference, filtered for individuals of African origin (defined with the AFR label) and with a minor allele frequency of 5%. Variants were called using the tool UnifiedGenotyper in GATK v2.4,After completion of imputation, four samples with high genotype missingness (>=0.1) were removed from subsequent analyses. Separately we also selected 120 WGS samples with a coverage of at least 0.4X to impute using the Software Impute2.Finally this combined resource was merged with 21 low coverage shotgun sequenced genomes which had been previously imputed.After merging these resources we then tested for differences in genotype missingness between datasets. To do so we first considered a set of 12 million SNPs common across all three datasets. We then calculated the missingness for each dataset and averaged across samples. We observed a genotype missing for the SNP capture and WGS imputed respectively of 12% and 13.5%.FROH. Separately, the hunter-gatherer and Neolithic farmer datasets were filtered for genotypes missingness and minor allele frequency using PLINK v1.9  obtaining respectively 51,289 SNPs and 41,426 SNPs. Using this set of SNPs we then identified ROH segments using PLINK v1.9. with the same parameters used in Gazal et al.ROH coefficients.ROH estimates obtained from imputed SNP capture with those calculated using imputed data from WGS data available for the same samples. We considered the same set of SNPs in both data types. For two samples that were whole genome screened and where the coverage was sufficiently high we also estimated the FROH coefficients using diploid genotype calls. For these two samples we applied the same protocol described in the imputation accuracy paragraph. In brief diploid genotypes with a depth below 10 or higher than 30 and a quality below 30 were excluded. As shown in To estimate the inbreeding coefficients of our imputed samples, we used a measure based on the proportion of the genome that is homozygous-by-descent , as employed in Cassidy et al.,For the sample, Xaghra9, which has sufficient coverage, we ran the software ROHan,-First degree: siblings and parent-offspring-Second degree: uncle-niece/aunt-nephew and grandparent-grandchild-Third degree: first cousins and great aunt-great nephew/great uncle-great nieceTo better understand the degree of relatedness between the parents of inbred samples we simulated different pedigree scenarios using dummy genotypes. We started from the same dataset described in the previous paragraph and we filtered for genotype missingness and minor allele frequency. This filtered resource was split by chromosome and then re-phased using SHAPEIT v.2.r837.Each of these scenarios was simulated 400 times using random sampled founders. ROH segments were found using PLINK with the same parameters described in the previous section (--homozyg-window-het 0 --homozyg-snp 50 --homozyg-kb 1 --homozyg-density 5000 --homozyg-gap 5000) and inbreeding coefficients estimates were also obtained using the same pipeline for both simulated and real genomes.In this work, we applied the software IBDSeq vr1206To test that no systematic bias was present between types of data, we compared the results obtained from those samples where it was possible to impute genome wide calls using both WGS and SNP capture data. We used a common set of SNPs for both data types that were pruned for genotype missingness and minor allele frequency, obtaining approximately 900 thousands markers per comparison. This set of common SNPs was then used to calculate the total amount of IBD that each sample type, WGS or SNP capture, shared with the rest of the dataset. As shown in To estimate the effective population size we used the IBD information obtained from IBDSeq as an input for the software IBDNe v.23Apr20.ae9.Separately we also estimated the effective population size of our Maltese group using the software hapROH v0.3a4.To investigate fine-scale population structure in our imputed dataset we used the software fineSTRUCTURE v2.The estimated matrix of chunk counts C obtaineTo visualize how geographical barriers affected migration between populations we used the software EEMS.http://www.nealelab.is/uk-biobank) using the UK BioBank resource. Prior to obtaining the PRS information we filtered individuals with more than 30% of BMI SNPs missing. We did not allow missing genotypes to be present in this analysis. SNPs in this dataset were filtered using a clumping/threshold approach through the software PLINK 1.9 with parameters (--clump-p1 0.01 --clump-kb 1000 --clump-r2 0.1). After filtering we obtained approximately 12 thousands SNPs that we used to compute the PRS in 247 ancient samples using the --score option in plink  for 247 individuals using the summary statistics calculated by the Neale Lab (in plink .


Phaseolus vulgaris L., known as common bean, is one of the most important grain legumes cultivated around the world for its immature pods and dry seeds, which are rich in protein and micronutrients. Common bean offers a cheap food and protein sources to ameliorate food shortage and malnutrition around the world. However, the genetic basis of most important traits in common bean remains unknown. This study aimed at identifying QTL and candidate gene models underlying twenty-six agronomically important traits in common bean. For this, we assembled and phenotyped a diversity panel of 200 P. vulgaris genotypes in the greenhouse, comprising determinate bushy, determinate climbing and indeterminate climbing beans. The panel included dry beans and snap beans from different breeding programmes, elite lines and landraces from around the world with a major focus on accessions of African, European and South American origin. The panel was genotyped using a cost-conscious targeted genotyping-by-sequencing (GBS) platform to take advantage of highly polymorphic SNPs detected in previous studies and in diverse germplasm. The detected single nucleotide polymorphisms (SNPs) were applied in marker-trait analysis and revealed sixty-two quantitative trait loci (QTL) significantly associated with sixteen traits. Gene model identification via a similarity-based approach implicated major candidate gene models underlying the QTL associated with ten traits including, flowering, yield, seed quality, pod and seed characteristics. Our study revealed six QTL for pod shattering including three new QTL potentially useful for breeding. However, the panel was evaluated in a single greenhouse environment and the findings should be corroborated by evaluations across different field environments. Some of the detected QTL and a number of candidate gene models only elucidate the understanding of the genetic nature of these traits and provide the basis for further studies. Finally, the study showed the possibility of using a limited number of SNPs in performing marker-trait association in common bean by applying a highly scalable targeted GBS approach. This targeted GBS approach is a cost-efficient strategy for assessment of the genetic basis of complex traits and can enable geneticists and breeders to identify novel loci and targets for marker-assisted breeding more efficiently. Phaseolus vulgaris L. (common bean) is one of the most important grain legumes, consumed as dietary staple worldwide, especially in Latin America and Africa   . MAS is on (MAS) . Marker-on (MAS) . Recentlon (MAS) , anthracon (MAS) , fusariuon (MAS)  and Anguon (MAS) ; producton (MAS) ; agronomon (MAS) , maturiton (MAS) , growth on (MAS) , heat ston (MAS) , pod shaon (MAS) , and seeon (MAS) . Althougon (MAS) . The stron (MAS)  and to con (MAS) . GBS useA diversity panel of two hundred common bean genotypes including dry beans and snap beans was assembled for this study. The panel comprised genotypes from different breeding programmes, elite lines and landraces from around the world. Diversity panels are often compiled by local regions and continents e.g., . AfricanThe evaluation was conducted in a greenhouse facility at Justus Liebig University Giessen, Germany during the winter season of 2019. The experiment was laid out in a completely randomized design with two replicates per genotype, each one grown in a different pot. For each replicate two subsamples were taken (2).The AED panel was harvested when the pods reached physiological maturity beginning from the eleventh week. The pods were harvested, placed in paper bags and dried to a constant weight in the oven for about 3-5 days at 40°C. The following data were collected for morpho-agronomic and seed quality traits. Growth habit, pod shattering, seed size and pod colour were recorded as categorical variables. Growth habit (GH) was scored on a scale of I-IV during the flowering stage and later confirmed at senescence, where I = determinate bushy type; II = indeterminate bushy type; III = determinate climber; IV = indeterminate climber . Pod shaThe AED panel was analyzed for seed, carbon and sulfur contents. Seed sample preparation was performed by drying the seeds, pooling approximately six seeds from two plant replicates and milling into fine powder using an electric milling machine  at maximum speed for about 1 min to obtain a homogenous powder. Analysis was carried out using the Elementar Analyzer Vario EL Cube  according to the company’s user’s manual. The analysis was carried out in triplicates. For each sample, seed protein content was estimated by multiplying seed nitrogen content with a conversion factor of 6.25 .The data collected on phenotypic traits were analyzed using R statistical software (version 1.2.5033). Summary statistics and analysis of variance (ANOVA) using fixed-factor model were conducted for every trait studied. Boxplots were created using the boxplot function in R to show phenotypic distributions based on the two major gene pools and based on major intra-gene pool subpopulations. Narrow-sense heritability is defined as the total variation in the population that is captured by additive effects. We calculated these using the heritability package in R, which estimates narrow-sense heritability based on a kinship matrix . The kinP. vulgaris (G19833) derived from the Andean pool (race Peru) (The AED panel was genotyped using the targeted genotyping by sequencing technology (SeqSNP) by Biosearch Technologies . Leaf samples for DNA isolation were collected from the genotypes at the seventh week. Fresh leaves from the third to fourth youngest were sampled using three 96-well plate LGC plant sample collection kit and supplied to Biosearch Technologies. The panel was genotyped with 1028 selected SNP markers from which 946 markers passed quality criteria of the commercial service provider. Average effective target SNP coverage was 229x produced by 75 bp single-end read sequencing on a Illumina NextSeq 500 machine. Data preprocessing and data analysis was performed by bowtie2 alignment of reads against the common bean reference G19833v2.1, an inbred landrace line of ce Peru) , variantDifferent methods were used to determine the population structure. In the first method, population structure analysis was performed using STRUCTURE 2.3.4  with a b2r and was performed alongside the association analysis using GenABEL  and published literature. A gene model was considered a candidate if the functional annotation or GO terms were related to the trait of interest.For the significant SNP markers, possible candidate gene models were identified based on proximity using a maximum of ± 100 kb distance . Pairwis2rh), ranging from 0.44 to 0.99, correlated strongly with the repeatability. Of the sixteen flowering, maturity, yield related and seed traits, 62.4% (10 traits) displayed narrow-sense heritability above 0.36, and six traits showed narrow-sense heritability over 0.7 providing the genetic basis for the GWAS. The three flowering time traits displayed the highest narrow-sense heritability (> 0.7), while eight yield related traits showed relatively low narrow-sense heritability compared with other groups . After SNP quality control, a total of 867 polymorphic SNPs were retained and used in the subsequent genetic analysis. The smallest number of polymorphic SNP markers were recorded on chromosome 4 with 48 polymorphic SNPs, while chromosome 9 had the highest number of polymorphic SNPs (101). The SNP distribution across the common bean chromosomes after filtering is shown in A total of 2500 SNP flanking sequences were selected from previous studies , some ofThe number of subgroups within the diversity panel was evaluated using Structure and GAPIT. In the Structure, the Q-matrix was defined by a ΔK index with a peak at K = 2, indicating the number of main subgroups which represent the Andean and Mesoamerican gene pools. The heatmap from GAPIT showed the two major clusters while the bar plot diagram showed each individual in k-coloured segments with lengths equivalent to each of the subgroup Figure 42r). LD decay in the population was estimated from the mean 2r as a function of inter-SNP distance. Calculations indicated that LD values decayed with the genetic distance in the population, with an average 2r of about 0.4 for very close markers < 500 Kb, and decays to about 0.1 for markers as distant as 5 Mb. The average 2r drops to 0.25 above 2 Mb; an average 2r of about 0.2 extends up to 4 Mb. The linkage disequilibrium analysis was conducted with the 867 polymorphic SNPs for the 192 genotypes and all chromosomes using the squared-allele frequency correlation r2. LD deca-16), indicating the strongest association between a marker and a trait, was observed on chromosome Pv10 for flowering time, explaining about 38.5% variation for the trait (Association of the phenotypic traits with 867 polymorphic SNPs was implemented using the fixed and random model Circulating Probability Unification (FarmCPU). Also, association analysis was performed with a compressed mixed linear model  implemenhe trait Figure 5Below we describe ten selected traits and QTL regions where we could identify novel candidate gene models potentially relevant for better understanding of the biological mechanisms underlying the studied traits including flowering time, yield related, seed quality and pod traits see Figure 6A. thaliana, Glycine max and Phaseolus vulgaris  and seed number (SN) and seed dry weight (SDWT) and days from flowering to maturity (DFTM) as has been reported before e.g. . Growth 2r = 0.2. The LD decay for chromosomes Pv06, Pv07, and Pv11 in the panel was exceptionally low  by applying a targeted genotyping-by-sequencing approach. In recent years, GWAS has mainly been performed in common bean using the BeanK_3 Bead array with 3900 to 4900 polymorphic SNP markers e.g.  or by usally low . This isally low .GWAS identified QTL associated with sixteen traits, some of which are novel while some overlap with previously reported QTL. QTL were reported as overlapping if located within the confidence interval defined as the peak SNP ± 100 kb of the associated SNP in our study. For instance, the QTL qFT-c and qDTBI-b/c observed for flowering time were not reported in earlier studies, while the QTL on chromosomes Pv01, Pv02, Pv10 and Pv11 are located on the same chromosomes and/or are overlapping with QTL previously reported for flowering time traits . HoweverCultivation of beans showing a determinate bushy growth habit with photoperiod insensitivity  is beneficial for commercial production as it results in a shortened in a shortened life cycle and tolerance to mechanical harvesting . The disCandidate gene models are discussed below in detail especially for some agronomically important traits exhibiting high heritability such as pod shattering and flowering time related traits, as our study revealed preliminary QTL harbouring novel interesting candidate gene models for these traits.PvIND, on chromosome Pv02 that is located near the St locus to affect pod strings, a factor influencing pod dehiscence. Another study reported a major locus on chromosome Pv02 which explained 32% of variation in pod suture string in a recombinant inbred population and mapped St locus to bordering PvIND on Pv02  through the programmes: DAAD In-Country/In-Region Scholarship (grant number: 57423580) and Short-Term Research Grant in Germany (grant number: 57500260).SU appreciates the German Academic Exchange Service (DAAD) for their funding during his study programme. We thank Annette Plank, Stavros Tzigos and Roland Kürschner for their assistance during the phenotypic evaluation. We are also grateful to Benjamin Wittkop and Stjepan Vukasovic for their help with the Elementar Analyzer. We thank Sven E. Weber and Iulian Gabur for help with R scripting. We appreciate the Department of Plant Science and Biotechnology, University of Nigeria Nsukka and Prof. Paul Bayeri of Crop Science Department, University of Nigeria Nsukka for their valuable comments and suggestions during this work. We thank Thomas Meyer-Lüpken from Van Waveren for critical review of the manuscript. We thank Van Waveren, IPK and CIAT, Uganda for providing the seeds.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interestAll claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Puccinia graminis f.sp. tritici (Pgt) causes stem rust disease in wheat that can result in severe yield losses. The factors driving the evolution of its virulence and adaptation remain poorly characterized. We utilize long-read sequencing to develop a haplotype-resolved genome assembly of a U.S. isolate of Pgt. Using Pgt haplotypes as a reference, we characterize the structural variants (SVs) and single nucleotide polymorphisms in a diverse panel of isolates. SVs impact the repertoire of predicted effectors, secreted proteins involved in host-pathogen interaction, and show evidence of purifying selection. By analyzing global and local genomic ancestry we demonstrate that the origin of 8 out of 12 Pgt clades is linked with either somatic hybridization or sexual recombination between the diverged donor populations. Our study shows that SVs and admixture events appear to play an important role in broadening Pgt virulence and the origin of highly virulent races, creating a resource for studying the evolution of Pgt virulence and preventing future epidemic outbreaks. Factors defining wheat stem rust pathogen (Pgt) virulence remain poorly characterized. Comparative population genomics based on Pgt haplotypes suggest that structural variation and admixture through somatic hybridization and sexual recombination play an important role in broadening Pgt virulence. Puccinia graminis f.sp. tritici (Pgt) is an obligate biotrophic pathogen causing stem rust disease in wheat. Epidemics of stem rust have been reported worldwide, including in Africa1, the Middle East2, North America3, Asia2, Europe4, and other regions5. The high levels of variability in virulence  and frequent emergence of races with new virulence combinations have been well-documented among Pgt populations7. However, many factors contributing to the evolution of Pgt virulence remain poorly understood8.Puccinia coronata f. sp. avenae9, Zymoseptoria tritici8, and Parastagonospora nodorum10, start uncovering the role of variation in a special class of genes, which encodes secreted proteins referred to as effectors, in adaptation to diverse crop genotypes. Fungal effectors are responsible for establishing a compatible interaction with the host by suppressing defense responses11 and modulating cellular processes13. The ability to sense these effectors is one of the strategies used by plants to recognize pathogens and trigger the immune response. The sequence divergence or loss-of-function mutations in effector-encoding genes or their complete deletion could render a resistant host unable to recognize a pathogen15. The interaction between the host immune system and the pathogen was postulated to diversify the arsenal of effector-encoding genes in pathogen populations13. The signatures of diversifying and direct selection detected in the effector-encoding regions from Parastagonospora nodorum and Puccinia coronata fungi further corroborate the effectors’ involvement in adaptation to genetically diverse hosts10. Thus, processes affecting the pathogen’s effector content should play a critical role in the evolution of new virulent strains adapted to resistant crop varieties.The first genome-scale diversity analyses performed on a limited number of agricultural pathogens, including pathogens of several cereal crops, such as 18. SVs are more likely to affect effector-encoding genes and contribute to pathogen evolution and adaptation than other types of variation20. Inter-species comparisons showed that the location of many of these effectors in the repetitive portions of the genome appears to promote a high rate of evolution18, which led to the development of the concept of a “two-speed genome”22. However, SVs in the pathogen populations remain poorly characterized due to the limitations of the short-read-based genome assemblies. The diversity analysis of the agricultural fungal pathogens has been largely focused on small SVs10, or detecting gene presence/absence variation based on the depth of read coverage10. Only recently, a reduction in the cost of long-read sequencing allowed for assembling multiple ~40-Mb-size genomes of the wheat pathogen Zymoseptoria tritici and characterizing population-scale SVs23. Characterization of the scope and distribution of SVs in Pgt populations could help to understand their impact of effector complement and potential role in the origin of virulent strains.Recent genomic studies revealed the importance of structural variants (SVs) in effector complement evolution24 and sexual recombination between diverged isolates are among two other factors that have the potential to diversify effector complements in Pgt and lead to the origin of new virulent strains. The origin of the Ug99 lineage of Pgt, showing a broad virulence spectrum, was associated with somatic hybridization25. Likewise, the Pgt isolates collected from an alternate host Berberis spp., which is required for the sexual reproduction of Pgt, showed broad virulence and the potential to cause stem rust epidemics6. However, the relative contribution of somatic hybridization and sexual recombination to the evolution of new Pgt lineages and virulent strains remains to be investigated.Both somatic hybridization25 to use as a pan-genome reference for characterizing genomic diversity in a global panel of Pgt isolates. We analyze the patterns of single nucleotide polymorphism (SNPs) and SVs across the Pgt genome to characterize the genetic composition of and genetic differentiation among regional samples of Pgt isolates with the goal of tracing their origin and identifying adaptive variation. Using comparative analysis of the haplotype-resolved Pgt assemblies, we characterize SVs in the diverse panel of Pgt isolates. Our results indicate that SVs, many of which harbor effector-encoding genes, are under stronger purifying selection than neutral variants in the genome. In combination with the enrichment of effectors in the highly repetitive regions of the Pgt genome, which are prone to generate new SVs at an elevated rate, our findings suggest that SVs contribute to the fast evolution of the effector repertoire in Pgt and likely facilitate adaptation to new host genotypes from distinct geographic regions. By studying global and local ancestry in the Pgt panel, we show that the origin of 8 out of 12 phylogenetic clades is associated with inter-lineage admixture in the populations undergoing either somatic hybridization or sexual recombination. The virulence of the Pgt panel is characterized by a diverse set of wheat lines carrying distinct stem rust resistance genes. The increase in virulence observed in the group of admixed isolates suggests that hybridization between distinct lineages likely play an important role in the evolution of Pgt virulence.In this study, the dikaryotic genome of Pgt, which has two nuclei, is sequenced using a combination of long-and short-read sequencing technologies. We use Oxford Nanopore long-read sequence data to construct a chromosome-level haplotype-resolved ~180 Mb genome assembly of the 99KS76A-1 isolate (race RKRQC). This assembly is combined with the previously assembled haplotypes of the Pgt21-0 isolate14. It was formerly identified as race RKQQC but was recently re-phenotyped and reclassified as race RKRQC. To create a haplotype-resolved assembly of the 99KS76A-1 genome, we used the combination of the Hi-C technique, long-read Oxford Nanopore (OXN) sequencing with short-read Illumina sequencing (2 × 300 bp). A two-step pipeline was utilized for scaffolding the contigs generated using Canu assembler26 as described in the Methods. The first step was based on a bin-assignment strategy25  between the two haplotypes of 99KS76A-1 might affect the variant discovery rate, read mapping was done to each of the E and F haplotypes separately. On average, about 40% of read pairs were uniquely mapped to individual haplotypes providing ~25X coverage per haploid genome. SVs between the haplotypes were identified by aligning haplotype F against haplotype E36 (see Methods). After filtering for read depth and allele frequency (see Methods), we identified 2,208,026 SNPs and small-scale indels in haplotype E. After adding variants discovered in the scaffolds missing in one of the haplotypes, we obtained 2,325,518 variants . Using this data, the average estimates of genetic diversity (π) in the Pgt panel were 4.6 × 10−3 for the introns, 3.4 × 10−3 for the genic regions, and 3.0 × 10−3 for exons.We investigated the patterns of genomic diversity in a panel of 77 diverse Pgt isolates collected in the United States and other parts of the world, including a group of isolates from the Ug99 lineage   affect linkage disequilibrium (LD), making it a useful summary of diversity patterns in the populations. We compared LD decay in the United States and non-US samples of Pgt isolates by fitting the LD decay model described by ref. tes Fig. . Among f10, likely in response to high selection pressure associated with the need to evade recognition by the host’s resistance genes. While in the entire Pgt panel, effector-encoding genes show a significantly lower difference in the levels of diversity (π = 3.2 × 10−3) compared to non-effector-encoding genes (π = 3.4 × 10−3)   Fig. , the relK = 6  significant increase in inter-population genetic differentiation FST compared to the effector-encoding and other genes located outside of the selective sweep regions  among the four assembled haplotypes of the 99KS76A-1 and Pgt21-0 isolates. A total of 20,576 SVs affecting 79 Mbp of the genome were detected. The size of each SV ranged from 50 bp to 1.3 Mbp, with most SVs being smaller than 5 kbp   Fig. , consistnes Fig. , indicatTo characterize the genomic distribution and frequency of SVs in the Pgt panel, we applied k-mer-based analysis to the raw Illumina sequencing data generated for the Pgt isolates (described in Methods). A diagnostic set of 31 nucleotide-long k-mers tagging both presence and absence alleles at each of the 11,517 SV sites (insertion and deletion)  was lower than that for SNPs (0.3), there were SVs showing high levels of genetic differentiation between the United States and non-US Pgt populations. Among 38 highly differentiated SVs with FST values above 95th percentile (FST = 0.7) of genome-wide FST distribution, we identified two SVs (insertion sites at chr5-E: 4106919-4106921 and chr14-E: 562512-562513) overlapping with the effector-encoding genes . This result, combined with the enrichment of effector-encoding genes within the selective sweeps regions  between inferred and true ancestry  between Clades IX and UN1  and UN1 (r2 = 0.85) could be modeled as a two-way admixture between Clades UN1 and VII, and between Clades VI-A and UN2  as a two-way admixture  between clades VI-A and VI-B  IV Fig. . Inferen IV Fig. . HoweverI-B Fig. . The disI-B Fig.  is consi25. By fitting a two-way admixture model (r2 = 0.72), we identified that Ug99 race haplotypes from clade I share ancestry with isolates from clade II  and clade IX (including Pgt21-0)  Fig.  and 18.  IX Fig. , we hypo IX Fig.  and 18,  IX Fig. , which cIn summary, by examining the local ancestry of individual Pgt isolates from clades showing evidence of admixture, we showed that the origin of three clades  is linked with sexual recombination between the diverged Pgt lineages Fig.  and 15. 50. To assess the impact of inter-lineage admixture on virulence, we compared the virulence scores  that were effective against all Pgt isolates from both source clades. Within this group of Sr genes, we counted how many Sr genes became ineffective against at least half of the isolates from the corresponding admixed clade. This loss of Sr gene efficiency could most likely be attributed to post-admixture processes affecting Pgt virulence. For example, out of 13 Sr genes, which are effective against all Pgt isolates from source clades IX and II, four (30%) lost their efficiency against Pgt from the admixed Ug99 clade (clade I). Using this approach, we show that, on average, 15% of Sr genes could have lost their efficiency after admixture among distinct Pgt lineages . The North American group of isolates showed lower levels of mean observed heterozygosity (Ho = 0.16), inbreeding coefficient (FIS = 0.03), and genetic diversity (π) than isolates outside of the US   Fig. . Howeverion Fig. , the Norion Fig. . The pos54.The comparison of the genomic distribution of the predicted effector-encoding genes and patterns of selection and genetic differentiation between the groups of Pgt isolates from distinct geographic regions suggests that effectors are more likely the selection targets of regional adaptation than other groups of genes. The signatures of selection around effector-encoding genes were evident for Pgt isolates grouped based on either virulence specificity or geographic regions, indicating that effectors could be associated with adaptation to new environmental conditions as well as adaptation to new genotypes of wheat cultivars. The latter is consistent with the earlier studies that showed that some fungal effectors display the signatures of selection in populations adapted to specific hosts25 generated using long-read sequence data allowed us to investigate the impact of SVs on effector repertoire in the Pgt diversity panel. The overlap between effectors and SVs that show the high levels of inter-population genetic differentiation is suggestive of SV’s importance in adaptive evolution55. The overlap of effector-encoding genes and some of the known avirulence factors (AvrSr3514 and AvrSr5015) with the regions affected by SVs supports this hypothesis.A set of highly contiguous haplotype-resolved genome assemblies21. Though several studies of fungal genomes found no evidence for this “two-speed genome” architecture57, our analyses of intraspecies genomic variation appear to be compatible with this model. We show that effectors tend to be enriched in the regions showing high levels of structural variation and TE content. This observation is consistent with the role of TEs in promoting structural variation through mechanisms associated with DNA recombination and replication processes59. Combined, our results indicate that the TE-driven SVs could have contributed to the evolution of the Pgt effector content.Previously, the comparative genome analysis between different species of fungi showed that the distribution of effector-encoding genes across the genome is not random and tends to be physically associated with the TE-rich repetitive regionsThe relative roles of sexual recombination and somatic hybridization in shaping Pgt diversity and the evolution of new virulence are poorly understood. Here, we model local fine-scale ancestry as a two-way admixture and show that chromosomal patterns of ancestry could be used to ascertain the contribution of sexual recombination and somatic hybridization to the origin of the Pgt lineages. We show that admixture between the distinct genetic groups of isolates is common in Pgt and that both sexual recombination and somatic hybridization impacted the genetic composition of Pgt isolates. The MOSAIC analyses suggest that the origin of 8 out of 12 phylogenetic clades in our Pgt panel could be modeled as a two-way admixture. The analyses of local ancestry distribution along the chromosomes of individual isolates from each of these eight clades suggest that admixture events leading to the origin of clades I, II, VII, IX, and VI-B were likely associated with somatic hybridization Fig. , whereas33, it is likely that the TKTTF origin involves recombination between haplotypes within the same clade and step-wise accumulation of mutations in avirulence factors. The isolates from closely related clade VII, which share about half of its ancestry with clade IV, show evidence of two-way admixture, which, based on the chromosomal patterns of local ancestry, likely results from somatic hybridization between isolates from clades VI-A and VI-B  from Ethiopia appears not linked with a population admixture. However, considering the results of an earlier study, which suggested that clade IV isolates sampled in 2013 originated from the sexually reproducing populationI-B Fig. . InteresI-B Fig. . The rec25. The origin of the second Ug99 haplotype remained uncertain. The analyses of local ancestry and clade-specific SNP alleles in our Pgt panel confirmed somatic hybridization as one of the most likely mechanisms of the Ug99 origin and Pgt21-0 as a possible donor of one of the haplotypes. This isolate belongs to a group of isolates from new clade IX, also including isolates from Ethiopia and Turkey. We identified the unknown source of the second Ug99 haplotype, which includes a group of isolates from Kenya and Ethiopia that are part of the previously defined clade II45.Previously, it was shown that somatic hybridization also contributed to the origin of the Ug99 race, with one of the haplotypes tracing its origin to isolate Pgt21-051 using the panel of wheat lines carrying the previously characterized stem rust resistance genes61. These results were consistent with earlier work suggesting that sexually reproducing Pgt isolates collected from barberry could be linked with rust epidemics6 and high levels of virulence diversity34.The analysis of ancestry segments in the isolates from clades VI-A, UN1, and UN2 revealed recombinant chromosomes, suggesting their origin in populations undergoing sexual reproduction. The role of sexual recombination in the origin of new virulence specificities was confirmed by phenotyping the progeny of two Pgt isolates51. One of the expected consequences of recent selection for recessive alleles of avirulence genes in populations with the prevalence of asexual reproduction is genome-wide loss of heterozygosity and diversity. These diversity trends were observed for the US sample of Pgt isolates and are consistent with the prevalence of asexual Pgt reproduction in the United States39 due to the eradication of the alternate Pgt host, barberry. Using these mechanisms, however, it is harder to explain the loss of efficiency against admixed Pgt isolates observed for 15% of Sr genes, which otherwise are highly effective against ancestral source Pgt clades. Though, it is possible that admixture could promote virulence by bringing together two haplotypes carrying virulent alleles of effectors unrecognizable by the host’s immune system. Alternatively, we could consider the scenario where broader virulence of admixed isolates is associated with their improved ability to suppress the host’s immune responses. In this case, admixture likely promotes virulence by combining avirulence suppressors evolved in distinct lineages. This strategy of new virulence origin was proposed for inter-specific hybrids of Blumeria graminis and Zymoseptoria tritici63. To what extent it is applicable to virulence evolution at the intra-specific level remains to be investigated. Further studies aimed at cloning and characterization of pathogen effectors from diverse pathosystems are needed to uncover the entire diversity of mechanisms that contribute to the evolution of virulence in agricultural pathogens. Resources generated in our study, including sequenced and phenotyped Pgt isolates, have the potential to facilitate the characterization of the Pgt avirulence factors, deepen our understanding of how this important pathogen evolves and adapts, and ultimately contribute to efforts aimed at reducing the risk of Pgt pandemics.There are several mechanisms by which sexual recombination and somatic hybridization could contribute to the origin of new virulent isolates. The loss of dominant avirulence factors in sexually recombining populations with some minor contribution from the suppressors of avirulence genes were previously postulated as the main driving factors behind the origin of virulent Pgt isolates64 was used, with minor modifications. We used a lysis buffer prepared by mixing 5 ml of Buffer A, 5 ml of Buffer B, 2 ml of Buffer C, 1 ml of 10% PVP 40, and 1 ml of sterile ddH2O. Buffer A composition: 0.35 M sorbitol, 0.1 M Tris-HCl (pH 8.0), 5 mM EDTA (pH 8.0) (autoclaved to sterilize). Buffer B composition: 0.2 M Tris-HCl (pH 8.0), 50 mM EDTA (pH 8.0), 2 M NaCl, 2% CTAB (autoclaved to sterilize). Composition of Buffer C: 5% Sarkosyl N-lauroylsarcosine sodium salt (Sigma L5125) (filtered through 0.22-micron membrane to sterilize). The lysis buffer was heated to 50 degrees after mixing and cooled to room temperature. Samples were grinded and added to the lysis buffer before adding 50 µl of RNAse A (10 mg/ml). Samples were rotated on Rotator Genie at the slowest speed at room temperature for 1 h, followed by adding 200 µl of Proteinase K and rotating for an additional 1–2 h. The tube was incubated for 5 min on ice, and then 200 µl of Proteinase K was added, mixed, and incubated for another 5 min. The samples were centrifuged at 5000×g for 12 min at 4 °C, supernatant was removed and placed into a new tube after adding 15 ml of phenol/chloroform/isoamyl alcohol , followed by rotating for 5 min at the slowest speed. The sample was centrifuged at 4000×g for 10 min at 4 °C, followed by removing the supernatant and adding 15 ml P/C/I. Sodium acetate and isopropanol were added and mixed, followed by incubation at room temperature for 5–10 min, and centrifugation at 8000×g for 30 min at 4 °C. Supernatant was poured slowly, and the pellet was washed with 70% ethanol and dissolved in 100 ul of 10 mM Tris-HCl buffer (pH 8.5)64.The wheat cultivar “Morocco” was used for spores grow-up and collection of 99KS76A. To obtain both high quality and high quantities of fungal DNA, collected urediniospores were dusted in a fine layer over the top of the diluted germination solution using a 40 µm metal sieve, and germinated spore mats were collected after 16 h. A high-quality fungal DNA isolation protocolOxford Nanopore (OXN) LSK-109 ligation sequencing kits with flow cell 9.4.1 was used to generate sequencing data for 99KS76A-1. Long-read DNA sequence data were generated using five flow cells using a MinION device. Raw fast5 files were converted to fastq files using guppy software. Resulting fastq reads were passed through the NanoFilt program to retain sequencing reads with a quality score >6 and read length >1500 bp.14 were used in this study.For Hi-C sequencing, a spore mat was sent to Dovetail Genomics for library preparation and sequencing. In addition, previously published Miseq and PacBio data developed for the 99KS76A-1 isolate14 were trimmed using Trimmomatic v0.3865 with the following parameters: LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:36. A total of 163,655,621 Hi-C read pairs, 4,175,267 OXN reads with the average length of 4258 bp, 264,532 PacBio reads with an average length of 2350 bp, and 31,390,809 Illumina read pairs (2 × 300 bp) were generated. Assembly was performed using Canu v1.726 followed by correcting assembly errors by aligning MiSeq reads (2 × 300 bp) to the assembled contigs using Pilon v1.2219 (4.36% error rate of the assembly was corrected). For this purpose, the trimmed MiSeq reads were aligned to the Canu assembly using BWA-mem v0.7.1766 with default settings and sorted with the samtools v1.867.To create haplotype-resolved assembly of the 99KS76A-1 isolate (race RKRQC), we have used the combination of High-throughput Chromosome Conformation Capture (Hi-C) technique, long-read Oxford Nanopore (OXN) sequencing with short-read Illumina sequencing (2 × 300 bp). The previously generated Illumina MiSeq reads (2 × 300 bp)25. The gene models predicted in our 99KS76A-1 assembly were aligned against the assembled contigs using BLASTN v2.9.068 and results were filtered to retain alignments with identity ≥95% and gene coverage ≥50%. In Puccinia spp., somatic cells are dikaryotic, consisting of two physically distinct haploid nuclei57. If two contigs shared at least 25 genes, they were considered to represent haplotypes from distinct Pgt nuclei. These homologous contigs were grouped into distinct bins (Supplementary Table 36 with nucmer (--maxmatch) and plotted by mummerplot . For each HG, noisy inter-allelic and weak Hi-C density signals (number of read pairs/length (contigA + contigB) <0.35) were removed by the ALLHiC Pruning function. Subsequently, together with information gathered from the previous bin-assignment step, contigs in each HG were partitioned into two groups (or haplotypes) through the ALLHiC Partition routine. The ALLHiC27 scaffolding resulted in a chromosome-level assembly of the 99KS76A-1 genome consisting of 36 chromosomes with a total length of ~161 Mbp .The haplotype assignment for the 99KS76A-1 genome scaffolds was performed by counting Hi-C links between the scaffolds , and alignment results were plotted using Dotplotly (-l -m 10000) to evaluate the collinearity between assemblies  295,186 transcripts previously assembled using the RNA-seq data generated for the Pgt-infected leaf tissues (includes both the wheat and Pgt transcripts)14, (2) 37,843 CDS reported for Pgt21-0 genome25; (3) 37,820 CDS reported for ug99 genome25; (4) 15,979 gene models reported for the genome of Pgt strain CDL 75-36-700-332; (5) 22,391 transcripts that were generated for the Australian Pgt isolates31. Genes encoding effector proteins were predicted using SignalP v5.072, ApoplastP v1.0.173, and Phobious v1.0174, with the removal of proteins targeted to mitochondria or having transmembrane domains by running TargetP v2.075 and TMHMM v2.077.RepeatMasker v4.1.0 with Repbase v. 25.12. was used to mask transposable elements in the final assembly. The evidence-based gene prediction was performed with the PASA pipeline v.r2014041733. Illumina reads were aligned to the non-redundant set of the 99KS76A-1 scaffolds using HISAT2 program78 with option --no-spliced-alignment. After removing reads mapping to multiple locations and marking duplicates, multi-sample variant calling was performed using the HaplotypeCaller of GATK v4.1.4.179 with the default settings. The variable sites have been filtered to remove those that (1) had more than two alleles, (2) minimum depth of total read coverage was below 3, (3) more than 25% of isolates have no genotype calls, or (4) minimum depth of alternative allele read coverage below 3. To improve the accuracy of rare variant calling with the minor allele present in only single isolate (singleton variants), we removed sites with a minimum depth of read coverage below 10. To assess the accuracy of genotype calling, we compared the allelic variants called for 99KS76A-1 by mapping short-read data to the reference genome of 99KS76A-1. Both SNP and indel calling showed 99% concordance, indicating the high accuracy of variant calling in our dataset.A diverse collection of 77 rust isolates  for each SNP site was computed using BCFtools v.1.9 fill-tags function. R package PopGenome v2.7.580 was used to calculate nucleotide diversity (π), TajimaD, and pairwise FST statistics. In these calculations, sites with missing data or located within the regions overlapping with repeated sequences identified by RepeatMasker v4.1.0 were excluded.The Pgt panel includes 18 F81 was used to randomly pick 5% of SNPs for LD calculation with the following parameters: --ld-window 20000, --ld-window-kb 7000, --ld-window-r2 0, --allow-extra-chr, --keep-allele-order, --thin 0.05. To reduce the effect of population structure on the estimates of the rate of LD decay, r2 was calculated for two samples of Pgt isolates (15 from the United States and 22 outside of the United States) that show no evidence of admixture at the optimal value of K = 6. LD decay was modeled by fitting the estimates of r2 to a model described in ref. LD analyses were performed on a dataset after removing variants with MAF <0.025 and heterozygous SNPs. Plink v1.940 and SweeD41. Both methods were shown to be robust to variation in demographic history and recombination rate heterogeneity across the genome. The datasets used for both XP-CLR and SweeD analyses were filtered by vcftools with –remove-indels –maf 0.01 and –max-missing 0.75. XP-CLR40 was run with a grid size of 100 bp, sliding window size of 0.05 cM, maximum of 50 SNPs within each window, and pairwise correlation coefficient of 0.8. The SweeD was run with the same grid size of 100. To select test statistic thresholds for detecting outliers, we conducted selection scans using both approaches in the simulated datasets, taking into account the demographic history of the two Pgt samples, 15 from the United States (US isolates) and 22 from Africa, Europe, and South America (non-US isolates). The two sets of Pgt isolates were selected to be genetically uniform and have no admixed genotypes at the optimal value of K = 6. The population size and split time history of these Pgt samples was inferred using the SMC + + tool82 using the process described in the software manual. The effective population sizes of non-US and US isolates were estimated to be 124,000 (N0) and 50,840 (N1), respectively, with a split time of 95,000–110,000 generations ago. These estimates of population history were used to simulate diversity datasets for 37 individuals  using the following MaCS83 command: macs 37 5e5 -i 1 -t 0.003 -r 0.00001 -I 2 22 15 -n 2 0.41 -ej 0.22 2 1 2. Effective population size-scaled diversity −t was calculated using the mutation rate of 5 × 10−984. The 4N-scaled recombination rate parameter -r was tested for ranges between 0.001 and 0.00001 and shown not to have a significant impact on the results of selective sweep statistics. Thus, we used a more conservative approach to simulate data with the lower value of -r 0.00001. The effective size of population 2 (N1) was 0.41 of the effective size of population 1 (N0). For each simulated dataset, we calculated Tajima’s D and diversity statistics using msstats program based on the C + + libsequence library85 and selected for downstream selective sweep analyses only those sets that match the observed diversity statistics within one standard deviation of Tajima’s D (0.74 ± 0.75) and diversity (3.3 × 10−3 ± 3.0 × 10−3). The selective sweep analyses using both methods were conducted for a total of 1000 datasets. The 95th percentiles of the distributions built using the highest test statistic values from each simulated dataset were 53.48 for XP-CLR and 4.27 for SweeD. Since these values were lower than the 95th percentiles of the test statistic distribution for XP-CLR (72.26) and SweeD (17.68) in the real-life dataset, the empirically defined thresholds were used in the study. The outliers above the XP-CLR and SweeD scan thresholds were identified, and the adjacent outlier regions were merged. The regions shared by both scans were classified as genomic regions under selection and the number of effector-encoding genes overlapping with these regions was calculated. To assess the chance of obtaining the set of genomic regions overlapping (159) with effector-encoding genes, we counted the number of genomic regions overlapping with effectors in 1000 randomized datasets . To improve structural variant detection, two methods were used. First method is based on Assemblytics42. After filtering the MUMmer results with parameters setting of -i 90 -r -q, Assemblytics42 was used for detecting SVs with unique anchor lengths of 500 bp, minimum variant size of 50 bp, and maximum variant size of 1000 kb. Since Assemblytics is more accurate in detecting small-size SVs than the large ones, the large SVs with size ≥30 kb were filtered out if they did not appear as consecutive in terms of coordinates along the chromosome. The large-size SVs (≥10 kbp) were identified using the second method. In the filtered MUMmer alignment results, only those alignments that showed consecutive coordinates were kept, and gaps between the alignments were considered SVs if their size was between 10 and 1500 kb. SVs obtained using these two methods were combined for further analysis. There are four types of SVs were detected using both methods: insertions, deletions, expansions, and contractions42 , four homologous chromosomes were compared to each other with MUMmer v4.0We assessed the accuracy of SV calling by comparing regions carrying SVs with individual long OXN reads spanning these regions in the 99KS76A-1 genome. Since each OXN read represents a single DNA molecule, this approach provides independent validation of identified SVs. For this analysis, we selected 1947 heterozygous SVs from ten homologous chromosomes  of 99KS76A-1. The OXN reads were mapped to both haplotypes of the 99KS76A-1 genome using minimap2, and those reads that span an entire SV and at least 1000 bp from each side were compared with the SV alleles detected by the alignment of the Pgt haplotypes. Out of 1889 analyzed SVs, 95% were confirmed to have both SV alleles at each variable site, indicating that the SV discovery by whole genome comparison has good accuracy. The actual concordance rate between the SVs detected by haplotype and OXN read alignments is likely higher. Due to the repetitive nature of some of the OXN reads and the high per-read error rate, some SVs were covered by reads mapping to more than single locations. These SVs were counted towards unconfirmed SVs.86, the presence or absence of these diagnostic k-mers was recorded to generate a matrix of SV genotypes for the whole Pgt panel using the following rules: (1) if the number of k-mers for the SV presence site (which include two boundaries) ≥15, and the number of k-mers for the SV absence site (which includes only one boundary) = 0, then genotype is 1/1; (2) if the number of k-mers for the SV presence site ≥15, and the number of k-mers for the SV absence site ≥5, then genotype is 0/1;3) if the number of k-mers for the SV presence site <5, and the number of k-mers for the SV absence site ≥5, then genotype is 0/0. The genotyping matrix was converted to the vcf format for constructing the NJ tree after filtering out SVs with no missing rate and minor allele frequency ≤10%.To genotype SVs in each Pgt isolate, sets of consecutive diagnostic 31 nucleotide-long k-mers that cover the boundaries SV sites were developed  were filtered to remove sites with more than 10% missing rate. The SNP dataset comparable to SVs was built by using only those sites that segregated between the four haplotypes of 99KS76A-1 and Pgt21-0. These sites were detected by comparing the 99KS76A-1 and Pgt21-0 haplotypes using MUMmer32. SNP positions were identified using the blastn2snp tool from JVarkit v.1.0. Since the CDL 775-36-700-3 genome assembly32 is largely haploid with the contigs from both nuclei collapsed together, genotypes were recorded as either 1/1 or 0/0 when only one of the two possible alleles is present at the aligned site. In case if no aligned sequences were detected or aligned sequences represented both alleles, genotypes are called missing.In addition to isolates in the Pgt diversity panel, we have also included CDL 775-36-700-3 and Pgt21-0 for population structure analysis. For these two isolates, the genotypes of SNP sites segregating in the Pgt diversity panel were inferred by aligning 100 bp SNP flanking sequences to the published genome assembly87, adegenet v2.1.388, ape v5.489, and ggtree v2.2.490 packages. Euclidean genetic distance between each individual isolate was calculated with the function “dist” in adegenet. NJ estimation of Saitou and Nei91 was built with “nj” function of ape, and was plotted using ggtree90.To construct a neighbor-joining (NJ) tree, SNP sites are filtered to keep those that have no missing data, and a minor allele present in at least three isolates. Distance-based phylogenetic analysis was computed using R v4.0.0, with vcfR v1.12.044 was performed with fivefold cross-validation (CV) for K ranging from 2 to 15 to estimate the optimal number of populations. At each value of K, ADMIXTURE was run ten times with different starting random seed numbers. Q matrix for the same values of K was merged using mean values and plotted using the R package pophelper v2.3.0. For identifying potential donor sources for each clade in the whole panel, SNPs were filtered by vcftools with --maf 0.1 and –max-missing 1, and phased by Beagle.28Jun21.220.jar92 with default settings. For inferring the local ancestry of each clade using donor sources, SNPs were phased and filtered by vcftools with --maf 0.05 and –max-missing 1 for each dataset. MOSAIC was used to estimate the copying probability of donor sources and infer local ancestry along chromosomes with default settings . The wheat seedlings were infected and scored at the USDA Cereal Disease Laboratory (CDL) at a biosafety level-3 facility (University of Minnesota). The Pgt urediniospores stored at −80 °C were heat-shocked for 6–10 min in a water bath at 42 °C and resuspended in Soltrol 170 light oil . Inoculations were performed by air spray at the one-leaf stage. Immediately after inoculation, plants were transferred to a dew chamber and incubated in the dark for 16 h at 22 °C and under 100% relative humidity. The seedlings were transferred to the greenhouse and grown at 22 °C day and 18 °C night with 16 h of photoperiod. The Pgt virulence scores ranging from 0 (highly avirulent) to 9 (highly virulent) on each differential were assessed 12–14 days after inoculation. According to the North American Pgt nomenclature60, the infection types induced by Pgt isolates were considered H (High) if the score was less than or equal 6 and L (Low) if the score was higher than 6.The virulence of each of the Pgt isolates was assessed by infecting the panel of wheat differential lines carrying known stem rust resistance genes (Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Reporting Summary


Zm00001d020615 and Zm00001d020623 were individually annotated as a polygalacturonase (PG) and a copper transporter 5.1 (COPT5.1), respectively. Candidate gene association analysis showed that three SNPs located in the exons of Zm00001d020615 were significantly associated with the dehydration rate, and AATTAA was determined as the superior haplotype. All these findings suggested that Zm00001d020615 was a key gene affecting moisture changes of maize at the physiological maturity stage. These results have demonstrated the genetic basis of ear moisture changes in maize and indicated a superior haplotype for cultivating maize varieties with low moisture content and high dehydration rates.Low moisture content (MC) and high dehydration rate (DR) at physiological maturity affect grain mechanical harvest, transport, and storage. In this study, we used an association panel composed of 241 maize inbred lines to analyze ear moisture changes at physiological maturity stage. A genome-wide association study revealed nine significant SNPs and 91 candidate genes. One SNP (SYN38588) was repeatedly detected for two traits, and 15 candidate genes were scanned in the linkage disequilibrium regions of this SNP. Of these, genes  Zea may L.) is one of the main food crops worldwide and is an important source of human nutrition, animal feed, and bioenergy. To further improve production efficiency and reduce production costs, mechanized grain harvesting has become a key technology for crop production [Maize , cob dehydration rate (CDR), KMC, and KDR values of 241 maize inbred lines on the 45th, 50th, and 55th days after pollination (DAP). GWAS was performed using the fixed and random circulating probability unification (FarmCPU) model to uncover the important SNPs and causal genes that control MC and DR. In addition, candidate gene association studies were conducted to elucidate the intragenic variations affecting the target traits. This study sought to (i) reveal the genetic basis of moisture change in maize kernel and cob, (ii) identify the hub genes affecting the maize moisture change, and (iii) contribute to the development of function markers for breeding low-MC and high-DR maize varieties through marker-assisted selection.p < 0.05) increased from 45 to 55 DAP (H2) was 57.34%  SNP markers were detected for CDR45–55, CDR50–55, and KMC45, respectively, which distributed on chromosomes 1, 2, 3, 5, and 7  in CDR45–55 associations  and copper transporter 5.1 (COPT5.1), respectively. According to previous studies, PG and COPT5.1 proteins are involved in regulating the MC and DR of soybean and rice seeds [Panicum virgatum  and nine (eight SNPs and one indel) polymorphic sites in the promoter (upstream 2000 bp), UTR, and gene body regions of Zm00001d020615 and Zm00001d020623, respectively (p < 0.05/19 = 2.63 × 10−3) in Zm00001d020615 were identified, involving two SNPs (S7_125166053 and S7_125166495) related to KDR45–50, and one CDR50–55-associated SNP (S7_125165191)  that control MC and DR at the physiological maturity stage in maize, we performed candidate gene association analyses for ectively . Candida5165191) . HoweverZm00001d020615, one missense variation (C/A) occurred in the fifth exon, and the other two synonymous variations  were located in the third and fifth exons, respectively (p < 0.05), whereas no significant difference in moisture-change-related phenotypes was observed between Hap2 and each of Hap1 and Hap3  the kernel dehydration-associated QTL in maize reported in previous studies (p = 9.58 × 10−11) was repeatedly detected for traits CDR45–55 and CDR50–55. Based on this SNP, we identified two further priority genes controlling moisture changes. A candidate gene association study revealed that three significant SNPs located in the coding sequence of Zm00001d020615 were associated with CDR50–55. In addition, we identified the favorable haplotype AATTAA, which should be given priority in marker-assisted selection breeding to cultivate maize varieties with low MC and high DR.Abundant phenotypic variation is an important factor for the successful analysis of the genetic basis of target traits. Population structure analysis showed that the association panel used in this study had high genetic diversity, including a tropical group, a stiff stalk (SS) group and a non-stiff stalk (NSS) group . The MC  studies . For insZm00001d020615, which encodes a PG. Based on previous research, most of the PGs in plants belong to cell-wall localized pectin degrading enzymes and are expressed in different tissues and development stages [PG031 affects seed coat permeability by regulating the intracellular space of parenchyma tissue and maintaining the integrity of the osteosclerosis layer [SlPG has been confirmed as a key gene for improving fruit firmness. After mutagenesis of the SlPG gene, the hydrolysis of cell wall polymers decreased, which led to water loss reduction and delay in the softening of tomato fruit [PSL1 gene functions as a PG that plays an important role in modifying cell wall biosynthesis of root and leaf and reducing water loss under drought stress [At physiological maturity, the MC and DR of maize are mainly determined by two factors: stress dehydration caused by field environmental conditions and physiological dehydration caused by seed development and maturation . Some spt stages . PGs aret stages . To contis layer . In tomato fruit . In ricet stress .Zm00001d020615 needs, firstly, to be verified. Then, gene Zm00001d020615 can be overexpressed/knocked out to create maize lines with high DR.In our study, SNP markers were provided for breeding new maize varieties with low MC and high DR at physiological maturity. By analyzing the utilization of superior alleles in 30 elite maize inbred lines, the superior allele ratios of three SNPs were found to be greater than 50%—one, PZE-102053209, was 90% . This inThe association panel consisted of 241 diverse maize inbred lines , which wH2) was calculated as follows [H2 = σG2/σP2, σG2 = (MSG-MSE)/rep, σP2 = (MSG-MSE)/rep + MSE. Herein, σG2, σP2, MSG, MSE, and rep represent genotypic variance, phenotypic variance, mean square of genotype, mean square of error, and number of replicates, respectively.For each line, the fresh weights (FWs) of the cob and kernel were measured separately on the 45th, 50th, and 55th DAP. Then, the dry weights (DWs) of the cob and kernel were measured after drying at 80 °C in an oven for 72 h. Three individuals were weighed for every line at each stage. The MC was calculated as follows: moisture content (%) = (FW − DW)/FW × 100%. According to the above method, cob moisture content (CMC) and kernel moisture content (KMC) were calculated on the 45th, 50th, and 55th DAP, respectively. Based on the MC values at two successive stages, DR was calculated as follows: dehydration rate (45–50 d) (%) = (MC on 45 d-MC on 50 d)/5 × 100%; dehydration rate (50–55 d) (%) = (MC on 50 d-MC on 55 d)/5 × 100%; dehydration rate (45–55 d) (%) = (MC on 45 d-MC on 55 d)/10 × 100%. The mean value across three replicates was used as the final phenotype value. The KMCs on 45, 50, and 55 DAP were designated as KMC45, KMC50, and KMC55. The CMCs on 45, 50, and 55 DAP were designated as CMC45, CMC50, and CMC55. The kernel DRs for the three time spans  were denoted as KDR45–50, KDR50–55, and KDR45–55. The cob DRs for the three time spans  were denoted as CDR45–50, CDR50–55, and CDR45–55. Descriptive statistical analysis was performed using SPSS25 software. The broad-sense heritability  [p = 0.05/Meff_G = 1.82 × 10−6 [https://www.ncbi.nlm.nih.gov/, accessed on 24 February 2022) databases, we determined the hub genes which were related to moisture changes.In our previous study, genotyping of the maize panel was performed by Illumina Maize SNP50K Bead Chip, and a total of 56,110 SNPs were detected . Accordi 27,507) ,43,44. T2 × 10−6 . In addip = 0.05/n (n represents the number of SNPs). The LD decay between pairwise SNPs was calculated using Haploview.JRE ver.18.0.2  software [t-test.We amplified the hub gene sequences, including the promoter (upstream 2000 bp), UTR, and gene body regions in 67 inbred lines, randomly selected from three subgroups of the association panel . The PCRsoftware . PhenotyIn our study, alleles associated with low MC and high DR were considered as superior alleles. The ratio of superior alleles for each significant SNP was calculated as the number of lines with superior alleles divided by the total number of lines . Accordi


DNA fingerprinting is a molecular technique applied to identify genetic differences between plant cultivars or lines and is used for genetic purity testing. The suitability of single nucleotide polymorphism (SNP) panels for the fingerprinting of tetraploid potato were investigated as a new high throughput, objective, and cost-effective method instead of simple sequence repeats (SSRs) and polyacrylamide gel electrophoresis (PAGE). One-hundred and ninety (190) potato cultivars, including various cultivars currently important in South Africa, were genotyped at 500 SNP positions utilising SeqSNP by LGC Biosearch Technologies. An optimal panel of 25 SNP markers was identified that could discriminate between South African potato cultivars on genetic allele dosage. The genotypes of these SNPs were validated on selected potato genotypes using KASP (Kompetitive Allele Specific PCR) SNP assays. A database of SNP genotype profiles was compiled for all the entries of the germplasm database. The panel of 21 successful SNPs accurately identified the unique potato cultivars in the database. The KASP SNP assays of the successful SNP panel are therefore available for potato DNA fingerprinting as new germplasm, or purity test requests are submitted to ARC-VIMP. This panel provides an objective method for assigning putative cultivar identity to unknown samples submitted for fingerprinting. DNA fingerprinting is a molecular technique applied to identify genetic differences between cultivars or lines and is used for genetic purity testing.Solanum tuberosum) DNA fingerprinting is that it can be done at very early stages of development, such as mini-tuber or in vitro leaf material, and it is less resource-intensive than morphological methods. Accidental mixes can therefore be identified early, before in vitro multiplication, to prevent costly mistakes later on. Cultivar genetic identity is important in the protection of plant breeders’ rights.The benefit of potato  provides genetic fingerprinting services. It is the largest vegetable commodity in South Africa, with a gross production value of R8.08 billion in 2019 ,4. All cSimple sequence repeat (SSR) fingerprinting is a popular method used for potato cultivar identification ,6,7,8 anhttps://www.biosearchtech.com (accessed on 1 August 2019); , the sequences were submitted to LGC Biosearch Technologies on 19 March 2021 for their KASP assay design software. Primers were supplied without primer sequences but with specific assay codes to allow re-ordering.g and the supernatant was transferred to a clean tube. Genomic DNA was precipitated with 0.6 volumes of isopropanol for 30 min at −20 °C. After centrifuging at 10,000× g for 10 min, the pellet was washed with 70% ethanol. The DNA pellet was air dried and resuspended in 1× TE buffer overnight, and DNA concentrations were determined with the Nanodrop ND-1000. Distilled water was used to prepare dilutions of 20 ng/μL.Using the CTAB extraction method following standard laboratory protocols, genomic DNA (gDNA) was isolated from field-grown, in vitro, and producer-supplied potato lines for verification with KASP SNP assays. The leaf samples (100 mg) were ground in the Genogrinder (SpexSamplePrep) at 1500 rpm for 4 min before adding the CTAB isolation buffer . A 30-min incubation period at 60 °C was followed by extraction with an equal volume of chloroform and isoamyl alcohol (24:1). The samples were centrifuged for 10 min at 10,000× KASP SNP assays were run according to the manufacturer’s manuals and recommendations ,40,41. 1https://cran.r-project.org/package=fitPoly (accessed on 26 May 2021)) was used for clustering and SNP dosage calling. It can fit the most accurate model for clustering polyploid genotyping data.R scripts were developed to analyse KASP SNP assay data. Scatter plots were constructed with the data points in the expected SeqSNP allele dosage colour. The software package in R called “fitPoly” ,30  were included in the database .https://www.rstudio.com/products/rstudio/ (accessed 5 November 2018)) that calculates the differences in allele dosages between two cultivars of interest at each of the SNP markers in the panel. Both SeqSNP and KASP dosages were included, if available. The input is the names of two cultivars that need to be distinguished with DNA fingerprinting. The dosage differences per marker are calculated and then sorted from high to low. The output is a list of markers that will give the highest confidence in the results if a difference in genotype between two suspected cultivars is detected. A script was developed in R Studio  Riiij = 4pi3pj, bi-allelic duplex (AABB) Riijj = 6pi2 pj2, bi-allelic triplex (ABBB) Rijjj = pi4pj3 and mono-allelic for the second allele (BBBB) Rjjjj = pj4. If the REF (pi) is 0.5, the probability of a variety having 0, 1, 2, 3, or 4 allele dosages can be computed as 0.0625, 0.25, 0.375, 0.25, and 0.0625, respectively. An example calculation of random match probabilities is presented in The probability that a match to a particular multiple-locus genotype would occur by chance is calculated using the “product rule”, taking the specific marker and its REF into account. Per-locus genotype frequencies are multiplied together to determine the match probability. For a diploid organism, the addition of a factor of 2 for each heterozygous locus is also included. Tetraploids are calculated differently but follow the same rule. For tetraploid individuals, the expected frequencies of genotype classes in progeny after random mating can be mono-allelic for one allele (AAAA) Riiii = pA panel of 25 SNPs were verified with KASP SNP assays for the fingerprinting of potato cultivars in the certification process of seed potatoes. The panel of 21 SNP markers, after eliminating the ineffective KASP SNP markers B, H, I, and M, is able to distinguish between all potato cultivars, except the previously detected similar or duplicated pairs.The development of an SNP genotype database, for a large number of potato cultivars crucial to the South African potato industry, was one of the main outputs of this project. SNP genotypes simplify the germplasm genotype database and enable us to compare the genetic profile of the unknown cultivar to the databased genotypes to determine the suggested identity of the cultivar. The KASP SNP assays developed for the selected SNP panel are suitable for genotyping samples locally as new germplasm, clonal identification or purity test requests are submitted to the ARC-VIMP. DNA fingerprinting based on SNP technology streamlines the process. Compared to conventional SSR and PAGE, the new technology offer improved efficiency, reliability, sensitivity, higher throughput and lower cost per sample. This 21 SNP panel also provides an objective method of assigning putative cultivar identity to unknown samples submitted for fingerprinting.The custom SNP panel for SNP fingerprinting was developed on a selection of only 190 potato cultivars. There exists, therefore, the possibility that a new cultivar cannot be distinguished from others by this panel. However, highly informative SNPs (PIC close to 0.5) were selected, which correspond to SNPs in the old founding cultivars (released before 1945 in the history of potato breeding ). This p