Plasmodiophora brassicae) of brassica crops, but resistance can break down quickly. Identification of novel sources of resistance is especially important when new pathotypes arise. In the current study, the reaction of 177 accessions of Brassica napus to four new, virulent pathotypes of P. brassicae was assessed. Each accession was genotyped using genotyping by sequencing to identify and map novel sources of clubroot resistance using mixed linear model (MLM) analysis. The majority of accessions were highly susceptible (70–100 DSI), but a few accessions exhibited strong resistance (0–20 DSI) to pathotypes 5X (21 accessions), 3A (8), 2B (7), and 3D (15), based on the Canadian Clubroot Differential system. In total, 301,753 SNPs were mapped to 19 chromosomes. Population structure analysis indicated that the 177 accessions belong to seven major populations. SNPs were associated with resistance to each pathotype using MLM. In total, 13 important SNP loci were identified, with 9 SNPs mapped to the A-genome and 4 to the C-genome. The SNPs were associated with resistance to pathotypes 5X (2 SNPs), 3A (4), 2B (5) and 3D (6). A Blast search of 1.6 Mb upstream and downstream from each SNP identified 13 disease-resistance genes or domains. The distance between a SNP locus and the nearest resistance gene ranged from 0.04 to 0.74 Mb. The resistant lines and SNP markers identified in this study can be used to breed for resistance to the most prevalent new pathotypes of P. brassicae in Canada.Genetic resistance is a successful strategy for management of clubroot ( Brassica napus L.), also known as oilseed rape, is grown around the globe. It is the largest crop in Canada, seeded on 9.1 M ha each year with a farm gate value of $16.7 billion Cdn in 2019 . Demand for a healthy oil for human consumption, biofuel production, and use of canola meal as a high quality feed for livestock, have produced strong prices and a steady market for canola products. Clubroot, caused by Plasmodiophora brassicae Woronin, is an important disease of canola and other brassica crops worldwide3. Use of clubroot-resistant (CR) canola cultivars has been the most effective and widely used strategy for clubroot management in Canada5. Management of clubroot disease using major resistance genes has been effective but not durable. For example, the resistance available in the first generation of CR canola cultivars in Canada has broken down rapidly to reveal the presence of many new pathotypes of P. brassicae7, which complicates breeding for resistance.Canola 9 are widely used in Europe, but the Williams10 system was selected for recommendations to producers in Canada because of its simplicity and suitability for the initial situation in Canada8. These three systems have recently been replaced in Canada by the Canadian Clubroot Differential (CCD) set6, which was designed to differentiate among the many new pathotypes recently identified in Canada. Pathotype 5X is the first new pathotype identified to be virulent on the first generation of Canadian clubroot resistant cultivars; 3A and 3D are the most common new pathotypes; and 2B is one of the most virulent new pathotypes in western Canada.Differential systems to identify pathotypes, which consist of selected cultivars with a consistent reaction to individual pathotypes, are available for 13 have been identified in B. napus (AC genome), but strong efforts have been made to identify novel sources of resistance to clubroot in other Brassica spp. and transfer them into canola. The majority of genes for clubroot resistance that have been identified are from B. rapa subsp. Rapifera9. The resistance in B. rapa has been utilized successfully in breeding for resistance to clubroot in B. napus8. More than 10 major clubroot resistance genes have been identified and mapped to chromosomes of B. rapa28. Also, six major clubroot resistance genes and at least 10 QTL have been mapped to B. oleracea (C genome)33.Only a couple of major clubroot resistance genes and some quantitative trait loci (QTL)B. napus34. Transcriptome-based associated has also been used to identify several QTL for resistance to clubroot in B. napus35.Genome-wide association studies (GWAS) provide a quick and precise approach for linkage mapping in QTL detection studies. GWAS uses the linkage disequilibrium (LD) between alleles within diverse populations to detect potential association between markers and the traits of interest. In the first application of GWAS to identify resistance to clubroot, a 60 K SNP array was used to map 10 QTL in 36. Genotyping by sequencing (GBS) provides a cost-effective approach for generating high density SNP panels in diverse accessions37, and GBS-association mapping has been used in many host–pathogen systems39. However, only a few genome-wide association mapping studies have been conducted in the B. napus–P. brassicae system35.Next-generation sequencing can be used to identify thousands of single nucleotide polymorphism (SNP) markers and provide a dense cover of SNPs across the host genome, even in plant species with large genomesB. napus from around the world was selected to conduct GBS-based GWAS in the B. napus-P. brassicae system. The objective was to identify novel sources of resistance to clubroot from a core collection of B. napus accessions by (1) screening the collection under controlled conditions for reaction to four new pathotypes of P. brassicae identified on canola in Canada, (2) assessing the genetic diversity and structure of the core collection, and (3) conducting association mapping of resistance to the four pathotypes of P. brassicae.In the current study, a core collection of 177 accessions from a large germplasm collection of Initially, the reaction of 671 accessions to pathotype 5X (the first pathotype identified that was virulent on the first generation of CR canola cultivars in Canada) was assessed. Only 21 accessions were resistant, defined as a disease severity index (DSI) ≤ 20. After this initial assessment had been completed, more new pathotypes were identified. The 21 accessions resistant to pathotype 5X and an additional 156 accessions were tested for resistance to the new (to western Canada) pathotypes 3A, 2B and 3D. The majority of accessions were highly susceptible (70–100 DSI), but 8 accessions were resistant to 3A (0–16 DSI), 7 accessions to 2B (0–11 DSI) and 15 accessions to 3D (0–20 DSI) Fig. .Figure 1The resistant accessions were clustered into four groups based on their responses to combinations of pathotypes. Group 1 contained 3 accessions that were resistant to all four pathotypes. Group 2 comprised 3 accessions that were resistant to 3 of the 4 pathotypes. Group 3 had 2 accessions that were resistant to 2 out of 4 pathotypes, and Group 4 contained 25 accessions that were resistant to single pathotypes Table .Table 1RB. napus accessions generated ~ 1.2 billion total reads and ~ 633 million good barcoded reads, which were split into three FASTQ fills. On average, there were 3.3 M read counts per sample (range ~ 1.8 to 7.7 M) and 3.1 M read counts mapped (range 76–96%). Sequence tags from each file were captured and merged to produce a master tag file of 4,253,499 sequence tags. The tags were then aligned to B. napus reference genome v4.1 using the TASSEL-GBS pipeline. A total of 2,217,292 (52.1%) tags were uniquely aligned to the reference, 1,220,090 (28.7%) aligned to multiple positions and 816,117 (19.2%) were not aligned. Uniquely mapped tags were used to calculate the tag density distribution at each site in the B. napus genome and for SNP calling.GBS analysis of the 177 B. napus and were kept for further analyses. The remaining SNPs were randomly distributed without specific chromosome assignment, and so were discarded.The raw sequence data for SNP calling were also analysed using the TASSEL-GBS pipeline. A total of 399,234 unfiltered SNPs and 355,680 filtered SNPs were called for the 177 accessions, with a mean individual depth of 8.5 ± 2.0 SD and mean site depth of 6.7 ± 11.4 SD. Of the 355,680 filtered SNPs, 301,753 SNPs (84.83%) were mapped to the 19 chromosomes of There were slightly more SNPs in the C-genome  than the A-genome . Chromosome A03 had the highest number of SNPs within the A-genome, while C03 contained the highest number of SNPs in the C- genome Table . The meaThe SNPs were annotated using SnpEff software. About 37% of SNPs were annotated within coding regions, 22% within introns, 31% within promoter regions, 0.3% within splice sites, and 9.7% mapped to other genetic regions  was 0.06 and the mean accession heterozygosity was 0.14. The average polymorphic information content (PIC) was the same for the A- and C-genomes (0.26). PIC was highest in chromosome C01 (0.27) and lowest (0.24) in A09  of 0.05 and minimum sample count of 80%, which resulted in 104,184 high quality SNPs. The mean MAF was the same for the A- and C-genomes (MAF = 0.14). Chromosome C01 had the highest MAF (0.16), followed by C03 and A07 (0.15), and lowest MAF 0.12) was in chromosomes A09 and C09  across the genome was 0.15. The mean LD was 0.10 in the A-genome and 0.19 in the C-genome. LD values ranged from 0.01 in A09 to 0.19 in C01 (Table r2 = 0.23) within 1.6 Mb  in the association panel was calculated using Pearson’s 10(p) was closest to the expected distribution in the P + Q + K compared to other models, so associations were identified using this model. A significance level of P ≤ 0.05/N (N: number of SNPs) based on the Bonferroni correction and a less stringent suggestive significance level at P ≤ 0.5/N were selected.Genome-wide association analysis for clubroot severity was conducted using a general linear model (GLM) for naïve and P + Q analysis, and a mixed linear model (MLM) for P + K and P + Q + K. Naïve refers to genotypes and phenotypes only, Q is structure, and K is kinship. The quantile–quantile (Q–Q) plots from all models revealed that, save for significant SNPs, the distribution of observed – logP. brassicae pathotypes distributed on chromosomes A01, A03, A04, A05, C03, C07 and C09, with two SNPs (one on A05 and one on C07) associated with resistance to 5X, four SNPs to 3A (three on A03 and one on C03), three SNPs to 2B (on A01) and six SNPs  associated with resistance to 3D  on chromosome A03 were detected in the same gene, BnaA03g05600D, and SNP C03_327139017 on chromosome C03 was detected in gene BnaC03g07170D. Both genes on chromosomes A03 and C03 encode galactose oxidase/kelch repeat superfamily protein  on chromosome A01 were associated with resistance to pathotypes 2B and 3D. These four SNPs were in the same At3g15700, was detected at 0.16 Mb from the SNPs (Table A Blast search of 1.6 Mb upstream and downstream regions (distance based on LD) from significant SNPs was performed to identify any nucleotide-binding-site/leucine-rich-repeat (NBS-LRR) resistance protein encoding genes. For pathotype 5X, a coiled coil / nucleotide binding site / leucine-rich repeat (CC-NBS-LRR or CNL) family was identified at 0.53 Mb from the significant SNP, A05_101850460, on chromosome A05. For the SNP on C07, no NBS-LRR gene was found , but ~ 10% showed strong resistance (0–20 DSI). This indicated that sources of strong resistance to clubroot were much less common in B. napus than in B. rapa25. In total, 13 SNPs were associated with resistance, of which nine SNPs were on the A-genome, and four on the C-genome. Although the A-genome (from B. rapa) appeared to carry more QTL for clubroot resistance than the C-genome, this result indicated that the C-genome (from B. oleracea) could also be a potential source for clubroot resistance. Interestingly, four SNPs on A01 were common for pathotypes 2B and 3D, which may indicate that there is a common QTL for resistance to the two pathotypes. Moreover, SNPs located on A03 and C03 were associated with resistance to pathotype 3A. A Blast search determined that these SNPs were located very close to or within the same gene on both chromosomes. Association of SNPs with a single gene on separate chromosomes indicated the occurrence of inter-genome gene duplication, which is a common phenomenon in B. napus42.In the current study, GWAS was used to identify and map new sources of resistance to four recently identified pathotypes  of B. napus, and several have been mapped to chromosomes A01, A02, A03, A08, C02, C03, C06, C07, and C0935. In those previous studies, the disease reaction to only a single strain of P. brassicae was assessed, and a SNP array or associative transcriptome was used rather than GBS, which might explain the difference in the number and identity of QTL detected. In a recent GWAS study, 45 QTLs were identified against 13 strains on chromosomes A01 to A10, and C02, C03 and C0543. In the current study, two pathotypes (3A and 2B) included in a recent study43 were analysed using a larger and potentially more diverse germplasm collection. The current study, however, identified different QTL from those in the previous study43. One possible explanation for the difference between the studies could be the analysis of DSI. A rank-based inverse normal transformation was used to make the DSI values nearly fit the normal distribution required for parametric model-based association analysis in the current study, while highly skewed DSIs were used in the previous study43. In addition, different sources of B. napus collections in the current study from the previous report43 could be an important factor contributing to the discrepancy. We conclude that all 13 QTL identified in the current study are likely to be novel because they were located at different physical locations on the chromosomes from the QTL identified previously43.QTL for clubroot resistance have been identified previously in P. brassicae in Brassica species via bi-parental mapping approaches. In our previous studies, eight genes / QTL loci for resistance to pathotype 3H or 5X or both were mapped into chromosomes A02 (Rcr8), A03  and A08 , and one gene into chromosome C07 (Rcr7)25. In the current study, 13 SNPs were identified from seven chromosomes  for resistance to four new pathotypes  of P. brassica from Canada. None of the previously identified genes/QTL for resistance to Canadian pathotypes reside in chromosomes A01, A04, A05, C03 or C09. Although three SNPs were identified in chromosome A03 where Rcr1, Rcr2, Rcr4 and Rcr5 were located and one SNP in chromosome C07 where Rcr7 was located, these four SNPs were located in different regions from the identified genes. Three closely linked SNP A03_50588774, A03_50605321 and A03_50605605 associated with resistance to 3A were identified in the 2.5 Mb region of chromosome A03, while Rcr1, Rcr2, Rcr4 and Rcr5 spanned in the region of 23–25 Mb of chromosome A03 in the B. napus reference genome. Furthermore, SNP C07_531619426 associated with resistance to 5X was located at 17.9 Mb of chromosome C07. However, Rcr7 was identified in B. oleraces, in a region equivalent to the 25 Mb of chromosome C07 in the B. napus genome.Extensive efforts have been made to map the genes for resistance to 47. In the current study, five putative resistance genes were identified within LD distance of the SNPs associated with resistance to the four pathotypes. The resistance genes belonged to the TNL family (one gene) and the CNL family (one gene), as well as other types of resistance genes. The gene BnaA03g065730, which mapped close to SNP A03_50588774, contained the LRR protein domain that is an essential part of NBS-LRR proteins48. Moreover, the gene BnaC03g07250D that was located at 0.04 Mb from SNP C03_327139017 encoded an avirulence-induced gene (AIG1) family protein that, according to the gene-for-gene hypothesis by Flor49, should belong to a disease resistance protein-encoding gene family.The majority of disease resistance genes identified in plants have been classified as TNL or CNL proteins, with ~ 70% of NBS-LRR genes in the Brassicaceae family being TNLs50. Similarly, the difference in LD between studies at the chromosomal level as well as at the genome level appeared to be associated with the density of molecular markers. In the current study, LD extended further in the C-genome relative to the A-genome. LD decayed very rapidly (r2 = 0.23) within 1.6 Mb, while the range of previous reported LD decay values was affected by the diversity within the germplasm collection that was examined53.Population structure can have a significant impact on GWAS. Structure analysis using the entire SNP panel from GBS indicated that the core collection was comprised of seven sub-populations. However, a previous study using a SNP panel obtained from the Brassica 60 K Illumina Infinium SNP array identified only two major subpopulationB. napus with high levels of resistance to one or more or the four important new pathotypes of P. brassicae examined. Genome-wide association mapping analysis detected and mapped 13 SNP loci associated with resistance to the four pathotypes. This information will be used in subsequent genetic analysis of bi-parental populations to verify the SNPs and fine map the functional genes responsible for resistance to each pathotype, and also used for marker-assisted breeding of resistance to clubroot in canola.In summary, the current study identified several accessions of B. napus accessions were obtained from three gene banks; the Plant Genetic Resources of Canada, the Centre for Genetic Resources of the Netherlands, and Agricultural Research Service of the United of America. Self-pollination was performed for each line under greenhouse conditions to reduce the level of heterozygosity. The accessions were evaluated for resistance to a field collection of pathotype 5X (strain LG02) that had been characterized by Dr. S.E. Strelkov using the Canadian Clubroot Differential6 system. Also, selected lines were tested for resistance to field collections of pathotypes 3A (strain F.P. 3–14), 2B (F.P. 183–14) and 3D (F.P. 1–14) of P. brassicae  and then genotyped using a GBS platform.A collection of 671 A core group of 177 accessions from 32 countries was selected for GWAS. This group included accessions of oilseed rape (146 accessions), fodder rape (21), Swede rape (7), rutabaga (2) and turnip (1) from Europe (123 accessions), Asia (29), North America (20), Oceania (2), South America (1), Africa (1), and one accession of unknown origin  sample of leaf tissue was collected from each accession, immediately frozen in liquid nitrogen, lyophilized in a freeze dryer for ~ 48 h and ground to a fine powder using a tissue lyser .P. brassicae were increased on susceptible canola and stored as frozen clubbed roots at − 20 °C until needed. Resting spores were extracted from the frozen clubs as described by Strelkov et al.54, and adjusted to a concentration of 1 × 107 resting spores mL−1. Spores of each pathotype were applied separately to the host entries.Resting spores of pathotypes 5X, 3A, 2B, and 3D of 16. Seed of each host genotype was pre-germinated on moistened filter paper in a Petri dish. One-week-old seedlings of each host line × pathotype combination with 12 plants each were inoculated by dipping the entire root system in the resting spore suspension for 10 s. The inoculated seedlings were then immediately planted in 6 cm × 6 cm × 6 cm plastic pots filled with Sunshine LA4 potting mixture, with one seedling per pot. The pots were thoroughly watered and transferred to a greenhouse at 21 °C ± 2 °C with a 16 h photoperiod. The potting mixture was kept saturated with tap water at pH 6.5 for the first week after inoculation and then watered and fertilized as required.The experiment was conducted in a growth chamber under the controlled environment following the method similar to that described by Suwabe et al.54.n is the number of plants in a class; N is the total number of plants in an experimental unit; and 0, 1, 2 and 3 are the symptom severity classes.Six weeks after inoculation, the seedlings were gently removed from the potting mix, the roots of each plant were washed with tap water, and each root was rated for clubroot symptom severity on a 0 to 3 scale, where: 0 = no clubs, 1 = a few small clubs on less than one-third of the roots, 2 = moderate clubs , and 3 = severe clubs (medium to large-sized clubs on > 2/3 of the roots). A disease severity index (DSI) was then calculated using the formula of Horiuchi and Horias, modified by Strelkov et al.Assessment of the disease reaction of each accession with a resistant response (DSI < 20) in the initial test was repeated two more times. Each of these repetitions provided a similar result.55 was used for SNP calling, and VCF and HapMap genotype files were generated. Initial SNP filtration was performed with the following settings: minor allele frequency (MAF) > 0.01 and missing data per site < 90%. Accessions with too much missing data were removed. Depth, missingness and heterozygosity were calculated using VCFtools V.0.1.1256. Genotyping and SNP calling were performed at the Genomic Diversity Facility, Cornell University (http://www.bio-tech. cornell.edu/brc /brc/ services). Beagle 5.157 was used to impute missing genotypes.The accession sequences were analyzed using genotyping by sequencing (GBS) in four major steps; DNA sample preparation, library construction, library sequencing and SNP calling. For sample preparation, DNA extraction was performed using the DNeasy 96 plant kit as per the manufacturer’s instruction (Qiagen). To reduce the genome complexity, DNA was digested with ApeKI, a methylation-sensitive restriction enzyme. For library construction, the fragments produced by digestion were directly ligated to enzyme-specific adapters followed by PCR amplification. For sequencing, the samples were divided into two pools of 96 samples each, and assessed in two runs of Illumina HiSeq 2500 . DNA alignment was generated with BWA software version 0.7.8-r455. The GBS-TASSEL pipelineB. napus reference genome v4.1 downloaded at https://wwwdev.genoscope.cns.fr/brassicanapus/data/ using SnpEff58, and Variant Effect Predictor (VEP)59, and variant locations were characterised as coding, intron, splice site, promoter and intergenic regions, intergenic, upstream, downstream, and synonymous.Variants were annotated to regions of the 60. Polymorphic information content (PIC) values were calculated for SNP markers using the formula (PIC = 1 − (maf2 + (1-maf)2))-(2maf2(1-maf)2)), where maf = is the minor allele frequency. The ratio of transitions to transversions was calculated using the Kimura 2-parameter model in MEGA761.Population-based genetic diversity, including allele frequencies, minor allele frequency (MAF), and average heterozygosity, were computed using TASSEL 5.2.18 software62. The admixture model and correlated allele frequency were applied with a burn-in period of 50,000 iterations and 100,000 replications of Markov Chain Monte Carlo (MCMC).Structure analysis of the accessions was conducted using fastSTRUCTURE v2.2B. napus genome was measured and a correlation matrix of r2 values was computed between all pairs of polymorphic SNPs with MAF ≥ 5% using the GAPIT V3 package63.LD decay across the 64. Associations were analyzed for all SNP markers with MAF ≥ 5%, and evenly distributed (1 SNP per 100 Kb) using the following models: general linear model (GLM) for naïve and P + Q, and mixed linear models (MLM) for P + K and P + Q + K, where naïve refers to genotypes and phenotypes only, Q is structure and K is kinship. A kinship matrix of the accessions was calculated and principle components analysis was used to account for population structure and accession relatedness. The association analysis was done using TASSEL v.560.Clubroot severity data  were transformed using rank-based inverse normal transformation using the rntransform function in the GenABEL R Library65, the sequence regions neighboring and within LD of the significant SNPs were searched for candidate genes encoding disease resistance proteins that were potentially responsible for the resistance to each pathotype of P. brassicae.Using Blast2Go softwareSupplementary Information 1.


Gracupica contra) complex in tropical Asia, a popular victim of the ongoing songbird crisis whose distinct Javan population (“Javan Pied Starling”) is extinct in the wild and subject to admixture in captivity. Comparing genomic profiles across the entire distribution, we detected three deeply diverged lineages at the species level characterized by a lack of genomic intermediacy near areas of contact. Our study demonstrates that the use of historical DNA can be instrumental in delimiting species in situations of taxonomic uncertainty, especially when modern admixture may obfuscate species boundaries. Results of our research will enable conservationists to commence a dedicated ex situ breeding program for the Javan Pied Starling, and serve as a blueprint for similar conservation problems involving terminally endangered species subject to allelic infiltration from close congeners.Urgent conservation action for terminally endangered species is sometimes hampered by taxonomic uncertainty, especially in illegally traded animals that are often cross‐bred in captivity. To overcome these problems, we used a genomic approach to analyze historical DNA from museum samples across the Asian Pied Starling ( We were Ripley, , and it es Table  across ts Figure . However2.2We used Qiagen DNeasy Blood and Tissue kits  for DNA extraction. The manufacturer's protocol was modified for the historical samples according to Chattopadhyay et al. , and DNA2.3Library preparation of historical samples was carried out in a sterile environment in a biosafety cabinet with laminar airflow. The cabinet was located in an isolated space between the historical DNA clean facility and post‐PCR area. Historical samples were first subjected to NEB Next FFPE DNA Repair Mix (New England BioLabs) to repair degraded and damaged regions , Collared Flycatcher , and Zebra Finch . Javan Myna  was followed with modifications . We used Trimmomatic v0.33  produced reads of three loci. In contrast, nonenriched negatives  did not produce any reads mapping to target loci. This suggests that reads obtained from the DNA of final negatives were derived from nondegraded RNA baits added to negative controls during in‐solution hybridization. As a precautionary step, we removed these three loci from all analyses.We generated multiple final variant sets to account for different biases. Firstly, variants were filtered to retain only those loci with <5% missing data, resulting in a total of 6333 SNPs (SNP set A1) across 18 individuals. Secondly, to account for linkage disequilibrium between SNPs located on the same locus, we selected one random SNP from each locus and retained a final SNP set of 936 variants (SNP set A2). To further check for linkage across loci, we ran PLINK v1.9  and possibly containing an allelic profile representative of the unsampled subspecies sordida. Therefore, to account for this bias, we constructed another SNP set allowing for no missingness in loci and keeping all other parameters equal. This alternative SNP‐calling regime resulted in a set of 871 SNPs (SNP set B1) across 20 individuals. To account for linkage, we again chose one random SNP from each locus, resulting in a total of 412 SNPs (SNP set B2) while managing to salvage one historical sample from Assam and one historical sample of jalla. Last, we constructed a third SNP set only containing a selection of loci that displayed the highest levels of polymorphism; that is, each locus contained >20 SNPs to examine whether these highly divergent loci carry an underlying genomic signal that corroborates the genome‐wide phylogenetic signal. For this final set, we obtained a total of 749 SNPs (SNP set C) across 25 loci.Our quality filters for missing data led to a loss of both historical samples from central Assam (northeast India), collected from close to the range of We investigated potential pairwise kinship relationships among individuals within the same population using SNPRelate  on a terrain map obtained from Natural Earth  from the line marking the terrain boundary and plotted these geographic distances against the PC2 values of our PCA plot from SNP set B2, which is representative of mainland Asian genomic differentiation in Asian Pied Starlings. The insular taxon jalla was excluded from this analysis as its distribution is widely disjunct and not geographically contiguous.We performed an analysis to inspect the relationship between geographic distance and genomic distance specifically across the boundary between the two adjacent taxa 2.9www.orientalbirdimages.org) and eBird. (www.ebird.org). We only included adult birds and we eliminated replicate pictures of identical birds posted multiple times. We counted multiple birds in the same picture separately. For the taxon floweri from Thailand, we examined a total of 13 birds on Oriental Bird Images and a total of 318 birds on eBird. For the taxon superciliaris from Myanmar, we examined one bird on Oriental Bird Images and a total of 23 birds on eBird. We focused on two important morphological traits during inspection: (a) extent of bare red/orange facial skin and (b) eye color. If the length of bare facial skin extending from the posterior end of the eye toward the nape was greater than the length of bare skin extending from the anterior end of the eye to the base of the bill, the bare facial skin patch was considered large, and vice versa. The eye color was examined to check whether it was pale or dark. Traits were recorded as “indeterminate” in photographs of insufficient quality or those in which the bird was too distant or depicted at an unsuitable angle.We performed a morphological inspection of Asian Pied Starling photographic material from Thailand and Myanmar deposited on two online photo repositories, Oriental Bird Images  as a reference.We assembled the cleaned DNA reads using HybPiper v1.3.1  due to poor sequence quality  as an outgroup .We constructed gene trees for all nuclear sequence loci containing >20 SNPs in RAxML‐NG v.0.9.0 . We first aligned the multifasta files for each locus  using the web‐based tool WebGestalt , we further performed gene ontology (GO) enrichment tests to identify whether functional loci associated with molecular, biological, or cellular processes might be particularly represented in these stretches of sequence. We annotated the entire set of target loci using Blast2GO  to investigate whether loci of high divergence carry a phylogenetic signal that reflects the overarching genome‐wide phylogenetic signal. In general, the phylogenetic signal across these 25 loci was mutually incongruent, and we failed to identify cohorts of candidate loci that would have converged on a particular phylogenetic signal—whether equal or different from the full dataset's signal Figure . Some geC Figure d. To exas Figure , which is Figure . Some ofs Figure .floweri emerged as sister to jalla, not to contra/superciliaris  floweri; and (c) jalla. Mitochondrial DNA information corroborated this result, furnishing pairwise sequence distances between the three lineages that are within the confines of sequence divergence thresholds used for species delimitation by the general bird DNA barcoding movement  and floweri (from Thailand)—at ~2%—is at the lower end of divergences typically displayed by good species.Our use of genome‐wide DNA markers established three distinct evolutionary lineages within the Asian Pied Starling complex Figure : (a) thent Table , althougjalla from Java has been previously proposed  is not an effective barrier to gene flow in these starlings  collected adjacent to the type locality of s Figure . Due to  Ripley, , our gen Ripley,  are consGracupica contra (Indian Pied Starling), with subspecies contra  and superciliaris ; (b) Gracupica floweri (Thai Pied Starling), monotypic (mostly Thailand and Cambodia); and (c) Gracupica jalla (Javan Pied Starling), monotypic .In summary, we propose a separation of the complex into the following three species: (a) 4.3G. jalla), is heavily traded in Indonesian markets and frequently admixed with floweri and superciliaris in illegal captivity due to a lack of recruitment of new individuals resulting from their extinction in the wild. Our study was only possible because of the availability of museum samples from a time preceding such introgression.Conservation management often hinges upon clear taxonomic resolution  and four other species of songbirds from the list of protected species because of advocacy work by the bird‐keepers’ lobby (Jakarta Hidayat, G. floweri.The captive e Figure . They arAt a time when the survival of more and more species hinges on conservation breeding (Chattopadhyay et al., 5The raw sequence data for this study are available at Sequence Read Archive, NCBI under BioProject PRJNA669910. Additional details and results of this study are available in the Supplementary MaterialClick here for additional data file.


Vitis 18k SNP chip. Two main recurrent parents were found, which can be regarded as “founders”: ‘Vulpea’, an Austrian variety parent-offspring related with at least ten Friuli Venezia Giulia cultivars, among them ‘Glera’, and ‘Refosco Nostrano’, first degree related with other six Friuli Venezia Giulia varieties. ‘Ribolla Gialla’ was shown to be another member of the impressively long list of offspring derived from the prolific ‘Heunisch Weiss’. Combining molecular markers and historical references was a high-performance strategy for retracing and adjusting the history of cultivars.‘Glera’ and ‘Ribolla Gialla’ are the most economically relevant local grapevine cultivars of Friuli Venezia Giulia region . ‘Glera’ is used to produce the world-renowned Prosecco wine. ‘Ribolla Gialla’ cultivation is constantly increasing due to the strong demand for sparkling wine and is the most important variety in Brda (Slovenia). Knowledge of local varieties history in terms of migration and pedigree relationships has scientific and marketing appeal. Following prospections, genotyping and ampelographic characterization of minor germplasm in Friuli Venezia Giulia, a further research was developed to understand the parentage relationships among the grapevine varieties grown in this region. An integrated strategy was followed combining the analysis of nuclear and chloroplast microsatellites with the  Genotyping performed with SSR (Simple Sequence Repeats) markers supports this kind of study, increasing knowledge on grapevine biodiversity, varieties migration and pedigree relationships. In this respect, many parents of traditional cultivars are still missing and difficult to find, because some ancestors may be lost due to genetic erosion. The agreement on a standard set of nine SSR-markers and the growing availability of genetic fingerprints has documented germplasm exchange not only among neighboring regions, but also among very distant countries in previously unknown detail. Indeed, the molecular approach does not need comparisons with selected varieties and allows easy massive data exchange and the use of all available genotyping informationhttp://catalogoviti.politicheagricole.it) and ‘Ribolla Gialla’ . Beyond minor and already known local varieties2, prospections of grapevine germplasm in FVG evidenced the presence of previously unknown varieties4.The main varieties grown in Friuli Venezia Giulia (FVG) region, North-East Italy, are ‘Pinot Gris’  and ‘Glera’ , followed by ‘Merlot’ , ‘Tocai Friulano’ , ‘Chardonnay’ , ‘Sauvignon Blanc’  (https://www.enolo.it/glera-veneto-ettari-proseccco-doc). The land cultivated with ‘Ribolla Gialla’ in Italy was really limited in 1982 (93 ha), but constantly increased reaching 284 ha in 2000, 435 ha in 2010 , and overcame 1,000 ha in the last years due to the strong demand for sparkling wine on the international market. ‘Ribolla Gialla’ sparkling wines are highly appreciated by consumers, forecasting a further increase of the cultivated area in the near future. ‘Rebula’  in Brda (Slovenian Collio) is the traditional and most important grapevine variety, nowadays covering 20% of the entire viticulture area. The first reliable mention of the variety by a priest Matija Vertovec dates to 1844 in a book Vinoreja5 and it has been the subject of several studies regarding genotyping and potential parentages and pedigree, without consistent reports9. Furthermore, the first mention of Ribolla of Rosazzo wine in Cividale del Friuli  dates to 140910.The most economically relevant local cultivars in FVG are ‘Glera’, used to produce the world-renowned Prosecco wine, and the ancient, recently re-discovered, ‘Ribolla Gialla’. ‘Glera’ DOP (Protected Denomination of Origin) area expanded from Veneto to FVG, for a total of about 25,000 ha; nowadays ‘Glera’ covers 3.9% of the Italian wine-growing area  simultaneously, researchers have turned to SNP approaches.Vitis 18k chip for grapevine genotyping, comprising 18,071 SNPs selected in 47 V. vinifera genotypes and 18 non-vinifera Vitis species11. This tool is expected to outperform SSRs because it inspects thousands of points in one analysis and overcomes the slow informative power of single SNPs, which are bi-allelic.In this study, an integrated strategy was followed combining the analysis of 12 nuclear and 8 chloroplast microsatellites with the 12, ‘Vitouska’ = ‘Malvasia Bianca Lunga’ and ‘Glera’ 13, and ‘Manzoni Bianco’ = ‘Pinot’ × ‘Riesling’15. Six accessions belonging to ‘Heunisch Weiss’ variety were analyzed, some of them being somatic variants16: ‘Heunisch Weiss’, ‘Heunisch Weiss Seedless’, ‘Heunisch Dreifarbig’, ‘Heunisch Rotgestreift’, ‘Rebula Stara’ and ‘Liseiret’; they came from three countries; Germany, Italy  and Slovenia. Two ‘Ribolla Gialla’ accessions were genotyped, one from the Italian Collio and the other from the Slovenian Brda.Seventy-nine grapevine accessions from different repositories in Italy, Germany and Slovenia were analyzed Table . The samGenomic DNA was extracted from young freeze-dried leaves harvested from 79 accessions, using the QIAGEN DNeasy 96 Plant Kit  according to the manufacturer’s protocols with small modifications: 1.6% PVP40 (Sigma Aldrich) was added to the AP1 buffer and samples were incubated at 65 °C for 5 minutes; DNA was eluted in milliQ water at 65 °C. DNA quantification was performed using Quant-iT™ PicoGreen™ dsDNA Assay Kit (ThermoFisher Scientific) by Synergy2 Fluorometer (Biotek); DNA quality was checked both on an Agilent 2200 Tapestation , using the DNA genomic ScreenTape (Agilent Technologies) for DNA integrity detection, and NanoDrop 8000 Spectrophotometer , for 260/230 and 260/280 ratios evaluation.17, plus ISV2 (VMC6e1), ISV4 (VMC6g1) and VMCNG4b918. The nSSR profiles were obtained using fluorescent primers and an ABI3130xl genetic analyser . SSR allele calling was performed with GeneMapper software version 5.0, with a home-made bin set produced with reference varieties. Identifications were made by comparing the obtained genetic profiles with the CREA Viticulture and Enology molecular database, literature information and the Vitis International Variety Catalogue .The 79 accessions were genotyped with 12 nuclear SSR (nSSR) markers. These markers are listed in Table 19. Two multiplex PCR were organized using fluorescent primers and SSR allele calling was as described for nSSRs.Chlorotypes were determined with eight chloroplast SSR markers out of the nine proposedAll accessions were genotyped using the Infinium® II Vitis18k SNP array, which includes 18,071 SNPs,  following the Infinium® HD Assay Ultra protocol  and scanned using an Illumina HiScan.20 and GenAlEx 6.5 software21, and in the VIVC with the “Relationships based on nine microsatellites” tool. The varieties proving to be members of trios or duos were included in the sample set. These varieties are in italics in Table A search for compatible trios (parents and offspring) and duos (parent-offspring) was made based on 9 to 12 nSSRs in the CREA Viticulture and Enology database with Cervus 3.022 (http://pngu.mgh.harvard.edu/purcell/plink). Parent-Offspring (PO) relationships were inferred analyzing four PLINK parameters: the probability of sharing no identical by descent (IBD) allele per locus (Z0), the probability of sharing both IBD alleles per locus (Z2), the probability of sharing one IBD allele per locus (Z1), the PI-HAT (Z2 + 0.5 * Z1). Reference values for PO are Z0 = 0, Z1 = 1, Z2 = 0, PI-HAT = 0.5.SNP data quality was evaluated using GenomeStudio Genotyping Module v2.0 of ILLUMINA. The 18,071 SNPs were selected with three criteria, minimum allele frequency (MAF) higher than 0.05, no calls (NC) lower than 5% and call quality values (p50GC) higher than 0.54. Accordingly, 11,929 SNPs were retained for subsequent analyses. PLINK Input Report Plug-in v2.1.4 for Genome Studio Genotyping module was used to convert genetic data into ‘map’ and ‘ped’ files for analysis with PLINK v1.07 software23 v. 1.02 was applied for additional SNPs pruning and improved clustering classification of SNPs previously selected using GenomeStudio tools. The aim was to better evaluate pedigree results on putative PO related cultivars conflicting with previous results reported in the literature. ASSIsT default parameters were applied; no SNP map position was given, because ASSIsT does not work with random or unmapped SNPs. The pedigree file was given with mother and father information missing, also for the pedigrees used as references. After pruning, the selected SNPs were analyzed again on a larger set of 192 genotypes to obtain a more consistent SNP classification into the four classes elaborated by ASSIsT: Robust, OneHomozygRare_HWE, OneHomozygRare_notHWE, DistortedAndUnexpSegreg. The more restricted set of higher-quality SNPs was used to recalculate Mendelian inconsistencies on putative PO-related cultivars.ASSIsT (Automatic SNP ScorIng Tool) softwarehttps://www.zsl.org/science/research-projects/software. Empirical data input consisted of both nSSR and SNP markers. The Parent-Parent-Offspring trios inferred with PLINK and GenomeStudio were used to define the main structure of the family into Colony. The following main settings were applied: markers error rate 0.00001, no sibship prior indicator, one medium run, FL (full likelihood) analysis method and medium precision in calculating FL. Only full-sib dyads with probability equal to 1 were retained. Colony reconstructs the possible genotypes of the parents absent in the dataset and complementing the full-sibs found; these genotypes were used to evaluate additional PO relationships suggested by the software.Full-sib relationships were inferred using Colony software version 2.0.6.5 , freely available at 24 was used to produce an unrooted dendrogram of genetic similarity using the first-round 11,929 selected SNP markers. Pairwise genetic distances were computed using the Kimura 2-parameter method. Missing data were removed for each sequence pair (pairwise deletion option). The dendrogram was constructed using the Unweighted Pair-Group Arithmetic Average Method (UPGMA). A bootstrap test of 500 replicates was used to define the percentage of replicate trees in which the associated genotypes clustered together; these values were shown next to the branches. Only branches with bootstrap values higher than 75 were taken into consideration.MEGA X software version 10.0.5Vitis 18k SNP.The pedigree relationships of FVG grapevine varieties and others from neighboring countries were investigated by a well-established low-throughput SSR approach and a high-throughput genotyping system based on an SNP chip array, the Vitis database (http://www.vitisdb.it/). VIVC prime names and variety numbers for FVG varieties are reported in Table SSR genotypes and ampelographic features of most varieties of interest for FVG are available on-line in the Italian Filtering reduced the initial 18,071 SNPs available in the chip of ILLUMINA to 11,929.25; these profiles are provided in Table All varieties were univocally identifiable with the 14 SNP set selected byTaking the ‘Heunisch’ samples and related synonyms (‘Liseiret’ and ‘Rebula Stara’), 18 out of 11,929 SNPs failed, all others showed the same allelic combination except one, ‘Heunisch Weiss Seedless’ (data not shown). Concerning the two ‘Ribolla Gialla’ samples, one from the Italian Collio and one from the Slovenian Brda, only 5 SNPs were missing, and the others showed the same allelic profile.8. These data support ‘Heunisch’ × ‘Vulpea’; moreover, ‘Pinella’ was shown to be half-sib of ‘Piccola Nera’. The other trios regarded varieties detected in the reconnaissance of old vineyards5 which have been given fantasy names, like ‘Gran Rap Neri’, ‘Pelena’, ‘Polposa’, ‘Sagrestana’ and ‘Venere’. ‘Gran Rap Neri’ derived from ‘Aghedene’ × ‘Corbina’. ‘Sagrestana’ and ‘Venere’ share the same parents, being derived from ‘Picolit’ × ‘Verduzzo Friulano’; ‘Pelena’ is another progeny of ‘Glera’ and ‘Malvasia Bianca Lunga’, like ‘Vitouska’13. A possible seventh trio was found: ‘Cjanorie’ could be the progeny of ‘Vulpea’ and ‘Vinoso Rosso’, even if the P-P-C error rate and Mendelian inconsistencies are higher than the references.PLINK, Parent-Parent-Child (P-P-C) error rates and Mendelian inconsistencies of already known kinships were used as reference for discovering new relationships; 12 nSSR data and the chlorotypes were also accounted for. The P-P-C trios found are shown in Table 26). ‘Vulpea’ was shown to be PO related to 8 varieties, many of them, i.e. ‘Cordenossa’, ‘Piculit Neri’, ‘Piciule’, ‘Schioppettino’, ‘Siora’ and ‘Aghedene’, are minor or even niche FVG varieties. In addition, a first-degree relationship was detected between ‘Vulpea’ and both ‘Glera’ cultivars, ‘Glera’ and ‘Glera Lunga’. ‘Verduzzo Friulano’ and ‘Forgiarin’ were shown to be PO related to the Slovenian ‘Volovnik’, also known under the synonym ‘Volovna’; molecular data exclude that the two FVG varieties could be the parents of ‘Volovnik’.Twenty-three duos were found Table , Fig. 1.Two pairs of varieties were shown to be full-sib related, ‘Vitouska’ and ‘Pelena’, ‘Sagrestana’ and ‘Venere’. Colony inferred some additional FS relationships, represented by dotted lines in Fig. ‘Marzemina Bianca’ and ‘Codelunghe’ were shown to be PO related to ‘Garganega’, like ‘Brambana’ to ‘Bianchetta Trevigiana’; moreover, ‘Ruacit’ and ‘Blanchias (Blancjàs)’ could also be FS related Fig. .VIVC). MI lower than or very close to the highest value for the reference pairs (0.0037) were computed for the pairs of varieties considered to be PO related; instead, MI 2 to 9 times higher than the highest value of reference were computed for pairs excluded as being PO related. Specifically, our data reject the PO relationships for ‘Marzemino’ and ‘Teroldego’ and for ‘Marzemino’ and ‘Refosco dal Peduncolo Rosso’ supported by other studies27. Furthermore, SNP data do not support the PO relationship for ‘Marzemino’ and ‘Marzemina Bianca’28 nor for ‘Glera Lunga’ and ‘Glera’8. Instead, Colony output supported the hypothesis that ‘Marzemino’ and ‘Refosco dal Peduncolo Rosso’ could be FS related  were retained and more consistently classified using a larger set of 192 genotypes. The 9,177 SNPs were used for Mendelian inconsistencies (MI) computation. The obtained results are reported in Table MI were also split in three classes: Robust, DistortedAndUnexpSegreg, and OneHomozygousRare (encompassing both OneHomozygousRare_HWE and OneHomozygousRare_not HWE) Table . Given tFinally, Colony inferred the nSSR genotype of the presumed common parent for FS related ‘Aghedene’, ‘Glera’ and ‘Glera Lunga’ varieties  with ‘Quaiara’ and ‘Rossetta/Sciavetta/Doretta’ accessions grown at CREA-Viticulture and Enology repository in Susegana (TV) evidenced matching morphology . In the 19th century, ‘Vulpea’ existed in nearly every Austrian and northeast Slovenian vineyard35 and in Siebenbürgen/Romania36. The Austrian Helbling provided the first accurate description in 1777 under the name ‘Schwarzer Abendroth’37. According to ampelographers of the 18th and 19th centuries the late maturating ‘Vulpea’ produced bunches with green, red and blue berries. The wine was of low quality, had a light red color and sour taste, however the cultivar was appreciated for its heavy crop. Similar to ‘Heunisch Weiss’, it was recommended for “eradication”35. ‘Vulpea’ is grown successfully in the warmer Italy34, thus indicating that ‘Vulpea’ spread from neighboring countries to northeast Italy, probably since ancient times.Surprisingly, ‘Vulpea’, which was classified in the 8. Therefore, according to present knowledge, all FVG cultivars first-degree related to ‘Vulpea’ should be offspring of this variety.The parents of ‘Vulpea’ are the Croatian ‘Bratkovina Crna’ and Hungarian ‘Gyoengy Feher’The discovery of Vulpea’s high impact on the FVG variety assortment and the involvement of the Croatian ‘Bratkovina Crna’ and Hungarian ‘Gyoengy Feher’ in its ancestry pointed more to Austria as the country of origin than to Romania or Moldavia. In the latter ‘Vulpea’, known as ‘Ciorcuta Rosie’, was also considered autochthonous.38; among these varieties ‘Refosco dal Peduncolo Rosso’ was widely cultivated, while the others were only grown locally. Instead, ‘Refošk’ (synonym ‘Teran’), corresponding to the FVG ‘Terrano’, is the most cultivated in Kras (Carst) and Istria7.‘Refosco Nostrano’ showed strong links with all other Refosco varieties, except for ‘Refosco dal Peduncolo Rosso’. ‘Refosco Nostrano’ was pivotal for some niche varieties, to our knowledge, only grown in a specific and restricted area of FVG: ‘Berzamino’, ‘Tazzelenghe’, ‘Refosco di Rauscedo’ and ‘Refosco Bianco’. In FVG all varieties were reproduced locally before the arrival of phylloxera. Afterwards, grafting was performed mostly in large nurseries where only some varieties, mainly the most productive, were propagated, endangering biodiversity conservation. In the small towns of Faedis and Nimis, far distant from the main nursery in Cividale del Friuli, grafting continued to be done on-site, saving some autochthonous varieties from extinction. In detail, ‘Berzamino’, ‘Curvin’ and ‘Tazzelenghe’  were found in Nimis, while ‘Refosco Nostrano’, ‘Vinoso Rosso’, ‘Piculit Neri’ and ‘Siora’ in Faedis. All these varieties were propagated for their high yield. Historically, all Refosco varieties  were widely distributed in FVG, from the hills to the sea‘Cjanorie’ appears to be a bridge between ‘Vulpea’ and ‘Refosco Nostrano’. ‘Cjanorie’ is PO related to ‘Vulpea’ and ‘Vinoso Rosso’. Moreover, no pair of varieties in PO with ‘Refosco Nostrano’ can be the parents of this ancient variety and Colony indicates an FS relationship between ‘Refosco di Rauscedo’ and ‘Vinoso Rosso’. Consequently, the only possible kinship for ‘Cjanorie’ seems to be that it is the progeny of ‘Vulpea’ and ‘Vinoso Rosso’.39. The same authors analyzed a so-called ‘Rebula Stara’, meaning “old Rebula”. This variety was shown to be different from ‘Ribolla Gialla’ and possibly PO related, according to 11 SSRs. It was a surprise to discover that ‘Rebula Stara’ corresponded to ‘Heunisch Weiss’ after molecular profile comparison with available databases. ‘Heunisch Weiss’ is a variety of paramount importance for the evolution of grapevine varietal assortment. The pair ‘Pinot’ and ‘Heunisch Weiss’ originated a dozen French varieties40, among them ‘Chardonnay’ and ‘Gamay’; in addition, ‘Heunisch Weiss’ could be the parent of more than a hundred varieties, even outside France, such as in Germany, Greece, Hungary and Romania8. Three years after the work by39, the hypothesis of a PO relationship between ‘Ribolla Gialla’ and ‘Heunisch Weiss’ using 20 SSRs was again supported by8. However, this parentage was ruled out by9, because five mismatching markers were found using a deep genotyping with 58 SSRs and these incompatibilities were considered too many. These authors did not take into consideration another aspect that can explain the mismatches found. In ancient varieties propagated vegetatively for centuries, molecular differences can accumulate as in a biological clock. For example, a wide clonal diversity was found in ‘Savagnin’41, a very ancient variety at least 900 years old42, using only 30 SSRs: 49 plants were analyzed41 and only 12 showed the same genotype, i.e. 24.5%. In conclusion, if the genotyping is restricted to only one plant, such differences cannot be evidenced, and molecular information could lead to erroneous conclusions. For this reason, six accessions of ‘Heunisch’, coming from three countries were compared in this work, some of them being phenotypic variants16, and two ‘Ribolla Gialla’ accessions. SNP data strongly support a first-degree relationship between ‘Heunisch Weiss’ and ‘Ribolla Gialla’.‘Ribolla Gialla’ cultivar of the Italian Collio corresponds to the ‘Rebula’ grown in Goriška Brda, the Slovenian Collio, as confirmed with genotypingVIVC variety number 5000) was added to our varietal list, to understand if there was some link among these varieties. All available genotyping data exclude that ‘Ribolla Gialla’ is currently grown in Greece as any one of the genotyped ‘Robola’ cultivars43. Moreover, ‘Robola/Goustolidi’ was shown to be another Greek variety PO related to ‘Heunisch Weiss’, supporting a previous finding8.‘Robola’ sounds very similar to ‘Rebula’ and ‘Ribolla’. ‘Robola’ is also homonym for different varieties grown in Greece. A ‘Robolla’ sample coming from Greece and corresponding to ‘Goustolidi’  and ‘Piccola Nera’ (FVG) showed possible PO relationship with ‘Heunisch Weiss’ according to 20 SSR markers‘Mocula’ and ‘Pignolo’ could be half-sib of ‘Aghedene’, ‘Glera’ and ‘Glera Lunga’. These additional pedigree relationships reinforce the hypothesis that the P2 vine really existed in the past. However, recovery of the P2 genotype is necessary to confirm these assumptions.The dendrogram constructed by UPGMA is a fairly faithful representation of already known pedigree relationships and the new ones found in this research, in agreement with the relatedness proposed and summarized in Fig. nd parent being very close to Traminer and the wild grape  and on the “attraction” by ‘Manzoni Bianco’ (‘Pinot’ × ‘Riesling Weiss’).The cluster analysis clearly separated the three most prominent founder varieties of this study ‘Vulpea’, ‘Refosco Nostrano’ and ‘Heunisch’, pointing to their distinct geographic and genetic backgrounds. These three varieties created large family groups. The fairly stringent grouping leading to the clear separation observed between variety families can be due to the conscious choice of additional cultivars Table S, which tGenotyping studies provide a more stringent and sometimes new perspective on the origin, spread and relatedness of grapevine cultivars. Research on pedigrees highlighted that outcrossing is the main strategy for the birth of new varieties, whilst very few cultivars derive from selfing; it has now become recognized that grape flowers are subjected not only to cleistogamy, but also to pollination by wind or insects. Shedding light on grapevine pedigree relationships helps in understanding the germplasm assortment evolution and cultivar history. Some varieties of FVG are shared with neighboring areas, in and outside Italy, like ‘Glera’, ‘Glera Lunga’, ‘Ribolla Gialla’ and ‘Piccola Nera’, others are specific to FVG. Even their history intertwines with local and foreign varieties. Surprisingly, ‘Vulpea’ was shown not only to be spread in FVG and Veneto since ancient times, but it was also recognized as a recurrent parent of at least ten FVG cultivars, both ancient and well-known, like ‘Glera’, as well as recent or neglected. Many ‘Refosco’ varieties are PO related to ‘Refosco Nostrano’, showing that Refosco’s represent a real family; ‘Refosco dal Peduncolo Rosso’ does not strictly belong to this family and is likely full-sib related to ‘Marzemino’. ‘Ribolla Gialla’ was shown to be PO related to ‘Heunisch Weiss’, therefore being another of the numerous offspring derived from this prolific variety.In conclusion, combining molecular markers and historical references was shown to be a high-performance strategy to retrace and adjust the history of cultivars.Supplementary informationSupplementary information 2


Genomic selection has been successfully implemented in many livestock and crop species. The genomic best linear unbiased predictor (GBLUP) approach, assigning equal variance to all SNP effects, is one of the reference methods. When large-effect variants contribute to complex traits, it has been shown that genomic prediction methods that assign a higher variance to subsets of SNP effects can achieve higher prediction accuracy. We herein compared the efficiency of several such approaches, including the Adaptive MultiBLUP (AM-BLUP) that uses local genomic relationship matrices (GRM) to automatically identify and weight genomic regions with large effects, to predict genetic merit in Belgian Blue beef cattle.We used a population of approximately 10,000 genotyped cows and their phenotypes for 14 traits, mostly related to muscular development and body dimensions. According to the trait, we found that 4 to 25% of the genetic variance could be associated with 2 to 12 genomic regions harbouring large-effect variants. Noteworthy, three previously identified recessive deleterious variants presented heterozygote advantage and were among the most significant SNPs for several traits. The AM-BLUP resulted in increased reliability of genomic predictions compared to GBLUP (+ 2%), but Bayesian methods proved more efficient (+ 3%). Overall, the reliability gains remained thus limited although higher gains were observed for skin thickness, a trait affected by two genomic regions having particularly large effects. Higher accuracies than those from the original AM-BLUP were achieved when applying the Bayesian Sparse Linear Mixed Model to pre-select groups of SNPs with large effects and subsequently use their estimated variance to build a weighted GRM. Finally, the single-step GBLUP performed best and could be further improved (+ 3% prediction accuracy) by using these weighted GRM.The AM-BLUP is an attractive method to automatically identify and weight genomic regions with large effects on complex traits. However, the method was less accurate than Bayesian methods. Overall, weighted methods achieved modest accuracy gains compared to GBLUP. Nevertheless, the computational efficiency of the AM-BLUP might be valuable at higher marker density, including with whole-genome sequencing data. Furthermore, weighted GRM are particularly useful to account for large variance loci in the single-step GBLUP. Genomic selection  has beenThe benefit of such approaches might increase in the near future as causative variants, or variants in high LD with those, are now frequently added to genotyping arrays to improve accuracy of genomic predictions . In addA genomic selection program has been recently implemented in the Walloon region (Southern Belgium) for the Belgian Blue beef (BBB) cattle breed known for its extreme muscular development. As for other breeds and traits in cattle, several variants with large effects have been identified and characterized in that breed. The 11 bp deletion in the myostatin gene  is fixedPIP). We start thus by describing the regions identified by AM-BLUP (using 1 Mb chunks) and compare them to results from BSLMM or from more traditional GWAS approaches relying on LMM. Note that in a few cases, the AM-BLUP identified several regions close to each other that should possibly be merged as one larger region capturing more genetic variance. However, we preferred to keep the boundaries as defined by the default settings of the software (see NCAPG-LCORL genes (non-SMC condensin I complex subunit G / ligand dependent nuclear receptor corepressor like) previously associated with height in cattle  responsible for the crooked-tail syndrome ).Although individual SNP weighting methods avoid some of the caveats associated with chunk-based strategies, the iterative procedure based on the SNP effect solutions or based on posterior SNP variance did not perform as well as BSLMM. The first strategy estimates all SNP effects simultaneously and does not eliminate or reduce variance of SNP adjacent to potential causative SNP automatically. It proved, however, efficient in simulations studies , but waCompared to other studies e.g., , 11, 23), 2311, 2Although weighting strategies might increase prediction accuracies, their impact on long term evolution of genetic progress, maintenance of genetic variance and genetic diversity should also be considered. Some studies suggested that higher weights to frequent or large effects SNPs might lead to their rapid fixation and reduce long term genetic gain . In parThe AM-BLUP is an attractive method to automatically identify regions in the genome with large effects on complex traits and to estimate the proportion of genetic variance they capture. As a result, an optimally weighted GRM is automatically created and can be used in weighted GBLUP or ssGBLUP settings to improve genomic prediction accuracy. Although the approach proved very efficient on human data, it was less adapted to medium density genotyping arrays and high LD levels as typically observed in cattle. An alternative strategy, using Bayesian methods to identify group of SNPs with large effects and estimating their variance with a REML approach resulted in higher accuracies. Nevertheless, the computational efficiency of the AM-BLUP might be valuable at higher marker density, including with whole-genome sequencing data, or to estimate variance associated with different functional categories of SNPs.p < 0.001) or with more than 10 incompatible parent/offspring pairs  were discarded. Imputation to a higher density using a reference panel of 1839 individuals genotyped for 35,207 SNPs common to the Illumina Bovine50K and Illumina BovineHD genotyping arrays was done using Beagle 3.3.2 [A set of 10,192 cows genotyped with four different versions of the Illumina Bovine Low Density genotyping array  was usedle 3.3.2 . The tarThe 10,192 selected cows were scored from 1993 to 2017 for 22 linear classification traits. From these, we selected 14 traits related to muscular development and conformation including length, chest-width, rib-shape, rump, tail-set, pelvis-length, buttock-muscling (side view), buttock-muscling (rear view), pelvis-width, shoulder-muscling, top-muscling and muscular development  and also height and skin thickness . Trait deviations (TD) were obtained by correcting these records for fixed effects estimated in the official genetic evaluations.GBLUP) that assumes that all SNP effects have the same variance, BayesR [BSLMM) from Zhou and Stephens [We start by describing three reference methods for whole genome prediction, the genomic BLUP  for the cows, μ is the general mean effect, g is the vector of additive genetic random effects normally distributed, i.e., g ~ N of centered SNP genotypes, where n is number of animals and m is the number of SNPs, with values xij equal to:Here, sij is the number of copies of the reference allele carried by individual i at marker j. The reference allele frequencies fj are used to centre these allele dosages. Under this model, SNP effects have equal variance.where GS, can be built with rules proposed by Amin et al. [Alternatively, a standardized GRM, n et al.  or Astlen et al. .W is a matrix of dimension (n x m) of normalized SNP genotypes with values wij equal to:Where D, a diagonal matrix with diagonal elements djj equal to 1 when all SNP have equal contribution to the genetic variance, allows the introduction of different weights (see below).The division by entclass1pt{minimavariance  showed tGS. It allows better comparisons with the other tested genomic prediction methods, and with the adaptive MultiBLUP approach in particular, because these rely also on standardized genotypes. In the present study, we used BLUPF90 [We will refer to the standardized GBLUP when using  BLUPF90  to applyThe BSLMM assumes that all SNPs have at least a relatively small effects but also that a few SNP can have a large effect . The modβ is the vector of SNP effects. The individual SNP effects βi are sampled from a mixture of two normal distributions, PIP) for each SNP that indicates the proportion of samples in which that SNP has a large effect. That proportion can be used for fine-mapping approaches but also for SNP prioritization. Here, we used the standardized SNP genotypes matrix  when applying BSLMM.Where In this model, SNP effects are sampled from a mixture of four normal distributions with mean zero and variances equal to 0, 0.0001 SNP effects were estimated using MCMC simulations and the software implemented in Moser et al. .LRT) is then performed to test whether the variance associated with the region is significantly different from 0 (the default threshold thr1 for genome-wide significance is set to 10− 5). In the second step, regions are formed around significant chunks by merging them with all adjacent chunks with a p-value < thr2 (where thr2 is a second threshold set by default to 0.01). A GRM is estimated for each of these K merged regions and the background kinship matrix is estimated with SNPs outside the selected regions. Variances associated to each of the K + 1 GRMs, k is the index for the region (or its GRM) and 0 is used for the background kinship). Finally, genomic predictions are obtained by using the K + 1 GRMs and the estimated variance components. This is equivalent to apply a GBLUP model with a single GRM where each SNP is weighted by its own variance :The adaptive MultiBLUP approach is described in Speed and Balding  and implGAM is the Adaptive MultiBLUP GRM and DAM is a diagonal matrix with the corresponding weights, Where k is the number of SNPs in region k encompassing SNP i and DAM is maintained constant as for instance in Wang et al. [Where mg et al. :\documethr1 and thr2 are used to declare regions significant and define their confidence interval (CI). To optimize these thresholds, we generated the empirical distribution of the test statistics under H0 through 1000 random permutations of the phenotypes [p-value was selected. The empirical threshold at α = 0.05 was set at the 5% quantile of this distribution of 1000 best p-values. The CI was subsequently determined with a bootstrapping strategy [p-value below 0.01 surrounding the most significant position . In addition, all significant regions less than 10 Mb apart were merged. For each significant region, 1000 bootstrap samples were obtained by randomly selecting individuals with replacement. For each sample, the most significant window was flagged. The 95% CI was then defined as the smallest set of windows being flagged 950 times or more (95%). These windows were then selected to build the GRM. Hereafter, we used BAM-BLUP as abbreviation (for Bootstrap-Adaptive-MultiBLUP) to refer to this method.Thresholds enotypes , withoutstrategy . For thiWe also tested whether the BSLMM could be used to identify regions of interest. To that end, we used the posterior inclusion probabilities (PIP) to perform SNP selection. Since several SNPs in high LD might capture the effect of the same variant, we decided to work with 25 SNPs windows. For each window, we computed the region PIP as the sum of 25 individual PIP (one per SNP). Following Barbieri and Berger , regionsD was equal to m.Other strategies to compute a GRM using weights for SNPs have been previously proposed. Wang et al.  developeAccuracy of GBLUP approaches using the different weighted GRM were compared. All the weighted GRM approaches were also applied in the single step GBLUP (ssGBLUP) method, which combines information from genotyped and non-genotyped individuals , 4. The LMM) framework. We set the significant threshold at 1.39e-6 after application of a Bonferroni correction for 36,191 independent tests. Peaks were declared independent when two significant SNPs were more than 10 Mb apart.Regions or SNPs identified by the adaptative MultiBLUP and BSLMM approaches were compared to results from a genome-wide association study (GWAS) performed using GLASCOW  that useTo compare the prediction accuracy from different models, the data was divided in training and validation sets. To that end, we removed all performances recorded after August 23th of 2018 which corresponded to individuals born more recently. As a result, the training set was composed of 7316 to 8532 individuals (according to the trait), whereas the validation group contained from 1356 to 1507 individuals . The prediction accuracy was estimated as the correlation between the trait deviation and the genomic estimated breeding values (predictive abilities). The reliability was obtained as the square of the accuracy divided by the heritability:Methods were also compared in terms of mean-square errors (MSE) between predicted and observed TD, eventually standardized by the variance of the TD. Bias of genomic predictions  were measured using the coefficients of the regression of the TD on the genomic predictions.Additional file 1.Additional file 2.Additional file 3.


To reduce the breeding costs and promote the application of genomic selection (GS) in Chinese Simmental beef cattle, we developed a customized low-density single-nucleotide polymorphism (SNP) panel consisting of 30,684 SNPs. When comparing the predictive performance of the low-density SNP panel to that of the BovineHD Beadchip for 13 traits, we found that this ~30 K panel achieved moderate to high prediction accuracies for most traits, while reducing the prediction accuracies of six traits by 0.04–0.09 and decreasing the prediction accuracy of one trait by 0.2. For the remaining six traits, the usage of the low-density SNP panel was associated with a slight increase in prediction accuracy. Our studies suggested that the low-density SNP panel (~30 K) is a feasible and promising tool for cost-effective genomic prediction in Chinese Simmental beef cattle, which may provide breeding organizations with a cheaper option and greater returns on investment.Chinese Simmental beef cattle play a key role in the Chinese beef industry due to their great adaptability and marketability. To achieve efficient genetic gain at a low breeding cost, it is crucial to develop a customized cost-effective low-density SNP panel for this cattle population. Thirteen growth, carcass, and meat quality traits and a BovineHD Beadchip genotyping of 1346 individuals were used to select trait-associated variants and variants contributing to great genetic variance. In addition, highly informative SNPs with high MAF in each 500 kb sliding window and in each genic region were also included separately. A low-density SNP panel consisting of 30,684 SNPs was developed, with an imputation accuracy of 97.4% when imputed to the 770 K level. Among 13 traits, the average prediction accuracy levels evaluated by genomic best linear unbiased prediction (GBLUP) and BayesA/B/Cπ were 0.22–0.47 and 0.18–0.60 for the ~30 K array and BovineHD Beadchip, respectively. Generally, the predictive performance of the ~30 K array was trait-dependent, with reduced prediction accuracies for seven traits. While differences in terms of prediction accuracy were observed among the 13 traits, the low-density SNP panel achieved moderate to high accuracies for most of the traits and even improved the accuracies for some traits. Vazquez et al. (2010) found that using 10,000 markers resulted in great prediction ability for production traits in Holstein cattle, and that the prediction ability was not improved as the number of markers continued to increase [Genomic prediction (GP), which uses genome-wide markers to predict direct genomic estimated breeding values (DGVs), has been widely studied in breeding programs for plants ,2,3 and increase .Many studies have been performed to develop a low-density SNP chip for cost-effective prediction in cattle ,21,22,23In China, the efficient genetic improvements brought about by GP and trends related to decreasing genotyping costs in contrast to increasing expenses for phenotyping have encouraged the application of GP to beef cattle. However, the cost of genotyping is still very high for breeding organizations in China; for instance, the cost of genotyping of BovineHD Beadchip is around 150 USD per animal. However, the cost of the low-density SNP chip is only 30 USD per sample. Chinese Simmental beef cattle play a dominant role in the Chinese beef industry because of their high adaptability and marketability. Simmental cattle and their crossbreeds account for more than 60% of the total cattle breeding stock across the country . The priAll the animals used in the study were treated in accordance with the guidelines established by the Council of China Animal Welfare. The protocols of the experiments were approved by the Science Research Department of the Institute of Animal Sciences, Chinese Academy of Agricultural Sciences (CAAS) . The approval ID and permit numbers are SYXK (Beijing) 2008-007 and SYXK (Beijing) 2008-008, respectively.2), eye muscle area at the 13th rib , and marbling (MB). The ADG was the rate of weight gain per day over the fattening duration. LW was measured before slaughter after fasting for 24 h. MB was visually scored on a seven-point scale depending on the degree of marbling on the cut surface of the 12th rib. In terms of the rib eye area (REA), a piece of the meat about 3 cm thick between the 12th and 13th rib was cut from the carcass. The rib eye area of both sides of the meat was measured as the area in square inches using a grid like the one pictured, and the two measurements were termed EMA12 and EMA13, respectively. Carcass traits and meat quality traits were measured in strict accordance with the guidelines proposed by the Institutional of Meat Purchase Specifications and GB/T 27643-2011 after slaughter.A total of 1346 Simmental cattle born between 2008 and 2015 were collected. Of the sample, 1114 animals were bulls. All individuals were collected from farms in Ulgai, Xilingol League, and Inner Mongolia of China, and they were transferred to Beijing Jinweifuren Farm for fattening under the same feeding conditions and measured for growth traits every 3 months. Then, a slaughter experiment was performed for various phenotype measurements when the cattle reached an average age of 20 months. A more detailed description of the management processes was reported in previous studies ,28. Thisp-value of Hardy–Weinberg equilibrium (HWE) chi-squared test >10−6; individual call rate >0.90. Alongside the quality control, missing genotypes were imputed using BEAGLE v4.1 software [DNA was extracted from blood samples via routine procedures and genotyped using the Illumina BovineHD Genotyping Beadchip. Raw genotype data were processed using PLINK (v1.90) before statistical analysis ,30. Indisoftware . The SNPThe genetic heritability was estimated via the single-trait animal model in ASREML (v4.1). The genetic correlations and phenotypic correlations of 13 traits were estimated using the bivariate animal model in ASREML (v4.1) . The bivn = 1199) and validation (n = 132) datasets using birth year before and after 2014, respectively, following the analysis in [Four separate strategies were implemented to select candidate SNPs separately, including genome-wide association study (GWAS), whole-genome regression (BayesB), sliding window, and gene annotation. Before the GWAS and BayesB analyses, the cattle population was split into training n = 119 and valp-values for each trait were chosen as the potential trait-associated SNPs. To identify the significantly trait-associated variants, the mixed model-based single locus regression analysis (MMRA) applied to perform GWAS for each trait was as follows: GenABEL . Lastly,SNPs contributing to a comparatively large proportion of genetic variance were selected by using whole-genome regression (BayesB), which uses autosomal markers to predict SNP effects simultaneously . BayesB v=4.234)  and the are from . The topSimilarly to , the highttp://asia.ensembl.org/index.html; accessed on 22 October 2019). The SNPs from the BovineHD Beadchip were annotated to the corresponding gene regions  using bedtools [Given that recent studies have shown that the number of genome features (like genes) that are represented by genetic markers may also influence the performance of GP ,35, we abedtools . ConsequWe applied GBLUP , BayesA/For GBLUP, the prediction model used to estimate the direct genomic breeding values (DGVs) was basically the same as in Equation (3); however, For the three Bayesian approaches, the prediction model used was the same as in Equation (3); however, they used a different prior distribution for are from .r2) was used to quantify the LD decay of the low-density SNP panel within the 2 Mb window. In addition, to check the feasibility of using low-density marker panels for genotype imputation and genomic prediction of Chinese Simmental beef cattle, we imputed this panel to the ~770 K density using Beagle v4.1 [The number and percentage of markers on each chromosome, and the pairwise marker interval were investigated to ensure the approximately uniform distribution of the low-density SNP panel across the autosomes. The correlation coefficient , which assigned animals randomly into five separate subsets of near-equal size. Each subset was used as the validation set only once, with the phenotype masked, while the remaining four subsets were treated as the training set. To reduce random sampling effects, the CV layout described above was replicated 20 times, and a new randomization was implemented for each replicate so that each of the subsets contained different individuals. The prediction accuracies were calculated as the correlation between the DGVs and the adjusted phenotypes in the validation set divided by the square root of the heritability. In addition, to assess the extent of bias of genomic prediction, the linear regression coefficients ) of the adjusted phenotypes (y) on the DGVs were calculated for the validation dataset. Here, phenotypes were adjusted for the environmental fixed effects, including sex, year, and the covariates of body weight upon entering the fattening farm, and the number of fattening days, and the residuals were saved as the adjusted phenotype. Unbiased models should not significantly deviate from 1, whereas values greater than 1 indicate a biased deflation prediction of DGVs and values smaller than 1 indicate a biased inflation prediction of DGVs.r2 value with the 770 K chip after quality control. We found that the LD decreased from 0.61 to 0.01 within the 2 Mb window   or BayesIn our study, heritability estimates ranging from low to high (from 0.11 to 0.56) were generated for 13 traits in Chinese Simmental beef cattle. The estimates for LW, CW, and RMW in this study were similar to those in a previous study on Chinese Simmental beef cattle, while the estimates for DP, LP, ST, and SR were 0.12, 0.21, 0.16, and 0.13 higher, respectively, than those found previously , which wThe usage of the low-density SNP panel achieved moderate to high accuracy in studied traits, even though it decreased the prediction accuracies in most of the traits in comparison with those using the BovineHD Beadchip. Among these 13 traits, ST, RMW, and SR displayed the greatest decreases, with average decreases of 0.20, 0.09, and 0.09 across the four methods, respectively; however, DP, LP, CR, and ADG exhibited slight decreases ranging from 0.02 to 0.07. These losses in accuracy were mainly attributed to the decrease in marker density in the low-density SNP panel, since the prediction accuracy should, in theory, be positively associated with the marker density . In contrast to the decrease in accuracy for the above seven traits, the accuracy of LW prediction using the low-density SNP panel was very close to that using the BovineHD Beadchip, and even the accuracies of LW, TD, EMA12, EMA13, and MB benefited from the usage of the low-density SNP panel, with average accuracy increases of 0.04–0.07. These unexpected improvements indicated that the low-density SNP panel containing candidate QTLs and informative SNPs for a low-density SNP panel could be feasible and useful for cost-effective prediction, even if the marker density is much smaller. Our study suggested that the genetic variances of these five traits were determined by a small number of variants with large effects. The selection of SNPs associated with traits or explaining a great percentage of the genetic variance allowed the QTL effects to be directly captured by the models . The higTo allow a more reliable assessment of the predictive performance of the low-density SNP panel, four methods with different model assumptions were applied. No consensus exists to date on the best genomic prediction tool, in spite of plenty of comparisons having been made for various prediction methods in both simulations ,52 and rThe customized low-density SNP panel will be applied for the cost-effective GP of the 13 traits of the Chinese Simmental beef cattle. The calves will be genotyped by the low-density SNP panel, and the genomic estimated breeding value (GEBV) will be calculated. Animals with GEBV ranking in the top 10% will be selected as candidates for breeding purposes. Compared to the prediction accuracy of the BovineHD Beadchip, the accuracy of the low-density SNP panel was slightly lower. For a more accurate estimation of genetic merit and candidate selection, the calves passing the first step selection will be genotyped by the BovineHD Beadchip. The two-step genotyping will greatly reduce the genotyping costs by avoiding BovineHD Beadchip genotyping for all animals. Meanwhile, the second step of genotyping can ensure the satisfactory prediction accuracy of GEBV and the selection of breeding calves. In this setting, we assume that the low-density SNP panel would be a feasible and applicable way for cost-effective genomic prediction in Chinese Simmental beef cattle. The size of the reference population, as well as the genetic relationship between the training and validation datasets, plays a key role in genomic prediction. In future, the low-density SNP panel will be updated as the number of animals in the reference population increases. For example, new variants with larger estimated effects will be included and less informative variants in the current panel with small effects will be removed, while the marker density of the low-density SNP panel will be kept at approximately 30 K. With the availability of whole-genome sequencing of the Chinese Simmental cattle, the SNP list of the SNP panel will be refined to enhance the prediction power. The causal variants of 13 traits identified using the whole-genome sequencing data will also added to the panel in the near future. A customized low-density SNP panel with 30,684 informative markers was developed and used to predict useful genomic estimated breeding values in 13 growth, carcass, and meat quality traits of Chinese Simmental beef cattle, aiming to reduce breeding costs and support the application of genomic prediction. To enable comparison, the prediction accuracies using BovineHD Beadchip were treated as references. We found that the performance of the ~30 K SNP genotyping array was trait-dependent; it reduced the predictive accuracies of seven traits but improved the accuracies of five traits. While differences in terms of prediction accuracy were observed among the 13 traits, the low-density SNP panel achieved moderate to high accuracy for most of the traits and even improved accuracy for some traits. Overall, the low-density SNP panel (~30 K) is a feasible and promising tool for cost-effective genomic prediction in Chinese Simmental beef cattle when only one or a few key economic traits are of interest.


Prunus salicina Lindl. cv. ‘Sanyueli’ v2.0 reference genome. These data allowed for describing a cluster of QTLs in the cultivar, ‘Angeleno,’ associated with the flavan-3-ol composition of mesocarp and exocarp, which explain up to 100% of the C/EC ratio. Additionally, we developed a C/EC metabolic marker, which was mapped between the markers with the highest log of odds (LOD) scores detected by the QTL analysis. The C/EC locus was located in the LG1, at an interval spanning 0.70 cM at 108.30–108.90 cM. Our results suggest the presence of a novel major gene controlling the preferential synthesis of C or EC in the Japanese plum fruits. This study is a significant advance in understanding the regulation of synthesizing compounds associated with fruit quality, postharvest, and human health promotion.Japanese plum fruits are rich in phenolic compounds, such as anthocyanins and flavan-3-ols, whose contents vary significantly among cultivars. Catechin (C) and epicatechin (EC) are flavan-3-ol monomers described in the fruits of this species and are associated with bitterness, astringency, antioxidant capacity, and susceptibility to enzymatic mesocarp browning. In this study, we aimed to identify quantitative trait loci (QTL) associated with the content of flavan-3-ol in Japanese plum fruits. We evaluated the content of C and EC in the mesocarp and exocarp of samples from 79 and 64 seedlings of an F1 progeny (<‘98–99’ × ‘Angeleno’>) in the first and second seasons, respectively. We also constructed improved versions of linkage maps from ‘98–99’ and ‘Angeleno,’ presently called single-nucleotide polymorphisms (SNPs) after mapping the already available GBS reads to  Prunus genus comprises species with a high economic interest in agriculture, including peach, apricot, cherry, and the Japanese plum, among others  is a self-incompatible, diploid (2n = 16) species, originated in China, that produces an edible drupe  and epicatechin (EC). C and EC are isomers that can form oligomers and polymers called proanthocyanidins (PAs); they are also known as condensed tannins . The C aPrunus persica [L.] Batsch), PpMYB7, has been found to be associated with PpLAR1 activation but not PpANR, which in turn was regulated by PpbZIP5 in response to ABA  is a quantitative trait that depends on various genetic and environmental factors . In the e to ABA . In addie to ABA . To datePrunus salicina cv. ‘Sanyueli,’ allowing for the construction of more dense and accurate linkage maps to identify QTLs controlling several traits, including the content of phenolic compounds, such as specific flavan-3-ols.Quantitative trait loci mapping is a tool that identifies loci controlling traits of interest . ThroughPrunus salicina Lindl.  and 151 seedlings of the F1 progeny from their cross (<‘98-99’ × ‘Angeleno’>) were established at the ‘Rinconada de Maipú’ Experimental Station, Santiago, Chile . The female parent, ‘98–99,’ is an early-medium maturing selection with red skin and yellow flesh. The male parent, ‘Angeleno,’ has a late-medium maturity, purple skin, yellow flesh, and excellent postharvest quality , 2020. WIdentification of flavan-3-ols was performed following °C in darkness until analysis. The HPLC-DAD analysis was done for the parents and the seedlings of the F1 population. The total amount  of each flavan-3-ol was measured in mg 100 g−1 of fresh weight (FW).From the frozen fruits, the exocarp  was separated from the mesocarp . Each tissue had three replicates, where each replicate corresponded to an independent fruit. The amount of flavan-3-ols from the tissues was measured following the high-performance liquid chromatography-diode array detector (HPLC-DAD) technique, as reported by p-values greater than 0.05 indicate normal distribution and homogeneous variances, respectively. Histograms for each flavan-3-ol and scatterplot matrices were analyzed in each tissue in T1. Segregation analyses were performed according to the previously determined threshold, testing for the single gene model (3:1 or 1:1). Scatter plots, distribution, homogeneity of variance, and segregation analysis were done using the “pairs,” “hist,” “leveneTest,” and “chisq.test” functions, respectively, of the “stats” and “car” packages of R software v4.0.2  was calculated for each seedling in both seasons. Based on the C/EC values, the seedlings were classified depending on a threshold empirically determined according to the obtained results (refer to “Results” section “Catechin/Epicatechin Segregation”) since a clear gap in the relative content values was observed in both seasons. The C/EC threshold corresponded to the value which separated the seedlings based on the predominant content of C or EC in both the tissues. Flavan-3-ol contents in the F1 progeny were tested for normality by the Shapiro–Wilk test. The test of Levene was employed to determine the homogeneity of variance for each trait. Shapiro–Wilk and Levene tests with e v4.0.2 , using mThe linkage maps were constructed using the SNPs markers of 151 seedlings from the F1 population < ‘98–99’ × ‘Angeleno’>. Genotypic data obtained from DNA fragment libraries were generated by the genotype by sequencing (GBS) technology , which w1. The sequences were aligned against Prunus salicina Lindl. cv. ‘Sanyueli’ v2.0  for significant associations was determined after 1,000 permutations, according to the permutation test (PT) available in the MapQTL software. The QTL analyses were plotted using MapChart v2.32 (The SNPs were transformed to the double haploid (DH) format of the JoinMap, according to the pseudo-test cross method of MapQTL. Markers segregating from the cv., ‘98–99’ or ‘Angeleno’ were classified as “a” or “b,” respectively. Independent QTL analyses were performed for each tissue and season. The non-parametric Kruskal–Wallis and parametric interval mapping (IM) tests were used to determine significant marker-trait associations. The empirical LOD significance threshold (rt v2.32 .Through ultra high-performance liquid chromatography-diode array detector-orbitrap-mass spectrometry (UHPLC-DAD-Orbitrap-MS) analysis, 21 phenolic compounds were identified from the Japanese plum fruits in T1 . Flavono–1 of FW and 14.65 to 19.40 mg 100 g–1 of FW in the flesh and skin, respectively. Also, the lowest contents in both the tissues were observed in the cv. ‘Angeleno.’ Regarding the F1 progeny in T1, the mean of the total content of flavan-3-ols was 2.46 and 20.69 mg 100 g–1 of FW in the flesh and skin, respectively. According to the Shapiro–Wilk tests, none of the evaluated traits showed a normal distribution. In T2, the absolute contents of C and EC were higher than those observed in T1  and 3.60% (skin) of the seedlings and in the flesh of the parent ‘98–99.’ However, the content of C was higher in ‘98–99,’ while ‘Angeleno’ showed a higher content of EC.Based on the contents of the identified flavan-3-ols, the relative content of each flavan-3-ol in T1 was calculated. Because of the low content of PA2 in the parents and the seedlings, this compound was excluded from the calculation of the relative values. The Shapiro–Wilk test indicates that the relative contents of flavan-3-ols presented a non-normal distribution . Also, bTo further analyze the contents of flavan-3-ols in the tissues on T1, pairwise scatter plots were constructed . Among tp-value > 0.05). Furthermore, the fruits of T2 showed 34 and 30 seedlings classified as C and EC in the flesh, while 35 and 29 seedlings belonged to C and EC in the skin, fitting with a 1:1 ratio . Seedling classification across seasons was maintained in 100% of the cases in the skin and over 95% in the flesh.To test the segregation pattern between C and EC contents among the seedlings, the C/EC ratio was calculated. According to the C/EC ratio in T1, in the flesh, we observed seedlings with a C/EC ratio less than 0.30 or greater than 0.50 . This diP. salicina as the reference genome, we obtained 500,779 tags, of which 401,975 (80.27%) were aligned to a unique position, and 98,804 (19.73%) could not be aligned to the reference. Initially, 81,264 SNPs were obtained from the alignment; however, after applying filters , we obtained 12,715 SNPs to be analyzed with the software, JoinMap v4.1. After discarding all the SNPs with identical segregation patterns, 1,126 SNPs were available for map construction.Aligning and mapping GBS-based reads to Prunus linkage groups, 517 and 609 SNPs were segregated from the female (‘98–99’) and the male parents (‘Angeleno’), respectively , a species known to have a high content of flavonoids. Regarding this, –1 FW, respectively, in whole fruits, i.e., skin and flesh mixed in the same sample; –1 FW for C and EC, respectively. The high content and variation in the contents of C and EC found in the studied progeny suggest that the Japanese plum is an important species for the genetics studies of the secondary metabolite profile.Japanese plum fruits are rich in phenolic compounds, such as phenolic acids, anthocyanins, flavonols, and flavan-3-ols families . In thisBoth the F1 progeny and the parents showed greater contents of flavan-3-ols in the skin than in the flesh in T1 and T2. Flavan-3-ol contents agree with the total amount of phenolic compounds of the Japanese plum fruits, which have higher accumulation in the skin than in the flesh . Also, wPrunus persica, two F2 populations (<‘Contender’ × ‘Ambra’> and <‘N.J. Weeping’ × ‘Bounty’>) showed a trimodal distribution for the maturity date, showing a correlation coefficient of 0.92 between years  avoids the loss of information due to specific points of sequence divergence among species, despite the collinearity shown by previously cited studies.Genetic linkage maps for the Japanese plum cultivars have been published previously. First, salicina , which asalicina , 2020, rsalicina , 2020 ansalicina , 2020, sIn this study, we reported a cluster of QTLs located in LG1, which indicates the existence of a locus controlling the synthesis of the flavan-3-ols present in the Japanese plum fruits. Furthermore, the main QTLs are related to C and EC contents, which suggests that the locus would be associated with the preferential synthesis of C or EC in the fruit tissues and not with the total amount of flavan-3-ols. The above can be explained because PAs  are produced by the polymerization of C and EC, and their difference would be caused by the changes in the content of the flavan-3-ol monomers .Results from the QTL analysis agree with those observed in the phenotypic data, which suggest a bimodal pattern in the C and EC contents . AdditioThe metabolic marker C/EC was mapped in the LG1 of ‘Angeleno’ at 108.30 cM in T1 and 108.90 cM in T2, between the markers with the maximum LOD scores observed in the traits C, EC, and C/EC-binary. These results consolidate the hypothesis that there is a genetic regulation of the synthesis of C and EC in Japanese plum fruits located in the lower part of chromosome 1, between markers S1_46249627 and S1_46553437.Prunus salicina Lindl. have been published by our team, evaluating the productive and postharvest traits  causing the preferential synthesis of C or EC. Crossing the variants’ positions with annotated genes in the region could shorten the list of candidate genes. The so-called variants should also be tested in other F1 progenies from ‘Angeleno’ crossed with other varieties, to gain knowledge about the genetic control of this trait in different genetic backgrounds of the Japanese plum.Within the suggested interval, there are three predicted genes described as flavonol synthase, which are involved in the flavonoid pathway . AdditioGaining insight into the synthesis of C and EC in fruits is of special interest since they are involved in many important fruit traits. In this sense, results reported in this study open up possibilities for the fine mapping of the locus containing the gene responsible for promoting the content of C or EC in the Japanese plum fruits, which could even be tested on other progenies of the current species. The precise determination of the locus would allow for identifying the allele of the gene responsible for the preferential content of C or EC in this species, allowing for a further genetic characterization using other Japanese plum segregating progenies.https://www.rosaceae.org/publication_datasets, accession number tfGDR1054.The data presented in the study are deposited in the Genome Database for Rosaceae repository, BB performed the bioinformatic analyses, linkage mapping, phenotypic data analysis, QTL analysis and wrote the first draft of the manuscript. IP developed the concept of the work, applied for funding, coordinated experimental work and data analysis, and supervised manuscript writing. RI developed the crosses and F1 populations and maintained them on the field in good conditions. JS coordinated harvest, quality analysis, and fruit sampling. DV-S, AS-G, WV, IC, ÁP-N, and HM participated in the quantification and analyses of the compounds. CG performed MS compound identification. HS and JM participated in the bioinformatic analysis. RI, PJ-M, MG, HS, JM, and JS participated in the critical revision, editing, and preparation of the final version of the manuscript. All the authors contributed to the article and approved the submitted version.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 interest.All 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. 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