Grapevine pangenome facilitates trait genetics and genomic breeding | Nature Genetics

Nature Genetics (2024)Cite this article Metrics details Grapevine breeding is hindered by a limited understanding of the genetic basis of complex agronomic traits. This study constructs a graph-based

Nature Genetics (2024)Cite this article

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Grapevine breeding is hindered by a limited understanding of the genetic basis of complex agronomic traits. This study constructs a graph-based pangenome reference (Grapepan v.1.0) from 18 newly generated phased telomere-to-telomere assemblies and 11 published assemblies. Using Grapepan v.1.0, we build a variation map with 9,105,787 short variations and 236,449 structural variations (SVs) from the resequencing data of 466 grapevine cultivars. Integrating SVs into a genome-wide association study, we map 148 quantitative trait loci for 29 agronomic traits (50.7% newly identified), with 12 traits significantly contributed by SVs. The estimated heritability improves by 22.78% on average when including SVs. We discovered quantitative trait locus regions under divergent artificial selection in metabolism and berry development between wine and table grapes, respectively. Moreover, significant genetic correlations were detected among the 29 traits. Under a polygenic model, we conducted genomic predictions for each trait. In general, our study facilitates the breeding of superior cultivars via the genomic selection of multiple traits.

The cultivated grapevine (Vitis vinifera ssp. vinifera L.) is an economically important perennial fruit crop that is grown widely for winemaking and fresh fruit in ~94 countries1,2. Previous studies have suggested that grapevine originated from a single domestication event in the Black and Caspian Sea regions more than 10,000 years ago, which subsequently spread across the northern hemisphere with gene flow from local wild populations1,2,3,4,5,6. However, other studies have suggested the potential for multiple domestication events7,8,9. Since domestication, grapevine cultivars have accumulated deleterious genomic variants, including single-nucleotide polymorphisms (SNPs) and SVs, in a heterozygous state, resulting in strong inbreeding depression2,10. Recent studies have highlighted the potential contribution of hidden genomic variants, including SVs10,11,12,13,14,15, to phenotypes, but the quantitative genetic basis of complex agronomic traits in grapevine has rarely been investigated at the genome scale.

Long-read sequencing technologies have revealed the prevalence of SVs in plant genomes. It is increasingly evident that SVs are more likely than SNPs to influence the phenotype of domestication traits13,16,17,18. At the population level, SVs tend to occur at low frequencies, reflecting negative selection signals10,13,19. Furthermore, the frequency of SVs may be related to their recent origin. For example, recent transposable element (TE) activity can generate new SVs that are initially present in only one individual or lineage20. In part because of their low population frequencies, SVs are typically in low linkage disequilibrium (LD) with SNPs. One practical implication of low LD is that SVs may encompass substantial missing heritability for quantitative traits10,13. Consistent with this viewpoint, the addition of SVs to population and quantitative genetic analyses has yielded new insights into local adaptation and agronomic traits12,13,17,21.

Grapevine genomes are highly heterozygous, partly because of the accumulation of genetic variation during clonal propagation, which has been carried out for thousands of years6,10,22,23. For example, the genomes of diploid Chardonnay and Cabernet Sauvignon contain more than 10% heterozygous sites including SNPs, insertion–deletions (indels) and SVs10,24,25. Although the commonly used reference genome from PN40024 was highly homozygous after nine generations of selfing, it is missing >10% of genes compared with heterozygous cultivars26,27. Across cultivars, only ~7% of the genes are shared, whereas ~8% are unique to each individual28. The high level of variability in grapevine merits the construction of a pangenome reference that incorporates presence–absence variation, improves the detection of genomic variants, including SV, and reduces reference biases29,30,31,32,33,34.

Here we assembled 18 haplotype-resolved telomere-to-telomere (T2T) assemblies representing eight diploid grapevine cultivars and one diploid wild grape. We then constructed a graph-based pangenome, which we call Grapepan v.1.0, using these new assemblies and 11 previously published chromosomal assemblies. These genotypes represent the global genetic diversity of grapes. Using Grapepan v.1.0, we built a variation map that includes SNPs, indels (2 bp ≤ indel < 50 bp) and SVs (≥50 bp) across a larger sample of 466 accessions, including 324 that were newly sequenced. We utilized this variation map in a genome-wide association study (GWAS) and the genomic prediction of 29 complex agronomic traits. This exercise identified quantitative trait loci (QTLs) for these agronomic traits, provided unique insights into the contribution of SVs to quantitative genetic variation and demonstrated the feasibility of breeding superior cultivars via genomic selection for multiple traits. The pangenome reference (Grapepan v.1.0), variation map, QTLs and our genomic selection models facilitate genomic breeding of grapevine.

HiFi reads, Hi-C reads and ultra-long nanopore reads were collected for nine representative diploid samples, including one accession of Vitis retordii, a wild species endemic to Asia, and eight grapevine cultivars (seven table grapes and one wine grape) (Supplementary Table 1). The nine samples resulted in 18 haplotypes that reached T2T-level assembly after gap filling (Supplementary Fig. 1). Genome sizes ranged from 479.15 to 539.30 Mb (Supplementary Table 2). The quality of haplotype assembly was confirmed by high contiguity (>99.9%), minimal switching error (<0.05%) and low Hamming error35 (<2.83%) (Fig. 1a,b and Supplementary Table 3). Benchmarking universal single-copy orthologs evaluation indicated an average completeness of 98.4% for these haplotypes (range 98.07% to 98.64%; Supplementary Fig. 2). We used the same pipeline to annotate all haplotypes and to ensure consistent results. Across the 18 haplotypes, the number of protein-coding genes ranged from 34,536 to 38,526 (Supplementary Table 2), and the TE sequence length per haplotype ranged from 263.86 Mb (54.68%) to 312.10 Mb (59.03%) (Supplementary Table 4). In addition, we identified centromere and telomere sequences in all assemblies (Fig. 1c and Supplementary Table 5). Consistent with previous studies27, the predominant repeat unit of the centromere was 107 bp long. Overall, these 18 assembled haplotypes and their annotations represent one of the highest-quality grapevine genomic datasets generated to date.

a, NGx plot showing the assembly continuity of the 18 newly assembled haplotypes compared with the published PNT2T assembly. Two haplotypes (haplotype 1 and haplotype 2) of the same individual are distinguished. b, Assessment of the assembly for nine sequenced grape accessions (BMNG, HMNG, MH, WG, MF, PN, SM, TS and BM). The quality values demonstrate the base-level accuracy of each sample. The phasing accuracy is indicated by the percentages of switch errors and hamming errors. c, Comparative genomics of 27 (published genomes only selected the primary haplotype) assemblies and one assembly of Muscadinia rotundifolia as outgroup for chromosome 1. d, Total length of MC pangenome sequences with different numbers of haplotypes; M represents megabase pairs and G represents gigabase pairs. e, Validation of pangenome deletions and insertions involved counting SVs of varying lengths and calculating the accuracy. f, PCA of the first two components of the 466 sequenced grape accessions. Different grape groups are distinguished by different colors. The samples used to construct Grapepan v.1.0 represent a wide range of genetic diversity. PC, principal component. g, The decay of LD was calculated based on three different datasets: SVs, SNPs and SVs + SNPs.

To represent genetic diversity among grapevines, we collected 11 previously published assemblies from five cultivars (one with haplotype-resolved assemblies and four primary assemblies) and four wild accessions (one with haplotype-resolved assemblies and three primary assemblies) (Fig. 1c and Supplementary Table 1). Using these samples, we investigated the heterozygosity within the genome. We found that the heterozygosities of Vitis retordii, V. arizonica, V. labrusca and V. vinifera ssp. sylvestris are significantly lower than those of the cultivars (excluding PNT2T, telomere-to-telomere grape genome of PN40024 (ref. 27)) (P < 0.05, Student’s t-test). For example, genome heterozygosity was 0.40% in V. labrusca compared with an average of 1.42% across V. vinifera cultivars, whereas it was 1.66% in an interspecific hybrid (Shine Muscat, V. labrusca × V. vinifera) (Supplementary Fig. 3a). We also identified genome-wide presence and absence variations between the two haplotypes from our newly sequenced accessions. The results showed that an average of 12.57% (4,645) of genes were in a hemizygous state, which was identified as the entire gene structure affected by SVs (Supplementary Fig. 3b). Utilizing gene annotations obtained from these 18 grape accessions, we identified core and variable gene families. Our analysis revealed a total of 30,268 gene families, with 19.07% identified as core families and 77.00% categorized as variable families, including 20.40% soft-core families and 56.60% nonessential families. In addition, 3.93% of the identified gene families were classified as private (Supplementary Fig. 4).

An unbiased pangenome reference is crucial for discovering global genetic diversity, including SVs, among grape genomes. Using 29 haplotypes from the sequenced samples, we constructed two graph-based pangenomes based on the PanGenome Graph Builder (PGGB) and Minigraph-Cactus (MC) programs. The total length of the MC-based pangenome (Grapepan v.1.0) reached 1.43 Gb, which is 2.88 times that of the PNT2T genome (Fig. 1d). We identified SVs from graph deconstruction and integrated assembly alignments to validate the sensitivity and precision of SV detection and then repositioned SVs using PNT2T coordinates. Altogether, we detected 236,449 reliable SVs with high precision (Fig. 1e), and we verified large SVs (>10 kb) by mapping HiFi reads (Supplementary Fig. 5). The PGGB pangenome provided a similar result to the MC pangenome (Supplementary Fig. 6). Based on the Grapepan v.1.0 SV map, we found a biased distribution of SVs throughout the genome, including two SV hotspots near the centromeres of chromosome (Chr) 9 and Chr19 (Supplementary Fig. 7a). A significant fraction of the observed SVs had low population frequencies (Supplementary Fig. 7b). On average, 88.6% of the SVs overlapped TEs, whereas 30.9% intersected gene structures (Supplementary Fig. 7c,d). The high concordance between TEs and SVs indicates that the former play a critical role in driving genetic variation events.

To expand the genome-wide SV map, we integrated short-read sequencing data from 466 accessions, including 324 that were newly sequenced (Supplementary Tables 6 and 7). The genotypes of pangenome-based SVs in these accessions were characterized, providing a more thorough understanding of SV frequencies across a diverse set of accessions. In total, the variation map contained 8,591,818 SNPs, 513,969 indels and 236,449 SVs.

We used the genome-wide SNPs to explore genetic relationships among grapes (Fig. 1f and Supplementary Fig. 8). Population structure analyses identified three major grape species or subspecies (V. labrusca, V. vinifera ssp. sylvestris and V. vinifera ssp. vinifera) within 466 collected grape accessions (Supplementary Figs. 9 and 10). We further analyzed modern cultivars of V. vinifera ssp. vinifera, which separated into three groups upon ADMIXTURE and PHATE analyses: winemaking varieties from Europe and the Middle East (Wine), and two groups of table grapes, one from Europe and Eastern Asia (Table1) and one from hybridization with V. labrusca (Table2, V. labrusca × V. vinifera). The Wine group had a high identity-by-state value (0.82), indicating a long history of shared genomic segments (Supplementary Fig. 11). The recent hybrid origin of the Table2 group may contribute to its lower identity-by-state value (0.79), low recessive deleterious burden and relatively high heterozygous burden compared with other groups. These observations support the vigorous phenotypes of Table2 grapes, because their deleterious burden is hidden in the heterozygous state owing to their hybrid origin (Supplementary Fig. 12). We assessed LD decay using both SNPs and SVs within the Wine and Table1 groups. LD decay was consistent across groups and rapid, but although was even more rapid among SVs (Fig. 1g and Supplementary Fig. 13). This rapid decay likely reflects the fact that SVs were typically observed in low frequencies (Supplementary Figs. 14 and 15). However, the rapid decay of LD between SVs and SNPs suggests that part of the missing heritability for quantitative traits could be hidden among SVs in the grape genome10.

To investigate the contribution of SVs to quantitative traits, we performed a phenotypic survey of 29 traits over 2 years (2016 and 2017) for the 324 newly sequenced accessions (Fig. 2a). The sample of 324 cultivars included 106 Wine grapes, 108 Table1 grapes and 110 Table2 grapes (Supplementary Fig. 16). The 29 traits comprised five phenotypic categories: bunch (six), contents (eight), berry traits (eight), fruit size (four) and skin (three) (Fig. 2a). GWAS has been used to investigate phenotypic traits related to the composition and dimensions of fruit using ~6,000 SNPs36. Our larger sample size and genome-scale variants facilitate genomic selection for multiple traits simultaneously. We began by analyzing correlations among quantitative traits over the 2 years (Supplementary Fig. 17) and by mapping phenotypes in principal component analysis (PCA) (Fig. 2b). In the berry content category, pairwise correlations for the traits fructose (Fru), glucose (Glu) and soluble solids content (SSC) were significantly positive (P < 0.001). Traits were also correlated between different categories. For example, titratable acid (TAC) of the content category had a significant negative correlation with four measurements of fruit size: berry weight (BeWe), berry volume (BV), berry length (BL) and berry width (BeWi) (P < 0.001; Supplementary Fig. 17). These correlations likely follow from the fact that the synthesis of acids typically ceases during veraison, and this cessation contributes to the dilution of acid concentrations as the fruit continues to ripen and expand36. These correlations among phenotypes may lead to an overlap of some candidate GWAS loci and have potential implications for the genomic selection of elite grape varieties with multiple desirable traits.

a, Schematic diagram of agronomic traits of grape fruits investigated, including five identified categories. Traits in each category are labeled. b, PCA map showing the relationships among all agronomic traits. The distance between variables and the origin measures the quality (index by cos2 value) of the variables. c, UMAP plot (base map) generated from 29 trait scores for three grape populations. Scores were scaled and centered (Z-score) across all individuals for each trait independently. The points indicate individual grapes and are colored by different populations. UMAP plots from content of Suc and BV traits were generated by mapping these scores to the UMAP base map. d, Box plot of mean pairwise phenotypic distances within the population (identity), and between all other populations (other). Sample sizes are 5,778, 5,778, 5,565, 23,112, 23,112 and 22,896 pairs. Boxes, 25% to 75% quartiles; horizontal line, median; whiskers, inner fence within 1.5× box height; circles, outliers within 1.5× box height; asterisks, outliers beyond 1.5× box height. Statistical significance was determined by two-sided Student’s t-tests. BB, berry bloom; BC, berry color; BD, bunch density; BeS, berry shape; BF, particularity of flavor; BuS, bunch shape; BuW, weight of a single bunch; Cit, content of citric acid; EDP, ease of detachment from pedicel; FF, firmness of flesh; FJ, juiciness of flesh; Mal, content of malic acid; SA, astringent of skin; SBN, number of subsidiary bunch; SL, length of seeds; SN, number of seeds; ST, thickness of skin; UMS, uniformity of time of physiological stage of full maturity of the berry; WPB, number of wings of the primary bunch.

To elucidate differences in 29 agronomic traits at the population level, we performed uniform manifold approximation and projection (UMAP) analyses. The first two components (UMAP1 and UMAP2) established trait differences between the Wine, Table1 and Table2 groups. Within-group trait distances were significantly lower than between-group distances (P < 0.001) (Fig. 2d). We then mapped the normalized values of agronomic traits onto the UMAP analysis to characterize the distribution of each trait among three groups. We found that 25 of 29 agronomic traits were evenly distributed without obvious group differences (Fig. 2c and Supplementary Fig. 18).

Previous GWAS analyses have faced challenges in simultaneously accommodating a large variety of cultivars, multiple phenotypic traits and high-resolution data analysis37,38,39, and no previous study of grapevine has included SVs in GWAS analyses. We performed GWAS analyses of SNPs and SVs using Grapepan v.1.0, based on a joint dataset containing 2 years of GWAS results (Fig. 3a). A total of 148 loci were significantly associated with agronomic traits, including 136 genomic regions that were detected by SNPs and 12 that were captured by SVs. Altogether, 27.61 Mb (~5.58%) of the genome was associated with at least one of the 29 agronomic traits (Supplementary Table 8). Of the 148 candidate regions, 26 (~17.57%) overlapped with loci identified by previous functional studies (Supplementary Table 9). For example, based on the SNP dataset, we detected a locus associated with the seedless or seedness trait on grapes in Chr18 (31.41–31.45 Mb), which contains a MADS-box gene, agamous-like 11 (AGL11), responsible for the development of ovules into seeds after fertilization40. Similarly, we identified a 95-bp deletion in the BL1 locus that was significantly associated with grape berry length. This variant, which was located in the exon region of Vitvi011427 and encodes a NAD(P)-linked oxidoreductase superfamily protein, had a phenotypic variation explained (PVE) value of 6.31% and was present in 20.4% of sequenced grapes (Fig. 3b and Supplementary Table 8). In addition, a significant 1.1 kb deletion in the SN6 locus had a PVE of 6.08%, was associated with a photolyase coding gene Vitvi030206 and had a frequency of 13.0% across sequenced grapes (Fig. 3c). A 139 bp insertion specific to Table2 grapes was located in the Suc1 locus associated with sucrose content, demonstrated a PVE of 6.60% and was present in 56.9% of Table2 grapes (Fig. 3d). This insertion is close to a gene homologous to AtRHM1, which encodes an enzyme involved in UDP-beta-l-rhamnose biosynthesis. We also performed GWAS of the SNP dataset from the PNT2T single reference genome. We found that 124 of 136 (91.18%) loci were detected by both the pangenome SNP and PNT2T reference SNP (Supplementary Fig. 19). Collectively, our GWAS analysis based on the pangenome integrated SVs and SNPs to improve mapping of important traits.

a, Integrated GWAS map for 29 grape agronomic traits. The ordinate represents the PVE of the trait. b–d, Three significant SVs for BL1 (b), SN6 (c) and Suc1 (d) GWAS loci and their populational frequencies. Left, Gene models with coding regions and transcription direction. The corresponding deletions and insertions are highlighted. Right, Proportions of different genotypes in three populations. e,f, The GWAS results and linkage for SSC7 (e) and BeWi9 (f) loci. Statistical significance was determined by generalized least squares F-test. g, The nucleotide diversity (π) and FST around this candidate region. The vertical dashed lines indicate the combined GWAS loci of BeWi or SSC traits, VL represents V. labrusca. h, Proportions of different genotypes at significant SNP sites in BeWi (nGT0/0 = 272, nGT0/1 = 51) and SSC (nGT0/0 = 288, nGT0/1 = 58) in 2016 (center line, median; box limits, first and third quartiles; whiskers, 1.5× interquartile range). nGT0/0 and nGT0/1 refer to the counts of different genotypes at significant SNP sites. Statistical significance was determined by two-sided Student’s t-tests. i, Gene annotation in candidate region (upper) and expression level (transcripts per kilobase of exon model per million mapped reads) of genes in the candidate region in different grape cultivars (lower).

We compared candidate GWAS loci for traits from different phenotypic categories and found a candidate locus (SSC7, Chr17:6.47–6.53 Mb) for SSC that was close to a candidate locus (BeWi9, Chr17:6.47–6.65 Mb) responsible for berry width. There were two most significant SNPs (17_6489512 and 17_6484258) with PVE values of 6.05% and 5.91% for SSC7 and BeWi9 loci, respectively (Fig. 3e,f). We constructed a local phylogenetic tree using the variations from the combined region (6.47–6.65 Mb). The tree featured a tight cluster within cultivated grape groups that had an extremely short inner branch length (Supplementary Fig. 20). The combined region had low genetic diversity compared with the rest of the genome, suggesting a selective sweep (Fig. 3g). The homozygous genotype of SNP 17_6489512 showed significantly lower soluble solids content (P = 6.778 × 10−4) and the homozygous genotype of SNP 17_6484258 showed significant higher berry width (P < 1.12 × 10 −15) (Fig. 3h). Based on the genome annotation in this locus, we identified two gene clusters, the NEPS ([−]-isopiperitenol and/or [−]-carveol dehydrogenase) family (five members) and NRT1 (nitrate or dipeptide and/or tripeptide transporters) family (four members), and examined the expression of these genes in berries (Fig. 3i). Vitvi031750 of the NEPS family and Vitvi031760 of the NRT1 family had significantly high expression in four grapevine cultivars of both haplotypes, whereas the expression of Vitvi031756 in two Wine grapes (Merlot and Cabernet Sauvignon) was higher than Table1 grapes (Fujiminori and Venus Seedless).

To determine whether the QTLs associated with complex traits were under selection during divergence between populations, we performed XP-EHH (cross population extended haplotype homozogysity)41 analyses between the two table grape groups (Table1 and Table2). We found a total of 21.45 Mb (4.4%) regions that were significantly differentiated (P < 0.05) (Fig. 4a). The top 5% outliers in fixation statistics (FST) analysis showed a similar pattern (Supplementary Fig. 21). Gene set enrichment analysis (GSEA) revealed enrichment of four Gene Ontology (GO) terms associated with hormone responses and stress responses among the set of genes in the diverged genomic regions (Fig. 4c). Comparing the highly differentiated regions with the regions associated with phenotypes, six GWAS candidate loci were located in divergent genomic regions, and these were associated with berry color (BC4 locus), skin astringent (SA1), berry shape (BeS2), bunch weight (BuW5), flesh firmness (FF6) and tartaric acid content (Tar4) (Supplementary Fig. 22). Among them, a BC4 locus containing multiple MYB genes on Chr2 associated with berry color42 based on the pangenome SV dataset (Supplementary Table 10). The FF6 locus explained 7.35% of the variation for flesh firmness, which differed between groups because the Table2 group had an 11.7% increase in flesh firmness compared with the Table1 group (P < 0.01) (Supplementary Fig. 23).

a,b, Selection of the XP-EHH genomic scan for the Table1 versus Table2 (a) and Wine versus Table1 (b). nSites = 8,508, FDR (Benjamini–Hochberg) correction. Arrows indicate the highly correlated loci associated with the agronomic traits analyzed by GWAS. c,d, GSEA analyzes genes ranked in divergent regions of the genome and included two comparisons, Table1 versus Table2 (c) and Wine versus Table1 (d). The vertical dashed lines indicate the leading-edge subset and the max rank of the enriched gene.

The different usage of grapevine cultivars (for winemaking or consumption as fresh fruit) might drive genetic and phenotypic divergence between cultivated populations. We found that approximately 21.35 Mb of the genomic region was significantly different between the Wine group and the Table1 group based on XP-EHH analysis (P < 0.05) (Fig. 4b). The GSEA results indicate that amino sugar, glutathione and chitin metabolic processes, and the toxin catabolic process are enriched in the differentiated genomic regions (Fig. 4d). We detected 45 candidate GWAS loci associated with population divergent regions that determine berry size across five traits: BeWi, BL, BV, BeWe and weight of a single bunch (Supplementary Table 8). Metabolites provide rich flavors to wine, and 32 related candidate loci were enriched based on eight metabolic phenotypes (TAC, SSC, Glu, Fru, Suc, Tar, malic acid and citric acid). Within the diverged regions, we also identified five GWAS candidate loci (BV12, BeWe6, BuW2, BV15 and BeWi9) associated with fruit size, and two GWAS candidate loci (TAC3 and SSC7) associated with metabolites (Supplementary Fig. 22 and Supplementary Table 11). Overall, our analysis suggests that divergent selection on agronomic traits is associated with different breeding targets.

Because most SVs are not linked with SNPs (Fig. 1g), they could potentially contribute to the missing heritability in association analyses and genomic scanning tests13; in fact, we have already shown that a few candidate GWAS regions were identified with only SVs and not SNPs. We further investigated the contribution of SVs to phenotypic traits by using the LDAK model to estimate the proportion of phenotypic variance that is explained by genetic variants. The use of only SVs or SNPs limited the power of prediction for most agronomic traits, with the heritability contributed by SNPs ranging from 0.01% to 52.3% and that contributed by SVs ranging from 0.5% to 86.1% (Fig. 5a). The response to quantitative traits was dominated by SNPs, which implies polygenic architecture with minor effects from many loci, whereas qualitative traits were mostly affected by SVs with potentially large effects. Our analysis indicated that SVs contributed more to the heritability of 15 traits than SNPs. For example, genome-wide SVs explained 74.6% of the variance in BeWi but genome-wide SNPs explained only 0.5% (Fig. 5a). Similarly, SVs contributed 35.8% of the captured heritability in the SSC, but SNPs contributed only 0.6%. One 5.6 kb deletion on Chr7 explained 6.23% of the PVE for SSC (SSC2, Chr7:2029369–2032050) (Fig. 5c). The Wine grape accessions with a heterozygous deletion of this SV had a significantly lower SSC than accessions without the deletion. We suspect this might be related to the regulation of Vitvi011368, a gene encoding an isoamylase. The GWAS result revealed a significant association between BL and an SNP (BL2 locus, Chr10_9052243, PVE: 6.63%) (Fig. 5d). SNPs contributed to 20.9% of the captured heritability in BL, whereas SVs further improved the captured heritability to 64.9%.

a, The heritability of 29 traits contributed by genomic SVs and SNPs. The contributions from SNP and SV were distinguished. b, The genetic correlations among 29 traits. FDR (Benjamini–Hochberg) corrected. c, The genotypes of the gene Vitvi011368 and SSC in each group. Top, schematic diagram of a deletion in the region including the gene Vitvi011368. Bottom, SSC2 values in each group with different genotypes (center line, median; box limits, first and third quartiles; whiskers, 1.5× interquartile range). The sample sizes, from left to right, are [6, 50, 54], [6, 40, 61] and [10, 42, 50]. Statistical significance was determined using two-sided Student’s t-tests. d, GWAS result and LD analysis of the BL2 locus, which contained a candidate gene, Vitvi018414. Differences in BL between populations and genotypes were estimated (center line, median; box limits, first and third quartiles; whiskers, 1.5× interquartile range). The sample sizes, from left to right, are [38, 70], [12, 96] and [103, 3]. Statistical significance was determined using two-sided Student’s t-tests. e, Linear regression analysis for phenotype prediction between SSC2 values and values predicted by LDpred2-auto. The confidence interval (CI) is shown by gray shading. The smoothed line represents a linear regression fit of the actual data, and the shading represents the CI. Sample size n = 29. f, Linear regression analysis for phenotype prediction between BL values and predicted values by lassoum2. The significance of the linear relationship between variables was evaluated through Pearson correlation coefficients. The smoothed line represents a linear regression fit of the actual data, and the shading represents the CI. Sample size n = 29. °Bx, degrees Brix; NS, nonsignificant.

We calculated the genetic correlation between traits to assess the genomic selection of multiple traits for grape breeding. We combined pangenome SVs and SNPs to calculate pairwise genetic correlations among 29 traits, 20.7% of which showed a significant signal (P < 0.05). The genetic correlations (rG value) between various traits of fruits ranged from 0.41 to 0.97, suggesting a potential for concurrent selection of multiple traits in future breeding efforts (Fig. 5b). Single BeWe (SV heritability = 63.7%) and BL (SV heritability = 54.0%) were the main hubs of pairwise genetic correlations. Polygenic scores (PGS) can be used to aggregate effects across many genetic variants into a single predictive score, enabling the assessment of genome selection43. We evaluated PGS on the basis of GWAS summary statistics (pangenome SNPs + SVs) based on two derivation methods for all 29 traits (Supplementary Fig. 24 and Supplementary Table 12). The PGS prediction accuracy averaged >50% across all traits. As expected, traits with higher captured heritability tend to show improved prediction accuracy. The predictive accuracies obtained in this study showed improvements of at least 16%, and often higher, relative to previously reports for grapevines36,37. In particular, we found that BL has a higher heritability (74.9%), exceeding the 36.4% heritability captured in SSC. The PGS prediction showed an accuracy of 57.46% in SSC and 79.53% in berry length (BL) (Fig. 5e,f). Collectively, our analyses enable substantial prediction of agronomic traits in the grape breeding program.

Accelerating the innovation of grape varieties is urgently required to adapt to future planting, rapidly changing market demands and climate change. Grape breeding exhibits a degree of reliance on older varieties; in particular, clonal reproduction allows the preservation of genotypes over extended periods, some of which are older than 900 years22. Advances in grapevine breeding lag far behind those made in annual cereal crops because of their long generation times (~3 years on average), high deleterious burden that leads to inbreeding and/or hybrid depression, high genomic heterozygosity, inefficient genetic transformations and limited knowledge about the genetic basis of complex agronomic traits.

Progress in understanding the complexity of the grapevine over the past two decades, from phenotypic characterization to marker identification and association analysis, has greatly benefited breeding efforts. Early breeding emphasized correlation analysis between phenotypic traits and low-density genetic markers, and selected phenotypic traits through marker-assisted selection36,39,44. Using association analyses, researchers have linked specific genetic variations to desirable phenotypic traits, providing breeders with valuable tools for the targeted selection of multiple phenotypes, including berry size, color and sugar content37,38,39,45. These efforts have led to significant contributions such as the development of seedless grape varieties and the enhancement of disease resistance in grapevines46,47. However, limitations inherent in detecting variations within a single reference genome hinder the identification of crucial variants associated with breeding traits and a comprehensive analysis of agronomic trait inheritance.

Advanced pangenome-based approaches underscore broader efforts aimed at discovering genetic variants in crop breeding48. Recent research has focused on North American wild grapevines and has established a nonreference pangenome inclusive of nine wild accessions32. Their sequencing encompasses the diversity of wild grapevine species, aiming to integrate resistance variants from wild species for use in rootstock improvement. By contrast, our pangenome (Grapepan v.1.0) focuses on discovering variants associated with agronomic traits in domesticated grapevines. We selected representative cultivated varieties to construct the pangenome. We also included table grape varieties to expand diversity across grape populations with different uses49. Therefore, our pangenome may contain more advantageous genotypes related to domesticated traits, thus directly serving breeding programs. We utilized a graph-based approach in which any variant is integrated as a node within the pangenome reference. Indeed, the most significant enhancement of the pangenome lies in the discovery of SVs30,50. The number of newly discovered SNPs differs only slightly compared with alignment with a single reference genome or previous pangenome versions32. Thus, our grape pangenome places greater emphasis on uncovering traits associated with SVs and revealing their inheritance patterns.

In Grapepan v.1.0, SVs often associated with repetitive sequences and TEs, suggesting that TE-mediated events are an important evolutionary force49,51,52. The low frequency of SVs in the grapevine genome can be attributed to recent TE activity and the evolutionary constraints imposed by natural selection. This poses challenges in precisely controlling the breeding process when relying solely on SNP for trait selection. This challenge is exacerbated by the incomplete capture of heritability for multiple traits, particularly from SVs, which might be related to two factors. First, LD decay can influence the resolution of genetic mapping and the identification of causal variants53. Second, SVs are often found to generate and explain a greater proportion of phenotypic variation in numerous traits compared with SNPs54. The rarity of SVs also makes it difficult to accurately estimate their frequency and effect size within a population10. Consequently, the statistical power to detect associations involving rare SVs is lower than that for SNPs. In addition, SVs are larger relative to SNPs and can engender more immediate functional consequences, such as perturbations in gene dosage or the disruption of critical gene regulatory elements16,31. For example, SVs contribute the largest share of heritability for approximately half of the molecular traits in tomatoes, the identification of SVs based on pangenome has greatly increased estimates of the heritability of metabolic traits17. In foxtail millet, the precision of 73.9% of traits with both SNP and SV markers increased by between 0.04% and 12.67% compared with SNP-only markers14. Fruit color serves as a key trait in grape breeding, renowned for its association with SV determination10,55,56. We confirmed the higher heritability in fruit color contributed by SV and emphasized the power and accuracy of SV-based GWAS and genomic selection. We have found that the inheritance of an isoamylase gene associated with a 5.6-kb deletion explained 6.23% of the variance in SSC. Collectively, a deep understanding of SVs based on the pangenome will greatly improve the efficiency of SV-associated analysis for grapevine breeding44,48.

The ultimate goal of genomic breeding is to build superior cultivars by combining beneficial alleles underlying multiple agronomic traits of interest, while purging or hiding moderate-to-strong deleterious variants (including SNPs and SVs)57,58. Interestingly, we found strong genetic correlations among the 29 grape agronomic traits investigated, which allowed us to predict multiple traits during the same breeding cycle and decrease the time and monetary costs of breeding. In particular, SV-based genomic selection will play an excellent role in programs integrating multiple breeding traits. Overall, our Grapepan v.1.0, variation map, and the genomic prediction will greatly facilitate grapevine genomic breeding.

We collected nine grape accessions for genome assemblies, including seven V. vinifera ssp. vinifera (six Table1 grapes and one Wine grape), one V. labrusca × vinifera hybrid (Table2) and one V. retordii grape (Supplementary Table 1). We generated HiFi, Hi-C and ONT (Oxford Nanopore) ultra-long 150-kb reads for three grape accessions (Manicure Finger, Muscat Hamburg and Shine Muscat) and ONT ultra-long reads for the other six samples (the HiFi and Hi-C reads were obtained from other studies that have been published or will be published soon59,60,61).

We also obtained 11 previously published grape genome haplotypes from 9 grape accessions10,24,25,27,62,63,64 (Supplementary Table 1). Notably, haplotype-resolved assemblies were achieved for Cabernet Sauvignon63 and V. arizonica grape. These 18 grape accessions were utilized for core and/or variable gene family analysis, and 29 corresponding haplotypes were used to construct the graph pangenome.

We collected 324 modern cultivars of V. vinifera ssp. vinifera, including 106 Wine grapes, 108 Table1 grapes and 110 Table2 grapes, from the experimental orchard of Zhengzhou Fruit Research Institute of the Chinese Academy of Agricultural Sciences (Zhengzhou, China). The genomic DNA of these accessions was used for genome sequencing (15-fold coverage) (Supplementary Table 6). These newly sequenced data were used for subsequent analyses, including GWAS analysis, genomic prediction, heritability estimation and genetic correlation calculation. To improve population genome analysis, we also collected previously published resequencing data from 139 grape accessions and three outgroups6,64,65,66. These data were download from the National Center for Biotechnology Information (NCBI) database (Supplementary Table 6). Altogether, 466 grape varieties were used to investigate population structure, genetic diversity and genetic differentiation. This study provides a detailed description of the grape samples used for genome assembly and short-reads sequencing (Supplementary Notes).

For nine genome assemblies, the HiFi and ONT reads with Hi-C reads were integrated for self-correction, trimmed and assembled using the Hifiasm67 program (v.0.19.5-r587). For each accession, the contig-level assemblies were anchored and oriented to 19 chromosomes based on the reference-guided software RagTag68 (v.1.0.1). The two sets of HiFi contigs were then validated, grouped, sorted and anchored with the Hi-C reads to generate two pseudochromosomes, by using Juicer (v.1.5)69 and 3D-DNA (v.201008)70. In addition, we utilized Juicebox (v.1.11.08) to visualize and check the Hi-C data. We analyzed the gaps in these assembled haplotypes. These gaps were filled based on reads mapping and assembled scaffolds using the ONT data. We used NextDenovo71 (v.2.5.0) to generate the ONT assemblies. We then used Minimap2 (v.2.26)72 to map the ONT reads to the genome. Combined with Integrative Genomics Viewer visualization73, we identified split reads and estimated gaps. The gaps were closed with ONT assemblies with HiFi reads polished. Therefore, we obtained 18 gap-free T2T haplotypes from 9 newly sequenced grapes. The completeness of genome assembly was checked according to the benchmarking universal single-copy orthologs score74, and phase errors were verified by Hamming error and switch error. Genome heterozygosity was estimated with a k-mer-based approach using kmc75 (v.3.2.2) and GenomeScope2.0 (ref. 76) programs.

We used the same pipeline to annotate gene structure and repetitive sequences in all 29 haplotypes. For gene annotation, expression data were collected from various tissues, including flowers, leaves, stems, roots and fruits (PRJNA565689 and PRJNA434655). To improve the annotation process, we used Hisat2 (v.2.10.2)77 to align RNA sequencing reads against the repeat-masked assemblies. The mapping states were extracted using StringTie78 (v.1.3.0). We mainly relied on customs scripts that integrated Braker79 (v.3.0.2), PASA80 (v.2.5.1) and MAKER81 (v.3.01.03) programs for our genome annotations. We developed gene models and conducted subsequent searches utilizing AUGUSTUS82 (v.3.4.0). Incomplete genes and low confidence gene structures were filtered based on hidden Markov models and Pfam83 (v.1.6) database. TEs were identified using multiple combined programs. We generated the nonredundant TE catalogs using RepeatModeler84 (v.2.0.4) based on all haplotypes. At the same time, we improved the nonredundant TE annotation based on the program EDTA85 (v.2.0.1). RepeatMasker86 (v.4.1.2) was used to execute homolog annotation.

Telomere repeat units were investigated utilizing TIDK (v.0.2.0). The entire genome underwent an in-depth search and we generated statistics pertaining to telomere regions and visualized the telomere peaks. To investigate tandem repetitive sequences, we used Tandem Repeats Finder87 (v.4.09) to generate the statistics of the number of repeats and position information. These statistics were combined to annotate the centromeric repeats.

We performed genome alignments based on 27 haplotypes (18 newly generated and 9 previously published) from 18 representative grape accessions. We additionally collected one assembly from outgroup Muscadinia rotundifolia88. Haplotype sequences were aligned using SyRI89 (v.1.6.3) and the resulting alignments visualized using Plotsr90 (v.1.0.0). A set of homologous genes was estimated based on gene family clustering using Orthofinder91 (v.2.5.2). The resulting statistical summaries were used for identifying the core and nonessential gene family sets.

To represent the genetic diversity of grapes, we constructed a graph pangenome that incorporated a total of 29 haplotype assemblies, including 18 newly sequenced haplotype assemblies from 9 phase-resolved accessions and 11 previously published assemblies based on continuous long read data (two phase-resolved and seven primary assemblies). Based on these genome assemblies, we used two tools to build graph pangenome, MC (v.2.6.11)92 and PGGB (v.0.5.4)93, respectively. We combined reads mapping and assembled alignments to validate the graph paths, edges and nodes in the grape pangenome. To address the issue of small fragments in assemblies, we implemented filtering steps to exclude minor, fragmented and diverse assemblies that could introduce the wrong structures.

The complex regions of the PGGB pangenome may introduce additional challenges and uncertainties during SV genotyping based on Illumina sequencing data. Therefore, we chose to use the MC pangenome (Grapepan v1.0) to generate the SV set for population-scale SV genotyping. We used the PNT2T assembly as the reference to order the position of variations. We deconstructed the MC pangenome to obtain an SV map. At the same time, we identified SVs by aligning assemblies using SVIM-asm94 (v.1.0.3). To validate these pangenome SVs, we compared the SV generated by MC and SVIM-asm and obtained three validation metrics (precise, recall ratio and f1 score) using the program Truvari95 (v.4.1.0). The high-quality pangenome SVs were filtered for downstream analysis. To performed GWAS using SVs in a large population, we genotype the pangenome SVs in 466 sequenced grape accessions. We used vcfbub (v.0.1.0) to filter SV, processing input variants. We used the re-genotyping tool PanGenie50 (v2.1.0) to determine unique k-mers in the graph with PanGenie index. We then executed the PanGenie command on each accession and combined these VCF (variant call format) files to generate a merged SV map. We then distinguished biallelic and multiallelic variants. The resulting biallelic bubble-based VCF was used for downstream GWAS analysis, whereas the multiallelic bubble-based VCF was used for haplotype graph classification.

In addition, we generated pangenome SNPs and indels maps based on these sequenced grape accessions. We mapped 466 short reads to Grapepan v.1.0 using the Giraffe command from the vg program (v.1.51.0) and generated a BAM file for each accession. Small variants were identified by GTX (http://www.gtxlab.com/product/cat) (v.2.2.1) based on the BAM files. The decay of LD between SNPs and SVs was estimated using nonlinear regression of pairwise r2. LD was calculated using PLINK96 (v.3.31) and LD decay graphs were plotted using PopLDdecay97 (v.3.26).

We analyzed population structure based on 466 whole-genome sequencing data. The maximum likelihood (ML) tree was constructed using IQ-TREE98 (v.1.6.619) based on the VT + F + R5 model. The reliability of the ML tree was estimated using the ultrafast bootstrap approach with 1,000 replicates. Figtree and an online tool iTOL (Interactive Tree of Life v.3, https://itol.embl.de) were used to display the ML tree. Population structure was analyzed using the ADMIXTURE99 (v.1.3) program with a block-relaxation algorithm. To explore the convergence of individuals, we ran the cross-validation error procedure with K from 2 to 8. PCA was performed using PLINK (v.1.9). Two-dimensional PHATE embedding was generated using the first 20 principal components in the program phateR (v.1.0.7).

The genome divergence between different grape groups was estimate based on XP-EHH by using the selscan program100 (v.2.0.0). To identify candidate regions potentially affected by selections, nucleotide diversity and population FST were calculated based on vcftools101 (v.0.1.13) and genomics_general (https://github.com/simonhmartin/genomics_general).

Phenotyping of traditional core-cultivated grapes was performed in the experimental fields of the Zhengzhou Institute of Fruit Trees (Zhengzhou, China) in the spring and summer of 2016 and 2017 (over two growing seasons). Three clones were investigated per grape accession. We investigated 29 agronomic traits in field experiments. The measurements and criteria for 21 phenotypes were based on standardized protocols from the International Organization of Vine and Wine, with minor adaptations made for certain traits as necessary. The remaining eight agronomic traits were defined based on our field experience. All specific methods for phenotypic measurement and definitions of phenotypes can be found in Supplementary Notes. Our 29 phenotypic traits included five phenotypic categories: six traits in bunch category (bunch shape, number of wings of the primary bunch, number of subsidiary bunches, bunch density, weight of a single bunch and ease of detachment from pedicel), eight traits in content category (SSC, Glu, Fru, Suc, TAC, Tar, malic acid and citric acid), eight traits in berry traits category (firmness of flesh, juiciness of flesh, particularity of flavor, number of seeds, length of seeds, berry shape, uniformity of time of physiological stage of full maturity of the berry and berry color), four traits in berry size category (BL, BeWi, BV and BeWe) and three traits in berry skin category (astringence of skin, thickness of skin and berry bloom). We used Scatterplot Matrix to plot the correlations between each two pairs of traits and highlight the significant differences.

For two consecutive growing seasons of phenotype data, we conducted independent GWAS analysis and created a joint dataset by combining the GWAS results from both years. We have constructed a pangenome dataset encompassing SNPs and SVs. Based on these datasets, we independently conducted GWAS analyses: reference-based SNPs, pangenome-based SNPs and pangenome-based SNPs + SVs. PermGWAS (v.2023.05) was used to perform GWAS analysis of agronomic traits. To account for population structure, we implemented a mixed linear model approach to estimate heritability for each trait. The top five principal components have been shown to effectively capture population structure. To reduce the influence of overfit structure, we considered both fixed effects (using four PCA components to capture population structure) and random effects (using a kinship matrix to model individual covariance). Furthermore, we use permutation analysis to verify the result of each GWAS. The minimal P value from each permutation was used to calculate a permutation-based threshold using the maxT/minP multiple testing procedure.

The PVE of the most significantly associated single variant was estimated using the following formula102:

Where N represents the sample size, s.e. is the standard error of the effect size of genetic variants, beta is the effect size of genetic variants, and MAF is the minor allele frequency of the target marker.

The genome SNP heritability (G_h2SNP) of a trait is the fraction of phenotypic variance explained by additive contributions from all pangenome SNPs. Similarly, we calculate the genome SV heritability (G_h2SV) using all pangenome SVs. The LDAK103 (v.5.2) program provides a model for estimating G_h2SNP and G_h2SV by deriving approximate relationships between the heritability of a variant and MAF. Consider the heritability model of the form:

where wj is the weighting for each SNP or SV (j), fj is its MAF and tau are the corresponding coefficients and are estimated from the SNP or SV. The parameter alpha determines how the expected heritability contributed by a variant depends on its MAF (https://dougspeed.com/technical-details/).

Our approach involves filtering out genotyping error and nearly linked mutation sites, followed by utilization of the LDAK-Thin model. To improve robustness, we inferred G_h2SNP and G_h2SV based on a kinship matrix generated by pangenome SNPs or SVs. Because LDAK cannot directly estimate alpha, we adopt an alternative strategy involving a trial of multiple alpha values by comparing the highest log likelihood and identified value estimates from the Gaussian distribution.

To establish a connection between genetic variation and phenotype, we used a method that combines LD between loci and multiple genetic variations, calculating PGS through application of the LDpred2 program (v.1.12.4)104. To reduce the overfitting issues arising from excessive variable numbers, we initially clustered all genomic variations. We then partitioned the dataset into three subsets: a training dataset comprising 70%, a tuning dataset consisting of 20% and a test dataset constituting 10%. We used the training dataset to generate final summary statistics.

To validate the candidate genes identified in our GWAS and divergent selection, we retrieved fruit expression data from two accessions at three distinct stages—green–hard, veraison and ripening—from the NCBI database (PRJNA565689), with each library consisting of three biological replicates. These sequenced reads were mapped to the PNT2T genome using the STAR program (v.2.4.2)105. Gene expression levels were quantified using featureCounts106 (v.2.0.2) in terms of transcripts per kilobase of exon model per million mapped reads. Based on the GO database (http://geneontology.org/), we conducted the background GO-Terms using whole-genome proteins. GO and GSEA enrichment analysis was performed using clusterProfiler107 (v.4.0) and a test threshold of false discovery rate (FDR) <0.05.

Details on all statistical analyses used in this paper, including the statistical tests used, the number of replicates and precision measures, are indicated in the corresponding figure legends. Statistical analysis of replicate data was performed using appropriate strategies in R (v.8.4.3).

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

All long-read and Hi-C sequencing data have been deposited in the NCBI database under the accession code BioProject: PRJNA1048106. All resequencing data generated have been deposited in the NCBI database under the accession code BioProject: PRJNA994294. All assemblies have been deposited under the accession codes BioProject: PRJNA1018808, PRJNA1018809, PRJNA1029477, PRJNA1029478, PRJNA1029479, PRJNA1029480, PRJNA1130629, PRJNA1130630, PRJNA1130639, PRJNA1130641, PRJNA1130642, PRJNA1130643, PRJNA1130644, PRJNA1130647, PRJNA1130648, PRJNA1130649, PRJNA1130650, PRJNA1130651. These data are also available at the National Genomics Data Center Genome Sequence Archive (https://ngdc.cncb.ac.cn/gsa/) with BioProject codes PRJCA024688 and PRJCA024753. The Grapepan v.1.0, all assembled genome sequences and annotations are available via Zenodo at https://doi.org/10.5281/zenodo.10851547 (ref. 108) and https://doi.org/10.5281/zenodo.10846425 (ref. 109).

All scripts and codes associated with this project are available via GitHub at https://github.com/zhouyflab/GrapePan and Zenodo at https://doi.org/10.5281/zenodo.13308856 (ref. 110).

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We thank members of the Zhou lab at AGIS and Z. Zhang from Washington State University for discussion and comments on the project. This work was supported by the National Key R&D Program of China (grant no. 2023YFF1000100 to Y.Z.), the National Natural Science Foundation of China (grant no. 32372662 to Y.Z. and grant no. 32272647 to J.F.), the National Natural Science Foundation of China (Overseas) to Y.Z. and the Priority Academic Program Development of Jiangsu Higher Education Institutions (RAPD).

These authors contributed equally: Zhongjie Liu, Nan Wang, Ying Su, Qiming Long, Yanling Peng, Lingfei Shangguan.

National Key Laboratory of Tropical Crop Breeding, Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China

Zhongjie Liu, Nan Wang, Ying Su, Qiming Long, Yanling Peng, Fan Zhang, Shuo Cao, Xu Wang, Hui Xue, Zhiyao Ma, Wenwen Liu, Xiaodong Xu, Chaochao Li, Xuejing Cao, Bilal Ahmad, Xiangnian Su, Yuting Liu, Guizhou Huang, Mengrui Du, Zhenya Liu, Yu Gan, Zhongxin Jin, Yiwen Wang, Sanwen Huang, Hua Xiao & Yongfeng Zhou

College of Horticulture, Nanjing Agricultural University, Nanjing, China

Lingfei Shangguan, Mengqing Ge, Yanhua Ren, Tianyu Dong, Dan Pei & Jinggui Fang

National Key Laboratory of Tropical Crop Breeding, Tropical Crops Genetic Resources Institute, Chinese Academy of Tropical Agricultural Sciences, Haikou, China

Chaochao Li, Zhongxin Jin, Sanwen Huang & Yongfeng Zhou

Zhengzhou Fruit Research Institute, Chinese Academy of Agricultural Sciences, Zhengzhou, China

Lei Sun, Xiucai Fan & Chonghuai Liu

Institute of Horticultural Crops, Xinjiang Academy of Agricultural Sciences, Urumqi, China

Chuan Zhang, Haixia Zhong & Xinyu Wu

College of Horticulture, Qingdao Agricultural University, Qingdao, China

Xiangpeng Leng & Jinggui Fang

State Key Laboratory of Integrated Management of Pest Insects and Rodents, Institute of Zoology, Chinese Academy of Sciences, Beijing, China

Jinfeng Chen

Department of Ecology and Evolutionary Biology, University of California, Irvine, CA, USA

Brandon Gaut

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Y.Z. conceived of and designed the research. Zhongjie Liu, C. Liu, Y.L., M.G., X.F., L. Sun, L. Shangguan, M.D. and Y.R. participated in the material preparation and phenotype investigation. Z.J., X. Wu, C.Z., L. Shangguan, J.F. and D.P. provided metabolites data. Y.S., X. Wang, C.Z., S.C. and T.D. contributed to assembly and annotation. Zhongjie Liu, N.W., Y.S. and Q.L. constructed graph pangenome and detected genetic variations. G.H. and X.L. performed gene expression analysis. Zhongjie Liu, F.Z., Y.G., X.S., Zhenya Liu, H.Z., Y.P. and Y.W. contributed to the heritability estimation and association study. N.W. and Zhongjie Liu wrote the paper. Y.Z., B.G. and S.H. revised the paper with contributions from H. Xiao, J.C., H. Xue, S.H., Y.S., N.W., J.F., L. Shangguan, C. Li, B.A., Z.M., X.X., W.L. and X.C. All of the authors read and approved the paper.

Correspondence to Jinggui Fang, Hua Xiao or Yongfeng Zhou.

The authors declare no competing interests.

Nature Genetics thanks Roberto Bacilieri and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Supplementary Notes, Methods, Figs. 1–24 and Tables 1–12.

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Liu, Z., Wang, N., Su, Y. et al. Grapevine pangenome facilitates trait genetics and genomic breeding. Nat Genet (2024). https://doi.org/10.1038/s41588-024-01967-5

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Received: 24 July 2023

Accepted: 01 October 2024

Published: 04 November 2024

DOI: https://doi.org/10.1038/s41588-024-01967-5

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