Callipo, P. | Search bioRxiv

bioRxiv2026

Leaf and cluster spectral signatures reveal trait-dependent prediction performance for grapevine cluster architecture and juice quality

Grapevine cluster architecture is a key selection target in breeding programs because it influences disease susceptibility, yield stability and juice quality. High-throughput phenotyping offers a rapid and non-destructive approach to capture biochemical and structural variation in these traits, yet the influence of plant organ reflectance and data partitioning strategies on trait prediction remains poorly understood. In this study, we evaluated how hyperspectral reflectance from different grapevine organs contributes to the prediction of cluster architecture and juice quality traits in two clonal populations of Riesling and Pinot. Using partial least squares regression (PLSR), we assessed the prediction accuracy of eight cluster architecture and six juice quality traits under two data partitioning strategies. Models based on cluster reflectance outperformed those using dry leaf reflectance for most traits, except for pH. Partitioning the dataset by cluster type increased trait variance and improved predictions for number of berries (R{superscript 2} = 0.53), berry diameter (R{superscript 2} = 0.79), and total acidity (R{superscript 2} = 0.48). Visible, red-edge and NIR spectra were most informative regions to predict the traits studied. Together, our results highlight the importance of organ-specific data and appropriate calibration strategies to improve phenomic models for the development of scalable proxies for grapevine improvement. HighlightSpectral phenomics reveals that prediction accuracy in grapevine depends on organ spectral signatures and traits, with cluster reflectance outperforming leaves, informing new phenotyping strategies for breeding improvement.

plant biology↗

bioRxiv2026

A dual genomic-epigenomic map of clonal evolution in grapevine

Grapevine is one of the worlds oldest and most economically important perennial crops and has been vegetatively propagated for centuries to millennia. This long history of clonal propagation has generated substantial intra-varietal diversity, but its molecular basis has remained difficult to resolve with short-read sequencing, which misses large structural variation and DNA base modifications. Here, we generated a high-quality, phased diploid reference genome for the elite cultivar Pinot noir and integrate it with Oxford Nanopore sequencing of 23 distinct clones to build an integrated, genome-wide map of clonal genetic and epigenetic variation in Vitis vinifera. The genome assembly reveals a deep history of ancient inbreeding, with approximately 12% of the genome occurring in extended runs of homozygosity. Across molecular layers, we observe contrasting evolutionary dynamics. Somatic genetic variation (67,277 SNPs and 4,037 SVs) is dominated by rare variants and strongly depleted from coding regions, consistent with strong purifying selection. A substantial fraction of the structural variation among clones is mechanistically linked to instability in repetitive DNA, particularly centromeric repeats. In contrast, CG context epigenetic variation is abundant (250,382 differentially methylated cytosines) and strongly enriched within gene bodies, highlighting it as a major source of clonal divergence. Most importantly, CG methylation patterns alone reconstruct clonal phylogenetic relationships with high fidelity, closely mirroring the SNP-based lineages. This congruence, absent in transient non-CG methylation, indicates that stable CG epialleles are mitotically inherited and preserve a persistent record. Together, our results show that clonal identity in grapevine is jointly encoded by genome and methylome, with stable CG methylation capturing propagation history at near-genetic resolution.

plant biology↗