Chepca, H. | bioRxiv Science

bioRxiv2026

Genetic analysis of grapevine wood composition identifies glycosyltransferases involved in the biosynthesis of stilbene glycosides

Although wood extractives may play important roles in defense against pathogens, few studies have explored the genetic determinism of their accumulation. In this work, we have used a non-targeted metabolomic approach to perform a genetic analysis of grapevine (Vitis vinifera) wood extractive composition in a progeny from a cross between Riesling and Gewurztraminer. Metabolic quantitative trait loci (mQTL) analyses of cane and trunk wood extractives revealed a number of mQTLs. Analysis of one of these mQTL has led to the identification of a cluster of six UDP glycosyltransferase (UGT) genes linked to the accumulation of resveratrol-glucoside (piceid) in both canes and trunks. Detailed biochemical and structural characterization of these six UGTs highlighted a surprising functional diversity among this small gene family, as well as one UGT gene as a key determinant of the constitutive accumulation of piceid in lignified grapevine tissues. Molecular evolution analyses showed that the UGTs in this gene cluster were globally under strong purifying selection, suggesting important biological roles of the corresponding proteins. Moreover, the functional characterization showed that distinct UGT genes were involved in the biosynthesis of cis- and trans- isomers of piceid, suggesting that these isomers may play different biological roles in grapevine.

plant biology↗

bioRxiv2025

Comparative assessment of genomic, phenomic, and metabolomic prediction models in biparental grapevine breeding populations

Accelerating grapevine breeding for disease resistance and climate adaptation remains constrained by long generation cycles. We benchmarked genomic (SNP), phenomic (NIRS), and metabolomic (untargeted LC-MS) prediction for 24 agronomic traits in a biparental population phenotyped over three years. Seven statistical frameworks and four tissue x timepoint combinations (wood; vineyard leaves at budbreak and flowering; greenhouse leaves at flowering) were evaluated, together with feature-wise BLUPs across samples. Cross-year and cross-population analyses with two additional populations assessed temporal robustness and transferability. Genomic prediction was most accurate (up to r = 0.83), metabolomic prediction was intermediate (up to r = 0.59), and phenomic prediction was lowest (up to r = 0.39) despite its lower acquisition cost. Metabolite features were more heritable than NIR wavelengths, for which most unexplained variation remained residual under the fitted model. Multi-omics integration produced limited overall gains. These results support genomic selection as the primary approach, with metabolomic or phenomic screening considered only for traits and sampling designs that show reproducible predictive signal.

genomics↗