Modern viticulture is facing important challenges due to worldwide climatic changes, spanning from extreme heat spells and water scarcity to increased vulnerability to late frost occurrences due to the acceleration of grapevines phenological development. Anticipated vegetative and reproductive cycles are also directly connected to relevant variations in ripening and harvest conditions, deeply influencing grape composition and wine quality. To tackle these challenges and develop more resilient cultivars, we set out to generate new genotypic materials in Vitis vinifera to enable the application of genomic selection (GS). GS has emerged as a powerful tool to accelerate breeding programs by supporting the prediction of phenotypic performance based on genomic data. To effectively implement GS, it is crucial to generate a robust training population that captures the genetic diversity and trait variability of interest. On these premises, a semidiallelic cross including 7 parental genotypes of interest was carried out in spring 2023, and the genotyping of the resulting seedlings is expected to be completed in summer-autumn 2025 via ddRAD-seq. This training population, named OBIETTIVO2100, will serve as a foundational resource for developing predictive models that can guide the selection of superior Vitis vinifera cultivars.
Generation of a semidiallelic cross in Vitis vinifera for enhanced genomic selection
Falchi R.;De Paoli E.
2026-01-01
Abstract
Modern viticulture is facing important challenges due to worldwide climatic changes, spanning from extreme heat spells and water scarcity to increased vulnerability to late frost occurrences due to the acceleration of grapevines phenological development. Anticipated vegetative and reproductive cycles are also directly connected to relevant variations in ripening and harvest conditions, deeply influencing grape composition and wine quality. To tackle these challenges and develop more resilient cultivars, we set out to generate new genotypic materials in Vitis vinifera to enable the application of genomic selection (GS). GS has emerged as a powerful tool to accelerate breeding programs by supporting the prediction of phenotypic performance based on genomic data. To effectively implement GS, it is crucial to generate a robust training population that captures the genetic diversity and trait variability of interest. On these premises, a semidiallelic cross including 7 parental genotypes of interest was carried out in spring 2023, and the genotyping of the resulting seedlings is expected to be completed in summer-autumn 2025 via ddRAD-seq. This training population, named OBIETTIVO2100, will serve as a foundational resource for developing predictive models that can guide the selection of superior Vitis vinifera cultivars.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
