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Predictive Breeding for Wine Quality (SelWineQ)

Project

Production processes

This project contributes to the research aim 'Production processes'. Which funding institutions are active for this aim? What are the sub-aims? Take a look:
Production processes


Project code: JKI-ZR-08-5205
Contract period: 01.11.2016 - 31.10.2019
Purpose of research: Applied research

Evaluation of new grapevine genotypes for their potential to produce high quality wines is a long lasting process. Seedlings require 3 to 4 years to yield sufficient grapes for micro-vinification. Quality assessment is based on sensory evaluation of qualified panels and requires replications for 15 to 20 years due to product variation based on annual climate variability. The development of suitable prediction models for the bottle neck trait 'wine quality' will speed up grapevine breeding considerably. A validated and reliable prediction model would enable an early removal of poor quality genotypes (negative selection). Within the project quality aspects are distinguished as (1) genetic quality potential (irrespective of the environment), as (2) metabolic quality potential (genotype by environment interaction) of the primary product (juice or must), and (3) product quality (analytical and sensory properties after winemaking). In order to predict the genetic quality potential a training-set (150 F1 plants = POP150) of a segregating white wine F1 population (in total 1000 F1 plants) will be deeply phenotyped in a non-targeted and a targeted metabolomics approach. Phenotyping of POP150 covers analyses of must and wine from 2 locations using FTIR, GC-ODP/MS and LC/MS as well as the sensory data from a descriptive analysis by a highly trained panel. Deep genotyping data will be generated based on a modified 'Genotyping by Sequencing' (GBS) method combined with novel high-throughput bioinformatics approaches on big data. Phenotypic and genotypic data will be used for modelling and a prediction of the genetic quality potential out of genomic data. As controls and in a comparative manner samples from wineries of reference cultivars with a proven genetic quality potential will be phenotyped along the process chain by metabolic and sensory description. This data set will show the range of metabolic plasticity in reference cultivars throughout different environments.

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Subjects

Framework programme

BMEL Frameworkprogramme 2008

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