S01 - Session O3 - Genomic selection in apple: lessons from preliminary studies

Authors: Hélène Muranty *, Michaela Jung, Morgane Roth, Xabi Cazenave, Andrea Patocchi, François Laurens, Charles-Eric Durel

Genomic selection (GS) in apple has the potential to enhance breeding efficiency through decreased generation interval and/or increased early selection intensity. In GS, a large training set of individuals with both phenotypic and genotypic data is used to construct a statistical prediction model which is then applied to estimate Genomic Breeding Values (GBV) of individuals that only have genotypic data. One of the key factors determining genomic selection efficiency is its prediction accuracy, i.e. the correlation between GBV and true breeding values. Several studies have now assessed prediction accuracy under various settings regarding genotypic data collection, traits studied, and building of the training and validation sets. The objective of the presentation is to learn from these studies to provide suggestions to apply GS in apple and identify remaining areas to explore.