Genomic and phenomic prediction of complex traits in apple

Apple (Malus × domestica Borkh.) is one of the most important perennial fruit crops worldwide, however, many commercial cultivars trace their roots back to chance seedlings in orchard rather than goal-oriented breeding efforts. The major reason for this is its long juvenile phase, making single breeding cycles take decades. This, with its great - and currently underexploited - genetic diversity, makes apple an ideal crop for predictive breeding approaches as this allows early selection long before the traits of interest are even expressed.
This project applies genomic and phenomic prediction frameworks across a wide range of desirable characteristics — from compact tree architecture to improved fruit quality, including anthocyanin overexpression in red-fleshed apples. The genetic architecture underlying these traits is investigated by partitioning variance into additive, dominance and epistatic components and mapping genome-wide interactions with major mutations, with the goal of building more accurate genomic prediction models. In parallel, phenomic prediction models are evaluated that integrate measurements at the leaf and fruit level across different phenological timepoints, identifying low-cost proxies for traits that are otherwise slow or expensive to assess. Together, these genomic and phenomic approaches aim to translate apple’s great genetic potential into accelerated, more targeted breeding progress.