Giovanni A. L. Broggini

Evaluation of genomic and phenomic prediction for application in apple breeding

This study evaluates genomic and phenomic prediction methods for apple breeding, showing that expanding training sets improves predictive ability but leave-one-family-out validation reduces it, while phenomic data are less effective.

Integrative multi-environmental genomic prediction in apple

This study applies statistical and deep learning models to multi-environmental genomic prediction in apple, showing improved predictive ability for certain traits.

Chromosome-scale de novo diploid assembly of the apple cultivar 'Gala Galaxy'

This study de novo assembles a diploid and heterozygous genome of the apple cultivar 'Gala Galaxy' using long- and short-read sequencing and optical mapping, providing a high-quality reference genome for apple breeding.