Andrea Patocchi

WISER improves breeding value estimation by addressing population structure and genotype-environment confounding through spectral whitening, outperforming LS-means and BLUP across multiple species and traits.

WISER improves phenotype estimation in omics studies by correcting population structure, outperforming LS and BLUP across multiple species and datasets.

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.

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

This study examines the genetic architecture and genomic prediction accuracy of 30 apple quantitative traits across six European locations, identifying novel associations and demonstrating the impact of genotype by environment interactions.

This paper uses genomic selection to predict apple fruit texture traits, achieving high accuracy by optimizing the training set size and composition.

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.