François N. D. Laurens

Leveraging genome-wide association studies and genomic prediction for distinctness, uniformity, and stability (DUS) testing in maize

The paper uses GWAS and machine learning to develop molecular markers and a genomic prediction framework for improving maize DUS testing using historical data.

WISER: spectral whitening of fixed effects for improved breeding value estimation in structured, unbalanced, and genotype-environment-confounded populations

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: an innovative and efficient method for correcting population structure in omics-based selection and association studies

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

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.

Genetic architecture and genomic prediction accuracy of apple quantitative traits across environments

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.

Combining genetic resources and elite material populations to improve the accuracy of genomic prediction in apple

This study combines genetic resources and elite material populations to improve genomic prediction accuracy in apple breeding by optimizing training set composition and population integration.