Renaud Rincent

This paper demonstrates that UAV-based phenomic selection can predict field performance of nursery-grown maize and pea genotypes without genotyping.

This study evaluates phenomic and genomic selection for pea yield traits, finding that integrating spectral and molecular data improves prediction accuracy for complex traits.

This study examines how domestication shapes multivariate phenotypic spaces across species, finding convergence, reduced phenotypic space, and a new index for cross-species comparison.

This study integrates multi-omics data to identify genes influencing yield in maize, showing improved resolution and predictive power compared to GWAS.

The paper reports on a competition to predict maize yield using genetic and environmental data, showing that diverse modeling approaches can achieve good results.

MegaLMM improves genomic predictions in new environments by incorporating environmental covariates into a statistical model, showing higher accuracy than traditional methods in maize breeding.

The paper uses multi-omics data from controlled conditions to predict platform and field traits in maize hybrids, showing higher predictive abilities than GBLUP for platform traits but only slight improvements for field traits.

BWGS is an R package for genomic selection that enables GEBV computation and validation in wheat breeding, showing that training population size and marker selection are key for predictive ability.

This paper introduces phenomic selection, a low-cost, high-throughput method using near-infrared spectroscopy to predict complex traits in wheat and poplar with accuracy comparable to molecular markers.