Agnès Doligez

Genetic control of leaf carbon- and water-related traits in grapevine under greenhouse and field conditions

The paper identifies genetic regions associated with leaf ecophysiological traits in grapevine under greenhouse and field conditions, showing that multi-environment analysis improves the detection of stable QTLs.

Robustness of high-throughput prediction of leaf ecophysiological traits using near infra-red spectroscopy and poro-fluorometry

This paper evaluates high-throughput tools for predicting leaf traits in grapevines under different water conditions, finding spectroscopy and poro-fluorometry effective for trait prediction and heritability estimation.

Interest of phenomic prediction as an alternative to genomic prediction in grapevine

This study applies phenomic prediction to grapevine using spectra to predict 15 traits, finding comparable predictive ability to genomic prediction across traits and populations.

Genome-wide association and prediction study in grapevine deciphers the genetic architecture of multiple traits and identifies genes under many new QTLs

This study identifies new QTLs and improves prediction accuracy for multiple grapevine traits using a diverse panel and multi-SNP models.

Across-population genomic prediction in grapevine opens up promising prospects for breeding

This study evaluates across-population genomic prediction in grapevine for 15 traits, finding predictive abilities of 0.6 for cross means and 0.26 for individual values, suggesting potential for genomic selection in breeding.

Harnessing multivariate, penalized regression methods for genomic prediction and QTL detection to cope with climate change affecting grapevine

The paper compares genomic prediction and QTL detection methods in grapevine under climate change, finding penalized regression methods like Elastic Net to be effective for prediction and QTL discovery.