R-scripts and data associated with the publication "Harnessing multivariate, penalized regression methods for genomic prediction and QTL detection of drought-related traits in grapevine". Article abstract: Viticulture has to cope with climate change and decrease pesticide inputs, while maintaining yield and wine quality. Breeding is a key lever to meet this challenge, and genomic prediction is a promising tool to accelerate breeding programs. Multivariate methods are potentially more accurate than univariate ones. Moreover, some prediction methods also provide marker selection, thus allowing quantitative trait loci (QTLs) detection and allowing the identification of positional candidate genes. To study both genomic prediction and QTL detection for drought-related traits in grapevine, we applied several methods, interval mapping as well as univariate and multivariate penalized regression, in a bi-parental progeny. We used a new denser genetic map, simulated two traits under four QTL configurations, and re-analyzed 14 traits measured in semi-controlled conditions under different watering conditions. According to our simulations, we recommend the penalized regression method Elastic Net (EN) for genomic prediction, and controlling the marginal False Discovery Rate on EN selected markers to prioritize the QTLs. Indeed, penalized methods were more powerful than interval mapping for QTL detection across various genetic architectures. Multivariate prediction did not perform better than its univariate counterpart, despite strong genetic correlation between traits. Using experimental data, penalized regression methods proved as very efficient for intra-population prediction whatever the genetic architecture of the trait, with accuracies reaching 0.68. These methods applied on the denser map found new QTLs controlling traits linked to drought tolerance and provided relevant candidate genes. Overall, these findings provide a strong evidence base for implementing genomic prediction in grapevine breeding.

Scripts and data associated with the SNP genetic map of Syrah x Grenache progeny are available at: https://doi.org/10.15454/QEDX2V.

Raw phenotypic data of Syrah x Grenache progeny in the phenotypic platform PhenoArch are available at: https://doi.org/10.15454/YTRKV6.

R, 3.6.3

RStudio, 1.2.5033