Harnessing multivariate, penalized regression methods for genomic prediction and QTL detection of drought-related traits in grapevine.

ABSTRACT: Viticulture has to cope with climate change and to decrease pesticide inputs, while maintaining yield and wine quality. Breeding is a key lever to meet this challenge, and genomic prediction 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 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 (IM) as well as univariate and multivariate penalized regression, in a bi-parental progeny. With a dense genetic map, we simulated two traits under four QTL configurations. Th

SUBMITTER: Brault C 

PROVIDER: S-EPMC8496232 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature