Multivariate statistical modelling for QTL detection and marker selection in a bi-parental grapevine population
2018
Brault, Charlotte | Perrot-Dockès, Marie | Doligez, Agnes | Chiquet, Julien | Le Cunff, Loic | Flutre, Timothée | Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro) | Mathématiques et Informatique Appliquées (MIA-Paris) ; Institut National de la Recherche Agronomique (INRA)-AgroParisTech
Genetic selection in grapevine and many similar species is a challenge due to their perennial status and outbred nature. Marker-assisted selection and genomic selection can hence be useful methods to ease and speed up breeding. In Coupel-Ledru et al (2014), a 191 progeny of Syrah x Grenache was phenotyped in two successive years for several ecophysiological traits under two conditions, well-watered and water deficit, on a high-throughput phenotyping platform coupled to a controlled–environment chamber. As offsprings were previously genotyped at 153 SSR markers, several QTLs were found for each trait separately, differing across years and conditions.But do these differences reflect biological processes or contrasted power between conditions? And how accurately do they allow predicting phenotypes depending on the conditions? To answer such questions, our aim is to explore the ability of sparse and regularized multivariate regression models and algorithms to select QTLs based on their predictive properties. In the present study, we perform variable selection with various flavours of the LASSO method (group Lasso, fused Lasso) adapted for multiple responses, extending the model and algorithm from Chiquet et al. (2017). We apply these methods on simulated data and on real data fromCoupel-Ledru et al. (2014).
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