Multivariate genomic model improves analysis of oil palm (Elaeis guineensis Jacq.) progeny tests

Marchal Alexandre, Legarra Andrés, Tisne Sébastien, Carasco-Lacombe Catherine, Manez Aurore, Suryana Edyana, Omoré Alphonse, Nouy Bruno, Durand-Gasselin Tristan, Sanchez Leopoldo, Bouvet Jean-Marc, Cros David. 2016. Multivariate genomic model improves analysis of oil palm (Elaeis guineensis Jacq.) progeny tests. Molecular Breeding, 36 (1):2, 13 p.

https://doi.org/10.1007/s11032-015-0423-1

Article de revue ; Article de recherche ; Article de revue à facteur d'impact

	Version publiée - Anglais Accès réservé aux personnels Cirad Utilisation soumise à autorisation de l'auteur ou du Cirad. 579129.pdf Télécharger (573kB) \| Demander une copie
	Version Online first - Anglais Accès réservé aux personnels Cirad Utilisation soumise à autorisation de l'auteur ou du Cirad. Marchal et al 2016_Mol breeding.pdf Télécharger (771kB) \| Demander une copie

Quartile : Q1, Sujet : HORTICULTURE / Quartile : Q1, Sujet : AGRONOMY / Quartile : Q2, Sujet : PLANT SCIENCES / Quartile : Q3, Sujet : GENETICS & HEREDITY

Résumé : Genomic selection is promising for plant breeding, particularly for perennial crops. Multivariate analysis, which considers several traits jointly, takes advantage of the genetic correlations to increase accuracy. The aim of this study was to empirically evaluate the potential of a univariate and multivariate genomic mixed model (G-BLUP) compared to the traditional univariate pedigree-based BLUP (T-BLUP) when analyzing progeny tests of oil palm, the world's major oil crop. The dataset comprised 478 crosses between two heterotic groups, A and B, with 140 and 131 parents, respectively, genotyped with 313 simple sequence repeat markers. The traits were bunch number and average bunch weight. We found that G-BLUP with a genomic matrix based on a similarity index gave a higher likelihood than T-BLUP. In addition, multivariate G-BLUP improved the accuracy of additive effects (breeding values or general combining abilities, GCAs), in particular for the less heritable trait, and of dominance effects (specific combining abilities, SCAs). The average increase in accuracy was 22.5 % for GCAs and 18.7 % for SCAs. Using 160 markers in group A and 90 in group B was enough to reach maximum GCA prediction accuracy. The contrasted history of the parental groups likely explained the higher benefit of G-BLUP over T-BLUP for group A than for group B. Finally, G-BLUP should be used instead of T-BLUP to analyze oil palm progeny tests, with a multivariate approach for correlated traits. G-BLUP will allow breeders to consider SCAs in addition to GCAs when selecting between the progeny-tested parents.

Mots-clés Agrovoc : Elaeis guineensis, amélioration des plantes, sélection, méthodologie, génomique, modèle mathématique, marqueur génétique, analyse multivariée, contrôle continu

Mots-clés géographiques Agrovoc : Sumatra

Mots-clés libres : Sélection génomique, Palmier à huile, Genomic selection, Oil palm

Classification Agris : F30 - Génétique et amélioration des plantes
U10 - Informatique, mathématiques et statistiques
U30 - Méthodes de recherche

Champ stratégique Cirad : Axe 1 (2014-2018) - Agriculture écologiquement intensive

Auteurs et affiliations