Multivariate genomic model improves analysis of oil palm (Elaeis guineensis Jacq.) progeny tests
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.
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Subjects: | F30 - Génétique et amélioration des plantes, U10 - Informatique, mathématiques et statistiques, U30 - Méthodes de recherche, Elaeis guineensis, amélioration des plantes, sélection, méthodologie, génomique, modèle mathématique, marqueur génétique, analyse multivariée, contrôle continu, http://aims.fao.org/aos/agrovoc/c_2509, http://aims.fao.org/aos/agrovoc/c_5956, http://aims.fao.org/aos/agrovoc/c_6951, http://aims.fao.org/aos/agrovoc/c_12522, http://aims.fao.org/aos/agrovoc/c_92382, http://aims.fao.org/aos/agrovoc/c_24199, http://aims.fao.org/aos/agrovoc/c_24030, http://aims.fao.org/aos/agrovoc/c_28921, http://aims.fao.org/aos/agrovoc/c_2736, http://aims.fao.org/aos/agrovoc/c_7518, |
Online Access: | http://agritrop.cirad.fr/579129/ http://agritrop.cirad.fr/579129/7/579129.pdf |
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dig-cirad-fr-5791292024-01-28T23:13:17Z http://agritrop.cirad.fr/579129/ http://agritrop.cirad.fr/579129/ 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. Molecular Breeding, 36 (1):2, 13 p.https://doi.org/10.1007/s11032-015-0423-1 <https://doi.org/10.1007/s11032-015-0423-1> 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 eng 2016 Molecular Breeding F30 - Génétique et amélioration des plantes U10 - Informatique, mathématiques et statistiques U30 - Méthodes de recherche Elaeis guineensis amélioration des plantes sélection méthodologie génomique modèle mathématique marqueur génétique analyse multivariée contrôle continu http://aims.fao.org/aos/agrovoc/c_2509 http://aims.fao.org/aos/agrovoc/c_5956 http://aims.fao.org/aos/agrovoc/c_6951 http://aims.fao.org/aos/agrovoc/c_12522 http://aims.fao.org/aos/agrovoc/c_92382 http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_24030 http://aims.fao.org/aos/agrovoc/c_28921 http://aims.fao.org/aos/agrovoc/c_2736 Sumatra http://aims.fao.org/aos/agrovoc/c_7518 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. article info:eu-repo/semantics/article Journal Article info:eu-repo/semantics/publishedVersion http://agritrop.cirad.fr/579129/7/579129.pdf text Cirad license info:eu-repo/semantics/restrictedAccess https://agritrop.cirad.fr/mention_legale.html https://doi.org/10.1007/s11032-015-0423-1 10.1007/s11032-015-0423-1 info:eu-repo/semantics/altIdentifier/doi/10.1007/s11032-015-0423-1 info:eu-repo/semantics/altIdentifier/purl/https://doi.org/10.1007/s11032-015-0423-1 |
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F30 - Génétique et amélioration des plantes U10 - Informatique, mathématiques et statistiques U30 - Méthodes de recherche Elaeis guineensis amélioration des plantes sélection méthodologie génomique modèle mathématique marqueur génétique analyse multivariée contrôle continu http://aims.fao.org/aos/agrovoc/c_2509 http://aims.fao.org/aos/agrovoc/c_5956 http://aims.fao.org/aos/agrovoc/c_6951 http://aims.fao.org/aos/agrovoc/c_12522 http://aims.fao.org/aos/agrovoc/c_92382 http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_24030 http://aims.fao.org/aos/agrovoc/c_28921 http://aims.fao.org/aos/agrovoc/c_2736 http://aims.fao.org/aos/agrovoc/c_7518 F30 - Génétique et amélioration des plantes U10 - Informatique, mathématiques et statistiques U30 - Méthodes de recherche Elaeis guineensis amélioration des plantes sélection méthodologie génomique modèle mathématique marqueur génétique analyse multivariée contrôle continu http://aims.fao.org/aos/agrovoc/c_2509 http://aims.fao.org/aos/agrovoc/c_5956 http://aims.fao.org/aos/agrovoc/c_6951 http://aims.fao.org/aos/agrovoc/c_12522 http://aims.fao.org/aos/agrovoc/c_92382 http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_24030 http://aims.fao.org/aos/agrovoc/c_28921 http://aims.fao.org/aos/agrovoc/c_2736 http://aims.fao.org/aos/agrovoc/c_7518 |
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F30 - Génétique et amélioration des plantes U10 - Informatique, mathématiques et statistiques U30 - Méthodes de recherche Elaeis guineensis amélioration des plantes sélection méthodologie génomique modèle mathématique marqueur génétique analyse multivariée contrôle continu http://aims.fao.org/aos/agrovoc/c_2509 http://aims.fao.org/aos/agrovoc/c_5956 http://aims.fao.org/aos/agrovoc/c_6951 http://aims.fao.org/aos/agrovoc/c_12522 http://aims.fao.org/aos/agrovoc/c_92382 http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_24030 http://aims.fao.org/aos/agrovoc/c_28921 http://aims.fao.org/aos/agrovoc/c_2736 http://aims.fao.org/aos/agrovoc/c_7518 F30 - Génétique et amélioration des plantes U10 - Informatique, mathématiques et statistiques U30 - Méthodes de recherche Elaeis guineensis amélioration des plantes sélection méthodologie génomique modèle mathématique marqueur génétique analyse multivariée contrôle continu http://aims.fao.org/aos/agrovoc/c_2509 http://aims.fao.org/aos/agrovoc/c_5956 http://aims.fao.org/aos/agrovoc/c_6951 http://aims.fao.org/aos/agrovoc/c_12522 http://aims.fao.org/aos/agrovoc/c_92382 http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_24030 http://aims.fao.org/aos/agrovoc/c_28921 http://aims.fao.org/aos/agrovoc/c_2736 http://aims.fao.org/aos/agrovoc/c_7518 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 Multivariate genomic model improves analysis of oil palm (Elaeis guineensis Jacq.) progeny tests |
description |
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. |
format |
article |
topic_facet |
F30 - Génétique et amélioration des plantes U10 - Informatique, mathématiques et statistiques U30 - Méthodes de recherche Elaeis guineensis amélioration des plantes sélection méthodologie génomique modèle mathématique marqueur génétique analyse multivariée contrôle continu http://aims.fao.org/aos/agrovoc/c_2509 http://aims.fao.org/aos/agrovoc/c_5956 http://aims.fao.org/aos/agrovoc/c_6951 http://aims.fao.org/aos/agrovoc/c_12522 http://aims.fao.org/aos/agrovoc/c_92382 http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_24030 http://aims.fao.org/aos/agrovoc/c_28921 http://aims.fao.org/aos/agrovoc/c_2736 http://aims.fao.org/aos/agrovoc/c_7518 |
author |
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 |
author_facet |
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 |
author_sort |
Marchal, Alexandre |
title |
Multivariate genomic model improves analysis of oil palm (Elaeis guineensis Jacq.) progeny tests |
title_short |
Multivariate genomic model improves analysis of oil palm (Elaeis guineensis Jacq.) progeny tests |
title_full |
Multivariate genomic model improves analysis of oil palm (Elaeis guineensis Jacq.) progeny tests |
title_fullStr |
Multivariate genomic model improves analysis of oil palm (Elaeis guineensis Jacq.) progeny tests |
title_full_unstemmed |
Multivariate genomic model improves analysis of oil palm (Elaeis guineensis Jacq.) progeny tests |
title_sort |
multivariate genomic model improves analysis of oil palm (elaeis guineensis jacq.) progeny tests |
url |
http://agritrop.cirad.fr/579129/ http://agritrop.cirad.fr/579129/7/579129.pdf |
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