Agritrop
Home

Genomic selection prediction accuracy in a perennial crop: case study of oil palm (Elaeis guineensis Jacq.)

Cros David, Denis Marie, Sanchez Leopoldo, Cochard Benoît, Flori Albert, Durand-Gasselin Tristan, Nouy Bruno, Omoré Alphonse, Pomies Virginie, Riou Virginie, Suryana Edyana, Bouvet Jean-Marc. 2015. Genomic selection prediction accuracy in a perennial crop: case study of oil palm (Elaeis guineensis Jacq.). Theoretical and Applied Genetics, 128 (3) : pp. 397-410.

Journal article ; Article de revue à facteur d'impact
[img] Published version - Anglais
Access restricted to CIRAD agents
Use under authorization by the author or CIRAD.
document_575284.pdf

Télécharger (980kB)

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

Abstract : Genomic selection (GS) can increase the genetic gain in plants. In perennial crops, this is expected mainly through shortened breeding cycles and increased selection intensity, which requires sufficient GS accuracy in selection candidates, despite often small training populations. Our objective was to obtain the first empirical estimate of GS accuracy in oil palm (Elaeis guineensis), the major world oil crop. We used two parental populations involved in conventional reciprocal recurrent selection (Deli and Group B) with 131 individuals each, genotyped with 265 SSR. We estimated within-population GS accuracies when predicting breeding values of non-progeny-tested individuals for eight yield traits. We used three methods to sample training sets and five statistical methods to estimate genomic breeding values. The results showed that GS could account for family effects and Mendelian sampling terms in Group B but only for family effects in Deli. Presumably, this difference between populations originated from their contrasting breeding history. The GS accuracy ranged from ?0.41 to 0.94 and was positively correlated with the relationship between training and test sets. Training sets optimized with the so-called CDmean criterion gave the highest accuracies, ranging from 0.49 (pulp to fruit ratio in Group B) to 0.94 (fruit weight in Group B). The statistical methods did not affect the accuracy. Finally, Group B could be preselected for progeny tests by applying GS to key yield traits, therefore increasing the selection intensity. Our results should be valuable for breeding programs with small populations, long breeding cycles, or reduced effective size. (Résumé d'auteur)

Mots-clés Agrovoc : Elaeis guineensis, Amélioration des plantes, Méthodologie, génomique, Génie génétique, Sélection récurrente, Génétique des populations, Modèle de simulation, Modèle mathématique, Phénotype, Génotype, Efficacité, Rendement des cultures

Mots-clés géographiques Agrovoc : Indonésie, Afrique, Angola, Congo, République démocratique du Congo, Côte d'Ivoire, Bénin, Nigéria

Classification Agris : F30 - Plant genetics and breeding
U10 - Computer science, mathematics and statistics
U30 - Research methods

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

Auteurs et affiliations

  • Cros David, CIRAD-BIOS-UMR AGAP (FRA) ORCID: 0000-0002-8601-7991
  • Denis Marie, CIRAD-BIOS-UMR AGAP (FRA)
  • Sanchez Leopoldo, INRA (FRA)
  • Cochard Benoît, CIRAD-BIOS-UMR AGAP (FRA)
  • Flori Albert, CIRAD-BIOS-UMR AGAP (FRA)
  • Durand-Gasselin Tristan, PalmElit (FRA)
  • Nouy Bruno, CIRAD-BIOS-UMR AGAP (FRA)
  • Omoré Alphonse, INRAB (BEN)
  • Pomies Virginie, CIRAD-BIOS-UMR AGAP (FRA) ORCID: 0000-0002-5481-5120
  • Riou Virginie, CIRAD-BIOS-UMR AGAP (FRA)
  • Suryana Edyana, SOCFINDO (IDN)
  • Bouvet Jean-Marc, CIRAD-BIOS-UMR AGAP (FRA)

Source : Cirad - Agritrop (https://agritrop.cirad.fr/575284/)

View Item (staff only) View Item (staff only)

[ Page générée et mise en cache le 2021-04-30 ]