Rice diversity panel provides accurate genomic predictions for complex traits in the progenies of biparental crosses involving members of the panel

Ben Hassen Manel, Cao Tuong-Vi, Bartholome Jérôme, Orasen G., Colombi C., Rakotomalala J., Razafinimpiasa L., Bertone C., Biselli Chiara, Volante Andrea, Desiderio Francesca, Jacquin Laval, Valè Giampiero, Ahmadi Nourollah. 2018. Rice diversity panel provides accurate genomic predictions for complex traits in the progenies of biparental crosses involving members of the panel. Theoretical and Applied Genetics, 131 (2) : pp. 417-435.

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

Abstract : So far, most potential applications of genomic prediction in plant improvement have been explored using cross validation approaches. This is the first empirical study to evaluate the accuracy of genomic prediction of the performances of progenies in a typical rice breeding program. Using a cross validation approach, we first analyzed the effects of marker selection and statistical methods on the accuracy of prediction of three different heritability traits in a reference population (RP) of 284 inbred accessions. Next, we investigated the size and the degree of relatedness with the progeny population (PP) of sub-sets of the RP that maximize the accuracy of prediction of phenotype across generations, i.e., for 97 F5–F7 lines derived from biparental crosses between 31 accessions of the RP. The extent of linkage disequilibrium was high (r2 = 0.2 at 0.80 Mb in RP and at 1.1 Mb in PP). Consequently, average marker density above one per 22 kb did not improve the accuracy of predictions in the RP. The accuracy of progeny prediction varied greatly depending on the composition of the training set, the trait, LD and minor allele frequency. The highest accuracy achieved for each trait exceeded 0.50 and was only slightly below the accuracy achieved by cross validation in the RP. Our results thus show that relatively high accuracy (0.41–0.54) can be achieved using only a rather small share of the RP, most related to the PP, as the training set. The practical implications of these results for rice breeding programs are discussed. (Résumé d'auteur)

Mots-clés Agrovoc : Oryza sativa, Génie génétique, Sélection, Amélioration des plantes, Séquence nucléotidique, Génotype, Modèle de simulation, technique de prévision, Modèle mathématique, Méthode d'amélioration génétique, Riz irrigué, Méthodologie

Mots-clés géographiques Agrovoc : Italie, Région méditerranéenne

Mots-clés complémentaires : Séquencage, Oryza sativa japonica

Mots-clés libres : Rice, Genomic selection, Progeny prediction, SNP, Genotyping by sequencing

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

  • Ben Hassen Manel, CIRAD-BIOS-UMR AGAP (FRA)
  • Bartholome Jérôme, CIRAD-BIOS-UMR AGAP (FRA) ORCID: 0000-0002-0855-3828
  • Orasen G., University of Milan (ITA)
  • Colombi C., Parco Tecnologico Padano (ITA)
  • Rakotomalala J., FOFIFA (MDG)
  • Razafinimpiasa L., FOFIFA (MDG)
  • Bertone C., University of Milan (ITA)
  • Biselli Chiara, CREA (ITA)
  • Volante Andrea, CREA (ITA)
  • Desiderio Francesca, CREA (ITA)
  • Jacquin Laval, CIRAD-BIOS-UMR AGAP (FRA)
  • Valè Giampiero, CREA (ITA)
  • Ahmadi Nourollah, CIRAD-BIOS-UMR AGAP (FRA) ORCID: 0000-0003-0072-6285 - auteur correspondant

Source : Cirad-Agritrop (

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