Genomic Prediction of Sunflower Hybrids Oil Content

Autor: Mangin, Brigitte, Bonnafous, Fanny, Blanchet, Nicolas, Boniface, Marie-Claude, Bret-Mestries, Emmanuelle, Carrere, Sebastien, Cottret, Ludovic, Legrand, Ludovic, Marage, Gwenola, Pegot - Espagnet, Prune, Munos, Stephane, Pouilly, Nicolas, Vear, Felicity, Vincourt, Patrick, Langlade, Nicolas
Přispěvatelé: Laboratoire des interactions plantes micro-organismes (LIPM), Institut National de la Recherche Agronomique (INRA)-Centre National de la Recherche Scientifique (CNRS), AGroécologie, Innovations, teRritoires (AGIR), Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT), Génétique Diversité et Ecophysiologie des Céréales (GDEC), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020]), ANR-11-BTBR-0005,SUNRISE,Ressources génétiques de tournesol pour l'amélioration de la stabilité de production d'huile sous c(2011), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées
Jazyk: angličtina
Rok vydání: 2017
Předmět:
Zdroj: Frontiers in Plant Science
Frontiers in Plant Science, 2017, 8, pp.1-12. ⟨10.3389/fpls.2017.01633⟩
Frontiers in Plant Science, Frontiers, 2017, 8, pp.1-12. ⟨10.3389/fpls.2017.01633⟩
Frontiers in Plant Science, Vol 8 (2017)
Frontiers in Plant Science (8), 1-12. (2017)
ISSN: 1664-462X
Popis: International audience; Prediction of hybrid performance using incomplete factorial mating designs is widely used in breeding programs including different heterotic groups. Based on the general combining ability (GCA) of the parents, predictions are accurate only if the genetic variance resulting from the specific combining ability is small and both parents have phenotyped descendants. Genomic selection (GS) can predict performance using a model trained on both phenotyped and genotyped hybrids that do not necessarily include all hybrid parents. Therefore. GS could overcome the issue of unknown parent GCA. Here, we compared the accuracy of classical GCA-based and genomic predictions for oil content of sunflower seeds using several GS models. Our study involved 452 sunflower hybrids from an incomplete factorial design of 36 female and 36 male lines. Re-sequencing of parental lines allowed to identify 468,194 non-redundant SNPs and to infer the hybrid genotypes. Oil content was observed in a multi-environment trial (MET) over 3 years, leading to nine different environments. We compared GCA-based model to different GS models including female and male genomic kinships with the addition of the female-by-male interaction genomic kinship, the use of functional knowledge as SNPs in genes of oil metabolic pathways, and with epistasis modeling. When both parents have descendants in the training set, the predictive ability was high even for GCA-based prediction, with an average MET value of 0.782. GS performed slightly better (+0.2%). Neither the inclusion of the female-by-male interaction, nor functional knowledge of oil metabolism, nor epistasis modeling improved the GS accuracy. GS greatly improved predictive ability when one or both parents were untested in the training set, increasing GCA-based predictive ability by 10.4% from 0.575 to 0.635 in the MET. In this scenario, performing GS only considering SNPs in oil metabolic pathways did not improve whole genome GS prediction but increased GCA-based prediction ability by 6.4%. Our results show that GS is a major improvement to breeding efficiency compared to the classical GCA modeling when either one or both parents are not well-characterized. This finding could therefore accelerate breeding through reducing phenotyping efforts and more effectively targeting for the most promising crosses.
Databáze: OpenAIRE
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