Genomic Selection for Yield and Seed Protein Content in Soybean: A Study of Breeding Program Data and Assessment of Prediction Accuracy
2017
Duhnen, Alexandra | Gras, Amandine | Teyssèdre, Simon | Romestant, Michel | Claustres, Bruno | Daydé, Jean | Mangin, Brigitte | Laboratoire des interactions plantes micro-organismes (LIPM) ; Institut National de la Recherche Agronomique (INRA)-Centre National de la Recherche Scientifique (CNRS) | 2n ; Rouergue Auvergne Gévaudan Tarnais | Rouergue Auvergne Gévaudan Tarnais | Physiologie, Pathologie et Génétique Végétales (PPGV) ; Ecole d'Ingénieurs de Purpan (INP - PURPAN) ; Institut National Polytechnique (Toulouse) (Toulouse INP) ; Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP) ; Université de Toulouse (UT)-Université de Toulouse (UT) | French region "Midi-Pyrenees" ; ANRT CIFRE grant
Soybean [Glycine max (L.) Merr.] is a major crop with high seed protein content. Genomic selection is expected to be a valuable tool in improving the efficiency of breeding programs, especially for complex traits such as yield. This study aimed to evaluate the accuracy of genomic selection for yield and seed protein content in a soybean breeding population. Having a structured population, we compared genomic prediction accuracy obtained using models calibrated across or within two subpopulations: early lines and late lines. Calibrations within subpopulations were more efficient. Using a medium density of markers and genomic best linear unbiased prediction(GBLUP) model, which assumes an additive polygenic architecture, we predicted ~32 and39% of phenotypic variation among late lines for seed protein content and yield, respectively. Prediction accuracy was further improved by including epistasis in the GBLUP model. Further, we assessed accuracies obtained using several Bayesian models that assume different distributions for marker effects: Bayesian ridge regression, Bayesian LASSO, Bayes Cp, and Bayes R. Overall, these approaches did not improve prediction accuracy. In this study, were ported preliminary results relevant to the study of the efficiency of genomic selection use in a breeding program.
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