Genomic Selection for Yield and Seed Protein Content in Soybean: A Study of Breeding Program Data and Assessment of Prediction Accuracy
| Autor: | Michel Romestant, Brigitte Mangin, Simon Teyssèdre, Jean Daydé, Amandine Gras, Bruno Claustres, Alexandra Duhnen |
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| Přispěvatelé: | Laboratoire des interactions plantes micro-organismes (LIPM), Centre National de la Recherche Scientifique (CNRS)-Institut National de la Recherche Agronomique (INRA), 2n, Rouergue Auvergne Gévaudan Tarnais, Physiologie, Pathologie et Génétique Végétales (PPGV), INP-PURPAN, Université Fédérale Toulouse Midi-Pyrénées, French region 'Midi-Pyrenees', ANRT CIFRE grant, Institut National de la Recherche Agronomique (INRA)-Centre National de la Recherche Scientifique (CNRS) |
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
0106 biological sciences
0301 basic medicine [SDV.SA]Life Sciences [q-bio]/Agricultural sciences Breeding program Bayesian probability Population Best linear unbiased prediction Biology 01 natural sciences genomic selection 03 medical and health sciences Bayes' theorem Statistics [SDV.BV]Life Sciences [q-bio]/Vegetal Biology sélection génomique soja education soya bean 2. Zero hunger amélioration génétique education.field_of_study business.industry Crop yield food and beverages Regression Biotechnology 030104 developmental biology Epistasis business Agronomy and Crop Science 010606 plant biology & botany |
| Zdroj: | Crop Science Crop Science, Crop Science Society of America, 2017, 57 (3), pp.1325-1337. ⟨10.2135/cropsci2016.06.0496⟩ |
| ISSN: | 0011-183X 1435-0653 |
| DOI: | 10.2135/cropsci2016.06.0496⟩ |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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