Multi-trait genomic prediction for grain yield and zinc content in rice
2019
Nha, C.T. | Descalsota, G.I.L. | Palanog, A. | Swe, Z.M. | Calayugan, M.I. | Amparado, A. | Inabangan-Asilo, M.A. | Mendioro, M.S. | Diaz, M.G.Q. | Lalusin, A.G. | Reinke, R. | Swamy, B.P.M.
Genomic prediction (GS) is an efficient tool for accelerating genetic gains and reducing the time per cycles in breeding programs. Multi-trait genomic prediction has been widely used in both plant and animal breeding pipelines. In this study, we evaluated the accuracy prediction of single-trait (ST) and multi-trait (MT) models for grain yield (YLD) and zinc (Zn) content traits in six RIL populations using BLUPs. Authors genotyped a total of 1477 lines using 7K SNP rice chip. The scheme or cross-validation 2 (CV2) used for ST-GS and MT-GS models where 70% of the lines used to form the training population and the rest as testing population. Prediction accuracy of ST-GS was estimated as 0.52-079 for Zn (ST-BayesB) and 0.22-0.39 for YLD (ST-BayesB), respectively. However, adding both traits in MT-GS showed significant increase in the predicted values for Zn (0.58-0.83) (MT-GK) and YLD (0.24-0.43) (MT-GK) compared with ST-GS model. Moreover, the similar trend of prediction accuracies of all genomic selection models increased significantly with increasing number of markers (50-6000 markers). Therefore, the benefits of MT-GS model may be further useful in combining various important characters encompassing high yield and high-zinc content for rice biofortification breeding programs.
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