Assessment of the Potential for Genomic Selection To Improve Husk Traits in Maize

In this study, we compared four GS models on a maize association population with 498 inbred lines belonging to four subpopulations, including 27 lines in stiff stalk, 67 lines in non-stiff stalk, 193 lines in tropical-subtropical, and 211 lines in mixture subpopulations. Genomic Best Linear Unbiased Prediction with principal components as cofactor, performed the best and was selected to examine the impact of interaction between sampling proportions and subpopulations. We found that predictions on inbred lines in a subpopulation were benefited from excluding individuals from other subpopulations for training if the training population within the subpopulation was large enough. Husk thickness exhibited the highest prediction accuracy among all husk traits. These results gave strategic insight to improve husk architecture.

Source: G3: Genes Genomes Genetics - October 4, 2020 Category: Genetics & Stem Cells Authors: Cui, Z., Dong, H., Zhang, A., Ruan, Y., He, Y., Zhang, Z. Tags: Genomic Prediction Source Type: research