Genetic Dissection of Grain Yield of Maize and Yield-Related Traits Through Association Mapping and Genomic Prediction - Ma, Juan; Cao, Yanyong (Frontiers in Plant Science, 2021)
High yield is the primary objective of maize breeding. Genomic dissection of grain yield and yield-related traits contributes to understanding yield formation and improving maize yield. In the present study, two genome-wide association study (GWAS) methods and genomic prediction were conducted on an association panel of 309 inbred lines. GWAS analyses revealed 22 significant trait-marker associations for grain yield per plant (GYP) and yield-related traits. Genomic prediction analyses showed that reproducing kernel Hilbert space outperformed the other four models based on GWAS-derived markers for GYP, ear weight, kernel number per ear and row, ear length, and ear diameter, whereas genomic best linear unbiased prediction showed a slight superiority over other modes in most trait-associated marker (TAM) subsets for thousand kernel weight and kernel row number. The prediction accuracy could be improved when significant single-nucleotide polymorphisms were fitted as fixed effects. Integrating information on population structure into the fixed model did not improve prediction performance. For GYP, the prediction accuracy of TAMs derived from fixed and random model circulating probability unification (FarmCPU) was comparable to that of compressed mixed linear model (CMLM). For yield-related traits, CMLM-derived markers performed better accuracies than FarmCPU-derived markers in most scenarios. Compared with all markers, TAMs could effectively improve the prediction accuracies for GYP and yield-related traits. For eight traits, moderate and high prediction accuracies were achieved using TAMs. Taken together, genomic prediction incorporating prior information detected by GWAS could be a promising strategy to improve maize grain yield.