Abstract
High yield is the primary objective of maize breeding. Genomic dissection of grain yield and yield-related traits contribute to understanding the yield formation and improving the yield of maize. In this study, two genome-wide association study (GWAS) methods and genomic prediction were made 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 (RKHS) 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 (GBLUP) showed a slight superiority over other modes in most subsets of the trait-associated marker (TAM) for thousand kernel weight and kernel row number. The prediction accuracy could be improved when significant single-nucleotide polymorphisms were fitted as the fixed effects. Integrating information on population structure into the fixed model did not improve the prediction performance. For GYP, the prediction accuracy of TAMs derived from fixed and random model Circulating Probability Unification (FarmCPU) was comparable to that of the compressed mixed linear model (CMLM). For yield-related traits, CMLM-derived markers provided 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 the grain yield of maize.
Highlights
Maize serves as an important cereal and forage crop and plays an important role in sustaining global food security
The main objectives of this study were to (1) identify significant single-nucleotide polymorphisms (SNPs) for eight traits using two genome-wide association study (GWAS) methods, (2) compare the prediction accuracies of different Genomic selection (GS) models, (3) investigate the prediction accuracy by treating significant SNPs and population structure as the fixed effects, and (4) evaluate the effects of trait-associated marker (TAM) derived from different GWAS methods on prediction accuracy
Descriptive statistics revealed that extensive phenotypic variations were observed in grain yield per plant (GYP) and seven yieldrelated traits in the panel under different environments (Supplementary Table 1)
Summary
Maize serves as an important cereal and forage crop and plays an important role in sustaining global food security. Improvement of grain yield is a major and longstanding breeding goal for maize. Kernel number per ear (KNE) and thousand kernel weight (HKW) are the major components of grain yield per plant (GYP). Kernel number per row (KNR) and kernel row number (KRN) are the important components of the KNE. Ear length (EL) and ear diameter (ED) affect GYP in different degrees.
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