Genome-Wide Association Study and Genomic Prediction on Plant Architecture Traits in Sweet Corn and Waxy Corn

Abstract

Sweet corn and waxy corn has a better taste and higher accumulated nutritional value than regular maize, and is widely planted and popularly consumed throughout the world. Plant height (PH), ear height (EH), and tassel branch number (TBN) are key plant architecture traits, which play an important role in improving grain yield in maize. In this study, a genome-wide association study (GWAS) and genomic prediction analysis were conducted on plant architecture traits of PH, EH, and TBN in a fresh edible maize population consisting of 190 sweet corn inbred lines and 287 waxy corn inbred lines. Phenotypic data from two locations showed high heritability for all three traits, with significant differences observed between sweet corn and waxy corn for both PH and EH. The differences between the three subgroups of sweet corn were not obvious for all three traits. Population structure and PCA analysis results divided the whole population into three subgroups, i.e., sweet corn, waxy corn, and the subgroup mixed with sweet and waxy corn. Analysis of GWAS was conducted with 278,592 SNPs obtained from resequencing data; 184, 45, and 68 significantly associated SNPs were detected for PH, EH, and TBN, respectively. The phenotypic variance explained (PVE) values of these significant SNPs ranged from 3.50% to 7.0%. The results of this study lay the foundation for further understanding the genetic basis of plant architecture traits in sweet corn and waxy corn. Genomic selection (GS) is a new approach for improving quantitative traits in large plant breeding populations that uses whole-genome molecular markers. The marker number and marker quality are essential for the application of GS in maize breeding. GWAS can choose the most related markers with the traits, so it can be used to improve the predictive accuracy of GS.

Genome-Wide Association Study and Prediction of Tassel Weight of Tropical Maize Germplasm in Multi-Parent Population

“…Bayes-type models integrate the variance of the effect values for each marker or chromosome segment as a prior distribution to improve prediction accuracy. We found that the prediction accuracy range for TW was 0.20-0.39, close to the precision for tassel-related traits in previous studies (0.25-0.48) [29,30].…”

“…Xu et al utilized the ridge regression best linear unbiased prediction (rrBLUP) model for GS of tassel length, tassel primary branch number, tassel secondary branch number, and tassel branch number, with prediction accuracies of 0.36, 0.41, 0.28, and 0.38, respectively [29]. Dang et al also employed the rrBLUP model for GS on tassel branch number in sweet corn and waxy corn, discovering that increasing the number of markers significantly associated with the target trait led to a notable improvement in prediction accuracy [30]. As the size of the training group increased from 10% to 90% of the total number of markers, the prediction accuracy for tassel branch number increased from 0.16 to approximately 0.48.…”

Integrated Metabolome and Transcriptome Analysis of Gibberellins Mediated the Circadian Rhythm of Leaf Elongation by Regulating Lignin Synthesis in Maize

“…This suggested that GAs could inhibit the expression of ZmMYBr41 and ZmMYBr87 to activate the expression of ZmHCT8 for modulating the accumulation of 5-O-caffeoylshikimic acid in lignin biosynthesis ( Figure 8 ). ZmMYBr41 turns out to be a gene associated with plant height [ 84 ], and ZmMYBr87 is related to cell wall synthesis [ 85 ], which further proves the involvement of ZmMYBr41 and ZmMYBr87 in regulating cell wall synthesis during leaf cell growth.…”