Genome-wide association analysis of four yield-related traits using a maize (Zea mays L.) F1 population

• Published in: PloS one 2024-06, Vol.19 (6), p.e0305357
• Main Authors: Zhang, Yong, Zeng, Ziru, Tuo, Feifei, Yue, Jin, Wang, Zhu, Jiang, Weiming, Chen, Xue, Wei, Xianya, Niu, Qunkai
• Format: Article
• Language: English
• Subjects: Agricultural production; Analysis; Annotations; Association analysis; Biosynthesis; Cellular signal transduction; Chromosomes; Corn; Correlation analysis; Crop yield; Crop yields; Filtrate; Genes; Genes, Plant; Genetic analysis; Genetic aspects; Genome-wide association studies; Genome-Wide Association Study; Genomes; Genomic analysis; Genomics; Genotype & phenotype; Heritability; Hybrids; Molecular modelling; Nucleotides; Phenotype; Plant growth; Plant hormones; Polymorphism, Single Nucleotide; Population genetics; Population studies; Protein biosynthesis; Protein synthesis; Quantitative Trait Loci; Signal transduction; Single nucleotide polymorphisms; Single-nucleotide polymorphism; Software; Statistical analysis; Variance analysis; Zea mays; Zea mays - genetics; Zea mays - growth & development
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0305357

Increasing the yield of maize F1 hybrid is one of the most important target for breeders. However, as a result of the genetic complexity and extremely low heritability, it is very difficult to directly dissect the genetic basis and molecular mechanisms of yield, and reports on genetic analysis of F1 hybrid yield are rare. Taking F1 hybrid as the research object and dividing the yield into different affect factors, this approach may be the best strategy for clarifying the genetic mechanism of yield. Therefore, in this study, a maize F1 population consisting of 300 hybrids with 17,652 single nucleotide polymorphisms (SNPs) markers was used for genome-wide association study (GWAS) to filtrate candidate genes associated with the four yield-related traits, i.e., kernel row number (KRN), kernel number per row (KNPR), ear tip-barrenness (ETB), and hundred kernel weight (HKW). Combined with the results of previous studies and functional annotation information of candidate genes, a total of six candidate genes were identified as being associated with the four traits, which were involved in plant growth and development, protein synthesis response, phytohormone biosynthesis and signal transduction. Our results improve the understanding of the genetic basis of the four yield-related traits and may be provide a new strategy for the genetic basis of maize yield.