Identifying yield-related genes in maize based on ear trait plasticity

• Published in: Genome Biology Vol. 24; no. 1; p. 94
• Main Authors: Liu, Minguo, Zhang, Shuaisong, Li, Wei, Zhao, Xiaoming, Wang, Xi-Qing
• Format: Journal Article
• Language: English
• Published: England BioMed Central 25.04.2023; BMC
• Subjects: Accuracy; Agricultural production; Algorithms; Automated ear phenotyping platform; Automation; Chromosome Mapping; Climate change; Corn; corn ears; Datasets; Ear phenotypes; Efficiency; Genes; genetic background; genetically modified organisms; genome; Genomics; Genotype; Genotypes; grain yield; Inbreeding; Integrated software; Maize; MAIZTRO; Phenotype; Phenotypes; Phenotypic plasticity; Phenotypic variations; Phenotyping; Quantitative Trait Loci; seeds; Transgenic maize inbred population; Zea mays; Phenotypic plasticity; Transgenic maize inbred population; Automated ear phenotyping platform; Maize; Ear phenotypes; MAIZTRO
• DOI: 10.1186/s13059-023-02937-6
• ISSN: 1474-760X; 1474-7596

Phenotypic plasticity is defined as the phenotypic variation of a trait when an organism is exposed to different environments, and it is closely related to genotype. Exploring the genetic basis behind the phenotypic plasticity of ear traits in maize is critical to achieve climate-stable yields, particularly given the unpredictable effects of climate change. Performing genetic field studies in maize requires development of a fast, reliable, and automated system for phenotyping large numbers of samples. Here, we develop MAIZTRO as an automated maize ear phenotyping platform for high-throughput measurements in the field. Using this platform, we analyze 15 common ear phenotypes and their phenotypic plasticity variation in 3819 transgenic maize inbred lines targeting 717 genes, along with the wild type lines of the same genetic background, in multiple field environments in two consecutive years. Kernel number is chosen as the primary target phenotype because it is a key trait for improving the grain yield and ensuring yield stability. We analyze the phenotypic plasticity of the transgenic lines in different environments and identify 34 candidate genes that may regulate the phenotypic plasticity of kernel number. Our results suggest that as an integrated and efficient phenotyping platform for measuring maize ear traits, MAIZTRO can help to explore new traits that are important for improving and stabilizing the yield. This study indicates that genes and alleles related with ear trait plasticity can be identified using transgenic maize inbred populations.