Complex genetic architecture underlying the plasticity of maize agronomic traits

• Published in: Plant communications Vol. 4; no. 3; p. 100473
• Main Authors: Jin, Minliang, Liu, Haijun, Liu, Xiangguo, Guo, Tingting, Guo, Jia, Yin, Yuejia, Ji, Yan, Li, Zhenxian, Zhang, Jinhong, Wang, Xiaqing, Qiao, Feng, Xiao, Yingjie, Zan, Yanjun, Yan, Jianbing
• Format: Journal Article
• Language: English
• Published: China Elsevier Inc 08.05.2023; Elsevier
• Subjects: Agricultural Science; Agriculture; complex traits; crop improvement; Environmental Sciences related to Agriculture and Land-use; Genotype; Jordbruksvetenskap; Miljö- och naturvårdsvetenskap; Phenotype; phenotypic plasticity; QTL-by-environment interaction; Quantitative Trait Loci - genetics; Zea mays; Zea mays - genetics; Zea mays - metabolism; QTL-by-environment interaction; complex traits; phenotypic plasticity; Zea mays; crop improvement
• ISSN: 2590-3462; 2590-3462
• DOI: 10.1016/j.xplc.2022.100473

Phenotypic plasticity is the ability of a given genotype to produce multiple phenotypes in response to changing environmental conditions. Understanding the genetic basis of phenotypic plasticity and establishing a predictive model is highly relevant to future agriculture under a changing climate. Here we report findings on the genetic basis of phenotypic plasticity for 23 complex traits using a diverse maize population planted at five sites with distinct environmental conditions. We found that latitude-related environmental factors were the main drivers of across-site variation in flowering time traits but not in plant architecture or yield traits. For the 23 traits, we detected 109 quantitative trait loci (QTLs), 29 for mean values, 66 for plasticity, and 14 for both parameters, and 80% of the QTLs interacted with latitude. The effects of several QTLs changed in magnitude or sign, driving variation in phenotypic plasticity. We experimentally validated one plastic gene, ZmTPS14.1, whose effect was likely mediated by the compensation effect of ZmSPL6 from a downstream pathway. By integrating genetic diversity, environmental variation, and their interaction into a joint model, we could provide site-specific predictions with increased accuracy by as much as 9.9%, 2.2%, and 2.6% for days to tassel, plant height, and ear weight, respectively. This study revealed a complex genetic architecture involving multiple alleles, pleiotropy, and genotype-by-environment interaction that underlies variation in the mean and plasticity of maize complex traits. It provides novel insights into the dynamic genetic architecture of agronomic traits in response to changing environments, paving a practical way toward precision agriculture. Jin et al. reveal a complex genetic architecture underlying variation in the mean and plasticity of maize agronomic traits that involves multiple alleles, pleiotropy, and genotype-by-environment interactions. By integrating variation in climate, genotype, and their interaction, the authors were able to predict complex traits across environments with higher accuracy, paving the way for precision agriculture.