Xue-Lian Han

Although phenotypic plasticity facilitates the understanding of trait variation, its study remains limited. To study phenotypic plasticity, in this study, 203 inbred maize lines were phenotyped for flowering time and plant height in Zhengzhou, Ningjin, Tieling, and Binxian and genotyped with 121,871 high-quality SNPs. The regression slopes and intercepts of flowering time and plant height on four meteorological factors in their corresponding, most significant correlation windows were used to estimate the phenotypic plasticity of the above traits and to further indirectly detect the interactions between quantitative trait nucleotides and meteorological factors. Of the two known and seven candidate genes identified in this study, ZmCCT, GRMZM2G035417, GRMZM2G069651, and GRMZM2G359322 can be used to explain why spring maize has a longer flowering time than summer maize, as these genes delay pollen development and flowering regulators under long day and low temperature; while ZmPIN1b, GRMZM2G062045, GRMZM2G370777, GRMZM2G077752, and GRMZM2G126397 can be used to explain why Tieling has higher plant height than other regions, as these genes enhance auxin transport and suppress dwarfing genes under increased precipitation and low temperature. This study explains the phenotypic plasticity of these traits.

Although 3VmrMLM-MEJA and several indirect indicators have been employed to identify QTN-by-environment interactions (QEIs) in genome-wide association studies (GWAS), there is no convenient, flexible, and accurate method to comprehensively identify QEIs. To address this issue, 3VmrMLM-random was first extended to 3VmrMLM-fixed. Next, the two single-environment QTN detection methods were integrated with trait differences and regression parameters to indirectly detect QEIs. Finally, these indirect indicators were extended to include environmental factors (EFs, such as temperature) and four environmental variation indicators. As a result, both 3VmrMLM-random and 3VmrMLM-fixed, alongside all the indirect indicators, were incorporated into a new tool, IIIVmrMLM.QEI, designed for effective QEI identification. Simulation studies demonstrated that 3VmrMLM-fixed showed significantly higher powers than existing fixed-SNP-effect methods (MLM and EMMAX) because it takes into account all the possible effects and controls for all the possible polygenic backgrounds. 3VmrMLM-random and 3VmrMLM-fixed exhibited superior combination power to 3VmrMLM-MEJA. In the re-analysis of Arabidopsis flowering time across three temperatures, 3VmrMLM-fixed (12) detected more known gene-by-environment interactions (GEIs) than both MLM (1) and EMMAX (1). Additionally, IIIVmrMLM.QEI (18) detected more known GEIs than 3VmrMLM-MEJA (6), when all indirect indicators were analyzed. All 18 GEIs were confirmed by haplotype analysis and associated with temperature variation in previous studies. Two and five GEIs were identified only by 3VmrMLM-fixed and 3VmrMLM-random, respectively, and 12 GEIs were identified only by indirect indicators, indicating the need to expand models and indirect indicators. This study provides a novel tool (https://github.com/YuanmingZhang65/IIIVmrMLM.QEI) for more comprehensive QEI detection.