Zhang Ya-Wen, Han Xue-Lian, Li Mei, Chen Ying, Zhang Yuan-Ming
College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China.
International Genome Center, Jiangsu University, Zhenjiang 212013, China.
Comput Struct Biotechnol J. 2024 Dec 2;23:4357-4368. doi: 10.1016/j.csbj.2024.11.046. eCollection 2024 Dec.
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 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.
尽管在全基因组关联研究（GWAS）中已采用3VmrMLM-MEJA和若干间接指标来识别数量性状核苷酸（QTN）与环境的互作（QEIs），但尚无便捷、灵活且准确的方法来全面识别QEIs。为解决这一问题，首先将3VmrMLM-随机模型扩展为3VmrMLM-固定模型。接下来，将两种单环境QTN检测方法与性状差异和回归参数相结合，以间接检测QEIs。最后，将这些间接指标扩展到包括环境因子（EFs，如温度）和四个环境变异指标。结果，3VmrMLM-随机模型和3VmrMLM-固定模型以及所有间接指标都被整合到一个名为IIIVmrMLM.QEI的新工具中，用于有效识别QEIs。模拟研究表明，3VmrMLM-固定模型比现有的固定单核苷酸多态性效应方法（MLM和EMMAX）具有显著更高的功效，因为它考虑了所有可能的效应并控制了所有可能的多基因背景。3VmrMLM-随机模型和3VmrMLM-固定模型比3VmrMLM-MEJA具有更强的组合功效。在对三个温度下的开花时间进行重新分析时，3VmrMLM-固定模型（12个）检测到的已知基因与环境互作（GEIs）比MLM（1个）和EMMAX（1个）都多。此外，当分析所有间接指标时，IIIVmrMLM.QEI（18个）检测到的已知GEIs比3VmrMLM-MEJA（6个）多。所有18个GEIs均通过单倍型分析得到证实，并且在先前的研究中与温度变化相关。分别仅通过3VmrMLM-固定模型和3VmrMLM-随机模型鉴定出2个和5个GEIs，仅通过间接指标鉴定出12个GEIs，这表明需要扩展模型和间接指标。本研究提供了一种用于更全面检测QEIs的新工具（https://github.com/YuanmingZhang65/IIIVmrMLM.QEI）。
