文献类型: 外文期刊
作者: Jin, Minliang 1 ; Liu, Haijun 2 ; Liu, Xiangguo 3 ; Guo, Tingting 1 ; Guo, Jia 3 ; Yin, Yuejia 3 ; Ji, Yan 5 ; Li, Zhenxian 6 ; Zhang, Jinhong 6 ; Wang, Xiaqing 1 ; Qiao, Feng 1 ; Xiao, Yingjie 1 ; Zan, Yanjun 5 ; Yan, Jianbing 1 ;
作者机构: 1.Huazhong Agr Univ, Natl Key Lab Crop Genet Improvement, Wuhan 430070, Peoples R China
2.Austrian Acad Sci, Gregor Mendel Inst, Vienna Bioctr, A-1030 Vienna, Austria
3.Jilin Acad Agr Sci, Inst Agr Biotechnol, Changchun 130033, Peoples R China
4.Hubei Hongshan Lab, Wuhan 430070, Peoples R China
5.Chinese Acad Agr Sci, Tobacco Res Inst, Key Lab Tobacco Improvement & Biotechnol, Qingdao 266000, Peoples R China
6.Inst Agr Sci Xishuangbanna Prefecture Yunnan Prov, Jinghong 666100, Peoples R China
7.Swedish Univ Agr Sci, UmeaPlant Sci Ctr, Dept Forestry Genet & Plant Physiol, S-90736 Umea, Sweden
关键词: complex traits; phenotypic plasticity; QTL-by-environment interaction; crop improvement; Zea mays
期刊名称:PLANT COMMUNICATIONS ( 影响因子:10.5; 五年影响因子:10.5 )
ISSN: 2590-3462
年卷期: 2023 年 4 卷 3 期
页码:
收录情况: SCI
摘要: 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 compen-sation effect of ZmSPL6 from a downstream pathway. By integrating genetic diversity, environmental vari-ation, 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.
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