玉米株型建成调控网络研究

Ideal plant architecture is the basis of maize high yield production. Plant height, stalk strength and leaf angle, which vary synergistically in general, are the major factors constituting plant architecture in maize. The applicant and participants have fine-mapped and cloned a series of key genes underlying plant architecture (PA), such as DLF1, qPH7, stiff1, HB53, and ABPH. These PA associated key genes function with multigenic effect controlling plant height, and synergistically regulate a certain number of PA sub-traits in a pleiotropic manner. On the basis of previous works, here, we propose to collaborate with all participants tightly, utilize modern molecular biological and genetic techniques, do the machine learning based on the comprehensive multi-omic biological big-data for the identification of regulatory networks of these PA associated key genes. Our proposal is dedicated to demonstrating the molecular mechanism of pleiotrophism, and systematically dissection of the molecular network associated with multigenic effect. Furthermore, we will conduct re-sequencing on an association panel of ~500 diverse inbred lines and maize relative-teosinte, followed by association mapping and evolutionary analysis, to identify favorable alleles for high-yield breeding and the selection sites during maize domestication and selection. Our proposal will not only enhance our understanding of the molecular mechanism of maize plant architecture, decipher the regulatory network during maize plant development, but also potentially provide a substantial number of new functional markers for maize high-yield breeding.

理想株型是玉米高产的基础，而株高、茎杆强度与茎叶夹角是玉米株型的重要构成因子，并表现出一定的协调变异规律。申请人及参与人团队前期分离和克隆了株型关键功能基因DLF1、qPH7、STIFF1、HB53和ABPH等，这些基因表现出明显的“多因一效”的调控关系共同影响株高，同时又各自表现出“一因多效”效应影响多个株型子性状协同变异。本项目拟在此基础上，进一步通过各参与团队的密切合作，利用现代分子生物学与遗传学技术，基于多维组学等生物大数据，进行机器学习挖掘与深度功能验证这些株型关键基因的调控网络，系统阐明这些株型关键基因“一因多效”调控株型各性状协同变异的分子机制，解析其“多因一效”共同调控株高变异的分子网络，进而，通过重测序、关联分析与进化分析，鉴定株型关键基因在玉米驯化与育种中的选择位点，挖掘并聚合优良等位基因，为玉米产量分子育种提供理论指导与基因资源。