作物株型发育与籽粒形成之间遗传互作调控网络解析新算法及其应用研究

For further understanding of complex genetic mechanisms of crop yield and high-yield crop breeding, it is important to investigate interaction networks between genes involved in plant architecture development and grain formation. In this study, we are going to develop new genetic models for unraveling genetic interactions between plant architecture development and grain formation. New methods for genome-wide association study based on genome, transcriptome, and proteome data will be developed (QTS, QTT and QTP), which could identify genes with epistasis effects, as well as their environment interactions. Integrated bioinformatic analysis platform BioPubInfo and miRNetwork showing networks for miRNA will be constructed. Moreover, we will generate transcriptome (including small RNA sequencing) and proteome data of Liang–You–Pei–Jiu permanent F2 population at a crucial time of grain formation. Based on collected rice omics data and our sequencing data, we will provide the gene interaction networks for plant architecture development and grain formation, respectively, and genetic interaction network unraveling the effects of plant architecture on grain formation using our newly developed models and bioinformatic platform. Meanwhile, we will analyze gene interaction networks for plant architecture and grain formation in maize based on the genome data of NAM population and perform comparative analysis of the regulation networks between maize and rice. We will finally provide the genetic interaction model for the development of plant architecture and grain formation of rice.

研究株型发育和籽粒形成之间基因互作调控网络，对进一步揭示作物产量复杂遗传机制及高产作物分子育种具有重要意义。本项目（1）拟开发解析株型发育和籽粒形成遗传互作网络新算法，该算法可以基于基因组、转录组和蛋白组数据进行全基因组关联分析（QTS、QTT和QTP），估算基因间上位性及基因与环境互作效应；构建生物信息学分析整合平台BioPubInfo，进一步开发阐明miRNA调控互作网络miRNetwork；（2）利用已有水稻组学数据和知识，并进一步测定两优培九永久F2群体籽粒形成关键时期转录组（包括小RNA）和蛋白质组数据，利用上述新算法及生物信息学平台，给出水稻株型发育和籽粒形成的各自遗传调控网络以及株型发育影响籽粒形成的互作调控网络。同时以玉米NAM群体基因组数据为基础进行并行分析，比较水稻和玉米株型发育与籽粒形成遗传互作调控网络及其保守性。本研究最终给出水稻株型发育和籽粒形成遗传互作调控模型。

本研究项目开展了解析水稻株型发育和籽粒形成之间基因互作调控网络算法及其调控网络络构建，对进一步揭示水稻产量复杂遗传机制及其高产分子育种具有重要意义。具体结果包括（1）开发解析株型发育和籽粒形成遗传互作网络新算法，该算法可以基于基因组、转录组和蛋白组数据进行全基因组关联分析，估算基因间上位性及基因与环境互作效应；编码和非编码RNA调控网络（特别是新类型环化RNA的分析与鉴定）；构建了2个生物信息学分析整合平台，提供mRNA-ncRNA调控互作网络；（2）针对水稻组，进行了不同时期编码和非编码RNA共同表达和调控测定（RNA-seq和其他非编码RNA转录组），鉴定了4万多个水稻新类型非编码调控因子，500多个mRNA-miRNA-circRNA网络，使水稻遗传调控因子数据集更为完整，给出水稻株型发育和籽粒形成的各自遗传调控网络以及株型发育影响籽粒形成的互作调控网络。共发表相关研究论文6篇，其中第一或第二标注论文5篇（平均影响因子8.25）。