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A wheat integrative regulatory network from large-scale complementary functional datasets enables trait-associated gene discovery for crop improvement

引用

Molecular Plant 2023年第2期16卷 393-414页

作者： Yongming Chen Yiwen Guo Panfeng Guan Yongfa Wang Xiaobo Wang Zihao Wang Zhen Qin Shengwei Ma Mingming Xin Zhaorong Hu Yingyin Yao Zhongfu Ni Qixin Sun Weilong Guo Huiru Peng Frontiers Science Center for Molecular Design Breeding Key Laboratory of Crop Heterosis and UtilizationBeijing Key Laboratory of Crop Genetic ImprovementChina Agricultural UniversityBeijing 100193China Hainan Yazhou Bay Seed Laboratory SanyaHainanChina State Key Laboratory of Plant Cell and Chromosome Engineering Institute of Genetics and Developmental BiologyChinese Academy of SciencesBeijingChina

Gene regulation is central to all aspects of organism growth,and understanding it using large-scale functional datasets can provide a whole view of biological processes controlling complex phenotypic traits in crops.However,the connection between massive functional datasets and trait-associated gene discovery for crop improvement is still lacking.In this study,we constructed a wheat integrative gene regulatory network(wGRN)by combining an updated genome annotation and diverse complementary functional datasets,including gene expression,sequence motif,transcription factor(TF)binding,chromatin accessibility,and evolutionarily conserved regulation.wGRN contains 7.2 million genome-wide interactions covering 5947 TFs and 127439 target genes,which were further verified using known regulatory relationships,condition-specific expression,gene functional information,and experiments.We used wGRN to assign genome-wide genes to 3891 specific biological pathways and accurately prioritize candidate genes associated with complex phenotypic traits in genome-wide association studies.In addition,wGRN was used to enhance the interpretation of a spike temporal transcriptome dataset to construct high-resolution networks.We further unveiled novel regulators that enhance the power of spike phenotypic trait prediction using machine learning and contribute to the spike phenotypic differences among modern wheat accessions.Finally,we developed an interactive webserver,wGRN(http://***/wGRN),for the community to explore gene regulation and discover trait-associated genes.Collectively,this community resource establishes the foundation for using large-scale functional datasets to guide trait-associated gene discovery for crop improvement.

关键词： wheat integrative gene regulatory network functional gene discovery phenotype prediction crop improvement

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