A Wheat Integrative Regulatory Network from Large-scale Complementary Functional Datasets Enables Trait-associated Gene Discovery for Crop Improvement
Gene regulation is central to all aspects of organism growth, and understanding it from 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 still lacks. Here, we constructed a wheat integrative gene regulatory network (wGRN) by using an updated genome annotation and combining 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 5,947 TFs and 127,439 target genes and was further verified using known regulatory relationships, condition-specific expression, gene functional information, and experiments.
We used wGRN to assign genome-wide genes to 3,891 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 predictive power of spike phenotypic traits using the machine learning method and contribute to the spike phenotypic differences among modern wheat accessions. An interactive webserver, wGRN (http://wheat.cau.edu.cn/wGRN), was finally developed for the community to explore gene regulations and discover trait-associated genes. Overall, this community resource establishes the foundation for using large-scale functional datasets to guide trait-associated gene discovery for crop improvement.
