摘要Gene regulation is central to all aspects of organism growth,and understanding it using large-scale func-tional 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 data-sets,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 127 439 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 asso-ciated 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 acces-sions.Finally,we developed an interactive webserver,wGRN(http://wheat.cau.edu.cn/wGRN),for the com-munity 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.