Genome-Wide Association Study (GWAS) have been widely used to detect the QTLs based on Linkage Disequilibrium (LD) relationships between SNPs and QTLs. However, in conventional GWAS false positive results cause serious concerns. In this research, we developed three different transcriptome-based GWAS approaches which are complementary to conventional SNP-based GWAS. The ability to identify trait-associated genes in these three different methods are supported by cross-validation, transposon knockout mutants, and the analysis of a gene regulatory networks. In summary, we provide novel methods of detecting trait associated loci to further understand the complex gene regulatory systems which will benefit plants, animals, and disease treatment development in the future
