Genome-wide trait-trait dynamics correlation study dissects the gene regulation pattern in maize kernels

Abstract Background Dissecting the genetic basis and regulatory mechanisms for the biosynthesis and accumulation of nutrients in maize could lead to the improved nutritional quality of this crop. Gene expression is regulated at the genomic, transcriptional, and post-transcriptional levels, all of which can produce diversity among traits. However, the expression of most genes connected with a particular trait usually does not have a direct association with the variation of that trait. In addition, expression profiles of genes involved in a single pathway may vary as the intrinsic cellular state changes. To work around these issues, we utilized a statistical method, liquid association (LA) to investigate the complex pattern of gene regulation in maize kernels. Results We applied LA to the expression profiles of 28,769 genes to dissect dynamic trait-trait correlation patterns in maize kernels. Among the 1000 LA pairs (LAPs) with the largest LA scores, 686 LAPs were identified conditional correlation. We also identified 830 and 215 LA-scouting leaders based on the positive and negative LA scores, which were significantly enriched for some biological processes and molecular functions. Our analysis of the dynamic co-expression patterns in the carotene biosynthetic pathway clearly indicated the important role of lcyE, CYP97A, ZEP1, and VDE in this pathway, which may change the direction of carotene biosynthesis by controlling the influx and efflux of the substrate. The dynamic trait-trait correlation patterns between gene expression and oil concentration in the fatty acid metabolic pathway and its complex regulatory network were also assessed. 23 of 26 oil-associated genes were correlated with oil concentration conditioning on 580 LA-scoutinggenes, and 5% of these LA-scouting genes were annotated as enzymes in the oil metabolic pathway. Conclusions By focusing on the carotenoid and oil biosynthetic pathways in maize, we showed that a genome-wide LA analysis provides a novel and effective way to detect transcriptional regulatory relationships. This method will help us understand the biological role of maize kernel genes and will benefit maize breeding programs.