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

Area of Science:

• Genetics and Genomics
• Plant Biology
• Nutritional Science

Background:

• Maize nutritional quality can be improved by understanding nutrient biosynthesis genetic basis.
• Gene expression regulation is complex, involving multiple levels and often lacking direct trait correlation.
• Dynamic gene expression profiles require advanced statistical methods for accurate analysis.

Purpose of the Study:

• To investigate complex gene regulatory patterns in maize kernels using a statistical method.
• To dissect dynamic trait-trait correlation patterns in gene expression.
• To identify key genes and regulatory relationships in carotenoid and oil biosynthesis pathways.

Main Methods:

• Applied liquid association (LA) statistical method to analyze expression profiles of 28,769 maize genes.
• Identified significant liquid association pairs (LAPs) and LA-scouting leaders.
• Analyzed dynamic co-expression patterns in carotenoid and fatty acid metabolic pathways.

Main Results:

• Identified 686 conditional correlation LAPs among the top 1000 LA pairs.
• Discovered 830 positive and 215 negative LA-scouting leaders enriched in biological processes.
• Highlighted key genes (lcyE, CYP97A, ZEP1, VDE) in carotene biosynthesis and identified 580 LA-scouting genes associated with oil concentration.

Conclusions:

• Genome-wide LA analysis is a novel and effective method for detecting transcriptional regulatory relationships in maize.
• This approach enhances understanding of maize kernel gene biological roles.
• The findings benefit maize breeding programs for improved nutritional quality.