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

Abstract

BackgroundDissecting 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.ResultsWe 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.ConclusionsBy 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.Electronic supplementary materialThe online version of this article (10.1186/s12870-017-1119-y) contains supplementary material, which is available to authorized users.

The Application of Metabolomics for the Study of Cereal Corn (Zea mays L.)

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Characterization and genetic dissection of maize ear leaf midrib acquired by 3D digital technology

“…Among these, GWAS provides us an effective approach to explore the genetic mechanisms of phenotype formation between individuals ( Xiao et al., 2016 ; Liu and Yan, 2018 ). Since the release of the maize B73 reference genome, GWAS has been widely used in maize genetics research such as traditional agronomic traits such as maize plant height (PH) and ear height (EH) ( Li et al., 2016 ), kernel length ( Dai et al., 2018 ), and moisture content ( Zhou et al., 2018 ), typical traits such as microscopic phenotypes ( Mazaheri et al., 2019 ; Zhang et al., 2021 ), nutrient composition ( Li et al., 2013 ; Xu et al., 2017 ; Baseggio et al., 2019 ), metabolism ( Chen et al., 2016 ), physiological and biochemical properties ( Liu et al., 2016 ; Xu et al., 2018 ), and heavy metal enrichment ( Zhao et al., 2017 ), and stress resistance ( Zhang et al., 2016 ; Shi et al., 2018 ; Cooper et al., 2019 ; Wang et al., 2019 ; Xie et al., 2019 ). GWAS has contributed significantly to the elucidation of the genetic mechanism of phenotype formation in maize.…”

Modeling dynamic correlation in zero‐inflated bivariate count data with applications to single‐cell RNA sequencing data

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