Combined large-scale phenotyping and transcriptomics in maize reveals a robust growth regulatory network

Leaves are vital organs for biomass and seed production because of their role in the generation of metabolic energy and organic compounds. A better understanding of the molecular networks underlying leaf development is crucial to sustain global requirements for food and renewable energy. Here, we combined transcriptome profiling of proliferative leaf tissue with in-depth phenotyping of the fourth leaf at later stages of development in 197 recombinant inbred lines of two different Zea mays populations. Previously, correlation analysis in a classical bi-parental mapping population identified 1740 genes correlated with at least one of fourteen traits (Baute et al., 2015). Here, we extended these results with data from a MAGIC population. As expected, the phenotypic variability was found to be larger in the latter population than in the bi-parental population, although general conclusions on the correlations amongst the traits are comparable. Data integration from the two diverse populations allowed us to identify a set of 226 genes that are robustly associated with diverse leaf traits. This set of genes is enriched for transcriptional regulators and genes involved in protein synthesis and cell wall metabolism. In order to investigate the molecular network context of the candidate gene set, we integrated our data with publicly available functional genomics data and identified a growth regulatory network of 185 genes. Our results illustrate the power of combining in-depth phenotyping with transcriptomics in mapping populations to dissect the genetic control of complex traits and present a set of candidate genes for use in biomass improvement.

Baute, J., Herman, D., Coppens, F., De Block, J., Slabbinck, B., Dell'Acqua, M., Pè, M.E., Maere, S., Nelissen, B., Inzé, D. (2016) Combined large-scale phenotyping and transcriptomics in maize reveals a robust growth regulatory network. Plant Physiol. 170(3):1848-67.

• 
• abstract
• 
• doi
• 
• PMID
•