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Related Experiment Video

Updated: Jul 30, 2025

Lateral Root Inducible System in Arabidopsis and Maize

Lateral Root Inducible System in Arabidopsis and Maize

Published on: January 14, 2016

Mining genes regulating root system architecture in maize based on data integration analysis.

Kunhui He¹, Zheng Zhao¹, Wei Ren¹

¹College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, Key Laboratory of Plant-Soil Interactions, MOE, China Agricultural University, Beijing, 100193, China.

TAG. Theoretical and Applied Genetics. Theoretische Und Angewandte Genetik

|May 15, 2023

Summary

Researchers developed a new strategy to identify maize root system architecture (RSA) genes by integrating public data. This approach identified 16 priority candidate genes, with one validated to influence root traits.

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Last Updated: Jul 30, 2025

Lateral Root Inducible System in Arabidopsis and Maize

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Published on: January 14, 2016

Author Spotlight: Improved Methods for Preparing Transverse Sections and Unrolled Whole Mounts of Maize Leaf Primordia for Fluorescence and Confocal Imaging

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Micron-scale Phenotyping Techniques of Maize Vascular Bundles Based on X-ray Microcomputed Tomography

Micron-scale Phenotyping Techniques of Maize Vascular Bundles Based on X-ray Microcomputed Tomography

Published on: October 9, 2018

Area of Science:

Plant Biology
Genetics
Bioinformatics

Background:

Root system architecture (RSA) is vital for crop productivity and stress tolerance in maize.
Identifying genes that regulate RSA in maize is challenging due to limited functional cloning of RSA genes.

Purpose of the Study:

To establish an integrated strategy for mining maize RSA genes using public data resources.
To construct a root gene co-expression network and identify candidate genes regulating maize RSA.

Main Methods:

Collected 589 known maize root genes and homologous genes.
Constructed a maize root gene co-expression network (13,874 genes) using weighted gene co-expression network analysis (WGCNA) on public transcriptome data.
Integrated WGCNA results with genome-wide association analysis (GWAS) of RSA traits to identify priority candidate genes.

Main Results:

A maize root gene co-expression network was built, identifying 53 root hub genes.
1082 new root candidate genes were predicted.
16 priority root candidate genes were identified by overlapping network predictions with GWAS data.
One candidate gene (Zm00001d023379, pyruvate kinase 2) was functionally validated to affect root open angle and shoot-borne root number in transgenic maize.

Conclusions:

The study presents an effective integration analysis method for discovering RSA regulatory genes in maize.
This approach provides a new pathway for mining candidate genes underlying complex traits like RSA.