Related Experiment Video
Updated: Mar 8, 2026

Identifying Mutations by High Resolution Melting in a TILLING Population of Rice
Published on: September 2, 2019
Comparative genomic hybridization analysis of rice dwarf mutants induced by gamma irradiation
J E Hwang1,2, S-H Kim1, I J Jung1
1Advanced Radiation Technology Institute, Korea Atomic Energy Research Institute, Jeongeup, Jeonbuk, Republic of Korea.
Abstract:
Comparative genomic hybridization (CGH) is a powerful tool used to analyze changes in copy number, polymorphisms, and structural variations in the genome. Gene copy number variation (CNV) is a common form of natural diversity in the genome, which can create new genes and alter gene structure. Thus, CNVs may influence phenotypic variation and gene expression. In this study, to detect CNVs, we irradiated rice seeds with gamma rays (300 Gy) and selected two dwarf mutagenized plants, GA-III-189 and -1052, in the M3 generation. These plants were subjected to CGH analysis using Agilent's RICE CGH array. Most of the CNVs identified were less than 10 kb in length. We detected 90 amplified and 18 deleted regions in GA-III-189, and 99 amplified and 11 deleted regions in GA-III-1052. Of note, CNVs were located on chromosome 12 in both GA-III-189 and -1052, which contained 39 commonly amplified regions in 29 genes. The commonly amplified genes included six genes encoding F-box domain-containing proteins. Alterations in these F-box domain-containing genes were confirmed by quantitative RT-PCR. Integration of CGH and gene expression data identified copy number aberrations and novel genes potentially involved in the dwarf phenotype. These CGH and gene expression data may be useful for uncovering the mechanisms underlying the dwarf phenotype.
More Related Videos
09:32An Array-based Comparative Genomic Hybridization Platform for Efficient Detection of Copy Number Variations in Fast Neutron-induced Medicago truncatula Mutants
Published on: November 8, 2017
09:43Author Spotlight: Streamlining Rice Breeding with CRISPR/Cas for Obtaining Optimal Phenotypic and Agronomic Traits
Published on: January 3, 2025