数字农科院2.0

Natural variation of CT2 affects the embryo/kernel weight ratio in maize

文献类型： 外文期刊

作者： Zhang, Yumin;Zhen, Sihan;Zhang, Chunxia;Zhang, Jie;Shangguan, Xiaoqing;Lu, Jiawen;Wu, Qingyu;Dirk, Lynnette M. A.;Downie, A. Bruce;Wang, Guoying;Zhao, Tianyong;Fu, Junjie

作者机构：

关键词： Maize;Embryo size;GWAS;CT2;Natural variation

期刊名称： JOURNAL OF GENETICS AND GENOMICS

ISSN： 1673-8527

年卷期： 2024 年 52 卷 3 期

页码：

收录情况： SCIE（2024版） ; ; CSCD（2023-2024年度） ; ; 科技核心（2024版）

摘要： Embryo size is a critical trait determining not only grain yield but also the nutrition of the maize kernel. Up to the present, only a few genes have been characterized affecting the maize embryo/kernel ratio. Here, we identify 63 genes significantly associated with maize embryo/kernel weight ratio using a genome-wide association study (GWAS). The peak GWAS signal shows that the natural variation in Zea mays COMPACT PLANT2 (CT2), encoding the heterotrimeric G protein a subunit, is significantly associated with the Embryo/Kernel Weight Ratio (EKWR). Further analyses show that a missense mutation of CT2 increases its enzyme activity and associates with EKWR. The function of CT2 on affecting embryo/kernel weight ratio is further validated by the characterization of two ct2 mutants, for which EKWR is significantly decreased. Subsequently, the key downstream genes of CT2 are identified by combining the differential expression analysis of the ct2 mutant and quantitative trait transcript analysis in the GWAS population. In addition, the allele frequency spectrum shows that CT2 was under selective pressure during maize domestication. This study provides important genetic insights into the natural variation of maize embryo/kernel weight ratio, which could be applied in future maize breeding programs to improve grain yield and nutritional content. Copyright (c) 2024, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, and Genetics Society of China. Published by Elsevier Limited and Science Press. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

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