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CT-Based Phenotyping and Genome-Wide Association Analysis of the Internal Structure and Components of Maize Kernels

文献类型： 外文期刊

作者： Li, Dazhuang 1 ; Wang, Jinglu 1 ; Zhang, Ying 1 ; Lu, Xianju 1 ; Du, Jianjun 1 ; Guo, Xinyu 1 ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Beijing Key Lab Digital Plant, Beijing 100097, Peoples R China

关键词： maize kernel; micro-CT; three-dimensional structure; deep learning; microscopic phenotype; GWAS

期刊名称：AGRONOMY-BASEL （ 2022影响因子：3.7； 五年影响因子：4.0 ）

ISSN：

年卷期： 2023 年 13 卷 4 期

页码：

收录情况： SCI

摘要： The structure of the maize kernels plays a critical role in determining maize yield and quality, and high-throughput, non-destructive microscope phenotypic characteristics acquisition and analysis are of great importance. In this study, Micro-CT technology was used to obtain images of maize kernels. An automatic CT image analysis pipeline was then developed to extract 20 traits related to the three-dimensional structure of kernel, embryo, endosperm, and cavity. The determination coefficients for five volume-related traits (embryo, endosperm, silty endosperm, embryo cavity, and endosperm cavity) were 0.95, 0.95, 0.77, 0.73, and 0.94, respectively. Further, we analyzed the phenotypic variations among a group of 303 inbred lines and conducted genome-wide association studies (GWAS). A total of 26 significant SNP loci were associated with these traits that are closely related to kernel volume, and 62 candidate genes were identified. Functional analysis revealed that most candidate genes corresponding to cavity traits encoded stress resistance proteins, while those corresponding to embryo and endosperm traits encoded proteins involved in regulating plant growth and development. These results will improve the understanding of the phenotypic traits of maize kernels and will provide new theoretical support for in-depth analysis of the genetic mechanism of kernel structure traits.

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