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Journal of Agriculture ›› 2024, Vol. 14 ›› Issue (4): 26-36.doi: 10.11923/j.issn.2095-4050.cjas2023-0092

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Grain-related Traits in Maize: Genome-wide Association Analysis and Candidate Gene Prediction

CHEN Xinyi(), LIU Chenyan, HUA Mingzhu, XU Xin, FENG Wenxiang, WANG Baohua, FANG Hui()   

  1. School of Life Sciences, Nantong University, Nantong 226019, Jiangsu, China
  • Received:2023-04-03 Revised:2023-09-15 Online:2024-04-17 Published:2024-04-17

Abstract:

To explore the natural variations in regulating the maize kernel development and to assist in the genetic improvement of maize yield traits, in this study, 150 maize inbred lines with rich genetic variations were selected as materials for investigation. Combining 34,342 SNP markers and three models, a genome-wide association analysis was conducted on five grain-related traits. The results revealed that a total of 18 independent loci were significantly associated with the target traits, with each locus accounting for 12.24% to 15.41% of the phenotypic variations. Additionally, significant epistatic interactions were identified among four pairs of SNPs associated with kernel length, collectively explaining 5.32% of the phenotypic variations. By integrating the dynamic transcriptome data of kernel development in the B73 inbred line and functional annotations of genes, 19 candidate genes were predicted and classified into four categories: 6 enzymes, 3 ribosomal proteins, 1 transcription factor, and 9 other proteins. These candidate genes provide new genetic resources for deciphering the molecular mechanisms of maize kernel development and enhancing maize kernel size and yield. Through this research, we have not only uncovered the natural variations that regulate the development of corn kernels but also provided new genetic resources for the genetic improvement of corn yield traits. These findings are expected to bring new breakthroughs in corn breeding efforts, enhance corn production, and thereby better meet human needs for food.

Key words: maize, kernel size, yield, genome-wide association analysis, candidate genes prediction