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Open AccessArticle
GWAS and Meta-QTL Analysis of Kernel Quality-Related Traits in Maize
by
Rui Tang
Rui Tang 1,2,
Zelong Zhuang
Zelong Zhuang 1,2,
Jianwen Bian
Jianwen Bian 1,2,
Zhenping Ren
Zhenping Ren 1,2,
Wanling Ta
Wanling Ta 1,2 and
Yunling Peng
Yunling Peng 1,2,*
1
College of Agronomy, Gansu Agricultural University, Lanzhou 730070, China
2
Gansu Provincial Key Laboratory of Aridland Crop Science, Gansu Agricultural University, Lanzhou 730070, China
*
Author to whom correspondence should be addressed.
Plants 2024, 13(19), 2730; https://doi.org/10.3390/plants13192730 (registering DOI)
Submission received: 17 August 2024
/
Revised: 22 September 2024
/
Accepted: 28 September 2024
/
Published: 29 September 2024
Abstract
The quality of corn kernels is crucial for their nutritional value, making the enhancement of kernel quality a primary objective of contemporary corn breeding efforts. This study utilized 260 corn inbred lines as research materials and assessed three traits associated with grain quality. A genome-wide association study (GWAS) was conducted using the best linear unbiased estimator (BLUE) for quality traits, resulting in the identification of 23 significant single nucleotide polymorphisms (SNPs). Additionally, nine genes associated with grain quality traits were identified through gene function annotation and prediction. Furthermore, a total of 697 quantitative trait loci (QTL) related to quality traits were compiled from 27 documents, followed by a meta-QTL analysis that revealed 40 meta-QTL associated with these traits. Among these, 19 functional genes and reported candidate genes related to quality traits were detected. Three significant SNPs identified by GWAS were located within the intervals of these QTL, while the remaining eight significant SNPs were situated within 2 Mb of the QTL. In summary, the findings of this study provide a theoretical framework for analyzing the genetic basis of corn grain quality-related traits and for enhancing corn quality.
Share and Cite
MDPI and ACS Style
Tang, R.; Zhuang, Z.; Bian, J.; Ren, Z.; Ta, W.; Peng, Y.
GWAS and Meta-QTL Analysis of Kernel Quality-Related Traits in Maize. Plants 2024, 13, 2730.
https://doi.org/10.3390/plants13192730
AMA Style
Tang R, Zhuang Z, Bian J, Ren Z, Ta W, Peng Y.
GWAS and Meta-QTL Analysis of Kernel Quality-Related Traits in Maize. Plants. 2024; 13(19):2730.
https://doi.org/10.3390/plants13192730
Chicago/Turabian Style
Tang, Rui, Zelong Zhuang, Jianwen Bian, Zhenping Ren, Wanling Ta, and Yunling Peng.
2024. "GWAS and Meta-QTL Analysis of Kernel Quality-Related Traits in Maize" Plants 13, no. 19: 2730.
https://doi.org/10.3390/plants13192730
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