GWAS and Meta-QTL Analysis of Kernel Quality-Related Traits in Maize
- PMID: 39409600
 - PMCID: PMC11479128
 - DOI: 10.3390/plants13192730
 
GWAS and Meta-QTL Analysis of Kernel Quality-Related Traits in Maize
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.
Keywords: GWAS; candidate genes; maize; meta-QTL; quality traits.
Conflict of interest statement
The authors declare no conflicts of interest.
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- 23ZYQA0322/Central-Guided Local Science and Technology Development Fund Project
 - 22ZD6NA009/Gansu Provincial Science and Technology Plan Major Project
 - 2022YFD1201804/National Key R&D Plan
 - 2022CYZC-46/Gansu Provincial Higher Education Industry Support Plan
 - 202401036, 202401046, 202401035/Innovation and Entrepreneurship Training Program for College Students at Gansu Agricultural University
 
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