Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2024 Sep 29;13(19):2730.
doi: 10.3390/plants13192730.

GWAS and Meta-QTL Analysis of Kernel Quality-Related Traits in Maize

Rui Tang  1   2 , Zelong Zhuang  1   2 , Jianwen Bian  1   2 , Zhenping Ren  1   2 , Wanling Ta  1   2 , Yunling Peng  1   2
Affiliations

GWAS and Meta-QTL Analysis of Kernel Quality-Related Traits in Maize

Rui Tang et al. Plants (Basel). .

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.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Manhattan plot and QQ plot of the BLUE value of seed quality traits. OC, oil content; PC, protein content; SC, starch content. The red dashed line is the threshold line.
Figure 1
Figure 1
Manhattan plot and QQ plot of the BLUE value of seed quality traits. OC, oil content; PC, protein content; SC, starch content. The red dashed line is the threshold line.
Figure 2
Figure 2
Heatmap of the tissue-specific expression patterns of candidate genes. DAP: Days after pollination, V18: Vegetative stage18, R1: Reproductive 1. Standardized data conversion with log2 (FPKM + 1).
Figure 3
Figure 3
Illustration of the projection and distribution of quantitative trait loci (QTL) and Meta QTL (MQTL) identified for quality traits on chromosome 6. The bars on the left side of the chromosome correspond to QTL associated with quality traits, while the black bars within the chromosome indicate marker density. The colored segments within the chromosome represent MQTL and, on the right side, molecular markers and genetic distances (cM) are displayed.
Figure 4
Figure 4
Circos plot illustrating the distribution of MQTL (Meta QTL) and significant SNPs from GWAS studies in maize. From the innermost to the outermost circle: the innermost circle represents the gene density within the MQTL, as well as genes associated with quality; the middle circle depicts the physical map position of the MQTL.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

References

    1. Godfray H.C.J., Beddington J.R., Crute I.R., Haddad L., Lawrence D., Muir J.F., Pretty J., Robinson S., Thomas S.M., Toulmin C. Food security: The challenge of feeding 9 billion people. Science. 2010;327:812–818. doi: 10.1126/science.1185383. - DOI - PubMed
    1. Reddappa S.B., Muthusamy V., Zunjare R.U., Chhabra R., Talukder Z.A., Maman S., Chand G., Pal D., Kumar R., Mehta B.K. Composition of kernel-amylose and-resistant starch among subtropically adapted maize. J. Food Compos. Anal. 2023;119:105236. doi: 10.1016/j.jfca.2023.105236. - DOI
    1. Zhao H.-L., Yao Q., Xiao Z.-Y., Qin S., Gong D.-M., Qiu F.-Z. Revealing the process of storage protein rebalancing in high quality protein maize by proteomic and transcriptomic. J. Integr. Agric. 2023;22:1308–1323. doi: 10.1016/j.jia.2022.08.031. - DOI
    1. Ogunyemi A.M., Otegbayo B.O., Fagbenro J.A. Effects of NPK and biochar fertilized soil on the proximate composition and mineral evaluation of maize flour. Food Sci. Nutr. 2018;6:2308–2313. doi: 10.1002/fsn3.808. - DOI - PMC - PubMed
    1. Wei X., He T. The Formation and Regulation of Maize Quality. Bot. Res. 2022;11:541.

LinkOut - more resources