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
. 2022 Nov 16:13:1035851.
doi: 10.3389/fpls.2022.1035851. eCollection 2022.

A meta-quantitative trait loci analysis identified consensus genomic regions and candidate genes associated with grain yield in rice

Kelvin Dodzi Aloryi  1 , Nnaemeka Emmanuel Okpala  1 , Aduragbemi Amo  2 , Semiu Folaniyi Bello  3 , Selorm Akaba  4 , Xiaohai Tian  1
Affiliations

A meta-quantitative trait loci analysis identified consensus genomic regions and candidate genes associated with grain yield in rice

Kelvin Dodzi Aloryi et al. Front Plant Sci. .

Abstract

Improving grain yield potential in rice is an important step toward addressing global food security challenges. The meta-QTL analysis offers stable and robust QTLs irrespective of the genetic background of mapping populations and phenotype environment and effectively narrows confidence intervals (CI) for candidate gene (CG) mining and marker-assisted selection improvement. To achieve these aims, a comprehensive bibliographic search for grain yield traits (spikelet fertility, number of grains per panicle, panicles number per plant, and 1000-grain weight) QTLs was conducted, and 462 QTLs were retrieved from 47 independent QTL research published between 2002 and 2022. QTL projection was performed using a reference map with a cumulative length of 2,945.67 cM, and MQTL analysis was conducted on 313 QTLs. Consequently, a total of 62 MQTLs were identified with reduced mean CI (up to 3.40 fold) compared to the mean CI of original QTLs. However, 10 of these MQTLs harbored at least six of the initial QTLs from diverse genetic backgrounds and environments and were considered the most stable and robust MQTLs. Also, MQTLs were compared with GWAS studies and resulted in the identification of 16 common significant loci modulating the evaluated traits. Gene annotation, gene ontology (GO) enrichment, and RNA-seq analyses of chromosome regions of the stable MQTLs detected 52 potential CGs including those that have been cloned in previous studies. These genes encode proteins known to be involved in regulating grain yield including cytochrome P450, zinc fingers, MADs-box, AP2/ERF domain, F-box, ubiquitin ligase domain protein, homeobox domain, DEAD-box ATP domain, and U-box domain. This study provides the framework for molecular dissection of grain yield in rice. Moreover, the MQTLs and CGs identified could be useful for fine mapping, gene cloning, and marker-assisted selection to improve rice productivity.

Keywords: Genome-Wide Association Studies; Meta-QTL analysis; candidate genes; grain yield; marker-assisted selection; rice.

PubMed Disclaimer

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Schematic work flow and the result of the present study.
Figure 2
Figure 2
Basic features of the QTLs. (A) Distribution of initial QTLs and the QTLs projected. (B) Trait-wise distribution of QTLs on the 12 chromosomes of rice. (C) PVE or R2 (%) of initial QTLs. (D) LOD scores of initial QTL involved in MQTLs.
Figure 3
Figure 3
Distribution of markers on the consensus map utilized for meta-analysis of QTLs. The number of loci mapped on individual rice chromosome is shown.
Figure 4
Figure 4
Distribution of MQTLs for grain yield traits on the 12 chromosomes of rice. Different colors on the left side of the maps indicate the initial QTLs involved in MQTLs.
Figure 5
Figure 5
Basic information of MQTLs identified in this study. (A) Frequency of MQTLs and the number of initial QTLs involved. (B) Comparison of CI of initial QTLs and MQTLs, exhibiting fold reduction in CI of initial QTLs and MQTLs.
Figure 6
Figure 6
Frequency of candidate genes encoding known protein families related to grain yield of rice.
Figure 7
Figure 7
Level 2 Gene ontology (GO) terms for CGs in the most stable and robust MQTLs intervals.
Figure 8
Figure 8
Protein-protein interaction of significant candidate genes regulating grain yield.
Figure 9
Figure 9
Expression profile of significant proteins encoded by 176 candidate genes underlying the following 8 MQTLs regions: MQTL1.1, MQTL2.6, MQTL3.5, MQTL4.2, MQTL5.2, MQTL5.5, MQTL10.2, and MQTL12.2.
See this image and copyright information in PMC

References

    1. Acuña-Galindo M. A., Mason R. E., Subramanian N. K., Dirk B. H. (2015). Meta-analysis of wheat QTL regions associated with adaptation to drought and heat stress. Crop Sci. 55, 477–492. doi: 10.2135/cropsci2013.11.0793 - DOI
    1. Ahmed E. Y. M., Zhang Y. P., Yu J. P., Rehman R. M. A., Zhang Z. Y., Zhang H. L., et al. (2016). Mapping of three QTLs for seed setting and analysis on the candidate gene for qSS-1 in rice (Oryza sativa l). J. Int. Agric. 15, 735–743. doi: 10.1016/S2095-3119(15)61299-0 - DOI
    1. Amo A., Soriano J. M. (2022). Unravelling consensus genomic regions conferring leaf rust resistance in wheat via meta-QTL analysis. Plant Genome. 15, e20185. doi: 10.1002/tpg2.20185 - DOI - PubMed
    1. Aquib A., Nafis S. (2021). Identifying meta-QTLs for stay-green in sorghum. bioRxiv. 4–11, 1–39. doi: 10.1101/2021.08.06.455413 - DOI
    1. Arcade A., Aymeric L., Matthieu F., Brigitte M., Fabien C., Alain C., et al. (2004). BioMercator: Integrating genetic maps and QTL towards discovery of candidate genes. Bioinformatics. 20, 2324–2326. doi: 10.1093/bioinformatics/bth230 - DOI - PubMed