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. 2023 Jul 27;9(8):e18731.
doi: 10.1016/j.heliyon.2023.e18731. eCollection 2023 Aug.

Unraveling genomic regions and candidate genes for multiple disease resistance in upland cotton using meta-QTL analysis

Wen-Qi Huo  1   2 , Zhi-Qiang Zhang  1   2 , Zhong-Ying Ren  2 , Jun-Jie Zhao  2 , Cheng-Xiang Song  2 , Xing-Xing Wang  2 , Xiao-Yu Pei  2 , Yan-Gai Liu  2 , Kun-Lun He  2 , Fei Zhang  2 , Xin-Yang Li  2 , Wei Li  1   2   3 , Dai-Gang Yang  1   2   3 , Xiong-Feng Ma  1   2   3
Affiliations

Unraveling genomic regions and candidate genes for multiple disease resistance in upland cotton using meta-QTL analysis

Wen-Qi Huo et al. Heliyon. .

Abstract

Verticillium wilt (VW), Fusarium wilt (FW) and Root-knot nematode (RKN) are the main diseases affecting cotton production. However, many reported quantitative trait loci (QTLs) for cotton resistance have not been used for agricultural practices because of inconsistencies in the cotton genetic background. The integration of existing cotton genetic resources can facilitate the discovery of important genomic regions and candidate genes involved in disease resistance. Here, an improved and comprehensive meta-QTL analysis was conducted on 487 disease resistant QTLs from 31 studies in the last two decades. A consensus linkage map with genetic overall length of 3006.59 cM containing 8650 markers was constructed. A total of 28 Meta-QTLs (MQTLs) were discovered, among which nine MQTLs were identified as related to resistance to multiple diseases. Candidate genes were predicted based on public transcriptome data and enriched in pathways related to disease resistance. This study used a method based on the integration of Meta-QTL, known genes and transcriptomics to reveal major genomic regions and putative candidate genes for resistance to multiple diseases, providing a new basis for marker-assisted selection of high disease resistance in cotton breeding.

Keywords: Cotton; Disease resistance; Genomic region; Meta-QTL; Quantitative trait loci.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
Information of QTLs for biological stress in cotton in previous QTL mapping studies used for meta-QTL analysis. (A) Time distribution of previous QTL mapping studies. (B) Proportion of QTLs for VW, FW and RKN resistance. (C) Distribution of QTLs on chromosomes.
Fig. 2
Fig. 2
Markers distribution on the consensus genetic map used for meta-QTL analysis.
Fig. 3
Fig. 3
Circular plot showing genome-wide distributions of MQTLs. Circles from the innermost to the outermost represent: genetic map, R2 values of initial QTLs, location of MQTLs on the genetic map, gene density map and the physical map.
Fig. 4
Fig. 4
Distribution of MQTLs on cotton chromosomes. Squares are the locations of disease resistance genes, and dots are the locations of SNPs.
Fig. 5
Fig. 5
Transcriptomic data associated with multiple disease resistance MQTL regions for cotton resistance to VW FW and RKN. (A) Venn diagram depicting the number of differentially expressed genes (DEGs) involved in three kinds of disease resistance. (B) Level 2 GO terms for DEGs in multiple disease resistance MQTL regions. (C) Top 30 KEGG enrichment pathways for DEGs in MQTL regions.
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References

    1. Chen Z.J., Scheffler B.E., Dennis E., Triplett B.A., Zhang T., Guo W., et al. Toward sequencing cotton (Gossypium) genomes. Plant Physiol. 2007;145:1303–1310. doi: 10.1104/pp.107.107672. - DOI - PMC - PubMed
    1. Cai Y-f, Xiaohong H., Mo J., Sun Q., Yang J., Liu J. Molecular research and genetic engineering of resistance to Verticillium wilt in cotton: a review. Afr. J. Biotechnol. 2012;8:7363–7372.
    1. Song R., Li J., Xie C., Jian W., Yang X. An overview of the molecular genetics of plant resistance to the Verticillium wilt pathogen Verticillium dahliae. Int. J. Biol. Macromol. 2020;21 doi: 10.3390/ijms21031120. - DOI - PMC - PubMed
    1. Sun M., Zhang Z., Ren Z., Wang X., Sun W., Feng H., et al. The GhSWEET42 glucose transporter participates in Verticillium dahliae infection in cotton. Front. Plant Sci. 2021;12 doi: 10.3389/fpls.2021.690754. - DOI - PMC - PubMed
    1. Xu L., Zhu L., Tu L., Liu L., Yuan D., Jin L., et al. Lignin metabolism has a central role in the resistance of cotton to the wilt fungus Verticillium dahliae as revealed by RNA-seq-dependent transcriptional analysis and histochemistry. J. Exp. Bot. 2011;62:5607–5621. doi: 10.1093/jxb/err245. - DOI - PMC - PubMed

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