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Genome-wide association study reveals novel quantitative trait loci and candidate genes of lint percentage in upland cotton based on the CottonSNP80K array

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Abstract

Key message

Thirty-four SNPs corresponding with 22 QTLs for lint percentage, including 13 novel QTLs, was detected via GWAS. Two candidate genes underlying this trait were also identified.

Abstract

Cotton (Gossypium spp.) is an important natural textile fiber and oilseed crop cultivated worldwide. Lint percentage (LP, %) is one of the important yield components, and increasing LP is a core goal of cotton breeding improvement. However, the genetic and molecular mechanisms underlying LP in upland cotton remain unclear. Here, we performed a genome-wide association study (GWAS) for LP based on 254 upland cotton accessions in four environments as well as the best linear unbiased predictors using the high-density CottonSNP80K array. In total, 41,413 high-quality single-nucleotide polymorphisms (SNPs) were screened, and 34 SNPs within 22 quantitative trait loci (QTLs) were significantly associated with LP. In total, 175 candidate genes were identified from two major genomic loci (GR1 and GR2), and 50 hub genes were identified through GO enrichment and weighted gene co-expression network analysis. Two candidate genes (Gh_D01G0162 and Gh_D07G0463), which may participate in early fiber development to affect the number of fiber protrusions and LP, were also identified. Their genetic variation and expression were verified by linkage disequilibrium blocks, haplotypes, and quantitative real-time polymerase chain reaction, respectively. The weighted gene interaction network analysis showed that the expression of Gh_D07G0463 was significantly correlated with that of Gh_D01G0162. These identified SNPs, QTLs and candidate genes provide important insights into the genetic and molecular mechanisms underlying variations in LP and serve as a foundation for LP improvement via marker-assisted breeding.

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Data availability

Genotyping data for 254 accessions in this study are available in the website (https://github.com/karmen-cc/TAG-2022.data). The transcriptomic data are downloaded from NCBI PRJNA546484. The python scripts for data analysis are deposited in the website (https://github.com/karmen-cc/chenchen.git).

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (32072116, 31671742 and 31601345), Seed-Industrialized Development Program in Shandong Province (2020LZGC002), China Agriculture Research System of MOF and MARA (CARS-15-05), the Taishan Scholar Project of Shandong Province (ts201511070), State Key Laboratory of Cotton Biology (CB2019A18) and the Innovation Project in Shandong Academy of Agricultural Sciences (CXGC2016A01).

Funding

This study was financially supported by the National Natural Science Foundation of China (Grant No. 32072116, 31671742 and 31601345), Seed-Industrialized Development Program in Shandong Province (Grant No. 2020LZGC002), China Agriculture Research System of MOF and MARA (CARS-15-05), the Taishan Scholar Project of Shandong Province (ts201511070), State Key Laboratory of Cotton Biology (Grant No. CB2019A18) and the Innovation Project in Shandong Academy of Agricultural Sciences (Grant No. CXGC2016A01).

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Author notes

  1. Yu Chen and Yang Gao contributed equally to this work.

Authors and Affiliations

  1. Key Laboratory of Cotton Breeding and Cultivation in Huang-Huai-Hai Plain, Institute of Industrial Crops, Ministry of Agriculture and Rural Affairs of China, Shandong Academy of Agricultural Sciences, Jinan, 250100, China

    Yu Chen, Yang Gao, Juan Zhou, Chuanyun Zhang, Zhangqiang Song, Xuehan Huo, Zhaohai Du, Chengjie Zhao, Shengli Wang, Jingxia Zhang, Furong Wang & Jun Zhang

  2. State Key Laboratory of Crop Genetics and Germplasm Enhancement, College of Agriculture, Nanjing Agricultural University, Nanjing, 210095, China

    Yang Gao

  3. State Key Laboratory of Cotton Biology, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang, 455000, China

    Pengyun Chen & Juwu Gong

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  1. Yu Chen
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  10. Chengjie Zhao
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  11. Shengli Wang
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  12. Jingxia Zhang
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  13. Furong Wang
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  14. Jun Zhang
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Contributions

JZ and FRW designed the experiments. YC, YG, JZ, CYZ, JWG, CJZ, ZHD and SLW performed field trials, phenotypic evaluation and data collection. YC, YG, XHH, ZQS, CJZ and JXZ contributed to the preparation of cotton DNA samples. YC, PYC and CJZ analyzed the data. ZQS performed the preparation of cotton RNA samples and qRT-PCR. YC drafted the manuscript. JZ and FRW revised the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Furong Wang or Jun Zhang.

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Communicated by Tobias Würschum.

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Chen, Y., Gao, Y., Chen, P. et al. Genome-wide association study reveals novel quantitative trait loci and candidate genes of lint percentage in upland cotton based on the CottonSNP80K array. Theor Appl Genet 135, 2279–2295 (2022). https://doi.org/10.1007/s00122-022-04111-1

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  • DOI: https://doi.org/10.1007/s00122-022-04111-1

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