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.2022 Jun 13:13:929168.
doi: 10.3389/fpls.2022.929168. eCollection 2022.

Detection of Stable Elite Haplotypes and Potential Candidate Genes of Boll Weight Across Multiple Environments via GWAS in Upland Cotton

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Detection of Stable Elite Haplotypes and Potential Candidate Genes of Boll Weight Across Multiple Environments via GWAS in Upland Cotton

Zhen Feng et al. Front Plant Sci..

Abstract

Boll weight (BW) is a key determinant of yield component traits in cotton, and understanding the genetic mechanism of BW could contribute to the progress of cotton fiber yield. Although many yield-related quantitative trait loci (QTLs) responsible for BW have been determined, knowledge of the genes controlling cotton yield remains limited. Here, association mapping based on 25,169 single-nucleotide polymorphisms (SNPs) and 2,315 insertions/deletions (InDels) was conducted to identify high-quality QTLs responsible for BW in a global collection of 290 diverse accessions, and BW was measured in nine different environments. A total of 19 significant markers were detected, and 225 candidate genes within a 400 kb region (± 200 kb surrounding each locus) were predicted. Of them, two major QTLs with highly phenotypic variation explanation on chromosomes A08 and D13 were identified among multiple environments. Furthermore, we found that two novel candidate genes (Ghir_A08G009110 andGhir_D13G023010) were associated with BW and thatGhir_D13G023010 was involved in artificial selection during cotton breeding by population genetic analysis. The transcription level analyses showed that these two genes were significantly differentially expressed between high-BW accession and low-BW accession during the ovule development stage. Thus, these results reveal valuable information for clarifying the genetic basics of the control of BW, which are useful for increasing yield by molecular marker-assisted selection (MAS) breeding in cotton.

Keywords: MAS; SNP; association mapping; boll weight; candidate genes.

Copyright © 2022 Feng, Li, Tang, Liu, Ji, Sun, Liu, Zhao, Huang, Zhang, Zhang and Yu.

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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
Map of the 290 cotton accessions.(A) Geographic distribution of the natural population; each accession is represented by a dot.(B) Pie chart of the proportions of diverse cotton-growing areas in 290 accessions. NIR: Northwest Inland region in China; NSER: Northern-Specific Early-Maturity region; YRR: Yellow River region; YZRR: Yangtze River region; and Amerasian: 27 accessions primarily introduced from six different countries (USA, Azerbaijan, Israel, Kyrgyzstan, Tajikistan, and Uzbekistan).(C) Breeding stage distribution of the GWAS panel; Unknown: accessions that were not found among the pedigrees.
Figure 2
Figure 2
Phenotypic variation analysis of boll weight.(A) Distributions of the mean values for boll weight in nine environments (E1: Anyang-2014, E2: Anyang-2015, E3: Anyang-2016, E4: Shihezi-2014, E5: Shihezi-2015, E6: Shihezi-2016, E7: Huanggang-2016, E8: Huanggang-2021, and E9: Sanya-2020-2021).(B) Correlation analysis of boll weight in nine environments (***P < 0.001, **P < 0.01, and *P < 0.05).
Figure 3
Figure 3
GWAS results of SNP and InDel markers and candidate gene analysis.(A,B) Manhattan plots of BW-BLUP for SNPs and InDels, respectively; significant BW-associated markers are distinguished by purple lines.(C) Heatmap of candidate gene expression patterns in 18 cotton tissues.(D) GO analysis of candidate genes associated with boll weight. The chart of purple, pink, and blue represented biological process, molecular function, and cellular component, respectively.
Figure 4
Figure 4
Variation analysis of the boll weight-related geneGhir_A08G009110 on candidate region.(A) Local Manhattan plots for BW-related genes on chromosome A08 and LD heatmap for the candidate region within the peak region of rsA08_30171616, including the exon–intron structure ofGhir_A08G009110.(B) Box plots for BW between the two haplotypes mentioned above (**P < 0.01, *P < 0.05).(C) Differentiation of the genetic diversity distribution of the favorable haplotype for rsA08_30171616 in five geographic areas.(D) Expression level analysis ofGhir_A08G009110 between “TM-1” (red) and “CRI16” (green) during ovule developmental stages (15, 20, and 25 DPA) by qRT-PCR (**P < 0.01, *P < 0.05).(E) Expression abundance analysis ofGhir_A08G009110 between “TM-1” (red) and “CRI12” (green) during ovule developmental stages (10, 20, 30, and 40 DPA) by RNA-seq (**P < 0.01, *P < 0.05).
Figure 5
Figure 5
Variation analysis of the boll weight-related geneGhir_D13G023010 on candidate region.(A) Local Manhattan plots for BW-related genes on chromosome D13 and LD heatmap for the candidate region within the peak region of rsD13_60955253, rsD13_60955261, and rsD13_60955462.(B) Genetic diversity across the three populations and exon–intron structure ofGhir_D13G023010.(C) Box plots for BW of the two haplotypes mentioned above (**P < 0.01, *P < 0.05).(D) Expression abundance analysis ofGhir_D13G023010 between “TM-1” (green) and “CRI12” (red) during ovule developmental stages (0, 10, 20, 30, and 40 DPA) by RNA-seq.
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References

    1. Bates D., Mächler M., Bolker B., Walker S. (2014). Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48. 10.18637/jss.v067.i01 - DOI
    1. Bolger A. M., Lohse M., Usadel B. (2014). Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 30, 2114–2120. 10.1093/bioinformatics/btu170 - DOI - PMC - PubMed
    1. Chen Z. J., Scheffler B. E., Dennis E., Triplett B. A., Zhang T., Guo W., et al. . (2007). Toward sequencing cotton (Gossypium) genomes. Plant Physiol. 145, 1303–1310. 10.1104/pp.107.107672 - DOI - PMC - PubMed
    1. Chen Z. J., Sreedasyam A., Ando A., Song Q., De Santiago L. M., Hulse-Kemp A. M., et al. . (2020). Genomic diversifications of five Gossypium allopolyploid species and their impact on cotton improvement. Nat. Genet. 52, 525–533. 10.1038/s41588-020-0614-5 - DOI - PMC - PubMed
    1. Covarrubias-Pazaran G.. (2016). Genome-assisted prediction of quantitative traits using the R package sommer. PLoS ONE. 11, e0156744. 10.1371/journal.pone.0156744 - DOI - PMC - PubMed

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