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Genome-wide association and transcriptome analysis of root color-related genes in Gossypium arboreum L.

Zibo ZhaoDaowu HuMuhammad Tehseen AzharHongge LiChenhui MaShoupu HeXiaoyang WangGaofei SunTahir MahmoodWashu DevXiongming Du
Published in: Planta (2021)
The significant number loci and candidate genes of root color in Gossypium arboreum are identified and provide a theoretical basis of root color for cotton. A stimulating phenomenon was observed on the 4th day of sowing in the root color of some G. arboreum accessions that turned red. To disclose the genetic mechanisms of root color formation via genome and transcript levels, we identified the significant number of SNPs and candidate genes that are related to root color through genome-wide association study (GWAS) and RNAseq analysis in G. arboreum. Initially, 215 no. of G. arboreum accessions was collected, and the colors of root on the 4th, 6th and 9th day of germination were recorded. The GWAS demonstrated that 225 significant SNPs and 47 candidate genes have been identified totally. The strongest signal SNP A04_91824 could greatly distinguish the root color with most "C" allele accessions have displayed white and "T" allele accessions displayed red. RNAseq was performed on accessions having the white and red root, and results revealed that 12 and 138 DEGs were detected on 2nd and 4th day, respectively. ACD6, UFGT, and LYM2 were the most related genes of root color, later, verified by qRT-PCR. The mature zone of red and the white roots was observed by the histological section method, and results shown that cells were more closely arranged in the white root, and both average cell length and cell width were longer in the red root. This study will be helpful to cotton breeders for utilization of several elite genes and related SNPs related to root color, in addition to find linkage with economically important traits of interests.
Keyphrases
  • genome wide
  • genome wide association study
  • gene expression
  • dna methylation
  • oxidative stress
  • cell therapy
  • cell death
  • bone marrow
  • transcription factor
  • data analysis
  • genome wide analysis
  • high density
  • plant growth