tetano
Editor, Senior Moderator
IEEE/ACM Trans Comput Biol Bioinform
. 2021 Jan 8;PP.
doi: 10.1109/TCBB.2021.3049836. Online ahead of print.
Detection of Phenotype-related Mutations of COVID-19 via the Whole Genomic Data
Jinxiong Lv, Shikui Tu, Lei Xu
Abstract
The coronavirus disease 2019 (COVID-19) epidemic continues to spread rapidly around the world and nearly 20 millions people are infected. This paper utilises both single-locus analysis and joint-SNPs analysis for detection of significant single nucleotide polymorphisms (SNPs) in the phenotypes of symptomatic vs. asymptomatic, the early collection time vs. the late collection time, the old vs. the young, and the male vs. the female. Also, this paper analyses the relationship between any two SNPs via linkage disequilibrium analysis, and visualises the patterns of cumulative mutations of SNPs over collection time. The results are in three folds. First, the SNP which locates at the nucleotide position 4321 is found to be an independent significant locus associated with all the first three phenotypes. Moreover, 12 significant SNPs are found in the first two studies. Second, gene orf1ab containing SNP-4321 is detected to be significantly associated with the first three phenotypes, and the three genes, S, ORF3a, and N, are detected significant in the first two phenotypes. Third, some of the detected genes or SNPs are related to the SARS-COV-2 as supported by literature survey, which indicates that the results here may be helpful for further investigation.
. 2021 Jan 8;PP.
doi: 10.1109/TCBB.2021.3049836. Online ahead of print.
Detection of Phenotype-related Mutations of COVID-19 via the Whole Genomic Data
Jinxiong Lv, Shikui Tu, Lei Xu
- PMID: 33417561
- DOI: 10.1109/TCBB.2021.3049836
Abstract
The coronavirus disease 2019 (COVID-19) epidemic continues to spread rapidly around the world and nearly 20 millions people are infected. This paper utilises both single-locus analysis and joint-SNPs analysis for detection of significant single nucleotide polymorphisms (SNPs) in the phenotypes of symptomatic vs. asymptomatic, the early collection time vs. the late collection time, the old vs. the young, and the male vs. the female. Also, this paper analyses the relationship between any two SNPs via linkage disequilibrium analysis, and visualises the patterns of cumulative mutations of SNPs over collection time. The results are in three folds. First, the SNP which locates at the nucleotide position 4321 is found to be an independent significant locus associated with all the first three phenotypes. Moreover, 12 significant SNPs are found in the first two studies. Second, gene orf1ab containing SNP-4321 is detected to be significantly associated with the first three phenotypes, and the three genes, S, ORF3a, and N, are detected significant in the first two phenotypes. Third, some of the detected genes or SNPs are related to the SARS-COV-2 as supported by literature survey, which indicates that the results here may be helpful for further investigation.