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DNA methylation marker identification and poly-methylation risk score in prediction of healthspan termination.

Meiqi YangMei WangXiaoyu ZhaoFeifei XuShuang LiangYifan WangNanxi WangMuhammed Lamin SambouHongbing ShenJuncheng Dai
Published in: Epigenomics (2024)
Aim: To elucidate the epigenetic consequences of DNA methylation in healthspan termination (HST), considering the current limited understanding. Materials & methods: Genetically predicted DNA methylation models were established (n = 2478). These models were applied to genome-wide association study data on HST. Then, a poly-methylation risk score (PMRS) was established in 241,008 individuals from the UK Biobank. Results: Of the 63,046 CpGs from the prediction models, 13 novel CpGs were associated with HST. Furthermore, people with high PMRSs showed higher HST risk (hazard ratio: 1.18; 95% CI: 1.13-1.25). Conclusion: The study indicates that DNA methylation may influence HST by regulating the expression of genes (e.g., PRMT6 , CTSK ). PMRSs have a promising application in discriminating subpopulations to facilitate early prevention.
Keyphrases
  • dna methylation
  • genome wide
  • gene expression
  • genome wide association study
  • copy number
  • poor prognosis
  • bioinformatics analysis
  • big data
  • binding protein
  • machine learning
  • long non coding rna