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MethylDetectR: a software for methylation-based health profiling.

Robert F Hillary1, Riccardo E Marioni1

  • 1Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, Midlothian, EH4 2XU, UK.

Wellcome Open Research
|May 12, 2021
PubMed
Summary
This summary is machine-generated.

DNA methylation profiles predict human traits and health risks. The MethylDetectR platform offers a secure, interactive tool to calculate and interpret these DNA methylation-based predictions, highlighting their utility and limitations.

Keywords:
DNA methylationShinyepidemiologyepigeneticspredictiontranslation

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Area of Science:

  • Epigenetics and Molecular Biology
  • Computational Biology and Bioinformatics
  • Genomics and Personalized Medicine

Background:

  • DNA methylation, the addition of methyl groups to DNA, regulates gene activity and is influenced by genetics and environment.
  • Individual DNA methylation profiles vary and can predict human traits and disease risks.
  • Existing methylation-based predictors require better communication regarding their applicability and limitations.

Purpose of the Study:

  • To develop a secure, web-based platform, MethylDetectR, for calculating and visualizing DNA methylation-derived trait estimates.
  • To provide users with tools to interpret methylation-based predictor scores in relation to population data and binary phenotypes.
  • To explore correlations between different methylation predictors and phenotypic traits in a large reference sample.

Main Methods:

  • Development of the secure, interactive web platform 'MethylDetectR'.
  • Automated calculation of trait estimates (e.g., age, lifestyle, HDL cholesterol) from blood DNA methylation data.
  • Comparative analysis of estimated trait scores against a reference sample (n=4,450) and correlation with binary phenotypes.

Main Results:

  • MethylDetectR enables fast and secure calculation of DNA methylation-derived trait estimates.
  • The platform facilitates comparison of individual scores against population data and analysis of trait variations by case/control status.
  • Correlations between methylation predictors and phenotypic traits were analyzed in the Generation Scotland cohort.

Conclusions:

  • MethylDetectR provides a valuable tool for understanding and applying DNA methylation-based predictors.
  • The platform enhances the interpretation of molecular health predictors by visualizing their performance and limitations.
  • This interactive tool aids in reporting estimated health profiles and exploring associations with health outcomes.