Centre for Genomic & Experimental Medicine
Centre for Genomic & Experimental Medicine


Welcome to ‘MethylDetectR’!

MethylDetectR example output
Figure 1. Percentile ranks for a selected individual compared against input sample. The lotto balls show the percentile ranks for a selected individual against the remainder of the input dataset for a variety of traits estimated from blood methylation data.

‘MethylDetectR’ consists of two online applications that allow for an estimation of various human traits from blood DNA methylation data.

DNA methylation is an important biological process that affects whether genes are turned on or off. This may influence overall health profiles and risk of disease.

The traits which can be estimated are:

  • age
  • alcohol consumption per week
  • body fat percentage
  • body mass index
  • ‘good’ or high-density lipoprotein cholesterol
  • smoking behaviour
  • waist-to-hip ratio




MethylDetectR Calculate Your Scores MethylDetectR   MethylDetectR - Demo

In ‘MethylDetectR – Calculate Your Scores’, users can securely upload blood methylation data and obtain methylation-based scores for the above traits. In ‘MethylDetectR’, users can view these scores and how they compare against other individuals in their input dataset. Users can also upload optional case/control data for binary traits of interest and view how methylation-based scores for traits vary across case and control groups. Please refer to the ‘MethylDetectR – Reference Document’ for detailed instructions on the use of either application.  A demo version of the 'MethylDetectR' app with in-built data is available by following the above link "MethylDetectR - Demo.


Consent and Data Protection

No data are stored in the platform. For information relating to data privacy and protection, please refer to the ‘Participant Information Sheet’ and ‘Participant Consent Statement’ documents prior to uploading data to the software. These documents describe general risks surrounding the upload of biological data to online software and the steps taken to mitigate the risk of motivated intruders gaining access to such data.

If you have any questions or concerns, please do not hesitate to contact either: Robert Hillary (robert.hillary@ed.ac.uk) or Riccardo Marioni (riccardo.marioni@ed.ac.uk).

Example input and output files, and associated scripts, are available at https://doi.org/10.5281/zenodo.4646300.