The Diagnostic Power of DNA Methylation
How epigenomic analysis of peripheral blood, coupled with machine learning, could give labs access to clinically relevant information
Bekim Sadikovic |
At a Glance
- DNA methylation can be an important diagnostic indicator of genetic disease
- When combined with machine learning, methylation analysis becomes a powerful tool that grows more accurate and effective over time
- Methylation analysis can help determine whether variants of unknown significance are pathogenic
- Creating open databases will help facilitate the continued growth of epigenetics
When my career started, constitutional genetics and epigenetics were limited to very specific purposes, such as imprinting disorders or specific methylation assays. About 10 to 15 years ago, this targeted approach began to give way to genome-wide methods using micro-arrays and similar technologies. Baylor College of Medicine, where I had my clinical fellowship, was one of the hubs at the forefront of introducing these technologies, and now they’ve carried out over 100,000 pediatric microarrays. In the process, they have discovered new clinical associations for dozens of new microdeletion and microduplication syndromes. The work set a precedent – and it got me to start thinking about methylation technologies as something that I could exploit in a similar way.
Read the full article now
Log in or register to read this article in full and gain access to The Pathologist’s entire content archive. It’s FREE and always will be!
Or register now - it’s free and always will be!
You will benefit from:
- Unlimited access to ALL articles
- News, interviews & opinions from leading industry experts
- Receive print (and PDF) copies of The Pathologist magazine
Or Login via Social Media
By clicking on any of the above social media links, you are agreeing to our Privacy Notice.
- E Aref-Eshghi et al., “Genomic DNA methylation signatures enable concurrent diagnosis and clinical genetic variant classification in neurodevelopmental syndromes”, Am J Hum Genet, 102, 156–174 (2018). PMID: 29304373.
- O Griffith, H Rehm, “Variant database collaborations – for cancer and beyond”, The Pathologist, 40, 40–43 (2018). Available at: bit.ly/2umjGXd.