Low-pass methylomes offer a more comprehensive alternative to microarrays
By Kathleen Barnes, SVP Population Health and Precision Medicine, Oxford Nanopore Technologies
Methylation quantification has been of tremendous interest to biologists for decades now, from elucidating basic biology to supporting drug discovery efforts or diagnosing certain diseases. The vast majority of methylation data generated to date has come from bisulfite sequencing or methylation-focused microarrays.
More recently, there’s an exciting new approach in the research community that stands to make methylation data even more useful, potentially for routine clinical use in the future. Methylation risk scoring follows the general idea of polygenic risk scores: capturing a large number of low-level signals from across the genome (or, in this case, the methylome) and pulling them together into a biologically relevant signature. The end result is a methylation risk score (MRS) that could be predictive of disease risk or serve as an early warning about disease onset.
The MRS is essentially where polygenic risk scores were five or six years ago. There is much to be learned, but the field is moving fast. In the not-too-distant future, it’s conceivable that an MRS could be incorporated into a patient’s medical record to help physicians model risk and tailor treatment plans.
Despite the promise of MRS, there’s a lot that has to be done, and it starts with cataloging methylome variants and performing the large-scale studies needed to link specific methylation profiles with key biological traits. Most of the work done in this area so far has been based on methylation microarrays, which help to identify hyper- and hypo-methylated regions to build out the foundation we’ll need for reliable MRS data. Arrays have the advantage of being very cost-effective, but they have a disadvantage too: they cover just a few percent of the methylome, limiting exploration to known regions and preventing serendipitous discovery.
To really solidify the foundation of MRS-guided healthcare, we need a more comprehensive tool for methylation detection and quantification. The only rational approach is whole methylome analysis, but this has largely been cost-prohibitive. Now, though, scientists have demonstrated that low-pass methylation coverage using nanopore sensing technology can be just as affordable as microarrays while spanning the entire methylome and maintaining the integrity of nucleic acid through sequencing. (Keen to see the scientific details? Check out this recent talk from Wasatch Biolabs CEO Chad Pollard as he explains his company’s use of nanopore technology to perform amplification-free methylome analysis.)
Low-pass methylation analysis makes it possible to run four to six samples per flow cell, generating accurate, whole-methylome data with sufficient coverage to spot differentially methylated regions that could be incorporated into an MRS. Multiplexing samples can bring the cost per sample down to less than that of a methylation microarray. That’s incredibly cost-effective for a reaction that generates not just full methylome coverage, but also the full genome sequence as well.
Scanning across the methylome leads to more robust analysis to feed into the predictive algorithms used to develop MRS signatures — and having the corresponding DNA sequence can empower some breakthrough scientific discovery about combinations of genetic and epigenetic signals. There is enormous potential to build clinically useful MRS-based classifiers and predictors from more comprehensive methylome data.
If you’ll be attending the annual meeting of the American Society of Human Genetics this week, stop by the Wasatch Biolabs CoLab presentation (From Arrays to Sequencing: Enhancing Methylation Analysis for Biomarker Discovery and Clinical Applications) on November 6 at 12 pm in theater 1 to learn more.