The Goals of Precision Medicine
One of the main services we offer our clients is the ability to extract meaningful insights from biological data. In the field of precision medicine, a fundamental goal is to be able to predict clinical phenotypes using patient data, from electronic health records for example.
The exciting thing is that genetic data from, say, a polygenic risk score (PRS) can be used synergistically with electronic health record (EHR) data to be more predictive. In case you didn’t know, a PRS is a metric of disease risk for a clinical phenotype based on the combination of multiple genetic markers. But again, the point is that the more genomic elements you have integrated into a predictive model, the better its predictive power.
What are Methylation Risk Scores?
So how does this transition into a methylation risk score? Well, in a recent study from npj Genomic Medicine, researchers calculated an epigenetic parallel to the polygenic risk score and called it a methylation risk score or MRS. They did this for more than 800 samples. These methylation risk scores consisted of a combination of the methylation states across the genome.
Genomic methylation is one of the key mechanisms that influence gene expression. In simpler terms, methylation is one way that genes are turned on or off. The authors at UCLA combined the methylation risk score with existing computational tools to predict clinical outcomes based on electronic health record data and polygenic risk scores. Including the MRS as a medical feature improved the imputation of lab tests by a whopping 37% using state-of-the-art computational methods for electronic health record data analysis. Not a bad result when you consider the convenience factor and ease of tissue acquisition – the tissue source is whole blood and was done on the EPIC Illumina Array.
It’s true that validation of array-based data is prudent and recommended. And some of you may remember our previous blog on this topic when we announced PacBio’s recent announcement that their new Revio platform will allow for direct measurement of methylation states on its sequencing runs. That puts things into context for older array technologies! But overall, the big picture still remains that with more readily available methylation data, this type of risk score could become a very useful diagnostic tool.
Outsourcing Bioinformatics Analysis: How We Can Help
From genetic sequencing to methylome studies, transforming raw sequence data of any kind into actionable biological insights is no small feat. We can help you tackle the challenging computational tasks of storing, analyzing, and interpreting genomic data. Bridge Informatics’ bioinformaticians are trained bench biologists, so they understand the biological questions driving your computational analysis. Click here to schedule a free introductory call with a member of our team.
Dan Ryder, MPH, PhD
Dan is the founder and CEO of Bridge Informatics, a professional services firm helping pharmaceutical companies translate genomic data into medicine. Unlike any other data analytics firm, Bridge forges sustainable communication change between their client’s biological and computational scientists. Dan is particularly passionate about improving communication between people of different scientific backgrounds, enabling bioinformaticians and software engineers to collectively succeed.
Prior to forming Bridge Informatics, Dan served in a variety of roles helping pharmaceutical clients solve early-phase drug discovery and development challenges.
Dan received both a Ph.D.
in Biochemistry and Molecular Biology and an MPH in Disease Control from the University of Texas Health Science Center at Houston (UTHealth Houston). He completed his postdoctoral studies in Molecular Pathways of Energy Metabolism at the University of Florida College of Medicine. Dan received his undergraduate degree in Microbiology from the University of Texas at Austin.
