Much of the risk for disease lies in our genes, but other factors also contribute. Sometimes, environment or exposures modify how genes operate. One way that gene activity is altered is by methylation of the DNA, a process whereby a methyl molecule attaches itself to a cytosine nucleotide in DNA. Specific methylation patterns are necessary for normal development and altered methylation plays a role in human diseases such as cancer and autoimmune diseases. It has recently become technically and financially feasible to measure DNA methylation at single-nucleotide resolution on a large scale across the genome, using a method called bisulfite sequencing. But the data from this sequencing often have many missing or imprecise measures, making them difficult to interpret and limiting the potential of such studies to describe the role of epigenetics in disease.
Drs. Celia Greenwood of the Lady Davis Institute for Medical Research and Karim Oualkacha of the Université du Québec à Montréal have assembled a team of experts to develop an algorithm and software package to analyze large-scale, high-dimensional DNA methylation data, so that we can profit from the hidden potential of these data. The initial focus of the methods and package will be scleroderma, a debilitating autoimmune disease leading to scarring across multiple tissues with unpredictable treatment response.
The team has support from a patient-advocacy organization, Scleroderma Quebec and from two Montreal companies, one that develops machine-learning methods and one that provides a high-performance computational platform. Understanding of DNA methylation and its contribution to disease will be revolutionized through these methods and software package.