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A primary goal of our research is to identify novel targets for developing better therapeutic compounds or other treatments. However, as new drug development is lengthy, a parallel but more immediate approach to improve disease management is to tailor existing treatments to individual patients. Psychiatric drugs are by no means “silver bullets” as they lack efficacy in a substantial number of patients and may cause a variety of side effects. Therefore, identifying biomarkers that can predict if, and how, a patient will respond to treatment is another important goal of our research.

Although our research makes use many biomarker platforms (e.g., genomics, epigenomics, transciptomics, proteomics, and metabolomics), DNA methylation has come to be the corner stone of our work. Methylation is a common epigenetic modification, which provides unique possibilities to better understand psychiatric and substance use disorders. For example, as methylation can be dynamic and modified by environment factors, it can shed a unique light on disease mechanisms underlying clinical features such as episodicity and the molecular impact of pathogenic events. Furthermore, because methylation changes can persist over time, it can explain long term health risks as a result of trauma or withdrawal symptoms that imply a “biological memory” of previous drug use. The translational potential of DNA methylation studies is also profound. In addition to tissue specificity, concordance in methylation marks between blood and brain often exists. This overlap makes methylation sites potential clinical biomarkers. Because methylation is modifiable by (pharmaceutical) interventions, this research can also suggest unique new treatments.

A key tool to achieve our research goals are large scale “omic” investigations where thousands to millions of biological features are measured simultaneously and correlated with clinical variables to find disease biomarkers. In addition, high-throughput technologies, allowing the cost-effective collection of a detailed molecular fingerprint for each patients, are key for precision medicine. Using “big data” analytics these individualized fingerprints can be used in algorithms aimed to deliver the right treatment to the right person at the right time. Therefore, a significant portion of our research efforts are also focused on developing and optimizing protocols for efficient methylome wide approaches such as MBD-seq and on creating robust and user friendly analysis tools such as RaMWAS, a novel Bioconductor package for analysis of methylation data.