With the continued drop in sequencing costs enabled by technological advancements, there is increasing opportunity to leverage DNA sequencing data in clinical practice. However, genetic data has mostly been used in research settings to explore questions about how diseases work and why they start. The clinical use of genetic technologies has been primarily limited to diagnosis of so-called “monogenic” diseases—diseases that are linked to a single, known mutation in the genetic sequence—or genes implicated in the susceptibility or treatment of cancers. Conversely, it is well known that the most common and costly chronic diseases like diabetes, cardiovascular disease, and Alzheimer’s disease are associated with “polygenic” risk (involving many genes working in combination). Quantifying the genetic contribution of polygenic diseases and bringing this information into routine clinical use is an open challenge in the translation of life science research to clinical practice.
New genotyping technologies are able to measure the entire genome or hundreds of thousands of specific targets, enabling the development of polygenic risk scores (PRSs) to estimate how a person’s genetic variation affects the risk of developing a particular disease. PRSs can be computed from targeted genotyping data or whole-genome sequencing data and provide insights into complex disease states that stem from a variety of genetic variations that are observed to be different with disease. Each such variation can have a small effect on disease in isolation, but the combined combinatorial effect can be highly significant and provide a powerful complement to the clinical presentation. For example, PRSs consisting of dozens to many thousands of genetic variants have been calculated for several dozen diseases, ranging from coronary artery disease to breast cancer to diabetes. PRSs are not yet routinely used by health professionals—but new research is exploring how PRSs might be leveraged in a clinical setting to provide benefit to patients.
In a recent publication, Marston et al. investigated the predictive utility of a PRS for coronary artery disease by using genetic data from over 300,000 individuals. Their results showed that younger adults with high risk for coronary artery disease according to the calculated PRS had a 3-4x greater risk of cardiac events than did those with low predicted risk. They concluded that these results could be used to aid clinicians in treatment of such individuals, specifically in the consideration for statin therapy based on an individual’s polygenic risk.
This study is just one of many in a trend of data-driven approaches being applied in a clinical setting. While polygenic risk scores still have many hurdles to overcome before becoming part of standard clinical practice, the promise of using data in novel ways to positively impact the way healthcare is practiced is massive. Currently, almost all clinical applications of genetics are in the area of rare, monogenic diseases, with some slightly broader applications in oncology. As discussed here, looking at genetics for common conditions could help identify the right treatments, including avoiding unnecessary treatments—and the potential health and financial side effects that often accompany them.