Polygenic risk scores in the clinic: Translating risk into action

Abstract

Polygenic risk scores (PRSs) are heralded as useful tools for risk stratification and personalized preventive care, but they are clinically useful only if they can be translated into action. The risk information conveyed by a PRS must be contextualized to enable this. Best practices are evolving but are likely to involve integrating a PRS into an absolute risk model and using guideline-driven care linked to a specific threshold of risk. Because this approach is not currently available for most diseases, it may be necessary to use different methods of presenting risk and linking it to appropriate clinical action. We discuss the trade-offs of each strategy and argue for transparent communication to providers and patients of the imprecision in both risk estimates and action thresholds for PRSs.

Figure 1

Contextualizing the risk information conveyed by a PRS to enable clinical action A PRS may need to be adjusted to ensure that the distribution of score values is independent of genetic ancestry. A first contextualization step is to present the resultant score. A second contextualization step is to frame this risk alongside a threshold for clinical action.

Figure 2

Three options for representing risk information from a PRS The change in risk estimate for an individual for a given condition can be represented as: a percentile rank, within a suitably chosen population (top); some measure of relative effect compared to a suitably chosen population, for example a relative risk or odds ratio (middle); or a measure of absolute risk, such as 5-year risk or lifetime-remaining risk for developing a condition (bottom).

Figure 3

Conceptual model of the relationship between continuous risk prediction and binary preventive action Although risk lies on a quantitative continuum, its clinical value is its ability to inform a binary choice about a clinical action. For the individual patient, both assessments (where they lie on the continuum of risk and whether they lie below or above a threshold for action) have inherent imprecisions and limitations. These gray areas allow physicians and patients to use more than an algorithmic comparison of two numbers to make medical decisions.