Implementation and implications for polygenic risk scores in healthcare

Abstract

Increasing amounts of genetic data have led to the development of polygenic risk scores (PRSs) for a variety of diseases. These scores, built from the summary statistics of genome-wide association studies (GWASs), are able to stratify individuals based on their genetic risk of developing various common diseases and could potentially be used to optimize the use of screening and preventative treatments and improve personalized care for patients. Many challenges are yet to be overcome, including PRS validation, healthcare professional and patient education, and healthcare systems integration. Ethical challenges are also present in how this information is used and the current lack of diverse populations with PRSs available. In this review, we discuss the topics above and cover the nature of PRSs, visualization schemes, and how PRSs can be improved. With these tools on the horizon for multiple diseases, scientists, clinicians, health systems, regulatory bodies, and the public should discuss the uses, benefits, and potential risks of PRSs.

Keywords: Clinical genetics; Genetic risk; PRS; Polygenic risk score; Public health; Risk stratification.

Fig. 1

Timeline of major events in genomics since the start of the Human Genome Project until today. Note the acceleration of advancements and increasing scale of studies

Fig 2

General scheme and important considerations for PRS development. This figure begins with collection of study participants and ends with assessment of PRS algorithm performance

Fig. 3

General scheme and important considerations for PRS validation. Note the importance of utilizing a population which was not in the original GWAS used for summary statistic generation but still of identical ancestry composition

Fig. 4

Examples of potential visualization schemes utilizing UK BioBank data for cardiovascular disease (CVD). Polygenic risk scores (PRSs) in a and b are on a 0–10 scale. b Relative risk (RR) can be represented as the scaled score with the colors green, yellow, and red indicating low, average, and high risk, respectively, to help increase understanding. a A normal distribution curve can also be used with a scaled score for relative risk along with a green to red gradient representing low to high relative risk of developing CVD within a subject’s lifetime. This graph can be represented with people or a solid color gradient. d Clinical factors can also be integrated into a more complex figure modeled from the Framingham Index for CVD with PRS quartiles serving as a static reference for other clinical factors to later adjust the absolute risk (AR) of CVD indicated by the percentage within each square (aka risk block). The AR has been adjusted based on age, sex, smoking history, and blood pressure (BP) in this example. c Given known patient factors, a sub-group of risk blocks can be excised from the larger figure for a said patient, showing the improvements to AR by reducing BP and not smoking while still acknowledging the impacts of age, sex, and genetic risk

Fig. 5

Distribution (%) of total individuals available in the GWAS catalog as of January 2019 [60]