Zachariah Jasser

Prediction of disease risk is an essential part of preventive medicine, often guiding clinical management. Risk prediction typically includes risk factors such as age, sex, family history of disease and lifestyle (e.g., smoking status); however, in recent years, there has been increasing interest to include genomic information into risk models. Polygenic risk scores (PRS) aggregate the effects of many genetic variants across the human genome into a single score and have recently been shown to have predictive value for multiple common diseases (e.g., colon cancer, coronary artery disease, type 2 diabetes, and Alzheimer’s disease). In its simplest and most common form, PRS are sums of the effects of single nucleotide polymorphisms (SNP), based on the estimated SNP effect sizes β (beta; obtained from GWAS summary statistics). Typically, PRS scores include hundreds-to- thousands of SNPs, thus PRS aggregates the contribution of an individual’s germline genome into a single number proportional to the risk for a given disease. PRS can provide physicians and patients with a general risk assessment over many diseases to direct screening and or treatment. However, in order to do so, we must first understand how these scores distribute over large populations to find useful results. Using data from the United Kingdom Biobank (UKB) and GWAS summary statistics for 24 diseases or conditions we calculated PRS for ~500,000 individuals. Results show that the majority of individual’s (>95%) are at an increased risk for at least 1 disease or clinically relevant condition (e.g., high cholesterol). For individuals determined to have a moderate risk for 1 or more diseases/conditions (Relative Risk > 1), an individual is at risk for 3.9 diseases/conditions on average. Interestingly, approximately 5% of the population is at high risk for 1 or more diseases/conditions (Relative Risk > 3). In summary, PRS aggregates the effects of many genetic variants and identifies a significant subset of the population at high risk for 1 or more diseases and or clinical conditions thus likely benefiting most from changes in clinical management (e.g., prevention, screening, intervention).