A framework for integrated clinical risk assessment using population sequencing data

Clinical risk prediction for genetic variants remains challenging even in established disease genes, as many are so rare that epidemiological assessment is not possible. Using data from 200,625 individuals, we integrate individual-level, variant-level, and protein region risk factors to estimate personalized clinical risk for individuals with rare missense variants. These estimates are highly concordant with clinical outcomes in breast cancer (BC) and familial hypercholesterolemia (FH) genes, where we distinguish between those with elevated versus population-level disease risk (logrank p<10-5, Risk Ratio=3.71 [3.53, 3.90] BC, Risk Ratio=4.71 [4.50, 4.92] FH), validated in an independent cohort ({chi}2 p=9.9x10-4 BC, {chi}2 p=3.72x10-16 FH). Notably in FH genes, we predict that 64% of biobank patients with laboratory-classified pathogenic variants are not at increased coronary artery disease (CAD) risk when considering all patient and variant characteristics. These patients have no significant difference in CAD risk from individuals without a monogenic variant (logrank p=0.68). Such assessments may be useful for optimizing clinical surveillance, genetic counseling, and intervention, and demonstrate the need for more nuanced approaches in population screening.