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Importance: Clinical risk prediction for monogenic coding variants remains challenging even in established disease genes, as variants are often so rare that epidemiological assessment is not possible. These variants are collectively common in population cohorts - one in six individuals carries a rare variant in nine clinically actionable genes commonly used in population health screening. Objective: To expand diagnostic risk assessment in genomic medicine by integrating monogenic, polygenic, and clinical risk factors, and to classify monogenic variant carriers as having elevated risk or population-level risk. Design, Setting, and Participants: Participants aged 40-70 years were recruited from 22 UK assessment centers from 2006-2010. Monogenic, polygenic, and clinical risk factors are used to generate integrated risk predictions for rare missense variant carriers in 200,625 individuals with exome sequencing data. Relative risks and classification thresholds are validated using 92,455 participants in the Geisinger MyCode cohort recruited from 70 US sites from 2007 onward. Conclusions and Relevance: Using integrated risk predictions, we identify 18.22% of UK Biobank (UKB) participants carrying variants of uncertain significance are at elevated risk for breast cancer (BC), familial hypercholesterolemia (FH), and colorectal cancer (CRC), accounting for 2.56% of the UKB in total. These predictions are concordant with clinical outcomes: individuals classified as having high risk have substantially higher risk ratios (RR=3.71 [3.53, 3.90] BC, RR=4.71 [4.50, 4.92] FH, RR=2.65 [2.15, 3.14] CRC, logrank p<10-5), findings that are validated in an independent cohort ({chi}2 p=9.9x10-4 BC, {chi}2 p=3.72x10-16 FH). Notably, we predict that 64% of UKB patients with laboratory-classified pathogenic FH variants are not at increased risk for coronary artery disease (CAD), and find no significant difference in CAD outcomes between these individuals and those without a monogenic disease-associated variant (logrank p=0.68). Current clinical practice guidelines discourage the disclosure of variants of uncertain significance to patients, but integrated modeling broadens this risk analysis, and identifies over 2.5-fold additional individuals who could potentially benefit from such information. This framework improves risk assessment within two similarly ascertained biobank cohorts, which may be useful in guiding preventative care and clinical management.

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