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Published on: February 26, 2013
Combining polygenic and clinical risk scores in atrial fibrillation risk prediction: Implications for population
Louise Segan1, William Wing Ho Ho2, Rose Crowley1
1The Baker Heart and Diabetes Research Institute, Melbourne, Victoria, Australia; Department of Cardiology, The Alfred Hospital, Melbourne, Victoria, Australia; University of Melbourne, Melbourne, Victoria, Australia; Monash University, Melbourne, Victoria, Australia.
Background:
Atrial fibrillation (AF) development is determined by clinical risk factors and genetic predisposition. Few studies have explored whether incorporating polygenic risk scores (PRS) improves clinical-risk prediction beyond existing models.
Objectives:
We evaluated the interaction between AF-PRS and the hypertension, age, raised body mass index, male sex, sleep apnea, and smoking-AF (HARMS2-AF) and Cohorts for Heart and Aging Research in Genetic Epidemiology for AF (CHARGE-AF) clinical-risk scores on incident AF risk among the United Kingdom Biobank.
Methods:
AF-PRS was examined in those with and without incident AF based on International Classification of Diseases, Tenth Revision coding and divided into tertiles defined as low, intermediate, and high-risk categories. Regression analysis examined the impact of AF-PRS combined with the HARMS2-AF and CHARGE-AF risk scores and AF risk.
Results:
Among 285,734 participants with available whole genome sequencing data (52% women, age 57 years [50-63], 84.6% Caucasian), AF incidence was 6.6% with a median time to AF 8.5 (5.0-11.2) over a median 12.9 years follow-up. High AF-PRS tertile was independently associated with incident AF risk, after adjustment for clinical-risk factors (hazard ratio 2.75, 95% confidence interval 2.62-2.89, P<.001). AF-PRS enhanced AF risk prediction when combined with the HARMS2-AF risk model area under curve (AUC) 0.828 improved to 0.839 with the addition of AF-PRS (DeLong P<.001) with overall net reclassification index of 13.5% (12.8%-14.1%), and the CHARGE-AF risk model (AUC 0.808 improved to 0.828 with the addition of AF-PRS (DeLong P<.001) with overall net reclassification index of 7.3% (6.7%-7.9%).
Conclusions:
Combining genetic and clinical risk using the HARMS2-AF and CHARGE-AF risk scores significantly improved AF risk prediction. Incorporating polygenic to clinical-risk scores may enhance population screening and promote targeted interventions to reduce the incidence of AF.
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