Abstract
Introduction: Genome-wide association studies (GWAS) have identified genetic associations for many common diseases and traits. However, incorporating genetic information into disease risk prediction has been challenging because any single collection of variants explains a small proportion of risk. We explored whether combining genetic information from related traits could improve our ability to predict ICH. Methods: We constructed an ICH meta-Genetic Risk Score (metaGRS) using 21 polygenic risk scores (PRSs) from GWASs of traits associated with ICH risk: systolic/diastolic blood pressure, pulse pressure, diabetes, hemoglobin A1c, total cholesterol, high/low-density lipoprotein, triglycerides, body mass index, waist-hip ratio, urine albumin-creatinine ratio, kidney disease, eGFR, white matter hyperintensities, small vessel stroke, insomnia, sleep duration, education attainment, alcohol use and smoking. Each PRS contained common, independent variants at p≤5x10 -4 with each trait. PRSs were calculated in 1,867 ICH cases/1,722 controls, using 1,019 cases/928 controls as a training dataset to derive logORs of the PRSs with ICH status, and a validation dataset of 848 cases/794 controls to construct the metaGRS as a weighted average. Results: Patients in higher metaGRS percentiles had higher odds of ICH (Table) , and a one standard deviation increase in metaGRS was associated with odds of ICH (OR 1.42; 95% CI: 1.28-1.57; p=1.6x10 -11 ). Compared to patients in the middle decile of the metaGRS distribution, patients in the top 10% and 5% had increased odds of ICH (OR 1.87; 95% CI: 1.07-3.30; p=0.03, OR 2.72; 95% CI: 1.54-4.92; p=7.4x10 -4 respectively). Conclusions: A metaGRS identified individuals at high risk for ICH with an odds ratio comparable to traditional risk factors, such as hypertension (OR ~1.6). Further studies are needed to investigate the role of incorporating genetic information into clinical care.
Published Version
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