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Predicting stroke through genetic risk functions: the CHARGE Risk Score Project
Carla A Ibrahim-Verbaas1, Myriam Fornage, Joshua C Bis
1From the Departments of Epidemiology (C.A.I.-V., P.J.K., N.A., R.G.W., A.D., A.H., A.G.U., C.M.v.D.), Neurology (C.A.I.-V., P.J.K., R.G.W., M.A.I.), Internal Medicine (A.G.U.), and Radiology (M.A.I.), Erasmus University Medical Center, Rotterdam, The Netherlands; Center for Medical Systems Biology, Leiden, The Netherlands (C.A.I.-V., N.A., C.M.v.D.); Institute for Molecular Medicine (M.F.) and Human Genetics Center (M.F., E.B.), University of Texas Health Science Center at Houston; Cardiovascular Health Research Unit (J.C.B., B.M.P.) and Departments of Medicine (J.C.B., B.M.P.), Epidemiology (B.M.P., S.R.H., W.T.L.), Health Services (B.M.P.), Biostatistics (K.R.), and Neurology (W.T.L.), University of Washington, Seattle; Group Health Research Institute, Group Health Cooperative, Seattle, WA (B.M.P.); Department of Biostatistics, Boston University School of Public Health, MA (S.H.C., A.L.D., S.D., L.X., A.B., P.A.W.); Department of Neurology (S.H.C., A.L.D., S.D., L.X., A.B., P.A.W., S.S.) and Cardiology section, Whitaker Cardiovascular Institute (J.D.F.), Boston University School of Medicine, MA; The National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA (S.H.C., J.D.F, C.J.O., C.S.F., A.L.D., S.D., L.X., A.B., P.A.W., S.S.); Department of Medicine, Harvard Medical School General Medicine Division (J.B.M.), Cardiovascular Research Center and Cardiology Division (S.K.), and Center for Human Genetic Research (S.K.), Massachusetts General Hospital, Boston; Division of Nephrology/Tufts Evidence Practice Center, Tufts University School of Medicine, Tufts Medical Center, Boston, MA (M.R.); Laboratory of Neurogenetics (M.N.) and Laboratory of Epidemiology and Population Sciences (L.J.L.), National Institute on Aging, National Institutes of Health, Bethesda, MD; Program in Medical and Population Genetics, Broad Institute of Harvard and Massachusetts Institute of Technology (MIT), Cambridge (S.K.); Center for Complex Disease Genomics, McKusick-Na
Background And Purpose:
Beyond the Framingham Stroke Risk Score, prediction of future stroke may improve with a genetic risk score (GRS) based on single-nucleotide polymorphisms associated with stroke and its risk factors.
Methods:
The study includes 4 population-based cohorts with 2047 first incident strokes from 22,720 initially stroke-free European origin participants aged ≥55 years, who were followed for up to 20 years. GRSs were constructed with 324 single-nucleotide polymorphisms implicated in stroke and 9 risk factors. The association of the GRS to first incident stroke was tested using Cox regression; the GRS predictive properties were assessed with area under the curve statistics comparing the GRS with age and sex, Framingham Stroke Risk Score models, and reclassification statistics. These analyses were performed per cohort and in a meta-analysis of pooled data. Replication was sought in a case-control study of ischemic stroke.
Results:
In the meta-analysis, adding the GRS to the Framingham Stroke Risk Score, age and sex model resulted in a significant improvement in discrimination (all stroke: Δjoint area under the curve=0.016, P=2.3×10(-6); ischemic stroke: Δjoint area under the curve=0.021, P=3.7×10(-7)), although the overall area under the curve remained low. In all the studies, there was a highly significantly improved net reclassification index (P<10(-4)).
Conclusions:
The single-nucleotide polymorphisms associated with stroke and its risk factors result only in a small improvement in prediction of future stroke compared with the classical epidemiological risk factors for stroke.
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