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Meta-Analysis
. 2014 Feb;45(2):403-12.
doi: 10.1161/STROKEAHA.113.003044. Epub 2014 Jan 16.

Predicting stroke through genetic risk functions: the CHARGE Risk Score Project

Carla A Ibrahim-Verbaas  1 , Myriam Fornage, Joshua C Bis, Seung Hoan Choi, Bruce M Psaty, James B Meigs, Madhu Rao, Mike Nalls, Joao D Fontes, Christopher J O'Donnell, Sekar Kathiresan, Georg B Ehret, Caroline S Fox, Rainer Malik, Martin Dichgans, Helena Schmidt, Jari Lahti, Susan R Heckbert, Thomas Lumley, Kenneth Rice, Jerome I Rotter, Kent D Taylor, Aaron R Folsom, Eric Boerwinkle, Wayne D Rosamond, Eyal Shahar, Rebecca F Gottesman, Peter J Koudstaal, Najaf Amin, Renske G Wieberdink, Abbas Dehghan, Albert Hofman, André G Uitterlinden, Anita L Destefano, Stephanie Debette, Luting Xue, Alexa Beiser, Philip A Wolf, Charles Decarli, M Arfan Ikram, Sudha Seshadri, Thomas H Mosley Jr, W T Longstreth Jr, Cornelia M van Duijn, Lenore J Launer
Affiliations
Meta-Analysis

Predicting stroke through genetic risk functions: the CHARGE Risk Score Project

Carla A Ibrahim-Verbaas et al. Stroke. 2014 Feb.

Abstract

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.

Keywords: genetic epidemiology; risk factors; stroke.

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Figures

Figure 1
Figure 1. Flow diagram: Construction of the Genetic Risk Score for Stroke
Figure 2
Figure 2. ROC curves for the discovery cohorts, ischemic stroke: CHARGE Genetic Risk Score for Stroke
Each lettered panel gives the sensitivity*(1-specificity) curves for the clinical prediction (sex+FSRS), prediction based on the GRS, and on the two combined. Panels: A=ARIC, B=CHS, C=FHS, D=RS.
Figure 2
Figure 2. ROC curves for the discovery cohorts, ischemic stroke: CHARGE Genetic Risk Score for Stroke
Each lettered panel gives the sensitivity*(1-specificity) curves for the clinical prediction (sex+FSRS), prediction based on the GRS, and on the two combined. Panels: A=ARIC, B=CHS, C=FHS, D=RS.
See this image and copyright information in PMC

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