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Abstract

BackgroundIntracerebral hemorrhage (ICH), the most fatal form of stroke, has an estimated heritability of 29%. Applying a meta-scoring approach, we developed a genomic risk score for ICH and determined its predictive power in comparison to standard clinical risk factors. MethodsUsing a meta-analytic approach, we combined genome-wide association data from individuals of European ancestry for ICH and ICH-related traits in a meta-genomic risk score (metaGRS) consisting of 2.6 million variants. We tested associations with ICH and the predictive performance of the metaGRS in addition to clinical risk factors in a held-out validation dataset (842 cases and 796 controls). Furthermore, we tested associations with risk of incident ICH in the population-based UK Biobank cohort (486,784 individuals, 1,526 events, median follow-up 11.3 years). ResultsOne SD increment in the metaGRS was significantly associated with 45% higher odds for ICH (OR 1.45; 95%CI: 1.30-1.63) in age- and sex-adjusted models and 31% higher odds for ICH (OR: 1.31, 95%CI: 1.16-1.48) in models further adjusted for clinical risk factors. The metaGRS identified individuals with almost 5-fold higher odds for ICH in the top score percentile (OR: 4.83, 95%CI: 1.56-21.2). Predictive models for ICH incorporating the metaGRS in addition to clinical predictors showed superior performance compared with clinical risk factors alone (c-index: 0.695 vs. 0.686). The metaGRS showed similar associations for both lobar and non-lobar ICH, which were independent of the known APOE risk locus for lobar ICH. In the UK Biobank, the metaGRS was associated with higher risk of incident ICH (HR: 1.15, 95%CI: 1.09-1.21). The associations were significant within both a relatively high-risk population of users of antithrombotic medications, as well as among a relatively low-risk population with a good control of vascular risk factors and no use of anticoagulants. ConclusionsWe developed and validated a genomic risk score that predicts lifetime risk of ICH beyond established clinical risk factors among individuals of European ancestry. Whether implementation of the score in risk prognostication models for high-risk populations, such as patients under antithrombotic treatment, could improve clinical decision making should be explored in future studies.

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