Intracerebral hemorrhage (ICH) is the most devastating type of stroke, being responsible for almost 50% of stroke-related morbidity and mortality. Given its severity, primary and secondary prevention is of critical importance. In this study, the authors, led by CGM Investigators Chris Anderson and Jonathan Rosand, developed and validated an ICH meta-genomic risk score (metaGRS) of 2.6 million variants, combining GWAS data from 21 ICH risk factors and related traits and tested its ability to predict ICH risk in relation to traditional clinical ICH predictors. ICH metaGRS was associated with 31% higher odds of ICH per standard deviation, and identified individuals with almost 5-fold higher odds of ICH in the top score percentile. In models incorporating both the metaGRS as well as a collection of traditional clinical predictors, the metaGRS showed comparable predictive performance to the most potent clinical predictor, hypertension, and, importantly, it improved the predictive performance on top of established risk factors. In an external validation in the UK Biobank, the metaGRS was associated with higher risk of incident ICH both in a relatively high-risk population of antithrombotic medications users, as well as among a relatively low-risk population with a good control of vascular risk factors and no use of anticoagulants. Overall, the results demonstrate that the incorporation of genomic information in clinical prediction models for ICH could enhance predictive performance and lay the groundwork for future analyses in larger genetic datasets for ICH to optimally combine genomic information to maximize predictive benefit.