Genomic risk score offers predictive performance comparable to clinical risk factors for ischaemic stroke

John Danesh, Martin Dichgans, Adam S. Butterworth, Joanna M. M. Howson, Michael Inouye, Tingting Wang, Agus Salim, Rainer Malik, Gad Abraham, Ekaterina Yonova-Doing
2019-12-20

SCID:  54.1/ypwggdej
Recent genome-wide association studies in stroke have enabled the generation of genomic risk scores (GRS) but their predictive power has been modest compared to established stroke risk factors. Here, using a meta-scoring approach, we develop a metaGRS for ischaemic stroke (IS) and analyse this score in the UK Biobank (n = 395,393; 3075 IS events by age 75). The metaGRS hazard ratio for IS (1.26, 95% CI 1.22-1.31 per metaGRS standard deviation) doubles that of a previous GRS, identifying a subset of individuals at monogenic levels of risk: the top 0.25% of metaGRS have three-fold risk of IS. The metaGRS is similarly or more predictive compared to several risk factors, such as family history, blood pressure, body mass index, and smoking. We estimate the reductions needed in modifiable risk factors for individuals with different levels of genomic risk and suggest that, for individuals with high metaGRS, achieving risk factor levels recommended by current guidelines may be insufficient to mitigate risk.
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2019-12-20
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John Danesh
Martin Dichgans
Adam S. Butterworth
Joanna M. M. Howson
Michael Inouye
Tingting Wang
Agus Salim
Rainer Malik
Gad Abraham
Ekaterina Yonova-Doing
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