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Nat Commun. 2019 Dec 20;10(1):5819. doi: 10.1038/s41467-019-13848-1.

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

Abraham G1,2,3, Malik R4, Yonova-Doing E5, Salim A6,7, Wang T8, Danesh J5,9,10,11,12,13, Butterworth AS5,9,10,11,12,13, Howson JMM5,12, Inouye M14,15,16,17,18,19, Dichgans M20,21,22.

Author information

1
Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia. gad.abraham@baker.edu.au.
2
Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK. gad.abraham@baker.edu.au.
3
Department of Clinical Pathology, University of Melbourne, Parkville, VIC, Australia. gad.abraham@baker.edu.au.
4
Institute for Stroke and Dementia Research, University Hospital, Ludwig-Maximilians-Universität LMU, Munich, Germany.
5
British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
6
Baker Heart and Diabetes Institute, Melbourne, VIC, Australia.
7
Department of Mathematics and Statistics, La Trobe University, Melbourne, VIC, Australia.
8
Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia.
9
British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK.
10
National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK.
11
National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge and Cambridge University Hospitals, Cambridge, UK.
12
Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK.
13
Department of Human Genetics, Wellcome Sanger Institute, Hinxton, UK.
14
Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia. mi336@medschl.cam.ac.uk.
15
Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK. mi336@medschl.cam.ac.uk.
16
Department of Clinical Pathology, University of Melbourne, Parkville, VIC, Australia. mi336@medschl.cam.ac.uk.
17
British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK. mi336@medschl.cam.ac.uk.
18
Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK. mi336@medschl.cam.ac.uk.
19
The Alan Turing Institute, London, UK. mi336@medschl.cam.ac.uk.
20
Institute for Stroke and Dementia Research, University Hospital, Ludwig-Maximilians-Universität LMU, Munich, Germany. martin.dichgans@med.uni-muenchen.de.
21
German Center for Neurodegenerative Diseases (DZNE), Munich, Germany. martin.dichgans@med.uni-muenchen.de.
22
Munich Cluster for Systems Neurology (SyNergy), Munich, Germany. martin.dichgans@med.uni-muenchen.de.

Abstract

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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