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.

Stroke risk is influenced by genetic and lifestyle factors and previously a genomic risk score (GRS) for stroke was proposed, albeit with limited predictive power. Here, Abraham et al. develop a metaGRS that is composed of several stroke-related GRSs and demonstrate improved predictive power compared with individual GRS or classic risk factors.

Details

Title
Genomic risk score offers predictive performance comparable to clinical risk factors for ischaemic stroke
Author
Abraham, Gad 1 ; Malik, Rainer 2 ; Yonova-Doing Ekaterina 3 ; Agus, Salim 4 ; Wang, Tingting 5 ; Danesh, John 6 ; Butterworth, Adam S 6   VIAFID ORCID Logo  ; Howson Joanna M M 7   VIAFID ORCID Logo  ; Inouye, Michael 8 ; Dichgans Martin 9   VIAFID ORCID Logo 

1  Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Australia (GRID:grid.1051.5) (ISNI:0000 0000 9760 5620); University of Cambridge, Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, Cambridge, UK (GRID:grid.5335.0) (ISNI:0000000121885934); University of Melbourne, Department of Clinical Pathology, Parkville, Australia (GRID:grid.1008.9) (ISNI:0000 0001 2179 088X) 
2  University Hospital, Ludwig-Maximilians-Universität LMU, Institute for Stroke and Dementia Research, Munich, Germany (GRID:grid.411095.8) (ISNI:0000 0004 0477 2585) 
3  University of Cambridge, British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, Cambridge, UK (GRID:grid.5335.0) (ISNI:0000000121885934) 
4  Baker Heart and Diabetes Institute, Melbourne, Australia (GRID:grid.1051.5) (ISNI:0000 0000 9760 5620); La Trobe University, Department of Mathematics and Statistics, Melbourne, Australia (GRID:grid.1018.8) (ISNI:0000 0001 2342 0938) 
5  Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Australia (GRID:grid.1051.5) (ISNI:0000 0000 9760 5620) 
6  University of Cambridge, British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, Cambridge, UK (GRID:grid.5335.0) (ISNI:0000000121885934); University of Cambridge, British Heart Foundation Centre of Research Excellence, Cambridge, UK (GRID:grid.5335.0) (ISNI:0000000121885934); University of Cambridge, National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, Cambridge, UK (GRID:grid.5335.0) (ISNI:0000000121885934); University of Cambridge and Cambridge University Hospitals, National Institute for Health Research Cambridge Biomedical Research Centre, Cambridge, UK (GRID:grid.5335.0) (ISNI:0000000121885934); Wellcome Genome Campus and University of Cambridge, Health Data Research UK Cambridge, Cambridge, UK (GRID:grid.5335.0) (ISNI:0000000121885934); Wellcome Sanger Institute, Department of Human Genetics, Hinxton, UK (GRID:grid.10306.34) (ISNI:0000 0004 0606 5382) 
7  University of Cambridge, British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, Cambridge, UK (GRID:grid.5335.0) (ISNI:0000000121885934); University of Cambridge and Cambridge University Hospitals, National Institute for Health Research Cambridge Biomedical Research Centre, Cambridge, UK (GRID:grid.5335.0) (ISNI:0000000121885934) 
8  Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Australia (GRID:grid.1051.5) (ISNI:0000 0000 9760 5620); University of Cambridge, Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, Cambridge, UK (GRID:grid.5335.0) (ISNI:0000000121885934); University of Melbourne, Department of Clinical Pathology, Parkville, Australia (GRID:grid.1008.9) (ISNI:0000 0001 2179 088X); University of Cambridge, British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, Cambridge, UK (GRID:grid.5335.0) (ISNI:0000000121885934); Wellcome Genome Campus and University of Cambridge, Health Data Research UK Cambridge, Cambridge, UK (GRID:grid.5335.0) (ISNI:0000000121885934); The Alan Turing Institute, London, UK (GRID:grid.499548.d) (ISNI:0000 0004 5903 3632) 
9  University Hospital, Ludwig-Maximilians-Universität LMU, Institute for Stroke and Dementia Research, Munich, Germany (GRID:grid.411095.8) (ISNI:0000 0004 0477 2585); German Center for Neurodegenerative Diseases (DZNE), Munich, Germany (GRID:grid.424247.3) (ISNI:0000 0004 0438 0426); Munich Cluster for Systems Neurology (SyNergy), Munich, Germany (GRID:grid.452617.3) 
Publication year
2019
Publication date
Dec 2019
Publisher
Nature Publishing Group
e-ISSN
20411723
Source type
Scholarly Journal
Language of publication
English
DOI
https://doi.org/10.1038/s41467-019-13848-1
ProQuest document ID
2329317835
Copyright
This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.