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

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

doi:10.1038/s41467-019-13848-1

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

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

Authors

Gad Abraham^{1

2

3}, Rainer Malik⁴, Ekaterina Yonova-Doing⁵, Agus Salim^{6

7}, Tingting Wang⁸, John Danesh^{5

9

10

11

12

13}, Adam S Butterworth^{5

9

10

11

12

13}, Joanna M M Howson^{5

11}, Michael Inouye^{14

15

16

17

18

19}, Martin Dichgans^{20

21

22}

Affiliations

¹ Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia. [email protected].
² Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK. [email protected].
³ Department of Clinical Pathology, University of Melbourne, Parkville, VIC, Australia. [email protected].
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. [email protected].
¹⁵ Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK. [email protected].
¹⁶ Department of Clinical Pathology, University of Melbourne, Parkville, VIC, Australia. [email protected].
¹⁷ British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK. [email protected].
¹⁸ Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK. [email protected].
¹⁹ The Alan Turing Institute, London, UK. [email protected].
²⁰ Institute for Stroke and Dementia Research, University Hospital, Ludwig-Maximilians-Universität LMU, Munich, Germany. [email protected].
²¹ German Center for Neurodegenerative Diseases (DZNE), Munich, Germany. [email protected].
²² Munich Cluster for Systems Neurology (SyNergy), Munich, Germany. [email protected].

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.

Publication types

Comparative Study
Research Support, Non-U.S. Gov't

MeSH terms

Aged
Blood Pressure
Body Mass Index
Brain Ischemia / epidemiology*
Brain Ischemia / genetics
Comorbidity
Datasets as Topic
Female
Genetic Predisposition to Disease*
Genome-Wide Association Study
Genomics / methods*
Humans
Male
Medical History Taking
Middle Aged
Polymorphism, Single Nucleotide
Predictive Value of Tests
Risk Assessment / methods
Smoking / epidemiology
United Kingdom / epidemiology

Abstract

Publication types

MeSH terms

Grants and funding