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

Author

Listed:
  • Gad Abraham

    (Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute
    University of Cambridge
    University of Melbourne)

  • Rainer Malik

    (University Hospital, Ludwig-Maximilians-Universität LMU)

  • Ekaterina Yonova-Doing

    (University of Cambridge)

  • Agus Salim

    (Baker Heart and Diabetes Institute
    La Trobe University)

  • Tingting Wang

    (Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute)

  • John Danesh

    (University of Cambridge
    University of Cambridge
    University of Cambridge
    University of Cambridge and Cambridge University Hospitals)

  • Adam S. Butterworth

    (University of Cambridge
    University of Cambridge
    University of Cambridge
    University of Cambridge and Cambridge University Hospitals)

  • Joanna M. M. Howson

    (University of Cambridge
    University of Cambridge and Cambridge University Hospitals)

  • Michael Inouye

    (Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute
    University of Cambridge
    University of Melbourne
    University of Cambridge)

  • Martin Dichgans

    (University Hospital, Ludwig-Maximilians-Universität LMU
    German Center for Neurodegenerative Diseases (DZNE)
    Munich Cluster for Systems Neurology (SyNergy))

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.

Suggested Citation

  • Gad Abraham & Rainer Malik & Ekaterina Yonova-Doing & Agus Salim & Tingting Wang & John Danesh & Adam S. Butterworth & Joanna M. M. Howson & Michael Inouye & Martin Dichgans, 2019. "Genomic risk score offers predictive performance comparable to clinical risk factors for ischaemic stroke," Nature Communications, Nature, vol. 10(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-13848-1
    DOI: 10.1038/s41467-019-13848-1
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    Cited by:

    1. Clara Albiñana & Zhihong Zhu & Andrew J. Schork & Andrés Ingason & Hugues Aschard & Isabell Brikell & Cynthia M. Bulik & Liselotte V. Petersen & Esben Agerbo & Jakob Grove & Merete Nordentoft & David , 2023. "Multi-PGS enhances polygenic prediction by combining 937 polygenic scores," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    2. Junqing Xie & Yuliang Feng & Danielle Newby & Bang Zheng & Qi Feng & Albert Prats-Uribe & Chunxiao Li & Nicholas J. Wareham & R. Paredes & Daniel Prieto-Alhambra, 2023. "Genetic risk, adherence to healthy lifestyle and acute cardiovascular and thromboembolic complications following SARS-COV-2 infection," Nature Communications, Nature, vol. 14(1), pages 1-10, December.

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