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Towards clinical utility of polygenic risk scores.

Authors
  • Lambert, Samuel A1, 2, 3, 4
  • Abraham, Gad1, 2, 5
  • Inouye, Michael1, 2, 3, 4, 5, 6
  • 1 Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK.
  • 2 Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia. , (Australia)
  • 3 MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK.
  • 4 Cambridge Substantive Site, Health Data Research UK, Wellcome Genome Campus, Hinxton, UK.
  • 5 Department of Clinical Pathology, University of Melbourne, Parkville, VIC 3010, Australia. , (Australia)
  • 6 The Alan Turing Institute, London, UK.
Type
Published Article
Journal
Human Molecular Genetics
Publisher
Oxford University Press
Publication Date
Nov 21, 2019
Volume
28
Issue
R2
Identifiers
DOI: 10.1093/hmg/ddz187
PMID: 31363735
Source
Medline
Language
English
License
Unknown

Abstract

Prediction of disease risk is an essential part of preventative medicine, often guiding clinical management. Risk prediction typically includes risk factors such as age, sex, family history of disease and lifestyle (e.g. smoking status); however, in recent years, there has been increasing interest to include genomic information into risk models. Polygenic risk scores (PRS) aggregate the effects of many genetic variants across the human genome into a single score and have recently been shown to have predictive value for multiple common diseases. In this review, we summarize the potential use cases for seven common diseases (breast cancer, prostate cancer, coronary artery disease, obesity, type 1 diabetes, type 2 diabetes and Alzheimer's disease) where PRS has or could have clinical utility. PRS analysis for these diseases frequently revolved around (i) risk prediction performance of a PRS alone and in combination with other non-genetic risk factors, (ii) estimation of lifetime risk trajectories, (iii) the independent information of PRS and family history of disease or monogenic mutations and (iv) estimation of the value of adding a PRS to specific clinical risk prediction scenarios. We summarize open questions regarding PRS usability, ancestry bias and transferability, emphasizing the need for the next wave of studies to focus on the implementation and health-economic value of PRS testing. In conclusion, it is becoming clear that PRS have value in disease risk prediction and there are multiple areas where this may have clinical utility. © The Author(s) 2019. Published by Oxford University Press. All rights reserved. For Permissions, please email: [email protected]

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