Department of Economics and Business Economics

From Basic Science to Clinical Application of Polygenic Risk Scores: A Primer

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperReviewResearchpeer-review

  • Naomi R Wray, The University of Queensland Diamantina Institute, University of Queensland, Australia.
  • ,
  • Tian Lin, The University of Queensland Diamantina Institute, University of Queensland, Australia.
  • ,
  • Jehannine Austin, Faculty of Pharmaceutical Sciences, The University of British Columbia, Vancouver, British Columbia
  • ,
  • John J McGrath
  • Ian B Hickie, Sydney Medical School - Central, University of Sydney, Edward Ford Building A27, The University of Sydney, NSW 2006, Sydney, AUSTRALIA.
  • ,
  • Graham K Murray, The University of Queensland Diamantina Institute, University of Queensland, Australia.
  • ,
  • Peter M Visscher, The University of Queensland Diamantina Institute, University of Queensland, Australia.

Importance: Polygenic risk scores (PRS) are predictors of the genetic susceptibilities of individuals to diseases. All individuals have DNA risk variants for all common diseases, but genetic susceptibility differences between people reflect the cumulative burden of these. Polygenic risk scores for an individual are calculated as weighted counts of thousands of risk variants that they carry, where the risk variants and their weights have been identified in genome-wide association studies. Here, we review the underlying basic science of PRS, providing a foundation for understanding the potential clinical utility and limitations of PRS.

Observations: Polygenic risk scores can be calculated for a wide range of diseases from a saliva or blood sample using genotyping technologies that are inexpensive. While genotyping only needs to be done once for each individual in their lifetime, the PRS can be recalculated as identification of risk variants improves. On their own, PRS will never be able to establish or definitively predict future diagnoses of common complex conditions because genetic factors only contribute part of the risk, and PRS will only ever capture part of the genetic contributions. Nonetheless, just as clinical medicine uses a multitude of other predictive measures, PRS either on their own or as part of multivariable predictive algorithms could play a role.

Conclusions and Relevance: Utility of PRS in clinical medicine and ethical issues related to their use should be evaluated in the context of realistic expectations of what PRS can and cannot deliver. For different diseases, PRS could have utility in community settings (stratification to better triage people into established screening programs) or could contribute to clinical decision-making for those presenting with symptoms but where formal diagnosis is unclear. In principle, PRS could contribute to treatment choices, but more data are needed to allow development of PRS in this context.

Original languageEnglish
JournalJAMA Psychiatry
Volume78
Issue1
Pages (from-to)101-109
Number of pages9
ISSN0003-990X
DOIs
Publication statusPublished - Jan 2021

    Research areas

  • CORONARY-ARTERY-DISEASE, GENETIC RISK, HERITABILITY, PREDICTIVE ACCURACY, PSYCHIATRY

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