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Towards fair and clinically relevant polygenic predictions.

Bjarni Jóhann Vilhjálmsson1

  • 1National Centre for Register-based Research, Aarhus BSS, Aarhus University, Aarhus, Denmark; Bioinformatics Research Centre, Department of Molecular Biology and Genetics, Aarhus University, Aarhus, Denmark; Novo Nordisk Foundation Centre for Genomics Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA.

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Polygenic scores (PGS) can aid preventive medicine, but challenges remain for their use in clinical prediction models. This article discusses the broader context and limitations of integrating PGS into healthcare.

Keywords:
clinical risk assessmentdiverse ancestrypolygenic scores

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Area of Science:

  • Genetics and genomics
  • Preventive medicine
  • Clinical informatics

Background:

  • The Electronic Medical Records and Genomics (eMERGE) network has advanced genomic data integration.
  • Polygenic scores (PGS) are emerging tools for assessing genetic risk.
  • Clinical application of PGS requires robust pipelines and validation.

Purpose of the Study:

  • To discuss the role of polygenic scores (PGS) in preventive medicine.
  • To highlight the limitations and challenges of incorporating PGS into clinical prediction models.
  • To provide context for the Lennon et al. proposed PGS pipeline.

Main Methods:

  • Review of the Lennon et al. clinical polygenic score (PGS) pipeline.
  • Discussion of the broader implications for preventive medicine.
  • Analysis of challenges in integrating PGS into electronic health records and prediction models.

Main Results:

  • Polygenic scores offer potential for personalized risk prediction.
  • Significant challenges exist in validating and implementing PGS in diverse clinical settings.
  • Current prediction models require further development to effectively utilize PGS.

Conclusions:

  • The integration of polygenic scores into preventive medicine is promising but faces technical and ethical hurdles.
  • Further research is needed to overcome limitations in PGS prediction accuracy and clinical utility.
  • Addressing these challenges is crucial for the responsible advancement of genomic medicine.