The need for polygenic score reporting standards in evidence-based practice: lipid genetics use case

Curr Opin Lipidol. 2021 Apr 1;32(2):89-95. doi: 10.1097/MOL.0000000000000733.

Abstract

Purpose of review: Polygenic scores (PGS) are used to quantify the genetic predisposition for heritable traits, with hypothesized utility for personalized risk assessments. Lipid PGS are primed for clinical translation, but evidence-based practice changes will require rigorous PGS standards to ensure reproducibility and generalizability. Here we review applicable reporting and technical standards for dyslipidemia PGS translation along phases of the ACCE (Analytical validity, Clinical validity, Clinical utility, Ethical considerations) framework for evaluating genetic tests.

Recent findings: New guidance suggests existing standards for study designs incorporating the ACCE framework are applicable to PGS and should be adopted. One recent example is the Clinical Genomics Resource (ClinGen) and Polygenic Score Catalog's PRS reporting standards, which define minimal requirements for describing rationale for score development, study population definitions and data parameters, risk model development and application, risk model evaluation, and translational considerations, such as generalizability beyond the target population studied.

Summary: Lipid PGS are likely to be integrated into clinical practice in the future. Clinicians will need to be prepared to determine if and when lipid PGS is useful and valid. This decision-making will depend on the quality of evidence for the clinical use of PGS. Establishing reporting standards for PGS will help facilitate data sharing and transparency for critical evaluation, ultimately benefiting the efficiency of evidence-based practice.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • Clinical Decision-Making
  • Dyslipidemias / genetics*
  • Evidence-Based Practice*
  • Genetic Testing*
  • Humans
  • Lipids
  • Multifactorial Inheritance*
  • Reproducibility of Results
  • Risk Assessment

Substances

  • Lipids