Polygenic Risk Scores Across Genomic Platforms for Reliable Breast Cancer Risk Stratification

  1. Peh Joo Ho
  2. Alexis Jiaying Khng
  3. Joanna Hui Juan Tan
  4. Pierre-Alexis Vincent Goy
  5. Kayla Aisha Kamila
  6. Zheng Li
  7. Weang Kee Ho
  8. Iain Bee Huat Tan
  9. Dawn Qingqing Chong
  10. Elaine Lo
  11. Liuh Ling Goh
  12. Hwee Lin Wee
  13. Mikael Hartman
  14. Rajkumar Dorajoo
  15. Nicolas Bertin
  16. Jingmei Li
Read the full article See related articles

Discuss this preprint

Start a discussion What are Sciety discussions?

Listed in

This article is not in any list yet, why not save it to one of your lists.
Log in to save this article

Abstract

Purpose

We evaluated differences in a 313-variant breast cancer polygenic risk score (PRS 313 ) across genomic platforms and their impact on risk stratification.

Methods

We compared PRS 313 derived from genotyping arrays (Global Screening Array [GSA], OncoArray-500K [OncoArray], Global Diversity Array [GDA], custom Axiom_PrecipV1 array [ThermoFisher]) and low-coverage genome sequencing (lc-WGS) in 2 cell lines and 92 individuals. Probes were designed for all variants on ThermoFisher (success rate: 259/313). Sanger sequencing was performed to profile indels. Concordance of high-risk classification (PRSscore>0.6) across platforms was assessed using Kappa statistics.

Results

PRS 313-lc-WGS was identical in the 4 cell line repeats. In saliva samples, indel concordance with Sanger sequencing varied widely (Kappa: 0.007–1.000). PRS313-ThermoFisher was predictable from other platforms using linear models, despite systematic differences. Greater agreement was observed between arrays with high imputation overlap (e.g., GDA∼GSA slope=0.986). Pre-calibration agreement in high-risk classification was moderate (Fleiss Kappa=0.552) and improved post-calibration (Kappa=0.650). Arrays with similar designs showed higher pre-calibration agreement (Kappa=0.745). Calibration narrowed high-risk proportions from 4–45% to 15–21% −28% were high-risk by any platform, while 8% were high-risk across all five.

Conclusion

Platform-specific biases affect PRS interpretation. Calibration enhances consistency in identifying high-risk individuals.

STATEMENT OF SIGNIFICANCE

This study compares the performance of a validated 313-variant breast cancer polygenic risk score across platforms, revealing systematic biases in risk stratification and raising concerns about including inconsistent indels in the model.

Article activity feed

  1. Version published to 10.1101/2025.04.10.648141 on bioRxiv