Multiple polygenic score approach in colorectal cancer risk prediction

TL;DR

This study shows that combining multiple polygenic scores improves colorectal cancer risk prediction beyond traditional methods.

Contribution

The novel use of multiple polygenic scores from non-colorectal cancer traits enhances risk prediction accuracy.

Findings

Adding multiple polygenic scores improved AUC by 0.017 when combined with known-loci CRC-PRS.

MPS significantly enhanced CRC risk prediction performance in multiple model configurations.

337 non-CRC PRSs were identified as predictive of CRC risk using machine learning.

Abstract

Recent studies have demonstrated that for various diseases, incorporating polygenic risk scores (PRSs) for other traits and diseases into the PRS-based risk prediction model may improve predictive performance – known as Multiple Polygenic Score (MPS) approach. We aimed to examine whether the MPS approach improves colorectal cancer (CRC) risk prediction. We included 2,187 non-CRC PRSs from the polygenic Score (PGS) Catalog and used machine learning (ML) models to select the most predictive non-CRC PRSs, utilizing individual-level data from 31,257 CRC cases and 33,408 controls. An independent dataset from the Genetic Epidemiology Research in Adult Health and Aging (GERA) cohort (4,852 cases and 67,939 controls) was randomly split into subsets for model estimation and validation. The model combined MPS with two existing CRC-PRSs based on known loci and genome-wide genotyping. We then assessed model performance by calculating the area under the receiver operating curve (AUC) in the validation set and performed 1,000 bootstrapped iterations to evaluate AUC improvements. The ML model selected 337 non-CRC PRSs predictive of CRC risk. Adding MPS to the CRC-PRSs significantly improved AUC by 0.017 (95% CI: 0.011–0.022, p < 0.0001) when combined with known-loci CRC-PRS, 0.005 (95% CI: 0.002–0.007, p = 0.0005) with genome-wide CRC-PRS, and 0.004 (95% CI: 0.002–0.006, p = 0.0005) with both the known loci and genome-wide CRC-PRSs. These findings demonstrate MPS’s potential to refine CRC risk prediction models and highlight opportunities for further advancements in risk prediction.

The online version contains supplementary material available at 10.1038/s41598-025-21956-w.

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