Comparing Pathway-Informed Polygenic Risk Score Strategies: A multi-cohort evaluation of Amyloid-β
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.Abstract
Objective
To systematically evaluate pathway-informed polygenic risk score (PRS) strategies and determine which approaches most effectively leverage biological annotations for risk prediction, using brain amyloid-β (Aβ) positivity as a case study.
Methods
We systematically benchmarked approaches for integrating pathway information into PRSs construction to predict brain Aβ positivity. Using two cohorts, the Alzheimer’s Disease Neuroimaging Initiative (ADNI, n = 969) and Australian Imaging, Biomarkers and Lifestyle (AIBL, n = 251), we compared Apolipoprotein E ( APOE ) genetic risk score (GRS), clumping and thresholding (C+T) PRS, pathway-guided single nucleotide polymorphism (SNP) selection PRS, and pathway-specific PRSs ensembled via machine learning. Pathways were derived from manually curated literature or from pathway databases via Functional Mapping and Annotation (FUMA).
Results
In cross-validation on the ADNI cohort, pathway-informed PRS using a narrow-set of pathways to guide SNP selection (PathPRS-SNP Lit without APOE locus) significantly outperformed the standard PRS model (median AUC = 0.742, p = 0.006) and the APOE locus model (median AUC = 0.736, p = 5.1 × 10 -5 ) based on the Mann–Whitney U test, achieving a median AUC of 0.763. This model showed enhanced ability to identify subgroups within the 10% lowest-and highest risk groups compared to the current standard of APOE locus alone (odds ratio = 0.67, 95% CI: 0.56–0.81; and OR = 13.23, 95% CI: 10.23–17.11), highlighting its clinical potential. Using a focused set l iterature-curated pathways outperformed using a broader set of database-derived pathways across configurations. When contrasting strategies for aggregating information across pathways, we observed that using pathways to guide selection of SNPs and then building a single PRS performed comparably to building PRS for each pathway and using machine learning (ML) to aggregate these, though the latter enabled pathway-level interpretability. S imilar trends were observed in the external AIBL validation dataset
Interpretation
Pathway-informed PRS can meaningfully improve genetic risk enrichment for Aβ positivity beyond APOE and standard C+T approaches, provided pathway definitions are carefully curated. The choice of pathway source has the strongest impact on predictive performance with aggregation strategies or ML model choice having far less impact. Our findings highlight the utility of literature-curated, pathway-informed PRSs for Aβ prediction and offer practical guidance for pathway-informed PRS construction in other polygenic traits.
