Data-adaptive gene and pathway-based tests forrare-variant associations with survival outcomes

2026-04-02

AI Summary

Current methods for rare-variant association analysis exhibit limited statistical power when applied to survival outcomes and struggle to accommodate heterogeneous genetic effects. To address these limitations, this work proposes aSPU—a novel, data-adaptive aggregation testing framework tailored for survival data—built upon Schoenfeld residuals from the Cox model. The method flexibly integrates heterogeneity in both magnitude and direction of genetic effects at the gene and pathway levels. aSPU maintains high statistical power across diverse genetic architectures and is accompanied by an efficient R package enabling rapid computation and simulation. In an application to post-radiotherapy bladder toxicity in prostate cancer patients, the approach successfully replicated known genetic signals and identified novel biologically relevant genes and pathways.

Abstract

Statistical methods for testing aggregate rare-variant genetic associations are typically based on either burden or dispersion tests (or a combination of the two). These methods lack statistical power in the presence of diverse genetic architectures. Moreover, few aggregate rare-variant association methods have been developed specifically for survival data. To address these issues, we propose data-adaptive gene- and pathway-based association tests based on Schoenfeld residuals in Cox proportional hazards models for association studies between an aggregate of rare-variants and survival outcomes. Our methods improve statistical power while maintaining flexibility across various genetic effect sizes and directions. We develop an efficient R package that enables fast computation and supports data simulation as well as gene- and pathway-level testing. Applying our approach to late bladder toxicity following radiotherapy for non-metastatic prostate cancer, we identify biologically relevant genes and pathways, replicate known signals, and capture additional associations. Our method provides a powerful, adaptive framework for survival-based genetic association studies of rare-variants. Keywords: aSPU, time-to-event outcomes, rare-variant associations, Cox regression, Schoenfeld residuals

Problem

• rare-variant associations
• survival outcomes
• genetic architectures
• aggregate association tests
• time-to-event outcomes

Innovation

• aSPU
• rare-variant associations
• Cox regression
• Schoenfeld residuals
• time-to-event outcomes