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

TL;DR

This paper introduces a novel data-adaptive gene and pathway-based testing method for rare-variant associations with survival outcomes, improving power and flexibility over existing approaches.

Contribution

The authors develop a new survival-based association test using Schoenfeld residuals, with an accompanying R package for efficient computation and data simulation.

Findings

Identified biologically relevant genes and pathways in prostate cancer radiotherapy data.

Replicated known genetic signals associated with bladder toxicity.

Captured additional novel associations beyond previous methods.

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