Bayesian variable selection in a Cox proportional hazards model with the "Sum of Single Effects" prior

TL;DR

This paper introduces CoxPH-SuSiE, a Bayesian variable selection method tailored for time-to-event data with high correlations and large datasets, improving genetic fine-mapping accuracy.

Contribution

It extends the SuSiE method to the Cox proportional hazards model, addressing challenges in genetic fine-mapping with correlated covariates and large-scale data.

Findings

CoxPH-SuSiE outperforms existing methods in simulated data.

Successfully fine-mapped asthma loci in UK Biobank data.

Identified 14 asthma risk SNPs, with 6 likely causal.

Abstract

Motivated by genetic fine-mapping applications, we introduce a new approach to Bayesian variable selection regression (BVSR) for time-to-event (TTE) outcomes. This new approach is designed to deal with the specific challenges that arise in genetic fine-mapping, including: the presence of very strong correlations among the covariates, often exceeding 0.99; very large data sets containing potentially thousands of covariates and hundreds of thousands of samples. We accomplish this by extending the "Sum of Single Effects" (SuSiE) method to the Cox proportional hazards (CoxPH) model. We demonstrate the benefits of the new method, "CoxPH-SuSiE", over existing BVSR methods for TTE outcomes in simulated fine-mapping data sets. We also illustrate CoxPH-SuSiE on real data by fine-mapping asthma loci using data from UK Biobank. This fine-mapping identified 14 asthma risk SNPs in 8 asthma risk loci, among which 6 had strong evidence for being causal (posterior inclusion probability greater than 50%). Two of the 6 putatively causal variants are known to be pathogenic, and others lie within a genomic sequence that is known to regulate the expression of GATA3.