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Cox-MK: a model-X knockoff framework for genome-wide survival association analysis.
Lin Hu1, Chen Yang1, Zihuan Tang1
1Department of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China.
Genetics
|May 13, 2026
Summary
We developed Cox-MK, a new genome-wide survival analysis tool. It improves the discovery of genetic variants for complex survival traits like asthma and heart disease by controlling false discoveries.
Area of Science:
- Genetics
- Bioinformatics
- Statistical Genetics
Background:
- Genome-wide survival association studies (GWAS) face challenges with large-scale testing and data censoring.
- Time-to-event (TTE) phenotypes are underutilized in GWAS due to statistical complexities.
Purpose of the Study:
- To introduce Cox-MK, a novel framework for genome-wide survival analysis.
- To enable SNP-level false discovery rate (FDR) control in large-scale biobank studies.
Main Methods:
- Integration of knockoff statistics with the saddlepoint approximation (SPA).
- Application to common and rare variants in UK Biobank data.
Main Results:
- Cox-MK demonstrates higher statistical power and calibrated FDR control compared to existing methods.
- Identified additional candidate genes for asthma and ischemic heart disease, including CD247 and EDNRA.
- Detected 47 additional SNPs for asthma and 16 for ischemic heart disease.
Conclusions:
- Cox-MK is an effective tool for prioritizing causal variants in complex survival phenotypes.
- The method enhances the utility of TTE phenotypes in GWAS.
- Provides robust FDR control for biobank-scale genetic association studies.
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