Applying weighted Cox regression to genome-wide association studies of time-to-event phenotypes

With the growing availability of time-stamped electronic health records linked to genetic data in large biobanks and cohorts, time-to-event (TTE) phenotypes are increasingly studied in genome-wide association studies (GWAS). Although numerous Cox-regression-based methods have been proposed for a large-scale GWAS, case ascertainment in TTE phenotypes has not been well addressed. In this paper, we propose a computationally efficient Cox-based method, named WtCoxG, that accounts for case ascertainment by fitting a weighted Cox PH null model. A hybrid strategy incorporating saddlepoint approximation largely increases its accuracy when analyzing low-frequency and rare variants. Notably, by leveraging external minor allele frequencies (MAF) from public resources, WtCoxG further boosts statistical power. Extensive simulation studies demonstrated that WtCoxG is more powerful than ADuLT and other Cox-based methods, while well controlling type I error rates. UK Biobank real data analysis validated that leveraging external MAF contributes to power gains of WtCoxG compared to ADuLT when analyzing type 2 diabetes and coronary atherosclerosis.