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 phenotypes are increasingly studied in genome-wide association studies. Although numerous Cox-regression-based methods have been proposed for a large-scale genome-wide association study, case ascertainment in time-to-event phenotypes has not been well addressed. Here we propose a computationally efficient Cox-based method, named WtCoxG, that accounts for case ascertainment by fitting a weighted Cox proportional hazards 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 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 effectively controlling type I error rates. UK Biobank real data analysis validated that leveraging external minor allele frequencies contributes to the power gains of WtCoxG compared with ADuLT in the analysis of type 2 diabetes and coronary atherosclerosis.