A Fast and Accurate Method for Genome-Wide Time-to-Event Data Analysis and Its Application to UK Biobank

• Activity Number: 43 - Statistical Genetics II – New Models for Complex Study Designs
• Type: Contributed
• Date/Time: Monday, August 3, 2020 : 10:00 AM to 2:00 PM
• Sponsor: Section on Statistics in Genomics and Genetics
• Author(s): Wenjian Bi* and Lars G Fritsche and Bhramar Mukherjee and Sehee Kim and Seunggeun Lee
• Companies: University of Michigan and University of Michigan and University of Michigan and University of Michigan and University of Michigan
• Keywords: time-to-event data; Cox proportional hazard model; survival analysis; gwas; saddlepoint approximation; phewas

Abstract

With increasing biobanking efforts connecting electronic health records and national registries to germline genetics, time-to-event phenotype has attracted increasing attention in the genetics studies of human diseases. However, existing methods and tools are not scalable when analyzing a large biobank with hundreds of thousands of samples and endpoints, and are not accurate when testing low-frequency and rare variants. Here we propose a scalable and accurate method, SPACox (SaddlePoint Approximation implementation based on Cox PH regression model), that is applicable for genome-wide scale time-to-event data analysis. SPACox requires fitting a Cox PH regression model only once across the genome-wide analysis and then uses a saddlepoint approximation (SPA) to calibrate the test statistics. Simulation studies show that SPACox is 76-252 times faster than other existing alternatives like gwasurvivr and can control type I error rates. Through the analysis of UK-Biobank inpatient data of 282,641 white British European-ancestry samples, we show that SPACox can efficiently analyze large sample size and identified 624 loci associated with time-to-event phenotypes of 12 common diseases.