Set-Based Genetic Association and Interaction Tests for Survival Outcomes Based on Weighted V Statistics

• 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): Chenxi Li* and Di Wu and Qing Lu
• Companies: Michigan State University and Michigan State University and University of Florida
• Keywords: Multi-variant test; Survival outcome; Weighted V statistic; Kernel function; Genetic heterogeneity

Abstract

Multi-variant tests with time-to-event outcomes are more powerful than single-variant tests with case-control outcomes to discover genetic associations and interactions on complex diseases. We develop a suite of novel multi-variant association and interaction tests with survival traits based on weighted V statistics, with one of them considering potential genetic heterogeneity. All the new tests can adjust for covariates to reduce confounding and/or improve power and can deal with left truncation and competing risks in the survival data. Simulation studies show that the new tests are faster, more accurate in small samples, and more robust against confounding than the existing multi-variant survival tests, and that when the genetic effect is heterogeneous across individuals/subpopulations, the association test considering genetic heterogeneity is more powerful than the existing tests, which do not account for genetic heterogeneity. We illustrate the utility of the new methods through a genome-wide association study of age to Alzheimer’s disease onset.