Bivariate Copula Modeling of Genetic Effects on AMD Progression with Intermittent Assessment Times

• Activity Number: 418 - Contributed Poster Presentations: Section on Statistics in Genomics and Genetics
• Type: Contributed
• Date/Time: Tuesday, August 1, 2017 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics in Genomics and Genetics
• Author(s): Tao Sun* and Yi Liu and Wei Chen and Ying Ding
• Companies: University of Pittsburgh and and University of Pittsburgh and University of Pittsburgh
• Keywords: Bivariate time-to-event ; Interval censoring ; Copula model ; GWAS ; Score test

Abstract

Age-related macular degeneration (AMD) is the major cause of blindness for the elderly populations in the developed countries. It is a polygenic and progressive neurodegenerative disease. Despite remarkable successes in discovering genetic variants associated with AMD risk, the genetic causes on its progression have not been elucidated. Using GWAS data from a large randomized trial, Age-Related Eye Disease Study (AREDS), where the participants were assessed every 6-12 months for up to 12 years, our research aims to evaluate the effects of genetic variants on the disease progression. In doing so, we derive the time intervals for progression-to-late-AMD in both eyes based on their assessment times and develop a computationally efficient copula-based score test, which explicitly models the between-eye correlation with interval censored time-to-event data. After identifying top genetic variants associated with AMD progression, we establish copula-based prediction models to predict the joint progression-free probability of two eyes within a subject.