A0763
Title: Bayesian variable selection for interval-censored outcomes in Genome-wide association studies
Authors: Jaihee Choi - Marquette University (United States) [presenting]
Ryan Sun - MD Anderson Cancer Center (United States)
Abstract: With the growing popularity of genetic and health databases such as the UK Biobank, there is increased access to Genome-wide association studies (GWAS) with interval-censored time-to-event outcomes. Gene set-based association tests have proven to be successful in identifying genes or risk loci associated with outcomes of interest while maintaining sufficient statistical power. However, fine-mapping the specific SNP or SNPs within these gene sets associated with the disease can lead to a better understanding of the genetic etiology of the disease. Though using interval-censored time-to-event outcomes can provide more information about the genetic pathology behind disease more than the binary or right-censored representation of the data, there currently are not many methods that work with interval-censored outcomes. A Bayesian framework is investigated for fine-mapping individual genetic variants associated with interval-censored data. This framework is applied to colorectal cancer data from the UK Biobank.
