A Three-Groups Bayesian Approach for Combining Information from RNA-Seq and GWAS Data with Applications to Parkinson's Disease

• Activity Number: 328 - Statistical Methods for Multi-Omics Data Integration
• Type: Contributed
• Date/Time: Wednesday, August 5, 2020 : 10:00 AM to 2:00 PM
• Sponsor: Section on Statistics in Genomics and Genetics
• Author(s): Daisy Philtron* and Benjamin A Shaby and Vivian Cheng
• Companies: Pennsylvania State University and Colorado State University and Pennsylvania State University
• Keywords: Hierarchical; Bayesian; GWAS; RNA-sequencing

Abstract

We develop, test, and apply a Bayesian hierarchical model that classifies genetic modifiers of disease as either null, deleterious, or beneficial. The model allows for the combination of information from RNA-sequencing studies with information from GWAS studies. We discuss the larger three-groups framework which provides a flexible approach to incorporating disparate data types, with the goal of sharing information for the same genetic targets across multiple data types to offer an improvement in discrimination relative to performing multiple parallel analyses. We include the results from simulation studies and from an application of the three-groups framework to Parkinson’s disease.