A Three-Groups Bayesian Approach to Identifying Beneficial and Deleterious Genes with Applications to Parkinson's Disease

• Activity Number: 626 - Bayesian Methods in Genetics and Genomics
• Type: Contributed
• Date/Time: Thursday, August 3, 2017 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistics in Genomics and Genetics
• Author(s): Daisy Philtron* and Benjamin A Shaby
• Companies: The Pennsylvania State University and Penn State University
• Keywords: Hierarchical modeling ; Data integration ; Whole Genome Sequencing ; Likelihood-based

Abstract

Parkinson's disease is a neurodegenerative disease with wide variation in age of onset for symptoms. In this work we use whole-genome sequencing data from affected families to identify genetic variants associated with early or late onset of symptoms. Variants associated with early onset are considered deleterious modifiers, while variants associated with late onset are considered beneficial. We use a Bayesian three-groups approach that combines association, penetrance, and linkage approaches in a single coherent hierarchical model. A strength of the model is the flexibility to incorporate additional data types such as cell survival or transcriptomics data. In this paper we will discuss the motivation, the model, and results from the analysis of 23 families with Parkinson's disease.