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Activity Number: 419 - Bayesian Computation and Spatial Modeling
Type: Contributed
Date/Time: Tuesday, July 31, 2018 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #329811 Presentation
Title: Fully Bayesian Analysis of Hierarchical Count Regression Models
Author(s): Jarad Niemi* and William Landau and Dan Nettleton
Companies: Iowa State University and Eli Lilly and Company and Iowa State University
Keywords: Markov chain Monte Carlo; graphics processing unit; RNA-seq; negative binomial; high performance computing; heterosis

Heterosis, or hybrid vigor, is the enhancement of the phenotype of hybrid progeny relative to their inbred parents. To identify genes displaying a heterosis pattern in their expression, we construct a gene-specific overdispersed count regression model. Since there are ~40,000 genes and ~10 samples, we build a hierarchical count regression model that provides a data-based borrowing of information across genes. To implement a fully Bayesian analysis, we construct a novel parallelized Markov chain Monte Carlo algorithm that efficiently utilizes the architecture of a graphical processing unit through embarrassingly parallel computations and parallel reductions. We demonstrate the utility of the method to identify gene expression heterosis through a variety of simulation studies and analyze an RNA-seq maize dataset to identify genes with 6 different types of heterosis.

Authors who are presenting talks have a * after their name.

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