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Activity Number: 415 - Statistical Methods for Gene Expression and RNA-Seq Analysis
Type: Contributed
Date/Time: Tuesday, July 30, 2019 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #303048
Title: Genome-Wide Detection of Allele-Specific Gene Expression by a Bayesian Logistic Regression Model
Author(s): Tieming Ji* and Jing Xie and Marco Ferreira
Companies: University of Missouri At Columbia and University of Missouri at Columbia and Virginia Tech
Keywords: Allelic imbalance; hierarchical generalized linear mixed model; high-throughput sequencing experiments; single nucleotide polymorphism

High-throughput sequencing experiments, which can determine allele origins, have been used to assess genome-wide allele-specific expression. Despite the amount of data generated from high-throughput experiments, statistical methods are often too simplistic to understand the complexity of gene expression. Existing methods do not test allele-specific expression of a gene as a whole and variation in allele-specific expression within a gene across exons separately and simultaneously. We propose a generalized linear mixed model to close these gaps, incorporating variations due to genes, SNPs and biological replicates. To improve reliability of inferences, we assign priors on each effect in the model. We utilize the Bayes factor to test the hypothesis of allele-specific gene expression for each gene and variations across SNPs within a gene. We apply our method to four tissue types from large offspring syndrome, and uncover intriguing predictions of regulatory allele-specific expression across exons and across tissue types. Simulation studies indicate the proposed method exhibits improved control of the false discovery rate and improved power over existing methods.

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

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