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Activity Number: 322
Type: Contributed
Date/Time: Tuesday, August 2, 2016 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract #320960 View Presentation
Title: Allele-Specific RNA Expression Analysis Using Bayesian Hierarchical Models
Author(s): Ignacio Alvarez* and Jarad Niemi and Dan Nettleton
Companies: Iowa State University and Iowa State University and Iowa State University
Keywords: RNAseq ; Bayesian modelling ; Allele-specific expression

RNA-sequencing (RNAseq) has been increasingly used as the method to measure gene expression. Compared to microarray, RNAseq has more information to detect allele-specific expression (ASE). We present statistical methods for modelling ASE and detecting genes where differential allele expression. We propose a Bayesian hierarchical model for binomial counts, which includes: shrinkage priors to handle sparsity and correction of the bias towards the reference genotype.

In plant breeding, hybrids are developed to take advantage of the genetic phenomenon known as heterosis. Heterosis occurs when hybrid offspring possess superior levels of some traits relative to their inbred parents. Recent genomic studies suggest phenotypic heterosis may be explained by heterosis in the expression levels of key genes. Genes where two distinct alleles at a heterozygous locus are differentially expressed might explain the heterosis. Our proposed Bayesian inference is expected to be a practical and powerful tool for the study of differential allele usage. The uneven expression of alleles might be related to the increased ability of adaptation of hybrids, leading to the occurrence of heterosis.

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

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