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Activity Number: 577 - Semiparametric Modeling in Biometric Data
Type: Contributed
Date/Time: Wednesday, August 2, 2017 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract #323427
Title: A Semi-parametric Bayesian Approach for Detection of Gene Expression Heterosis with RNA-Seq Data
Author(s): Ran Bi* and Peng Liu
Companies: Iowa State University and Iowa State University
Keywords: RNA-Seq ; Bayesian nonparametric method ; Gene expression ; Heterosis ; Mixture model
Abstract:

Heterosis, also called hybrid vigor, refers to the improvements in the phenotype of hybrid offspring compared with its two inbred parents. Although heterosis phenomenon is widely applied in agriculture, the mechanism of heterosis is still unknown. In an effort to understand phenotypic heterosis at the molecular level, researchers compare expression levels of thousands of genes in parental inbred lines and their hybrid offspring using RNA-sequencing (RNA-seq) technology to seek evidence of gene expression heterosis. Standard existing statistical approaches for RNA-seq analysis are not directly applicable for testing heterosis. To address this issue, we develop a semi-parametric Bayesian approach to identify gene expression heterosis, with a Dirichlet process as the prior model for the distribution of fold changes between each inbred parent versus the hybrid offspring respectively. Simulation results demonstrate that our proposed method outperforms other methods used to detect gene expression heterosis.


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

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