Empirical Bayes Analysis of RNA-seq Data for Detection of Gene Expression Heterosis

  • Jarad Niemi
  • Eric Mittman
  • Will Landau
  • Dan Nettleton
Article

DOI: 10.1007/s13253-015-0230-5

Cite this article as:
Niemi, J., Mittman, E., Landau, W. et al. JABES (2015) 20: 614. doi:10.1007/s13253-015-0230-5

Abstract

An important type of heterosis, known as hybrid vigor, refers to the enhancements in the phenotype of hybrid progeny relative to their inbred parents. Although hybrid vigor is extensively utilized in agriculture, its molecular basis is still largely unknown. In an effort to understand phenotypic heterosis at the molecular level, researchers are measuring transcript abundance levels of thousands of genes in parental inbred lines and their hybrid offspring using RNA sequencing (RNA-seq) technology. The resulting data allow researchers to search for evidence of gene expression heterosis as one potential molecular mechanism underlying heterosis of agriculturally important traits. The null hypotheses of greatest interest in testing for gene expression heterosis are composite null hypotheses that are difficult to test with standard statistical approaches for RNA-seq analysis. To address these shortcomings, we develop a hierarchical negative binomial model and draw inferences using a computationally tractable empirical Bayes approach to inference. We demonstrate improvements over alternative methods via a simulation study based on a maize experiment and then analyze that maize experiment with our newly proposed methodology.

Supplementary materials accompanying this paper appear on-line.

Keywords

Hierarchical model Negative binomial RNA-seq Bayesian LASSO  Parallel computing Hybrid vigor 

Supplementary material

13253_2015_230_MOESM1_ESM.pdf (55 kb)
Supplementary material 1 (pdf 55 KB)

Funding information

Funder NameGrant NumberFunding Note
National Institute of General Medical Sciences
  • R01GM109458

Copyright information

© International Biometric Society 2015

Authors and Affiliations

  1. 1.Department of StatisticsIowa State UniversityAmesUSA

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