Campus Units

Statistics

Document Type

Article

Publication Version

Accepted Manuscript

Publication Date

11-13-2018

Journal or Book Title

Journal of the American Statistical Association

Volume

114

Issue

526

First Page

610

Last Page

621

DOI

10.1080/01621459.2018.1497496

Abstract

Heterosis, or hybrid vigor, is the enhancement of the phenotype of hybrid progeny relative to their inbred parents. Heterosis is extensively used in agriculture, and the underlying mechanisms are unclear. To investigate the molecular basis of phenotypic heterosis, researchers search tens of thousands of genes for heterosis with respect to expression in the transcriptome. Difficulty arises in the assessment of heterosis due to composite null hypotheses and nonuniform distributions for p-values under these null hypotheses. Thus, we develop a general hierarchical model for count data and a fully Bayesian analysis in which an efficient parallelized Markov chain Monte Carlo algorithm ameliorates the computational burden. We use our method to detect gene expression heterosis in a two-hybrid plant-breeding scenario, both in a real RNA-seq maize dataset and in simulation studies. In the simulation studies, we show our method has well-calibrated posterior probabilities and credible intervals when the model assumed in analysis matches the model used to simulate the data. Although model misspecification can adversely affect calibration, the methodology is still able to accurately rank genes. Finally, we show that hyperparameter posteriors are extremely narrow and an empirical Bayes (eBayes) approach based on posterior means from the fully Bayesian analysis provides virtually equivalent posterior probabilities, credible intervals, and gene rankings relative to the fully Bayesian solution. This evidence of equivalence provides support for the use of eBayes procedures in RNA-seq data analysis if accurate hyperparameter estimates can be obtained. Supplementary materials for this article are available online.

Comments

This is a manuscript of an article published as Landau, Will, Jarad Niemi, and Dan Nettleton. "Fully Bayesian Analysis of RNA-seq Counts for the Detection of Gene Expression Heterosis." Journal of the American Statistical Association 114, no. 526 (2019): 610-621. doi: 10.1080/01621459.2018.1497496. Posted with permission.

Copyright Owner

American Statistical Association

Language

en

File Format

application/pdf

Available for download on Wednesday, November 13, 2019

Published Version

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