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.
Copyright Owner
American Statistical Association
Copyright Date
2018
Language
en
File Format
application/pdf
Recommended Citation
Landau, Will; Niemi, Jarad; and Nettleton, Dan, "Fully Bayesian Analysis of RNA-seq Counts for the Detection of Gene Expression Heterosis" (2018). Statistics Publications. 247.
https://lib.dr.iastate.edu/stat_las_pubs/247
Included in
Agriculture Commons, Genetics Commons, Statistical Methodology Commons, Statistical Models Commons
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.