Fully Bayesian Analysis of RNA-seq Counts for the Detection of Gene Expression Heterosis

dc.contributor.author Nettleton, Dan
dc.contributor.author Landau, Will
dc.contributor.author Niemi, Jarad
dc.contributor.author Niemi, Jarad
dc.contributor.author Nettleton, Dan
dc.contributor.department Statistics
dc.date 2019-09-12T01:24:59.000
dc.date.accessioned 2020-07-02T06:57:26Z
dc.date.available 2020-07-02T06:57:26Z
dc.date.copyright Mon Jan 01 00:00:00 UTC 2018
dc.date.embargo 2019-11-13
dc.date.issued 2018-11-13
dc.description.abstract <p>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 <em>p</em>-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.</p>
dc.description.comments <p>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." <em>Journal of the American Statistical Association</em> 114, no. 526 (2019): 610-621. doi: <a href="https://doi.org/10.1080/01621459.2018.1497496">10.1080/01621459.2018.1497496</a>. Posted with permission.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/stat_las_pubs/247/
dc.identifier.articleid 1241
dc.identifier.contextkey 14906284
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath stat_las_pubs/247
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/90562
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/stat_las_pubs/247/2019_Nettleton_FullyBayesianManuscript.pdf|||Fri Jan 14 22:54:33 UTC 2022
dc.source.uri 10.1080/01621459.2018.1497496
dc.subject.disciplines Agriculture
dc.subject.disciplines Genetics
dc.subject.disciplines Statistical Methodology
dc.subject.disciplines Statistical Models
dc.subject.keywords CUDA
dc.subject.keywords Empirical Bayes
dc.subject.keywords Graphics processing unit
dc.subject.keywords Hierarchical model
dc.subject.keywords Hybrid vigor
dc.subject.keywords Negative-binomial
dc.title Fully Bayesian Analysis of RNA-seq Counts for the Detection of Gene Expression Heterosis
dc.type article
dc.type.genre article
dspace.entity.type Publication
relation.isAuthorOfPublication 7d86677d-f28f-4ab1-8cf7-70378992f75b
relation.isAuthorOfPublication 31b412ec-d498-4926-901e-2cb5c2b5a31d
relation.isOrgUnitOfPublication 264904d9-9e66-4169-8e11-034e537ddbca
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