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

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. This article has supplementary material online.

Bayesian Computing with INLA: A Review

“…Recent examples of applications using the R-INLA package for statistical analysis, include disease mapping (Schrödle and Held, 2011b,a;Ugarte et al, 2014Ugarte et al, , 2016Papoila et al, 2014;Goicoa et al, 2016;, age-period-cohort models (Riebler and Held, 2016), evolution of the Ebola virus (Santermans et al, 2016), studies of relationship between access to housing, health and well-being in cities (Kandt et al, 2016), study of the prevalence and correlates of intimate partner violence against men in Africa (Tsiko, 2015), search for evidence of gene expression heterosis (Niemi et al, 2015), analysis of traffic pollution and hospital admissions in London (Halonen et al, 2016), early transcriptome changes in maize primary root tissues in response to moderate water deficit conditions by RNA-Sequencing (Opitz et al, 2016), performance of inbred and hybrid genotypes in plant breeding and genetics (Lithio and Nettleton, 2015), a study of Norwegian emergency wards (Goth et al, 2014), effects of measurement errors (Kröger et al, 2016;Muff and Keller, 2015), network meta-analysis (Sauter and Held, 2015), time-series analysis of genotyped human campylobacteriosis cases from the Manawatu region of New Zealand (Friedrich et al, 2016), modeling of parrotfish habitats (Roos et al, 2015b), Bayesian outbreak detection (Salmon et al, 2015), studies of long-term trends in the number of Monarch butterflies (Crewe and Mccracken, 2015), long-term effects on hospital admission and mortality of road traffic noise (Halonen et al, 2015), spatio-temporal dynamics of brain tumours (Iulian et al, 2015), ovarian cancer mortality (García-Pérez et al, 2015), the effect of preferential sampling on phylodynamic inference (Karcher et al, 2016), analysis of the impact of climate change on abundance trends in central Europe (Bowler et al, 2015), investigation of drinking patterns in US Counties from 2002 to 2012 …”

High-dimensional hierarchical models and massively parallel computing

“…However, fitting them is computationally demanding because of the high number of genes and low number of observations per gene. Many approaches ease the computation with empirical Bayes methods, where the hyperparameters φ are set constant at values calculated from the data that approximate the respective target densities before the MCMC begins (Hardcastle (2012); Ji et al (2014); Niemi et al (2015)). However, empirical Bayes approaches ignore uncertainty in the hyperparameters, so a fully Bayesian solution may be preferred.…”

Challenges Analyzing RNA-Seq Gene Expression Data

“…One year later, six methods where compared by [20]: DESeq, DEGseq, edgeR, NBPSeq, TSPM and baySeq using both real and simulated data with the result that all six methods produce similar fold changes and reasonable overlapping of differentially expressed genes based on p-value, edgeR being little bit superior. However, all six methods suffer from over-sensitivity as reported by the authors.…”