Abstract Details
Activity Number:
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194
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Type:
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Contributed
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Date/Time:
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Monday, August 10, 2015 : 10:30 AM to 12:20 PM
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Sponsor:
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ENAR
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Abstract #316028
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Title:
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Hierarchical Modeling and Differential Expression Analysis for RNA-Seq Experiments with Inbred and Hybrid Genotypes
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Author(s):
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Andrew Lithio* and Dan Nettleton
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Companies:
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Iowa State University and Iowa State University
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Keywords:
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empirical Bayes ;
split-plot ;
genetics ;
Integrated Nested Laplace Approximation ;
ShrinkBayes ;
INLA
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Abstract:
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The performance of inbred and hybrid plant genotypes is of interest in plant breeding and genetics. High-throughput sequencing of RNA (RNA-seq) has proven to be a useful tool in the study of the molecular genetic responses of inbreds and hybrids to environmental stresses. Commonly used experimental designs and sequencing methods lead to complex data structures that require careful attention in data analysis. We demonstrate an analysis of RNA-seq data from a split-plot design involving drought stress applied to two inbred genotypes and two hybrids formed by crosses between the inbreds. Our modeling strategy incorporates random effects for whole-plot and split-plot experimental units and uses sample-specific normalization to account for variation in gene length and base content, as well as differences in sequencing intensity across experimental units. Thoughtful parameterization that accounts for the natural relationships among genotypes and hierarchical modeling allow for borrowing of information across genes to improve estimation of dispersion parameters, genotype effects, treatment effects, and interaction effects of primary interest.
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Authors who are presenting talks have a * after their name.
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