A powerful and flexible statistical framework for testing hypotheses of allele-specific gene expression from RNA-seq data

Variation in gene expression is thought to make a significant contribution to phenotypic diversity among individuals within populations. Although high-throughput cDNA sequencing offers a unique opportunity to delineate the genome-wide architecture of regulatory variation, new statistical methods nee...

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Bibliographic Details
Published inGenome research Vol. 21; no. 10; pp. 1728 - 1737
Main Authors Skelly, Daniel A., Johansson, Marnie, Madeoy, Jennifer, Wakefield, Jon, Akey, Joshua M.
Format Journal Article
LanguageEnglish
Published United States Cold Spring Harbor Laboratory Press 01.10.2011
Subjects
Alleles
Alternative Splicing
Bayes Theorem
Gene Expression
Humans
Markov Chains
Method
Models, Genetic
Monte Carlo Method
Polymorphism, Single Nucleotide
ROC Curve
Saccharomyces cerevisiae
Saccharomyces cerevisiae - genetics
Saccharomyces cerevisiae Proteins - genetics
Sequence Analysis, RNA
Transcription, Genetic
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Summary:Variation in gene expression is thought to make a significant contribution to phenotypic diversity among individuals within populations. Although high-throughput cDNA sequencing offers a unique opportunity to delineate the genome-wide architecture of regulatory variation, new statistical methods need to be developed to capitalize on the wealth of information contained in RNA-seq data sets. To this end, we developed a powerful and flexible hierarchical Bayesian model that combines information across loci to allow both global and locus-specific inferences about allele-specific expression (ASE). We applied our methodology to a large RNA-seq data set obtained in a diploid hybrid of two diverse Saccharomyces cerevisiae strains, as well as to RNA-seq data from an individual human genome. Our statistical framework accurately quantifies levels of ASE with specified false-discovery rates, achieving high reproducibility between independent sequencing platforms. We pinpoint loci that show unusual and biologically interesting patterns of ASE, including allele-specific alternative splicing and transcription termination sites. Our methodology provides a rigorous, quantitative, and high-resolution tool for profiling ASE across whole genomes.
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ISSN:1088-9051
1549-5469
1549-5469
DOI:10.1101/gr.119784.110