title: A powerful and flexible statistical framework for testing hypotheses of allele-specific gene expression from RNA-seq data.
abstract: 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.
authors.affiliation: Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA. akeyj@u.washington.edu
authors.forename: Daniel A
authors.initials: DA
authors.lastname: Skelly
authors.forename: Marnie
authors.initials: M
authors.lastname: Johansson
authors.forename: Jennifer
authors.initials: J
authors.lastname: Madeoy
authors.forename: Jon
authors.initials: J
authors.lastname: Wakefield
authors.forename: Joshua M
authors.initials: JM
authors.lastname: Akey
mesh_terms: D000483:Alleles; D017398:Alternative Splicing; D001499:Bayes Theorem; D015870:Gene Expression; D006801:Humans; D008390:Markov Chains; D008957:Models, Genetic; D009010:Monte Carlo Method; D020641:Polymorphism, Single Nucleotide; D012372:ROC Curve; D012441:Saccharomyces cerevisiae / Q000235:genetics; D029701:Saccharomyces cerevisiae Proteins / Q000235:genetics; D017423:Sequence Analysis, RNA; D014158:Transcription, Genetic
chemical_list: D029701:Saccharomyces cerevisiae Proteins