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

Daniel A. Skelly, Marnie Johansson, Jennifer Madeoy, Jon Wakefield, Joshua M. Akey

Research output: Contribution to journalArticle

111 Scopus citations

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 genomewide 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.

Original languageEnglish (US)
Pages (from-to)1728-1737
Number of pages10
JournalGenome Research
Volume21
Issue number10
DOIs
StatePublished - Oct 2011

All Science Journal Classification (ASJC) codes

  • Genetics
  • Genetics(clinical)

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