title: Assessing allele-specific expression across multiple tissues from RNA-seq read data.
abstract: BACKGROUND
  RNA sequencing enables allele-specific expression (ASE) studies that complement standard genotype expression studies for common variants and, importantly, also allow measuring the regulatory impact of rare variants. The Genotype-Tissue Expression (GTEx) project is collecting RNA-seq data on multiple tissues of a same set of individuals and novel methods are required for the analysis of these data.
  
  
  RESULTS
  We present a statistical method to compare different patterns of ASE across tissues and to classify genetic variants according to their impact on the tissue-wide expression profile. We focus on strong ASE effects that we are expecting to see for protein-truncating variants, but our method can also be adjusted for other types of ASE effects. We illustrate the method with a real data example on a tissue-wide expression profile of a variant causal for lipoid proteinosis, and with a simulation study to assess our method more generally.
authors.affiliation: Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland.
authors.forename: Matti
authors.initials: M
authors.lastname: Pirinen
authors.affiliation: Department of Genetic Medicine and Development and, Institute for Genetics and Genomics in Geneva (iG3), University of Geneva, Geneva, Switzerland, Swiss Institute of Bioinformatics, Geneva, Switzerland, Department of Genetics, Stanford University, Palo Alto, CA, USA, New York Genome Center, New York, NY, USA, Department of Systems Biology, Columbia University, New York, NY, USA.
authors.forename: Tuuli
authors.initials: T
authors.lastname: Lappalainen
authors.affiliation: Department of Medicine, University of California, San Francisco, CA, USA.
authors.forename: Noah A
authors.initials: NA
authors.lastname: Zaitlen
authors.affiliation: Department of Genetic Medicine and Development and, Institute for Genetics and Genomics in Geneva (iG3), University of Geneva, Geneva, Switzerland, Swiss Institute of Bioinformatics, Geneva, Switzerland.
authors.forename: Emmanouil T
authors.initials: ET
authors.lastname: Dermitzakis
authors.affiliation: Wellcome Trust Centre for Human Genetics and Department of Statistics, University of Oxford, Oxford, UK and.
authors.forename: Peter
authors.initials: P
authors.lastname: Donnelly
authors.affiliation: Wellcome Trust Centre for Human Genetics and Oxford Centre for Diabetes, Endocrinology and Metabolism, Oxford, UK.
authors.forename: Mark I
authors.initials: MI
authors.lastname: McCarthy
authors.affiliation: Wellcome Trust Centre for Human Genetics and.
authors.forename: Manuel A
authors.initials: MA
authors.lastname: Rivas
mesh_terms: D000483:Alleles; D016326:Extracellular Matrix Proteins / Q000235:genetics / Q000378:metabolism*; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D006801:Humans; D008065:Lipoid Proteinosis of Urbach and Wiethe / Q000235:genetics / Q000378:metabolism*; D009928:Organ Specificity; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*; D020033:Protein Isoforms; D012313:RNA / Q000032:analysis* / Q000235:genetics
chemical_list: C490607:ECM1 protein, human; D016326:Extracellular Matrix Proteins; D020033:Protein Isoforms; D012313:RNA
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