Journal
BIOINFORMATICS
Volume 31, Issue 15, Pages 2497-2504Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btv074
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Funding
- Academy of Finland [257654]
- Wellcome Trust [095552/Z/11/Z, 098381, 090532]
- National Institutes of Health [R01-MH101814, MH-090941]
- Royal Society Wolfson Merit Award
- Clarendon Scholarship from the University of Oxford
- NDM Studentship from the University of Oxford
- Green Templeton College Award from the University of Oxford
- Wellcome Trust [095552/Z/11/Z] Funding Source: Wellcome Trust
- Academy of Finland (AKA) [257654, 257654] Funding Source: Academy of Finland (AKA)
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Motivation: 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.
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