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Bioinformatics. 2015 Aug 1;31(15):2497-504. doi: 10.1093/bioinformatics/btv074. Epub 2015 Mar 27.

Assessing allele-specific expression across multiple tissues from RNA-seq read data.

Author information

1
Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland.
2
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.
3
Department of Medicine, University of California, San Francisco, CA, USA.
4
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.
5
Wellcome Trust Centre for Human Genetics and Department of Statistics, University of Oxford, Oxford, UK and.
6
Wellcome Trust Centre for Human Genetics and Oxford Centre for Diabetes, Endocrinology and Metabolism, Oxford, UK.
7
Wellcome Trust Centre for Human Genetics and.

Abstract

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.

PMID:
25819081
PMCID:
PMC4514921
DOI:
10.1093/bioinformatics/btv074
[Indexed for MEDLINE]
Free PMC Article

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