Format

Send to

Choose Destination
Am J Hum Genet. 2016 Apr 7;98(4):697-708. doi: 10.1016/j.ajhg.2016.02.020. Epub 2016 Mar 31.

Imputing Gene Expression in Uncollected Tissues Within and Beyond GTEx.

Author information

1
Department of Public Health Sciences, University of Chicago, Chicago, IL 60637, USA.
2
Division of Genetic Medicine, Department of Medicine, Vanderbilt University and Vanderbilt Genetics Institute, Nashville, TN 37232, USA; Academic Medical Center, University of Amsterdam, Amsterdam 1105 AZ, the Netherlands.
3
Section of Genetic Medicine, University of Chicago, Chicago, IL 60637, USA; Institute for Genomics and Systems Biology, University of Chicago, Chicago, IL 60637, USA.
4
Section of Genetic Medicine, University of Chicago, Chicago, IL 60637, USA.
5
Division of Genetic Medicine, Department of Medicine, Vanderbilt University and Vanderbilt Genetics Institute, Nashville, TN 37232, USA.
6
Section of Genetic Medicine, University of Chicago, Chicago, IL 60637, USA; Department of Statistics, University of Chicago, Chicago, IL 60637, USA.
7
Department of Public Health Sciences, University of Chicago, Chicago, IL 60637, USA. Electronic address: lchen@health.bsd.uchicago.edu.

Abstract

Gene expression and its regulation can vary substantially across tissue types. In order to generate knowledge about gene expression in human tissues, the Genotype-Tissue Expression (GTEx) program has collected transcriptome data in a wide variety of tissue types from post-mortem donors. However, many tissue types are difficult to access and are not collected in every GTEx individual. Furthermore, in non-GTEx studies, the accessibility of certain tissue types greatly limits the feasibility and scale of studies of multi-tissue expression. In this work, we developed multi-tissue imputation methods to impute gene expression in uncollected or inaccessible tissues. Via simulation studies, we showed that the proposed methods outperform existing imputation methods in multi-tissue expression imputation and that incorporating imputed expression data can improve power to detect phenotype-expression correlations. By analyzing data from nine selected tissue types in the GTEx pilot project, we demonstrated that harnessing expression quantitative trait loci (eQTLs) and tissue-tissue expression-level correlations can aid imputation of transcriptome data from uncollected GTEx tissues. More importantly, we showed that by using GTEx data as a reference, one can impute expression levels in inaccessible tissues in non-GTEx expression studies.

KEYWORDS:

GTEx; eQTL; multi-tissue imputation; tissue-tissue expression-level correlation; transcriptome

PMID:
27040689
PMCID:
PMC4833292
DOI:
10.1016/j.ajhg.2016.02.020
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Elsevier Science Icon for PubMed Central
Loading ...
Support Center