Imputing Gene Expression in Uncollected Tissues Within and beyond GTEx

Jiebiao Wang, Eric R. Gamazon, Brandon L. Pierce, Barbara Elaine Stranger, Hae Kyung Im, Robert D. Gibbons, Nancy J. Cox, Dan L. Nicolae, Lin S. Chen*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

22 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)697-708
Number of pages12
JournalAmerican journal of human genetics
Issue number4
StatePublished - Apr 7 2016


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

ASJC Scopus subject areas

  • Genetics
  • Genetics(clinical)


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