The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans

By Kristin G. Ardlie, David S. DeLuca, A. Segrè, Timothy J. Sullivan, Taylor Young, Ellen T. Gelfand, Casandra A. Trowbridge, J. Maller, T. Tukiainen, M. Lek and 123 more. 2015

4,590 citations recorded by the source corpus.

Expression, genetic variation, and tissues Human genomes show extensive genetic variation across individuals, but we have only just started documenting the effects of this variation on the regulation of gene expression. Furthermore, only a few tissues have been examined per genetic variant. In order to examine how genetic expression varies among tissues within individuals, the Genotype-Tissue Expression (GTEx) Consortium collected 1641 postmortem samples covering 54 body sites from 175 individuals. They identified quantitative genetic traits that affect gene expression and determined which of these exhibit tissue-specific expression patterns. Melé et al. measured how transcription varies among tissues, and Rivas et al. looked at how truncated protein variants affect expression across tissues. Science, this issue p. 648, p. 660, p. 666; see also p. 640 Sampling RNA from multiple tissues across the human body illuminates how genetic variation affects gene expression. [Also see Perspective by Gibson] Understanding the functional consequences of genetic variation, and how it affects complex human disease and quantitative traits, remains a critical challenge for biomedicine. We present an analysis of RNA sequencing data from 1641 samples across 43 tissues from 175 individuals, generated as part of the pilot phase of the Genotype-Tissue Expression (GTEx) project. We describe the landscape of gene expression across tissues, catalog thousands of tissue-specific and shared regulatory expression quantitative trait loci (eQTL) variants, describe complex network relationships, and identify signals from genome-wide association studies explained by eQTLs. These findings provide a systematic understanding of the cellular and biological consequences of human genetic variation and of the heterogeneity of such effects among a diverse set of human tissues.

Subfields: Genetics, Molecular Biology

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Citations in the corpus

Gene expression inference with deep learning (2015, methodology): “The complete performances of D-GEX with other dropout rates on both GEO-va and GEO-te are given in Supplementary Table S2 and S3.”
Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types (2017, methodology): “We analyzed the same 48 diseases and traits using stratified LD score regression7 in conjunction with chromatin data from the Roadmap Epigenomics and ENCODE projects1,2 (see URLs) instead of gene expression data, with three goals: (1) to validate the results from our analysis of gene expression data”