We have long advocated the use of gene expression datasets for understanding the mechanisms underlying complex human disease, and we believe that the unique nature of data in the Genotype-Tissue Expression (GTEx) program will allow us to enhance our understanding of the functionality for a large class of variants, will lead to great insight into the genetic components of common diseases, and will permit the exploration of several novel scientific hypotheses on both transcriptome biology and the genetic architecture of human disease. The opportunity to analyze the GTEx datasets motivates the development of new statistical methods, software and knowledge databases that will facilitate the use of these results by the larger scientific community for additional investigations. All proposed research is informed by our near-complete immersion in studies relating genotype to phenotype (and in developing methods for relating genotype to phenotype) for many different complex traits. Thus, our specific aims are: 1) to discover cross-tissue and tissue-specific regulatory variation and to use the resulting information for discovery of novel risk pathways, and for partitioning of disease heritability into components corresponding to different classes of functional variants; 2) to test hypotheses that are finally feasible to investigate due to the uniqe design of the GTEx program, including the role of gender and other environmental exposures on regulatory variation, and the role of loss of function and missense variants in transcription; and 3) to use systems and network approaches for a better understanding of the organization of gene expression across tissues. All software and the databases developed through this project will be made publicly available immediately.

Public Health Relevance

Understanding the genetic architecture of complex human traits, including that of common diseases, can be greatly enhanced by a better understanding of the role of genetic variation in the inter- and intra-tissue expression variability. The unique structure of the Genotype-Tissue Expression (GTEx) data will allow us to explore novel scientific hypotheses, and motivates development of new statistical methods and knowledge databases. Our project will lead to a partitioning of disease variability into components corresponding to different classes of functional variants, will allow users to query results of transcriptome association studies in all GTEx tissues, and to use this information to discover novel genes and pathways for complex phenotypes.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Research Project (R01)
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Special Emphasis Panel (ZRG1-GGG-H (50))
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Addington, Anjene M
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University of Chicago
Internal Medicine/Medicine
Schools of Medicine
United States
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Gamazon, Eric R; Trendowski, Matthew R; Wen, Yujia et al. (2018) Gene and MicroRNA Perturbations of Cellular Response to Pemetrexed Implicate Biological Networks and Enable Imputation of Response in Lung Adenocarcinoma. Sci Rep 8:733
Zhang, Mingfeng; Lykke-Andersen, Soren; Zhu, Bin et al. (2018) Characterising cis-regulatory variation in the transcriptome of histologically normal and tumour-derived pancreatic tissues. Gut 67:521-533
Agrawal, A; Chou, Y-L; Carey, C E et al. (2018) Genome-wide association study identifies a novel locus for cannabis dependence. Mol Psychiatry 23:1293-1302
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Barbeira, Alvaro N; Dickinson, Scott P; Bonazzola, Rodrigo et al. (2018) Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics. Nat Commun 9:1825
Salisbury-Ruf, Christi T; Bertram, Clinton C; Vergeade, Aurelia et al. (2018) Bid maintains mitochondrial cristae structure and function and protects against cardiac disease in an integrative genomics study. Elife 7:
Varma, V R; Varma, S; An, Y et al. (2017) Alpha-2 macroglobulin in Alzheimer's disease: a marker of neuronal injury through the RCAN1 pathway. Mol Psychiatry 22:13-23
Mohammadi, Pejman; Castel, Stephane E; Brown, Andrew A et al. (2017) Quantifying the regulatory effect size of cis-acting genetic variation using allelic fold change. Genome Res 27:1872-1884
GTEx Consortium; Laboratory, Data Analysis &Coordinating Center (LDACC)—Analysis Working Group; Statistical Methods groups—Analysis Working Group et al. (2017) Genetic effects on gene expression across human tissues. Nature 550:204-213
Wheeler, Heather E; Gamazon, Eric R; Frisina, Robert D et al. (2017) Variants in WFS1 and Other Mendelian Deafness Genes Are Associated with Cisplatin-Associated Ototoxicity. Clin Cancer Res 23:3325-3333

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