In recent years, studies that associate genetic variation with gene expression (""""""""eQTL studies"""""""") have become a major tool for identifying regulatory genetic variation. However, the difficulty of securing primary tissue samples means that up to now these eQTL studies have been conducted in a limited range of cell and tissue types. Most notably the largest studies have been conducted in EBV-transformed lymphoblastoid cell lines, and it is unclear to what extent eQTLs identified in these cell lines wil be relevant to human disease mapping. The GTEx Project will provide data to remedy this situation, collecting RNA-seq and genotype data on 30 tissues in hundreds of individuals. However, current analytic tools are limited in their ability to fully exploit the richness of these data. In particular, available methods fall short in their ability to jointly analyze data on all tssues to maximize power, while simultaneously allowing for differences among eQTLs present in each tissue. Here we propose to develop novel statistical methods to help address these issues. We will apply these methods to identify eQTLs in the GTEx project data, integrate the GTEx data with other relevant data such as those available from the ENCODE project, and disseminate the results on the internet in a convenient form. We will also provide researchers with convenient tools to cross-reference results of the GTEx project with results of genome-wide association studies. The overall goal of the project is to build and apply an infrastructure for improved eQTL analyses, helping to maximize the utility and accessibility of GTEx data to the broad community of scientists who would like to use these data.

Public Health Relevance

This project will generate and apply statistical tools for analyzing large-scale studies that aim to understand the impact of genetic variation on transcriptomes, and to better understand the different regulatory mechanisms underlying different tissue types, a fundamental issue in biology. Understanding these mechanisms, identifying the regulatory genetic variants, and correlating them with human disease, has the potential help understand the biology of disease, eventually leading to new treatment strategies.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Research Project (R01)
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Special Emphasis Panel (ZRG1)
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Addington, Anjene M
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University of Chicago
Biostatistics & Other Math Sci
Schools of Arts and Sciences
United States
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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
Banovich, Nicholas E; Li, Yang I; Raj, Anil et al. (2018) Impact of regulatory variation across human iPSCs and differentiated cells. Genome Res 28:122-131
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
Dolan, M Eileen; El Charif, Omar; Wheeler, Heather E et al. (2017) Clinical and Genome-Wide Analysis of Cisplatin-Induced Peripheral Neuropathy in Survivors of Adult-Onset Cancer. Clin Cancer Res 23:5757-5768
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
Peckham-Gregory, Erin C; Chakraborty, Rikhia; Scheurer, Michael E et al. (2017) A genome-wide association study of LCH identifies a variant in SMAD6 associated with susceptibility. Blood 130:2229-2232
Saha, Ashis; Kim, Yungil; Gewirtz, Ariel D H et al. (2017) Co-expression networks reveal the tissue-specific regulation of transcription and splicing. Genome Res 27:1843-1858
Mercader, Josep M; Liao, Rachel G; Bell, Avery D et al. (2017) A Loss-of-Function Splice Acceptor Variant in IGF2 Is Protective for Type 2 Diabetes. Diabetes 66:2903-2914
Bai, Xue; Mangum, Kevin D; Dee, Rachel A et al. (2017) Blood pressure-associated polymorphism controls ARHGAP42 expression via serum response factor DNA binding. J Clin Invest 127:670-680

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