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.

Agency
National Institute of Health (NIH)
Type
Research Project (R01)
Project #
5R01MH101825-02
Application #
8706983
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Addington, Anjene M
Project Start
Project End
Budget Start
Budget End
Support Year
2
Fiscal Year
2014
Total Cost
Indirect Cost
Name
University of Chicago
Department
Biostatistics & Other Math Sci
Type
Schools of Arts and Sciences
DUNS #
City
Chicago
State
IL
Country
United States
Zip Code
60637
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