Heritable variation in gene regulation plays an important role in human phenotypic variation, including variation in medically relevant phenotypes, such as susceptibility to disease. Expression quantitative trait loci (eQTL) mapping has become a widely used tool for identifying genetic variants that affect gene regulation. In this approach, mRNA expression levels are viewed as quantitative traits, and these expression phenotypes are mapped to particular genomic loci by correlating gene expression levels and genome-wide genotypes across many individuals. To date, nearly all genome-wide eQTL studies have focused on mRNA expression levels and have only rarely examined protein expression levels. As a result, we know little about the extent to which eQTLs are also associated with variation in protein expression levels, and we have almost no examples of genetic variants that directly affect post-transcriptional regulatory variation. To address this ga, I propose to carry out a large-scale protein expression study using a panel of 70 HapMap Yoruba lymphoblastoid cell lines (LCLs) for which genome sequences and mRNA expression levels by RNA-seq were previously collected. The data I propose to collect will allow me to provide a description of the genetic variation that underlies variation in mRNA and protein expression levels at an unprecedented scope and resolution. I expect to gain a better understanding of transcriptional and post-transcriptional regulatory mechanisms, their associated regulatory elements, and ultimately - their role in shaping human phenotypic variation.

Public Health Relevance

The key goal of this project is to map human genetic variation that drives variation in post-transcriptional gene regulation or protein expression - ultimately to advance our understanding the mechanisms of human trait variation including susceptibility to disease.

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Postdoctoral Individual National Research Service Award (F32)
Project #
5F32HG006972-02
Application #
8537208
Study Section
Special Emphasis Panel (ZRG1-F04-D (20))
Program Officer
Gatlin, Christine L
Project Start
2012-09-01
Project End
2013-11-30
Budget Start
2013-09-01
Budget End
2013-11-30
Support Year
2
Fiscal Year
2013
Total Cost
$14,265
Indirect Cost
Name
University of Chicago
Department
Genetics
Type
Schools of Medicine
DUNS #
005421136
City
Chicago
State
IL
Country
United States
Zip Code
60637
Khan, Zia; Ford, Michael J; Cusanovich, Darren A et al. (2013) Primate transcript and protein expression levels evolve under compensatory selection pressures. Science 342:1100-4