September 21, 2016
The University of North Carolina at Chapel Hill has been awarded a three-year, $1.2 million grant to address statistical challenges that arise in the analysis of next-generation expression quantitative trait loci (eQTL) studies. In such studies, the goal is to identify and quantify how known genetic variants regulate gene expression across multiple tissues.
The principal investigators are Andrew Nobel, PhD, professor, and Fred Wright, PhD, adjunct professor, both in the Department of Biostatistics in the UNC Gillings School of Global Public Health. Nobel also is a professor in UNC’s Department of Statistics and Operations Research, and Wright also serves as a professor of statistics and biological sciences at North Carolina State University.
Their project was selected for funding by the National Human Genome Research Institute of the National Institutes of Health (NIH) following a competitive application process.
eQTL studies seek to identify genomic variants that influence the expression of particular genes, thereby influencing higher-level biological functions. The study of eQTLs has proven to be a useful tool in understanding the biological pathways that underlie disease in humans and other populations.
“Until recently, most eQTL analyses in humans were carried out using samples from a single tissue, namely blood,” said Nobel. “Large, multi-tissue eQTL studies such as those being carried out by the NIH Genotype-Tissue Expression (GTEx) consortium have the potential to elucidate the tissue-specific nature of genetic regulation, with important implications for genetic components of disease risk.’’
Nobel and Wright have been members of, and active contributors to, the GTEx consortium since it began in 2010. Their laboratories have contributed software and statistical analyses to the ongoing work of the Consortium, which had its first paper featured as the cover article of Science in 2015.
“We will use this grant to extend our existing work and develop new, network-based software and analysis methods,” Nobel said. “These methods will have applications for biomedical researchers working in genomics and other fields.”