There is now an explosion of new genome scale data relating genetic variation within the human population to phenotype, and particularly to common disease. Microarray technology has identified 100s of loci where the presence of particular variants is associated with altered risk of many common diseases;complete sequencing of individual exomes in cancer samples has discovered many somatic mutations in a variety of genes;and sequencing of 1000 human genomes has provided an almost complete inventory of common population variants. Further, these data are only the first in an ever-increasing flood, as even faster and cheaper sequencing technologies come on line. The results hold promise for major advances in treatment and diagnosis of common human diseases. Extracting the expected benefits is not straightforward, and will necessitate acquiring detailed knowledge of the mechanisms linking genetic variation to disease. This project focuses on one aspect of this challenge - using the new genomic data to identify new therapeutic opportunities. We will investigate those principles underlying complex trait disease that are particularly relevant to tha goal. We introduce a three stage mechanistic framework, relating genomic variation to the function of impacted gene products, the impact of these altered functions on pathways, processes and subsystems;and finally the consequences for complex trait disease phenotypes. We will develop computational methods to address key questions concerning three major aspects of the framework (1) How large are the changes in protein function brought about by the genomic variants underlying complex trait disease? What role do different classes of genomic and protein level mechanism, such as expression, non-synonymous changes and splicing, play in these variants? (2) How complete is the set genes with strong influence on the disease phenotypes discovered by current technologies, and how can missing genes be imputed from the genomic and network data? (3) What is the distribution of coupling between the activity of genes involved in disease mechanism and disease phenotypes? The results will deepen understanding of these aspects of complex disease, and provide a basis for identifying potential new drug targets from GWAS and other genomic studies.

Public Health Relevance

New technologies are now providing extensive information on human genetic variation associated with increased risk of a wide range of common human disease, such as Alzheimer's, diabetes, heart disease, and many cancers. These data hold the promise for the development of new therapies, and realizing those benefits requires the acquisition of complementary knowledge of the mechanisms that link genetic variation to disease risk. This project is focused on analysis of the relationship between genetic variation and common disease with the goal of identifying new therapeutic opportunities.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM104436-02
Application #
8738688
Study Section
Special Emphasis Panel (ZGM1-GDB-7 (CP))
Program Officer
Krasnewich, Donna M
Project Start
2013-09-20
Project End
2017-05-31
Budget Start
2014-06-01
Budget End
2015-05-31
Support Year
2
Fiscal Year
2014
Total Cost
$288,080
Indirect Cost
$98,080
Name
University of Maryland College Park
Department
Miscellaneous
Type
Other Domestic Higher Education
DUNS #
790934285
City
College Park
State
MD
Country
United States
Zip Code
20742
Cao, Chen; Moult, John (2014) GWAS and drug targets. BMC Genomics 15 Suppl 4:S5