The NIH has awarded six three-year grants totaling approximately $13 million to support new computational approaches for searches involving millions of genomic variants to identify those linked to disease susceptibility or other traits.
The grants, announced yesterday, will be administered by NIH’s National Human Genome Research Institute (NHGRI) and National Cancer Institute.
“We are looking for approaches that can find the causal variants out of the many variants associated with a disease, or at least narrow down the set,” Lisa Brooks, Ph.D., program director of the NHGRI Genetic Variation Program, said in a statement.
While genome-wide association studies (GWAS) may find hundreds of variants that appear to be associated with a disease, there are believed to be tens of millions of genetic variants. It remains a challenge to find out which variants actually have a role in the disease process, and what that role might be, NIH added.
Most variants—including many that contribute to disease risk, response to drugs, and traits such as height—are in genomic regions that do not code for proteins. These variants usually affect the regulation of genes, residing within “switches” in the genome that determine when and where proteins are made.
“For variants sitting outside the coding regions, it is difficult to know which parts of the genome they affect, let alone how the variants cause differences in function,” stated Mike Pazin, Ph.D., program director in the NHGRI Functional Genomics Program. “However, we know that 90% of associated variants found in GWAS are outside of the protein-coding areas. Eventually, we want to understand mechanistically how the variants function in regulating genes, and how differences in the way they function affect disease risk.”
Details on the grant awards, which NIH said will be awarded “pending the availability of funds,” (and principal investigators):
- $2.6 million to the Broad Institute of MIT and Harvard, toward analyzing how DNA variants associated with common immune diseases cause individuals to differ in their immune responses. (Nir Hacohen, Ph.D.)
- $2.5 million to the Broad Institute toward interpreting the importance of non-coding variants in human disease by studying their activity patterns and how variants influence epigenomic marks affecting gene regulation. (Manolis Kellis, Ph.D.)
- $2.3 million to University of California, San Diego and Ludwig Institute for Cancer Research toward creating computational models that can predict or narrow down non-coding sequence variants that contribute to the development of disease, using age-related macular degeneration (AMD) as a test case. (Bing Ren, Ph.D.)
- $2.2 million to the University of North Carolina, Chapel Hill toward developing computational approaches to predict structural changes in RNA caused by genetic variants. (Alain Laederach, Ph.D. and Kevin Weeks, Ph.D.)
- $1.9 million to University of Washington/UW Medicine and HudsonAlpha Institute for Biotechnology, toward testing a system they developed, Combined Annotation Dependent Depletion (CADD), which aims to identify which individual genetic variants contribute to disease. (Jay Shendure, M.D., Ph.D. and Greg Cooper, Ph.D.)
- $1.4 million to Stanford University toward developing methods for interpreting non-coding genetic variation, and predicting disease-causing variants in genomes. Researchers will develop statistical models and identify variants contributing to hundreds of diseases and traits. (Stephen Montgomery, Ph.D.)