Sep 23 2015
Six new grants from the National Institutes of Health will support researchers to develop new computational approaches for searching among millions of genomic variants to find those that make a difference in disease susceptibility or in other traits. The awards are for three years each, and total approximately $13 million, pending the availability of funds. They are administered by the National Human Genome Research Institute (NHGRI) and the National Cancer Institute, both parts of NIH.
Comparing the genomes of many people suggests that there are tens of millions of genetic variants, or DNA spelling differences. For the last decade, scientists have used genome-wide association studies (GWAS) to find regions of the genome associated with diseases and traits. In GWAS, the genomes of thousands of people with and without a disease are compared to find the genomic regions containing variants that affect disease risk. Although GWAS may find hundreds of variants that appear to be associated with a disease, it remains a challenge to find out which variants actually have a role in the disease process, and what that role might be.
"Before we can understand how a variant or gene functionally contributes to a disease - and then develop prevention and therapeutic strategies - we have to identify which genes and variants actually are involved in raising the risk," said Lisa Brooks, Ph.D., program director of the NHGRI Genetic Variation Program. "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."
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.
"We know a great deal about the protein-coding genes and what they do," said Mike Pazin, Ph.D., program director in the NHGRI Functional Genomics Program. "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. However, we know that 90 percent 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."
The researchers are developing computational approaches to combine many different sets of data to identify disease-causing variants or narrow down the set of candidate variants. They will use data from experiments to determine the accuracy of the computational predictions.
The following grants have been awarded (pending availability of funds):
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts; $2.6 million, three years
Principal Investigator: Nir Hacohen, Ph.D.
To understand the DNA drivers of common human diseases (using immune diseases as test cases), the researchers plan to analyze how DNA variants that are associated with common immune diseases cause individuals to differ in their immune responses.
- Broad Institute of MIT and Harvard; $2.5 million, three years
Principal Investigator: Manolis Kellis, Ph.D.
The researchers plan to interpret the importance of non-coding variants in human disease by studying their activity patterns and how variants influence chemical modifications called epigenomic marks, which affect gene regulation. They will develop statistical methods to identify locations in the genome where variants are more likely to affect the regulation of genes.
- University of North Carolina, Chapel Hill; $2.2 million, three years
Principal Investigators: Alain Laederach, Ph.D. and Kevin Weeks, Ph.D.
Although non-coding regions of the genome are not translated into proteins, they may be transcribed into RNA. Such RNA carries out various regulatory functions in a cell. The researchers have shown that disrupting RNA structure can lead to diseases in people, including an inherited eye cancer, retinoblastoma. They would like to develop computational approaches to predict structural changes in RNA that are caused by genetic variants.
- Stanford University, Stanford, California; $1.4 million, three years
Principal Investigator: Stephen Montgomery, Ph.D.
The researchers will develop methods for interpreting non-coding genetic variation and for predicting disease-causing variants in the subjects' genomes. They plan to develop various statistical models based on large amounts of information from individuals, and identify variants that contribute to hundreds of diseases and traits.
- University of California, San Diego/Ludwig Institute for Cancer Research; $2.3 million, three years
Principal Investigator: Bing Ren, Ph.D.
Previous studies have identified a number of DNA sequence variants strongly associated with age-related macular degeneration (AMD), which is the leading cause of blindness among seniors. In this project, the researchers will create computational models that can predict or narrow down non-coding sequence variants that contribute to the development of disease, using AMD as a test case.
- University of Washington/UW Medicine, Seattle and HudsonAlpha Institute for Biotechnology, Huntsville, Alabama; $1.9 million, three years (supported by the National Cancer Institute)
Principal Investigators: Jay Shendure, M.D., Ph.D. and Greg Cooper, Ph.D.
The researchers seek to develop better ways to identify non-coding genetic variants that contribute to human disease. They plan to test a system they developed called Combined Annotation Dependent Depletion (CADD), which aims to identify which individual genetic variants contribute to disease. They will test and further refine the method as well as explore other related approaches in genome sequencing studies of both rare and common diseases.
The following grants have been awarded: R01HG008131; R01HG008155; R01HG008133; R01HG008150; R01HG008135; and R01CA197139.
NIH/National Human Genome Research Institute