A project to identify new genes involved in the development of Parkinson’s disease has received $1 million in funding from the National Science Foundation (NSF).
Genome wide association studies (GWAS) have had some success in identifying genes that play a role in Parkinson’s disease. This approach involves scanning markers across the complete sets of DNA, or genomes, of many people to find genetic variations associated with a specific disease. Once these genetic associations are found, researchers can use them to develop improved strategies to detect, treat and prevent the disease.
However, this approach is limited as it can identify only a fraction of the genes potentially involved in the disease, and has had limited success in identifying disease-causing genetic variants.
“Genetics can play a key role in understanding the causes of and developing treatments for some diseases,” Benjamin Shaby, assistant professor of statistics at Penn State and one of the project’s researchers, said in a press release. “Studying a single type of genetic information usually results in very poor ability to detect weak signals.”
To circumvent this problem, Shaby and Daisy Philtron, assistant research professor of statistics at Penn State, will develop tools to combine different study approaches — GWAS along with family-based genetic studies — to identify those genetic variants that GWAS may not be able to detect.
“We will develop new models that can study several types of genetic information simultaneously,” Shaby said. “Our approach will allow for integration of disparate data types such as microarray data, genome-wide association data, and pedigree data.”
The researchers will use statistical tools, called hierarchical models, to combine the different sources of information. The model’s principle is that each gene in a person’s genome is inserted into one of three possible groups — the “null group,” the “deleterious group” or the “beneficial group.”
As the name indicates, genes in the “null group” have no link with disease progression, while genes in the “deleterious group” or “beneficial group” have negative and positive roles in disease progression.
The three-group structure has advantages compared to traditional methods of gene analysis. First, the outcomes are not influenced by the simultaneous analysis of thousands of genes; second, it doesn’t exclude data from different experiments, meaning that in the end the results are reinforced.
These two parameters render the new analysis more powerful to detect weak signals, and reduce the risk of false positive results — genes identified as having an effect when in reality they don’t. Also, the model’s structure allows for the ongoing integration of new data.
“The work is exciting because of its inherent flexibility to incorporate new data types as they become available,” Philtron said. “We hope that our integrated analysis will detect important signals that may be missed in analyses of individual data types.”
The project, called “Combining Heterogeneous Data Sources to Identify Genetic Modifiers of Diseases,” will run for five years.
Researchers will use Parkinson’s disease as their model disease, but the newly developed model can then be applied to other complex diseases with a genetic component.