Johns Hopkins researchers have developed a new scoring system intended to help identify genes that might be important factors in the development of sporadic Parkinson’s disease.
This new approach is expected to accelerate the identification of relevant genetic biomarkers that require further study while helping researchers avoid “dead-end paths.”
New technologies that analyze a cell’s genetic content have greatly increased knowledge of gene variations that can cause disease. These genome-wide association studies (GWAS) were initially designed to provide a general overview of disease-associated genetics. As a result, they have provided large amounts of data that can be difficult to decipher because they only identify regions of the genetic code where there could be hundreds of genes potentially affected by a mutation.
As such, these studies can identify genetic mutations, but they do not always pinpoint a single gene. Scientists believe there is, at best, a 50 percent chance that the gene closest to a mutation will be active in the cell types affected by a disease.
“We are in a scenario where we can collect massive amounts of genetic data using GWAS, but are realizing that does not provide the biological context we need in order to understand the results,” Andrew McCallion, PhD, associate professor at the Johns Hopkins University School of Medicine and senior author of the study, said in a press release.
To help read this data, McCallion and his team developed a method to filter the information. They focused on Parkinson’s disease for which GWAS have identified 49 genome regions with a mutation related to the disease.
“Strategies [that can systematically identify biologically pertinent gene candidates] are necessary for the community to take full advantage of the immense body of GWAS data now in the public domain,” the researchers wrote.
The team analyzed the genetic content of different dopaminergic brain cell populations — those affected in Parkinson’s — collected from mice at early stages of development (embryonic and early postnatal). This approach allowed the team to build a genetic profile of 13 dopaminergic cell types and identify gene regulatory networks in these cells.
Based on the collected data, researchers established a framework to systematically prioritize Parkinson’s candidate genes, and validated their system in the 49 genome regions GWAS had already found.
Using this novel strategy, they were able to pare down the list of potential genes from an initial 1,751 to 112.
Of the genes involved in Parkinson’s disease in these specific regions, their strategy captured all but one. “However, the one we didn’t capture is not expressed in dopaminergic neurons,” McCallion said. “This gives us confidence that the other genes pointed out will be important to the disease.”
The team now wants to investigate if age, environment, and disease state in dopaminergic brain cells can help to further fine-tune their scoring system.
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