How to uncover shared genetic risk factors between different brain diseases?
Yuk Yee Leung, Ph.D.
University of Pennsylvania
Philadelphia, PA - United States
Traditionally, genes represent a cell’s DNA code for creation of a protein. Many DNA sequences, however, are never used for creation of a protein and such sequences are called the “non-coding” DNA. Together both genes and non-coding DNA represent what is referred to as the human genome. Large scale genome-wide association studies (called GWAS) have found dozens of common genetic changes with Alzheimer’s and other dementias, including frontotemporal dementia (FTD), progressive supranuclear palsy (PSP), amyotrophic lateral sclerosis (ALS), Parkinson’s Disease (PD) and corticobasal degeneration (CBD). Studies show that many of these genetic changes are in the non-coding regions and these changes modulate the levels of other genes/protein-coding genes turning “on” and “off”.
Recent studies suggest that there is a shared genetic risk between FTD, ALS as well as between PSP and PD. However, it is not yet clear which genes are impacted in these brain diseases. Based on these findings, Dr. Yuk Yee Leung believes that a better understanding of how these genetic changes contribute to Alzheimer’s and other dementias will yield promising insights about the biological mechanisms of the brain diseases.
Dr. Leung and colleagues will apply their new technique called INFERNO (INFERring the molecular mechanisms of NOncoding genetic variants) method to data collected from GWAS studies. INFERNO is a computational tool that aims to identify genetic changes and characterize their effect on downstream biological processes. The researchers plan to apply INFERNO on GWAS data for six Alzheimer’s and other dementias and uncover shared versus distinct biological mechanisms across these brain diseases.
As part of their preliminary work, the researchers have applied the INFERNO tool on two established genetic datasets and have identified 64 different non-coding regions in the human genome for the six Alzheimer’s and other dementias proposed in this study. Dr. Leung and colleagues will analyze the data from this work and continue to apply the INFERNO tool on other datasets.
If successful, the study results could help improve our understanding of these diseases and identify potential new avenues for therapy development.
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