Using Big Data to Identify Genetic Markers of Alzheimer’s Disease

Big Data

An old man having Alzheimer's disease / Photo by: pathdoc via Shutterstock


Researchers from the University of Washington have created a big data framework for detecting robust expression markers of Alzheimer’s disease. They called the framework DECODER (discovering concordant expression markers) and resulted from a meta-analysis of three different studies that focused on nine brain regions of worms to form a pool of data from where conclusions can be derived, according to Samuel Vennin, reporting for physicsworld. 

The researchers first established common features in each brain region to be able to use the whole database. When they compared the overlap among the top 1,000 amyloid beta-associated genes in each region, they discovered that basic mechanisms leading to the development of the disease were common across regions

The studies were scored to quantify the gene-concordant associations with neuropathology levels (such as amyloid-beta levels) in multiple brain regions. They found out that the global concordance-based scores were statistically more robust and informative than scores computed from each individual area. The top-scoring genes were also more likely to be part of a 144-gene AD pathway taken as a reference, which highlighted the biological relevance of the designed scores.

Common to all pathways was NDUFA9, a gene that is part of the Complex I sub-unit in the mitochondria and has a big role in mitochondrial respiration and synthesis of adenosine triphosphate. 

The researchers plan to replicate the results in humans but since there are huge differences in human mitochondria and those of worms, estimating the results for humans will not be easy. But the framework developed by the researchers will become more powerful with time as more studies on brain gene expression and neuropathology are conducted.