What's the science?

There are currently no therapies available to treat or prevent Alzheimer’s disease. This may be due to the complexity and heterogeneity of the disease. Although we know that the accumulation of beta-amyloid peptides and tau proteins occurs in the brain in Alzheimer’s disease and that certain susceptibility genes are involved, we do not understand the sequence of events that lead from genetic risk to brain pathology and cognitive decline. Brain gene expression data and network based analysis can potentially gather nuanced information about the complex interactions among genes that lead to brain pathology. This week in Nature Neuroscience, Mostafavi and colleagues use network-based analysis of gene expression data from the aging prefrontal cortex to elucidate a network involved in aging and Alzheimer’s disease.

How did they do it?

Prefrontal cortex RNA-sequencing (RNA-seq) data (gene transcript levels representing gene expression) from participants in longitudinal datasets of aging called the Religious Orders Study (ROS) and the Memory and Aging Project (MAP) were used. These datasets also contain post-mortem brain pathology data and longitudinal measures of cognitive performance. They ran a standard association analysis to look for genes whose expression levels associate with Alzheimer’s disease and cognitive decline. They then used an approach called gene module-trait network analysis that links key networks of genes that are co-expressed (related expression patterns) to cognitive decline and Alzheimer’s disease traits, and then selects the most strongly and directly associated networks. The goal of this was to gain more information on gene networks and their associated biological pathways than can be obtained from analyzing single gene-disease associations.

What did they find?

The known risk variant for Alzheimer’s disease in the APOE gene (strongest Alzheimer's risk gene) only explained 2.2% of the variance in Alzheimer’s disease (heterogeneity) and 5.1% of cognitive decline. When examining 21 other known Alzheimer’s risk variants, they only explain 2.1% of disease variance and 7.6% of cognitive decline, emphasizing that these genes alone do not explain disease heterogeneity and decline. In the gene module-trait network analysis, 47 modules of genes (from the RNA-seq gene expression data) were identified representing related networks of genes. 11 of these modules were associated with cognitive decline or Alzheimer’s disease related traits (beta-amyloid or tau), and they found that these modules replicated in their association (with Alzheimer’s disease) in an independent gene expression dataset.