Analysis of brain region-specific co-expression networks reveals clustering of established and novel genes associated with Alzheimer disease | Alzheimer's Research & Therapy | Full Text

Analysis of brain region-specific co-expression networks reveals clustering of established and novel genes associated with Alzheimer disease | Alzheimer's Research & Therapy | Full Text

Analysis of brain region-specific co-expression networks reveals clustering of established and novel genes associated with Alzheimer disease

• Daniel Lancour, 
• Josée Dupuis, 
• Richard Mayeux, 
• Jonathan L. Haines, 
• Margaret A. Pericak-Vance, 
• Gerard C. Schellenberg, 
• Mark Crovella, 
• Lindsay A. Farrer & 
• Simon Kasif 

Alzheimer's Research & Therapy volume 12, Article number: 103 (2020) Cite this article

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Abstract

Background

Identifying and understanding the functional role of genetic risk factors for Alzheimer disease (AD) has been complicated by the variability of genetic influences across brain regions and confounding with age-related neurodegeneration.

Methods

A gene co-expression network was constructed using data obtained from the Allen Brain Atlas for multiple brain regions (cerebral cortex, cerebellum, and brain stem) in six individuals. Gene network analyses were seeded with 52 reproducible (i.e., established) AD (RAD) genes. Genome-wide association study summary data were integrated with the gene co-expression results and phenotypic information (i.e., memory and aging-related outcomes) from gene knockout studies in Drosophila to generate rankings for other genes that may have a role in AD.

Results

We found that co-expression of the RAD genes is strongest in the cortical regions where neurodegeneration due to AD is most severe. There was significant evidence for two novel AD-related genes including EPS8 (FDR p = 8.77 × 10−3) and HSPA2 (FDR p = 0.245).

Conclusions

Our findings indicate that AD-related risk factors are potentially associated with brain region-specific effects on gene expression that can be detected using a gene network approach.