A gene network and expression analysis identified genetic changes in late-onset Alzheimer disease (LOAD) brains, which have the potential to be novel biomarkers and may lead to the development of therapeutic avenues for LOAD. Findings were published in Alzheimer’s and Dementia.

Whole exome sequencing from 5561 individuals and brain expression data were analyzed for this study. Evolutionary action was predicted by calculating the genetic Alzheimer disease (AD) burden based on variants. Networks were built using protein-protein interactions from a Markov clustering method.

The study authors hypothesized that genes with greater mutational load burden likely facilitate the pathogenesis of LOAD, therefore genes with higher mutational load compared with healthy individuals were extracted. These 216 genes (>99th percentile) were enriched for high-impact variants among the individuals with AD homozygous for apolipoprotein E (APOE) e2 (AD-e2; P =1.0×10-14) and among AD homozygous for APOE e4 (AD-e4; P =4.1×10-6) compared with healthy controls carrying the APOE e4 variant (HC-e4).

Using these candidate genes to predict risk for AD, the 216 identified genes were able to predict AD-e2 compared against HC-e4 with an average area under the receiving operating characteristic curve (AUC) of 0.92±0.03, AD-e2 vs HC-e4 (AUC, 0.79±0.06), and AD-e4 vs HC-e4 (AUC, 0.71±0.08). With these 216 candidates, an imputation analysis found 94 genes had the highest predictive power.

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Among the differentially expressed genes, the brain expression profiles of 174 were assessed. Significant deviations of expression in at least 1 brain region were observed for 75 of the genes (P £.01) among the patients with AD compared with controls. Most of these differentially expressed genes (n=65) had previously been identified in AD genome-wide association studies.

With a network analysis, 26 clusters of genes were identified among patients with AD. Most of these clusters (n=15; false discovery rate q <.05) had significant biological enrichment, including synaptic integrity, neuron projection, axon guidance, dendritic spine, vesicular traffic, and lipid catabolism. Many of these clusters, biological processes, and underlying genes have previously been related with AD.

To assess the likelihood of generating potential therapeutic targets, potential interactions with pharmacological compounds were assessed. A total of 39 genes were predicted to interact with 390 compounds, indicating a potential for therapeutic targets.

This study was limited by the lack of diversity among the genetic and expression samples available. It remains unclear whether the biomarkers identified would be useful among a non-White population.

The study authors concluded that, with a diverse set of tests, they were able to confirm previous results relating genes and expression with AD and to identify novel genes and expression networks which may serve as robust biomarkers or potential therapeutic targets for LOAD.


Kim YW, Al-Ramahi I, Koire A, et al. Harnessing the paradoxical phenotypes of APOE ɛ2 and APOE ɛ4 to identify genetic modifiers in Alzheimer’s disease. Alzheimer’s Dement. 2020;1-16. doi:10.1002/alz.12240