Gene network and expression analysis identified genetic changes in the brain of late-onset Alzheimer’s disease (LOAD) that may be novel biomarkers and that could lead to the development of therapeutic pathways for LOAD. The results were published in Alzheimer’s and dementia.
For this study, total exome sequencing from 5561 individuals and brain expression data were analyzed. The evolutionary impact was predicted by calculating the genetic Alzheimer’s disease (AD) burden from variants. Networks were built using protein-protein interactions from a Markov clustering method.
The study’s authors hypothesized that genes with a higher mutational load are likely to facilitate the pathogenesis of LOAD. Therefore, genes with a higher mutation load compared to healthy individuals were extracted. These 216 genes (> 99th percentile) were found in persons with AD who were homozygous for apolipoprotein E (APOE) e2 (AD-e2; P = 1.0 × 10-14) and in AD who were homozygous for APOE e4 (AD) were enriched for high impact variants -e4; P = 4.1 × 10 -6) compared to healthy controls that carry the APOE e4 variant (HC-e4).
Using these candidate genes to predict the AD risk, the 216 identified genes AD-e2 compared to HC-e4 with an average area under the receiving operating curve (AUC) of 0.92 ± 0.03, AD-e2 compared to HC- predict. e4 (AUC, 0.79 ± 0.06) and AD-e4 versus HC-e4 (AUC, 0.71 ± 0.08). An imputation analysis of these 216 candidates showed that 94 genes had the highest predictive power.
Among the differentially expressed genes, the brain expression profiles of 174 were scored. In 75 of the genes (P £ 0.01) in patients with AD, significant expression deviations were observed in at least 1 brain region compared to controls. Most of these differentially expressed genes (n = 65) were previously identified in genome-wide AD association studies.
Network analysis identified 26 clusters of genes in patients with AD. Most of these clusters (n = 15; false detection rate q <0.05) showed 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 been previously linked to 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, suggesting potential for therapeutic targets.
This study was limited by the lack of diversity between the genetic and expression samples available. It remains unclear whether the identified biomarkers would be useful for a non-white population.
The study authors concluded that a series of tests could confirm previous results regarding genes and expression with AD and identify new genes and expression networks that could serve as robust biomarkers or potential therapeutic targets for LOAD.
Kim YW, Al-Ramahi I., Koire A. et al. Use of the paradoxical phenotypes of APOE ɛ2 and APOE ɛ4 to identify genetic modifiers in Alzheimer’s disease. Alzheimer’s dementia. 2020; 1-16. doi: 10.1002 / alz.12240
This article originally appeared on Psychiatry Advisor