Disease Gallery – Alzheimer’s Disease

precisionlife performed a study to identify combinatorial genomic signatures associated with Alzheimer’s disease (AD), which can be used for better stratification of patients into sub-groups and the development of more effective and personalized therapeutic interventions.

We identified almost 35,000 SNP networks strongly associated with AD. Clustering of the SNPs by the patients in which they occur enabled us to build a detailed architecture and stratification of the disease population. We identified 8 clusters of networks representing 8 patient sub-groups that differ in their disease associated genes and pathways.

Alzheimer’s disease (AD), a complex neurodegenerative disease, is one of the most common causes of dementia. As the global population is aging, it has become a major socioeconomic burden and concern for health and social care providers. The heterogeneity (clinical, genetic and pathophysiological) existing within AD patients has been one of the main reasons for the failure of more than 200 clinical trials in the last few years (1,2). Hence, there is an obvious need for improved patient stratification strategies that will enable development of novel and personalized treatments for AD (3).

precisionlife performed a study to identify combinatorial genomic signatures associated with Alzheimer’s disease, which can be used for better stratification of patients into sub-groups and development of personalized therapeutic interventions. The dataset consisted of 556,968 SNPs in 4,043 samples (1,985 cases and 2,058 controls) from the NIA-LOAD dataset (4) for cases diagnosed with late onset Alzheimer’s disease.

The precisionlife platform identified almost 35,000 SNP networks strongly associated with the case population. These networks consisted of 2 to 5 SNP genotypes each and cannot therefore be discovered using standard GWAS tools. Clustering of the SNPs by the patients in which they occur enabled us to build a detailed architecture and stratification of the disease population. This view helped us identify 8 clusters of networks representing 8 patient sub-groups that differ in their combinatorial disease signatures.

    Functional annotation of the genes associated with disease for these 8 sub-groups all showed a strong correlation with Alzheimer’s disease related processes. Furthermore, comparative pathway analyses of the sub-groups indicated enrichment of different biological processes that appear to be clinically relevant for Alzheimer’s (and other neurodegenerative) disease.

    These include targets that are associated with:

    • Interferon gamma signaling
    • Transmission across chemical synapses
    • Regulation of post-synaptic membrane potential
    • Regulation of adaptive immunity
    • Neuron differentiation
    • Glutamate receptor signaling

    1. Ferreira, D., Wahlund, L. O., & Westman, E. (2018). The heterogeneity within Alzheimer’s disease. Aging (Albany NY), 10(11), 3058.
    2. Mukherjee, S., Mez, J., Trittschuh, E. H., Saykin, A. J., Gibbons, L. E., Fardo, D. W., … & Gross, A. L. (2018). Genetic data and cognitively defined late-onset Alzheimer’s disease subgroups. Molecular psychiatry, 1.
    3. Kovacs, G. G. (2012). Clinical stratification of subtypes of Alzheimer’s disease. The Lancet Neurology, 11(10), 839-841.
    4. Lee JH, Cheng R, Graff-Radford N, Foroud T, Mayeux R, et al (2008). Analyses of the National Institute on Aging Late-Onset Alzheimer’s Disease Family Study: implication of additional loci. Arch. Neurology, 65(11):1518-26.