Journal of Stem Cell Research & Therapy

Multiscale genomic approach and big data analysis support a shared etiology between type 2 diabetes and Alzheimerâ??s disease

4^th International Conference and Exhibition on Cell & Gene Therapy

August 10-12, 2015 London, UK

Giulio Maria Pasinetti

Posters-Accepted Abstracts: J Stem Cell Res Ther

Abstract:

The convergence of several unique features of Alzheimer’s disease (AD) [e.g., heterogeneity, complex polygenic etiology,
and prolonged asymptomatic phase] indicates the need for large cohorts of well-characterized populations from diverse
backgrounds volunteers for: 1) Longitudinal epidemiological studies to discover/validate putative risk factors, and 2) clinical
studies for prospective validation of potential preventive interventions. In addition, many epidemiological studies indicate
that people with diabetes are at higher risk of eventually developing AD. A longitudinal database involving at-risk populations
is essential to address the future needs of a prevention initiative. Along with ‘Big-Data’, the field of therapy development will
require novel computational capabilities to not only sort out the complex interactions between type 2 diabetes (T2D) and
cognitive deterioration in AD, but also to discover-validate technologies for early and accurate detection. We used data from
public genome-wide association studies (GWAS) to explore the association single-nucleotide polymorphisms (SNPs) between
T2D and AD. We then integrated pathway data with gene ontology data, expressional quantitative trait loci (eQTL), and coexpression
networks to explore the function of the shared SNPs. We found a significant overlap (p=4.9E-19) between SNPs of
T2D and AD. 927 SNPs were associated with both AD and T2D, and we found that they influence 190 genes in brain tissue
and 416 in T2D-relevant peripheral tissues. Interestingly, we found that certain mitochondria and innate immune response
pathways are particularly enriched. Collectively, we found that T2D and AD share common genetic risk factors, which may
partially explain the epidemiological observation of the disease incidence correlation.