Dissecting Molecular Phenotypes in Late-Onset Alzheimer’s Disease Through Combined Systems Approach
Monday, September 24, 2018
11:30 AM-1:30 PM
BIOMED PhD Research Proposal
Dissecting Molecular Phenotypes in Late-Onset Alzheimer’s Disease (LOAD) Through a Combined Systems Approach
John S. Malamon, PhD Candidate, School of Biomedical Engineering, Science and Health Systems, Drexel University
Andres Kriete, PhD, Associate Dean for Academic Affairs and Teaching Professor, School of Biomedical Engineering, Science and Health Systems, Drexel University
Individually, Exome chip, RNA-seq, GWAS, and other NGS analysis techniques have taught us a great deal about the potential suspects involved in late-onset Alzheimer’s disease (LOAD), however they have failed to weave together a cohesive plot detailing how these factors combine to contribute to larger, systematic dysfunctions and associated endophenotypes. In addition, we face numerous challenges when considering the many forms of heterogeneities present in these data. To this end, we have developed a comprehensive and highly extensible framework for identifying novel regulatory networks, inferring functional relationships, and confirming dysregulation in these pathways.
Our study design combines clinical data, co-expression networks derived from RNA-seq data, functional enrichment, and expression quantitative trait loci (eQTL) derived from single nucleotide variant (SNV) data to accomplish four fundamental goals: (1) identify regulatory systems involved in and affected by LOAD; (2) examine the relationship between gene expression and genotype; (3) better describe cell-specific contributions to disease susceptibility; and (4) deconvolute aging phenotypes through machine learning.
Our results reveal pathways involved in innate immunity, p53-induced apoptosis, synaptic activity, membrane polarization, and vascular endothelial growth factor (VEGF) signaling. Our results replicate previous findings of an innate immune pathway and indicate global changes in receptivity and immune response. Finally, eQTL analysis revealed genetic perturbations and allele-specific effects in the MAPT and human leukocyte antigen (HLA) regions.