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We have applied a novel and integrative analysis framework for next-generation sequencing (NGS) data to 503 human subjects provided by the Religious Orders Study and Memory and Aging Project (ROSMAP) to examine changes in transcriptomic organization and common variants in association with late-onset Alzheimer’s disease (LOAD). Our framework identified seven reproducible, co-regulated modules after quality control (QC), clinical segregation, preservation filtering, and functional ontology analysis. These modules were specifically enriched in several innate and adaptive immune system processes, the synaptic vesicle cycle, and Hippo signaling. Topological and functional erosion of these modules due to shedding of genes and loss of in-module connectivity was diagnostic of disease progression. Perturbation analysis revealed that only 1% of eQTLs overlapped genes participating in these co-regulated modules. Common variants nevertheless identified components of the immune systems like human leukocyte antigen (HLA) complex and microtubule-associated protein tau (MAPT) regions in association with LOAD. Our results implicate microglial function, adaptive immune response, and the structural degeneration of neurons as contributors to the transcriptional deregulation observed along with common genetic variants in the progression of LOAD.

Late-onset Alzheimer’s disease (LOAD) and age are significantly correlated such that one-third of Americans beyond 85 years of age are afflicted. We have designed and implemented a pilot study that combines systems biology approaches with traditional next-generation sequencing (NGS) analysis techniques to identify relevant regulatory pathways, infer functional relationships and confirm the dysregulation of these biological pathways in LOAD. Our study design is a most comprehensive systems approach combining co-expression network modeling derived from RNA-seq data, rigorous quality control (QC) standards, functional ontology, and expression quantitative trait loci (eQTL) derived from whole exome (WES) single nucleotide variant (SNV) genotype data. Our initial results reveal several statistically significant, biologically relevant genes involved in sphingolipid metabolism. To validate these findings, we performed a gene set enrichment analysis (GSEA). The GSEA revealed the sphingolipid metabolism pathway and regulation of autophagy in association with LOAD cases. In the execution of this study, we have successfully tested an integrative approach to identify both novel and known LOAD drivers in order to develop a broader and more detailed picture of the highly complex transcriptional and regulatory landscape of age-related dementia.