Genetic and Multi-omic Risk Assessment of Alzheimer’s Disease Implicates Core Associated Biological Domains

Abstract

Background: Alzheimer’s disease (AD) is the predominant dementia globally, with heterogeneous presentation and penetrance of clinical symptoms, variable presence of mixed pathologies, potential disease subtypes, and numerous associated endophenotypes. However, there is no methodology to objectively rank endophenotypes for disease risk, nor to enumerate the genes associated with each endophenotype at a genome scale. Consequently, therapeutic development is challenged by the uncertainty of which endophenotypic areas, and specific subordinate targets, to prioritize for further translational research. Methods: Here we report the development of an informatic pipeline that ranks genes for AD risk genome wide and organizes them into disease associated endophenotypes--which we call AD biological domains. The AD risk ranking draws from genetic association studies, predicted variant impact, and linkage with dementia associated phenotypes to create a genetic risk score. This is paired with a multi-omic risk score utilizing extensive sets of both transcriptomic and proteomic studies to identify systems level changes in expression associated with AD. These two elements combined constitute our target risk score (TRS) that ranks AD risk genome wide. The ranked genes are organized into endophenotypic space through the development of 19 biological domains associated with AD in the described genetics and genomics studies and accompanying literature. The biological domains are constructed from exhaustive gene ontology (GO) term compilations, allowing automated assignment of genes into objectively defined disease-associated biology. This rank and organize approach, performed genome-wide, allows the characterization of aggregations of AD risk across biological domains. Results: The top AD-risk associated biological domains are Synapse, Immune Response, Lipid Metabolism, Mitochondrial Metabolism, Structural Stabilization, and Proteostasis, with slightly lower levels of risk enrichment present within the other 13 biological domains. Synapse and Mitochondrial Metabolism are the most down-regulated biological domains, with mitochondrial function being the most enriched, while Immune Response is the most up-regulated biological domain. Conclusions: The TRS ranked genes which are organized into the biological domains provides an objective methodology that can be automated into workflows to localize risk within specific biological endophenotypes, and drill down into the most significantly associated sets of GO-terms and annotated genes for potential therapeutic targets.