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

Abstract

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. Beyond the difficulty of designing treatments that address the core pathological characteristics of the disease, therapeutic development is challenged by the uncertainty of which endophenotypic areas and specific targets implicated by those endophenotypes to prioritize for further translational research. However, publicly-funded consortia driving large-scale open science efforts have produced multiple omic analyses that address both disease risk relevance and biological process involvement of genes across the genome. Here we report the development of an informatic pipeline that 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 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. 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. This provides an objective methodology 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.

Microglial MHC-I induction with aging and Alzheimer’s is conserved in mouse models and humans

“…The potential impact of studies in this pathway is supported by high TREAT-AD scores ( Fig. 2 ) for many of the genes in this pathway 82…”

“…The common regulation of these genes in microglia across aging and AD and in mouse and man provides a compelling result and to determine if these genes may warrant further mechanistic study, TREAT-AD scores 82 were collected for each of these genes and compared to the distribution of all annotated genes ( Figure 12 ). The TREAT-AD score was developed as part of NIH consortia to prioritize AD targets for investigation and potentially therapeutic targets.…”

New directions for Alzheimer's disease research from the Jackson Laboratory Center for Alzheimer's and Dementia Research 2022 workshop

“…One method to address how altered regulation of specific genes impacts disease etiology is to capture the gene in the context of the larger biological process. We have designed biological domains (or endophenotypes) of AD that are constructed from extensive Gene Ontology (GO) term collection, leveraging the gene to GO‐term annotation relationships, to place that gene in a broader biological context 47 . The up‐ and downregulated genes within any one biological domain can be used to “heat‐map” the subordinate processes relative to the disease state.…”

Open drug discovery in Alzheimer's disease

“…If targets are judged to not be tractable, the starting gene lists are expanded to incorporate other genes that are in the same network (using co-expression or pathway annotation information). 16 In addition to selection of individual understudied targets, we are developing resources to interrogate a set of functionally connected targets, by developing TEPS for multiple targets within one specific protein module. Deep proteomic profiling study of AD and control brains from AMP-AD revealed new AD-related protein coexpression modules that were highly preserved across cohorts and brain regions.…”

“…Following this initial scoring, targets are evaluated for therapeutic potential, with an emphasis on tractability. If targets are judged to not be tractable, the starting gene lists are expanded to incorporate other genes that are in the same network (using co‐expression or pathway annotation information) 16 …”