Transcriptomic stratification of late-onset Alzheimer's cases reveals novel genetic modifiers of disease pathology

Abstract

Late-Onset Alzheimer's disease (LOAD) is a common, complex genetic disorder wellknown for its heterogeneous pathology. The genetic heterogeneity underlying common, complex diseases poses a major challenge for targeted therapies and the identification of novel disease-associated variants. Case-control approaches are often limited to examining a specific outcome in a group of heterogenous patients with different clinical characteristics. Here, we developed a novel approach to define relevant transcriptomic endophenotypes and stratify decedents based on molecular profiles in three independent human LOAD cohorts. By integrating post-mortem brain gene co-expression data from 2114 human samples with LOAD, we developed a novel quantitative, composite phenotype that can better account for the heterogeneity in genetic architecture underlying the disease. We used iterative weighted gene co-expression network analysis (WGCNA) to reduce data dimensionality and to isolate gene sets that are highly co-expressed within disease subtypes and represent specific molecular pathways. We then performed single variant association testing using whole genome-sequencing data for the novel composite phenotype in order to identify genetic loci that contribute to disease heterogeneity. Distinct LOAD subtypes were identified for all three study cohorts (two in ROSMAP, three in Mayo Clinic, and two in Mount Sinai Brain Bank). Single variant association analysis identified a genome-wide significant variant in TMEM106B (p-value < 5×10 −8 , rs1990620 G) in the ROSMAP cohort that confers protection from the inflammatory LOAD subtype. Taken together, our novel approach can be used to stratify LOAD into distinct molecular subtypes based on affected disease pathways.

Brain Insulin Signaling, Alzheimer Disease Pathology, and Cognitive Function

“…Finally, there are widespread changes in gene expression in AD postmortem brain in relation to both disease pathology and its clinical expression. 36 Preclinical models of amyloid, tau, and other pathologies have also been instructive in delineating genes associated with these pathologies. Type 2 diabetes and obesity-related transcription changes have been found in both AD brains and AD mouse models.…”

Aged Tmem106b knockout mice display gait deficits in coincidence with Purkinje cell loss and only limited signs of non‐motor dysfunction

“…Variants in TMEM106B have initially been identified as an important genetic risk factor for FTD (41). Subsequently, genetic linkages were identified with a much broader spectrum of other neurodegenerative disorders including limbic-predominant age-related TDP-43 encephalopathy, Parkinson's disease and late-onset Alzheimer's disease (22,23,38). Moreover, genetic links were also confirmed with dissociation of cognitive performance in elderlies and differential aging in humans (16,30,45,47) as well as with increased neuronal proportion in Alzheimer's disease (16).…”

“…Variations in TMEM106B were shown to affect the course of disease in both GRN-and C9orf72 carriers (7,8,28,40,42). Likewise, variants in TMEM106B have been associated with other neurodegenerative diseases such as hippocampal sclerosis, Limbicpredominant age-related TDP-43 encephalopathy, Parkinson's disease, late-onset Alzheimer's disease and chronic traumatic encephalopathy (3,12,13,(22)(23)(24)31,38,48). Furthermore, TMEM106B was shown to play a role in the dissociation of cognition and neuropathology in elderly people (45) and TMEM106B was identified as a key genetic determinant of differential aging in the cerebral cortex (30,47).…”

Omics Data and Their Integrative Analysis to Support Stratified Medicine in Neurodegenerative Diseases

“…In another study, researchers categorized people with late-onset AD into six biologically coherent subgroups based on clinical symptomatology and genetic backgrounds [ 53 ]. Additional stratification analyses of AD patients were also obtained by analyzing whole genome sequencing and whole transcriptome data from post-mortem brain tissues of AD patients, identifying clusters of patient-specific transcriptional signatures and demonstrating the high molecular variability and complexity of gene expression in AD [ 33 , 34 , 35 ] ( Table 1 ). In particular, Neff et al (2021), by analyzing transcriptomes across different AD affected brain regions, identified three major molecular subtypes of AD independent of age and disease severity, each one characterized by different combinations of dysregulated pathways and a unique set of key regulator genes, suggesting that specific gene modules are subtype-specific, and subtypes may be driven by a specific, yet diverse set of disease mechanisms that lead to AD [ 35 ].…”