Genome-wide association results from: Transcriptomic stratification of late-onset Alzheimer’s cases reveals novel genetic modifiers of disease pathology

Late-Onset Alzheimer’s disease (LOAD) is a common, complex genetic
disorder well-known 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, rs1990620G) 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.