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

by Nikhil Milind, Christoph Preuss, Annat Haber, Guruprasad Ananda, Shubhabrata Mukherjee, Cai John, Sarah Shapley, Benjamin A. Logsdon, Paul K. Crane, Gregory W. Carter 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-c ontrol 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 independen t 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-ex pression 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 phe notype in order to identify genetic loci that contribute to disease heterogeneity. Distinct LOAD subtypes were identified for a...
Source: PLoS Genetics - Category: Genetics & Stem Cells Authors: Source Type: research