Alzheimer's disease heterogeneity explained by polygenic risk scores derived from brain transcriptomic profiles

Introduction: Alzheimer's disease (AD) is heterogeneous, both clinically and neuropathologically. We investigated whether polygenic risk scores (PRSs) integrated with transcriptome profiles from AD brains can explain AD clinical heterogeneity. Methods: We conducted co-expression network analysis and identified gene sets (modules) that were preserved in three AD transcriptome datasets and associated with AD-related neuropathological traits including neuritic plaques (NPs) and neurofibrillary tangles (NFTs). We computed the module-based PRSs (mbPRSs) for each module and tested associations with mbPRSs for cognitive test scores, cognitively defined AD subgroups, and brain imaging data. Results: Of the modules significantly associated with NPs and/or NFTs, the mbPRSs from two modules (M6 and M9) showed distinct associations with language and visuospatial functioning, respectively. They matched clinical subtypes and brain atrophy at specific regions. Discussion: Our findings demonstrate that polygenic profiling based on co-expressed gene sets can explain heterogeneity in AD patients, enabling genetically informed patient stratification and precision medicine in AD. HIGHLIGHTS: Co-expression gene-network analysis in Alzheimer's disease (AD) brains identified gene sets (modules) associated with AD heterogeneity. AD-associated modules were selected when genes in each module were enriched for neuritic plaques and neurofibrillary tangles. Polygenic risk scores from two selected modules were linked to the matching cognitively defined AD subgroups (language and visuospatial subgroups). Polygenic risk scores from the two modules were associated with cognitive performance in language and visuospatial domains and the associations were confirmed in regional-specific brain atrophy data.