Enhancing Predicted Gene Expression Models of Alzheimer's Disease Leveraging Single Cell Datasets

Music City Center 

The genetic architecture of Alzheimer's disease and related dementias (ADRD) is complex and polygenic. The advent of machine learning models that leverage expression quantitative trait loci (eQTL) databases to inform genomic prediction (e.g., PrediXcan) have dramatically improved the statistical power and biological interpretation in genomic studies of ADRD. We build on these methods by improving the cellular resolution leveraging single nucleus RNA sequencing datasets and deep quantitative traits harmonized as part of the AD sequencing project phenotype harmonization consortium (ADSP-PHC). First, we demonstrate the accuracy of predicted expression models for numerous cell types by validating model builds in an independent dataset, focusing on predicted gene expression models from excitatory neurons for an example. Next, we demonstrate novel associations with amyloid, tau, and cognitive decline leveraging these novel prediction models. Finally, we characterize our top gene candidate by exploring associations with observed expression at the bulk and single cell level to demonstrate the power of well-informed models of gene expression at single cell resolution.