Our study investigates the use of single-cell gene expression profiles to improve the identification of disease-causing variants in rare disease patients. We integrate established databases of pathogenic variants with genome-wide pathogenicity scores, human phenotype ontology annotations, and extensive expression data maps. We evaluate the effectiveness of various pseudobulk methods and compare the utility of tissue-specific versus cell type-specific expression data in variant prioritization.