Projecting genetic associations through gene expression patterns highlights disease etiology and drug mechanisms

Transcriptome-wide association studies have helped uncover the role of individual genes in disease-relevant mechanisms, explain researchers from the CU Department of Biomedical Informatics. However, modern models of the architecture of complex traits predict that gene-gene interactions play a crucial role in disease origin and progression. Researchers introduce PhenoPLIER, a computational approach that maps gene-trait associations and pharmacological perturbation data into a common latent representation for a joint analysis and observe that diseases are significantly associated with gene modules expressed in relevant cell types, and our approach is accurate in predicting known drug-disease pairs and inferring mechanisms of action.