scGRNom: a computational pipeline of integrative multi-omics analyses for predicting cell-type disease genes and regulatory networks

Abstract

Understanding cell-type-specific gene regulatory mechanisms from genetic variants to diseases remains challenging. To address this, we developed an open-source computational pipeline, scGRNom, to predict the cell-type disease genes and regulatory networks from multi-omics data, including cell-type chromatin interactions, epigenomics, and single-cell transcriptomics. With applications to Schizophrenia and Alzheimers Disease, our predicted cell-type regulatory networks link transcription factors and enhancers to disease genes for excitatory and inhibitory neurons, microglia, and oligodendrocytes. The enrichments of cell-type disease genes reveal cross-disease and disease-specific functions and pathways. Finally, machine learning analysis found that cell-type disease genes shared by diseases have improved clinical phenotype predictions.