RT Journal Article
SR Electronic
T1 scGRNom: a computational pipeline of integrative multi-omics analyses for predicting cell-type disease genes and regulatory networks
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.06.11.147314
DO 10.1101/2020.06.11.147314
A1 Ting Jin
A1 Peter Rehani
A1 Mufang Ying
A1 Jiawei Huang
A1 Shuang Liu
A1 Panagiotis Roussos
A1 Daifeng Wang
YR 2021
UL http://biorxiv.org/content/early/2021/02/22/2020.06.11.147314.abstract
AB 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 Alzheimer’s 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.Competing Interest StatementThe authors have declared no competing interest.