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

Ting Jin,Panagiotis Roussos,Shuang Liu,Jiawei Huang,Peter Rehani,Daifeng Wang,Mufang Ying

doi:10.1186/s13073-021-00908-9

Abstract

Understanding cell-type-specific gene regulatory mechanisms from genetic variants to diseases remains challenging. To address this, we developed a computational pipeline, scGRNom (single-cell Gene Regulatory Network prediction from multi-omics), to predict cell-type disease genes and regulatory networks including transcription factors and regulatory elements. With applications to schizophrenia and Alzheimer’s disease, we predicted disease genes and regulatory networks for excitatory and inhibitory neurons, microglia, and oligodendrocytes. Further enrichment analyses revealed cross-disease and disease-specific functions and pathways at the cell-type level. Our machine learning analysis also found that cell-type disease genes improved clinical phenotype predictions. scGRNom is a general-purpose tool available at https://github.com/daifengwanglab/scGRNom.

Highlights

Recent genome-wide association studies (GWAS) studies have identified a variety of genetic risk variants associated with multiple brain diseases
We found that the enhancers in our celltype gene regulatory network (GRN) are enriched with GWAS single nucleotide polymorphisms (SNPs) in human brain diseases, including psychiatric disorders and Alzheimer’sAlzheimer’s disease (AD)
We provided the numbers of GWAS SNPs for AD and SCZ that interrupt the binding sites of at least one of all possible transcription factor binding sites (TFBSs) and the binding sites of the regulatory transcriptional factors (TFs) in each cell-type GRN (Additional file 3: Table S1)

Summary

Introduction

Recent genome-wide association studies (GWAS) studies have identified a variety of genetic risk variants associated with multiple brain diseases. A recent study found 109 pleiotropic loci significantly associated with at least two brain disorders [1]. Recent studies have revealed shared symptoms at both psychiatric and physical levels between neurodegenerative and neuropsychiatric diseases [3]. Alzheimer’s disease (AD) and schizophrenia (SCZ) are neurodegenerative and neuropsychiatric diseases, respectively. Both are significantly associated with genetic variants and have complex underlying cellular and molecular mechanisms from genotype to phenotype [6, 7]. Amyloid beta plaques primarily originate from the apolipoprotein E-encoding gene APOE and its multiple variants.

Methods

Results

Discussion

Conclusion