An analytical method for the identification of cell type-specific disease gene modules

Area of Science:

• Neuroscience and Genetics
• Computational Biology and Bioinformatics

Background:

• Genome-wide association studies (GWAS) link genetic variants to brain diseases but often lack cell-type specificity.
• Understanding which specific cell types are vulnerable is crucial for elucidating disease mechanisms.
• Disease-associated genes are frequently expressed across multiple brain cell types, yet pathology may be confined to specific cell populations.

Purpose of the Study:

• To investigate the cell type-specificity of genetic contributions to brain-related diseases.
• To identify disease gene modules within specific human brain cell types.
• To develop a computational method for detecting cell type-specific disease modules.

Main Methods:

• Construction of human brain cell type-specific gene interaction networks using gene expression data.
• Integration of nucleus gene expression data with a reference tissue-specific gene interaction network.
• Statistical identification of significant cell type-specific disease gene modules from constructed networks.

Main Results:

• Distinct gene networks and functions were observed between neurons and glia cells.
• Cell type-specific disease gene modules for autism spectrum disorder (ASD) showed distinct dysregulated functions in different cell types.
• Comparative analysis revealed shared neuronal module functions (synapse-related) across ASD, schizophrenia, and bipolar disorder, with distinct glial module functions.

Conclusions:

• Disease manifestation in specific cell types is driven by statistically significant disease gene modules.
• Identification of cell type-specific modules facilitates targeted biomarker and therapeutic development.
• The developed method (CtsDGM R package) aids in understanding disease heterogeneity and inter-disease similarities/dissimilarities.