Cell-Type-Specific Proteogenomic Signal Diffusion for Integrating Multi-Omics Data Predicts Novel Schizophrenia Risk...

Area of Science:

• Genomics
• Proteomics
• Neuroscience
• Systems Biology

Background:

• Schizophrenia (SCZ) research generates vast omics data, necessitating integrated analysis to understand complex genetic underpinnings.
• Modeling the interplay between genome, transcriptome, and proteome is crucial for dissecting SCZ etiology.
• Existing methods struggle to amplify weak genetic signals and identify convergent disease modules.

Purpose of the Study:

• To introduce Markov affinity-based proteogenomic signal diffusion (MAPSD), a novel computational method.
• To model intra-cellular protein trafficking and tissue-specific protein abundances.
• To identify SCZ risk loci, associated gene modules, and their biological functions.

Main Methods:

• MAPSD integrates multi-omics data (genome, transcriptome, proteome) to enhance signal detection.
• The method models protein trafficking and single-cell protein abundances.
• Statistical analyses were performed to identify significant risk loci and gene enrichments.

Main Results:

• MAPSD successfully amplified signals at SCZ risk loci with small effect sizes.
• Convergent disease-associated gene modules in the brain were revealed.
• Identified SCZ risk genes are enriched in neuronal cells (cerebral cortex, cerebellum) and implicated in neurodevelopmental pathways.

Conclusions:

• MAPSD is a powerful tool for integrating multi-omics data to understand SCZ pathogenesis.
• The identified genes and pathways offer insights into neurodevelopmental alterations in SCZ.
• MAPSD facilitates drug repurposing and is applicable to other polygenic diseases.