Candidate gene prioritization for non-communicable diseases based on functional information: Case studies

Area of Science:

• Genetics
• Bioinformatics
• Computational Biology

Background:

• Prioritizing candidate genes is crucial for understanding complex non-communicable diseases.
• Existing methods for gene prioritization in protein-protein interaction (PPI) networks can be improved by integrating functional information.
• Identifying disease-associated genes aids in developing diagnostic and therapeutic strategies.

Purpose of the Study:

• To propose a novel candidate gene prioritization method for non-communicable diseases.
• To integrate functional information and disease risk transfer into weighted disease PPI networks.
• To investigate pathobiological similarities among Type 2 diabetes, coronary artery disease, and dilated cardiomyopathy.

Main Methods:

• Developed a gene prioritization method using weighted disease PPI networks with functional information.
• Incorporated disease risk transfer between genes as a weighting factor.
• Applied the method to Type 2 diabetes, coronary artery disease, and dilated cardiomyopathy as case studies.

Main Results:

• The proposed method effectively prioritized candidate genes for non-communicable diseases.
• Literature review and pathway enrichment analysis validated the top-ranked genes.
• The method outperformed existing approaches in gene prioritization.

Conclusions:

• The novel method enhances candidate gene prioritization for complex non-communicable diseases.
• Integrating functional information and disease risk transfer improves prioritization accuracy.
• The study reveals pathobiological similarities among T2D, CAD, and DCM through common top-ranked genes.