Identifying Alzheimer's disease-associated genes using PhenoGeneRanker

Area of Science:

• Neuroscience
• Genomics
• Computational Biology

Background:

• Alzheimer's disease (AD) is a progressive neurodegenerative disorder affecting millions globally, with no effective treatments currently available.
• The complex genetic architecture of AD has been explored, yet its precise pathophysiology remains elusive.
• Identifying novel AD-associated genes is crucial for understanding disease mechanisms and developing therapeutic strategies.

Purpose of the Study:

• To discover new potential Alzheimer's disease-associated genes by integrating multimodal gene and phenotype datasets.
• To prioritize candidate genes using network biology approaches for enhanced discovery.
• To investigate the genomic underpinnings of AD through advanced computational methods.

Main Methods:

• Construction of a multiplex heterogeneous network integrating patient similarity and gene similarity networks.
• Utilization of phenotypic and omics datasets from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.
• Application of the PhenoGeneRanker algorithm to traverse the network and identify potential AD-associated genes.

Main Results:

• The top-ranked genes identified by PhenoGeneRanker included known AD-related genes.
• These top genes were significantly enriched in Gene Ontology (GO) terms relevant to Alzheimer's disease.
• Several novel genes, not previously linked to AD, showed supporting literature evidence for their potential role in the disease.

Conclusions:

• Network biology integration of multimodal data effectively prioritizes potential Alzheimer's disease-associated genes.
• The study identified novel candidate genes that warrant further investigation for their role in AD pathophysiology.
• This approach advances the discovery of genetic factors contributing to Alzheimer's disease, paving the way for future research.