Identifying Alzheimer's disease-associated genes using PhenoGeneRanker

Abstract

Alzheimer's disease (AD) is a neurogenerative disease that affects millions worldwide with no effective treatment. Several studies have been conducted to decipher to genomic underpinnings of AD. Due to its complex nature, many genes have been found to be associated with AD. Despite these findings, the pathophysiology of the disease is still elusive. To discover new putative AD-associated genes, in this study, we integrated multimodal gene and phenotype datasets of AD using network biology methods to prioritize potential AD-related genes. We constructed a multiplex heterogeneous network composed of patient and gene similarity networks utilizing phenotypic and omics datasets of AD patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. We applied PhenoGeneRanker to traverse this network to discover potential AD-associated genes. To assess the impact of each network layer and seed gene, we also run PhenoGeneRanker on different variants of the network and seed genes. Our results showed that top-ranked genes captured several known AD-related genes and were enriched in Gene Ontology (GO) terms related to AD. We also observed that several top-ranked genes that are not in AD-associated gene list had literature supporting their potential relevance to AD.

Keywords: APP; Alzheimer’s Disease genes; PhenoGeneRanker; gene prioritization; network biology; network propagation.

Figure 1:

Overall pipeline to identify AD-associated genes. Three patient networks were generated from patients’ demographic, cognitive, and neuro-imaging data modalities. Two gene networks were built using gene expression and PPI data. PhenoGeneRanker was applied to the entire network to rank genes based on their potential association with AD seed genes. Finally, genes were ranked with associate p-value.

Figure 2:

Percentage of known AD genes appearing in the top-ranked genes of PhenoGeneRanker with seed combinations and multiple networks. A. Percentage of known AD genes appearing in the top-ranked genes of PhenoGeneRanker with using different seed genes on the original network of the reference run. B. Percentage of known AD genes appearing in the top-ranked genes of PhenoGeneRanker with using different network components. The reference run uses two gene and three patient layers with APP as a seed gene. Without PPI layer, APOE is used as the seed gene because APP is not present in the gene co-expression network. Other runs are the same as the reference run except for the one change described in the figure legend. See Section 3.2 for the description of random layer generation.

Figure 3:

Effect of different hyperparameters on the percentage of AD gene coverage. CDF plots for different values of A) δ, B) ζ, C) λ, D) r, E) τ, and F) η. For each run, all the other hyperparameters were set to their default values (see Table 4) and APP was used as the seed gene.

Figure 3:

Effect of different hyperparameters on the percentage of AD gene coverage. CDF plots for different values of A) δ, B) ζ, C) λ, D) r, E) τ, and F) η. For each run, all the other hyperparameters were set to their default values (see Table 4) and APP was used as the seed gene.