PheWAS-Based Systems Genetics Methods for Anti-Breast Cancer Drug Discovery

Abstract

Breast cancer is a high-risk disease worldwide. For such complex diseases that are induced by multiple pathogenic genes, determining how to establish an effective drug discovery strategy is a challenge. In recent years, a large amount of genetic data has accumulated, particularly in the genome-wide identification of disorder genes. However, understanding how to use these data efficiently for pathogenesis elucidation and drug discovery is still a problem because the gene⁻disease links that are identified by high-throughput techniques such as phenome-wide association studies (PheWASs) are usually too weak to have biological significance. Systems genetics is a thriving area of study that aims to understand genetic interactions on a genome-wide scale. In this study, we aimed to establish two effective strategies for identifying breast cancer genes based on the systems genetics algorithm. As a result, we found that the GeneRank-based strategy, which combines the prognostic phenotype-based gene-dependent network with the phenotypic-related PheWAS data, can promote the identification of breast cancer genes and the discovery of anti-breast cancer drugs.

Keywords: PheWAS; breast cancer; drug discovery; systems genetics.

Figure 1

Pipeline of HotNet2-based anti-breast cancer drug discovery. A total of 522 breast cancer-associated single nucleotide polymorphisms (SNPs) were derived from the phenome-wide association study (PheWAS) [9]. The strongly linked variants of these SNPs were obtained by linkage disequilibrium (LD) analysis on the basis of the 1000 Genomes Project (r² ≥ 0.8). Then, the genes potentially regulated by the PheWAS-derived loci were identified through the combinatorial application of various information, such as physical proximity to the gene, gene expression quantitative trait loci (eQTL), and the locations of variants overlapped with DNase I-hypersensitive site (DHS) peaks. Finally, a total of 1742 breast cancer-associated genes were identified from the PheWAS data. After HotNet2 calculation, significant subnetworks including 227 genes were successfully identified from the original PheWAS data. Finally, these agents that target HotNet2-derived pathogenic genes were predicted to be potential anti-breast cancer drugs. PPI: protein–protein interaction. MeSH: Medical Subject Headings.

Figure 2

Pipeline of GeneRank-based anti-breast cancer drug discovery. Based on the 1742 PheWAS-identified breast cancer-related genes (a), we combined the gene-dependent network (b) to rank the original PheWAS data using the GeneRank algorithm (c). To cause the topology of the biological network and the original PheWAS to have the same weight, we set d = 0.5. Then, we performed a series of enrichment analyses on the original PheWAS (PheWAS-Rank gene set) and PheWAS-Rank top 100 genes (PheWAS-Rank gene set) (d). Finally, the agents that target the PheWAS-Rank gene set were predicted to be potential anti-breast cancer drugs (e).

Figure 3

KEGG functional analysis on the top 100 genes of the original PheWAS-derived gene list (Table S1) and the PheWAS-Rank gene list (Table S4) (p.adjust < 0.05) (Table S6). The PheWAS gene set enriched 41 KEGG pathways (a,c); the PheWAS-Rank gene set enriched 104 KEGG pathways (b,c); there were 36 KEGG pathways that were enriched in both of the gene lists (c). KEGG: Kyoto Encyclopedia of Genes and Genomes.

Figure 4

GO functional analysis (biological processes) of the top 100 genes of the PheWAS-Rank gene list (Table S4) (p.adjust < 0.05). The abscissa represents the GeneRatio. GO: Gene Ontology.

Figure 5

The original PheWAS top 100 gene list and PheWAS-Ranked top 100 gene list was validated with a Kolmogorov–Smirnov test using 63 known anti-breast cancer active drugs.