Multivariate Analysis of Potential Pleiotropic Genes For Breast, Ovarian And Cervical Cancers Using Gene-Based Association Analysis

Abstract Although genome-wide association studies (GWAS) have a dramatic impact on susceptibility locus discovery in gynecological malignancies, the single nucleotide polymorphisms (SNPs) identified by this prevailing univariate approach only explain a small percentage of heredity. The extensive previous studies have repeatedly shown breast, ovarian and cervical cancers have common genetic mechanisms and the overlapping pathophysiological pathways. Novel multivariate analytical methods are necessary to identify shared pleiotropic genes. In this study, a total of 40,859 SNPs mapped in 11,597 gene regions were performed to identify potential common variants by using metaCCA and VEGAS2 analysis. Gene enrichment and protein-protein interaction (PPI) network analysis were used to explore potential biological pathways and connectivity. After metaCCA analysis, 4,203 SNPs (P<1.22×10−6) and 1,886 pleotropic gene (P<4.31×10−6) were identified. By screening the results of gene-based P-values, the existence of 3 confirmed pleiotropic genes and 16 novel genes that achieved statistical significance in the metaCCA analysis and were also associated with at least one cancer in the VEGAS2 analysis were identified. The enrichment analysis showed the biological pathways of these genes were mainly enriched in 4 signaling pathways and 11 differentially expressed genes were found to encode interacting proteins in PPI network analysis. Altogether, we identified novel genetic variants of breast, ovarian and cervical cancers and provided evidence of biological functions which developed new insights for the diagnosis and treatment of these cancers.