BackgroundThe clinically high comorbidity between polycystic ovary syndrome (PCOS) and breast cancer (BC) has been extensively reported. However, limited knowledge exists regarding their shared genetic basis and underlying mechanisms.MethodLeveraging summary statistics from the largest genome-wide association studies (GWASs) to date, we conducted a comprehensive genome-wide cross-trait analysis of PCOS and BC. A variety of genetic statistical methods were employed to uncover potential shared genetic causes.ResultsOur analysis revealed genetic overlap between the three trait pairs. After partitioning the genome into 2,495 independent regions, we identified two loci, chr8: 75,011,700–76,295,483 and chr17: 6,305,079–7,264,458, with significant localized genetic correlations. Pleiotropic analysis under a composite null hypothesis identified 1,183 significant pleiotropic single nucleotide polymorphisms (SNPs) across three trait pairs. FUMA mapped 26 pleiotropic loci, with regions 16q12.2 and 6q25.1 duplicated across all three trait pairs, while COLOC detected three loci with colocalization evidence. Gene-based analysis identified 23 unique candidate pleiotropic genes, including the FTO shared by all trait pairs, as well as SER1, RALB, and others in two trait pairs. Pathway enrichment analysis further highlighted key biological pathways, primarily involving the significant biological pathways were the metabolism of regulation of autophagy, regulation of cellular catabolic process, and positive regulation of catabolic process. Latent Heritable Confounder Mendelian randomization (LHC-MR) supported a positive causal relationship between PCOS and both BCALL and ERPBC but not with ERNBC.ConclusionIn conclusion, our genome-wide cross-trait analysis identified a shared genetic basis between PCOS and BC, specific identical genetic mechanisms and causality between PCOS and various BC subtypes, which could better explains the genetics of the co-morbidity of PCOS and ERPBC rather than PCOS and ERNBC. These findings provide new insights into the biological mechanisms underlying the co-morbidity of these two complex diseases, which have important implications for clinical disease intervention, treatment, and improved prognosis.
Read full abstract