The shared genetic landscape of polycystic ovary syndrome and breast cancer: convergence on ER+ breast cancer but not ER- breast cancer

Background: The clinically high co-morbidity of polycystic ovary syndrome(PCOS) and breast cancer(BC) has been widely reported, but little is known about their shared genetic basis and mechanisms. Method: Using summary statistics from the largest GWASs to date, we performed a large-scale genome-wide cross-trait analysis of PCOS and BC with multiple genetic statistical methods to unlock potential shared genetic causes. Results: The study found some genetic overlap between the three trait pairs, and after dividing the entire genome into 2,495 independent regions, we observed chr8: 75,011,700- 76,295,483, and chr17: 6,305,079- 7,264,458 to be loci with significant localized genetic correlations. Pleiotropic analysis under a composite null hypothesis identified 1,183 significant potential pleiotropic SNPs in 3 trait pairs, FUMA mapped 26 pleiotropic loci in which 16q12.2 and 6q25.1were found to be duplicated across all three pairs of traits and then 3 colocalized loci detected by COLOC. The gene-based analysis identified 23 unique candidate pleiotropy genes, including the FTO locus shared by three trait pairs as well as SER1, RALB, and others. Whereas pathway enrichment analysis further highlighted the biological pathways present between PCOS-BCALL and PCOS-ERPBC that are primarily involved in prostate glandular acinus development, prostate glandular acinus morphogenesis and cellular response to stress. Latent Heritable Confounder Mendelian randomization (LHC-MR) further supports positive causality of PCOS in BCALL and ERPBC but not ERNBC. Conclusion: In 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.