Integrative multi-omics analyses to identify the genetic and functional mechanisms underlying ovarian cancer risk regions

To identify credible causal risk variants (CCVs) associated with different histotypes of epithelial ovarian cancer (EOC), we performed genome-wide association analysis for 470,825 genotyped and 10,163,797 imputed SNPs in 25,981 EOC cases and 105,724 controls of European origin. We identified five new histotype-specific EOC risk regions (P-value < 5 x 10-8) and confirmed previously reported associations for 27 risk regions. Conditional analyses identified an additional 11 signals independent of the primary signal at six risk regions (P-value < 10-5). Fine mapping identified 4,008 CCVs in these regions, of which 1,452 CCVs were located in ovarian cancer-related chromatin marks with significant enrichment in active enhancers, active promoters, and active regions for CCVs from each EOC histotype. Transcriptome-wide association and colocalization analyses across histotypes using tissue-specific and cross-tissue data sets identified 86 candidate susceptibility genes in known EOC risk regions and 32 genes in 23 additional genomic regions that may represent novel EOC risk loci (FDR < 0.05). Finally, by integrating genome-wide HiChIP interactome analysis with TWAS, variant effect predictor, transcription factor ChIP-seq, and motifbreakR data, we identified candidate gene-CCV interactions at each locus. This included risk loci where TWAS identified one or more candidate susceptibility genes (e.g., HOXD-AS2, HOXD8 and HOXD3 at 2q31) and other loci where no candidate gene was identified (e.g., MYC and PVT1 at 8q24) by TWAS. In summary, this study describes a functional framework and provides a greater understanding of the biological significance of risk alleles and candidate gene targets at EOC susceptibility loci identified by GWAS. Overall design: Fine mapping of risk regions and epigenomic annotation to identify credible causal risk variants (CCVs) at each risk locus. Cell type specific epigenomic enrichment and partitioning heritability analysis and finding expression QTL based approaches to identify candidate susceptibility genes in EOC risk regions and 3D looping analysis to identify gene-CCV interactions at EOC risk loci