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Fulltext Genome-wide association studies identify susceptibility loci for epithelial ovarian cancer in east Asian women

Lawrenson K, Song F, Hazelett DJ, Kar SP, Tyrer J, Phelan CM, et al.

Gynecol Oncol, 2019 05;153(2):343-355.
PMID: 30898391 DOI: 10.1016/j.ygyno.2019.02.023

OBJECTIVE: Genome-wide association studies (GWASs) for epithelial ovarian cancer (EOC) have focused largely on populations of European ancestry. We aimed to identify common germline variants associated with EOC risk in Asian women.
METHODS: Genotyping was performed as part of the OncoArray project. Samples with >60% Asian ancestry were included in the analysis. Genotyping was performed on 533,631 SNPs in 3238 Asian subjects diagnosed with invasive or borderline EOC and 4083 unaffected controls. After imputation, genotypes were available for 11,595,112 SNPs to identify associations.
RESULTS: At chromosome 6p25.2, SNP rs7748275 was associated with risk of serous EOC (odds ratio [OR] = 1.34, P = 8.7 × 10-9) and high-grade serous EOC (HGSOC) (OR = 1.34, P = 4.3 × 10-9). SNP rs6902488 at 6p25.2 (r2 = 0.97 with rs7748275) lies in an active enhancer and is predicted to impact binding of STAT3, P300 and ELF1. We identified additional risk loci with low Bayesian false discovery probability (BFDP) scores, indicating they are likely to be true risk associations (BFDP <10%). At chromosome 20q11.22, rs74272064 was associated with HGSOC risk (OR = 1.27, P = 9.0 × 10-8). Overall EOC risk was associated with rs10260419 at chromosome 7p21.3 (OR = 1.33, P = 1.2 × 10-7) and rs74917072 at chromosome 2q37.3 (OR = 1.25, P = 4.7 × 10-7). At 2q37.3, expression quantitative trait locus analysis in 404 HGSOC tissues identified ESPNL as a putative candidate susceptibility gene (P = 1.2 × 10-7).
CONCLUSION: While some risk loci were shared between East Asian and European populations, others were population-specific, indicating that the landscape of EOC risk in Asian women has both shared and unique features compared to women of European ancestry.
Fulltext Genetic Data from Nearly 63,000 Women of European Descent Predicts DNA Methylation Biomarkers and Epithelial Ovarian Cancer Risk

Yang Y, Wu L, Shu X, Lu Y, Shu XO, Cai Q, et al.

Cancer Res, 2019 Feb 01;79(3):505-517.
PMID: 30559148 DOI: 10.1158/0008-5472.CAN-18-2726

DNA methylation is instrumental for gene regulation. Global changes in the epigenetic landscape have been recognized as a hallmark of cancer. However, the role of DNA methylation in epithelial ovarian cancer (EOC) remains unclear. In this study, high-density genetic and DNA methylation data in white blood cells from the Framingham Heart Study (N = 1,595) were used to build genetic models to predict DNA methylation levels. These prediction models were then applied to the summary statistics of a genome-wide association study (GWAS) of ovarian cancer including 22,406 EOC cases and 40,941 controls to investigate genetically predicted DNA methylation levels in association with EOC risk. Among 62,938 CpG sites investigated, genetically predicted methylation levels at 89 CpG were significantly associated with EOC risk at a Bonferroni-corrected threshold of P < 7.94 × 10-7. Of them, 87 were located at GWAS-identified EOC susceptibility regions and two resided in a genomic region not previously reported to be associated with EOC risk. Integrative analyses of genetic, methylation, and gene expression data identified consistent directions of associations across 12 CpG, five genes, and EOC risk, suggesting that methylation at these 12 CpG may influence EOC risk by regulating expression of these five genes, namely MAPT, HOXB3, ABHD8, ARHGAP27, and SKAP1. We identified novel DNA methylation markers associated with EOC risk and propose that methylation at multiple CpG may affect EOC risk via regulation of gene expression. SIGNIFICANCE: Identification of novel DNA methylation markers associated with EOC risk suggests that methylation at multiple CpG may affect EOC risk through regulation of gene expression.
Integrative multi-omics analyses to identify the genetic and functional mechanisms underlying ovarian cancer risk regions

Dareng EO, Coetzee SG, Tyrer JP, Peng PC, Rosenow W, Chen S, et al.

Am J Hum Genet, 2024 May 07.
PMID: 38723632 DOI: 10.1016/j.ajhg.2024.04.011

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 histotype-specific EOC risk regions (p value <5 × 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 datasets 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 (false discovery rate <0.05). Finally, by integrating genome-wide HiChIP interactome analysis with transcriptome-wide association study (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 a genome-wide association study.
Polygenic risk modeling for prediction of epithelial ovarian cancer risk

Dareng EO, Tyrer JP, Barnes DR, Jones MR, Yang X, Aben KKH, et al.

Eur J Hum Genet, 2022 Jan 14.
PMID: 35027648 DOI: 10.1038/s41431-021-00987-7

Polygenic risk scores (PRS) for epithelial ovarian cancer (EOC) have the potential to improve risk stratification. Joint estimation of Single Nucleotide Polymorphism (SNP) effects in models could improve predictive performance over standard approaches of PRS construction. Here, we implemented computationally efficient, penalized, logistic regression models (lasso, elastic net, stepwise) to individual level genotype data and a Bayesian framework with continuous shrinkage, "select and shrink for summary statistics" (S4), to summary level data for epithelial non-mucinous ovarian cancer risk prediction. We developed the models in a dataset consisting of 23,564 non-mucinous EOC cases and 40,138 controls participating in the Ovarian Cancer Association Consortium (OCAC) and validated the best models in three populations of different ancestries: prospective data from 198,101 women of European ancestries; 7,669 women of East Asian ancestries; 1,072 women of African ancestries, and in 18,915 BRCA1 and 12,337 BRCA2 pathogenic variant carriers of European ancestries. In the external validation data, the model with the strongest association for non-mucinous EOC risk derived from the OCAC model development data was the S4 model (27,240 SNPs) with odds ratios (OR) of 1.38 (95% CI: 1.28-1.48, AUC: 0.588) per unit standard deviation, in women of European ancestries; 1.14 (95% CI: 1.08-1.19, AUC: 0.538) in women of East Asian ancestries; 1.38 (95% CI: 1.21-1.58, AUC: 0.593) in women of African ancestries; hazard ratios of 1.36 (95% CI: 1.29-1.43, AUC: 0.592) in BRCA1 pathogenic variant carriers and 1.49 (95% CI: 1.35-1.64, AUC: 0.624) in BRCA2 pathogenic variant carriers. Incorporation of the S4 PRS in risk prediction models for ovarian cancer may have clinical utility in ovarian cancer prevention programs.
Fulltext Correction: Polygenic risk modeling for prediction of epithelial ovarian cancer risk

Dareng EO, Tyrer JP, Barnes DR, Jones MR, Yang X, Aben KKH, et al.

Eur J Hum Genet, 2022 May;30(5):630-631.
PMID: 35314806 DOI: 10.1038/s41431-022-01085-y