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... Show morePolygenic 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. Show less
Quantifying the genetic correlation between cancers can provide important insights into themechanisms driving cancer etiology. Using genome-wide association study summary sta-tistics across six... Show moreQuantifying the genetic correlation between cancers can provide important insights into themechanisms driving cancer etiology. Using genome-wide association study summary sta-tistics across six cancer types based on a total of 296,215 cases and 301,319 controls ofEuropean ancestry, here we estimate the pair-wise genetic correlations between breast,colorectal, head/neck, lung, ovary and prostate cancer, and between cancers and 38 otherdiseases. We observed statistically significant genetic correlations between lung and head/neck cancer (rg = 0.57, p = 4.6 × 10−8), breast and ovarian cancer (rg = 0.24, p = 7 × 10−5 ),breast and lung cancer (rg = 0.18, p =1.5 × 10−6) and breast and colorectal cancer (rg = 0.15,p = 1.1 × 10−4 ). We also found that multiple cancers are genetically correlated with non-cancer traits including smoking, psychiatric diseases and metabolic characteristics. Functionalenrichment analysis revealed a significant excess contribution of conserved and regulatoryregions to cancer heritability. Our comprehensive analysis of cross-cancer heritability sug-gests that solid tumors arising across tissues share in part a common germline genetic basis. Show less
