Multiple novel prostate cancer susceptibility signals identified by fine-mapping of known risk loci among Europeans.

Genome-wide association studies (GWAS) have identified numerous common prostate cancer (PrCa) susceptibility loci. We have fine-mapped 64 GWAS regions known at the conclusion of the iCOGS study using large-scale genotyping and imputation in 25 723 PrCa cases and 26 274 controls of European ancestry. We detected evidence for multiple independent signals at 16 regions, 12 of which contained additional newly identified significant associations. A single signal comprising a spectrum of correlated variation was observed at 39 regions; 35 of which are now described by a novel more significantly associated lead SNP, while the originally reported variant remained as the lead SNP only in 4 regions. We also confirmed two association signals in Europeans that had been previously reported only in East-Asian GWAS. Based on statistical evidence and linkage disequilibrium (LD) structure, we have curated and narrowed down the list of the most likely candidate causal variants for each region. Functional annotation using data from ENCODE filtered for PrCa cell lines and eQTL analysis demonstrated significant enrichment for overlap with bio-features within this set. By incorporating the novel risk variants identified here alongside the refined data for existing association signals, we estimate that these loci now explain ∼38.9% of the familial relative risk of PrCa, an 8.9% improvement over the previously reported GWAS tag SNPs. This suggests that a significant fraction of the heritability of PrCa may have been hidden during the discovery phase of GWAS, in particular due to the presence of multiple independent signals within the same region.

Common genetic variants associated with disease from genome-wide association studies are mutually exclusive in prostate cancer and rheumatoid arthritis.

UNLABELLED: WHAT'S KNOWN ON THE SUBJECT? AND WHAT DOES THE STUDY ADD?: The link between inflammation and cancer has long been reported and inflammation is thought to play a role in the pathogenesis of many cancers, including prostate cancer (PrCa). Over the last 5 years, genome-wide association studies (GWAS) have reported numerous susceptibility loci that predispose individuals to many different traits. The present study aims to ascertain if there are common genetic risk profiles that might predispose individuals to both PrCa and the autoimmune inflammatory condition, rheumatoid arthritis. These results could have potential public heath impact in terms of screening and chemoprevention. OBJECTIVES: To investigate if potential common pathways exist for the pathogenesis of autoimmune disease and prostate cancer (PrCa). To ascertain if the single nucleotide polymorphisms (SNPs) reported by genome-wide association studies (GWAS) as being associated with susceptibility to PrCa are also associated with susceptibility to the autoimmune disease rheumatoid arthritis (RA). MATERIALS AND METHODS: The original Wellcome Trust Case Control Consortium (WTCCC) UK RA GWAS study was expanded to include a total of 3221 cases and 5272 controls. In all, 37 germline autosomal SNPs at genome-wide significance associated with PrCa risk were identified from a UK/Australian PrCa GWAS. Allele frequencies were compared for these 37 SNPs between RA cases and controls using a chi-squared trend test and corrected for multiple testing (Bonferroni). RESULTS: In all, 33 SNPs were able to be analysed in the RA dataset. Proxies could not be located for the SNPs in 3q26, 5p15 and for two SNPs in 17q12. After applying a Bonferroni correction for the number of SNPs tested, the SNP mapping to CCHCR1 (rs130067) retained statistically significant evidence for association (P = 6 × 10(-4) ; odds ratio [OR] = 1.15, 95% CI: 1.06-1.24); this has also been associated with psoriasis. However, further analyses showed that the association of this allele was due to confounding by RA-associated HLA-DRB1 alleles. CONCLUSIONS: There is currently no evidence that SNPs associated with PrCa at genome-wide significance are associated with the development of RA. Studies like this are important in determining if common genetic risk profiles might predispose individuals to many diseases, which could have implications for public health in terms of screening and chemoprevention.

The effect of sample size on polygenic hazard models for prostate cancer.

We determined the effect of sample size on performance of polygenic hazard score (PHS) models in prostate cancer. Age and genotypes were obtained for 40,861 men from the PRACTICAL consortium. The dataset included 201,590 SNPs per subject, and was split into training and testing sets. Established-SNP models considered 65 SNPs that had been previously associated with prostate cancer. Discovery-SNP models used stepwise selection to identify new SNPs. The performance of each PHS model was calculated for random sizes of the training set. The performance of a representative Established-SNP model was estimated for random sizes of the testing set. Mean HR98/50 (hazard ratio of top 2% to average in test set) of the Established-SNP model increased from 1.73 [95% CI: 1.69-1.77] to 2.41 [2.40-2.43] when the number of training samples was increased from 1 thousand to 30 thousand. Corresponding HR98/50 of the Discovery-SNP model increased from 1.05 [0.93-1.18] to 2.19 [2.16-2.23]. HR98/50 of a representative Established-SNP model using testing set sample sizes of 0.6 thousand and 6 thousand observations were 1.78 [1.70-1.85] and 1.73 [1.71-1.76], respectively. We estimate that a study population of 20 thousand men is required to develop Discovery-SNP PHS models while 10 thousand men should be sufficient for Established-SNP models.

A Genetic Risk Score to Personalize Prostate Cancer Screening, Applied to Population Data.

BACKGROUND: A polygenic hazard score (PHS), the weighted sum of 54 SNP genotypes, was previously validated for association with clinically significant prostate cancer and for improved prostate cancer screening accuracy. Here, we assess the potential impact of PHS-informed screening. METHODS: United Kingdom population incidence data (Cancer Research United Kingdom) and data from the Cluster Randomized Trial of PSA Testing for Prostate Cancer were combined to estimate age-specific clinically significant prostate cancer incidence (Gleason score ≥7, stage T3-T4, PSA ≥10, or nodal/distant metastases). Using HRs estimated from the ProtecT prostate cancer trial, age-specific incidence rates were calculated for various PHS risk percentiles. Risk-equivalent age, when someone with a given PHS percentile has prostate cancer risk equivalent to an average 50-year-old man (50-year-standard risk), was derived from PHS and incidence data. Positive predictive value (PPV) of PSA testing for clinically significant prostate cancer was calculated using PHS-adjusted age groups. RESULTS: The expected age at diagnosis of clinically significant prostate cancer differs by 19 years between the 1st and 99th PHS percentiles: men with PHS in the 1st and 99th percentiles reach the 50-year-standard risk level at ages 60 and 41, respectively. PPV of PSA was higher for men with higher PHS-adjusted age. CONCLUSIONS: PHS provides individualized estimates of risk-equivalent age for clinically significant prostate cancer. Screening initiation could be adjusted by a man's PHS. IMPACT: Personalized genetic risk assessments could inform prostate cancer screening decisions.