Association analysis identifies 65 new breast cancer risk loci, predicts target genes for known risk loci and demonstrates a strong overlap with somatic driver genes in breast tumours. Genome-wide association studies for breast cancer have identified common genetic variation that influences susceptibility to this disease, but much of the genetic risk remains unexplained. Doug Easton and colleagues report a genome-wide association study for breast cancer in more than 122,000 cases and 105,000 controls. The authors genotyped a subset of these cases using OncoArray, a new, custom genome-wide single-nucleotide polymorphism (SNP) array for cancer genomics. Overall, they identify 65 loci newly associated with breast cancer susceptibility, and estimate that, together with 107 previously identified breast cancer susceptibility loci, these explain about 18 per cent of the familial relative risk of breast cancer. Polygenic risk scores may be used in risk prediction models and may improve early detection and targeted prevention of the disease. Breast cancer risk is influenced by rare coding variants in susceptibility genes, such as BRCA1, and many common, mostly non-coding variants. However, much of the genetic contribution to breast cancer risk remains unknown. Here we report the results of a genome-wide association study of breast cancer in 122,977 cases and 105,974 controls of European ancestry and 14,068 cases and 13,104 controls of East Asian ancestry1. We identified 65 new loci that are associated with overall breast cancer risk at P < 5 × 10−8. The majority of credible risk single-nucleotide polymorphisms in these loci fall in distal regulatory elements, and by integrating in silico data to predict target genes in breast cells at each locus, we demonstrate a strong overlap between candidate target genes and somatic driver genes in breast tumours. We also find that heritability of breast cancer due to all single-nucleotide polymorphisms in regulatory features was 2–5-fold enriched relative to the genome-wide average, with strong enrichment for particular transcription factor binding sites. These results provide further insight into genetic susceptibility to breast cancer and will improve the use of genetic risk scores for individualized screening and prevention.

Stratification of women according to their risk of breast cancer based on polygenic risk scores (PRSs) could improve screening and prevention strategies. Our aim was to develop PRSs, optimized for prediction of estrogen receptor (ER)-specific disease, from the largest available genome-wide association dataset and to empirically validate the PRSs in prospective studies. The development dataset comprised 94,075 case subjects and 75,017 control subjects of European ancestry from 69 studies, divided into training and validation sets. Samples were genotyped using genome-wide arrays, and single-nucleotide polymorphisms (SNPs) were selected by stepwise regression or lasso penalized regression. The best performing PRSs were validated in an independent test set comprising 11,428 case subjects and 18,323 control subjects from 10 prospective studies and 190,040 women from UK Biobank (3,215 incident breast cancers). For the best PRSs (313 SNPs), the odds ratio for overall disease per 1 standard deviation in ten prospective studies was 1.61 (95%CI: 1.57-1.65) with area under receiver-operator curve (AUC) = 0.630 (95%CI: 0.628-0.651). The lifetime risk of overall breast cancer in the top centile of the PRSs was 32.6%. Compared with women in the middle quintile, those in the highest 1% of risk had 4.37- and 2.78-fold risks, and those in the lowest 1% of risk had 0.16- and 0.27-fold risks, of developing ER-positive and ER-negative disease, respectively. Goodness-of-fit tests indicated that this PRS was well calibrated and predicts disease risk accurately in the tails of the distribution. This PRS is a powerful and reliable predictor of breast cancer risk that may improve breast cancer prevention programs.

We have developed a set of web-based SNP selection tools (freely available at http://www.niehs.nih.gov/snpinfo) where investigators can specify genes or linkage regions and select SNPs based on GWAS results, linkage disequilibrium (LD), and predicted functional characteristics of both coding and non-coding SNPs. The algorithm uses GWAS SNP P-value data and finds all SNPs in high LD with GWAS SNPs, so that selection is from a much larger set of SNPs than the GWAS itself. The program can also identify and choose tag SNPs for SNPs not in high LD with any GWAS SNP. We incorporate functional predictions of protein structure, gene regulation, splicing and miRNA binding, and consider whether the alternative alleles of a SNP are likely to have differential effects on function. Users can assign weights for different functional categories of SNPs to further tailor SNP selection. The program accounts for LD structure of different populations so that a GWAS study from one ethnic group can be used to choose SNPs for one or more other ethnic groups. Finally, we provide an example using prostate cancer and demonstrate that this algorithm can select a small panel of SNPs that include many of the recently validated prostate cancer SNPs.

Paul Pharoah and colleagues report the results of a large genome-wide association study of ovarian cancer. They identify new susceptibility loci for different epithelial ovarian cancer histotypes and use integrated analyses of genes and regulatory features at each locus to predict candidate susceptibility genes, including OBFC1. To identify common alleles associated with different histotypes of epithelial ovarian cancer (EOC), we pooled data from multiple genome-wide genotyping projects totaling 25,509 EOC cases and 40,941 controls. We identified nine new susceptibility loci for different EOC histotypes: six for serous EOC histotypes (3q28, 4q32.3, 8q21.11, 10q24.33, 18q11.2 and 22q12.1), two for mucinous EOC (3q22.3 and 9q31.1) and one for endometrioid EOC (5q12.3). We then performed meta-analysis on the results for high-grade serous ovarian cancer with the results from analysis of 31,448 BRCA1 and BRCA2 mutation carriers, including 3,887 mutation carriers with EOC. This identified three additional susceptibility loci at 2q13, 8q24.1 and 12q24.31. Integrated analyses of genes and regulatory biofeatures at each locus predicted candidate susceptibility genes, including OBFC1, a new candidate susceptibility gene for low-grade and borderline serous EOC.

Background : Numerous studies have associated bladder cancer with exposure to carcinogens present in tobacco smoke and other environmental or occupational exposures. Approximately 50% of all humans inherit two deleted copies of the GSTM1 gene which encodes for the carcinogendetoxification enzyme glutathione S -transferase M1. Recent findings suggest that the GSTM1 gene may modulate the internal dose of environmental carcinogens and thereby affect the risk of developing bladder cancer. Purpose : We investigated whether the absence of the GSTM1 gene affects bladder cancer risk and whether there are racial differences in GSTM1 genotype frequency. Methods : Using a polymerase chain reaction (PCR)-based method, we examined the frequency of the homozygous deleted genotype (GSTM1 0/0) in 229 patients with transitional cell carcinoma of the bladder and 211 control subjects who were enrolled from the Urology Clinics at Duke University Medical Center and the University of North Carolina Hospitals. Control subjects were urology clinic patients who primarily presented with benign prostatic hypertrophy or impotence, who had no history of any cancer other than nonmelanoma skin cancer, and who were frequency matched to case patients on race, sex, and age (10-year age intervals). In order to explore racial differences in GSTM1 gene frequency, genotype was also determined in a community-based sample of 466 paid, healthy, unrelated volunteers from Durham and Chapel Hill, N.C. The presence or absence of the GSTM1 gene locus was determined by using a differential PCR, a semiquantitative technique in which multiple genes are coamplified. Results : Overall, the GSTM1 0/0 genotype conferred a 70% increased risk of bladder cancer (odds ratio [OR] = 1.7; 95% confidence interval [CI] = 1.2−2.5; P = .004). Absence of the GSTM1 gene encoding the glutathione S -transferase M1enzyme significantly increased risk to persons with exposure to the carcinogens in tobacco smoke (OR = 1.8; 95% CI = 1.2−3.0; P = .01) but poses little increased risk to persons without such exposure. Persons with smoking exposure of more than 50 pack-years who had the GSTM1 0/0 genotype had a sixfold greater risk relative to persons in the lowest risk group (i.e., nonsmokers who were GSTM1 +/+ or +/0). In the pooled clinic control and community sample groups (677 individuals), the GSTM1 0/0 genotype occurred less frequently among Blacks (35%) than among Whites (49%, P<.001). Conclusions : These findings support a protective role for the GSTM1 gene in bladder cancer. From these findings, it is estimated that 25% of all bladder cancer may be attributable to the at-risk GSTM1 0/0 genotype. [J Natl Cancer Inst 85: 1159–1164, 1993]