Breast cancer risk is influenced by rare coding variants in susceptibility genes such as BRCA1 and many common, mainly non-coding variants. However, much of the genetic contribution to breast cancer risk remains unknown. We report results from a genome-wide association study (GWAS) 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 associated with overall breast cancer at p<5x10-8. The majority of credible risk SNPs in the new 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 SNPs 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 utility of genetic risk scores for individualized screening and prevention.

IntroductionHow much of the disease burden in a population could be eliminated if the effects of certain causal factors were eliminated from the population? To address this question, epidemiologists calculate the population attributable fraction. As noted in a recent editorial in the Journal, population attributable fraction estimates can help guide policymakers in planning public health interventions.' Despite numerous articles on population attributable fraction estimation, 2-7 errors in computation and interpretation persist. In addition, in certain settings, the value of a population attributable fraction estimate may be questionable. This commentary considers computational and conceptual issues relevant to population attributable fraction estimation that are infrequently discussed elsewhere, with illustrations from the breast cancer literature.

We studied the risk of early loss of pregnancy by collecting daily urine specimens from 221 healthy women who were attempting to conceive. Urinary concentrations of human chorionic gonadotropin (hCG) were measured for a total of 707 menstrual cycles with use of an immunoradiometric assay that is able to detect hCG levels as low as 0.01 ng per milliliter, with virtually 100 percent specificity for hCG in the presence of luteinizing hormone. Our criterion for early pregnancy--an hCG level above 0.025 ng per milliliter on three consecutive days--was determined after we compared the hCG levels in the study group with the levels in a comparable group of 28 women who had undergone sterilization by tubal ligation. We identified 198 pregnancies by an increase in the hCG level near the expected time of implantation. Of these, 22 percent ended before pregnancy was detected clinically. Most of these early pregnancy losses would not have been detectable by the less sensitive assays for hCG used in earlier studies. The total rate of pregnancy loss after implantation, including clinically recognized spontaneous abortions, was 31 percent. Most of the 40 women with unrecognized early pregnancy losses had normal fertility, since 95 percent of them subsequently became clinically pregnant within two years.

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 describe a log-linear method for analysis of case-parent-triad data, based on maximum likelihood with stratification on parental mating type. The method leads to estimates of association parameters, such as relative risks, for a single allele, and also to likelihood ratio chi2 tests (LRTs) of linkage disequilibrium. Hardy-Weinberg equilibrium need not be assumed. Our simulations suggest that the LRT has power similar to that of the chi2 "score" test proposed by Schaid and Sommer and that both can outperform the transmission/disequilibrium test (TDT), although the TDT can perform better under an additive model of inheritance. Because a restricted version of the LRT is asymptotically equivalent to the TDT, the proposed test can be regarded as a generalization of the TDT. The method that we describe generalizes easily to accommodate maternal effects on risk and, in fact, produces powerful and orthogonal tests of the contribution of fetal versus maternal genetic factors. We further generalize the model to allow for effects of parental imprinting. Imprinting effects can be fitted by a simple, iterative procedure that relies on the expectation-maximization algorithm and that uses standard statistical software for the maximization steps. Simulations reveal that LRT tests for detection of imprinting have very good operating characteristics. When a single allele is under study, the proposed method can yield powerful tests for detection of linkage disequilibrium and is applicable to a broader array of causal scenarios than is the TDT.

Among healthy women trying to conceive, nearly all pregnancies can be attributed to intercourse during a six-day period ending on the day of ovulation. For practical purposes, the timing of sexual intercourse in relation to ovulation has no influence on the sex of the baby.

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, 9q31.1) and one for endometrioid EOC (5q12.3). We then meta-analysed 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 an additional three 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 novel susceptibility gene for low grade/borderline serous EOC.

This article describes how genetic components of disease susceptibility can be evaluated in case-control studies, where cases and controls are sampled independently from the population at large. Subjects are assumed unrelated, in contrast to studies of familial aggregation and linkage. The logistic model can be used to test collapsibility over phenotypes or genotypes, and to estimate interactions between environmental and genetic factors. Such interactions provide an example of a context where non-hierarchical models make sense biologically. Also, if the exposure and genetic categories occur independently and the disease is rare, then analyses based only on cases are valid, and offer better precision for estimating gene-environment interactions than those based on the full data.