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.

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.

Most common breast cancer susceptibility variants have been identified through genome-wide association studies (GWAS) of predominantly estrogen receptor (ER)-positive disease1. We conducted a GWAS using 21,468 ER-negative cases and 100,594 controls combined with 18,908 BRCA1 mutation carriers (9,414 with breast cancer), all of European origin. We identified independent associations at P < 5 × 10−8 with ten variants at nine new loci. At P < 0.05, we replicated associations with 10 of 11 variants previously reported in ER-negative disease or BRCA1 mutation carrier GWAS and observed consistent associations with ER-negative disease for 105 susceptibility variants identified by other studies. These 125 variants explain approximately 14% of the familial risk of this breast cancer subtype. There was high genetic correlation (0.72) between risk of ER-negative breast cancer and breast cancer risk for BRCA1 mutation carriers. These findings may lead to improved risk prediction and inform further fine-mapping and functional work to better understand the biological basis of ER-negative breast cancer.

Given their increased risk of second cancers and co-morbid chronic conditions, the lack of difference in behavioral risk factors confirms the need for a focus on improving the health behaviors of cancer survivors. Collaborative chronic disease management models may be particularly appropriate in this regard.

The results of this systematic review and meta-analysis suggest that physical activity is associated with a reduced risk of both proximal colon and distal colon cancers, and that the magnitude of the association does not differ by subsite. Given this finding, future research on physical activity and colon cancer should focus on other aspects of the association that remain unclear, such as whether sedentary behavior and nonaerobic physical activity are associated with the risk of colon cancer.

Genome-wide association studies have identified breast cancer risk variants in over 150 genomic regions, but the mechanisms underlying risk remain largely unknown. These regions were explored by combining association analysis with in silico genomic feature annotations. We defined 205 independent risk-associated signals with the set of credible causal variants (CCVs) in each one. In parallel, we used a Bayesian approach (PAINTOR) that combines genetic association, linkage disequilibrium, and enriched genomic features to determine variants with high posterior probabilities of being causal. Potentially causal variants were significantly over-represented in active gene regulatory regions and transcription factor binding sites. We applied our INQUSIT Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:

BackgroundSelf-report remains the most practical and cost-effective method for epidemiologic sleep studies involving large population-based samples. Several validated questionnaires have been developed to assess sleep, but these tools are lengthy to administer and may be impractical for epidemiologic studies. We examined whether a 3-item sleep questionnaire, similar to those typically used in epidemiologic studies, closely corresponded with objective measures of sleep as assessed using actigraphy monitoring.MethodsEligible participants were Western Australian women aged 18 to 80 years. Participants completed a sleep questionnaire, wore a wrist actigraph for 7 nights, and completed a brief daily sleep log. Objective actigraphy measurements for 56 participants were summarized by mean and mode and compared with the subjective reports, using weighted kappa and delta.ResultsData collected from the questionnaire showed poor agreement with objectively measured sleep, with kappas ranging from −0.19 to 0.14.ConclusionsOur results indicate that sleep questions typically used in epidemiologic studies do not closely correspond with objective measures of sleep as assessed using actigraphy. The findings have implications for studies that have used such sleep questions. A means of appropriately measuring sleep as a risk factor in epidemiologic studies remains to be determined.

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