The clinical utility of machine-learning (ML) algorithms for breast cancer risk prediction and screening practices is unknown. We compared classification of lifetime breast cancer risk based on ML and the BOADICEA model. We explored the differences in risk classification and their clinical impact on screening practices.We used three different ML algorithms and the BOADICEA model to estimate lifetime breast cancer risk in a sample of 112,587 individuals from 2481 families from the Oncogenetic Unit, Geneva University Hospitals. Performance of algorithms was evaluated using the area under the receiver operating characteristic (AU-ROC) curve. Risk reclassification was compared for 36,146 breast cancer-free women of ages 20–80. The impact on recommendations for mammography surveillance was based on the Swiss Surveillance Protocol.The predictive accuracy of ML-based algorithms (0.843 ≤ AU-ROC ≤ 0.889) was superior to BOADICEA (AU-ROC = 0.639) and reclassified 35.3% of women in different risk categories. The largest reclassification (20.8%) was observed in women characterised as ‘near population’ risk by BOADICEA. Reclassification had the largest impact on screening practices of women younger than 50.ML-based reclassification of lifetime breast cancer risk occurred in approximately one in three women. Reclassification is important for younger women because it impacts clinical decision- making for the initiation of screening. The guidelines classify women into moderate (17% ≤ lifetime risk <30%) or high (lifetime risk ≥30%) breast cancer risk calculated with the BOADICEA model based on different scenarios of family history.13Breast cancer screening with biennial mammograms for women 50–74-years old has been recommended by the U.S. Preventive Services Task Force since 2009.14 However, about 25% of all breast cancers are diagnosed in younger women.16 Moreover, mammography is less effective as a screening tool for younger women, who are more likely to have dense breast tissue, compromising the efficiency of routine mammograms in this age group. This contributes to diagnostic delays and increased morbidity and mortality.17 In the era of personalised medicine, a screening strategy based on individual breast cancer risk may improve the benefit–harm ratio of mammography, and increase the efficiency of screening programmes.19 Many medical societies and professional groups proposed that risk-based screening would be more effective, less morbid and more cost-effective.24Age over 50 years is the sole risk factor considered for entering a population-based screening programme.27 no consistent model has been incorporated into routine clinical practice and/or screening programmes due to limited discriminatory accuracy and applicability. The discriminatory ability, area under the receiver operating characteristic (AU-ROC) curve, of these models is between 0.53 and 0.64.31 A comprehensive risk prediction model with an improved discriminatory power to classify women into clinically meaningful risk groups will enable targeting high-risk women, while reducing interventions in those at low risk.Although many models are used to predict breast cancer risk, such as the Breast Cancer Risk Assessment Tool , the International Breast Intervention Study (IBIS) model, the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA model),33 A series of ML techniques, including our own work, have been developed and used in breast cancer prediction and prognosis, demonstrating that the application of ML methods could improve the prediction accuracy of cancer susceptibility, recurrence and survival models.39 Previous studies presented the discriminatory accuracy, sensitivity, specificity and calibration performance of different ML algorithms. However, clinical utility, in terms of potential clinical consequences of using new ML prediction models, is rarely examined. The objective of the current study is to assess the impact of using ML-based breast cancer risk prediction on screening practices. Using data from a large clinical population, we quantified performance measure and reclassification of lifetime breast cancer risk generated from ML algorithms and from the BOADICEA model. We also examined the clinical impact of reclassification of breast cancer risk on screening practices based on the Swiss Surveillance Protocol.13Machine-learning (ML) algorithms offer an alternative approach to standard prediction modelling that may address current limitations and improve the accuracy of breast cancer prediction models.40 Data used in this study were collected as part of routine medical records. The Regional Research Ethics Committee at the University Hospitals of Geneva has approved the data collection and management processes. Informed consent was obtained from all participants included in the study before genetic testing.The Oncogenetic Unit at the Geneva University Hospitals has been offering genetic counselling and testing for hereditary cancer syndromes since 1994 to patients and asymptomatic individuals concerned with their family history. The common reasons for genetic consultation are familial aggregation of breast or colorectal cancer or suspicion of hereditary cancer syndromes, mainly due to breast, ovarian or colon cancer. For each individual seen in consultation, demographic, personal and family history, previous genetic test results and a detailed family pedigree are collected and recorded with the ‘Progeny' software.41 Extracted data were suitable for risk calculations with the BOADICEA model for multiple female members from each family. There were about 13% missing values. BRCA1/BRCA2 status, oestrogen receptor and progesterone receptor status contributed 11%. In addition to ‘positive' and ‘negative', missing values for BRCA pathogenic variants and hormone receptor testing were characterised as ‘unknown' in subsequent analyses. This approach is also consistent with the flexibility of the BOADICEA model in handling missing information.For the purposes of this study, information regarding pathology reports, archived tumour tissue and cancer treatment, was extracted from medical records for cancer patients and affected relatives, whenever possible. Data from genetic consultation records and Progeny files were extracted with R packages ‘tm’ and ‘gdata’.Lifetime risk predictions were generated with the web-based batch processing from the BOADICEA web application (version 3.0) using data from 2481 families with 112,587 family members. The lifetime breast cancer risk for each woman in every family was calculated using data from all other family members, and by assigning every female family member once as the targeted woman for risk calculation.We generated breast cancer lifetime risk predictions for all female members within each family.42 adaptive boosting (ADA) and random forest (RF).45 The input for the ML algorithms used identical risk factors as the ones included in the BOADICEA model in order to have fair comparisons among the different risk prediction models. The variables included in each comparison are presented in Supplementary Table Based on previous reports of method reliability, effectiveness and performance in identifying, tracking and exploiting salient features in similar samples with the same data structures, we selected three ML algorithms, i.e., Markov Chain Monte Carlo generalised linear mixed model (MCMC GLMM),47 SMOTE implements known ML techniques to adaptively select the most appropriate strategy for a given unbalanced task. To ensure the reliability of ML predictions and the consistency of the forecasts, we used 10-fold cross-validation with 20 repetitions. This strategy provides a powerful preventive measure against model overfitting.50In our supervised classification, we rebalanced the breast cancer patients and cancer-free controls to reduce potential bias with the R packages ‘unbalanced'  and ‘SMOTE' (Synthetic Minority Over-sampling TEchnique).n = 45,110 women using the AU-ROC, while the performance of ML techniques is presented with the mean AU-ROC from 10-fold cross-validations.BOADICEA cannot be applied for females older than 80 years, for males and for deceased individuals. Thus, we excluded all predictions generated for those individuals when we compared the performance of ML algorithms with the BOADICEA model. The performance of BOADICEA was evaluated from n = 36,146 breast cancer-free women.According to the Swiss Surveillance Protocol, we applied the following cut-offs for lifetime breast cancer risk: <17% as near-population risk, ≥17% and < 30% as moderate risk and ≥30% as high-risk group. We excluded women who were under 20-years old or had been diagnosed with breast cancer to be consistent with the clinical utility of the protocol. We estimated differences in breast cancer risk classification using the BOADICEA model and the best-performing ML algorithm, based on the data from Frequencies, percentages, means and standard deviations were used to describe the demographics and clinical characteristics of 36,146 breast cancer-free women. We present classifications by age and risk categories using the BOADICEA model as the reference standard. Differences in classification for mammography surveillance according to the Swiss Surveillance Protocol were calculated for the moderate- and high-risk groups.A consort flow diagram Fig.  presentsThe mean age of the 45,110 women was 49.82 (±11.02) years old. There were 4911 breast cancer patients with average age onset at 51.76 (±9.79) years old. Among them, 554 had a second breast cancer diagnosis. There were 119 cases with first-ductal carcinoma in situ (DCIS). Table n = 36,146 breast cancer-free women, 2617 (7.24%) had a diagnosis of another type of cancer. In the total sample, only few breast cancer-free women  were tested for BRCA1 and/or BRCA2 germline pathogenic variants, including both complete and targeted testing. Most of these women had a targeted genetic testing, i.e., the search for a pathogenic variant previously identified in the family, since consultations were limited to situations that are highly suggestive for a hereditary syndrome and, whenever possible, genetic testing was offered first to breast cancer patients belonging to the family.Table When using the BOADICEA model as the reference standard, and based on the lifetime breast cancer risk cut-offs from the Swiss clinical guidelines, 58.8% of all samples were categorised as near-population risk, 32.3% as moderate risk and 8.8% as high risk . When we reclassified women with ML algorithms, applying cut-offs of 17% and 30% resulted in shifting relatively large proportions of women between different risk groups. This indicates that for ML algorithms, categorisation of different risk groups  should be probably based on different cut-offs, based on a clinically meaningful decision of their sensitivity and specificity.51Given that breast cancer screening guidelines were established after the release of several commonly used risk prediction models, including BOADICEA, the guideline cut-offs (risk categorisation) have been greatly influenced by these models. According to several validation studies of the BOADICEA model, about 80–90% of women were classified as having a lifetime breast cancer risk between 5 and 25% (near population or moderate risk).There are several barriers for using risk prediction models in a wide variety of settings. First, each risk prediction model uses different risk factors. The panel of risk factors used in the development of each model limits its applicability and validity in broader segments of the population. ML models can be applied in medical consultation contexts where similar data inputs were collected. Currently, the most feasible way of following the Swiss Surveillance Protocol is through consultation with a medical specialist. In this context, clinical decisions about risk management options are likely influenced by risk calculations from such prediction models. Secondly, existing infrastructures for collection and assessment of clinical data limit the development of risk prediction models and their generalisability in broader segments of the population. ML approaches have the potential to achieve better accuracy, and can incorporate different types of information, including mammographic images, family history, germline genetic data and clinical factors. However, currently there are no comprehensive systems that incorporate data from such diverse sources, e.g., screening programmes, medical consultations and medical records. In order to develop a risk prediction model that can be used to enhance national screening programmes, the usefulness of accessible risk factors from screening practice, e.g., breast density and previous benign breast disease, should be assessed. Based on the predictive ability of each risk factor, and the feasibility of collecting relevant data in the screening setting, a parsimony panel of risk factors would be applied in ML modelling to develop a comprehensive model that supports effective clinical decision-making. However, limited resources have been invested into this promising new analytic approach.34 They have been partially evaluated for external validity using internal statistical cross-validation, a process where each fold iteration relies on separate and independent training and testing datasets. For fully assessing the external validity, we need to evaluate prospective samples from populations intrinsically different from the development sample, in respect to location, time or methods/criteria used for data collection, which is a gradual process commonly applied to prediction models.30 Current screening guidelines already incorporate risk estimates from existing prediction tools based on inputs from medical consultation contexts. Thus, it is important to study the potential clinical utility of ML as a promising alternative analytic approach, even with limited information from screening practice. Finally, breast cancer surveillance guidelines define ‘population level risk' as having a lifetime risk <17% calculated from the BOADICEA model. This risk estimate does not necessarily mean ‘low' risk in the general population due to potential misclassifications. In our reclassification results, the BOADICEA model classified 21,293 (58.9%) samples into the population-level risk. Thus, our sample is ‘suitable' for the comparison and covers sufficiently women with a wide range of risk estimates based on current recommendations.Our results are reliable because we used a limited number of well-established breast cancer risk factors without feature selection and relatively non-complex ML models, which helps mitigate the ‘black-box' nature of ML algorithms. They are also reliable due to the large sample size, completeness and high accuracies of the data. Our models have been evaluated for internal validity, since we have reproduced similar accuracy performance in this study compared with our previous study.52 Moreover, we used retrospective cross-sectional data, which limit the ability of ML algorithms to generate 5- or 10-year risk estimates. Analysing prospective longitudinal data with ML algorithms may reveal additional implications for clinical decision support.One limitation of the study is that the performance of our approaches was evaluated with k-Fold cross-validation process in the same dataset, which could result in an optimistic model performance. However, the k-Fold cross-validation process generally results in a less biased or less optimistic estimate of the model skill compared with other commonly used methods, e.g., simple train/test split.53 Future studies can also include resource rearrangement involving health policymakers and other stakeholders, in terms of cost-effectiveness and adaptability in different clinical settings. A prospective clinical trial would provide more functional and extended evaluation of the performance of ML algorithms, and findings can be compared with ongoing personalised breast cancer screening trials like ‘My PeBS' and ‘WISDOM'.55In summary, we calculated lifetime breast cancer risk with ML algorithms and compared their discriminatory accuracy, classification and impact on mammography screening with the BOADICEA model according to the Swiss Surveillance Protocol. Differences in classification and impact on breast cancer surveillance were considerable. The ability of our model to detect individuals with high suspicion of breast cancer, should be further evaluated with other datasets and prospective samples. Future studies can enhance the performance of ML algorithms through incorporation of additional clinical data, such as lifestyle, medications, breast images, exact histology of benign breast diseases and co-morbidities.Supplementary Table 1


Paraoxonase 1 (PON1), a multifactorial antioxidant enzyme, has a defensive role against oxidative stress, which is believed to contribute to cancer development. This study aimed to investigate the association of PON1-L55M functional polymorphism with breast cancer risk. In the experimental study, blood samples were collected from 150 healthy women controls and 150 breast cancer subjects. The L55M genotyping was performed by polymerase chain reaction-restriction fragment length polymorphism. PON1-L55M polymorphism and breast cancer risk in homozygote , dominant , and allelic  models. Our analysis showed that the genotypes distribution is in Hardy-Weinberg equilibrium for both case and control groups. Our data revealed that there are significant associations between PON1-L55M genetic variation could be a genetic risk factor for breast cancer risk and it could be considered as a molecular biomarker for screening of susceptible women.Our results suggest that the  PON1) in detoxification of cancer-causing oxidative stress encourages scientists to evaluate PON1 gene variations in susceptibility to breast cancer . PON1 binds to HDL and helps to detoxify organophosphorus compounds such as paraoxon and lipid peroxidation-soluble radicals .Breast cancer (BC) in women is the most common reason for cancer mortality around the world. The rate of this malignancy is varying worldwide, but it is increasing in areas that until recently had a low prevalence of the disease . Many etiologic factors, including nutritional, lifestyle, environmental carcinogenicity, as well as oxidative stress have been considered to increase the risk of breast cancer . The role of paraoxonase 1 (PON1 gene is located on the long arm of chromosome 7 (7q21.3) which is contained 9 exons and encodes a protein with 355 residues . Although several pharmaceutical, dietary, and life-style modulators of PON1 are identified, by far the main influence on the activity of PON1, which could differ by above 40 times between persons, is through PON1 genetic variations . There are several main polymorphisms in PON1 gene that could affect the gene function. The PON1-Q192R (rs662) missense single nucleotide polymorphism (SNP) regulates a substrate-dependent influence on activity. The 192R isoform could hydrolyze some substrates such as paraoxon 192Q isoform could hydrolyze some other substrates such as lipid-peroxides and diazoxon . Both the missense L55M (rs854560) and the upstream T-108C (rs705379) SNPs are correlated with various activities and serum levels of PON1. The PON1-55M isoform leads to a lower level of mRNA and then its activity compared to 55L isoform. Besides, the -108C genotype of rs705379 variation could result in higher promoter activity compared to the -108T genotype . There are several studies investigating the role of three mentioned SNPs with various common malignancies e.g. breast cancer. Regarding to the rs854560 polymorphism, there are some reports. For example, Stevens et al., (2006) reported that this polymorphism might be correlated with elevated susceptibility of breast cancer in USA. Naidu et al., (2010) suggested that the L55M SNP may be a genetic biomarker for breast cancer in Malaysian population. However, there is no identified study investigating the association of this polymorphism with breast cancer in Iranian population. This study aimed to investigate the association of PON1-L55M genetic polymorphism with breast cancer susceptibility in a case-control trial.Humans SubjectsIn a hospital-based case-control study, 300 participants including 150 subjects with breast cancer and 150 age-matched healthy controls were enrolled from Pasteur pathobiology and genetics laboratory and also Rohani hospital . The breast cancer diagnosis was approved histologically for all breast cancer subjects. Healthy controls individuals without oncological history were referring to the same hospital for routine tests, and after completing the questionnaire and oral interview, it became clear that they did not display positive outcomes. Lastly, 2 mL peripheral blood was obtained from all participants. Besides, the current study was performed according to the criteria outlined in the Helsinki Declaration. SNP genotypingPON1-L55M SNP genotyping was performed by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) method according to the previous study . In brief, PCR was done in a 20 µL overall volume. About 50 ng of genomic DNA, 10 µL of 2X premix, and 0.1 nmoL of the primers (forward primer: 5’-GAAGAGTGATGTATAGCCCCAG-3’ and reverse primer: 5’-TTTAATCCAGAGCTAATGAAAGCC-3’) were used to PCR. After initial denaturation at 95°C for 5 min, the PCR mixture was subjected to 30 repeatitive cycles of denaturation (at 94°C for 1 min), annealing (at 61°C 45 sec), and extension (at 72°C for 1 min). The PCR products with length of 171-bp was treated with the NlaIII restriction endonuclease  at 37°C for 16 hours, and the digested mixtures were electrophoresed on 2% agarose gel. The T (L) allele had no restriction site for NlaIII enzyme, while the A (M) allele was digested to 127 and 44-bp fragments.After, blood sample collection, genomic DNA was extracted from whole blood by a commercial Kit  and the Statistical analysisrs854560 polymorphism and risk of breast cancer was evaluated by odds ratios (ORs) and 95% confidence intervals (CIs) which were estimated by logistic regression. A p-value of less than 0.05 was considered statistically significant. These statistical analyses were performed by SPSS Statistical software version 19 .Hardy-Weinberg equilibrium (HWE) was computed in both case and control groups by the Chi-Square test. The differences of allele and genotype frequencies between case and control groups were calculated by Chi-Square test. The strength of association between 2, respectively. Additionally, 41.33% of patients were postmenopausal. The highest percentage of histologic grade was related to grade I (52.00%). The tumor type for 67.33% of patients was detected as invasive ductal carcinoma (IDC) and 32.00% was detected as invasive lobular carcinoma (ILC). While one of the patients was considered with a mixed tumor (IDC+ILC). Tumor size in 65.33% of patients was less than 2 cm while 34.67% of patients have a tumor with size ≥2 cm. Finally, lymph node metastasis was observed in 52.00% of patients  and control  groups. rs854560 SNP in our case-control study. Frequencies of genotypes LL, LM, and MM were 44.00%, 39.33%, and 16.67%, respectively for the healthy control group and 31.33%, 43.33%, and 25.34% respectively for the breast cancer group. Alleles L and M rates were 63.67% and 36.33% respectively for the control group and 53.00% and 47.00% respectively in the breast cancer group. As detailed in p= 0.018). In addition, carriers of allele M were at a high risk for breast cancer . Besides, allele analysis revealed that there is a significant association between allele M and breast cancer risk .Genotype frequencies of  PON1 antioxidant gene in a case-control study. The data from our experimental study revealed that there is a significant association between MM, LM+MM, M genotypes and allele and breast cancer risk in our studied population. This shows the main role of this gene in the development of breast cancer. In a meta-analysis published in 2019, it was reported that there are significant associations between PON1-L55M genetic variation and other cancers such as hematologic cancer and prostate cancer . However, there is another key polymorphism in the PON1 gene which should be noted. It is Q192R missense variation which is associated with decreased risk of breast cancer . Breast cancer is the most common cancer in women that the etiology of this malignancy is not fully understood. It seems that oxidative stress and free radicals play an important role in the pathogenesis and progression of breast cancer . In this study, we investigated the association of the common polymorphism L55M in thePON1 . Therefore, it is accepted that the polymorphisms of these genes are key determinants of cancer susceptibility to toxic or environmental toxic chemicals . Single nucleotide polymorphisms based on their gene locations could affect the function of the gene . For example, genetic variations in promoter regions could affect gene expression level and non-coding variation could alter gene expression via interfering with splicing procedure . But, the genetic variations in coding sequences (exon variations) could affect the protein structure and function . So based on what was mentioned, the L55M polymorphism as an exon variation, could affect the protein PON1 structure and/or function. Investigation of molecular effects of genetic variation through in vitro and in vivo is too cost- and time-consuming. However, the computational analysis could be useful to evaluate these effects and other molecular evaluations . Therefore, assessment of L55M polymorphism on the PON1 protein function by using bioinformatics tools could be a helpful approach. Oxidative stress could result in impaired in biological membranes, intracellular organelles, and macromolecules such as proteins and DNA . One of the main damage is the oxidation of lipids by free radicals resulting from these stresses, which results in the production of active compounds such as aldehydes, ketones and hydroxyl acids . These radicals may have an external source or may be due to oxidation-reduction reactions in the body. The imbalance in the formation and removal of these free radicals, including reactive oxygen species (ROS), has been shown to cause genetic dysfunction, interference with cellular signaling, metastasis, neurodegenerative diseases, and aging . The human body has many enzymatic systems for the protection of genotoxic damage that activate, indirectly, through the reduction of substrates having the potential to produce free radicals, such as cytochrome P450c17a or directly via or indirectly, through free radical scavenging, such as PON1-L55M polymorphism is a genetic risk factor for breast cancer risk and it could be considered as a possible molecular biomarker for screening of susceptible women. However, there are some limitations in case-control study which should be mentioned. For example, we did not evaluate the gene-gene and gene-environmental factors and also our sample size was somewhat small. Besides, combination effects of PON1-L55M gene polymorphism and some oxidative stress markers such as total antioxidant capacity, malondialdehyde with breast cancer could be useful.Our findings suggest that the


P < 5 × 10−8, tripling the number known to 46. Seventeen identified MD loci also are associated with breast cancer risk in an independent meta-analysis (P < 0.05). Mendelian randomization analyses show that genetic estimates of dense area (DA), nondense area (NDA), and percent density (PD) are all significantly associated with breast cancer risk (P < 0.05). Pathway analyses reveal distinct biological processes involving DA, NDA and PD loci. These findings provide additional insights into the genetic basis of MD phenotypes and their associations with breast cancer risk.Mammographic density (MD) phenotypes are strongly associated with breast cancer risk and highly heritable. In this GWAS meta-analysis of 24,192 women, we identify 31 MD loci at  Mammographic density represents one the strongest predictors of breast cancer risk. Here the authors perform genome-wide association study meta-analysis of women screened with full-field digital mammography and identify 31 previously unreported loci associated with mammographic density phenotypes. Women with ≥75% density on a mammogram have a 4 to 5-fold increased risk of breast cancer compared to women with little or no dense tissue, independent of other known risk factors3. PD is a composite of two phenotypes: the dense area (DA) reflecting the amount of fibroglandular tissue in the breast, and the nondense area (NDA) consisting of predominantly fatty tissues that appear radiotranslucent or dark on a mammogram4. Recent studies have shown that NDA is associated with decreased breast cancer risk independently of DA, suggesting that breast adipose tissues play an important role in normal mammary gland growth and function6. PD, DA, and NDA each have heritability estimates of over 50% in twin studies9. However, only 15 independent genome-wide significant loci with P < 5 × 10−8 have been identified to date, together explaining less than 1-3% of the total variance of mammographic density (MD) phenotypes14.Percent density (PD), the percentage of the breast area that appears radiodense or light on a mammogram, is one of the strongest risk factors for breast cancer, but the biological basis for this association is poorly understood15. These findings triple the total number of independent genome-wide significant MD loci now mapped to 46, enabling the first genetic pathway analyses and Mendelian randomization analyses to evaluate the causal nature of the association of MD phenotypes with breast cancer risk. Pathway analyses reveal distinct biological processes involving DA, NDA, or PD loci. Mendelian randomization analyses show that genetic estimates of DA, NDA, and PD are all significantly associated with breast cancer risk. These findings provide additional insights into the genetic basis of MD phenotypes and their relationship with breast cancer risk.In this genome-wide association study (GWAS) meta-analysis of 24,192 women screened with full-field digital mammography (FFDM), we identify 31 MD loci, of which 17 also are associated with breast cancer in an independent study of over 200,000 breast cancer cases and controls16. The first study included 20,311 women screened using Hologic FFDM machines, and the second study included an independent sample of 3881 women screened using General Electric (GE) FFDM machines. Women in the GE cohort were 2.7 years younger, had lower BMI, and were less likely to be postmenopausal compared with women in the Hologic cohort  and NDA , and DA was moderately negatively correlated with NDA  in both cohorts, as expected.This GWAS meta-analysis comprised a total of 24,192 non-Hispanic white women with MD phenotypes measured centrally using Cumulus softwareP < 5 × 10−8. In the GE study, 3 of these SNPs could not be confirmed and were excluded, while 18 additional SNPs with P < 5 × 10−5 in the Hologic study reached genome-wide significance in the combined meta-analysis. In total, 52 SNPs at 40 independent chromosomal regions (loci) were associated with DA, NDA, and/or PD in the same directions in both studies, and met the conventional genome-wide significance threshold of P < 5 × 10−8 , age at mammography, and principal components of European ancestry  in addition to ln(BMI); and only 2 of the 13 NDA SNPs (rs6718628 and rs4132228) were associated with BMI at P < 0.05 in models unadjusted for BMI  and PD (P = 0.083) in the same directions as previously reported13. However, rs3817198 at the LSP1 locus on 11p15.5 showed no evidence of association, although the imputation accuracy was relatively low for this SNP (imputation r2 = 0.55). We also report new MD phenotype associations at P < 5 × 10−8 with SNPs at five prior loci. At the MTMR11 locus on 1q21.1 previously associated with PD12, we found that rs11205303 was associated with both DA (P = 6.8 × 10−20) and PD (P = 1.5 × 10−11). At the RALB/INHBB locus on 2q14.2 recently associated with absolute dense volume10, we found that rs4849864 was associated with NDA (P = 2.6 × 10−13) and rs17625845 was associated with DA (P = 2.8 × 10−9). At the AREG locus on 4q13.3 previously associated with DA13, we found that rs71219402 was associated with NDA (P = 1.4 × 10−8) while rs149689338 was the lead SNP for DA (P = 8.2 × 10−10). At the PRDM6 locus on 5q23.2 previously associated with PD13, we found that rs335160 was associated with DA (P = 6.7 × 10−14) while rs335143 was the lead SNP for PD (P = 1.0 × 10−8). At the MRTFA (or MKL1) locus on 22q13.1-22q13.2 previously associated with DA13, we found that rs73169057 was associated with NDA (P = 9.6 × 10−9) while rs6001984 was the lead SNP for DA (P = 8.3 × 10−11).We confirmed associations with SNPs at 13 of 15 previously identified MD locir2 = 0.02), attained P values of 1.4 × 10−10 and 7.6 × 10−9, respectively, in a linear regression model that included both SNPs. rs10802015 near SPAG17 (sperm associated antigen 17) is located 726 kb away from rs1779445, an intronic variant of TBX15 (T-box 15) that was also a significant eQTL for WARS2  in mammary tissue. At 2q14.2, we found an NDA SNP rs11123556 at the INHBB/GLI2 locus that was conditionally independent from the strongest NDA SNP rs4849864 (r2 = 0.002) at the RALB/INHBB locus previously associated only with absolute dense volume;10 the two NDA SNPs attained p values of 1.9 × 10−14 and 4.7 × 10−13, respectively, in a linear regression model that included both SNPs. Finally, at the ZNF365 locus on 10q21.2, we found a NDA SNP rs1949355 that was uncorrelated with rs10995190 (r2 = 0.07) previously associated only with DA and PD  after adjusting for rs10995190, which was associated with DA (P = 9.4 × 10−27) and PD (P = 1.6 × 10−12) but not NDA (P = 0.12) in single-SNP models.Conditional analyses adjusting for the lead MD SNP in each region identified two independent subregions within three different chromosomal locations. At 1p12, two conditionally independent NDA SNPs, rs10802015 and rs1779445 , 10p12.1, 10q21.1, 11p15.4, 19q13.33, 20q13.13) were associated with both MD and breast cancer risk.We evaluated whether the newly identified MD loci were also associated with breast cancer risk using data from an independent sample of 122,977 cases and 105,974 controls of European ancestry from the Breast Cancer Association Consortium (BCAC) and Discovery, Biology and Risk of Inherited Variants in Breast Cancer Consortium (DRIVE)05 Table . Of thes4, we performed Mendelian randomization analyses using the weighted median method18 and summary statistics for all prior and new MD loci from this study and BCAC/DRIVE15. For each standard deviation (SD) increment in DA, NDA, and PD, the estimated odds ratios  for breast cancer were: 1.45 , 0.84 , and 1.68 , respectively. Sensitivity analyses using mode-based estimates that are also robust to violations of the instrumental variable assumptions, but less powerful than the weighted median method showed similar results , 0.78 (0.71–0.86), and 1.52 (1.39–1.66) in premenopausal women; and 1.38 (1.31–1.44), 0.79 (0.73–0.85), and 1.53 (1.44–1.64) in post-menopausal women6. These findings support a biological basis for the positive association of DA and PD with breast cancer risk, and inverse association of NDA with breast cancer risk, in observational studies.To explore the extent to which DA, NDA, and PD may be associated with breast cancer through shared underlying genetic factors and biologic pathways21 of 779,828 SNPs using summary statistics from this study and the BCAC/DRIVE breast cancer GWAS15 yielded estimates of 0.27 (P = 5.5 × 10−6), −0.14 (P = 0.014), and 0.27 (P = 7.7 × 10−10) for the genetic correlations of DA, NDA, and PD, respectively, with breast cancer. The significant positive genetic correlations of DA and PD with breast cancer, and significant inverse association of NDA with breast cancer were consistent with the Mendelian randomization results, as well as evidence from observational studies6, supporting the shared genetic bases of all three MD phenotypes and breast cancer.To complement the Mendelian randomization analyses, we estimated the genetic correlation between each MD phenotype and breast cancer based on all SNPs genome-wide. LD Score regressionTo identify potentially functional variants at the 31 MD loci, we examined whether any of the lead SNPs were associated with: protein-coding variants (Supplementary Table r2 ≥ 0.80) with nonsynonymous mutations, and all of these SNPs were also significant (FDR < 0.05) expression quantitative trait loci (eQTLs) (Supplementary Tables ADCY3 (adenylate cyclase 3), which catalyzes the formation of the secondary messenger cyclic adenosine monophosphate involved in signal transduction and metabolic processes. The lead SNPs for NDA rs6718628 and PD rs11676272 were also significant eQTLs associated with increased ADCY3 expression in subcutaneous and visceral fat, and whole blood. On 15q25.2, the lead NDA SNP rs1812707 was strongly correlated (r2 = 0.88) with the V661L missense mutation variant in ADAMTSL3, involved in protein glycosylation and catabolism, and was also a significant eQTL in fibroblasts and subcutaneous fat for the GOLGA6L5P pseudogene, about which little is known.MD SNPs at three loci  were strongly correlated (r2 = 0.99) and PD rs1704773 (r2 = 0.89) were tightly linked with the W154X nonsense mutation in FUT2 (fucosyltransferase 2) resulting in a truncated protein. FUT2 is involved in the production of histo-blood group antigens, and exhibits the non-secretor phenotype  when inactivating mutations are present. The lead DA and PD SNPs were also significantly associated with decreased expression of FUT2 in mammary tissue and fibroblasts, and increased expression in fibroblasts of the nearby MAMSTR gene, encoding a transcriptional regulator. This regulatory activity may be mediated in part by strong correlation (r2 ≥ 0.80) with an intronic variant of MAMSTR within a promoter and enhancer-like region in mammary epithelial cells and mammary fibroblasts , 2p23.3, 3p14.1, 11p15.2, 11q24.3, 12q22, 15q25.2) included ADAMTS8, ADAMTS9-AS2, ADCY3, ARNTL, CENPO, DNAJC27, GOLGA6L5P, NCOA1, NTN4, and WARS2. Target genes regulated by PD SNPs at 5 loci overlapped with those for DA (19q13.33) or NDA , except for DUSP4 (8p12) and LINC00445 (13q13.3), which were regulated by PD SNPs only.The lead MD SNPs at 13 loci were significantly associated (FDR < 0.05) with gene expression levels in normal human mammary tissue, primary fibroblast cells, subcutaneous fat, visceral fat, or whole blood, which were the tissues most closely related to cell types in the breast available in GTExr2 ≥ 0.80) variants nearby, were located within regions with promoter or enhancer activity in normal human mammary epithelial cells or mammary fibroblasts using data from ENCODE25 (Supplementary Table ZNF365 was perfectly linked (r2 = 1.0) with an enhancer-region variant in mammary fibroblasts. ZNF365 is involved in regulating neuronal growth and DNA repair. On 14q24.1 the lead DA SNP was an intronic variant in RAD51B located within an enhancer-like region in mammary fibroblasts. The RAD51 protein family is essential for DNA repair by homologous recombination, and interacts with the major breast and ovarian cancer susceptibility genes BRCA1 and BRCA2. On 18q21.33 the lead DA SNP was an intronic variant in BCL2 located within an enhancer-like region in mammary fibroblasts. BCL2 suppresses apoptosis and constitutive expression is thought to cause follicular lymphoma.MD SNPs at 13 loci, or strongly correlated (26 (Supplementary Table 27. We found that NDA loci (n = 17) were significantly enriched in the open chromatin regions of fibroblast cell lines derived from three different normal human tissues at the Bonferroni threshold of P < 0.0004 accounting for 125 tests. NDA loci were observed within regulatory regions more often than expected under the null distribution in fibroblasts isolated from the lung , skin , and heart . Suggestive enrichment of NDA loci at P < 0.01 was also found in the regulatory regions of 13 additional fibroblast cell lines from different tissues, including mammary fibroblasts . The regulatory activity of NDA loci in mammary fibroblasts was further supported by our functional analyses showing that 6 of 13 new NDA loci were associated with promoter or enhancer regions in mammary fibroblasts (Supplementary Table n = 28) and PD (n = 20) loci were not significantly enriched in the regulatory regions of any normal human cell types. However, suggestive evidence of enrichment (P < 0.01) was found specifically in 6 fibroblast cell lines for DA loci, and 2 fibroblast cell lines for PD loci, and not in any other normal human cell type  method29 to perform gene set enrichment analyses of all prior and new genome-wide significant loci. We found that NDA loci (n = 17) were significantly enriched by over ninefold for genes involved in mammary gland development (Supplementary Table n = 28) were significantly enriched for genes involved in reproduction, apoptosis, metabolism, and signaling (Supplementary Table n = 20) were significantly enriched for genes involved in anatomical structure development and metabolism (Supplementary Table n = 46) were involved in regulation of gene expression, nucleic acid binding and metabolism, and cell proliferation  for DA, NDA, and PD loci using DAVIDloci n = 7 were si31 was 0.30 (SE = 0.02), 0.34 (SE = 0.02), and 0.31 (SE = 0.02) for DA, NDA, and PD, respectively. These results were comparable to a previous GWAS that reported estimates of 0.31 (SE = 0.07), 0.25 (SE = 0.07), and 0.29 (SE = 0.07) for absolute dense volume, nondense volume, and percent dense volume, respectively10. SNP-based heritability estimates represent the upper bound on the total proportion of phenotypic variance that could be explained by GWAS of common variants, and the lower bound for narrow-sense heritability estimated from twin studies because they do not account for rare variants that are not in linkage disequilibrium with the genotyped SNPs32. Heritability estimates from twin studies can also be influenced by nonadditive genetic effects and shared environmental effects32. Altogether, the newly identified and previously known MD loci explained 12.3%, 9.1%, and 8.7% of the SNP-based heritability for DA, NDA, and PD, respectively, compared with 3.3%, 2.1%, and 3.2% explained by previously known MD loci.The proportion of phenotypic variance explained by all genotyped SNPs estimated using GCTA4. PD, DA, and NDA are all highly heritable and significantly associated with breast cancer risk in observational studies6. However, the biological bases for how these breast tissue phenotypes are related to breast cancer development are poorly understood. In this GWAS meta-analysis, we identified 31 loci for MD phenotypes, tripling the total number of genome-wide significant loci from 15 previously to 46 presently. Seventeen of the MD loci also were associated with breast cancer risk in an independent large meta-analysis, identifying potential new breast cancer susceptibility alleles at seven loci. Mendelian randomization and genetic correlation analyses provided further evidence of the shared genetic etiology of all three MD phenotypes with breast cancer.High MD is one of the strongest and most common risk factors for breast cancer, and has been estimated to account for up to one-third of all breast cancersP < 5 × 10−8) associated with all three MD phenotypes. Intronic variants in PPARG (peroxisome proliferator activated receptor gamma) were associated with DA, PD, and breast cancer risk in the same direction, and with NDA and breast cancer risk in the opposite direction, as expected. PPARG is a member of the nuclear receptor family of ligand-activated transcription factors, and a regulator of adipocyte differentiation33. PPARG has been shown to inhibit transcription of aromatase, the rate-limiting enzyme in estrogen biosynthesis, in primary breast adipocytes34. Higher aromatase expression has been observed in dense breast tissue35, and treatment with aromatase inhibitors lowers breast cancer risk although associations with MD changes have been less consistent36. Estrogen may influence breast cancer risk as well as breast tissue composition through its proliferative effects on mammary cells37. PPARG is therefore plausibly involved in the development of DA, NDA, and PD, as well as breast cancer.Among the seven loci newly associated with both MD and breast cancer risk, one locus on 3p25.2 was significantly  involved in autosomal dominant adult-onset leukodystrophy33. On 10p12.1, rs2642278 was positively associated with DA and breast cancer risk, and an eQTL for MKX (mohawk homeobox), which plays a role in cell adhesion33. On 10q21.1, rs1892368 is an intronic variant in PRKG1 (protein kinase cGMP-dependent 1) that was inversely associated with both DA and breast cancer risk (P = 4.8 × 10−7). PRKG1 is a key mediator of the nitric oxide/cGMP signaling pathway important in many signal transduction processes33. On 11p15.4, rs11040963 was inversely associated with both DA and breast cancer risk (P = 4.5 × 10−4), and located 8 kb upstream of MRPL17  involved in protein synthesis in mitochondria33. On 20q13.13, rs17196752 was positively associated with DA and breast cancer risk, and uncorrelated (r2 = 0.0001) with the closest known breast cancer risk allele rs612290615. rs17196752 is an intronic eQTL within an enhancer-like region in mammary fibroblasts that down-regulated the mammary tissue expression of SMIM25 . While little is known about SMIM25 function, rs17196752 has been associated with white blood cell traits that have been linked to cancer and other systemic diseases38.Five additional loci were newly associated with both DA and breast cancer risk in the same direction, identifying FUT2 protein-truncating mutation associated with the nonsecretor phenotype for histo-blood group antigens. Both SNPs also were associated with increased expression of the MAMSTR transcriptional regulator in fibroblasts, and a MAMSTR regulatory region in mammary epithelial cells and mammary fibroblasts. rs492602 has been associated with serum lipid levels39 but not breast cancer, and rs1704773 was associated with PD and breast cancer (P < 0.05) in opposite directions, indicating that FUT2 and MAMSTR are candidate genes for DA and PD but may not be directly associated with breast cancer risk.Finally, on 19q13.33, SNPs for DA (rs492602) and PD (rs1704773) were tightly linked with a 15. The DA SNP rs11684853 was tightly linked (r2 = 0.99) with variants in an enhancer element with strong activity in mammary epithelial cells and mammary fibroblasts. The PD SNP rs34331777 was about 100 kb away from the tumor suppressor gene OSR1 (Odd-skipped related 1) encoding a zinc-finger transcription factor that acts on the p53 and Wnt/β-catenin signaling pathways40, and the MIR4757 microRNA that could influence the translation of multiple target mRNAs with different effects on MD and breast cancer risk.Among the ten new MD loci associated with known breast cancer susceptibility alleles, all but one were associated with MD phenotypes and breast cancer risk in consistent directions. SNPs on 2p24.1 were associated with both DA and PD in directions opposite to their known associations with breast cancer risk41. Increased collagen alignment and stiffness of the extracellular matrix (ECM), associated with dense breast tissue, have been shown to induce malignant phenotypes in normal mammary epithelial cells42. Fibroblasts produce collagen and other ECM components, as well as proteases involved in ECM remodeling. In addition, fibroblast signaling is a critical determinant of normal mammary epithelial and adipocyte cell development and differentiation43, and cancer-associated fibroblasts can stimulate breast tumor progression44. Our finding that loci for all three MD phenotypes were enriched in the regulatory regions of fibroblasts more than in any other cell type is consistent with a key role of fibroblasts in regulating the stromal environment and normal breast tissue composition, as well as aberrant growth in breast tumors.There are several potential biological mechanisms through which higher DA and PD may increase breast cancer risk. Dense areas of the breast contain more epithelial cells, fibroblasts, and collagen than nondense areas that contain more adipocytes45. DNA damage may increase DA and PD, and decrease NDA, by repressing CD36 expression in mammary fibroblasts, which induces increased ECM deposition and decreased lipid storage in nonmalignant breast tissue44. CD36 is a widely expressed glycoprotein receptor that binds to a broad range of ligands, including ECM proteins and lipids, and modulates adipocyte differentiation, lipid metabolism, angiogenesis, apoptosis, cell-ECM interactions, and immune signaling45. Importantly, CD36 expression is primarily controlled by the PPARG transcription factor45, which we found to be associated with all three MD phenotypes and breast cancer risk. The etiologic role of DNA repair and apoptosis genes in dense breast tissue is further supported by our findings that variants in the RAD51B (14q24.1) DNA repair gene, and FAF1 (1p32.3) and BCL2 (18q21.33) apoptosis genes are significantly associated with DA, and that DA loci are enriched for genes in the apoptosis pathway.Mammary epithelial cells from high density tissues also have been shown to have greater DNA damage response signaling and shorter telomeres compared with mammary epithelial cells from low density tissues6, but the etiologic nature of this association and underlying mechanisms are uncertain. Our findings demonstrating a significant inverse association of genetically estimated NDA and breast cancer risk in both Mendelian randomization and genetic correlation analyses provide strong evidence that this association is caused by shared underlying genetic and biological pathways. A limitation of this study is that relatively few women were diagnosed with breast cancer following the mammogram, precluding mediation analyses of the extent to which SNP associations with breast cancer risk are explained by MD phenotypes. A limitation of Mendelian randomization studies generally is the potential for bias due to horizontal pleiotropy, although the weighted median method is a relatively robust and statistically powerful approach19. While NDA and BMI effects are difficult to disentangle, the adipose tissues within the breast may play a more direct role in breast cancer etiology than distant adipose tissues. Mammary adipocytes secrete adipokines that modulate the stromal environment, and constitute a local source of lipids and metabolites that influence mammary epithelial cell growth and function46. In vivo models have shown that the mammary adipose environment is critical for mammary gland growth and development46.NDA has been inversely associated with breast cancer risk independently of DA in observational studies15 in opposite directions, implicating GLI2, NCOA1, and NTN4 as candidate genes for both NDA and breast cancer. GLI2 at 2q14.2 encodes a zinc finger transcription factor that mediates hedgehog signaling33. NCOA1 expression was upregulated in mammary tissue by the NDA SNP at 2p23.3, and encodes a transcriptional coactivator for steroid and nuclear hormone receptors that stimulates transcriptional activity in a hormone-dependent fashion33. NTN4 expression was upregulated in mammary tissue by the NDA SNP at 12q22, and encodes a member of the netrin protein family involved in neuronal growth, angiogenesis, and tumorigenesis33. Netrin 4 has been implicated in controlling epithelial cell branching morphogenesis in the breast47. These findings provide insights into the genetic basis for the inverse association of NDA with breast cancer risk.The finding that NDA loci, but not DA or PD loci, are significantly enriched for genes involved in mammary gland development supports a key role of the nondense fatty tissues in breast health. Three of the four new NDA loci enriched for genes involved in mammary gland development were associated with NDA and breast cancer riskIn summary, this GWAS of 24,192 women from two independent population-based cohorts screened using Hologic or GE digital mammography, and MD phenotypes measured centrally using Cumulus, identified 31 MD loci, and new candidate genes for MD and breast cancer risk. The study findings support the etiologic role of NDA as well as DA and PD as modifiable risk factors for breast cancer that provide potential for intervention. With this study, all 46 genome-wide significant loci identified to date explain 12.3%, 9.1%, and 8.7% of the SNP-based heritability for DA, NDA, and PD. Future studies are needed to discover additional MD loci and to elucidate the different roles that the fatty and dense breast tissue components play in breast health and cancer risk.49. RPGEH is population-based and participants were not selected based on any disease phenotype. All participants completed a health survey, and over 100,000 individuals provided a DNA sample that was genotyped genome-wide; this sample constitutes the Genetic Epidemiology Research on Adult Health and Aging (GERA) study49. Written informed consent was obtained from all participants. Institutional Review Board approvals for this study were obtained from KPNC, Stanford University, and the Icahn School of Medicine at Mount Sinai.We conducted a GWAS meta-analysis within the Research Program on Genes, Environment and Health (RPGEH) administered by Kaiser Permanente Northern California (KPNC) Division of Research50. Hologic images were randomly assembled into 23 batches of up to 1100 images each, including randomly selected replicates for quality control.Hologic study: The first GWAS included 20,311 non-Hispanic white women who underwent bilateral screening mammography at age 39–80 years during 2004–2013 at one of 36 KPNC clinics using Hologic FFDM machines. Processed (for presentation) images were retrieved from the KPNC imaging archive. Hologic images were downsampled from a pixel size of 70 microns to 200 microns. The resulting image resolution exceeded that of the computer monitors used to view the images, and was therefore unlikely to influence the density measurements. A median filter with a 3-pixel radius was applied to the downsampled Hologic images to reduce digital noise and make the images appear more like screen film mammograms, which we found to improve the reproducibility of density measurements51. GE images were randomly assembled into 6 batches of up to 700 images each, including randomly selected replicates for quality control.GE study: The second GWAS included an independent sample of 3881 non-Hispanic white women who underwent bilateral screening mammography at age 38–77 years during 2004–2013 at one of 11 KPNC clinics using GE FFDM machines. GE images processed using Tissue Equalization software were retrieved from the KPNC imaging archive, and downsampled from a pixel size of 94 microns to 200 microns. We found that denoising of the GE images did not improve the reproducibility of the density measurements and therefore did not denoise the downsampled images51. Women with a history of bilateral breast cancer (0.06%) were also excluded. For women with a history of unilateral breast cancer, we measured MD phenotypes using the contralateral (unaffected) breast image from the closest prediagnostic exam following the RPGEH survey when available, or prior to the survey otherwise50. For women with no history of breast cancer, we used the left breast image from the closest post-survey exam, except for a random 10% subset of women for whom the right breast image was used to blind the reader to cancer history. All density measurements were performed using a single craniocaudal view.Women were excluded if their mammograms contained breast implants (3.6%), did not contain the entire breast (1%), or were unreadable/unavailable (2.6%)16 (provided by M.J.Y.), a computer-assisted method that requires the reader to select the pixel intensity threshold for distinguishing the dense and nondense portions of the breast image. The reader must also define the pectoral muscle boundary, whereas Cumulus6 detects the outer edge of the breast automatically for most FFDM images. Cumulus computes the PD as the DA divided by the total breast area. The NDA is equal to the total area minus the DA. All Cumulus measurements were performed by a single radiological technologist (R.Y.L.) trained by the software developer (M.J.Y.) and a breast imaging specialist (J.A.L.), who was certified in the Cumulus method and provided the gold standard measurements used for training and longitudinal evaluation.MD phenotypes were measured centrally using Cumulus6In the first study, 23 batches of up to 1100 Hologic images were read consecutively over a period of eight months. Each batch contained 10% quality control images, including random replicates used to assess reader reproducibility and images with gold standard measurements by JAL used for calibration and periodic retraining. The batch-adjusted Pearson R for PD, DA, and NDA were: 0.952, 0.925, and 0.996 in the Hologic cohort. Following the completion of all Hologic density assessments, the reader underwent a training period to attain high reproducibility on GE images. Then, in the second study, 6 batches of up to 700 GE images were read consecutively over a period of 3 months. The batch-adjusted Pearson R for PD, DA, and NDA were: 0.961, 0.941, and 0.995 in the GE cohort. When multiple measurements were obtained per image, the mean values were used in subsequent analyses.52. This array is estimated to report on 93% of common variants with minor allele frequency (MAF) > 0.05, and 73% of less common variants (MAF between 0.01 and 0.05) at r2 > 0.80, based on the 1000 Genomes Project (http://1000genomes.org) European population. Arrays were processed using the Affymetrix Axiom reagent kit 1.0 (96.7%) or 2.0 (3.3%). Genotype quality control procedures have been described previously49. Samples were excluded if the genotyping call rate was <0.97, or if there was evidence of trisomy, monosomy, male or ambiguous sex (PLINK v1.0753 X chromosome F-statistic >0.2), or excess heterozygosity (PLINK53 F-statistic <−0.03). Among first-degree female relatives (457 pairs and 14 trios), only the youngest woman was retained for analysis. Principal components of ancestry were computed for the genotyped SNPs using EIGENSOFT4.254, and women were excluded if their principal components were not consistent with European ancestry48. Over 30 million variants were imputed from the 1000 Genomes Project reference panel using IMPUTE2.2.257, after pre-phasing the genotyped SNPs using SHAPEIT v2.r64458. After excluding variants with MAF < 0.01 or imputation r2 ≤ 0.359, there remained 9,906,178 variants available for analysis.Over 650,000 SNPs were genotyped at the UCSF Institute for Human Genetics, Genomics Core Facility using a custom Affymetrix array optimized for individuals of European ancestryThe MD phenotypes for each study cohort were transformed separately to attain standard normal distributions with mean 0 and variance 1, to facilitate estimation of the combined meta-analytic effects and enable interpretation of effect sizes in SD units. The distributions of MD phenotypes differed between women in the two cohorts and required different transformations  and GE cohort (n = 3881) were performed with PLINK53 using linear regression models of each SNP as an additive dosage effect60, adjusted for ln(BMI), age at mammography, genotyping reagent kit, and the first ten principal components of European ancestry49. BMI was determined from electronic health records for the patient visit closest to the date of mammography. There was a linear relationship of age . Novel loci were identified by conditional analyses adjusting for the nearest known SNP for any MD phenotype. To be considered novel, SNPs were required to meet the genome-wide significance threshold of P < 5 × 10−8 in conditional analyses adjusting for the nearest known MD SNP, and to have low LD (r2 < 0.10) with previously reported genome-wide significant SNPs for any MD phenotype on the same chromosome.Conditionally independent SNPs within the same chromosomal region were identified by conditional analyses adjusting for the lead SNP. To be considered statistically significant, both SNPs were required to have: meta-analytic 62 were used to assess the presence of inflated significance levels due to uncontrolled population substructure. LocusZoom v1.363 plots of the 400 kb region centered around each novel lead SNP were used to visualize the GWAS meta-analysis significance levels, linkage disequilibrium with the lead SNP, local recombination rates from HapMap, and nearby genes.Quantile-quantile plots and genomic inflation factors15. Associations of MD SNPs with risk of estrogen receptor (ER)-negative breast cancer were evaluated in a subset of 21,468 cases and 100,594 controls64. Summary statistics were obtained from: http://bcac.ccge.medschl.cam.ac.uk/bcacdata/. MD SNPs were considered to be potentially novel breast cancer loci if they were associated with breast cancer with P < 0.05 and P > 5 × 10−8 in the BCAC/DRIVE data, and were uncorrelated (r2 ≤ 0.01) with previously reported genome-wide significant breast cancer SNP in the NHGRI-EBI GWAS catalog65 (https://www.ebi.ac.uk/gwas/).We evaluated associations of newly identified MD SNPs with breast cancer risk in 122,977 cases and 105,974 controls of European ancestry from the BCAC and Discovery, Biology and Risk of Inherited Variants in Breast Cancer Consortium (DRIVE)18. We considered the first reported SNP at all independent prior and new genome-wide significant loci (Table n = 28), NDA (n = 17), and PD (n = 20), and estimated their associations with the relevant MD phenotype in the GWAS meta-analysis. Summary statistics for SNP associations with breast cancer in 122,977 cases and 105,974 controls of European ancestry were obtained from BCAC/DRIVE15.Mendelian randomization analyses were performed to evaluate the potential causal associations of MD phenotypes with breast cancer risk. We used the weighted median method to estimate the causal effect because it is more efficient and robust to violations of instrumental variable assumptions than other Mendelian randomization methods for summary statistics, and provides consistent estimates even when up to half of the information comes from invalid instrumental variablesci Table  for DA , primary fibroblast cells (n = 300), subcutaneous fat (n = 385), visceral fat (n = 313), and whole blood cells (n = 369) using data from the Genome-Tissue Expression (GTEx) project version 7 23. Significant cis-eQTLs within 1 Mb of the gene transcription start site were identified by computing q-values for SNP-gene pairs involving one of the lead MD SNPs using the R qvalue package, and controlling for a false discovery rate (FDR) of 0.05. We also assessed whether lead MD SNPs or nearby proxies (r2 ≥ 0.80 in Europeans from the 1000 Genomes Project) were located within promoter or enhancer regions in human primary mammary epithelial cells and human primary mammary fibroblasts using data from the ENCODE and Roadmap Epigenomic consortia (https://www.encodeproject.org/)25. Promoter-like regions were identified by combining DNase hypersensitivity and histone modification H3K4me3 signals in the same cell type25. Enhancer-like regions were identified based on DNase hypersensitivity and histone modification H3K27ac signals in mammary epithelial cells, and DNAse hypersensitivity only in mammary fibroblasts25.We evaluated whether MD SNPs were associated with gene expression levels in: human mammary tissue (https://github.com/robertkleinlab/uesEnrichment)26. Open chromatin regions were defined using DNase I hypersensitive sites sequencing data from the ENCODE and Roadmap Epigenomic consortia27. The empirical P value for the observed enrichment of independent MD SNPs in the regulatory regions of each cell line was computed by generating 10,000 sets of randomly selected SNPs that were matched to MD SNPs by the distance to the nearest transcription start site and number of correlated SNPs. We applied a Bonferroni correction for the 125 cell lines tested to determine the significance threshold of P < 0.0004.We tested whether previously identified and new loci for each MD phenotype were enriched in regulatory regions in 125 diverse human cell and tissue types using the UES method  in mammary tissue, primary fibroblast cells, subcutaneous fat, visceral fat, or whole blood (Table 29.We performed gene set enrichment analyses to identify biological pathways implicated by prior and new MD loci using DAVID v6.8od Table , and 8. 31. Array-based heritability was estimated separately for the Hologic and GE study cohorts, and the resulting estimates were then combined using inverse-variance weighting. We also estimated the proportion of phenotypic variance explained by all independent prior and new genome-wide significant MD loci. For each MD phenotype, the residual variance of linear regression models were estimated using 100-fold cross-validation, where model 1 included all independent genome-wide significant loci in addition to the adjustment variables in the GWAS model, and model 2 included only the non-SNP covariates. In both models, the SNP effects and covariates were nested within the Hologic or GE studies to account for differences across studies. The proportion of variance explained by the genome-wide significant loci for each MD phenotype was computed by 1 − V1/V2, where V1 and V2 represent the estimated residual variances of models 1 and 2 respectively.We estimated the proportion of phenotypic variance explained by the additive genetic effects of all genotyped SNPs using GCTA v1.02Further information on research design is available in the Nature Research Reporting Summary linked to this article.Supplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Reporting Summary


BRCA1/2 gene having the greatest survival benefit. On May 14th, 2013, Angelina Jolie shared the news of her bilateral risk-reducing mastectomy (BRRM), on the basis of her BRCA1 pathogenic variant status. We evaluated the impact of this news on rates of CRRM in women with increased risk for developing breast cancer after being diagnosed with unilateral breast cancer. The prospective cohort study included all women with at least a moderate lifetime risk of developing breast cancer who attended our family history clinic (1987–2019) and were subsequently diagnosed with unilateral breast cancer. Rates of CRRM were then compared between patients diagnosed with breast cancer before and after Angelina Jolie’s announcement (pre- vs. post-AJ). Of 386 breast cancer patients, with a mean age at diagnosis of 48 ± 8 years, 268 (69.4%) were diagnosed in the pre-AJ period, and 118 (30.6%) in the post-AJ period. Of these, 123 (31.9%) underwent CRRM, a median 42 (interquartile range: 11–54) days after the index cancer surgery. Rates of CRRM doubled following AJ’s news, from 23.9% pre-AJ to 50.0% post AJ (p < 0.001). Rates of CRRM were found to decrease with increasing age at breast cancer (p < 0.001) and tumour TNM stage (p = 0.040), and to increase with the estimated lifetime risk of breast cancer (p < 0.001) and tumour grade (p = 0.015) on univariable analysis. After adjusting for these factors, the step-change increase in CRRM rates post-AJ remained significant . The AJ effect appears to have been associated with higher rates of CRRM amongst breast cancer patients with increased cancer risk. CRRM rates were highest amongst younger women and those with the highest lifetime risk profile. Clinicians need to be aware of how media news can impact on the delivery of cancer related services. Communicating objective assessment of risk is important when counselling women on the merits of risk-reducing surgery.Contralateral risk-reducing mastectomy (CRRM) rates have tripled over the last 2 decades. Reasons for this are multi-factorial, with those harbouring a pathogenic variant in the  Data from the US Surveillance, Epidemiology and End Results (SEER) program2 has shown a 300% increase in rates of CRRM (2002—3.9% to 2012—12.7%). This significant increase is surprising, given that overall rates of contralateral breast cancer are decreasing3, likely due to improved preventive effects of anti-endocrine therapy4, as well as there being no improvements in long term outcomes associated with CRRM in the majority of breast cancer patients5.In recent years, there has been an increase in the number of unilaterally affected breast cancer patients undergoing removal of the opposite healthy breastBRCA1/2), a survival benefit from CRRM has been shown, particularly in those with a pathogenic variant in BRCA1 and BRCA28 . This group of patients have an annual contralateral breast cancer (CBC) rate of approximately 2–3%9, compared to approximately 0.5% in patients with sporadic breast cancer4. Amongst high-risk patients who test negative for the BRCA1 or BRCA2 mutation, the risk of CBC appears to remain elevated, although some studies have suggested otherwise10.However, in women deemed to be at increased risk of developing breast cancer based on their family history or carrier status of a pathogenic genetic mutation  shared with the world her experience of bilateral risk reducing mastectomies (BRRMs)19 over breast conservation in the USA resulted in a 25% reduction in the use of breast conservation, sparking a media frenzy with headlines claiming that such a personally publicised decision “set us back 10 years”20.Healthcare professionals need to be aware of the impact high profile personalities can have on health related issues. In 1987, Nancy Reagan’s well publicised decision to undergo a mastectomy22. In addition, breast cancer patients may substantially overestimate their risk, possibly influenced by the fact that some healthcare professionals have a poor understanding of CBC risk themselves24. A challenging clinical scenario for breast clinicians is the newly diagnosed breast cancer patient who requests a CRRM. An understanding of the multiple factors discussed is critical to ensure this group of women receive evidence-based recommendations26.The reasons for choosing a CRRM are complex—patient’s personal experience, family history, radiological imaging, tumour factors, surgeon related factors and desire for symmetry/cosmesis need to be consideredBRCA status was examined. To our knowledge, this is the first study to assess whether the “Angelina Jolie effect” affected the rate of CRRM amongst patients at an increased risk of developing a further breast cancer.We analysed the trends of CRRM over a 29-year period amongst increased risk patients diagnosed with a unilateral breast cancer. Tumour and patient characteristics were assessed. The impact, if any, of the news of Angelina Jolie’s BRRM and her 29. In the NBCPC, testing for BRCA1 and BRCA2 mutations has been available within our services since 1996. Additional genes are tested based on family history pedigree and cancer phenotype. Panel testing is not routinely performed. Ethics approval is covered by FHrisk Research Ethics Committee reference number 10/H1008/24.Patients with an increased risk of developing breast cancer due to family history have been attending a dedicated Breast Cancer Family History Risk and Prevention Clinic  for over 3 decades (1987- to date), with 14,198 women having been assessed as of February 2020. This dedicated service offers women appropriate risk assessment, radiological surveillance, lifestyle advice and risk-reducing strategies . Lifetime and residual risk of breast cancer has been assessed using questionnaire information on family history and standard risk factors using Claus tables and Tyrer-Cuzick programme30 who were subsequently diagnosed with a unilateral breast cancer were included. Data relating to patient demographics and pathology, as per National Health Screening Breast Screening Programme guidelines31, were assessed.The Family History database, a prospectively maintained database of all patients seen in the family history clinic was interrogated. All patients with a moderate or high risk of developing breast cancer (> 17% lifetime risk at any time during follow up)The results of genetic testing and risk categorisation were also collected. However, data were only recorded for the most recent risk status; hence it was not known whether this information was available to the patient at the time the decisions regarding CRRM were made. Those patients who underwent CRRM were then identified, and rates were compared between those diagnosed with breast cancer in the periods before (pre-AJ) and after (post-AJ) Angelina Jolie’s announcement of BRRM (14th May 2013). As such, the pre-AJ group comprised of patients diagnosed with breast cancer between 14th Feb 1990–13th May 2013, and the post-AJ group between 14th May 2013–29th Nov 2019.All the authors confirm that all experimental protocols were approved the University of Manchester and the University of Birmingham. All methods were carried out in accordance with relevant guidelines and regulations set by the University of Manchester and the University of Birmingham. Informed consent was obtained from all subjects (no subjects under 18) in accordance to the Ethics Committee at Manchester University NHS Foundation Trust. Our study protocols were covered by the Family History Risk application that gained ethics approval by the North West 7 Research Ethics Committee (10/H10008/24).Comparisons between patients diagnosed with breast cancer in the periods pre- and post-AJ were performed using independent samples t-tests for normally distributed variables, Mann–Whitney U tests for ordinal variables, and Fisher’s exact tests for nominal variables.The change in the rate of CRRM post-AJ was initially analysed using a Fisher’s exact test to compare between the two periods. Trends over time were then assessed using a segmented regression approach. Prior to this analysis, we calculated two variables, the first stating the number of years from the AJ date to the date of the index cancer diagnosis , and the second being an indicator variable, indicating whether the diagnosis was pre- or post-AJ. A binary logistic regression model was then produced, with these two factors and an interaction term as covariates, giving the following model structure:1), as could the step-change increase in the proportion undergoing CRRM in the period after the AJ announcement (β2). The coefficient for the interaction term (β3) then allowed for the slopes in CRRM rates to be compared between the pre- and post-AJ periods.From this model, the “slope” in the proportion undergoing CRRM per year in the pre-AJ period could be estimated , with p < 0.001), with 55.1% vs. 36.6% being in the 40 + % lifetime risk category, and 58.3% vs. 35.2% being high (Non-BRCA) lifetime risk. The post-AJ cohort was also more likely to be lymph node negative , and had different distributions of molecular subtypes (p = 0.002), with fewer cases of triple-negative disease (17.9% vs. 30.1%).A total of 387 women were diagnosed with unilateral breast cancer and met the inclusion criteria of the study, with the first diagnosis being made in February 1990. Of these, one patient underwent CRRM prior to breast cancer being diagnosed , and so was excluded. Of the N = 386 patients included in the analysis, 268 (69.4%) were diagnosed prior to 14th May 2013, and were classified as pre-AJ, with the remainder  being post-AJ. Comparisons between these two cohorts  of patients underwent CRRM, which was performed a median of 42 days (interquartile range: 11–154) after the index cancer surgery. Of those undergoing CRRM, this was performed during the index cancer surgery in 16.5% of cases, with the longest interval between breast cancer and CRRM being 58 months, and intervals being less than 28 months for the remainder. There were three patients diagnosed with breast cancer prior to the AJ announcement, and underwent CRRM in the post-AJ period, who were included in the pre-AJ group for analysis. However, in two of these patients, the interval from breast cancer to CRRM was less than three months; hence the decision to have CRRM was likely made prior to the AJ announcement. The third patient had an interval of 10 months between breast cancer and CRRM.p < 0.001), with a two-fold increase from 23.9% (64/268) to 50.0% (59/118). For the subgroup of BRCA1/2 carriers (N = 105), a similar increase in CRRM rates from 47.6% (40/84) to 90.5% (19/21) was observed between the periods (p < 0.001). The trends in CRRMs were further assessed using a segmented regression approach, the results of which are summarised in Table p = 0.907]. Immediately after the AJ announcement, a significant step change increase in the CRRM rate was observed . In the post-AJ period, the rates of CRRM were not found to be changing significantly over time , although there appeared to be a tendency for a decline.A simple comparison between the pre- and post-AJ periods found a significant difference in rates of CRRM p < 0.00, with a p < 0.001) and TNM tumour stage (p = 0.040), and to increase significantly with risk  and tumour grade (p = 0.015) on univariable analysis (Table p < 0.001). After adjusting for these factors, the step-change increase in CRRM rates post-AJ remained significant . However, the subsequent decline in the CRRM rate was found to be more pronounced on multivariable analysis, with a near-significant OR of 0.83 per year (p = 0.076) in the post-AJ period.Across the cohort as a whole, rates of CRRM were found to decrease significantly with increasing age at breast cancer  database, where data on risk was not available. A study from Switzerland33 showed no change in the rates of CRRM over time, and concluded that differences in medico-social and cultural factors, perceptions towards breast cancer and access to plastic surgery may account for differences between the US and Europe.These observed trends in the pre-AJ period are in contrast to overall trends from the UK 36. A recent study from Denmark35 confirmed that uptake of CRRM amongst women before the age of 45 years was significantly higher in those harbouring a pathogenic variant of the BRCA1/2 gene and those from high-risk families, compared to those with a lower genetic risk.There are a number of studies that have assessed the impact of genetic testing on CRRM ratesBRCA carriers chose a BRRM, compared to 36% in the US37, suggesting cultural factors and access to breast reconstruction may contribute to this.To our knowledge, this is the first study to report variations in rates of CRRM according to baseline lifetime risk of developing breast cancer. Patients attending the family history clinic are likely to have differing motivations for CRRM, compared to those with a sporadic cancer, although this has not been formally assessed. In addition, international variations exist towards attitudes to risk-reducing surgery. For example, in Poland, only 2.7% of p < 0.001) who, amongst BRCA1 and BRCA2 carriers, derive the greatest survival benefit8. Tuttle et al.1 found that favourable prognostic factors  were associated with increasing rates of CRRM. They proposed that this was because this group of patients with a good breast cancer prognosis were likely to derive the most benefit from CRRM, as “their survival time is longer and thus their subsequent risk of contralateral breast cancer is greater”. These factors were not found to be significant independent predictors of CRRM in our study, although our cohort reflects breast cancers in the context of early detection, with later stage breast cancers being uncommon. In addition, on account of the relatively small sample size, it is possible that the study was underpowered to detect the associations between these factors and CRRM.In our study, CRRM was most commonly performed in younger breast cancer patients (BRCA1/2 carriers in North America (49.3%), compared to 0% in Norway38. Our own studies suggest that attitudes towards risk reducing surgery amongst breast surgeons in the UK are closely aligned with the USA39. As expected, this rate was highest amongst the youngest (< 45 years) and those with the highest risk profile (40 + % estimated lifetime risk), which is in keeping with previous studies38.Overall uptake of CRRM in our cohort was 31.9% over the study period as a whole. There are wide variations in uptake of CRRM worldwide with the highest for 14. There was an up to six month delay in this increase—as women undergoing BRRM (in comparison to CRRM) follow a time consuming detailed protocol, including psychological assessment40. This is in contrast to CRRM, where the rise in number was almost immediate, suggesting that surgeons and multi-disciplinary teams may be willing to consider CRRM as part of the primary treatment, rather than undertaking a lengthier protocol similar to women being considered for BRRM. In addition, the effect seems to be sustained over a period of at least four years, although there is some decline in number of CRRMs following the initial surge. Long term follow up will be important to assess whether rates of CRRMs ultimately return to the pre-AJ baseline and, if so, how long the AJ effect persisted.We recently reported an increase in the number of high-risk women undergoing BRRM following the Angelina Jolie’s’ article in the New York Times in May 201341. In the USA, women being diagnosed with DCIS are increasingly choosing bilateral mastectomies41, with a recent study42 showing that, despite adjusting for family history, rates of CBC amongst those diagnosed with DCIS are not increased sufficiently to warrant a recommendation of CRRM. Further work needs to determine the current trend of CRRM amongst women with DCIS.Our study demonstrated that almost a fifth of patients undergoing CRRM did so on the basis of a Stage 0  index breast cancer. This group with pre-invasive breast cancer is considered to have excellent long-term outcomes following breast conserving surgery or mastectomy. Mastectomy rates for DCIS declined between 1998 and 2004, with rates increasing to 2011BRCA genes, uptake of CRRM in the post-AJ period was extremely high, at 90%. Data from a recent prospective study43 assessing the effect of a germline BRCA1 or BRCA2 mutation on breast cancer outcomes in patients with young-onset breast cancer found that this group (tested positive for BRCA1 or BRCA2) had similar survival compared to non-carriers. Therefore, clinicians need to be aware that this group of high-risk patients warrant further consideration when discussing risk-reducing strategies.Amongst women who tested positive for a pathogenic variant in either BRCA1, carriers whose tumours nearly always require chemotherapy. However, the times from surgery to CRRM were generally short, with a median of 42 days, hence this should have had minimal influence on the results.There are some limitations to the current study. We have reported the results from a single centre; hence the generalizability of findings to other centres and other countries cannot be confirmed. This also resulted in a relatively small sample size, although this did yield sufficient statistical power to obtain high levels of significance when analysing the AJ effect, even across risk groups. A high proportion of cases underwent genetic testing, allowing sub-analysis based on carrier status. However, the details of the genetic testing was not available to all patients prior to CRRM, hence may not have directly influenced their decision. Whilst there appeared to be a tendency for CRRM rates to be in decline after the initial step-change increase, the relatively short period of follow up in the post-AJ period was insufficient to conclusively assess whether this decline was significant. In addition, in the later years of the study, it is possible that rates of CRRM may have been underestimated, as patients may have opted to undergo CRRM after the follow up period had ended. This may be more pronounced in those undergoing neoadjuvant chemotherapy, which may delay the primary surgical decision, and may be particularly relevant in 4. It has not been possible to adjust for these multiple factors in our current study. However, access to breast reconstruction has remained constant over the period of the study. Despite this, there the type of breast reconstruction (autologous or implant based) may be a confounding factors, although data were not available to assess this. In addition, access to neoadjuvant chemotherapy, changing trends in MRI and both patient and physician knowledge on objective risk assessment are other potentially important confounding factors for which data were not available to be assessed in the current study. . In addition to these factors that it was not possible to adjust for, it is possible that residual confounding may exist for factors that were accounted for in the multivariable analysis, particularly those that are correlated with both the outcome (CRRM) and primary factor (AJ), such as the estimated lifetime risk. As such, the results of the multivariable analyses should be interpreted in the light of the limitations of both unmeasured variables and residual confounding.We have previously reviewed the multiple risk factors for developing contralateral breast cancers and potential drivers for CRRM25, which proposed a protocol to be used in the decision-making process regarding CBC, based on a review of the literature. The current study furthers our understanding into potential drivers for variations in rates of CRRM, highlighting that highly publicised case-studies of CRRM may have a sizable influence on patient decision making. In light of this, it is important to ensure that objective assessments of risk are clearly communicated when counselling women on the merits of risk-reducing surgery, to ensure that they have sufficient information to make a rational decision, and are not unduly influenced by high-profile cases.We recently published the “Manchester Guidelines for CRRM”The “Angelina Jolie effect” appears to be have been associated with a sustained increase in the rates of CRRM amongst increased risk breast cancer patients in our single-centre study. When considering the trends over time of CRRM, it may be important to assess those with a significant family history and/or those harbouring a pathogenic variant in one of the breast cancer susceptibility genes as a separate group to those with sporadic breast cancer. Further studies should address the multiple factors that contribute to a decision for CRRM.