Height and Body Mass Index as Modifiers of Breast Cancer Risk in BRCA1/2 Mutation Carriers: A Mendelian Randomization Study

Abstract

Background

BRCA1/2 mutations confer high lifetime risk of breast cancer, although other factors may modify this risk. Whether height or body mass index (BMI) modifies breast cancer risk in BRCA1/2 mutation carriers remains unclear.

Methods

We used Mendelian randomization approaches to evaluate the association of height and BMI on breast cancer risk, using data from the Consortium of Investigators of Modifiers of BRCA1/2 with 14 676 BRCA1 and 7912 BRCA2 mutation carriers, including 11 451 cases of breast cancer. We created a height genetic score using 586 height-associated variants and a BMI genetic score using 93 BMI-associated variants. We examined both observed and genetically determined height and BMI with breast cancer risk using weighted Cox models. All statistical tests were two-sided.

Results

Observed height was positively associated with breast cancer risk (HR = 1.09 per 10 cm increase, 95% confidence interval [CI] = 1.0 to 1.17; P = 1.17). Height genetic score was positively associated with breast cancer, although this was not statistically significant (per 10 cm increase in genetically predicted height, HR = 1.04, 95% CI = 0.93 to 1.17; P = .47). Observed BMI was inversely associated with breast cancer risk (per 5 kg/m² increase, HR = 0.94, 95% CI = 0.90 to 0.98; P = .007). BMI genetic score was also inversely associated with breast cancer risk (per 5 kg/m² increase in genetically predicted BMI, HR = 0.87, 95% CI = 0.76 to 0.98; P = .02). BMI was primarily associated with premenopausal breast cancer.

Conclusion

Height is associated with overall breast cancer and BMI is associated with premenopausal breast cancer in BRCA1/2 mutation carriers. Incorporating height and BMI, particularly genetic score, into risk assessment may improve cancer management.

Breast cancer is the most common cancer in women and a leading cause of cancer deaths globally (1). Inheritance of a BRCA1 or BRCA2 mutation is associated with an increased lifetime risk of breast cancer (2,3). However, penetrance of BRCA1/2 mutations is likely modified by lifestyle, reproductive factors, and genetic variants (4–8). Multiple genes have been found to modify the association between BRCA1/2 and breast cancer risk (9–11). Accurate breast cancer risk prediction in BRCA1/2 mutation carriers is crucial in preventing morbidity and mortality, while optimizing primary and secondary prevention.

The relationship between anthropometric characteristics such as height or body mass index (BMI) and breast cancer risk has been extensively studied (12,13). Adult height was found to be positively associated with breast cancer risk (14). Higher BMI is positively associated with postmenopausal breast cancer, but inversely associated with premenopausal breast cancer (15). However, the associations of height and BMI with breast cancer risk in BRCA1/2 mutation carriers remain unclear. Retrospective studies are subject to potential biases, whereas prospective studies are often underpowered.

Notably, both height and BMI have a strong genetic basis. Genome-wide association studies (GWAS) (16–18) have identified variants that are associated with either trait. In aggregate, these variants explain a sizable proportion of the variation in each trait.

Mendelian randomization (MR) is a method to assess the association between an exposure and a disease using genetic markers associated with the exposure as instrumental variables. Because genes are inherited randomly, MR can be used to minimize the effects of recall bias, reverse causation, measurement error, and residual confounding (19). The assumptions underlying MR include the following: genetic variants are associated with the exposure of interest, variants only affect the outcome through the exposure, and variants are weakly or not associated with confounders in the exposure-outcome pathway (20,21). A causal relationship between exposure and disease could be concluded if these assumptions are held. In this study, we used MR approaches to evaluate the association between height and BMI and breast cancer, using data from the Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA), including 22 588 women, with 14 676 BRCA1 and 7912 BRCA2 mutation carriers.

Methods

Information about CIMBA and genotyping protocols can be found in the Supplementary Methods (available online) and previous publications (9–11). All participants provided written informed consent in accordance with the local institutional review boards.

Single-Nucleotide Polymorphism Selection

Single-nucleotide polymorphisms (SNPs) associated with height and BMI were identified from the Genetic Investigation of Anthropometric Traits publications (16,17). SNPs achieving genome-wide statistical significance (P < 5 × 10⁻⁸) with height or BMI were eligible. We excluded SNPs with an imputation quality of less than  0.5. For height, we included 586 SNPs (85 genotyped) (Supplementary Table 1, available online). For BMI, we included 93 SNPs (12 genotyped) (Supplementary Table 2, available online).

Statistical Analysis

We calculated weighted genetic scores (GS) for height and BMI using methods described previously, based on a polygenic additive model (ie, ignoring interactions between variants) (14,22). We calculated each GS using the formulas

$${\mathrm{G}\mathrm{S}}_{\mathrm{h}\mathrm{e}\mathrm{i}\mathrm{g}\mathrm{h}\mathrm{t}}=\sum _{i=1}^{586}{\mathrm{\beta }}_i{\mathrm{S}\mathrm{N}\mathrm{P}}_i\mathrm{}\mathrm{a}\mathrm{n}\mathrm{d}\mathrm{}{\mathrm{G}\mathrm{S}}_{\mathrm{B}\mathrm{M}\mathrm{I}}=\sum _{i=1}^{93}{\mathrm{\beta }}_i{\mathrm{S}\mathrm{N}\mathrm{P}}_i,$$

where β_i is the reported per-allele effect of the ith SNP for height and BMI (16,17) and SNP_i is the effect allele dosage (0, 1, 2) of the ith SNP. We rescaled GSs to calculate the genetically predicted height and BMI by performing linear regressions of observed height and BMI on the corresponding GS in noncases. For height, we obtained from the regression equation β₀ (intercept = 165.648) and β₁ (slope = 5.119). The corresponding values for BMI were β₀ (22.058) and β₁ (6.408). We used these values to calculate the scaled height-GS and BMI-GS using this equation: Scaled-GS = β₀ + β₁GS. We estimated the variation explained by each GS and the association between each GS and traditional breast cancer risk factors, using linear regressions for continuous variables and logistic regressions for categorical variable.

Next, we modeled height-GS and BMI-GS with breast cancer risk using weighted Cox models. The primary outcome was breast cancer diagnosis. Observations were censored at ovarian cancer diagnosis, prophylactic mastectomy/salpingo-oophorectomy, death, or end of follow-up, whichever came first. Time to event was computed from birth to age at breast cancer diagnosis or censoring. Mutation carriers were not randomly selected and those with breast cancer had a higher probability of being identified. To account for nonrandom sampling, we applied a weighted cohort approach (23). Weights were assigned based on observed incidence rates of breast cancer for BRCA1/2 carriers (24). To account for interdependence between carriers from the same family, we used a robust sandwich variance estimation approach. Stratified analyses were performed by BRCA1/2 or menopausal status. Menopausal status was modeled as a time-varying covariate: The variable was coded as premenopausal from birth until age at censoring and was switched to postmenopausal at the age of natural menopause or bilateral salpingo-oophorectomy. If age at natural menopause or bilateral salpingo-oophorectomy was missing, we imputed the mean age as 46 years, because the mean and median ages at natural menopause in this population were 46 and 48 years, respectively. These ages were broadly consistent with those from a prior registry study of mutation carriers (25). Imputing missing age at menopause as 50 did not materially change the results. The analyses were also adjusted for the first eight principal components (as a proxy for population structure and ethnicity), birth cohort, and country of enrollment.

We also examined the association between height and BMI with breast cancer by modeling individual height and BMI variants separately. We assessed the direct association between each SNP and height and BMI (β_XG) and its association with breast cancer risk (β_YG). β_XG for each SNP was extracted from prior GWAS and represents the per-allele effect on height or BMI. β_YG was calculated using multivariable-adjusted weighted Cox model for each SNP using data from CIMBA, ie, breast cancer ∼ β_YGX (where X = 0, 1, 2 for the allele corresponding to increased height or BMI), principal components, birth cohort, mutated gene, and country of enrollment. We statistically combined these two effect estimates to measure the association between height and BMI and breast cancer risk (β_YX) (26,27). The causal effect (β_YX) was calculated using the Wald estimator β_YX = β_YG/β_XG. The standard error for this estimate was estimated using the method proposed by Burgess (27):

$${\mathrm{S}\mathrm{E}}_{YX}=\sqrt{{\left(\frac{{\mathrm{S}\mathrm{E}}_{YG}}{{\mathrm{\beta }}_{XG}}\right)}^2.}$$

β_YX can be interpreted as the log hazard ratio (HR) for breast cancer per 1 unit increase in genetically determined height and BMI. We then combined the effects of individual height- and BMI-associated variants using an inverse-variance fixed-effects meta-analysis. We also used the Egger test to assess for possible pleiotropic effects of the variants (ie, the effects are not mediated via the exposure), one of the assumptions for MR (28).

In a subset of participants with observed height or BMI (34%), we performed a formal instrumental variable analysis to estimate the unbiased effect of height and BMI on breast cancer risk using two-stage residual inclusion regression (29). In stage 1, we conducted a linear regression of observed height or BMI on corresponding GS, principal components, birth cohort, country, mutation status, and residuals. In stage 2, we used a Cox model to fit breast cancer risk against height and BMI, birth cohort, country, mutation status, and residuals from stage 1. We performed 10 000 bootstraps to obtain the variance estimates.

Lastly, we examined the association between observed height and BMI and breast cancer risk in participants with measurements using weighted Cox models, adjusted for traditional breast cancer risk factors including birth cohort, menopausal status, age at menarche (continuous), and parity (continuous). BMI was reported at date of questionnaire (baseline), usually close to the date of genetic testing and recalled for young adulthood (age 18). The BMI-GS mentioned above was rescaled to BMI reported at baseline because previous GWAS were based on adult BMI.

The proportional hazards assumption was tested by adding an interaction term of age and either height-GS or BMI-GS. In models with menopausal status as the time-varying variable, test for heterogeneity by menopausal status was also a test for proportional hazard assumption. Analyses were performed using SAS 9.4 (SAS Institute, Cary, NC) and Stata 14.0 (StataCorp, College Station, TX). All statistical tests were two-sided and a P value less than .05 was considered statistically significant unless stated otherwise. For association tests of individual SNPs, Bonferroni adjustment was conducted.

Results

Baseline Characteristics

Table 1 presents the baseline characteristics of the 22 588 participants (14 676 BRCA1 and 7912 BRCA2 mutation carriers) with genotype information. There were 11 451 cases of breast cancer in the overall consortium. The mean age of individuals at the time of breast cancer diagnosis (cases) was similar to the age of individuals who did not develop breast cancer at the time of censoring (controls). However, the birth year of cases tended to be earlier than controls. Height was available in 7657 participants (4502 BRCA1 carriers and 3155 BRCA2 carriers) and BMI at date of questionnaire was available in 7516 participants (4401 BRCA1 carriers and 3115 BRCA2 carriers).

Table 1.

Baseline characteristics of participants in the CIMBA consortium with genotype information*

Variable	BRCA1 carriers	BRCA2 carriers
	N = 14 676	N = 7912
Case participants, n	7360	4091
Year of birth, median (IQR)	1956 (1948–1964)	1954 (1945–1961)
Age at diagnosis, mean (SD), y	41.1 (9.3)	44.2 (10.0)
Control participants, n	7316	3821
Year of birth, median (IQR)	1963 (1953–1972)	1962 (1950–1971)
Age at censoring, mean (SD), y	42.0 (12.5)	44.0 (13.5)
Ethnicity, n (%)
Caucasian, not otherwise specified	13 435 (91.5)	7126 (90.1)
Ashkenazi Jewish	1241 (8.5)	786 (9.9)
Height in cm, n	4502	3155
Mean (SD)	164.8 (6.8)	164.5 (6.9)
Weight at date of questionnaire in kg, n	4436	3133
Mean (SD)	68.1 (13.9)	69.0 (14.5)
BMI at date of questionnaire in kg/m2, n	4401	3115
Mean (SD)	25.1 (5.1)	25.6 (5.2)
Weight in early adulthood in kg, n	3152	2296
Mean (SD)	57.6 (9.2)	57.9 (9.6)
BMI in early adulthood in kg/m2, n	3134	2283
Mean (SD)	21.2 (3.3)	21.4 (3.3)
Age at menarche in years, n	4425	3034
Mean (SD), y	13.0 (1.5)	13.0 (1.5)
Parous, n (%)
Yes	3914 (77.8)	2681(79.7)
No	1117 (22.2)	682 (20.3)
Age at first live birth in years, n	3711	2579
Mean (SD), y	25.3 (4.9)	25.3 (4.9)
Menopausal status, n (%)
Premenopausal	2330 (47.4)	1610 (46.4)
Postmenopausal	2588 (52.6)	1858 (53.6)
Age at menopause, mean (SD), y	44.7 (6.0)	45.6 (6.0)

*BMI = body mass index; CIMBA = Consortium of Investigators of Modifiers of BRCA1/2; IQR = interquartile range.

Height Analysis

Observed height was positively associated with breast cancer risk (HR per 10 cm = 1.09, 95% CI = 1.02 to 1.17, P = .02) (Table 2). In stratified analysis, we found that height was a stronger predictor of risk in BRCA2 carriers (HR = 1.17, 95% CI = 1.04 to 1.31) than in BRCA1 carriers (HR = 1.06, 95% CI = 0.97 to 1.16), but the interaction was not statistically significant. The country-specific estimates showed low levels of heterogeneity (Supplementary Figure 1A, available online).

Table 2.

Association of height and breast cancer risk using observed height, among 7657 participants

	N/events	HR (95% CI)	P*
Per 10-cm increase in observed height
All participants (confounding adjustment sequentially)
Unadjusted	7657/3653	1.14 (1.06 to 1.22)	2.0 × 10-4
Adjusted for principal components	7657/3653	1.15 (1.07 to 1.23)	1.6 × 10-4
Additionally adjusted for country	7657/3653	1.17 (1.09 to 1.26)	1.3 × 10-4
Additionally adjusted for birth cohort	7657/3653	1.09 (1.01 to 1.17)	.02
Additionally adjusted for mutation status	7657/3653	1.09 (1.02 to 1.17)	.01
Additionally adjusted for menopausal status	7657/3653	1.09 (1.02 to 1.17)	.02
Additionally adjusted for parity and age at menarche	7090/3383	1.10 (1.02 to 1.18)	.01
By mutation status†
BRCA1 carrier	4502/2154	1.06 (0.97 to 1.16)	.19
BRCA2 carrier	3155/1499	1.17 (1.04 to 1.31)	.007
Pinteraction			.18
By menopausal status‡
Premenopausal	7657/2197	1.10 (1.01 to 1.20)	.03
Postmenopausal	3076/1402	1.07 (0.95 to 1.19)	.26
Pinteraction‡			.64

^*P values were calculated from weighted Cox models. All P values are two-sided. HR = hazard ratio; CI = confidence interval.

^†Adjusted for principal components, birth cohort, country of enrollment, and menopausal status.

^‡Adjusted for principal components, mutation status, birth cohort, and country of enrollment.

We found that height-GS was strongly associated with observed height by case/control and mutation status (all P < 10^–93) (Table 3). The height-GS explained 13.4% of the variation in height (Supplementary Figure 2A, available online). As shown in Supplementary Figure 3A (available online), there was a strong correlation (r = 0.44) between the estimated effect size for individual variants in our study and those reported in previous GWAS. Height-GS was positively associated with weight, baseline age, and age at menarche but the associations were weak.

Table 3.

Associations of the height genetic score (height-GS) with height and traditional breast cancer risk factors^*

Variable	Number of participants	Summary effect	SE	P†	% variation explained
Measured height, cm
All participants	7657	1.012	0.029	7.0 × 10−241	13.4
BRCA1 carriers	4502	1.025	0.038	3.8 × 10−149	14.0
BRCA2 carriers	3155	0.996	0.047	2.5 × 10−94	12.6
Case participants	3653	1.028	0.041	2.6 × 10−128	14.7
Control participants	4004	1.000	0.042	1.1 × 10−116	12.3
Traditional risk factors
BMI, kg/m2	7516	−0.010	0.024	.66	–
Weight, kg	7569	0.813	0.065	2.7 × 10−35	–
Age at baseline, y	8578	0.123	0.036	7.3 × 10−4	–
Age at menarche, y	7459	0.028	0.007	9.3 × 10−5	–
Parous, yes vs no	8394	−0.010	0.011	.35	–
Age at first live birth, y	6290	−0.014	0.025	.58	–
Menopausal status, pre vs post	8386	0.011	0.009	.20	–
Age at menopause, y	4336	−0.082	0.037	.03	–

^*Regression coefficient is presented for continuous variables and natural log-scale odds ratio for binary variables, per unit increase of the H-GS. BMI = body mass index; SE = standard error.

^†P values were calculated from linear regression models for all variables except for parity and menopausal status (logistic regression models). All P values are two-sided.

Height-GS was positively associated with breast cancer risk with an effect weaker than that for observed height, although it was not statistically significant (HR = 1.04 per 10-cm increase in genetically predicted height, 95% CI = 0.93 to 1.17; P = .47) (Table 4). Effect was not different when stratified for menopausal or mutation status.

Table 4.

Association of height genetic score and breast cancer risk in 22 588 participants in CIMBA, per 10-cm increase in genetically predicted height

Breast cancer group	N/events	HR (95% CI)	P*	Heterogeneity (I2)
Height-GS†
All participants (confounding adjustment sequentially)
Unadjusted	22 588/11 451	1.11 (1.00 to 1.23)	.05	–
Adjusted for principal components	22 588/11 451	1.04 (0.93 to 1.17)	.48	–
Additionally adjusted for country	22 588/11 451	1.03 (0.92 to 1.16)	.57	–
Additionally adjusted for birth cohort	22 588/11 451	1.04 (0.92 to 1.16)	.56	–
Additionally adjusted for mutation status	22 588/11 451	1.04 (0.93 to 1.17)	.45	–
Additionally adjusted for menopausal status	22 588/11 451	1.04 (0.93 to 1.17)	.47
By mutation status‡
BRCA1 carrier	14 676/7360	1.03 (0.91 to 1.18)	.62	–
BRCA2 carrier	7912/4091	1.07 (0.87 to 1.32)	.50	–
Pinteraction			.95
By menopausal status§
Premenopausal	22 588/7410	1.09 (0.96 to 1.24)	.20	–
Postmenopausal	8459/3926	0.95 (0.79 to 1.13)	.55	–
Pinteraction			.18
Meta-analysis method§
All participants	22 588/11451	1.05 (0.93 to 1.19)	.42	17.0%
BRCA1 carrier	14 676/7360	1.04 (0.90 to 1.20)	.57	11.8%
BRCA2 carrier	7912/4091	1.09 (0.87 to 1.36)	.45	6.6%
Pinteraction			.75
Two-stage residual inclusion method
All participants	7657/3653	1.09 (0.93 to 1.27)	.27	–
BRCA1 carrier	4502/2154	1.16 (0.96 to 1.40)	.13	–
BRCA2 carrier	3155/1499	1.05 (0.80 to 1.37)	.74	–

^*P values were calculated using weighted Cox models. All P values are two-sided. CIMBA = Consortium of Investigators of Modifiers of BRCA1/2; H-GS = height genetic score; HR = hazard ratio; CI = confidence interval.

^†H-GS combining 586 height-associated single-nucleotide polymorphisms (SNPs).

^‡Adjusted for principal components, birth cohort, country of enrollment, and menopausal status.

^§Adjusted for principal components, mutation status, birth cohort, and country of enrollment.

^‖Hazard ratios were calculated using inverse-variance meta-analysis and rescaled to the corresponding units by calculating the height measurements per z score among controls. Effect estimates for breast cancer for each SNP were calculated from weighted Cox model adjusting for principal components, birth cohort, country of enrollment, menopausal status, and mutation status.

When combining the breast cancer risk estimates for individual height variant using inverse-variance meta-analysis, the result was similar (HR = 1.05, 95% CI = 0.93 to 1.19; P = .42) (Table 4). There was low heterogeneity among SNPs (I²=17.0%). The Egger test for small-study effects was not statistically significant (P = .61, Supplementary Figure 4A, available online), so we failed to reject the assumption of no pleiotropic effects for MR analysis. The two-stage residual inclusion analysis found a similar risk estimate to that for observed height (HR = 1.09, 95% CI = 0.93 to 1.27; P = .27).

BMI Analysis

For reported BMI at date of questionnaire, we found an inverse association with breast cancer risk after multivariable adjustment (HR per 5-kg/m² increase = 0.94, 95% CI = 0.90 to 0.98; P = .007) (Table 5). The inverse association was stronger in BRCA2 vs BRCA1 carriers [HR = 0.90 (95% CI = 0.84 to 0.97) vs 0.96 (95% CI = 0.91 to 1.01)] and for premenopausal vs postmenopausal breast cancer [HR = 0.92 (95% CI = 0.87 to 0.97) vs 0.97 (95% CI: 0.91 to 1.04), but there was no statistically significant interaction (P_interaction > .05 for each comparison). We found a stronger inverse association of BMI in young adulthood with breast cancer risk (HR = 0.83, 95% CI = 0.76 to 0.90; P = 2.1 × 10^–5). The country-specific estimates showed moderate levels of heterogeneity (Supplementary Figure 1B, available online).

Table 5.

Association of BMI and breast cancer risk using observed body mass index (BMI)

Breast cancer group	N/events	HR (95% CI)	P*
Per 5-kg/m2 increase in BMI at date of questionnaire
All participants (confounding adjustment sequentially)
Unadjusted	7516/3594	0.92 (0.88 to 0.97)	4.3 × 10-4
Adjusted for principal components	7516/3594	0.93 (0.89 to 0.97)	7.3 × 10-4
Additionally adjusted for country	7516/3594	0.92 (0.88 to 0.96)	3.1 × 10-4
Additionally adjusted for birth cohort	7516/3594	0.94 (0.90 to 0.98)	.003
Additionally adjusted for mutation status	7516/3594	0.95 (0.91 to 0.99)	.01
Additionally adjusted for menopausal status	7516/3594	0.94 (0.90 to 0.98)	.007
Additionally adjusted for parity and age at menarche	6964/3331	0.93 (0.89 to 0.98)	.003
By mutation status†
BRCA1 carrier	4401/2114	0.96 (0.91 to 1.01)	.11
BRCA2 carrier	3115/1480	0.90 (0.84 to 0.97)	.003
Pinteraction‡			.26
By menopausal status§
Premenopausal	7516/2153	0.92 (0.87 to 0.97)	.001
Postmenopausal	3029/1389	0.97 (0.91 to 1.04)	.40
Pinteraction‡			.14
Per 5-kg/m2 increase in BMI in young adulthood
All participants (confounding adjustment sequentially)
Unadjusted	5417/2520	0.83 (0.76 to 0.91)	3.1 × 10-5
Adjusted for principal components	5417/2520	0.83 (0.76 to 0.91)	5.4 × 10-5
Additionally adjusted for country	5417/2520	0.81 (0.74 to 0.88)	2.8 × 10-6
Additionally adjusted for birth cohort	5417/2520	0.81 (0.74 to 0.88)	1.8 × 10-6
Additionally adjusted for mutation status	5417/2520	0.83 (0.76 to 0.90)	1.7 × 10-5
Additionally adjusted for menopausal status	5417/2520	0.83 (0.76 to 0.90)	2.1 × 10-5
Additionally adjusted for parity and age at menarche	5210/2436	0.82 (0.75 to 0.90)	2.7 × 10-5
By mutation status†
BRCA1 carrier	3134/1462	0.87 (0.78 to 0.97)	.01
BRCA2 carrier	2283/1058	0.74 (0.63 to 0.85)	4.5 × 10-5
Pinteraction			.06
By menopausal status‡
Premenopausal	5417/1519	0.85 (0.78 to 0.94)	.002
Postmenopausal	2181/977	0.79 (0.69 to 0.91)	.001
Pinteraction			.35

^*P values calculated using weighted Cox models. All P values are two-sided; HR = hazard ratio; CI = confidence interval.

^†Adjusted for principal components, birth cohort, country of enrollment, and menopausal status.

^‡Adjusted for principal components, mutation status, birth cohort, and country of enrollment.

BMI-GS was strongly associated with reported BMI at date of questionnaire among controls and cases (each P < 10^–14) (Table 6). BMI-GS accounted for 2.6% of the variation in BMI at date of questionnaire (Supplementary Figure 2B, available online). We found a strong correlation between the effect on BMI by individual variants in prior reported GWAS and in CIMBA (r = 0.52, Supplementary Figure 3B, available online). Similarly, the BMI-GS was associated with reported BMI in young adulthood, with stronger effects among controls (P < 10^–15, r² = 2.3%). The BMI-GS was positively associated with height and inversely associated with age at menarche.

Table 6.

Associations of the body mass index genetic score (BMI-GS) with BMI and traditional breast cancer risk factors^*

Variable	Number of participants	Summary effect*	SE	P†	% variation explained
Observed BMI at date of questionnaire, kg/m2
All participants	7516	0.832	0.059	2.8 × 10-44	2.6
BRCA1 carrier	4401	0.786	0.076	9.9 × 10-25	2.4
BRCA2 carrier	3115	0.903	0.094	2.1 × 10-21	2.9
Case participants	3594	0.651	0.083	6.9 × 10-15	1.7
Control participants	3922	1.000	0.084	3.3 × 10-32	3.5
Premenopausal control participants	2246	0.941	0.110	2.6 × 10-17	3.1
Postmenopausal control participants	1441	1.113	0.139	3.0 × 10-15	4.2
Observed BMI in young adulthood, kg/m2
All participants	5417	0.794	0.083	2.3 × 10-21	1.6
BRCA1 carrier	3134	0.770	0.108	1.2 × 10-12	1.6
BRCA2 carrier	2283	0.830	0.131	2.8 × 10-10	1.7
Case participants	2520	0.540	0.110	1.0 × 10-6	0.9
Control participants	2897	1.000	0.122	3.3 × 10-16	2.3
Premenopausal control participants	1589	0.995	0.170	5.3 × 10-9	2.1
Postmenopausal control participants	1100	1.044	0.193	8.2 × 10-8	2.6
Traditional risk factors
Height	7657	0.319	0.079	5.4 × 10-5	–
Age at baseline, y	8578	−0.020	−0.020	.79	–
Age at menarche, y	7459	−0.090	0.018	5.0 × 10-7	–
Parous, yes vs no	8394	0.024	0.027	.38	–
Age at first live birth, y	6290	−0.113	0.063	.07	–
Menopausal status, pre vs post	8386	−0.019	0.022	.39	–
Age at menopause, y	4336	−0.172	0.093	.06	–

^*Regression coefficient is presented for continuous variables and natural log-scale odds ratio for binary variables, per unit increase of the weighted BMI genetic score. SE = standard error.

^†P values calculated from linear regression models for all variables except for parity and menopausal status (logistic regression models). All P values are two-sided.

In the analysis of BMI-GS and breast cancer risk, each 5-kg/m² increment in genetically predicted BMI was associated with a 13% reduction in breast cancer risk (HR = 0.87, 95% CI = 0.76 to 0.98; P = .02) (Table 7). The association was slightly stronger among BRCA2 mutation carriers and for premenopausal breast cancer, although there was no statistically significant interaction (P_interaction > .05 for each).

Table 7.

Association of body mass index genetic score (BMI-GS) and breast cancer risk among 22 588 participants in CIMBA, per 5-kg/m² increase in genetically predicted BMI

Breast cancer group	N/events	HR (95% CI)	P*	Heterogeneity (I2)
BMI-GS†
All participants (confounding adjustment sequentially)
Unadjusted	22 588/11 451	0.93 (0.81 to 1.05)	.24
Adjusted for principal components	22 588/11 451	0.89 (0.78 to 1.01)	.07
Additionally adjusted for country	22 588/11 451	0.90 (0.79 to 1.03)	.13
Additionally adjusted for birth cohort	22 588/11 451	0.88 (0.77 to 0.999)	.049
Additionally adjusted for mutation status	22 588/11 451	0.88 (0.78 to 0.99)	.04
Additionally adjusted for menopausal status	22 588/11 451	0.87 (0.76 to 0.98)	.02
By mutation status‡
BRCA1 carrier	14 676/7360	0.88 (0.76 to 1.02)	.09
BRCA2 carrier	7912/4091	0.83 (0.65 to 1.05)	.11
Pinteraction			.63
By menopausal status§
Premenopausal	22 588/7410	0.84 (0.73 to 0.98)	.02
Postmenopausal	8459/3926	0.89 (0.72 to 1.09)	.26
Pinteraction			.68
Meta-analysis method‖
All participants	22 588/11 451	0.87 (0.76 to 0.98)	.03	3.5%
BRCA1 carrier	14 676/7360	0.88 (0.76 to 1.03)	.10	15.7%
BRCA2 carrier	7912/4091	0.82 (0.65 to 1.04)	.10	0.0%
Pinteraction			.63
Two-stage residual inclusion method
All participants	7516/3594	0.86 (0.70 to 1.07)	.18	–
BRCA1 carrier	4401/2114	0.93 (0.69 to 1.23)	.61	–
BRCA2 carrier	3115/1480	0.82 (0.61 to 1.12)	.23	–

^*P values were calculated using weighted Cox models. All P values are two-sided. CIMBA = Consortium of Investigators of Modifiers of BRCA1/2; SE = standard error; HR = hazard ratio; CI = confidence interval.

^†BMI-GS was constructed by combining 93 BMI-associated single-nucleotide polymorphisms (SNPs).

^‡Adjusted for principal components, birth cohort, country of enrollment, and menopausal status.

^§Adjusted for principal components, mutation status, birth cohort, and country of enrollment.

^‖Hazard ratios were calculated using inverse-variance meta-analysis and rescaled to the corresponding units by calculating the BMI measurements per z score among controls. Effect estimates for breast cancer for each SNP were calculated from weighted Cox model adjusting for principal components, birth cohort, country of enrollment, menopausal status, and mutation status.

When we statistically combined the effect of individual BMI variants on breast cancer risk, we found a similar association (HR = 0.87, 95% CI = 0.76 to 0.98, P = .03) (Table 7). There was low overall heterogeneity (I² = 3.5%). The Egger test was not statistically significant (P = .44, Supplementary Figure 4B, available online), suggesting that pleiotropic effects may not exist. The two-stage residual inclusion method yielded similar results (HR = 0.86, 95% CI = 0.70 to 1.07; P = .18).

Individual Height- and BMI-Associated Variants

Of the 586 height-related variants, 50 were found to be associated with breast cancer risk at P less than .05 (Table 8). Of the 93 BMI-related variants, seven were associated with breast cancer risk. One SNP (rs10744956) was statistically significant after Bonferroni adjustment.

Table 8.

Height or body mass index (BMI) single-nucleotide polymorphisms (SNPs) statistically significantly associated (P < .05) with breast cancer risk in CIMBA

Rsid	Chromosome	Position	Nearest gene	Reference allele in CIMBA	Effect allele in CIMBA	Effect allele frequency in CIMBA	Imputation quality*	Association with breast cancer in CIMBA
								HR†	SE	P‡
Height
rs10744956	15	51 269 629	AP4E1	A	G	0.80	0.98	0.096	0.023	3 × 10-5
rs7740107	6	130 374 461	L3MBTL3	T	A	0.75	1	0.081	0.021	1 × 10-4
rs10995319	10	52 762 887	PRKG1	T	C	0.23	0.96	0.075	0.022	6 × 10-4
rs8058684	16	53 515 118	RBL2	G	A	0.32	1	0.061	0.019	.001
rs11049611	12	28 600 244	CCDC91	C	T	0.28	1	−0.065	0.020	.002
rs8103992	19	19 665 643	PBX4	A	C	0.78	0.98	−0.064	0.022	.004
rs11618507	13	30 172 751	SLC7A1	G	T	0.20	1	0.061	0.023	.007
rs11244750	10	127 673 877	FANK1	C	T	0.35	0.83	0.055	0.021	.009
rs7701414	5	131 585 958	PDLIM4	A	G	0.46	1	0.047	0.018	.01
rs7733195	5	172 994 624	FAM44B	G	A	0.37	1	0.049	0.019	.01
rs2306694	12	56 680 636	CS	A	G	0.06	1	0.096	0.038	.01
rs6435143	2	203 194 256	NOP5/NOP	A	C	0.56	0.84	0.050	0.020	.01
rs2284746	1	17 306 675	MFAP2	C	G	0.50	0.84	−0.049	0.020	.01
rs1797625	3	112 826 415	C3orf17	A	T	0.36	0.91	−0.049	0.020	.01
rs10495098	1	218 516 310	TGFB2	G	T	0.41	0.84	−0.051	0.021	.01
rs301901	5	37 046 626	NIPBL	A	G	0.45	0.93	−0.046	0.019	.02
rs42039	7	92 244 422	CDK6	C	T	0.26	1	0.051	0.021	.02
rs1576900	9	18 629 792	ADAMTSL1	G	A	0.30	0.91	0.048	0.020	.02
rs891088	19	7 184 762	INSR	A	G	0.27	0.83	0.052	0.022	.02
rs273945	7	137 611 566	CREB3L2	A	C	0.58	1	−0.042	0.018	.02
rs2682587	19	44 082 429	XRCC1	C	A	0.18	1	0.053	0.023	.02
rs1257763	9	96 893 945	PTPDC1	A	G	0.96	0.72	−0.118	0.053	.03
rs2888877	7	92 228 400	CDK6	T	C	0.79	0.96	−0.050	0.022	.03
rs8042424	15	101 762 539	CHSY1	C	T	0.24	0.82	−0.052	0.024	.03
rs7716219	5	54 955 071	SLC38A9	T	C	0.70	0.97	−0.044	0.020	.03
rs7727731	5	64 674 446	ADAMTS6	C	T	0.12	0.67	−0.076	0.035	.03
rs16964211	15	51 530 495	CYP19A1	G	A	0.06	0.99	−0.085	0.040	.03
rs11880992	19	2 176 403	DOT1L	G	A	0.42	0.95	−0.040	0.019	.03
rs2302580	4	8 608 634	CPZ	C	T	0.44	1	−0.040	0.019	.03
rs12538407	7	23 521 316	IGF2BP3	A	G	0.39	0.99	0.040	0.019	.03
rs2300921	3	185 651 001	SFRS10	T	C	0.41	0.98	0.041	0.019	.03
rs897080	2	44 774 202	C2orf34	C	T	0.79	0.90	−0.050	0.024	.03
rs4357716	11	69 163 161	MYEOV	C	T	0.14	1	0.055	0.026	.03
rs11659752	18	77 222 862	NFATC1	T	G	0.30	0.82	−0.045	0.021	.04
rs2166898	2	121 612 659	GLI2	G	A	0.17	0.55	0.067	0.032	.04
rs6020202	20	48 634 821	SNAI1	G	A	0.24	1	0.045	0.022	.04
rs3760318	17	29 247 715	CENTA2	G	A	0.37	1	−0.039	0.019	.04
rs6746356	2	174 815 898	SP3	A	C	0.24	0.79	0.050	0.024	.04
rs9428104	1	118 855 587	SPAG17	A	G	0.75	1	0.044	0.021	.04
rs2834442	21	35 690 786	KCNE2	T	A	0.66	0.98	−0.040	0.019	.04
rs1546391	3	114 697 457	ZBTB20	C	G	0.09	0.97	−0.069	0.033	.04
rs12926008	16	2 488 211	CCNF	T	C	0.65	0.53	0.054	0.026	.04
rs2123731	19	4 929 473	UHRF1	A	G	0.27	0.70	−0.049	0.024	.04
rs2289195	2	25 463 483	DNMT3A	G	A	0.41	0.65	0.046	0.023	.04
rs16834765	1	32 371 442	PTP4A2	C	T	0.05	0.82	0.091	0.045	.04
rs10958476	8	57 095 808	PLAG1	T	C	0.20	1	0.045	0.022	.045
rs17369123	1	172 355 841	DNM3	C	T	0.16	0.98	0.049	0.025	.046
rs1415701	6	130 345 835	L3MBTL3	G	A	0.26	1	0.041	0.021	.047
rs11152213	18	57 852 948	MC4R	A	C	0.23	0.99	−0.043	0.022	.047
rs4802134	19	38 346 685	SIPA1L3	A	G	0.75	1	−0.041	0.021	.049
BMI
rs13107325	4	103 188 709	SLC39A8	C	T	0.09	0.76	0.101	0.037	.007
rs10182181	2	25 150 296	ADCY3	A	G	0.45	0.64	−0.056	0.023	.02
rs7903146	10	114 758 349	TCF7L2	C	T	0.30	1	0.044	0.020	.03
rs9925964	16	31 129 895	KAT8	A	G	0.39	0.99	−0.040	0.019	.03
rs4740619	9	15 634 326	C9orf93	T	C	0.46	0.97	0.040	0.019	.03
rs1558902	16	53 803 574	FTO	T	A	0.42	1.00	−0.038	0.018	.04
rs2207139	6	50 845 490	TFAP2B	A	G	0.16	0.99	0.049	0.024	.04

^*Imputation quality of 1 indicates genotyped SNPs. Rsid = Reference SNP cluster ID; CIMBA = Consortium of Investigators of Modifiers of BRCA1/2; HR = log hazard ratio; SE = standard error.

^†Per-allele association with breast cancer was adjusted for principal components, birth cohort, menopausal status, age at menopause, country of enrollment, and mutation status in weighted Cox models.

^‡P values were calculated using weighted Cox models. All P values are two-sided.

Discussion

Using data from a large international study of women with a BRCA1/2 mutation and analyzed by several MR methods, we found that both observed and genetically predicted BMI were associated with a reduced risk of breast cancer whereas observed and genetically predicted height were associated with an increased risk of breast cancer.

We found that each 10-cm increment in observed height was associated with a 9% increase in breast cancer risk, whereas a 10-cm increment in genetically predicted height was associated with a 4%–8% increase in risk in BRCA1/2 mutation carriers. Our findings are broadly consistent with previous studies in the general population (14,20). A recent meta-analysis of prospective studies of height reported a relative risk (RR) of 1.17 per 10-cm increase, and the MR analysis using 168 height-associated variants found an odds ratio (OR) of 1.22 per 10-cm increase in genetically predicted height (14). A subsequent MR analysis with 423 height-associated variants reported a similar result (OR = 1.19) (20). One study of height in 719 BRCA1/2 mutation carriers showed a statistically nonsignificant positive relationship with premenopausal breast cancer and a statistically significant positive relationship with postmenopausal breast cancer (30). Thus, height is likely a predictor for breast cancer risk in BRCA1/2 mutation carriers and the general population.

Several studies have examined the relationship between BMI and breast cancer risk in BRCA1/2 carriers (5,30,31) with inconsistent findings, possibly because of limited sample size. In the general population, every 5-kg/m² increase in BMI was positively associated with postmenopausal breast cancer (RR = 1.12) and inversely associated with premenopausal breast cancer (RR = 0.92) (15). We found that for BRCA1/2 carriers observed BMI at date of questionnaire was inversely associated with premenopausal breast cancer but was not statistically significantly associated with postmenopausal breast cancer. Our MR analysis found that a 5-kg/m² increase in genetically predicted BMI was associated with a 16% reduction in premenopausal breast cancer. Similarly, a MR analysis in the general population found that each 5-kg/m² increase in genetically predicted BMI had an OR of 0.65, with consistent effects across menopausal status (22). Altogether, there is strong evidence for the protective effect of higher BMI on premenopausal breast cancer in both the general population and BRCA1/2 mutation carriers. Unlike with MR, the association with observed BMI is potentially subject to recall bias or reverse causation. Conversely, BMI-GS may only capture early-life body weight and cannot predict weight changes later in life, which are influenced by lifestyle factors. The association between BMI at age 18 and premenopausal breast cancer (HR = 0.83) was quite similar to that for BMI-GS and premenopausal breast cancer (HR = 0.84), supporting the notion that early-life BMI/adiposity play a role in breast carcinogenesis. The seemingly inconsistent findings for observed BMI and postmenopausal breast cancer might reflect differences in study populations and methodology. Our study may be underpowered to assess the impact of observed and genetically predicted BMI on postmenopausal breast cancer, given the smaller number of cases. An ongoing prospective consortium of BRCA1/2 carriers may clarify the relationship between BMI and postmenopausal breast cancer. Hence higher BMI, particularly genetically predicted BMI, is associated with lower risk of premenopausal breast cancer, although the relationship with postmenopausal breast cancer remains inconclusive.

There are several potential mechanisms for the associations between height or BMI and breast cancer. For height, early-life exposures including nutritional and hormonal status could affect obtained height and account for the association between height and breast cancer risk (32,33). The insulin-like growth factor (IGF) signaling pathway has been implicated in the pathogenesis of multiple malignancies, with possibly stronger effects on premenopausal breast cancer (34,35). Recent investigations have also implicated the LIN28B–let-7 microRNA pathway, which affects adult height, mammalian body size, and carcinogenesis (36–38). Furthermore, potential mechanisms that could account for the association between BMI and reduced risk of breast cancer include circulating IGF-1 (15), greater likelihood of having anovulatory cycles, and lower circulating levels of estradiol/progesterone (39).

Several SNPs included in the present analysis were reported to be statistically significantly associated with breast cancer risk in the general population. Guo et al. (22) reported rs7903146 near TCF7L2 (OR = 0.96) and rs1558902 (OR = 0.93) near FTO. Our findings were similar. Interestingly, rs7903146 is in weak linkage disequilibrium (r² =  0.45) with rs7904519 near TCF7L2, which was reported in a previous GWAS (40). Moreover, rs1558902 was in strong linkage disequilibrium (r² =   0.92) with rs17817449 near FTO (40,41).

The strengths of our study include a large sample size, inclusion of numerous height and BMI variants, an MR approach that reduces confounding, and consistent findings between observed and genetically predicted phenotypes. Our study has several limitations. Observed height and BMI for breast cancer cases were typically measured approximiately 5–6 years after initial diagnosis. Whereas height is unlikely to be affected by breast cancer diagnosis, changes in weight after diagnosis may affect the relationship between observed BMI and breast cancer risk. The height-GS explained 13.4% of height variation, compared with 15.9% in previous GWAS (17). The BMI-GS accounted for 2.6% of BMI variation, compared to 2.7% in previous GWAS (16,17). Although both GSs had sufficient strength to be valid instrumental variables (F statistic >> 10), they are not very strong, leading to wide CIs in the MR analysis. Although the GSs were correlated with some breast cancer risk factors, these associations were much weaker compared with height or BMI, suggesting minimal residual confounding and upholding MR assumptions. Another limitation is that our study only included women of European ancestry, which limits generalizability to women of other racial/ethnic groups.

Our study suggests that for BRCA1/2 mutation carriers, a higher BMI is associated with lower risk of premenopausal breast cancer, whereas greater height may be associated with increased risk of overall breast cancer. The inconsistent findings between observed and genetically predicted BMI and postmenopausal breast cancer warrants future studies. These findings may have implications for risk stratification to help carriers and their physicians to decide age-appropriate risk-tailored interventions, including increased surveillance and prophylactic surgeries. Future studies could elucidate the biological mechanisms underlying these associations.

Funding

CIMBA: The CIMBA data management and data analysis were supported by Cancer Research – UK grants C12292/A20861, C12292/A11174. GCT and ABS are NHMRC Research Fellows. iCOGS: the European Community's Seventh Framework Programme under grant agreement No. 223175 (HEALTH-F2–2009-223175) (COGS), Cancer Research UK (C1287/A10118, C1287/A 10710, C12292/A11174, C1281/A12014, C5047/A8384, C5047/A15007, C5047/A10692, C8197/A16565), the National Institutes of Health (CA128978) and Post-Cancer GWAS initiative (1U19 CA148537, 1U19 CA148065, and 1U19 CA148112 - the GAME-ON initiative), the Department of Defence (W81XWH-10–1-0341), the Canadian Institutes of Health Research (CIHR) for the CIHR Team in Familial Risks of Breast Cancer (CRN-87521), and the Ministry of Economic Development, Innovation and Export Trade (PSR-SIIRI-701), Komen Foundation for the Cure, the Breast Cancer Research Foundation, and the Ovarian Cancer Research Fund. The PERSPECTIVE project was supported by the Government of Canada through Genome Canada and the Canadian Institutes of Health Research, the Ministry of Economy, Science and Innovation through Genome Québec, and The Quebec Breast Cancer Foundation.

BCFR: UM1 CA164920 from the National Cancer Institute. The content of this article does not necessarily reflect the views or policies of the National Cancer Institute or any of the collaborating centers in the Breast Cancer Family Registry (BCFR), nor does mention of trade names, commercial products, or organizations imply endorsement by the US Government or the BCFR. BFBOCC: Lithuania (BFBOCC-LT): Research Council of Lithuania grant SEN-18/2015. BIDMC: Breast Cancer Research Foundation. BMBSA: Cancer Association of South Africa (PI Elizabeth J. van Rensburg). CNIO: Spanish Ministry of Health PI16/00440 supported by FEDER funds, the Spanish Ministry of Economy and Competitiveness (MINECO) SAF2014–57680-R and the Spanish Research Network on Rare diseases (CIBERER). COH-CCGCRN: Research reported in this publication was supported by the National Cancer Institute of the National Institutes of Health under grant number R25CA112486, and RC4CA153828 (PI: J. Weitzel) from the National Cancer Institute and the Office of the Director, National Institutes of Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. CONSIT: Associazione Italiana Ricerca sul Cancro (AIRC; IG2014 no.15547) to P. Radice. Italian Association for Cancer Research (AIRC; grant no.16933) to L. Ottini. Associazione Italiana Ricerca sul Cancro (AIRC; IG2015 no.16732) to P. Peterlongo. Jacopo Azzollini is supported by funds from Italian citizens who allocated the 5×1000 share of their tax payment in support of the Fondazione IRCCS Istituto Nazionale Tumori, according to Italian laws (INT-Institutional strategic projects “5×1000”). DEMOKRITOS: European Union (European Social Fund – ESF) and Greek national funds through the Operational Program “Education and Lifelong Learning” of the National Strategic Reference Framework (NSRF) - Research Funding Program of the General Secretariat for Research & Technology: SYN11_10_19 NBCA. Investing in Knowledge Society through the European Social Fund. DFKZ: German Cancer Research Center. EMBRACE: Cancer Research UK Grants C1287/A10118 and C1287/A11990. DGE and FL are supported by an NIHR grant to the Biomedical Research Centre, Manchester, UK. The investigators at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust are supported by an NIHR grant to the Biomedical Research Centre at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust. Ros Eeles and Elizabeth Bancroft are supported by Cancer Research UK Grant C5047/A8385. Ros Eeles is also supported by NIHR support to the Biomedical Research Centre at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust. FCCC: The University of Kansas Cancer Center (P30 CA168524) and the Kansas Bioscience Authority Eminent Scholar Program. A.K.G. was funded by R0 1CA140323, R01 CA214545, and by the Chancellors Distinguished Chair in Biomedical Sciences Professorship. FPGMX: FISPI05/2275 and Mutua Madrileña Foundation (FMMA). GC-HBOC: German Cancer Aid (grant no 110837, Rita K. Schmutzler) and the European Regional Development Fund and Free State of Saxony, Germany (LIFE - Leipzig Research Centre for Civilization Diseases, project numbers 713–241202, 713–241202, 14505/2470, 14575/2470). GEMO: Ligue Nationale Contre le Cancer; the Association “Le cancer du sein, parlons-en!” Award, the Canadian Institutes of Health Research for the “CIHR Team in Familial Risks of Breast Cancer” program, and the French National Institute of Cancer (INCa). GEORGETOWN: the Non-Therapeutic Subject Registry Shared Resource at Georgetown University (NIH/NCI grant P30-CA051008), the Fisher Center for Hereditary Cancer and Clinical Genomics Research, and Swing For the Cure. G-FAST: Bruce Poppe is a senior clinical investigator of FWO. Mattias Van Heetvelde obtained funding from IWT. HCSC: Spanish Ministry of Health PI15/00059, PI16/01292, and CB-161200301 CIBERONC from ISCIII (Spain), partially supported by European Regional Development FEDER funds. HEBCS: Helsinki University Hospital Research Fund, Academy of Finland (266528), the Finnish Cancer Society, and the Sigrid Juselius Foundation. HEBON: the Dutch Cancer Society grants NKI1998–1854, NKI2004–3088, NKI2007–3756, the Netherlands Organization of Scientific Research grant NWO 91109024, the Pink Ribbon grants 110005 and 2014–187.WO76, the BBMRI grant NWO 184.021.007/CP46, and the Transcan grant JTC 2012 Cancer 12–054. HEBON thanks the registration teams of Dutch Cancer Registry (IKNL; S. Siesling, J. Verloop) and the Dutch Pathology database (PALGA; L. Overbeek) for part of the data collection. HRBCP: Hong Kong Sanatorium and Hospital, Dr Ellen Li Charitable Foundation, The Kerry Group Kuok Foundation, National Institute of Health1R 03CA130065, and North California Cancer Center. HUNBOCS: Hungarian Research Grants KTIA-OTKA CK-80745 and OTKA K-112228. ICO: The authors would like to particularly acknowledge the support of the Asociación Española Contra el Cáncer (AECC), the Instituto de Salud Carlos III (organismo adscrito al Ministerio de Economía y Competitividad) and “Fondo Europeo de Desarrollo Regional (FEDER), una manera de hacer Europa” (PI10/01422, PI13/00285, PIE13/00022, PI15/00854, PI16/00563, and CIBERONC), and the Institut Català de la Salut and Autonomous Government of Catalonia (2009SGR290, 2014SGR338, and PERIS Project MedPerCan). Asociación Española Contra el Cáncer, Spanish Health Research Foundation, Carlos III Health Institute, organismo adscrito al Ministerio de Economía y Competitividad, “Fondo Europeo de Desarrollo Regional (FEDER), una manera de hacer Europa”, Catalan Health Institute, and Autonomous Government of Catalonia (ISCIIIRETIC RD06/0020/1051, RD12/0036/008, PI13/00285, PIE13/00022, PI15/00854, PI16/00563, and 2009SGR283). IHCC: PBZ_KBN_122/P05/2004. ILUH: Icelandic Association “Walking for Breast Cancer Research” and by the Landspitali University Hospital Research Fund. INHERIT: Canadian Institutes of Health Research for the “CIHR Team in Familial Risks of Breast Cancer” program (grant #CRN-87521) and the Ministry of Economic Development, Innovation and Export Trade (grant #PSR-SIIRI-701). IOVHBOCS: Ministero della Salute and “5×1000” Istituto Oncologico Veneto grant. IPOBCS: Liga Portuguesa Contra o Cancro. kConFab: The National Breast Cancer Foundation, and previously by the National Health and Medical Research Council (NHMRC), the Queensland Cancer Fund, the Cancer Councils of New South Wales, Victoria, Tasmania, and South Australia, and the Cancer Foundation of Western Australia. MAYO: NIH grants CA116167, CA192393, and CA176785, an NCI Specialized Program of Research Excellence (SPORE) in Breast Cancer (CA116201), and a grant from the Breast Cancer Research Foundation. MCGILL: Jewish General Hospital Weekend to End Breast Cancer, Quebec Ministry of Economic Development, Innovation and Export Trade. MTi is supported by the European Union Seventh Framework Programme (2007Y2013)/European Research Council (Grant No. 310018). MODSQUAD: MH CZ—DRO (MMCI, 00209805), MEYS—NPS I - LO1413 to LF, and by Charles University in Prague project UNCE204024 (MZ). MSKCC: the Breast Cancer Research Foundation, the Robert and Kate Niehaus Clinical Cancer Genetics Initiative, the Andrew Sabin Research Fund, and a Cancer Center Support Grant/Core Grant (P30 CA008748). NAROD: 1R01 CA149429–01. NCI: the Intramural Research Program of the US National Cancer Institute, NIH, and by support services contracts NO2-CP-11019–50, N02-CP-21013–63, and N02-CP-65504 with Westat, Inc, Rockville, MD. NICCC: Clalit Health Services in Israel, the Israel Cancer Association, and the Breast Cancer Research Foundation (BCRF), New Yotk, NY. NNPIO: the Russian Federation for Basic Research (grants 15–04-01744, 16–54-00055, 17–54-12007). NRG Oncology: U10 CA180868, NRG SDMC grant U10 CA180822, NRG Administrative Office and the NRG Tissue Bank (CA 27469), the NRG Statistical and Data Center (CA 37517), and the Intramural Research Program, NCI. OSUCCG: Ohio State University Comprehensive Cancer Center. PBCS: Italian Association of Cancer Research (AIRC) [IG 2013 N.14477] and Tuscany Institute for Tumors (ITT) grant 2014–2015-2016. SEABASS: Ministry of Science, Technology and Innovation, Ministry of Higher Education (UM.C/HlR/MOHE/06), and Cancer Research Initiatives Foundation. SMC: the Israeli Cancer Association. SWE-BRCA: the Swedish Cancer Society. UCHICAGO: NCI Specialized Program of Research Excellence (SPORE) in Breast Cancer (CA125183), R01 CA142996, 1U01CA161032, American Cancer Society (MRSG-13–063-01-TBG, CRP-10–119-01-CCE), Breast Cancer Research Foundation, Susan G. Komen Foundation (SAC110026), and Ralph and Marion Falk Medical Research Trust, the Entertainment Industry Fund National Women's Cancer Research Alliance. UCLA: Jonsson Comprehensive Cancer Center Foundation; Breast Cancer Research Foundation. UCSF: UCSF Cancer Risk Program and Helen Diller Family Comprehensive Cancer Center. UKFOCR: Cancer Research UK. UPENN: National Institutes of Health (NIH); Breast Cancer Research Foundation; Susan G. Komen Foundation for the Cure, Basser Center for BRCA. UPITT/MWH: Hackers for Hope Pittsburgh. VFCTG: Victorian Cancer Agency, Cancer Australia, National Breast Cancer Foundation. WCP: Dr Karlan is funded by the American Cancer Society Early Detection Professorship (SIOP-06–258-01-COUN) and the National Center for Advancing Translational Sciences (NCATS), Grant UL1TR000124. JM was supported by NIH grant F32CA162847.

Notes

Authors: Frank Qian, Shengfeng Wang, Jonathan Mitchell, Lesley McGuffog, Daniel Barrowdale, Goska Leslie, Jan C. Oosterwijk, Wendy K. Chung, D. Gareth Evans, Christoph Engel, Karin Kast, Cora M. Aalfs, Muriel A. Adank, Julian Adlard, Bjarni A. Agnarsson, Kristiina Aittomäki, Elisa Alducci, Irene L. Andrulis, Banu K. Arun, Margreet G.E.M. Ausems, Jacopo Azzollini, Emmanuelle Barouk-Simonet, Julian Barwell, Muriel Belotti, Javier Benitez, Andreas Berger, Ake Borg, Angela R. Bradbury, Joan Brunet, Saundra S. Buys, Trinidad Caldes, Maria A. Caligo, Ian Campbell, Sandrine M. Caputo, Jocelyne Chiquette, Kathleen B.M. Claes, J. Margriet Collée, Fergus J. Couch, Isabelle Coupier, Mary B. Daly, Rosemarie Davidson, Orland Diez, Susan M. Domchek, Alan Donaldson, Cecilia M. Dorfling, Ros Eeles, Lidia Feliubadaló, Lenka Foretova, Jeffrey Fowler, Eitan Friedman, Debra Frost, Patricia A. Ganz, Judy Garber, Vanesa Garcia-Barberan, Gord Glendon, Andrew K. Godwin, Encarna B. Gómez Garcia, Jacek Gronwald, Eric Hahnen, Ute Hamann, Alex Henderson, Carolyn B. Hendricks, John L. Hopper, Peter J. Hulick, Evgeny N. Imyanitov, Claudine Isaacs, Louise Izatt, Ángel Izquierdo, Anna Jakubowska, Katarzyna Kaczmarek, Eunyoung Kang, Beth Y. Karlan, Carolien M. Kets, Sung-Won Kim, Zisun Kim, Ava Kwong, Yael Laitman, Christine Lasset, Min Hyuk Lee, Jong Won Lee, Jihyoun Lee, Jenny Lester, Fabienne Lesueur, Jennifer T. Loud, Jan Lubinski, Noura Mebirouk, Hanne E.J. Meijers-Heijboer, Alfons Meindl, Austin Miller, Marco Montagna, Thea M. Mooij, Patrick J. Morrison, Emmanuelle Mouret-Fourme, Katherine L. Nathanson, Susan L. Neuhausen, Heli Nevanlinna, Dieter Niederacher, Finn C. Nielsen, Robert L. Nussbaum, Kenneth Offit, Edith Olah, Kai-Ren Ong, Laura Ottini, Sue K. Park, Paolo Peterlongo, Georg Pfeiler, Catherine M. Phelan, Bruce Poppe, Nisha Pradhan, Paolo Radice, Susan J. Ramus, Johanna Rantala, Mark Robson, Gustavo C. Rodriguez, Rita K. Schmutzler, Christina G. Hutten Selkirk, Payal D. Shah, Jacques Simard, Christian F. Singer, Johanna Sokolowska, Dominique Stoppa-Lyonnet, Christian Sutter, Yen Yen Tan, Manuel R. Teixeira, Soo H. Teo, Mary Beth Terry, Mads Thomassen, Marc Tischkowitz, Amanda E. Toland, Katherine M. Tucker, Nadine Tung, Christi J. van Asperen, Klaartje van Engelen, Elizabeth J. van Rensburg, Shan Wang-Gohrke, Barbara Wappenschmidt, Jeffrey N. Weitzel, Drakoulis Yannoukakos; GEMO Study Collaborators, HEBON, EMBRACE, Mark H. Greene, Matti A. Rookus, Douglas F. Easton, Georgia Chenevix-Trench, Antonis C. Antoniou, David E. Goldgar, Olufunmilayo I. Olopade, Timothy R. Rebbeck, Dezheng Huo

Affiliations of authors: Department of Medicine, The University of Chicago, Chicago, IL (FQ); Center for Clinical Cancer Genetics, The University of Chicago, Chicago, IL (SW, OIO, DH); Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China (SW); Division of Gastroenterology, Department of Hepatology and Nutrition, Children's Hospital of Philadelphia, Philadelphia, PA (JM); Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK (LM, DB, GL, DF, EMBRACE, DFE, ACA); Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands (JCO); Departments of Pediatrics and Medicine, Columbia University, New York, NY (WKC); Division of Evolution and Genomic Sciences, Genomic Medicine, Manchester Academic Health Sciences Centre, University of Manchester, Central Manchester University Hospitals, NHS Foundation Trust, Manchester, UK (DGE); Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany (CE); LIFE - Leipzig Research Centre for Civilization Diseases, University of Leipzig, Leipzig, Germany (CE); Department of Gynecology and Obstetrics, Technical University of Dresden, Dresden, Germany (KK); Department of Clinical Genetics, Academic Medical Center, Amsterdam, the Netherlands (CMA., KvE); Family Cancer Clinic, Netherlands Cancer Institute, Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands (MAA); Yorkshire Regional Genetics Service, Chapel Allerton Hospital, Leeds, UK (JA); Department of Pathology, National Institute of Oncology, Budapest, Hungary (BAA); School of Medicine, University of Iceland, Reykjavik, Iceland (BAA); Department of Clinical Genetics, Helsinki University Hospital, University of Helsinki, Helsinki, Finland (KA); Immunology and Molecular Oncology Unit, Veneto Institute of Oncology IOV - IRCCS, Padua, Italy (EA, MM); Fred A. Litwin Center for Cancer Genetics, Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital, Toronto, ON, Canada (ILA, GG); Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada (ILA); Department of Breast Medical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX (BKA); Department of Medical Genetics, University Medical Center, Utrecht, the Netherlands (MGEMA); Unit of Medical Genetics, Department of Medical Oncology and Hematology, Fondazione IRCCS (Istituto Di Ricovero e Cura a Carattere Scientifico) Istituto Nazionale dei Tumori (INT), Milan, Italy (JA); Oncogénétique, Institut Bergonié, Bordeaux, France (EB-S); Leicestershire Clinical Genetics Service, University Hospitals of Leicester NHS Trust, Leicester, UK (JB); Service de Génétique, Institut Curie, Paris, France (MB, SMC, EM-F, DS-L); Human Cancer Genetics Program, Spanish National Cancer Research Centre, Madrid, Spain (JB); Centro de Investigación en Red de Enfermedades Raras (CIBERER), Valencia, Spain (JB); Department of Obstetrics and Gynecology, Medical University of Vienna, Vienna, Austria (AB, YYT); Department of Oncology, Lund University and Skåne University Hospital, Lund, Sweden (AB); Department of Medicine, Abramson Cancer Center, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA (ARB, SMD, KLN, PDS.); Genetic Counseling Unit, Hereditary Cancer Program, IDIBGI (Institut d'Investigació Biomèdica de Girona), Catalan Institute of Oncology, CIBERONC, Girona, Spain (JB, ÁI); Department of Medicine, Huntsman Cancer Institute at the University of Utah, Salt Lake City, UT (SSB); Molecular Oncology Laboratory, Hospital Clinico San Carlos, IdISSC, CIBERONC, Madrid, Spain (TC, VG-B); Section of Genetic Oncology, Department of Laboratory Medicine, University and University Hospital of Pisa, Pisa, Italy (MAC); Peter MacCallum Cancer Center, Melbourne, Victoria, Australia (IC); Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, Victoria, Australia (IC); Unité de recherche en santé des populations, Centre des maladies du sein Deschênes-Fabia, Hôpital du Saint-Sacrement, Québec, QC, Canada (JC); Centre for Medical Genetics, Ghent University, Ghent, Belgium (KBMC, BP); Department of Clinical Genetics, Erasmus University Medical Center, Rotterdam, the Netherlands (JMC); Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN (FJC); Unité d'Oncogénétique, CHU Arnaud de Villeneuve, Montpellier, France (IC); Department of Clinical Genetics, Fox Chase Cancer Center, Philadelphia, PA (MBD); Department of Clinical Genetics, South Glasgow University Hospitals, Glasgow, UK (RD); Oncogenetics Group, Clinical and Molecular Genetics Area, Vall d'Hebron Institute of Oncology (VHIO), University Hospital, Vall d'Hebron, Barcelona, Spain (OD); Clinical Genetics Department, St Michael's Hospital, Bristol, UK (AD); Department of Genetics, University of Pretoria, Arcadia, South Africa (CMD, EJvR); Oncogenetics Team, The Institute of Cancer Research and Royal Marsden NHS Foundation Trust, London, UK (RE); Molecular Diagnostic Unit, Hereditary Cancer Program, ICO-IDIBELL (Catalan Institute of Oncology, Bellvitge Biomedical Research Institute), CIBERONC, Barcelona, Spain (LF); Department of Cancer Epidemiology and Genetics, Masaryk Memorial Cancer Institute, Brno, Czech Republic (LF); The Ohio State University, Columbus Cancer Council, Columbus, OH (JF); The Susanne Levy Gertner Oncogenetics Unit, Chaim Sheba Medical Center, Ramat Gan, Israel (EF, YL); Sackler Faculty of Medicine, Tel Aviv University, Ramat Aviv, Israel (EF); Schools of Medicine and Public Health, Division of Cancer Prevention & Control Research, Jonsson Comprehensive Cancer Center, University of California Los Angeles, CA (PAG); Cancer Risk and Prevention Clinic, Dana-Farber Cancer Institute, Boston, MA (JG); Department of Pathology and Laboratory Medicine, Kansas University Medical Center, Kansas City, KS (AKG); Department of Clinical Genetics and GROW, School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, the Netherlands (EBGG); Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland (JG, AJ, KK, JL); Centers for Hereditary Breast and Ovarian Cancer, Integrated Oncology and Molecular Medicine, University Hospital of Cologne, Cologne, Germany (EH, RKS, BW); Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany (UH); Institute of Genetic Medicine, Centre for Life, Newcastle Upon Tyne Hospitals NHS Trust, Newcastle upon Tyne, UK (AH); City of Hope Clinical Cancer Genetics Community Research Network, Duarte, CA (CBH); Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia (JLH); Center for Medical Genetics, NorthShore University HealthSystem, Evanston, IL (PJH, CGHS); The University of Chicago Pritzker School of Medicine, Chicago, IL (PJH); N.N. Petrov Institute of Oncology, St. Petersburg, Russia (ENI); Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC (CI); Clinical Genetics, Guy’s and St Thomas’ NHS Foundation Trust, London, UK (LI); Department of Surgery, Seoul National University Bundang Hospital, Seongnam, Korea (EK); Women's Cancer Program at the Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA (BYK, JL); Department of Human Genetics, Radboud University Medical Center, Nijmegen, the Netherlands (CMK); Department of Surgery, Daerim Saint Mary's Hospital, Seoul, Korea (S-WK); Department of Surgery, Soonchunhyang University Hospital Bucheon, Bucheon, Korea (ZK); Hong Kong Hereditary Breast Cancer Family Registry, Happy Valley, Hong Kong (AK); Department of Surgery, The University of Hong Kong, Pok Fu Lam, Hong Kong (AK); Department of Surgery, Hong Kong Sanatorium and Hospital, Happy Valley, Hong Kong (AK); Unité de Prévention et d’Epidémiologie Génétique, Centre Léon Bérard, Lyon, France (CL); Department of Surgery, Soonchunhyang University College of Medicine and Soonchunhyang University Hospital, Seoul, Korea (MHL, JL); Department of Surgery, Ulsan University College of Medicine and Asan Medical Center, Seoul, Korea (JWL); Genetic Epidemiology of Cancer team, Institut Curie, Paris, France (FL, NM); U900, INSERM, Paris, France (FL, NM); PSL University, Paris, France (FL, NM); Mines ParisTech, Fontainebleau, France (FL, NM); Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD (JTL, MHG); Department of Clinical Genetics, VU University Medical Center, Amsterdam, the Netherlands (HEJM-H); Division of Gynaecology and Obstetrics, Technische Universität München, Munich, Germany (AM); NRG Oncology, Statistics and Data Management Center, Roswell Park Cancer Institute, Buffalo, NY (AM); Department of Epidemiology, Netherlands Cancer Institute, Amsterdam, the Netherlands (TMM, MAR); Centre for Cancer Research and Cell Biology, Queens University Belfast, Belfast, UK (PJM); Department of Population Sciences, Beckman Research Institute of City of Hope, Duarte, CA (SLN); Department of Obstetrics and Gynecology, Helsinki University Hospital, University of Helsinki, Helsinki, Finland (HN); Department of Gynecology and Obstetrics, University Hospital Düsseldorf, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany (DN); Center for Genomic Medicine, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark (FCN); Cancer Genetics and Prevention Program, University of California San Francisco, San Francisco, CA (RLN); Clinical Genetics Research Laboratory, Department of Cancer Biology and Genetics, Memorial Sloan-Kettering Cancer Center, New York, NY (KO, NP); Clinical Genetics Service, Department of Medicine, Memorial Sloan-Kettering Cancer Center, New York, NY (KO, MR); Department of Molecular Genetics, National Institute of Oncology, Budapest, Hungary (EO); West Midlands Regional Genetics Service, Birmingham Women’s Hospital Healthcare NHS Trust, Birmingham, UK (K-RO); Department of Molecular Medicine, University La Sapienza, Rome, Italy (LO); Departments of Preventive Medicine and Biomedical Sciences, and Cancer Research Institute, Seoul National University, Seoul, Korea (SKP); IFOM, The FIRC (Italian Foundation for Cancer Research) Institute of Molecular Oncology, Milan, Italy (PP); Department of Urology, Medical University of Vienna, Vienna, Austria (GP); Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL (CMP); Unit of Molecular Bases of Genetic Risk and Genetic Testing, Department of Research, Fondazione IRCCS (Istituto Di Ricovero e Cura a Carattere Scientifico) Istituto Nazionale dei Tumori (INT), Milan, Italy (PR); School of Women's and Children's Health, University of New South Wales Sydney, New South Wales, Australia (SJR); The Kinghorn Cancer Centre, Garvan Institute of Medical Research, Sydney, New South Wales, Australia (SJR); Clinical Genetics, Karolinska Institutet, Stockholm, Sweden (JR); Division of Gynecologic Oncology, NorthShore University HealthSystem, University of Chicago, Evanston, IL (GCR); Genomics Center, Centre Hospitalier Universitaire de Québec Research Center, Laval University, Québec City, QC, Canada (JSi); Department of Obstetrics and Gynecology and Comprehensive Cancer Center, Medical University of Vienna, Vienna, Austria (CFS); Laboratoire de génétique médicale, Nancy Université, Centre Hospitalier Régional et Universitaire, Vandoeuvre-les-Nancy, France (JSo); Department of Tumour Biology, Institut Curie, INSERM U830, Paris, France (DS-L, GEMO Study Collaborators); Université Paris Descartes, Paris, France (DS-L, GEMO Study Collaborators); Institute of Human Genetics, University Hospital Heidelberg, Heidelberg, Germany (CS); Department of Genetics, Portuguese Oncology Institute, Porto, Portugal (MRT); Biomedical Sciences Institute (ICBAS), University of Porto, Porto, Portugal (MRT); Cancer Research Malaysia, Subang Jaya, Selangor, Malaysia (SHT); Breast Cancer Research Unit, Cancer Research Institute, University Malaya Medical Centre, Kuala Lumpur, Malaysia (SHT); Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY (MBT); Department of Clinical Genetics, Odense University Hospital, Odense C, Denmark (MTh); Program in Cancer Genetics, Departments of Human Genetics and Oncology, McGill University, Montréal, QC, Canada (MTi); Department of Medical Genetics, Addenbrooke's Hospital, Cambridge, UK (MTi); Department of Cancer Biology and Genetics, Comprehensive Cancer Center, The Ohio State University, Columbus, OH (AET); School of Medicine, University of New South Wales, Sydney, New South Wales, Australia (KMT); Hereditary Cancer Centre, Prince of Wales Hospital, Sydney, New South Wales, Australia (KMT); Department of Medical Oncology, Beth Israel Deaconess Medical Center, Boston, MA (NT); Department of Clinical Genetics, Leiden University Medical Center, Leiden, the Netherlands (CJvA); Department of Gynaecology and Obstetrics, University of Ulm, Ulm, Germany (SW-G); Clinical Cancer Genetics, City of Hope, Duarte, CA (JNW); Molecular Diagnostics Laboratory, INRASTES, National Centre for Scientific Research ‘Demokritos’, Athens, Greece (DY); The Hereditary Breast and Ovarian Cancer Research Group Netherlands (HEBON), Coordinating Center, Netherlands Cancer Institute, Amsterdam, the Netherlands (HEBON); Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK (DFE); Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia (GC-T); Department of Dermatology, Huntsman Cancer Institute, University of Utah School of Medicine, Salt Lake City, UT (DEG); Harvard T.H. Chan School of Public Health, Boston, MA (TRR); Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA (TRR); Department of Public Health Sciences, The University of Chicago, Chicago, IL (DH)

The funders had no role in the design of the study; the collection, analysis, and interpretation of the data; the writing of the manuscript; and the decision to submit the manuscript for publication. The authors declare no conflicts of interest.

We gratefully acknowledge all of the families and clinicians who contributed to the studies; Sue Healey, in particular taking on the task of mutation classification with the late Olga Sinilnikova; Maggie Angelakos, Judi Maskiell, Gillian Dite, Helen Tsimiklis; members and participants in the New York site of the Breast Cancer Family Registry; members and participants in the Ontario Familial Breast Cancer Registry; Vilius Rudaitis and Laimonas Griškevičius; Drs Janis Eglitis, Anna Krilova, and Aivars Stengrevics; Yuan Chun Ding and Linda Steele for their work in participant enrollment and biospecimen and data management; Bent Ejlertsen and Anne-Marie Gerdes for the recruitment and genetic counseling of participants; Alicia Barroso, Rosario Alonso, and Guillermo Pita; Manoukian Siranoush, Bernard Peissel, Cristina Zanzottera, Milena Mariani, Daniela Zaffaroni, Bernardo Bonanni, Monica Barile, Irene Feroce, Mariarosaria Calvello, Alessandra Viel, Riccardo Dolcetti, Giuseppe Giannini, Laura Papi, Gabriele Lorenzo Capone, Liliana Varesco, Viviana Gismondi, Maria Grazia Tibiletti, Daniela Furlan, Antonella Savarese, Aline Martayan, Stefania Tommasi, and Brunella Pilato; the personnel of the Cogentech Cancer Genetic Test Laboratory, Milan, Italy. Ms JoEllen Weaver and Dr Betsy Bove; Marta Santamariña, Ana Blanco, Miguel Aguado, Uxía Esperón, and Belinda Rodríguez; IFE - Leipzig Research Centre for Civilization Diseases (Markus Loeffler, Joachim Thiery, Matthias Nüchter, Ronny Baber). We thank all participants, clinicians, family doctors, researchers, and technicians for their contributions and commitment to the DKFZ study and the collaborating groups in Lahore, Pakistan (Muhammad U. Rashid, Noor Muhammad, Sidra Gull, Seerat Bajwa, Faiz Ali Khan, Humaira Naeemi, Saima Faisal, Asif Loya, Mohammed Aasim Yusuf) and Bogota, Colombia (Diana Torres, Ignacio Briceno, Fabian Gil). Genetic Modifiers of Cancer Risk in BRCA1/2 Mutation Carriers (GEMO) study is a study from the National Cancer Genetics Network UNICANCER Genetic Group, France. We wish to pay tribute to Olga M. Sinilnikova, who with Dominique Stoppa-Lyonnet initiated and coordinated GEMO until she sadly passed away on June 30, 2014. The team in Lyon (Olga Sinilnikova, Mélanie Léoné, Laure Barjhoux, Carole Verny-Pierre, Sylvie Mazoyer, Francesca Damiola, Valérie Sornin) managed the GEMO samples until the biological resource center was transferred to Paris in December 2015 (Noura Mebirouk, Fabienne Lesueur, Dominique Stoppa-Lyonnet). We want to thank all the GEMO collaborating groups for their contribution to this study: Coordinating Centre, Service de Génétique, Institut Curie, Paris, France: Muriel Belotti, Ophélie Bertrand, Anne-Marie Birot, Bruno Buecher, Sandrine Caputo, Anaïs Dupré, Emmanuelle Fourme, Marion Gauthier-Villars, Lisa Golmard, Claude Houdayer, Marine Le Mentec, Virginie Moncoutier, Antoine de Pauw, Claire Saule, Dominique Stoppa-Lyonnet; and Inserm U900, Institut Curie, Paris, France: Fabienne Lesueur, Noura Mebirouk. Contributing Centres: Unité Mixte de Génétique Constitutionnelle des Cancers Fréquents, Hospices Civils de Lyon - Centre Léon Bérard, Lyon, France: Nadia Boutry-Kryza, Alain Calender, Sophie Giraud, Mélanie Léone. Institut Gustave Roussy, Villejuif, France: Brigitte Bressac-de-Paillerets, Olivier Caron, Marine Guillaud-Bataille. Centre Jean Perrin, Clermont–Ferrand, France: Yves-Jean Bignon, Nancy Uhrhammer. Centre Léon Bérard, Lyon, France: Valérie Bonadona, Christine Lasset. Centre François Baclesse, Caen, France: Pascaline Berthet, Laurent Castera, Dominique Vaur. Institut Paoli Calmettes, Marseille, France: Violaine Bourdon, Catherine Noguès, Tetsuro Noguchi, Cornel Popovici, Audrey Remenieras, Hagay Sobol. CHU Arnaud-de-Villeneuve, Montpellier, France: Isabelle Coupier, Pascal Pujol. Centre Oscar Lambret, Lille, France: Claude Adenis, Aurélie Dumont, Françoise Révillion. Centre Paul Strauss, Strasbourg, France: Danièle Muller. Institut Bergonié, Bordeaux, France: Emmanuelle Barouk-Simonet, Françoise Bonnet, Virginie Bubien, Michel Longy, Nicolas Sevenet. Institut Claudius Regaud, Toulouse, France: Laurence Gladieff, Rosine Guimbaud, Viviane Feillel, Christine Toulas. CHU Grenoble, France: Hélène Dreyfus, Christine Dominique Leroux, Magalie Peysselon, Rebischung. CHU Dijon, France: Amandine Baurand, Geoffrey Bertolone, Fanny Coron, Laurence Faivre, Caroline Jacquot, Sarab Lizard. CHU St-Etienne, France: Caroline Kientz, Marine Lebrun, Fabienne Prieur. Hôtel Dieu Centre Hospitalier, Chambéry, France: Sandra Fert Ferrer. Centre Antoine Lacassagne, Nice, France: Véronique Mari. CHU Limoges, France: Laurence Vénat-Bouvet. CHU Nantes, France: Stéphane Bézieau, Capucine Delnatte. CHU Bretonneau, Tours and Centre Hospitalier de Bourges France: Isabelle Mortemousque. Groupe Hospitalier Pitié-Salpétrière, Paris, France: Chrystelle Colas, Florence Coulet, Florent Soubrier, Mathilde Warcoin. CHU Vandoeuvre-les-Nancy, France: Myriam Bronner, Johanna Sokolowska. CHU Besançon, France: Marie-Agnès Collonge-Rame, Alexandre Damette. CHU Poitiers, Centre Hospitalier d’Angoulême and Centre Hospitalier de Niort, France: Paul Gesta. Centre Hospitalier de La Rochelle: Hakima Lallaoui. CHU Nîmes Carémeau, France: Jean Chiesa. CHI Poissy, France: Denise Molina-Gomes. CHU Angers, France: Olivier Ingster; Ilse Coene and Brecht Crombez; Alicia Tosar and Paula Diaque; Drs Sofia Khan, Taru A. Muranen, Carl Blomqvist, Irja Erkkilä, and Virpi Palola. The Hereditary Breast and Ovarian Cancer Research Group Netherlands (HEBON) consists of the following Collaborating Centers: Coordinating center: Netherlands Cancer Institute, Amsterdam, NL: M.A. Rookus, F.B.L. Hogervorst, F.E. van Leeuwen, S. Verhoef, M.K. Schmidt, N.S. Russell, D.J. Jenner; Erasmus Medical Center, Rotterdam, NL: J.M. Collée, A.M.W. van den Ouweland, M.J. Hooning, C. Seynaeve, C.H.M. van Deurzen, I.M. Obdeijn; Leiden University Medical Center, NL: C.J. van Asperen, J.T. Wijnen, R.A.E.M. Tollenaar, P. Devilee, T.C.T.E.F. van Cronenburg; Radboud University Nijmegen Medical Center, NL: C.M. Kets, A.R. Mensenkamp; University Medical Center Utrecht, NL: M.G.E.M. Ausems, R.B. van der Luijt, C.C. van der Pol; Amsterdam Medical Center, NL: C.M. Aalfs, T.A.M. van Os; VU University Medical Center, Amsterdam, NL: J.J.P. Gille, Q. Waisfisz, H.E.J. Meijers-Heijboer; University Hospital Maastricht, NL: E.B. Gómez-Garcia, M.J. Blok; University Medical Center Groningen, NL: J.C. Oosterwijk, A.H. van der Hout, M.J. Mourits, G.H. de Bock; The Netherlands Foundation for the Detection of Hereditary Tumors, Leiden, NL: H.F. Vasen; The Netherlands Comprehensive Cancer Organization (IKNL): S. Siesling, J.Verloop; The Dutch Pathology Registry (PALGA): L.I.H. Overbeek; Hong Kong Sanatorium and Hospital; the Hungarian Breast and Ovarian Cancer Study Group members (Janos Papp, Aniko Bozsik, Timea Pocza, Zoltan Matrai, Miklos Kasler, Judit Franko, Maria Balogh, Gabriella Domokos, Judit Ferenczi, Department of Molecular Genetics, National Institute of Oncology, Budapest, Hungary) and the clinicians and patients for their contributions to this study; the Oncogenetics Group (VHIO) and the High Risk and Cancer Prevention Unit of the University Hospital Vall d’Hebron, and the Cellex Foundation for providing research facilities and equipment; the ICO Hereditary Cancer Program team led by Dr Gabriel Capella; Dr Martine Dumont for sample management and skillful assistance; Ana Peixoto, Catarina Santos, and Pedro Pinto; members of the Center of Molecular Diagnosis, Oncogenetics Department and Molecular Oncology Research Center of Barretos Cancer Hospital; Heather Thorne, Eveline Niedermayr, all the kConFab research nurses and staff, the heads and staff of the Family Cancer Clinics, and the Clinical Follow Up Study (which received funding from the NHMRC, the National Breast Cancer Foundation, Cancer Australia, and the National Institutes of Health [USA]) for their contributions to this resource, and the many families who contribute to kConFab; the KOBRA Study Group; Csilla Szabo (National Human Genome Research Institute, National Institutes of Health, Bethesda, MD); Eva Machackova (Department of Cancer Epidemiology and Genetics, Masaryk Memorial Cancer Institute and MF MU, Brno, Czech Republic); and Michal Zikan, Petr Pohlreich, and Zdenek Kleibl (Oncogynecologic Center and Department of Biochemistry and Experimental Oncology, First Faculty of Medicine, Charles University, Prague, Czech Republic); Anne Lincoln, Lauren Jacobs; the NICCC National Familial Cancer Consultation Service team led by Sara Dishon, the lab team led by Dr Flavio Lejbkowicz, and the research field operations team led by Dr Mila Pinchev; the investigators of the Australia New Zealand NRG Oncology group; members and participants in the Ontario Cancer Genetics Network; Leigha Senter, Kevin Sweet, Caroline Craven, Julia Cooper, and Michelle O'Conor; Yip Cheng Har, Nur Aishah Mohd Taib, Phuah Sze Yee, Norhashimah Hassan, and all the research nurses, research assistants, and doctors involved in the MyBrCa Study for assistance in patient recruitment, data collection, and sample preparation; Philip Iau, Sng Jen-Hwei, and Sharifah Nor Akmal for contributing samples from the Singapore Breast Cancer Study and the HUKM-HKL Study, respectively; the Meirav Breast Center team at the Sheba Medical Center; Håkan Olsson, Helena Jernström, Karin Henriksson, Katja Harbst, Maria Soller, Ulf Kristoffersson; from Gothenburg Sahlgrenska University Hospital: Anna Öfverholm, Margareta Nordling, Per Karlsson, Zakaria Einbeigi; from Stockholm and Karolinska University Hospital: Anna von Wachenfeldt, Annelie Liljegren, Annika Lindblom, Brita Arver, Gisela Barbany Bustinza; from Umeå University Hospital: Beatrice Melin, Christina Edwinsdotter Ardnor, Monica Emanuelsson; from Uppsala University: Hans Ehrencrona, Maritta Hellström Pigg, Richard Rosenquist; from Linköping University Hospital: Marie Stenmark-Askmalm, Sigrun Liedgren; Cecilia Zvocec, Qun Niu; Joyce Seldon and Lorna Kwan; Dr Robert Nussbaum, Beth Crawford, Kate Loranger, Julie Mak, Nicola Stewart, Robin Lee, Amie Blanco and Peggy Conrad and Salina Chan; Simon Gayther, Paul Pharoah, Carole Pye, Patricia Harrington, and Eva Wozniak; Geoffrey Lindeman, Marion Harris, Martin Delatycki, Sarah Sawyer, Rebecca Driessen, and Ella Thompson for performing all DNA amplification.