We studied the interplay between 39 breast cancer (BC) risk SNPs and established BC risk (body mass index, height, age at menarche, parity, age at menopause, smoking, alcohol and family history of BC) and prognostic factors (TNM stage, tumor grade, tumor size, age at diagnosis, estrogen receptor status and progesterone receptor status) as joint determinants of BC risk. We used a nested case–control design within the National Cancer Institute's Breast and Prostate Cancer Cohort Consortium (BPC3), with 16 285 BC cases and 19 376 controls. We performed stratified analyses for both the risk and prognostic factors, testing for heterogeneity for the risk factors, and case–case comparisons for differential associations of polymorphisms by subgroups of the prognostic factors. We analyzed multiplicative interactions between the SNPs and the risk factors. Finally, we also performed a meta-analysis of the interaction ORs from BPC3 and the Breast Cancer Association Consortium. After correction for multiple testing, no significant interaction between the SNPs and the established risk factors in the BPC3 study was found. The meta-analysis showed a suggestive interaction between smoking status and SLC4A7-rs4973768 (Pinteraction = 8.84 × 10−4) which, although not significant after considering multiple comparison, has a plausible biological explanation. In conclusion, in this study of up to almost 79 000 women we can conclusively exclude any novel major interactions between genome-wide association studies hits and the epidemiologic risk factors taken into consideration, but we propose a suggestive interaction between smoking status and SLC4A7-rs4973768 that if further replicated could help our understanding in the etiology of BC.

INTRODUCTION

Several genome-wide association studies (GWAS) have identified single-nucleotide polymorphisms (SNPs) associated with breast cancer (BC) risk (117). The possible interplay between genetic variants and established epidemiologic BC risk factors is gradually being explored (1822). Finding gene–environment interactions can be useful in several areas such as allowing a more specific risk assessment that could be useful for early detection or prevention strategies and moreover to further our understanding of biological pathways and mechanisms of disease etiology.

In a previous work conducted in a smaller set of cases and controls in the context of the National Cancer Institute (NCI)'s Breast and Prostate Cancer Cohort Consortium (BPC3), we have reported the lack of interactions between 17 GWAS SNPs and 9 epidemiologic risk factors for BC (21). The results from other groups were similar to what we found; however, a recent large study by Nickels et al. performed in the context of the Breast Cancer Association Consortium (BCAC) showed several highly significant gene–environment interactions (22). Given these new findings, we felt the need to extend our previous work and doubling our overall sample size, we studied a further 22 SNPs reported to show genome-wide statistically significant associations with BC risk. We also used updated and extended information on established BC risk factors (body mass index (BMI), height, age at menarche, parity, age at first full-term pregnancy, number of full-term pregnancies, age at menopause, smoking, pack-years of smoking, alcohol, family history of BC and ever use of oral contraceptives), as well as on prognostic factors of BC (estrogen receptor (ER) status, progesterone receptor (PR) status, tumor size, TNM stage, tumor grade and age at diagnosis) available in the central BPC3 database. Both genetic and non-genetic information were available for a total of 35 661 individuals (16 285 cases and 19 376 controls).

In this study, first, we analyzed BC risk SNPs stratifying by the established BC risk factors and BC prognostic factors. Second, we tested for gene–environment interactions and, taking advantage of the work done by Nickels et al., we combined the interaction ORs from BPC3 and BCAC (22) in a meta-analysis. This was the largest effort up to date, using data on up to 79 000 individuals in order to discover any possible interplay between genes and environment in relation to BC risk.

RESULTS

In total, 16 285 BC cases and 19 376 controls of European descent from BPC3 were included in the analysis of this study. The relevant characteristics of the study subjects are presented in Supplementary Material, Table S1. At the time of recruitment, 88% of the subjects in this study were peri- or postmenopausal (14 468 cases, 16 761 controls). For Nurses' Health Study (NHS), two SNPs (ZMIZ1-rs1045485 and 11q13-rs614367) showed departure from the Hardy–Weinberg equilibrium among the controls (P = 8.4 × 10−4 and 6 × 10−4, respectively). Therefore, for the analyses involving these two SNPs, subjects from NHS were removed. The genotyping success rate was 98.88% in the study population.

SNPs main effects

The SNPs included in the analyses are listed in Table 1. The results of the main effects analyses of the association between the 39 SNPs and BC risk are shown in Table 2. In the overall analysis (considering ER− and ER+ together), we found significant associations (at the conventional 0.05 level) with BC risk for 29 of the SNPs (P-values ranging from 0.035 to 5.81 × 10−32). The directions of the associations were consistent with those reported in previous papers for all the SNPs. The results of the tests of heterogeneity of the main effects of SNPs and BC risk factors across cohorts were not significant (data not shown). The results of the main effects analyses for the epidemiologic risk factors are reported in Supplementary Material, Table S2.

Table 1.

Information on the selected SNPs

SNP Gene Chr Location (bp) (hg19)a Major allele/minor allele References 
rs11249433b NOTCH2 1p11 ***121280363 T/C (9,15
rs10931936 CASP8 2q33 202143678 C/T (23
rs1045485b CASP8 2q33 202149339 G/C (4
rs13387042b Intergenic 2q35 219905582 A/G (2
rs4973768b SLC4A7 3p24 27415763 C/T (8
rs4415084b,c Intergenic 5p12 44662265 C/T (5
rs10941679b Intergenic 5p12 44706248 A/G (5
rs10069690 TERT 5p15 1279540 C/T (12
rs889312b MAP3K1 5q11 56031634 A/C (1
rs17530068 Intergenic 6q14 82192859 T/C (15
rs13437553 Intergenic 6q14 82303985 T/C (15
rs1917063d Intergenic 6q14 82322957 C/T (15
rs9344191e Intergenic 6q14 82197935 T/G (15
rs2180341b,f RNF146 6q22 127600380 A/G (6
rs3757318 Intergenic 6q25 151913863 G/A (10
rs9383938 Intergenic 6q25 151987107 G/T (15,24
rs2046210b Intergenic 6q25 151948116 C/T (7,14
rs13281615b Intergenic 8q24 128355368 A/G (8
rs1562430 Intergenic 8q24 128387602 T/C (10
rs1011970 CDKN2BAS 9p21 22061884 G/T (16
rs865686 Intergenic 9q31 110888228 T/G (16
rs2380205 Intergenic 10p15 5886484 C/T (16
rs10995190 ZNF365 10q21 68278432 G/A (16,25
rs16917302 ZNF365 10q21 64260948 A/C (11,25
rs1250003g ZMIZ1 10q22 80846564 T/C (16,26
rs3750817b FGFR2 10q26 123332327 C/T (20
rs2981582b FGFR2 10q26 123352067 C/T (8
rs3817198b LSP1 11p15 1908756 T/C (1
rs909116 LSP1 11p15 1941696 T/C (10
rs614367 Intergenic 11q13 69328514 C/T (17
rs999737b,h RAD51L1 14q24 69034432 C/T (9
rs3803662b TNRC9 16q12 52586091 C/T (1,2
rs2075555b COL1A1 17q21 48274041 C/A (3
rs6504950b COX11 17q22 53056221 G/A (8
rs12982178 USHBP1 19p13 17371318 T/C (12
rs8170 C19Orf62 19p13 17389454 G/A (15
rs2284378i RALY 20q11 32587845 C/T (15
rs4911414 Intergenic 20q11 32729194 G/T (15
rs311499j GMEB2 20q13 62217339 C/T (11
SNP Gene Chr Location (bp) (hg19)a Major allele/minor allele References 
rs11249433b NOTCH2 1p11 ***121280363 T/C (9,15
rs10931936 CASP8 2q33 202143678 C/T (23
rs1045485b CASP8 2q33 202149339 G/C (4
rs13387042b Intergenic 2q35 219905582 A/G (2
rs4973768b SLC4A7 3p24 27415763 C/T (8
rs4415084b,c Intergenic 5p12 44662265 C/T (5
rs10941679b Intergenic 5p12 44706248 A/G (5
rs10069690 TERT 5p15 1279540 C/T (12
rs889312b MAP3K1 5q11 56031634 A/C (1
rs17530068 Intergenic 6q14 82192859 T/C (15
rs13437553 Intergenic 6q14 82303985 T/C (15
rs1917063d Intergenic 6q14 82322957 C/T (15
rs9344191e Intergenic 6q14 82197935 T/G (15
rs2180341b,f RNF146 6q22 127600380 A/G (6
rs3757318 Intergenic 6q25 151913863 G/A (10
rs9383938 Intergenic 6q25 151987107 G/T (15,24
rs2046210b Intergenic 6q25 151948116 C/T (7,14
rs13281615b Intergenic 8q24 128355368 A/G (8
rs1562430 Intergenic 8q24 128387602 T/C (10
rs1011970 CDKN2BAS 9p21 22061884 G/T (16
rs865686 Intergenic 9q31 110888228 T/G (16
rs2380205 Intergenic 10p15 5886484 C/T (16
rs10995190 ZNF365 10q21 68278432 G/A (16,25
rs16917302 ZNF365 10q21 64260948 A/C (11,25
rs1250003g ZMIZ1 10q22 80846564 T/C (16,26
rs3750817b FGFR2 10q26 123332327 C/T (20
rs2981582b FGFR2 10q26 123352067 C/T (8
rs3817198b LSP1 11p15 1908756 T/C (1
rs909116 LSP1 11p15 1941696 T/C (10
rs614367 Intergenic 11q13 69328514 C/T (17
rs999737b,h RAD51L1 14q24 69034432 C/T (9
rs3803662b TNRC9 16q12 52586091 C/T (1,2
rs2075555b COL1A1 17q21 48274041 C/A (3
rs6504950b COX11 17q22 53056221 G/A (8
rs12982178 USHBP1 19p13 17371318 T/C (12
rs8170 C19Orf62 19p13 17389454 G/A (15
rs2284378i RALY 20q11 32587845 C/T (15
rs4911414 Intergenic 20q11 32729194 G/T (15
rs311499j GMEB2 20q13 62217339 C/T (11

aGenome Reference Consortium Human, build 37 (http://genome.ucsc.edu/cgi-bin/hgGateway).

bThis SNP was included in the first analyses conducted in this dataset.

c5p12-rs4415084 or surrogate 5p12-rs920329.

d6q14-rs1917063 or surrogate 6q14-rs9344208.

e6q14-rs9344191 or surrogate 6q14-rs9449341.

fECHDC1, RNF146-rs2180341 or surrogate ECHDC1, RNF146-rs9398840.

gZMIZ1-rs1250003 or surrogate ZMIZ1-rs704010.

hRAD51L1-rs999737 or surrogate RAD51L1-rs10483813.

iRALY-rs2284378 or surrogate RALY-rs6059651, RALY-rs8119937.

jGMEB2-rs311499 or surrogate GMEB2-rs311498.

Table 2.

Associations between selected SNPs and BC risk

SNP Closest gene Chr. Cases
 
Controls
 
ORhet (95% CI)a ORhom (95% CI)a ORallele (95% CI)a Ptrendb P2d.f.c 
A/A A/B B/B A/A A/B B/B 
rs11249433 NOTCH2 1p11 4596 7232 2783 6097 8381 3055 1.14 (1.08–1.20) 1.21 (1.13–1.29) 1.10 (1.07–1.14) 7.06E−10 1.63E−09 
rs10931936 CASP8 2q33 4470 3697 775 5762 4549 846 1.05 (0.99–1.12) 1.18 (1.06–1.31) 1.07 (1.03–1.12) 2.10 E−03 5.59 E−03 
rs1045485 CASP8 2q33 9261 2690 200 10148 3058 236 0.96 (0.91–1.02) 0.94 (0.77–1.14) 0.96 (0.92–1.02) 1.75 E−01 3.98 E−01 
rs13387042 Intergenic 2q35 4599 6945 3074 4618 8821 4203 0.79 (0.75–0.83) 0.73 (0.69–0.78) 0.85 (0.82–0.88) 2.66 E−24 3.04 E−26 
rs4973768 SLC4A7 3p24 3647 7397 3572 4657 8847 4063 1.07 (1.01–1.13) 1.13 (1.06–1.21) 1.06 (1.03–1.10) 1.01 E−04 5.07 E−04 
rs4415084d Intergenic 5p12 2764 4125 1546 4218 5636 1964 1.11 (1.04–1.18) 1.19 (1.09–1.29) 1.09 (1.05–1.14) 1.69 E−05 8.33 E−05 
rs10941679 Intergenic 5p12 7823 5750 1083 9912 6467 1075 1.13 (1.07–1.18) 1.29 (1.17–1.41) 1.13 (1.09–1.17) 2.42 E−11 2.00 E−10 
rs10069690 TERT 5p15 7848 5652 1025 9810 6533 1197 1.08 (1.03–1.13) 1.07 (0.98–1.17) 1.06 (1.02–1.10) 2.19 E−03 3.21 E−03 
rs889312 MAP3K1 5q11 7170 6042 1375 9155 7110 1388 1.09 (1.04–1.14) 1.26 (1.16–1.37) 1.11 (1.07–1.15) 3.41 E−09 1.37 E−08 
rs17530068 Intergenic 6q14 8703 5769 928 10068 6285 976 1.06 (1.01–1.11) 1.11 (1.01–1.22) 1.06 (1.02–1.10) 2.69 E−03 1.09 E−02 
rs13437553 Intergenic 6q14 3926 2492 400 5055 2978 460 1.08 (1.00–1.15) 1.15 (1.00–1.32) 1.07 (1.02–1.13) 9.42 E−03 3.42 E−02 
rs1917063e Intergenic 6q14 5780 3521 524 7385 4179 603 1.08 (1.02–1.14) 1.12 (0.99–1.27) 1.07 (1.02–1.12) 3.69 E−03 1.36 E−02 
rs9344191f Intergenic 6q14 4972 3566 645 6435 4317 741 1.07 (1.01–1.13) 1.14 (1.02–1.28) 1.07 (1.02–1.12) 4.04 E−03 1.60 E−02 
rs2180341g RNF146 6q22 5253 3244 529 7100 4583 722 0.95 (0.90–1.01) 0.99 (0.88–1.12) 0.97 (0.93–1.02) 2.51 E−01 2.54 E−01 
rs3757318 Intergenic 6q25 7679 1443 66 9736 1645 56 1.13 (1.04–1.22) 1.40 (0.98–2.01) 1.14 (1.06–1.22) 5.05 E−04 2.10 E−03 
rs9383938 Intergenic 6q25 7563 1568 104 9620 1841 86 1.08 (1.01–1.17) 1.56 (1.17–2.08) 1.11 (1.04–1.19) 1.60 E−03 1.57 E−03 
rs2046210 Intergenic 6q25 5829 6700 2035 7304 7993 2313 1.05 (1.00–1.10) 1.10 (1.02–1.18) 1.05 (1.01–1.08) 4.35 E−03 1.68 E−02 
rs13281615 Intergenic 8q24 2905 4306 1666 4231 5925 2164 1.06 (1.00–1.13) 1.13 (1.04–1.22) 1.06 (1.02–1.10) 2.46 E−03 1.01 E−02 
rs1562430 Intergenic 8q24 5355 7123 2381 5549 8214 2995 0.90 (0.85–0.94) 0.82 (0.77–0.88) 0.90 (0.88–0.93) 8.35 E−10 5.71 E−09 
rs1011970 CDKN2BAS 9p21 10500 4399 426 12020 4754 506 1.06 (1.01–1.12) 0.96 (0.84–1.09) 1.03 (0.99–1.08) 1.20 E−01 3.45 E−02 
rs865686 Intergenic 9q31 6459 7072 1894 6814 8059 2486 0.92 (0.88–0.97) 0.81 (0.75–0.86) 0.90 (0.88–0.93) 1.11 E−09 4.81 E−09 
rs2380205 Intergenic 10p15 5004 7435 2895 5388 8499 3412 0.94 (0.90–0.99) 0.91 (0.86–0.97) 0.95 (0.93–0.98) 3.44 E−03 1.07 E−02 
rs10995190 ZNF365 10q21 11352 3663 285 12456 4463 361 0.89 (0.85–0.94) 0.83 (0.71–0.98) 0.90 (0.86–0.94) 2.73 E−06 1.49 E−05 
rs16917302 ZNF365 10q21 7599 1574 86 9408 2060 101 0.94 (0.88–1.01) 1.04 (0.78–1.39) 0.96 (0.89–1.02) 1.86 E−01 2.58 E−01 
rs1250003h ZMIZ1 10q22 5637 7330 2345 6631 8074 2604 1.07 (1.02–1.12) 1.06 (1.00–1.14) 1.04 (1.01–1.07) 1.62 E−02 1.92 E−02 
rs3750817 FGFR2 10q26 5510 6564 1963 5964 8330 2774 0.84 (0.80–0.89) 0.76 (0.70–0.81) 0.86 (0.84–0.89) 1.75 E−18 8.18 E−18 
rs2981582 FGFR2 10q26 4712 7101 2774 6540 8431 2641 1.18 (1.12–1.24) 1.48 (1.39–1.59) 1.21 (1.17–1.25) 5.81 E−32 3.02 E−31 
rs3817198 LSP1 11p15 4132 3881 947 5658 5463 1261 0.98 (0.92–1.03) 1.03 (0.94–1.14) 1.00 (0.96–1.04) 9.22 E−01 4.19 E−01 
rs909116 LSP1 11p15 4320 7339 3198 4602 8264 3871 0.95 (0.90–1.00) 0.88 (0.83–0.94) 0.94 (0.91–0.97) 1.26 E−04 6.15 E−04 
rs614367 Intergenic 11q13 9422 3567 411 9998 3359 348 1.13 (1.07–1.19) 1.25 (1.08–1.45) 1.12 (1.07–1.18) 5.15 E−07 3.30 E−06 
rs999737i RAD51L1 14q24 8557 4878 688 9979 6078 1010 0.93 (0.89–0.98) 0.80 (0.72–0.89) 0.91 (0.88–0.95) 2.81 E−06 9.31 E−06 
rs3803662 TNRC9 16q12 6890 6177 1434 9290 6899 1311 1.21 (1.15–1.27) 1.49 (1.37–1.62) 1.21 (1.17–1.26) 2.41 E−28 3.14 E−27 
rs2075555 COL1A1 17q21 6283 1987 175 8879 2740 226 1.04 (0.97–1.11) 1.09 (0.89–1.33) 1.04 (0.98–1.10) 2.01 E−01 4.37 E−01 
rs6504950 COX11 17q22 7946 5664 1051 9245 7066 1348 0.93 (0.89–0.97) 0.91 (0.83–0.99) 0.94 (0.91–0.97) 6.88 E−04 2.13 E−03 
rs12982178 USHBP1 19p13 6752 3308 388 8292 4052 520 1.00 (0.95–1.06) 0.93 (0.81–1.07) 0.99 (0.94–1.04) 6.15 E−01 5.50 E−01 
rs8170 C19Orf62 19p13 10713 4780 557 12034 5359 654 1.00 (0.96–1.05) 0.96 (0.85–1.08) 0.99 (0.96–1.03) 7.82 E−01 7.59 E−01 
rs2284378j RALY 20q11 4354 3871 961 5286 4917 1170 0.96 (0.90–1.02) 1.01 (0.92–1.12) 0.99 (0.95–1.03) 6.20 E−01 2.85 E−01 
rs4911414 Intergenic 20q11 4177 3954 1048 5132 5047 1311 0.97 (0.91–1.03) 1.00 (0.91–1.10) 0.99 (0.95–1.03) 6.03 E−01 5.04 E−01 
rs311499k GMEB2 20q13 7987 1162 66 9976 1505 70 0.96 (0.88–1.04) 1.13 (0.81–1.59) 0.98 (0.91–1.05) 5.57 E−01 4.64 E−01 
SNP Closest gene Chr. Cases
 
Controls
 
ORhet (95% CI)a ORhom (95% CI)a ORallele (95% CI)a Ptrendb P2d.f.c 
A/A A/B B/B A/A A/B B/B 
rs11249433 NOTCH2 1p11 4596 7232 2783 6097 8381 3055 1.14 (1.08–1.20) 1.21 (1.13–1.29) 1.10 (1.07–1.14) 7.06E−10 1.63E−09 
rs10931936 CASP8 2q33 4470 3697 775 5762 4549 846 1.05 (0.99–1.12) 1.18 (1.06–1.31) 1.07 (1.03–1.12) 2.10 E−03 5.59 E−03 
rs1045485 CASP8 2q33 9261 2690 200 10148 3058 236 0.96 (0.91–1.02) 0.94 (0.77–1.14) 0.96 (0.92–1.02) 1.75 E−01 3.98 E−01 
rs13387042 Intergenic 2q35 4599 6945 3074 4618 8821 4203 0.79 (0.75–0.83) 0.73 (0.69–0.78) 0.85 (0.82–0.88) 2.66 E−24 3.04 E−26 
rs4973768 SLC4A7 3p24 3647 7397 3572 4657 8847 4063 1.07 (1.01–1.13) 1.13 (1.06–1.21) 1.06 (1.03–1.10) 1.01 E−04 5.07 E−04 
rs4415084d Intergenic 5p12 2764 4125 1546 4218 5636 1964 1.11 (1.04–1.18) 1.19 (1.09–1.29) 1.09 (1.05–1.14) 1.69 E−05 8.33 E−05 
rs10941679 Intergenic 5p12 7823 5750 1083 9912 6467 1075 1.13 (1.07–1.18) 1.29 (1.17–1.41) 1.13 (1.09–1.17) 2.42 E−11 2.00 E−10 
rs10069690 TERT 5p15 7848 5652 1025 9810 6533 1197 1.08 (1.03–1.13) 1.07 (0.98–1.17) 1.06 (1.02–1.10) 2.19 E−03 3.21 E−03 
rs889312 MAP3K1 5q11 7170 6042 1375 9155 7110 1388 1.09 (1.04–1.14) 1.26 (1.16–1.37) 1.11 (1.07–1.15) 3.41 E−09 1.37 E−08 
rs17530068 Intergenic 6q14 8703 5769 928 10068 6285 976 1.06 (1.01–1.11) 1.11 (1.01–1.22) 1.06 (1.02–1.10) 2.69 E−03 1.09 E−02 
rs13437553 Intergenic 6q14 3926 2492 400 5055 2978 460 1.08 (1.00–1.15) 1.15 (1.00–1.32) 1.07 (1.02–1.13) 9.42 E−03 3.42 E−02 
rs1917063e Intergenic 6q14 5780 3521 524 7385 4179 603 1.08 (1.02–1.14) 1.12 (0.99–1.27) 1.07 (1.02–1.12) 3.69 E−03 1.36 E−02 
rs9344191f Intergenic 6q14 4972 3566 645 6435 4317 741 1.07 (1.01–1.13) 1.14 (1.02–1.28) 1.07 (1.02–1.12) 4.04 E−03 1.60 E−02 
rs2180341g RNF146 6q22 5253 3244 529 7100 4583 722 0.95 (0.90–1.01) 0.99 (0.88–1.12) 0.97 (0.93–1.02) 2.51 E−01 2.54 E−01 
rs3757318 Intergenic 6q25 7679 1443 66 9736 1645 56 1.13 (1.04–1.22) 1.40 (0.98–2.01) 1.14 (1.06–1.22) 5.05 E−04 2.10 E−03 
rs9383938 Intergenic 6q25 7563 1568 104 9620 1841 86 1.08 (1.01–1.17) 1.56 (1.17–2.08) 1.11 (1.04–1.19) 1.60 E−03 1.57 E−03 
rs2046210 Intergenic 6q25 5829 6700 2035 7304 7993 2313 1.05 (1.00–1.10) 1.10 (1.02–1.18) 1.05 (1.01–1.08) 4.35 E−03 1.68 E−02 
rs13281615 Intergenic 8q24 2905 4306 1666 4231 5925 2164 1.06 (1.00–1.13) 1.13 (1.04–1.22) 1.06 (1.02–1.10) 2.46 E−03 1.01 E−02 
rs1562430 Intergenic 8q24 5355 7123 2381 5549 8214 2995 0.90 (0.85–0.94) 0.82 (0.77–0.88) 0.90 (0.88–0.93) 8.35 E−10 5.71 E−09 
rs1011970 CDKN2BAS 9p21 10500 4399 426 12020 4754 506 1.06 (1.01–1.12) 0.96 (0.84–1.09) 1.03 (0.99–1.08) 1.20 E−01 3.45 E−02 
rs865686 Intergenic 9q31 6459 7072 1894 6814 8059 2486 0.92 (0.88–0.97) 0.81 (0.75–0.86) 0.90 (0.88–0.93) 1.11 E−09 4.81 E−09 
rs2380205 Intergenic 10p15 5004 7435 2895 5388 8499 3412 0.94 (0.90–0.99) 0.91 (0.86–0.97) 0.95 (0.93–0.98) 3.44 E−03 1.07 E−02 
rs10995190 ZNF365 10q21 11352 3663 285 12456 4463 361 0.89 (0.85–0.94) 0.83 (0.71–0.98) 0.90 (0.86–0.94) 2.73 E−06 1.49 E−05 
rs16917302 ZNF365 10q21 7599 1574 86 9408 2060 101 0.94 (0.88–1.01) 1.04 (0.78–1.39) 0.96 (0.89–1.02) 1.86 E−01 2.58 E−01 
rs1250003h ZMIZ1 10q22 5637 7330 2345 6631 8074 2604 1.07 (1.02–1.12) 1.06 (1.00–1.14) 1.04 (1.01–1.07) 1.62 E−02 1.92 E−02 
rs3750817 FGFR2 10q26 5510 6564 1963 5964 8330 2774 0.84 (0.80–0.89) 0.76 (0.70–0.81) 0.86 (0.84–0.89) 1.75 E−18 8.18 E−18 
rs2981582 FGFR2 10q26 4712 7101 2774 6540 8431 2641 1.18 (1.12–1.24) 1.48 (1.39–1.59) 1.21 (1.17–1.25) 5.81 E−32 3.02 E−31 
rs3817198 LSP1 11p15 4132 3881 947 5658 5463 1261 0.98 (0.92–1.03) 1.03 (0.94–1.14) 1.00 (0.96–1.04) 9.22 E−01 4.19 E−01 
rs909116 LSP1 11p15 4320 7339 3198 4602 8264 3871 0.95 (0.90–1.00) 0.88 (0.83–0.94) 0.94 (0.91–0.97) 1.26 E−04 6.15 E−04 
rs614367 Intergenic 11q13 9422 3567 411 9998 3359 348 1.13 (1.07–1.19) 1.25 (1.08–1.45) 1.12 (1.07–1.18) 5.15 E−07 3.30 E−06 
rs999737i RAD51L1 14q24 8557 4878 688 9979 6078 1010 0.93 (0.89–0.98) 0.80 (0.72–0.89) 0.91 (0.88–0.95) 2.81 E−06 9.31 E−06 
rs3803662 TNRC9 16q12 6890 6177 1434 9290 6899 1311 1.21 (1.15–1.27) 1.49 (1.37–1.62) 1.21 (1.17–1.26) 2.41 E−28 3.14 E−27 
rs2075555 COL1A1 17q21 6283 1987 175 8879 2740 226 1.04 (0.97–1.11) 1.09 (0.89–1.33) 1.04 (0.98–1.10) 2.01 E−01 4.37 E−01 
rs6504950 COX11 17q22 7946 5664 1051 9245 7066 1348 0.93 (0.89–0.97) 0.91 (0.83–0.99) 0.94 (0.91–0.97) 6.88 E−04 2.13 E−03 
rs12982178 USHBP1 19p13 6752 3308 388 8292 4052 520 1.00 (0.95–1.06) 0.93 (0.81–1.07) 0.99 (0.94–1.04) 6.15 E−01 5.50 E−01 
rs8170 C19Orf62 19p13 10713 4780 557 12034 5359 654 1.00 (0.96–1.05) 0.96 (0.85–1.08) 0.99 (0.96–1.03) 7.82 E−01 7.59 E−01 
rs2284378j RALY 20q11 4354 3871 961 5286 4917 1170 0.96 (0.90–1.02) 1.01 (0.92–1.12) 0.99 (0.95–1.03) 6.20 E−01 2.85 E−01 
rs4911414 Intergenic 20q11 4177 3954 1048 5132 5047 1311 0.97 (0.91–1.03) 1.00 (0.91–1.10) 0.99 (0.95–1.03) 6.03 E−01 5.04 E−01 
rs311499k GMEB2 20q13 7987 1162 66 9976 1505 70 0.96 (0.88–1.04) 1.13 (0.81–1.59) 0.98 (0.91–1.05) 5.57 E−01 4.64 E−01 

Bp, base pair; chr, chromosome; CI, confidence interval; ORhet, odds ratio of heterozygotes versus homozygotes for the major allele; ORhom, odds ratio of homozygotes for the minor alleles versus homozygotes for the major allele; ORallele, odds ratio of each increasing number of minor alleles; SNP, single E-nucleotide polymorphism; A, major allele in HapMap CEU subjects; B, minor allele in HapMap CEU subjects (http://hapmap.ncbi.nlm.nih.gov/).

a Odds ratios have been adjusted for age and subcohort (defined by country in EPIC and study phase in NHS).

bP-values for trend (two sided) were derived from Cochran–Armitage trend test (d.f. = 1).

cP-values for the Cochran–Armitage trend test (two sided; d.f. = 2) were obtained by coding genotypes as three categories: major allele homozygotes (reference), heterozygotes and for minor-allele homozygotes.

d5p12-rs4415084 or surrogate 5p12-rs920329.

e6q14-rs1917063 or surrogate 6q14-rs9344208.

f6q14-rs9344191 or surrogate 6q14-rs9449341.

gECHDC1, RNF146-rs2180341 or surrogate ECHDC1, RNF146-rs9398840.

hZMIZ1-rs1250003 or surrogate ZMIZ1-rs704010.

iRAD51L1-rs999737 or surrogate RAD51L1-rs10483813.

jRALY-rs2284378 or surrogate RALY-rs6059651, RALY-rs8119937.

kGMEB2-rs311499 or surrogate GMEB2-rs311498.

Stratified and case–case analysis

In the stratified analysis, SNP C19Orf62-rs8170 showed an increased risk of BC in the ER− stratum (ORallele = 1.20, 95% CI = 1.09–1.31, P1d.f. = 1 × 10−4). In addition, 11q13-rs614367 showed a preferential association with ER+ BC (ORallele = 1.16, 95% CI = 1.10–1.22, P1d.f. = 9.6 × 10−8). The results of the stratified analyses are shown in Supplementary Material, Table S3.

The case–case analyses showed a significant difference in the distribution of alleles of C19Orf62-rs8170 between ER+ and ER− cases (P1d.f. = 6.8 × 10−7) and a non-significant difference between PR+ and PR− cases (P1d.f. = 5.4 × 10−4). For FGFR2-rs2981582, we observed significant differences in the distribution of alleles with respect to ER status (P1d.f. = 1.2 × 10−5). This was also observed for 11q13-rs614367 (P1d.f. = 1.1 × 10−4). A significant result was also observed for 5p12-rs10941679 with respect to PR status (P1d.f. = 1.5 × 10−4). We did not observe any other statistically significant difference. The strongest, non-significant evidence for a difference in the case–case analysis was observed for USHBP1-rs12982178 with respect to ER status (P1df = 5.6 × 10−4) and PR status (P1df = 3.4 × 10−4). The results of the case–case analyses are shown in Supplementary Material, Table S4.

Gene–environment interactions

The results of the interaction analysis of the 39 SNPs and the established risk factors are presented in Supplementary Material, Table S5. After correcting for multiple testing, no interactions were significant considering the adjusted threshold (P < 1.34 × 10−4) in BPC3. The strongest interaction result in BPC3 was observed between 6q25-rs2046210 and alcohol consumption (Pinteraction = 0.002) (see Table 3).

Table 3.

Results of SNP-risk factors interaction analyses in BPC3, where interaction P-value < 0.01

Risk factora SNP Chr. Nearest plausible gene Cases Controls OR (95% CI)a P_intb Categories 
Alcohol rs2046210 6q25 Intergenic 14 169 17 026 1.05 (1.01–1.08) 1.95E−03 ALL 
    4515 5722 0.96 (0.91–1.02)  0 g/day 
    7436 8820 1.07 (1.02–1.12)  >0–<10 g/day 
    1158 1414 1.17 (1.04–1.31)  10–20 g/day 
    1060 1070 1.15 (1.02–1.30)  ≥20 g/day 
Height rs2180341c 6q22 RNF146 9004 12 376 0.97 (0.93–1.02) 6.37E−03 ALL 
    2206 3342 1.04 (0.95–1.13)  <1.60 m 
    2563 3628 1.05 (0.97–1.14)  1.60–1.64 m 
    2512 3206 0.87 (0.80–0.95)  1.65–1.69 m 
    1723 2200 0.92 (0.83–1.02)  ≥1.70 m 
Oral contraceptives use rs1562430 8q24 Intergenic 14 691 16 597 0.90 (0.87–0.93) 6.41E−03 ALL 
    7610 8559 0.87 (0.83–0.90)  Never 
    7081 8038 0.95 (0.90–0.99)  Ever 
Height rs311499d 20q13 GMEB2 9197 11 529 0.98 (0.91–1.06) 8.32E−03 ALL 
    2291 2946 0.79 (0.68–0.92)  <1.60 m 
    2709 3461 1.07 (0.94–1.23)  1.60–1.64 m 
    2474 3011 1.09 (0.94–1.27)  1.65–1.69 m 
    1723 2111 0.97 (0.81–1.15)  ≥1.70 m 
Smoking rs4973768 3p24 SLC4A7 14 463 17 433 1.06 (1.03–1.10) 1.98E−02 ALL 
    7286 9392 1.02 (0.98–1.07)  Never 
    7177 8041 1.10 (1.05–1.16)  Ever 
Risk factora SNP Chr. Nearest plausible gene Cases Controls OR (95% CI)a P_intb Categories 
Alcohol rs2046210 6q25 Intergenic 14 169 17 026 1.05 (1.01–1.08) 1.95E−03 ALL 
    4515 5722 0.96 (0.91–1.02)  0 g/day 
    7436 8820 1.07 (1.02–1.12)  >0–<10 g/day 
    1158 1414 1.17 (1.04–1.31)  10–20 g/day 
    1060 1070 1.15 (1.02–1.30)  ≥20 g/day 
Height rs2180341c 6q22 RNF146 9004 12 376 0.97 (0.93–1.02) 6.37E−03 ALL 
    2206 3342 1.04 (0.95–1.13)  <1.60 m 
    2563 3628 1.05 (0.97–1.14)  1.60–1.64 m 
    2512 3206 0.87 (0.80–0.95)  1.65–1.69 m 
    1723 2200 0.92 (0.83–1.02)  ≥1.70 m 
Oral contraceptives use rs1562430 8q24 Intergenic 14 691 16 597 0.90 (0.87–0.93) 6.41E−03 ALL 
    7610 8559 0.87 (0.83–0.90)  Never 
    7081 8038 0.95 (0.90–0.99)  Ever 
Height rs311499d 20q13 GMEB2 9197 11 529 0.98 (0.91–1.06) 8.32E−03 ALL 
    2291 2946 0.79 (0.68–0.92)  <1.60 m 
    2709 3461 1.07 (0.94–1.23)  1.60–1.64 m 
    2474 3011 1.09 (0.94–1.27)  1.65–1.69 m 
    1723 2111 0.97 (0.81–1.15)  ≥1.70 m 
Smoking rs4973768 3p24 SLC4A7 14 463 17 433 1.06 (1.03–1.10) 1.98E−02 ALL 
    7286 9392 1.02 (0.98–1.07)  Never 
    7177 8041 1.10 (1.05–1.16)  Ever 

aPer-allele OR of the SNP in various categories of the risk factor.

bP-value of likelihood ratio test between models with and without interaction terms.

cECHDC1, RNF146-rs2180341 or surrogate ECHDC1, RNF146-rs9398840.

dGMEB2-rs311499 or surrogate GMEB2-rs311498.

In the meta-analysis with the BCAC data the pooled OR of the interaction between NOTCH2-rs11249433 and parity was significant (ORmeta = 1.13, 95% CI = 1.07–1.20, Pmeta = 4.83 × 10−5). The interaction OR of LSP1-rs3817198 and number of full-term pregnancies, which was reported to be significant in BCAC (22) but not in our study, was not significant, considering the multiple testing, in the meta-analysis (ORmeta = 1.03, 95% CI = 1.01–1.05, Pmeta = 0.0113). In addition, we observed heterogeneity between BPC3 and BCAC for the interaction ORs (Pheterogeneity = 6.39 × 10−5). This was also the case for the interaction between CASP8-rs1045485 and alcohol consumption (ORmeta = 1.14, 95% CI = 0.98–1.31, Pmeta = 0.0813). The strongest evidence for interaction was observed between smoking and SLC4A7-rs4973768 (ORmeta = 1.08, 95% CI = 1.03–1.13, Pmeta = 8.84 × 10−4) as shown in Table 4. Forest plots of the interactions shown in Table 4 are presented in Figure 1. Detailed results of the meta-analysis are shown in Supplementary Material, Table S6.

Table 4.

Interaction odds ratios from BPC3, BCAC and meta-analysis showing interaction P-value < 10−3

Study SNP Chr. Nearest plausible gene Location (bp) (hg19)a BC risk factor Cases Controls OR (95% CI) Ptrendb Phetc 
BPC3 rs11249433 NOTCH2 121280363 Ever FTP 13 812 16 809 1.07 (0.95–1.20) 2.40E−01  
BCAC      28 469 29 228 1.16 (1.08–1.24) 5.27E−05  
META      42 281 46 037 1.13 (1.07–1.20) 4.83E−05 0.55 
BPC3 rs4973768 SLC4A7 27415763 Ever smoke 14 463 17 433 1.09 (1.02–1.16) 1.21E−02  
BCAC      16 737 18 263 1.07 (1.01–1.14) 2.76E−02  
META      31 200 35 696 1.08 (1.03–1.13) 8.84E−04 0.96 
BPC3 rs3817198 11 LSP1 1908756 Number of FTP 7591 10 679 0.97 (0.94–1.00) 6.26E−02  
BCAC      23 064 22 151 1.06 (1.04–1.09) 2.38E−06  
META      30 655 32 830 1.03 (1.01–1.05) 1.13E−02 6.39E−05 
BPC3 rs1045485 CASP8 202149339 Alcohol 11 907 13 083 0.96 (0.81–1.15) 6.83E−01  
BCAC      6081 9305 1.59 (1.24–2.05) 3.05E−04  
META      17 988 22 388 1.14 (0.98–1.31) 8.13E−02 0.006 
Study SNP Chr. Nearest plausible gene Location (bp) (hg19)a BC risk factor Cases Controls OR (95% CI) Ptrendb Phetc 
BPC3 rs11249433 NOTCH2 121280363 Ever FTP 13 812 16 809 1.07 (0.95–1.20) 2.40E−01  
BCAC      28 469 29 228 1.16 (1.08–1.24) 5.27E−05  
META      42 281 46 037 1.13 (1.07–1.20) 4.83E−05 0.55 
BPC3 rs4973768 SLC4A7 27415763 Ever smoke 14 463 17 433 1.09 (1.02–1.16) 1.21E−02  
BCAC      16 737 18 263 1.07 (1.01–1.14) 2.76E−02  
META      31 200 35 696 1.08 (1.03–1.13) 8.84E−04 0.96 
BPC3 rs3817198 11 LSP1 1908756 Number of FTP 7591 10 679 0.97 (0.94–1.00) 6.26E−02  
BCAC      23 064 22 151 1.06 (1.04–1.09) 2.38E−06  
META      30 655 32 830 1.03 (1.01–1.05) 1.13E−02 6.39E−05 
BPC3 rs1045485 CASP8 202149339 Alcohol 11 907 13 083 0.96 (0.81–1.15) 6.83E−01  
BCAC      6081 9305 1.59 (1.24–2.05) 3.05E−04  
META      17 988 22 388 1.14 (0.98–1.31) 8.13E−02 0.006 

aGenome Reference Consortium Human, build 37 (http://genome.ucsc.edu/cgi-bin/hgGateway).

bPer-allele OR of the SNP in various categories of the risk factor.

cHeterogeneity P-value between studies.

Figure 1.

Forest plots of the interaction odds ratios shown in Table 4.

Figure 1.

Forest plots of the interaction odds ratios shown in Table 4.

DISCUSSION

An important extension of GWAS is to investigate whether genetic polymorphisms modify the effects of established BC risk factors and whether they show a stronger association in subgroups of BC cases. In this paper, we report findings from a consortium of large prospective studies on the possible interactions between 39 polymorphisms that have been associated previously with BC risk and established risk factors for the disease. Moreover, we conducted stratified analysis, considering the strata defined by the risk and prognostic factors and finally a case–case analysis considering the tumor prognostic factors alone. Data were examined using a nested case–control design within the BPC3. This work complements a previous report where a smaller set of cases and controls was analyzed for only 17 SNPs (21).

As a first step, we tested the main effect of the SNPs on BC risk and we found associations for all of the previously reported ER+ BC risk SNPs at the 0.05 significance level (P-values ranging from 0.035 to 5.81 × 10−32) except for CASP8-rs1045485, RNF146-rs2180341, ZNF365-rs16917302, LSP1-rs3817198, COL1A1-rs2075555 and GMEB2-rs311499 (1,3,6,10,11,16,21). The possible involvement of LSP1-rs3817198 and CASP8-rs1045485 with BC risk was investigated in several studies and their association was consistently found in case–control studies (1,4,7,10,22) but not in prospective studies (9,18,21). The other SNPs were either found in small studies (COL1A1-rs2075555) (3) or selected populations such as Ashkenazi Jews (RNF146-rs2180341) (6) or BRCA2 mutation carriers (GMEB2-rs311499, ZNF365-rs16917302) (11).

As a second step, we investigated the possible differential association between SNPs and prognostic factors. The stratified and case–case analyses in groups determined by prognostic factors showed a preferential association of C19Orf62-rs8170 with ER− BC, which confirms previous findings (15). With respect to receptor-specific BC risk, our results were in general agreement with previous reports, suggesting that several SNPs are predominantly associated with ER+ BC: NOTCH2-rs11249433 (9), 2q35-rs13387042 (2,27), TNRC9-rs3803662 (2), 5p12-rs4415084, 5p12-rs10941679 (5), FGFR2-rs2981582 (1), FGFR2-rs3750817 (20) and MAP3K1-rs889312 (28). Others are more predominantly associated with ER− or PR− BC: C19Orf62-rs8170, 6q14-rs17530068 and 6q14-rs13437553 (15), whereas we did not replicate the preferential association of 20q11-rs4911414 with ER− BC (15). Our results are also consistent with previous reports of SNPs C19ORF62-rs8170, USHBP1-rs12982178 and TERT-rs10069690 being specifically associated with ER− BC risk (12,15), while we could not replicate the association of RALY-rs2284378 with ER− BC (15). A possible explanation for this is lack of statistical power in the ER− group, which included 2127 cases in our study. Since part of the individuals used in this report overlap with Refs. (12,15), the results presented here cannot be considered an independent replication.

In the last years, there has been a keen interest in investigating gene–environment interactions using SNPs identified by GWAS and established risk factors, especially in common cancer types such as breast, for which multiple susceptibility loci have been identified and several risk factors are known. Finding gene–environment interactions can be useful in two areas: in order to allow a more specific risk assessment and aid targeted early detection or prevention strategies and to further our understanding of biological pathways and mechanisms of disease etiology (29). Despite the vast international effort, only a few established examples of gene–environment interactions exist, such as the one between NAT2 polymorphisms and smoking in relation to bladder cancer risk (30), and between ALDH2 polymorphisms and alcohol in relation to esophageal cancer risk (31,32). For BC, several large studies, including our own (18,19,21), focusing on GWAS loci have reported no gene–environment interactions. We considerably expanded our previous study (21). Moreover, taking advantage of the work of Nickels et al., who reported significant interactions between NOTCH2-rs11249433 and parity as well as between LSP1-rs3817198 and number of full-term pregnancies (22), we conducted a meta-analysis of the results from BCAC and BPC3 in order to investigate gene–environment interactions on up to ∼79 000 individuals. In BPC3, we did not observe any significant interactions between the selected SNPs and any of the epidemiologic risk factors, when we considered the adjusted significance threshold (P < 1.34 × 10−4). In the meta-analysis, we observed that our estimate of the interaction between NOTCH2-rs11249433 and parity (ORinteraction = 1.07 95% CI = 0.95–1.20, Ptrend = 0.24) pointed towards an increased risk, as did the one reported by BCAC (ORinteraction = 1.16, 95% CI = 1.08–1.24, Ptrend = 5.27 × 10−5), but our estimate did not reach statistical significance. However, our result does not weaken the meta-analysis estimate, on the contrary, it makes the association even stronger (ORmeta = 1.13, 95% CI = 1.07–1.20, Pmeta = 4.83 × 10−5). It is therefore plausible that the interaction observed by BCAC is true, although modest, but not observed in BPC3 because of insufficient sample size. We did not observe the interaction reported from Nickels et al. for LSP1-rs3817198 with the number of FTP, which is not surprising since we did not replicate the association with BC risk that was reported by BCAC for this SNP either. The discrepancy in the findings between cohort studies and case–control studies seem to be consistent since also the previous studies from BPC3 (21) and BCAC showed discordant results. Nickels et al. suggest that the difference might be due to a misclassification of parity in the cohorts, considering that the information was collected only at the time of enrolment. This seems unlikely considering the age of enrolment of the women in the BPC3 cohorts and therefore the most likely explanation is that our power was limited to detect the modest magnitude of the interaction reported by BCAC.

The most interesting result from the meta-analysis is the interaction of smoking status and SLC4A7-rs4973768. This association, although not significant, has biological plausibility. The SLC4A7 gene affects bicarbonate transport but is also thought to be responsible for the influx of lead into erythrocytes (33). It is well known that smokers have a higher concentration of lead in the blood than non-smokers (34,35), since lead is present in cigarette tar. In addition, several studies suggest a positive association between lead exposure and risk of several kinds of cancer, including breast (3639). Moreover, rs4973768 has been reported to have a functional impact on the SLC4A7 gene (33). To follow this up, we have used the regulomeDB web site (40), observing that the polymorphic variant rs552647, which is in high LD (r2 = 0.963) with rs4973768, is predicted to affect binding with the transcriptional repressor CTCF, a master regulator of gene expression. It is therefore conceivable that this polymorphic variant could modify the ability of SLC4A7 to introduce deleterious lead inside cells. Since lead is not normally present in the organism and is introduced by environmental exposure it is reasonable that a polymorphic variant that influences SLC4A7 functionality might exert its effect in subjects with an increased exposure to lead such as smokers. In the BPC3, we also note that this SNP is significantly associated with BC risk among smokers (ORallele, smokers = 1.10, 95% CI = 1.05–1.16, Ptrend = 2.1 × 10−5, ORallele, non-smokers = 1.02, 95% CI = 0.98–1.07, Ptrend = 0.28). Therefore, this statistical interaction, although not significant, is highly suggestive, since it could describe a biological process which may contribute to breast carcinogenesis. This effect could be an example of a pure or qualitative interaction (i.e. there is an effect only in the presence of both the susceptible genotype and the environmental factor, as described by Thomas (29)), since it seems that the effect of SLC4A7 genotypes is present only in smokers. A similar finding has been described for polymorphisms in the NAT2 gene in bladder cancer: in fact, several studies have consistently shown that slow acetylator phenotype increases the risk of developing the disease only among smokers (30).

BC is a complex disease involving multiple environmental and genetic risk factors and, in this analysis, we have considered only a small fraction of the loci that potentially could influence the disease risk. A possible alternative strategy to pursue this route is to perform gene–environment-wide interaction studies, i.e. using the information of a GWAS to agnostically find novel gene–environment interactions. This innovative approach has shown some promising results (32,4146), although several questions on how to apply this strategy still remain unsolved (29,47). In addition, our data confirm what has been suggested by others, namely that one possible way to further our understanding of BC would be to conduct GWAS in more homogeneous groups of cases defined by receptor status or histological subtype (2,5,15,16,48,49). Continuing to implement gene–environment interaction analysis, possibly using more comprehensive statistical approaches and study designs, alongside novel genotyping techniques, will still be useful. More importantly, it will be necessary not to restrict the field to GWAS hits and established epidemiologic risk factors.

In conclusion, in this study of up to almost 79 000 women we confirmed several known associations between polymorphic variants and BC risk. Considering the significance threshold adjusted for multiple testing, we did not observe any novel interaction between genetic variants and BC risk factors nor any novel difference in the association between risk SNPs and tumor characteristics. The results obtained in this paper strongly suggest that there are no biologically relevant interactions for the majority of the current GWAS variants and the epidemiologic risk factors taken into consideration. However, we did observe a suggestive novel gene–environment interaction (SLC4A7 genotypes and smoking behavior) that if confirmed could further our understanding on BC susceptibility. Finally, our results are compatible with a significant interaction between NOTCH2-rs11249433 and parity reported by Nickels et al. The results obtained in this paper are conclusive in excluding any other major interactions between the current GWAS hits and the epidemiologic risk factors taken into consideration.

MATERIALS AND METHODS

Study subjects

The BPC3 has been described extensively elsewhere (50). Briefly, it consists of large, well-established prospective cohorts assembled in Europe, Australia and the United States that have both DNA samples and extensive questionnaire information collected before BC diagnosis. The cohorts used in this analysis are: the European Prospective Investigation into Cancer and Nutrition (EPIC) (51), the Women's Health Initiative (WHI) (52), the Melbourne Collaborative Cohort Study (53), the NHS (54), the Women's Health Study (55), the American Cancer Society Cancer Prevention Study II (56), the Prostate, Lung, Colorectal, Ovarian Cancer Screening Trial (57) and the Multiethnic Cohort (58).

Cases were women who had been diagnosed with invasive BC after enrolment in one of the BPC3 cohorts. The diagnosis was confirmed by medical records or tumor registries (the method varied among cohorts). Subjects were considered eligible controls if they were free of BC until the follow-up time for the matched case subject. Matching criteria were: age at baseline, menopausal status at baseline and cohort. All study subjects were of Caucasian ethnicity. Relevant institutional review boards from each cohort approved the project and informed consent was obtained from all subjects.

SNP selection and genotyping

The SNPs included in the analyses (Table 1) were reported to show a genome-wide statistical significant association with BC risk (P < 5 × 10−7). In this study, either the SNP from the original publication or a surrogate in complete linkage disequilibrium (r2 = 1 in HapMap CEU) was genotyped. In particular, for the following SNPs, we have genotyped either the original SNP or the surrogate: rs4415084 (surrogate rs920329), rs9344191 (surrogate rs9449341), rs1250003 (surrogate rs704010), rs999737 (surrogate rs10483813), rs2284378 (surrogates rs8119937 and rs6059651), rs2180341 (surrogate rs9398840), rs311499 (surrogate rs311498) and rs9344208 (surrogate rs1917063). The BC susceptibility SNPs recently reported by BCAC were not included in this analysis (59).

Genotyping was performed using TaqMan assays (Applied Biosystems, Foster City, CA, USA) as specified by the producer. Genotyping of the BC cases and controls was performed in four laboratories (German Cancer Research Center (DKFZ), University of Southern California, U.S. NCI, Harvard School of Public Health). Laboratory personnel were blinded to whether the subjects were cases or controls. Duplicate samples (∼8%) were included and concordance of these samples was >99.99%.

Data filtering and statistical analysis

The study subjects were BC patients and controls for which at least 90% of the SNPs had been successfully genotyped (35 661 subjects in total). Each SNP was tested for Hardy–Weinberg equilibrium among the controls. All unconditional statistical models that were used in this study have been adjusted for age at recruitment and cohort (defined as country in EPIC and study phase in NHS).

We investigated the association between genetic variants and BC risk by fitting an unconditional logistic regression model. The genotypes were treated either as nominal variables, comparing heterozygotes and minor allele homozygotes to the reference group of major allele homozygotes (co-dominant model), or as interval variables in a log-additive model. This was also carried out separately for each cohort and tests for heterogeneity between cohorts were performed.

In order to investigate the possible interactions between SNPs and risk factors, two models for each pair of SNP and risk factor were explored: one with only SNP and risk factor and one including additional SNP-risk factor interaction term(s). The genetic variants were treated as interval variables (counts of minor alleles) and the non-genetic risk factors were treated as continuous (BMI, height, age at menarche, age at menopause, pack-years of smoking, alcohol in g/day, age at first full-term pregnancy and number of full-term pregnancies) or dichotomous (parity, family history, ever use of oral contraceptives). For smoking status, we used three categories (current, former and never smoker).

We then applied the likelihood ratio test to compare the two models and to assess departures from the log-additive model for the joint effect of SNP and risk factor. For BMI, the interaction analysis was performed separately for pre- and postmenopausal women.

The interaction of genes and use of hormone replacement therapy will be explored in a separate study and was not examined in this analysis.

Stratified, unconditional analyses using cases and controls were performed for risk factors of BC. Cochrane's Q-test was used to test for heterogeneity between the strata. The strata for the risk factors were defined as: BMI (BMI < 25, 25 ≤ BMI < 30, BMI ≥ 30), height (height ≤ 1.60, 1.60–1.65,1.65–1.70 m, height ≥ 1.70 m), age at menarche (early, ≤11 years; intermediate, 12–13 years; late, ≥14 years), age at menopause (early, ≤44 years; intermediate, 45–49 years; late, ≥50 years), smoking (never smoker, ever smoker), pack-years of smoking (0, >0–<10, ≥10–<20, ≥20), alcohol (0, >0–<10, ≥10–<20, ≥20 g/day), ever full-term pregnancy (yes, no), age at first full-term pregnancy (<20, 20–24, 25–29, >29), number of full-term pregnancies (0, 1, 2, 3, ≥4), family history (mother diagnosed with BC or not, as information on first-degree relatives was sparse), ever use of oral contraceptives (yes, no).

Additional case–case analyses using an unconditional logistic regression model were performed to test for the effect ***of heterogeneity of prognostic factors. The prognostic factors were defined as follows: TNM staging (Stage 1, Stage 2, Stages 3–4), grade (well differentiated, moderately differentiated and poorly differentiated), tumor size (<2, 2–5 cm, >5 cm), age at diagnosis (younger than 55, older than 55) and ER, PR status (negative, positive). Using the matching to select controls, we also performed subgroup analyses for the prognostic factors in order to have a complete assessment of the preferential association of the SNPs with the tumor characteristics.

A fixed-effects meta-analysis of the interaction ORs was performed to combine the results from our study and that of the BCAC study (22) giving us a final sample size of 79 534 subjects (34 817 BC cases and 44 717 controls). We analyzed 19 SNPs that were used in both studies, the non-genetic variables in the BPC3 study were recoded so as to correspond to the categories used in the BCAC study (22). Before doing the meta-analysis, heterogeneity between the studies was investigated using Cochrane's Q-test.

The significance threshold was adjusted, taking into account the large number of tests carried out. Since some of the SNPs map to the same regions and might be in linkage disequilibrium, for each locus we calculated the effective number of independent SNPs, Meff, using the SNP Spectral Decomposition approach (simpleM method) (60). The study-wise Meff obtained was 31. For the interaction analyses, the P-value threshold was obtained by dividing the conventional significance threshold of 0.05 by the product of Meff and the number of risk factors (n = 12). Thus, for the interaction analyses and for the heterogeneity tests the threshold for statistical significance was 1.34 × 10−4 (0.05/(31 × 12)), for the stratified analyses it was 0.05/(31 × 18) = 9 × 10−5 and for the case–case analyses it was 0.05/(31 × 6) = 2.7 × 10−4.

All statistical tests were two sided, and all statistical analyses were performed with SAS version 9.2.

SUPPLEMENTARY MATERIAL

Supplementary Material is available at HMG online.

FUNDING

This work was supported by U.S. National Institutes of Health, National Cancer Institute (U19-CA148065, cooperative agreements U01-CA98233-07 to D.J.H., U01-CA98710-06 to M.J.T., U01-CA98216-06 to E.R. and R.K., and U01-CA98758-07 to B.E.H.) and Intramural Research Program of National Institutes of Health and National Cancer Institute, Division of Cancer Epidemiology and Genetics. The WHI program is funded by the National Heart, Lung, and Blood Institute, National Institutes of Health, U.S. Department of Health and Human Services through contracts HHSN268201100046C, HHSN268201100001C, HHSN268201100002C, HHSN268201100003C, HHSN268201100004C, and HHSN271201100004C. A full listing of WHI investigators can be found at: https://cleo.whi.org/researchers/Documents%20%20Write%20a%20Paper/WHI%20Investigator%20Short%20List.pdf. The Founding Sources had no role in the study design; in the collection, analysis and interpretation of data and in the decision to submit the paper for publication.

ACKNOWLEDGEMENTS

The authors thank Angelika Stein (DKFZ, Heidelberg, Germany) for expert technical assistance.

Conflict of Interest statement. None declared.

REFERENCES

1
Easton
D.F.
Pooley
K.A.
Dunning
A.M.
Pharoah
P.D.
Thompson
D.
Ballinger
D.G.
Struewing
J.P.
Morrison
J.
Field
H.
Luben
R.
et al.  
Genome-wide association study identifies novel breast cancer susceptibility loci
Nature
 
2007
447
1087
1093
2
Stacey
S.N.
Manolescu
A.
Sulem
P.
Rafnar
T.
Gudmundsson
J.
Gudjonsson
S.A.
Masson
G.
Jakobsdottir
M.
Thorlacius
S.
Helgason
A.
et al.  
Common variants on chromosomes 2q35 and 16q12 confer susceptibility to estrogen receptor-positive breast cancer
Nat. Genet.
 
2007
39
865
869
3
Murabito
J.M.
Rosenberg
C.L.
Finger
D.
Kreger
B.E.
Levy
D.
Splansky
G.L.
Antman
K.
Hwang
S.J.
A genome-wide association study of breast and prostate cancer in the NHLBI's Framingham Heart Study
BMC Med. Genet.
 
2007
8
Suppl. 1
S6
4
Cox
A.
Dunning
A.M.
Garcia-Closas
M.
Balasubramanian
S.
Reed
M.W.
Pooley
K.A.
Scollen
S.
Baynes
C.
Ponder
B.A.
Chanock
S.
et al.  
A common coding variant in CASP8 is associated with breast cancer risk
Nat. Genet.
 
2007
39
352
358
5
Stacey
S.N.
Manolescu
A.
Sulem
P.
Thorlacius
S.
Gudjonsson
S.A.
Jonsson
G.F.
Jakobsdottir
M.
Bergthorsson
J.T.
Gudmundsson
J.
Aben
K.K.
et al.  
Common variants on chromosome 5p12 confer susceptibility to estrogen receptor-positive breast cancer
Nat. Genet.
 
2008
40
703
706
6
Gold
B.
Kirchhoff
T.
Stefanov
S.
Lautenberger
J.
Viale
A.
Garber
J.
Friedman
E.
Narod
S.
Olshen
A.B.
Gregersen
P.
et al.  
Genome-wide association study provides evidence for a breast cancer risk locus at 6q22.33
Proc. Natl Acad. Sci. USA
 
2008
105
4340
4345
7
Zheng
W.
Long
J.
Gao
Y.T.
Li
C.
Zheng
Y.
Xiang
Y.B.
Wen
W.
Levy
S.
Deming
S.L.
Haines
J.L.
et al.  
Genome-wide association study identifies a new breast cancer susceptibility locus at 6q25.1
Nat. Genet.
 
2009
41
324
328
8
Ahmed
S.
Thomas
G.
Ghoussaini
M.
Healey
C.S.
Humphreys
M.K.
Platte
R.
Morrison
J.
Maranian
M.
Pooley
K.A.
Luben
R.
et al.  
Newly discovered breast cancer susceptibility loci on 3p24 and 17q23.2
Nat. Genet.
 
2009
41
585
590
9
Thomas
G.
Jacobs
K.B.
Kraft
P.
Yeager
M.
Wacholder
S.
Cox
D.G.
Hankinson
S.E.
Hutchinson
A.
Wang
Z.
Yu
K.
et al.  
A multistage genome-wide association study in breast cancer identifies two new risk alleles at 1p11.2 and 14q24.1 (RAD51L1)
Nat. Genet.
 
2009
41
579
584
10
Turnbull
C.
Ahmed
S.
Morrison
J.
Pernet
D.
Renwick
A.
Maranian
M.
Seal
S.
Ghoussaini
M.
Hines
S.
Healey
C.S.
et al.  
Genome-wide association study identifies five new breast cancer susceptibility loci
Nat. Genet.
 
2010
42
504
507
11
Gaudet
M.M.
Kirchhoff
T.
Green
T.
Vijai
J.
Korn
J.M.
Guiducci
C.
Segre
A.V.
McGee
K.
McGuffog
L.
Kartsonaki
C.
et al.  
Common genetic variants and modification of penetrance of BRCA2-associated breast cancer
PLoS Genet.
 
2010
6
e1001183
12
Haiman
C.A.
Chen
G.K.
Vachon
C.M.
Canzian
F.
Dunning
A.
Millikan
R.C.
Wang
X.
Ademuyiwa
F.
Ahmed
S.
Ambrosone
C.B.
et al.  
A common variant at the TERT-CLPTM1L locus is associated with estrogen receptor-negative breast cancer
Nat. Genet.
 
2011
43
1210
1214
13
Long
J.
Cai
Q.
Sung
H.
Shi
J.
Zhang
B.
Choi
J.Y.
Wen
W.
Delahanty
R.J.
Lu
W.
Gao
Y.T.
et al.  
Genome-wide association study in east Asians identifies novel susceptibility loci for breast cancer
PLoS Genet.
 
2012
8
e1002532
14
Kim
H.C.
Lee
J.Y.
Sung
H.
Choi
J.Y.
Park
S.K.
Lee
K.M.
Kim
Y.J.
Go
M.J.
Li
L.
Cho
Y.S.
et al.  
A genome-wide association study identifies a breast cancer risk variant in ERBB4 at 2q34: results from the Seoul Breast Cancer Study
Breast Cancer Res.
 
2012
14
R56
15
Siddiq
A.
Couch
F.
Chen
G.
Lindström
S.
Eccles
D.
Millikan
R.
Michailidou
K.
Stram
D.
Beckmann
L.
Rhie
S.
et al.  
A meta-analysis of genome-wide association studies of breast cancer identifies two novel susceptibility loci at 6q14 and 20q11
Hum. Mol. Genet.
 
2012
21
5373
5384
16
Antoniou
A.C.
Kuchenbaecker
K.B.
Soucy
P.
Beesley
J.
Chen
X.
McGuffog
L.
Lee
A.
Barrowdale
D.
Healey
S.
Sinilnikova
O.M.
et al.  
Common variants at 12p11, 12q24, 9p21, 9q31.2 and in ZNF365 are associated with breast cancer risk for BRCA1 and/or BRCA2 mutation carriers
Breast Cancer Res.
 
2012
14
R33
17
Lambrechts
D.
Truong
T.
Justenhoven
C.
Humphreys
M.K.
Wang
J.
Hopper
J.L.
Dite
G.S.
Apicella
C.
Southey
M.C.
Schmidt
M.K.
et al.  
11q13 is a susceptibility locus for hormone receptor positive breast cancer
Hum. Mutat.
 
2012
33
1123
1132
18
Travis
R.C.
Reeves
G.K.
Green
J.
Bull
D.
Tipper
S.J.
Baker
K.
Beral
V.
Peto
R.
Bell
J.
Zelenika
D.
et al.  
Gene-environment interactions in 7610 women with breast cancer: prospective evidence from the Million Women Study
Lancet
 
2010
375
2143
2151
19
Milne
R.L.
Gaudet
M.M.
Spurdle
A.B.
Fasching
P.A.
Couch
F.J.
Benitez
J.
Arias Perez
J.I.
Zamora
M.P.
Malats
N.
Dos Santos Silva
I.
et al.  
Assessing interactions between the associations of common genetic susceptibility variants, reproductive history and body mass index with breast cancer risk in the breast cancer association consortium: a combined case-control study
Breast Cancer Res.
 
2010
12
R110
20
Prentice
R.L.
Huang
Y.
Hinds
D.A.
Peters
U.
Pettinger
M.
Cox
D.R.
Beilharz
E.
Chlebowski
R.T.
Rossouw
J.E.
Caan
B.
et al.  
Variation in the FGFR2 gene and the effects of postmenopausal hormone therapy on invasive breast cancer
Cancer Epidemiol. Biomarkers Prev.
 
2009
18
3079
3085
21
Campa
D.
Kaaks
R.
Le Marchand
L.
Haiman
C.A.
Travis
R.C.
Berg
C.D.
Buring
J.E.
Chanock
S.J.
Diver
W.R.
Dostal
L.
et al.  
Interactions between genetic variants and breast cancer risk factors in the breast and prostate cancer cohort consortium
J. Natl. Cancer Inst.
 
2011
103
1252
1263
22
Nickels
S.
Truong
T.
Hein
R.
Stevens
K.
Buck
K.
Behrens
S.
Eilber
U.
Schmidt
M.
Haberle
L.
Vrieling
A.
et al.  
Evidence of gene-environment interactions between common breast cancer susceptibility loci and established environmental risk factors
PLoS Genet.
 
2013
9
e1003284
23
Sueta
A.
Ito
H.
Kawase
T.
Hirose
K.
Hosono
S.
Yatabe
Y.
Tajima
K.
Tanaka
H.
Iwata
H.
Iwase
H.
et al.  
A genetic risk predictor for breast cancer using a combination of low-penetrance polymorphisms in a Japanese population
Breast Cancer Res. Treat.
 
2012
132
711
721
24
Fletcher
O.
Johnson
N.
Orr
N.
Hosking
F.J.
Gibson
L.J.
Walker
K.
Zelenika
D.
Gut
I.
Heath
S.
Palles
C.
et al.  
Novel breast cancer susceptibility locus at 9q31.2: results of a genome-wide association study
J. Natl. Cancer Inst.
 
2011
103
425
435
25
Cai
Q.
Long
J.
Lu
W.
Qu
S.
Wen
W.
Kang
D.
Lee
J.Y.
Chen
K.
Shen
H.
Shen
C.Y.
et al.  
Genome-wide association study identifies breast cancer risk variant at 10q21.2: results from the Asia Breast Cancer Consortium
Hum. Mol. Genet.
 
2011
20
4991
4999
26
Palmer
J.R.
Ruiz-Narvaez
E.A.
Rotimi
C.N.
Cupples
L.A.
Cozier
Y.C.
Adams-Campbell
L.L.
Rosenberg
L.
Genetic susceptibility Loci for subtypes of breast cancer in an African American population
Cancer Epidemiol. Biomarkers Prev.
 
2013
22
127
134
27
Milne
R.L.
Benitez
J.
Nevanlinna
H.
Heikkinen
T.
Aittomaki
K.
Blomqvist
C.
Arias
J.I.
Zamora
M.P.
Burwinkel
B.
Bartram
C.R.
et al.  
Risk of estrogen receptor-positive and -negative breast cancer and single-nucleotide polymorphism 2q35-rs13387042
J. Natl. Cancer Inst.
 
2009
101
1012
1018
28
Broeks
A.
Schmidt
M.K.
Sherman
M.E.
Couch
F.J.
Hopper
J.L.
Dite
G.S.
Apicella
C.
Smith
L.D.
Hammet
F.
Southey
M.C.
et al.  
Low penetrance breast cancer susceptibility loci are associated with specific breast tumor subtypes: findings from the Breast Cancer Association Consortium
Hum. Mol. Genet.
 
2011
20
3289
3303
29
Thomas
D.
Gene–environment-wide association studies: emerging approaches
Nat. Rev. Genet.
 
2010
11
259
272
30
García-Closas
M.N.
Malats
N.
Silverman
D.
Dosemeci
M.
Kogevinas
M.
Hein
D.W.
Tardón
A.
Serra
C.
Carrato
A.
García-Closas
R.
et al.  
NAT2 slow acetylation and GSTM1 null genotypes increase bladder cancer risk: results from the Spanish Bladder Cancer Study and meta-analyses
Lancet
 
2005
20
649
659
31
Brooks
P.J.
Goldman
D.
Li
T.K.
Alleles of alcohol and acetaldehyde metabolism genes modulate susceptibility to oesophageal cancer from alcohol consumption
Hum. Genomics
 
2009
3
103
105
32
Wu
C.
Kraft
P.
Zhai
K.
Chang
J.
Wang
Z.
Li
Y.
Hu
Z.
He
Z.
Jia
W.
Abnet
C.C.
et al.  
Genome-wide association analyses of esophageal squamous cell carcinoma in Chinese identify multiple susceptibility loci and gene-environment interactions
Nat. Genet.
 
2012
44
1090
1097
33
Whitfield
J.B.
Dy
V.
McQuilty
R.
Zhu
G.
Montgomery
G.W.
Ferreira
M.A.
Duffy
D.L.
Neale
M.C.
Heijmans
B.T.
Heath
A.C.
et al.  
Evidence of genetic effects on blood lead concentration
Environ. Health Perspect.
 
2007
115
1224
1230
34
Chiba
M.
Masironi
R.
Toxic and trace elements in tobacco and tobacco smoke
Bull. World Health Organ.
 
1992
70
269
275
35
Chelchowska
M.
Ambroszkiewicz
J.
Jablonka-Salach
K.
Gajewska
J.
Maciejewski
T.M.
Bulska
E.
Laskowska-Klita
T.
Leibschang
J.
Tobacco smoke exposure during pregnancy increases maternal blood lead levels affecting neonate birth weight
Biol. Trace Elem. Res.
 
2013
155
169
175
36
Alatise
O.I.
Schrauzer
G.N.
Lead exposure: a contributing cause of the current breast cancer epidemic in Nigerian women
Biol. Trace Elem. Res.
 
2010
136
127
139
37
McElroy
J.A.
Shafer
M.M.
Gangnon
R.E.
Crouch
L.A.
Newcomb
P.A.
Urinary lead exposure and breast cancer risk in a population-based case-control study
Cancer Epidemiol. Biomarkers Prev.
 
2008
17
2311
2317
38
Florea
A.M.
Busselberg
D.
Metals and breast cancer: risk factors or healing agents?
J. Toxicol.
 
2011
2011
159619
39
Rashidi
M.
Rameshat
M.H.
Gharib
H.
Rouzbahani
R.
Ghias
M.
Poursafa
P.
The association between spatial distribution of common malignancies and soil lead concentration in Isfahan, Iran
J. Res. Med. Sci.
 
2012
17
348
354
40
Boyle
A.P.
Hong
E.L.
Hariharan
M.
Cheng
Y.
Schaub
M.A.
Kasowski
M.
Karczewski
K.J.
Park
J.
Hitz
B.C.
Weng
S.
et al.  
Annotation of functional variation in personal genomes using RegulomeDB
Genome Res.
 
2012
22
1790
1797
41
Cornelis
M.C.
Tchetgen
E.J.
Liang
L.
Qi
L.
Chatterjee
N.
Hu
F.B.
Kraft
P.
Gene-environment interactions in genome-wide association studies: a comparative study of tests applied to empirical studies of type 2 diabetes
Am. J. Epidemiol.
 
2012
175
191
202
42
Hamza
T.H.
Chen
H.
Hill-Burns
E.M.
Rhodes
S.L.
Montimurro
J.
Kay
D.M.
Tenesa
A.
Kusel
V.I.
Sheehan
P.
Eaaswarkhanth
M.
et al.  
Genome-wide gene-environment study identifies glutamate receptor gene GRIN2A as a Parkinson's disease modifier gene via interaction with coffee
PLoS Genet.
 
2011
7
e1002237
43
Hancock
D.B.
Artigas
M.S.
Gharib
S.A.
Henry
A.
Manichaikul
A.
Ramasamy
A.
Loth
D.W.
Imboden
M.
Koch
B.
McArdle
W.L.
et al.  
Genome-wide joint meta-analysis of SNP and SNP-by-smoking interaction identifies novel loci for pulmonary function
PLoS Genet.
 
2012
8
e1003098
44
Rudolph
A.
Hein
R.
Lindström
S.
Beckmann
L.
Behrens
S.
Liu
J.
Aschard
H.
Bolla
M.K.
Wang
J.
Truong
T.
et al.  
Genetic modifiers of menopausal hormone replacement therapy and breast 1 cancer risk: a genome-wide interaction study
Endocr. Relat. Cancer
 
2013
20
875
887
45
Tang
H.
Wei
P.
Duell
E.J.
Risch
H.A.
Olson
S.H.
Bueno-de-Mesquita
H.B.
Gallinger
S.
Holly
E.A.
Petersen
G.M.
Bracci
P.M.
et al.  
Genes-environment interactions in obesity- and diabetes-associated pancreatic cancer: a GWAS data analysis
Cancer Epidemiol. Biomarkers Prev.
 
2014
23
98
106
46
Manning
A.K.
LaValley
M.
Liu
C.T.
Rice
K.
An
P.
Liu
Y.
Miljkovic
I.
Rasmussen-Torvik
L.
Harris
T.B.
Province
M.A.
et al.  
Meta-analysis of gene-environment interaction: joint estimation of SNP and SNP × environment regression coefficients
Genet. Epidemiol.
 
2011
35
11
18
47
Hutter
C.M.
Mechanic
L.E.
Chatterjee
N.
Kraft
P.
Gillanders
E.M.
Gene-environment interactions in cancer epidemiology: a national cancer institute think tank report
Genet. Epidemiol.
 
2013
37
643
657
48
Antoniou
A.C.
Kartsonaki
C.
Sinilnikova
O.M.
Soucy
P.
McGuffog
L.
Healey
S.
Lee
A.
Peterlongo
P.
Manoukian
S.
Peissel
B.
et al.  
Common alleles at 6q25.1 and 1p11.2 are associated with breast cancer risk for BRCA1 and BRCA2 mutation carriers
Hum. Mol. Genet.
 
2011
20
3304
3321
49
Kraft
P.
Haiman
C.A.
GWAS identifies a common breast cancer risk allele among BRCA1 carriers
Nat. Genet.
 
2010
42
819
820
50
Hunter
D.J.
Riboli
E.
Haiman
C.A.
Albanes
D.
Altshuler
D.
Chanock
S.J.
Haynes
R.B.
Henderson
B.E.
Kaaks
R.
Stram
D.O.
et al.  
A candidate gene approach to searching for low-penetrance breast and prostate cancer genes
Nat. Rev. Cancer
 
2005
5
977
985
51
Riboli
E.
Hunt
K.J.
Slimani
N.
Ferrari
P.
Norat
T.
Fahey
M.
Charrondiere
U.R.
Hemon
B.
Casagrande
C.
Vignat
J.
et al.  
European Prospective Investigation into Cancer and Nutrition (EPIC): study populations and data collection
Public Health Nutr.
 
2002
5
1113
1124
52
Anderson
G.
Cummings
S.
Freedman
L.S.
Furberg
C.
Henderson
M.
Johnson
S.R.
Kuller
L.
Manson
J.
Oberman
A.
Prentice
R.L.
Rossouw
J.E.
Design of the Women's Health Initiative Clinical Trial and Observational Study
Control. Clin. Trials
 
1998
19
61
109
53
Giles
G.G.
English
D.R.
The Melbourne Collaborative Cohort Study
IARC Sci. Publ.
 
2002
156
69
70
54
Colditz
G.A.
Hankinson
S.E.
The Nurses’ Health Study: lifestyle and health among women
Nat. Rev. Cancer
 
2005
5
388
396
55
Rexrode
K.
Lee
I.
Cook
N.
Hennekens
C.H.
Burning
J.E.
Baseline characteristics of participants in the Women's Health Study
J. Womens Health Gend. Based Med.
 
2000
9
19
27
56
Calle
E.E.
Rodriguez
C.
Jacobs
E.J.
Almon
M.L.
Chao
A.
McCullough
M.L.
Feigelson
H.S.
Thun
M.J.
The American Cancer Society Cancer Prevention Study II Nutrition Cohort: rationale, study design, and baseline characteristics
Cancer
 
2002
94
2490
2501
57
Hayes
R.B.
Reding
D.
Kopp
W.
Subar
A.F.
Bhat
N.
Rothman
N.
Caporaso
N.
Ziegler
R.G.
Johnson
C.C.
Weissfeld
J.L.
et al.  
Etiologic and early marker studies in the prostate, lung, colorectal and ovarian (PLCO) cancer screening trial
Controlled Clinical Trials
 
2000
21
349
355
58
Kolonel
L.N.
Henderson
B.E.
Hankin
J.H.
Nomura
A.M.Y.
Wilkens
L.R.
Pike
M.C.
Stram
D.O.
Monroe
K.R.
Earle
M.E.
Nagamine
F.S.
A Multiethnic Cohort in Hawaii and Los Angeles: Baseline Characteristics
Am. J. Epidemiol.
 
2000
151
346
357
59
Michailidou
K.
Hall
P.
Gonzalez-Neira
A.
Ghoussaini
M.
Dennis
J.
Milne
R.L.
Schmidt
M.K.
Chang-Claude
J.
Bojesen
S.E.
Bolla
M.K.
et al.  
Large-scale genotyping identifies 41 new loci associated with breast cancer risk
Nat. Genet.
 
2013
45
353
361
361e—2
60
Gao
X.
Starmer
J.
Martin
E.R.
A multiple testing correction method for genetic association studies using correlated single nucleotide polymorphisms
Genet. Epidemiol.
 
2008
32
361
369