A genome-wide gene-environment interaction study of breast cancer risk for women of European ancestry
verfasst von:
Pooja Middha, Xiaoliang Wang, Sabine Behrens, Manjeet K. Bolla, Qin Wang, Joe Dennis, Kyriaki Michailidou, Thomas U. Ahearn, Irene L. Andrulis, Hoda Anton-Culver, Volker Arndt, Kristan J. Aronson, Paul L. Auer, Annelie Augustinsson, Thaïs Baert, Laura E. Beane Freeman, Heiko Becher, Matthias W. Beckmann, Javier Benitez, Stig E. Bojesen, Hiltrud Brauch, Hermann Brenner, Angela Brooks-Wilson, Daniele Campa, Federico Canzian, Angel Carracedo, Jose E. Castelao, Stephen J. Chanock, Georgia Chenevix-Trench, Emilie Cordina-Duverger, Fergus J. Couch, Angela Cox, Simon S. Cross, Kamila Czene, Laure Dossus, Pierre-Antoine Dugué, A. Heather Eliassen, Mikael Eriksson, D. Gareth Evans, Peter A. Fasching, Jonine D. Figueroa, Olivia Fletcher, Henrik Flyger, Marike Gabrielson, Manuela Gago-Dominguez, Graham G. Giles, Anna González-Neira, Felix Grassmann, Anne Grundy, Pascal Guénel, Christopher A. Haiman, Niclas Håkansson, Per Hall, Ute Hamann, Susan E. Hankinson, Elaine F. Harkness, Bernd Holleczek, Reiner Hoppe, John L. Hopper, Richard S. Houlston, Anthony Howell, David J. Hunter, Christian Ingvar, Karolin Isaksson, Helena Jernström, Esther M. John, Michael E. Jones, Rudolf Kaaks, Renske Keeman, Cari M. Kitahara, Yon-Dschun Ko, Stella Koutros, Allison W. Kurian, James V. Lacey, Diether Lambrechts, Nicole L. Larson, Susanna Larsson, Loic Le Marchand, Flavio Lejbkowicz, Shuai Li, Martha Linet, Jolanta Lissowska, Maria Elena Martinez, Tabea Maurer, Anna Marie Mulligan, Claire Mulot, Rachel A. Murphy, William G. Newman, Sune F. Nielsen, Børge G. Nordestgaard, Aaron Norman, Katie M. O’Brien, Janet E. Olson, Alpa V. Patel, Ross Prentice, Erika Rees-Punia, Gad Rennert, Valerie Rhenius, Kathryn J. Ruddy, Dale P. Sandler, Christopher G. Scott, Mitul Shah, Xiao-Ou Shu, Ann Smeets, Melissa C. Southey, Jennifer Stone, Rulla M. Tamimi, Jack A. Taylor, Lauren R. Teras, Katarzyna Tomczyk, Melissa A. Troester, Thérèse Truong, Celine M. Vachon, Sophia S. Wang, Clarice R. Weinberg, Hans Wildiers, Walter Willett, Stacey J. Winham, Alicja Wolk, Xiaohong R. Yang, M. Pilar Zamora, Wei Zheng, Argyrios Ziogas, Alison M. Dunning, Paul D. P. Pharoah, Montserrat García-Closas, Marjanka K. Schmidt, Peter Kraft, Roger L. Milne, Sara Lindström, Douglas F. Easton, Jenny Chang-Claude, CTS Consortium, ABCTB Investigators, kConFab Investigators
Genome-wide studies of gene–environment interactions (G×E) may identify variants associated with disease risk in conjunction with lifestyle/environmental exposures. We conducted a genome-wide G×E analysis of ~ 7.6 million common variants and seven lifestyle/environmental risk factors for breast cancer risk overall and for estrogen receptor positive (ER +) breast cancer.
Methods
Analyses were conducted using 72,285 breast cancer cases and 80,354 controls of European ancestry from the Breast Cancer Association Consortium. Gene–environment interactions were evaluated using standard unconditional logistic regression models and likelihood ratio tests for breast cancer risk overall and for ER + breast cancer. Bayesian False Discovery Probability was employed to assess the noteworthiness of each SNP-risk factor pairs.
Results
Assuming a 1 × 10–5 prior probability of a true association for each SNP-risk factor pairs and a Bayesian False Discovery Probability < 15%, we identified two independent SNP-risk factor pairs: rs80018847(9p13)-LINGO2 and adult height in association with overall breast cancer risk (ORint = 0.94, 95% CI 0.92–0.96), and rs4770552(13q12)-SPATA13 and age at menarche for ER + breast cancer risk (ORint = 0.91, 95% CI 0.88–0.94).
Conclusions
Overall, the contribution of G×E interactions to the heritability of breast cancer is very small. At the population level, multiplicative G×E interactions do not make an important contribution to risk prediction in breast cancer.
Douglas F. Easton, Jenny Chang-Claude Joint senior authors.
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Abkürzungen
BMI
Body mass index
COGS
Collaborative Oncological Gene-Environment Study
SNPs
Single nucleotide polymorphisms
GWAS
Genome-wide association study
GEWIS
Genome-wide gene-environment interaction study
G×E
Gene–environment interaction
BCAC
Breast Cancer Association Consortium
ER +
Estrogen receptor positive
ER-
Estrogen receptor negative
Q–Q
Quantile–quantile
BFDP
Bayesian false discovery probability
MAF
Minor allele frequency
ORint
Interaction odds ratio
CI
Confidence intervals
ORmeta
Meta-analyzed odds ratio
LD
Linkage disequilibrium
IGF1
Insulin-like growth factor 1
LINGO2
Leucine rich repeat and Ig domain containing 2
SPATA13
Spermatogenesis associated 13
MKL1
Megakaryoblastic leukemia 1
LDSC
Linkage disequilirium score regression
Background
Breast cancer is a complex disease involving interplay between lifestyle/environmental and genetic risk factors. Risk factors such as parity, breastfeeding, age at menarche, age at first full-term pregnancy, body mass index (BMI), height, mammographic density, exogenous hormonal use, and alcohol consumption are well-established [1‐7]. Through continued collaborative efforts such as the Collaborative Oncological Gene-environment Study (COGS) and the OncoArray project [8], more than 200 common single nucleotide polymorphisms (SNPs) associated with risk of breast cancer have been identified [9‐11].
Traditional genome-wide association study (GWAS) analyses assess the marginal effects of variants and might miss variants which only show an effect within certain strata in the population. These potential gene–environment interactions where SNPs are associated with disease risk in conjunction with lifestyle/environmental risk factors can be investigated through genome-wide gene-environment interaction studies (GEWIS) [12‐15].
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Very few genome-wide studies of gene-environment (G×E) interactions in breast cancer have been conducted to date, and three focused on the use of menopausal hormonal therapy as the single environmental risk factor [16‐18]. An exploratory analysis of G×E interactions examined ten environmental risk factors and 71,527 SNPs selected from prior evidence, using data from approximately 35,000 cases and controls in the Breast Cancer Association Consortium (BCAC). That study identified two potential G×E interactions associated with breast cancer risk [19]. In the present study, we performed a comprehensive genome-wide analysis of gene–environment interactions for risk of overall breast cancer, as well as estrogen receptor positive (ER +) breast cancer using data from 72,285 cases and 80,354 controls participating in the BCAC.
Methods
Study sample
Analyses were conducted using data from 46 studies (16 prospective cohorts, 14 population-based case–control studies, and 16 non-population based studies) participating in the BCAC. We excluded participants if they were genotypically male, of non-European descent, or had a breast tumor of unknown invasiveness or in-situ breast cancer. Women with prevalent breast cancer at the time of recruitment or with unknown reference age (defined as age at diagnosis for cases and age at interview for controls) were also excluded from the analyses. Further, studies with fewer than 150 cases and 150 controls for the risk factor under evaluation were excluded from those analyses. Each participating study obtained informed consent from the participants and was approved by their local ethics committee.
Risk factor data
Risk factor data from individual studies was checked for quality using a multi-step harmonization process based on a common data dictionary. Time-dependent risk factor variables were derived with respect to the reference date defined as date of diagnosis for cases and date of interview for controls. Analyses were conducted with the following risk factors among all women: age at menarche (per 2 years), parity (per 1 birth), adult height (per 5 cm), ever use of oral contraceptives (yes/no), and current smoking (yes/no). The analysis of age at first full-term pregnancy (per 5 years) was conducted among parous women only, and that of body mass index (BMI, per 5 kg/m2) was conducted among postmenopausal women only. Menopausal status was either self-reported or assigned as postmenopausal if the reference age was greater than 54 years.
Genetic data
All samples were genotyped either using the iCOGS [20, 21] or OncoArray [9, 10, 22]. Briefly, iCOGS is a customized iSelect SNP genotyping array, consisting of ~ 211,000 SNPs [20, 21], whereas OncoArray includes ~ 533,000 SNPs of which nearly 260,000 were selected as a GWAS backbone (Illumina HumanCore) [22]. Detailed information is provided elsewhere [9, 10, 20‐22]. Data were imputed to the 1000 Genomes Reference Panel (phase 3 version 5). Overall, 28,176 cases and 32,209 controls of European ancestry who were genotyped by the iCOGS array, and 44,109 cases and 48,145 controls who were genotyped using the OncoArray array were included in this analysis.
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Genetic variants with imputation quality score < 0.5 in iCOGS or < 0.8 in OncoArray, or with minor allele frequency < 0.01, were excluded from the analyses. Variants in known breast cancer regions were also excluded from the analysis since interactions between known susceptibility variants and risk factors have been explored previously [23, 24]. After applying all exclusions, 7,672,870 genetic variants (SNPs and indels) were included in the analysis.
Statistical analysis
Unconditional logistic regression was employed to assess the associations of SNPs and risk factors with breast cancer risk. Genotypes were assessed using the expected number of copies of the alternative allele (‘dosage’) as the covariate under a log-additive model. Interactions between genetic variants and risk factors were tested by comparing the fit of logistic regression models with and without an interaction term using likelihood ratio tests. All models were adjusted for reference age, study, and ten ancestry-informative principal components. To account for potential differential main effects of risk factors by study design, all models included an interaction term between risk factor and an indicator variable for study design (population-based vs. non-population based). Analyses with current smoking were further adjusted for former smoking.
Analyses were performed separately for overall and ER + breast cancer risk, and also separately by genotyping array. Array-specific results were combined using METAL [25]. Quantile–quantile (Q-Q) plots were assessed to examine the consistency of the distribution of p-values with the null distribution. Interaction P value less than 5E-07 was considered suggestive evidence of interaction. We also calculated Bayesian False Discovery Probabilities (BFDP) for all suggestive interactions, assuming a 1 × 10–5 prior probability of a true association for each SNP-risk factor pair. Overall, G×E interactions with BFDP < 15% were considered noteworthy [26]. For noteworthy SNP-risk pairs, we evaluated the G×E interaction also for ER-negative breast cancer risk. For noteworthy interactions, we conducted stratified analyses by categories of the risk factor. All analyses were conducted using R version 3.5.1.
We estimated the overall genome-wide contribution of G×E associations for each risk factor to the familial relative risk of breast cancer using LD score regression [27]. The analysis used the G×E interaction summary statistics and was restricted to HapMap3 SNPs with MAF > 5% in European population from the 1000 Genomes Project. Under the log-additive model, the G×E heritability on the frailty scale can be estimated by hf2 = hobs2 × var(X)/P(1-P), where hobs2 is the observed heritability given by LD score regression, var(X) is the variance of the risk factor under evaluation, and P is the proportion of cases in the sample. The proportion of the familial relative risk (FRR) of breast cancer due to G×E interactions is then given by hf2/2log(λ) where λ is the familial relative risk to first degree relatives of cases (assumed to be 2) [28].
Results
Studies included in the analysis are summarized in Additional file 1: Table S1. The number of cases and controls in each analysis varied from 61,617 cases and 74,698 controls for parity to 48,276 cases and 60,587 controls for current smoking (Additional file 1: Table S2). Consistent with the literature, increasing age at first full-term pregnancy, higher adult height, ever use of oral contraceptives, and current smoking were associated with increased overall breast cancer risk, whereas increasing age at menarche, being parous, increasing number of full-term pregnancies, and breast feeding were associated with decreased breast cancer risk (Additional file 1: Table S3).
The genome-wide analysis of interactions with seven environmental risk factors yielded two SNP-risk factor pairs at BFDP < 15%, one for risk of overall breast cancer and one for ER + breast cancer risk (Table 1, Fig. 1, 2, Additional file 1: Figure S1A-S1B). No inflation in the test statistics was observed for either of the environmental risk factors. The heritability on the frailty scale of breast cancer risk explained by G×E interaction is shown in Additional file 1: Figure S2. The estimated proportion of the frailty scale heritability explained by G×E interactions was very low for all factors, being highest for age at first full-term pregnancy (~ 1.5% for both overall and ER + breast cancer risk), age at menarche and post-menopausal BMI.
Table 1
Genetic variants with suggestive (Pint ≤ 5E−07) GxE interactions for overall and estrogen receptor positive (ER +) breast cancer risk
Risk factor
SNP
Chr
Position1
Alleles2
EAF3
ORmarg
(95% CI)
Pmarg
ORint
(95% CI)
Pint
BFDP4
Overall breast cancer risk
Number of full-term pregnancies (per 1 birth)
rs10928872
2
129,833,111
A/T
0.08
1.00
(0.97–1.03)
0.97
0.93
(0.91–0.96)
1.34E-07
0.98
Number of full-term pregnancies (per 1 birth)
rs36064687
2
129,832,988
AT/A
0.08
1.00
(0.97–1.04)
0.99
0.93
(0.91–0.96)
1.34E-07
0.98
Number of full-term pregnancies (per 1 birth)
rs79929694
2
129,841,483
A/G
0.08
1.00
(0.96–1.03)
0.96
0.93
(0.91–0.96)
1.43E-07
0.98
Number of full-term pregnancies (per 1 birth)
rs77107485
2
129,843,663
T/G
0.08
1.00
(0.97–1.04)
0.89
0.93
(0.91–0.96)
2.79E-07
0.98
Number of full-term pregnancies (per 1 birth)
rs79722231
2
129,834,483
T/C
0.08
1.00
(0.97–1.04)
0.92
0.93
(0.91–0.96)
2.80E-07
0.98
Age at menarche (per 2 years)
rs73277506
7
21,112,413
C/T
0.02
1.03
(0.96–1.10)
0.39
1.26
(1.16–1.38)
1.46E-07
0.75
Current smoking (yes/no)
rs11322161
8
105,120,195
GC/G
0.29
1.00
(0.98–1.02)
0.63
1.19
(1.12–1.27)
9.26E-08
0.49
Adult height (per 5 cm)
rs80018847
9
28,326,896
A/G
0.14
1.00
(0.98–1.03)
0.88
0.94
(0.92–0.96)
4.34E-08
0.11
Adult height (per 5 cm)
rs1360506
9
28,339,154
G/C
0.16
1.00
(0.97–1.03)
0.96
0.95
(0.93–0.98)
2.13E-07
1.00
Adult height (per 5 cm)
rs1237669
17
41,912,024
T/G
0.91
1.01
(0.98–1.04)
0.62
0.94
(0.91–0.96)
5.00E-07
0.16
OC use (yes/no)
rs147290549
18
7,713,860
C/T
0.50
1.01
(0.99–1.02)
0.23
1.10
(1.06–1.14)
4.55E-07
0.59
OC use (yes/no)
rs664040
18
7,716,250
C/A
0.50
1.01
(0.99–1.02)
0.22
1.10
(1.06–1.14)
4.93E-07
0.59
Current smoking
(yes/no)
rs75489324
21
26,302,665
G/C
0.02
0.98
(0.92–1.05)
0.87
0.58
(0.47–0.72)
4.58E-07
0.94
ER + breast cancer risk
Age at menarche (per 2 years)
rs73277506
7
21,112,413
C/T
0.02
1.04
(0.97–1.12)
0.25
1.28
(1.17–1.41)
3.94E-07
0.76
Current smoking (yes/no)
rs11322161
8
105,120,195
GC/G
0.29
1.00
(0.98–1.02)
0.95
1.20
(1.12–1.29)
4.32E-07
0.81
Age at menarche (per 2 years)
rs4770552
13
24,594,430
C/T
0.86
1.02
(1.00–1.05)
0.10
0.91
(0.88–0.94)
4.62E-08
0.11
Age at menarche (per 2 years)
rs113684695
13
24,600,947
ACCTCGT
GATCCGC/A
0.86
1.02
(1.00–1.05)
0.11
0.91
(0.88–0.94)
5.48E-08
0.11
Age at menarche (per 2 years)
rs9510997
13
24,592,857
G/T
0.86
1.02
(1.00–1.05)
0.10
0.91
(0.88–0.94)
6.26E-08
0.11
Age at menarche (per 2 years)
rs7321200
13
24,593,519
A/G
0.87
1.01
(0.99–1.04)
0.33
0.91
(0.87–0.94)
7.92E-08
0.11
Age at menarche (per 2 years)
rs4770553
13
24,595,995
C/T
0.85
1.02
(0.99–1.04)
0.22
0.92
(0.89–0.95)
4.31E-07
0.76
Age at menarche (per 2 years)
rs9551041
13
24,601,810
A/G
0.85
1.02
(0.99–1.04)
0.21
0.92
(0.89–0.95)
4.58E-07
0.76
Age at menarche (per 2 years)
rs1886805
13
24,602,494
G/A
0.85
1.02
(0.99–1.04)
0.21
0.92
(0.89–0.95)
4.62E-07
0.76
Age at menarche (per 2 years)
rs4770555
13
24,601,751
T/C
0.85
1.02
(0.99–1.04)
0.21
0.92
(0.89–0.95)
4.63E-07
0.76
Age at menarche (per 2 years)
rs4770556
13
24,601,760
A/G
0.85
1.02
(0.99–1.04)
0.21
0.92
(0.89–0.95)
4.68E-07
0.76
Age at menarche (per 2 years)
rs1886804
13
24,602,174
T/G
0.85
1.02
(0.99–1.04)
0.21
0.92
(0.89–0.95)
4.72E-07
0.76
Age at menarche (per 2 years)
rs1886803
13
24,602,165
T/C
0.85
1.02
(0.99–1.04)
0.21
0.92
(0.89–0.95)
4.72E-07
0.76
Age at menarche (per 2 years)
rs9553145
13
24,602,061
G/C
0.85
1.02
(0.99–1.04)
0.21
0.92
(0.89–0.95)
4.75E-07
0.76
Age at menarche (per 2 years)
rs4769305
13
24,592,448
T/C
0.85
1.02
(0.99–1.05)
0.17
0.92
(0.89–0.95)
4.76E-07
0.76
Age at menarche (per 2 years)
rs9553144
13
24,601,943
C/G
0.85
1.02
(0.99–1.04)
0.21
0.92
(0.89–0.95)
4.78E-07
0.76
Number of full-term pregnancies (per 1 birth)
rs930198
23
79,492,077
T/C
0.24
1.00
(0.98–1.02)
0.93
0.96
(0.94–0.97)
3.10E-07
0.99
SNP: Single Nucleotide Polymorphism; Chr: Chromosome; Ref: Reference Allele; Alt: Alternative Allele; EAF: Effect Allele Frequency; ORmarg: SNP Marginal Odds ratio; CI: Confidence Interval; Pmarg: Marginal p-value (meta-analyzed); ORint: Interaction p-value (meta-analyzed); Pint: Interaction p-value; BFDP: Bayesian False Discovery Probability; ABF: Approximate Bayes Probability; OC use: Ever use of oral contraceptives; ER + : Estrogen receptor positive breast cancer risk
1Build 37 Position
2Reference/Alternate alleles in Europeans (forward strand)
3Effect allele frequency in controls in OncoArray dataset
4Bayesian False Discovery Probability at prior probability of 1 × 10–05
Fig. 1
Manhattan plot of genome-wide interactions of adult height on overall breast cancer risk. The genome-wide significance threshold of P < 5 x 10−8 is indicated by the dashed black line. Genome-wide significant findings are highlighted in blue
Fig. 2
Manhattan plot of genome-wide interaction of age at menarche for ER + breast cancer risk. The genome-wide significance threshold of P < 5 x 10−8 is indicated by the dashed black line. Genome-wide significant findings are highlighted in blue.
×
×
For overall breast cancer risk, there was evidence of interaction between SNP rs80018847 and adult height (ORint = 0.94, 95% CI 0.92–0.96, Pint = 4.34E−08, BFDP = 11%) without an apparent marginal effect of the rs80018847 variant (ORmarg = 1.00, 95% CI 0.98–1.03, Pmarg = 0.88). By categories of adult height defined a priori, the estimated per allele ORmeta of rs80018847-G varied from 1.03 (95% CI 0.94–1.13, Pmeta = 0.53) for women shorter than 158 cm, 1.13 (1.02–1.25) for women 158–162 cm in height, to ORmeta of 1.01 (95% CI 0.93–1.09, Pmeta = 0.88) for women who were 168 cm or taller risk (Additional file 1: Table S4). Therefore, there is no linear relationship between the SNP and categories of adult height. The interaction with height was also observed for ER + breast cancer (ORint 0.95, 95% CI 0.93–0.97, Pint = 5.62E-06) but not for ER negative (ER-) breast cancer risk (ORint = 0.98, 95% CI 0.93–1.03, Pint = 0.77). The regional plot for overall breast cancer shows another SNP (rs1360506) at this locus in high linkage disequilibrium (LD) (r2 = 0.81) with rs80018847 (Additional file 1: Figure S3).
For risk of ER + breast cancer, a statistically significant interaction was observed between SNP rs4770552 and age at menarche (ORint = 0.91, 95% CI 0.88–0.94, Pint = 4.62E−08, BFDP = 11%). There was weak evidence for a marginal association between the rs4770552-T allele and ER + breast cancer (ORmarg = 1.02, 95% CI 1.00–1.05, Pmarg = 0.10). The per allele ORmeta appeared to decrease with increasing age at menarche, from 1.07 (95% CI 1.00–1.15, Pmeta = 0.04) for age at menarche less than 13 years to 0.92 (95% CI 0.77–1.09, Pmeta = 0.33) for age at menarche greater than 15 years (Additional file 1: Table S4). There was weaker evidence of interaction between SNP rs4770552 and age at menarche for overall breast cancer risk (ORint = 0.93, 95% CI 0.90–0.96, Pint = 5.47E−06), but no interaction for ER- breast cancer risk (ORint = 0.98, 95% CI 0.89–1.08), Pint = 0.66). At this locus, we found suggestive evidence of interactions between further 13 SNPs and age at menarche for ER + breast cancer risk. However, these 13 SNPs are in high LD (r2 = 0.8–1.0) with SNP rs4770552 (Additional file 1: Figure S4).
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Discussion
This is the largest genome-wide gene-environment interaction study for breast cancer to date. We found evidence of one novel susceptibility loci interacting with adult height associated with increased breast cancer risk overall, and one interaction for increased risk of ER + breast cancer with age at menarche. It is important to note, however, that while these associations reached conventional levels of genome-wide statistical significance, they may still represent chance associations. Based on the assumed prior distribution of effect sizes, the BFDP for both loci were 11%, considered noteworthy. Nevertheless, studies with an even larger sample size are required to confirm or refute these associations.
Many observational studies have shown an association between increasing adult height and increased breast cancer risk, in both premenopausal and postmenopausal women [7, 29, 30]. A meta-analysis estimated that each 10 cm increment in height was associated with a 17% increase in breast cancer risk [31]. The biological link between height and breast cancer is poorly understood, but some studies have suggested that increased height corresponds to more stem cells at risk of acquiring driver mutations [32]. Another hypothesis is that adult height could be a surrogate for nutritional intake, potentially implying a role for insulin-like growth factor 1 (IGF1) [33]. The functional basis of the potential interaction between adult height and the SNP rs80018847 is unclear. This SNP is in an intronic region of the leucine rich repeat and Ig domain containing 2 gene (LINGO2) on the short arm of chromosome 9 (9p13). This gene encodes a transmembrane protein belonging to the LINGO/LERN protein family [34]. Studies in mouse embryos have shown expression of LINGO2 specifically in the central nervous system [34], but it has not been implicated in breast cancer to date.
Early age at menarche is known to be associated with elevated risk of breast cancer. There is an approximate 5% decrease in risk with each year delay in the initiation of menstruation [35]. It has been postulated that younger age at menarche corresponds to longer cumulative hormonal exposure and therefore elevated levels of estradiol [3, 36]. SNP rs4770552 is an intronic variant within the spermatogenesis associated 13 gene (SPATA13) at 13q12. SPATA13 encodes a guanine nucleotide exchange factor (GEF) for RhoA, Rac1 and CDC42 GTPases [37, 38]. Although the role of this gene in breast cancer is still unclear, there could be an indirect link via the role of RhoA GTPases in breast tumorigenesis. Rho GTPase signaling is altered in human breast cancers, and dysregulation of Rho GTPase may have differential effects on the development of breast tumors depending on the stage and subtype [39]. Activation of RhoA results in release of megakaryoblastic leukemia 1 (MKL1), which in turn has been observed to alter the transcriptional activity of ERα, known to play a critical role in breast tumors [40]. Therefore, SNP rs4770552 may potentially indirectly interact with the regulatory region of SPATA13 and affect the breast tumorigenesis process via activation of RHoA GTPases.
Given that the marginal effects of the common genetic variants are small and the associations of environmental risk factors with breast cancer are modest, interactions are also expected to be weak (Additional file 1: Figure S5). Although this is the largest breast cancer dataset available to date with more than 60,000 cases and 70,000 controls, the study is underpowered to detect weak interactions. Also, this study included only women of European ancestry and the findings may not be generalizable to women of other ancestries.
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Using LDSC regression, we estimated the overall heritability due to G×E for each of the risk factors. The estimated frailty scale heritability (≤ 0.015) can be compared with corresponding heritability for the SNP main effects (for which heritability is about 0.47) or the overall heritability based on the familial risk (~ 1.4) [28, 41]. The implication is that G×E interactions make very little contribution to the heritability of breast cancer, at least for the known risk factors and common genetic variants that can be evaluated using genome-wide arrays, and hence do not make an important contribution to risk prediction at the population level. This is consistent with the fact that detection of G×E interactions is rare. This does not rule out the possibility that G×E interactions could be identified in additional large studies or that such interactions may provide important clues to mechanisms.
Conclusions
In conclusion, we identified two novel genome-wide gene–environment interactions for overall and ER + breast cancer risk for women of European ancestry. These results contribute to our global body of knowledge on genetic susceptibility for breast cancer by generating plausible biological hypotheses, but they require replication and further functional studies.
Acknowlegements
We thank all the individuals who took part in these studies and all the researchers, clinicians, technicians and administrative staff who have enabled this work to be carried out. ABCFS thank Maggie Angelakos, Judi Maskiell, Gillian Dite. ABCS thanks the Blood bank Sanquin, The Netherlands. ABCTB Investigators: Christine Clarke, Deborah Marsh, Rodney Scott, Robert Baxter, Desmond Yip, Jane Carpenter, Alison Davis, Nirmala Pathmanathan, Peter Simpson, J. Dinny Graham, Mythily Sachchithananthan. Samples are made available to researchers on a non-exclusive basis. BCEES thanks Allyson Thomson, Christobel Saunders, Terry Slevin, BreastScreen Western Australia, Elizabeth Wylie, Rachel Lloyd. The BCINIS study would not have been possible without the major contribution of Ms. H. Rennert, and the contributions of Dr. M. Pinchev, Dr. O. Barnet, Dr. N. Gronich, Dr. K. Landsman, Dr. A. Flugelman, Dr. W. Saliba, Dr. E. Liani, Dr. I. Cohen, Dr. S. Kalet, Dr. V. Friedman of the NICCC in Haifa, and all the contributing family medicine, surgery, pathology and oncology teams in all medical institutes in Northern Israel. The BREOGAN study would not have been possible without the contributions of the following: Manuela Gago-Dominguez, Jose Esteban Castelao, Angel Carracedo, Victor Muñoz Garzón, Alejandro Novo Domínguez, Maria Elena Martinez, Sara Miranda Ponte, Carmen Redondo Marey, Maite Peña Fernández, Manuel Enguix Castelo, Maria Torres, Manuel Calaza (BREOGAN), José Antúnez, Máximo Fraga and the staff of the Department of Pathology and Biobank of the University Hospital Complex of Santiago-CHUS, Instituto de Investigación Sanitaria de Santiago, IDIS, Xerencia de Xestion Integrada de Santiago-SERGAS; Joaquín González-Carreró and the staff of the Department of Pathology and Biobank of University Hospital Complex of Vigo, Instituto de Investigacion Biomedica Galicia Sur, SERGAS, Vigo, Spain. CBCS thanks study participants, co-investigators, collaborators and staff of the Canadian Breast Cancer Study, and project coordinators Agnes Lai and Celine Morissette. CGPS thanks staff and participants of the Copenhagen General Population Study. For the excellent technical assistance: Dorthe Uldall Andersen, Maria Birna Arnadottir, Anne Bank, Dorthe Kjeldgård Hansen. The Danish Cancer Biobank is acknowledged for providing infrastructure for the collection of blood samples for the cases. CNIO-BCS thanks Guillermo Pita, Charo Alonso, Nuria Álvarez, Pilar Zamora, Primitiva Menendez, the Human Genotyping-CEGEN Unit (CNIO). Investigators from the CPS-II cohort thank the participants and Study Management Group for their invaluable contributions to this research. They also acknowledge the contribution to this study from central cancer registries supported through the Centers for Disease Control and Prevention National Program of Cancer Registries, as well as cancer registries supported by the National Cancer Institute Surveillance Epidemiology and End Results program. The authors would like to thank the California Teachers Study Steering Committee that is responsible for the formation and maintenance of the Study within which this research was conducted. A full list of California Teachers Study (CTS) team members is available at https://www.calteachersstudy.org/team. We thank the participants and the investigators of EPIC (European Prospective Investigation into Cancer and Nutrition). ESTHER thanks Hartwig Ziegler, Sonja Wolf, Volker Hermann, Christa Stegmaier, Katja Butterbach. PROCAS thank NIHR for funding. The GENICA Network: Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, and University of Tübingen, Germany [HB, RH, Wing-Yee Lo], Department of Internal Medicine, Johanniter GmbH Bonn, Johanniter Krankenhaus, Bonn, Germany [YDK, Christian Baisch], Institute of Pathology, University of Bonn, Germany [Hans-Peter Fischer], Molecular Genetics of Breast Cancer, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Germany [UH], Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University Bochum (IPA), Bochum, Germany [Thomas Brüning, Beate Pesch, Sylvia Rabstein, Anne Lotz]; and Institute of Occupational Medicine and Maritime Medicine, University Medical Center Hamburg-Eppendorf, Germany [Volker Harth]. KARMA and SASBAC thank the Swedish Medical Research Counsel. kConFab/AOCS wish to thank 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 has received funding from the NHMRC, the National Breast Cancer Foundation, Cancer Australia, and the National Institute of Health (USA)) for their contributions to this resource, and the many families who contribute to kConFab. LMBC thanks Gilian Peuteman, Thomas Van Brussel, EvyVanderheyden and Kathleen Corthouts. MARIE thanks Petra Seibold, Nadia Obi, Ursula Eilber and Muhabbet Celik. The MCCS was made possible by the contribution of many people, including the original investigators, the teams that recruited the participants and continue working on follow-up, and the many thousands of Melbourne residents who continue to participate in the study. We thank the coordinators, the research staff and especially the MMHS participants for their continued collaboration on research studies in breast cancer. The MISS study group acknowledges the former Principal Investigator, professor Håkan Olsson. NBHS and SBCGS thank study participants and research staff for their contributions and commitment to the studies. For NHS and NHS2 the study protocol was approved by the institutional review boards of the Brigham and Women’s Hospital and Harvard T.H. Chan School of Public Health, and those of participating registries as required. We would like to thank the participants and staff of the NHS and NHS2 for their valuable contributions as well as the following state cancer registries for their help: AL, AZ, AR, CA, CO, CT, DE, FL, GA, ID, IL, IN, IA, KY, LA, ME, MD, MA, MI, NE, NH, NJ, NY, NC, ND, OH, OK, OR, PA, RI, SC, TN, TX, VA, WA, WY. The authors assume full responsibility for analyses and interpretation of these data. OBCS thanks Arja Jukkola-Vuorinen, Mervi Grip, Saila Kauppila, Meeri Otsukka, Leena Keskitalo and Kari Mononen for their contributions to this study. The OFBCR thanks Teresa Selander, Nayana Weerasooriya and Steve Gallinger. ORIGO thanks E. Krol-Warmerdam, and J. Blom for patient accrual, administering questionnaires, and managing clinical information. PBCS thanks Louise Brinton, Mark Sherman, Neonila Szeszenia-Dabrowska, Beata Peplonska, Witold Zatonski, Pei Chao, Michael Stagner. We thank the SEARCH and EPIC teams. UCIBCS thanks Irene Masunaka. UKBGS thanks Breast Cancer Now and the Institute of Cancer Research for support and funding of the Generations Study, and the study participants, study staff, and the doctors, nurses and other health care providers and health information sources who have contributed to the study. We acknowledge NHS funding to the Royal Marsden/ICR NIHR Biomedical Research Centre. The authors thank the WHI investigators and staff for their dedication and the study participants for making the program possible.
Declarations
Ethics approval and consent to participate.
Each participating study obtained informed consent from the participants and was approved by their local ethics committee.
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Each participating study obtained informed consent from the participants to publish and was approved by their local ethics committee.
Competing Interests
The authors do not have any conflict of interest.
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A genome-wide gene-environment interaction study of breast cancer risk for women of European ancestry
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Pooja Middha Xiaoliang Wang Sabine Behrens Manjeet K. Bolla Qin Wang Joe Dennis Kyriaki Michailidou Thomas U. Ahearn Irene L. Andrulis Hoda Anton-Culver Volker Arndt Kristan J. Aronson Paul L. Auer Annelie Augustinsson Thaïs Baert Laura E. Beane Freeman Heiko Becher Matthias W. Beckmann Javier Benitez Stig E. Bojesen Hiltrud Brauch Hermann Brenner Angela Brooks-Wilson Daniele Campa Federico Canzian Angel Carracedo Jose E. Castelao Stephen J. Chanock Georgia Chenevix-Trench Emilie Cordina-Duverger Fergus J. Couch Angela Cox Simon S. Cross Kamila Czene Laure Dossus Pierre-Antoine Dugué A. Heather Eliassen Mikael Eriksson D. Gareth Evans Peter A. Fasching Jonine D. Figueroa Olivia Fletcher Henrik Flyger Marike Gabrielson Manuela Gago-Dominguez Graham G. Giles Anna González-Neira Felix Grassmann Anne Grundy Pascal Guénel Christopher A. Haiman Niclas Håkansson Per Hall Ute Hamann Susan E. Hankinson Elaine F. Harkness Bernd Holleczek Reiner Hoppe John L. Hopper Richard S. Houlston Anthony Howell David J. Hunter Christian Ingvar Karolin Isaksson Helena Jernström Esther M. John Michael E. Jones Rudolf Kaaks Renske Keeman Cari M. Kitahara Yon-Dschun Ko Stella Koutros Allison W. Kurian James V. Lacey Diether Lambrechts Nicole L. Larson Susanna Larsson Loic Le Marchand Flavio Lejbkowicz Shuai Li Martha Linet Jolanta Lissowska Maria Elena Martinez Tabea Maurer Anna Marie Mulligan Claire Mulot Rachel A. Murphy William G. Newman Sune F. Nielsen Børge G. Nordestgaard Aaron Norman Katie M. O’Brien Janet E. Olson Alpa V. Patel Ross Prentice Erika Rees-Punia Gad Rennert Valerie Rhenius Kathryn J. Ruddy Dale P. Sandler Christopher G. Scott Mitul Shah Xiao-Ou Shu Ann Smeets Melissa C. Southey Jennifer Stone Rulla M. Tamimi Jack A. Taylor Lauren R. Teras Katarzyna Tomczyk Melissa A. Troester Thérèse Truong Celine M. Vachon Sophia S. Wang Clarice R. Weinberg Hans Wildiers Walter Willett Stacey J. Winham Alicja Wolk Xiaohong R. Yang M. Pilar Zamora Wei Zheng Argyrios Ziogas Alison M. Dunning Paul D. P. Pharoah Montserrat García-Closas Marjanka K. Schmidt Peter Kraft Roger L. Milne Sara Lindström Douglas F. Easton Jenny Chang-Claude CTS Consortium ABCTB Investigators kConFab Investigators
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