Abstract
Background
Most genetic variants associated with breast cancer risk have been discovered in women of European ancestry, and only a few genome-wide association studies (GWAS) have been conducted in minority groups. This research disparity persists in post-GWAS gene-environment interaction analyses. We tested the interaction between hormonal and lifestyle risk factors for breast cancer, and ten GWAS-identified single nucleotide polymorphisms (SNPs) among 2,107 Hispanic women with breast cancer and 2,587 unaffected controls, to gain insight into a previously reported gene by ancestry interaction in this population.
Methods
We estimated genetic ancestry with a set of 104 ancestry-informative markers selected to discriminate between Indigenous American and European ancestry. We used logistic regression models to evaluate main effects and interactions.
Results
We found that the rs13387042-2q35(G/A) SNP was associated with breast cancer risk only among postmenopausal women who never used hormone therapy [per A allele odds ratio (OR): 0.94 (95% confidence interval 0.74–1.20), 1.20 (0.94–1.53) and 1.49 (1.28–1.75) for current, former and never hormone therapy users, respectively, P-interaction 0.002] and premenopausal women who breastfed >12 months [OR: 1.01 (0.72–1.42), 1.19 (0.98–1.45) and 1.69 (1.26–2.26) for never, <12 months, and >12 months breastfeeding, respectively, P-interaction 0.014].
Conclusions
The correlation between genetic ancestry, hormone replacement therapy use, and breastfeeding behavior partially explained a previously reported interaction between a breast cancer risk variant and genetic ancestry in Hispanic women.
Impact
These results highlight the importance of understanding the interplay between genetic ancestry, genetics, and non-genetic risk factors and their contribution to breast cancer risk.
Keywords: Breast cancer, Hispanics, Latinas, Gene-environment interaction, Genetic ancestry
Introduction
Breast cancer is a common disease caused by genetic and non-genetic factors (e.g. hormonal and lifestyle factors) and possibly, by the interaction between the two(1). Since the discovery of the high penetrance breast cancer causing genes, BRCA1 and BRCA2, multiple breast cancer genome-wide association studies (GWAS) have unveiled new genetic variants with moderate to small contributions to breast cancer risk−(2–19). When combined, all common genetic variants explain approximately 30% of familial risk(5), and it has been postulated that an important proportion of the unexplained familial risk might be buried within complex gene by gene and gene by environment interactions(1). Most of the risk-associated variants were originally discovered in samples that included women of European ancestry, and, to our knowledge, only four U.S.-based GWAS have been conducted in or included racial/ethnic minority groups(4, 19–21). The gene by environment interaction analyses that followed the GWAS have also been mostly conducted in populations of European origin(22–34). Studies assessing the relationship between intermediate to low penetrance variants and non-genetic risk factors (from here on referred to as “environmental”) have had little success in identifying consistent interactions(22–34). A study including over 70,000 women of European or Asian descent from 24 studies, reported three statistically robust interactions: rs3817198-LSP1 and parity, rs17468277-CASP8 and alcohol consumption, and rs11249433-1p11.2 and parity(26). A later study by the same consortium evaluated interactions for an additional set of 47 breast cancer susceptibility loci and reported non-statistically significant interactions for three additional single nucleotide polymorphisms (SNPs) (rs6828523 and height; rs4808801 and number of full-term pregnancies; and rs11242675 and smoking)(34). Beyond the statistical evidence there are no clear biological mechanisms explaining the reported interactions.
We previously found statistically significant interactions between Indigenous American genetic ancestry and genotypes for three out of ten GWAS-discovered breast cancer risk variants in 2,107 Hispanic/Latina (from here on referred to as Hispanics) women with breast cancer and 2,587 unaffected controls (rs13387042-2q35, rs17157903-RELN, and rs7696175-TLR1)(35). For these three SNPs, the risk allele showed stronger associations among the group of women with the highest proportion of Indigenous American ancestry. The analyses were aimed at detecting heterogeneity by genetic ancestry based on the underlying hypothesis that if an interaction was observed, it could reflect either a difference in genetic predisposition between populations, or differences in environmental exposures by ancestry that would modify the associations with the genetic variants(35).
In the present study, we investigated multiple environmental breast cancer risk factors to evaluate if the previously observed heterogeneity by ancestry could be due to the correlation between genetic ancestry and those risk factors. First, we tested the interaction between the 3 GWAS-identified breast cancer risk SNPs that had previously shown interaction by ancestry(35) and 15 environmental breast cancer risk factors in a total of 4,694 Hispanic women (2,107 cases and 2,587 controls). Secondly, we assessed the interaction between the 7 other GWAS-identified breast cancer risk SNPs and environmental risk factors to evaluate the possibility of additional interactions.
Materials and Methods
The present analysis was conducted as part of the Breast Cancer Health Disparities Study (36). This collaborative effort to study breast cancer in Hispanic and non-Hispanic White women, combined and harmonized data from two population-based case-control studies conducted in the US: the 4-Corners Breast Cancer Study (4-CBCS) (37) and the San Francisco Bay Area Breast Cancer Study (SFBCS) (38, 39); and a population-based multicenter case-control study conducted in Mexico (MBCS) (40). Details about the Breast Cancer Health Disparities Study have been previously published (35, 36,41–45). All participants signed a written informed consent, and the study was approved by the Institutional Review Board for Human Subjects at each institution. The present analysis is based on 4,697 women of Hispanic/Native American origin living in the U.S. or Mexico with complete genotype and exposure data including 603 Hispanic cases and 730 controls from 4-CBCS, 812 Mexican cases and 989 controls from MBCS, and 692 Hispanic cases and 871 controls from SFBCS.
Genetic Data
The Breast Cancer Health Disparities Study is focused on variants in genes related to inflammation, hormones, metabolism and risk of breast cancer in Hispanic and non-Hispanic White women (36). The genotyping platform included 10 GWAS-identified SNPs associated with breast cancer risk that were published at the time of platform design(3, 6,R11, 12, 14) [rs13387042-2q35 region (G/A), rs17157903-7q22 (C/T) within the RELN gene, rs2067980-5q11 (A/G) near the MRPS30 gene, rs2180341-6q22.1-q22.33 (A/G) within the RNF146 gene, rs2981582-10q26 (C/T) within the FGFR2 gene, rs3803662-16q12.1 (C/T) within the TOX3 gene, rs3817198-11p15.5 (T/C) within the LSP1 gene, rs7696175-4p14 (C/T) near the TLR1 gene, rs889312-5q11.2 (A/C) near the MAP3k1 gene and rs999737-14q23-q24.2 (C/T) within the RAD51L1 gene]. The platform also included a set of 104 ancestry-informative markers (AIMs) used to infer genetic ancestry among study participants. Details about these AIMs have been previously published(36). All markers were genotyped using a multiplexed bead array assay based on GoldenGate chemistry (Illumina, San Diego, California) attaining a genotyping call rate of 99%. In the present study we analyzed the ten GWAS-identified SNPs associated with breast cancer risk in Europeans or Asians, with a focus on three variants that had previously shown statistically significant interactions with genetic ancestry in Hispanics: rs13387042-2q35, rs17157903-7q22 and rs7696175-4p14(35).
Environmental risk factors (Reproductive/lifestyle)
From available questionnaire data that had been harmonized between the three different studies we selected a set of breast cancer risk factors to explore if the interaction between SNPs and genetic ancestry could be due to the correlation between environmental factors and genetic ancestry. The variables we analyzed were: menopausal status (premenopausal, postmenopausal), age at menopause (<50 or ≥50 years), age at menarche (<11; 11–13; ≥13), age at diagnosis/interview (<40; 40–50; 50–60; >60), alcohol intake (no alcohol; ≤10 gms daily; >10 gms daily), smoking status (ever, never), body mass index (<25; 25–29.9; ≥30 kg/m2), height (below mean; above mean), waist-to-hip ratio (below mean; above mean), number of full-term pregnancies/age at first-full term pregnancy (no children; 1 or 2 children <25 years old; 1 or 2 children ≥25 years old; ≥3 children <25 years old; ≥3 children ≥25 years old), breastfeeding (no breastfeeding; ≤12 months; >12 months), use of hormone therapy (current, former, never), use of oral contraceptives (ever, never), and family history of breast cancer (yes, no).
Genetic ancestry estimation
Indigenous American ancestry was modeled as continuous or categorical variable depending on the analysis. The cutoffs for three ancestry categories were defined based on sample size as previously described: low Indigenous American ancestry (0–28%), intermediate Indigenous American ancestry (29 to 70%), and high Indigenous American ancestry (71 to 100%)(36). We acknowledge that Hispanic populations would be best modeled as resulting from a three-way admixture process, with a European, an Indigenous American and an African component. However, the African influence in most Hispanic populations is minor (between 0 and 8%) and studies that include women of mostly Mexican or Central American origin, such as ours, do not have sufficient power to evaluate the association of this minor component with health outcomes. We focused the analyses on the Indigenous American/European proportions and estimated genetic ancestry using an unsupervised two-way admixture model. We compared the Indigenous American ancestry estimates obtained with this model, with those obtained with a supervised three-way admixture model in a subset of 1769 Hispanics that were included in a previous study(46) and found that the estimates were highly correlated (Pearson correlation coefficient = 0.94, mean absolute difference between pairs of estimates = 0.09, standard deviation = 0.06).
Statistical Analysis
Indigenous American ancestry proportions were compared between the different categories of environmental risk factors by study and by case/control status using the non-parametric Kruskal-Wallis equality-of-populations rank test, which is appropriate for variables that deviate from normality, as was the case for genetic ancestry in the analyzed samples. We estimated ancestry-specific odds ratios (OR) for the non-genetic risk factors using logistic regression stratified by ancestry category (low, intermediate, and high Indigenous American ancestry) and adjusting for study, and tested the heterogeneity of the effects by evaluating a logistic regression model that included an environmental risk factor and genetic ancestry interaction term (ExAncestry). We also used logistic regression to evaluate SNP by environmental risk factor interactions (GxE), with adjustment for study, genetic ancestry and age, and to test GxE interactions stratified by ancestry, including age and study as covariates. For those stratified analyses that had results suggestive of an interaction with an environmental risk factor, we conducted likelihood ratio tests (LRT) to compare a model with the SNP by ancestry interaction (GxAncestry) term and adjusted for the environmental factor, to a model with GxAncestry and GxE interaction terms. In the first analyses where we tested the interaction between the three SNPs previously associated with genetic ancestry and environmental risk factors, we considered as statistically significant any interaction with a P value <0.003, which corresponds to a Bonferroni correction for the number of risk factors tested (0.05/15). For analyses that included the other 7 SNPs that did not show interaction by genetic ancestry, we took both the environmental risk factors and the number of SNPs into account when we considered statistical significance. For those analyses we considered as statistically significant a P value <4.8×10−4.
Results
Many risk factors showed associations with breast cancer risk that were statistically significant at the 5% level and concordant with the direction of associations that have been previously reported for these risk factors (Table 1). P values were higher in the low Indigenous American ancestry group, which is to be expected given that the sample size for that group was smaller than that for the other two ancestry groups. We did not find statistically significant heterogeneity by ancestry category for any of the environmental risk factors. However, among postmenopausal women, current use (vs. never use) of hormone therapy showed a positive association in all strata but a suggestive stronger association among Hispanics with high Indigenous American ancestry (P=0.05).
Table 1.
Association between breast cancer risk and non-genetic risk factors by Indigenous American ancestry in Hispanics from the Breast Cancer Health Disparities Study
| Low Indigenous American ancestry (N=557) |
Interm. Indigenous American ancestry (N=3098) |
High Indigenous American ancestry (N=1078) |
||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Category | OR | 95%CI | P | OR | 95%CI | P | OR | 95%CI | P | |
| Age at dx/interview (Ref: ≤40) | 41–50 | 1.39 | 0.78–2.48 | 0.258 | 1.47 | 1.15–1.85 | 0.001 | 1.13 | 0.77–1.63 | 0.524 |
| 51–60 | 1.11 | 0.62–1.98 | 0.715 | 1.38 | 1.08–1.75 | 0.009 | 1.03 | 0.69–1.51 | 0.886 | |
| >60 | 1.36 | 0.75–2.43 | 0.301 | 1.36 | 1.06–1.74 | 0.014 | 1.10 | 0.72–1.66 | 0.661 | |
| Family history BC (Ref: No) | Yes | 1.71 | 1.07–2.73 | 0.023 | 1.37 | 1.08–1.72 | 0.008 | 2.63 | 1.38–4.98 | 0.003 |
| Anthropometrics | ||||||||||
| BMI (kg/m2) (Ref: <25) | 25–29 | 0.83 | 0.54–1.28 | 0.406 | 0.74 | 0.60–0.90 | 0.004 | 0.74 | 0.51–1.06 | 0.104 |
| ≥30 | 0.81 | 0.53–1.22 | 0.318 | 0.62 | 0.51–0.75 | <0.001 | 0.69 | 0.47–0.98 | 0.041 | |
| W-H ratio (Ref: Below the mean) | Above the mean | 1.09 | 0.73–1.61 | 0.672 | 1.07 | 0.91–1.24 | 0.378 | 0.79 | 0.59–1.06 | 0.122 |
| Height (Ref: Below the mean) | Above the mean | 1.03 | 0.70–1.51 | 0.873 | 1.19 | 1.02–1.37 | 0.02 | 1.43 | 1.07–1.88 | 0.013 |
| Lifestyle | ||||||||||
| Smoking (Ref: Never) | Ever | 1.13 | 0.76–1.66 | 0.539 | 1.08 | 0.91–1.27 | 0.35 | 1.04 | 0.76–1.42 | 0.789 |
| Daily alcohol intake (Ref: None) | ≤10gms/day | 0.95 | 0.64–1.40 | 0.801 | 1.01 | 0.83–1.23 | 0.903 | 0.88 | 0.43–1.80 | 0.735 |
| >10gms/day | 1.49 | 0.88–2.51 | 0.137 | 1.41 | 1.02–1.94 | 0.034 | 0.37 | 0.03–3.54 | 0.392 | |
| Reproductive characteristics | ||||||||||
| Parity/AFFTP (Ref: nulliparous) | 1–2 children/<25 years of age | 1.09 | 0.58–2.03 | 0.794 | 0.69 | 0.50–0.93 | 0.018 | 0.56 | 0.33–0.95 | 0.033 |
| 1–2 children/≥25 years of age | 1.34 | 0.74–2.43 | 0.331 | 0.69 | 0.51–0.94 | 0.018 | 0.91 | 0.53–1.53 | 0.728 | |
| ≥3 children/<25 years of age | 0.97 | 0.54–1.69 | 0.903 | 0.43 | 0.32–0.56 | <0.001 | 0.57 | 0.36–0.89 | 0.016 | |
| ≥3 children/≥25 years of age | 1.32 | 0.63–2.70 | 0.457 | 0.54 | 0.38–0.75 | <0.001 | 0.60 | 0.32–1.08 | 0.091 | |
| Breastfeeding if parous (Ref: No) | ≤12 months | 0.85 | 0.54–1.30 | 0.453 | 0.86 | 0.71–1.03 | 0.108 | 0.75 | 0.51–1.11 | 0.156 |
| >12 months | 0.70 | 0.43–1.12 | 0.137 | 0.56 | 0.45–0.68 | <0.001 | 0.52 | 0.33–0.80 | 0.003 | |
| Oral contraceptive use (Ref: Never) | Ever | 1.13 | 0.78–1.62 | 0.512 | 1.05 | 0.90–1.20 | 0.544 | 1.12 | 0.87–1.43 | 0.376 |
| Age at menarche (Ref: ≤11 yrs.) | 12 to 13 | 1.20 | 0.80–1.80 | 0.370 | 0.85 | 0.70–1.01 | 0.078 | 1.04 | 0.75–1.44 | 0.798 |
| >13 | 0.83 | 0.51–1.32 | 0.434 | 0.80 | 0.66–0.97 | 0.03 | 0.80 | 0.55–1.13 | 0.209 | |
| Menopause (Ref: Premenopausal) | Postmenopausal | 0.97 | 0.67–1.38 | 0.853 | 1.01 | 0.87–1.17 | 0.857 | 0.91 | 0.70–1.16 | 0.438 |
| Postmenopausal women only | N=325 | N=1793 | N=591 | |||||||
| Age at menopause (Ref: age ≤50) | . ≥50 yrs. | 1.22 | 0.75–1.96 | 0.409 | 1.12 | 0.91–1.37 | 0.276 | 1.17 | 0.81–1.69 | 0.392 |
| BMI (Ref: <25) | 25–29 | 0.93 | 0.52–1.65 | 0.816 | 0.83 | 0.63–1.09 | 0.193 | 0.86 | 0.50–1.46 | 0.585 |
| ≥30 | 0.67 | 0.38–1.16 | 0.154 | 0.67 | 0.51–0.86 | 0.003 | 0.83 | 0.49–1.39 | 0.476 | |
| Hormone therapy use (Ref: Never)* | Former | 0.64 | 0.36–1.15 | 0.137 | 0.88 | 0.69–1.13 | 0.321 | 1.19 | 0.76–1.86 | 0.456 |
| Current | 2.03 | 1.18–3.47 | 0.01 | 1.71 | 1.33–2.19 | <0.001 | 5.90 | 2.13–16.33 | 0.001 | |
Abbreviations: Interm: Intermediate; dx: diagnosis; BC: breast cancer; BMI: body mass index; W-H ratio: Waist to hip ratio; AFFP: Age at first full-term pregnancy.
P value for interaction between ancestry category and reproductive/lifestyle variable = 0.05
Environmental risk factors and Indigenous American ancestry
Average Indigenous American ancestry differed by risk factor category, and tended to be higher among women with no family history of breast cancer, women with more children at a younger age, a longer duration of breastfeeding, a higher body mass index, a higher waist-to-hip ratio, shorter height, no smoking or alcohol consumption history, no history of oral contraceptive use, with a younger age at diagnosis, menopause before age 50 years, and no history of postmenopausal hormone therapy use (Table 2). Despite the difference in mean Indigenous American ancestry between studies, the direction of association between ancestry and environmental risk factors is similar for most variables.
Table 2.
Mean (standard deviation) Indigenous American ancestry among controls in each study by levels of risk factors in Hispanics from the Breast Cancer Health Disparities Study (differences in ancestry significant at the 5% level or lower are in bold)
| 4-CBCS | MBCS | SFBCS | |||||
|---|---|---|---|---|---|---|---|
| Variable | N | Mean (SD) | N | Mean (SD) | N | Mean (SD) | |
| Age at dx/interview | ≤40 | 79 | 0.45 (0.17) | 108 | 0.70 (0.19) | 63 | 0.40 (0.21) |
| 41–50 | 202 | 0.42 (0.18) | 299 | 0.69 (0.19) | 238 | 0.45 (0.20) | |
| 51–60 | 177 | 0.43 (0.17) | 237 | 0.65 (0.20) | 190 | 0.41 (0.19) | |
| >60 | 151 | 0.40 (0.16) | 172 | 0.64 (0.20) | 207 | 0.41 (0.18) | |
| Family history BC | No | 452 | 0.42 (0.17) | 766 | 0.68 (0.19) | 600 | 0.43 (0.19) |
| Yes | 104 | 0.41 (0.17) | 50 | 0.61 (0.18) | 98 | 0.39 (0.20) | |
| Anthropometrics | |||||||
| BMI (kg/m2) | <25 | 193 | 0.38 (0.17) | 160 | 0.64 (0.21) | 139 | 0.37 (0.19) |
| 25–29 | 210 | 0.43 (0.16) | 330 | 0.68 (0.18) | 228 | 0.44 (0.19) | |
| ≥30 | 191 | 0.45 (0.17) | 311 | 0.67 (0.19) | 330 | 0.43 (0.19) | |
| Waist-to-hip ratio | Below mean | 380 | 0.40 (0.17) | 229 | 0.63 (0.19) | 446 | 0.41 (0.19) |
| Above mean | 205 | 0.46 (0.16) | 564 | 0.68 (0.19) | 233 | 0.45 (0.19) | |
| Height | Below mean | 230 | 0.45 (0.17) | 506 | 0.70 (0.19) | 267 | 0.48 (0.18) |
| Above mean | 367 | 0.40 (0.17) | 295 | 0.61 (0.19) | 412 | 0.39 (0.19) | |
| Lifestyle | |||||||
| Smoking | Never | 395 | 0.44 (0.17) | 604 | 0.69 (0.19) | 298 | 0.44 (0.19) |
| Ever | 201 | 0.37 (0.16) | 212 | 0.62 (0.20) | 148 | 0.40 (0.18) | |
| Daily alcohol intake | Never | 385 | 0.43 (0.17) | NA | NA | 435 | 0.45 (0.19) |
| ≤10gms/day | 173 | 0.39 (0.16) | NA | NA | 171 | 0.41 (0.20) | |
| >10gms/day | 39 | 0.40 (0.18) | NA | NA | 92 | 0.34 (0.16) | |
| Reproductive characteristics | |||||||
| Parity/AFFTP | Nulliparous | 57 | 0.41 (0.17) | 91 | 0.65 (0.19) | 81 | 0.42 (0.20) |
| 1–2 children/<25 years of age | 126 | 0.42 (0.14) | 106 | 0.68 (0.20) | 127 | 0.41 (0.19) | |
| 1–2 children/≥25 years of age | 113 | 0.39 (0.17) | 165 | 0.66 (0.20) | 138 | 0.38 (0.21) | |
| ≥3 children/<25 years of age | 250 | 0.43 (0.18) | 356 | 0.69 (0.19) | 275 | 0.46 (0.17) | |
| ≥3 children/≥25 years of age | 51 | 0.43 (0.16) | 80 | 0.61 (0.20) | 76 | 0.41 (0.20) | |
| Breastfeeding | Never | 214 | 0.41 (0.15) | 102 | 0.67 (0.18) | 223 | 0.42 (0.19) |
| ≤12 months | 173 | 0.41 (0.17) | 517 | 0.66 (0.20) | 231 | 0.41 (0.20) | |
| >12 months | 153 | 0.45 (0.20) | 106 | 0.73 (0.18) | 163 | 0.44 (0.18) | |
| Oral contraceptive use | No | 270 | 0.45 (0.17) | 404 | 0.68 (0.19) | 236 | 0.45 (0.18) |
| Yes | 320 | 0.39 (0.16) | 409 | 0.66 (0.19) | 456 | 0.41 (0.19) | |
| Age at Menarche (yrs.) | ≤11 | 135 | 0.40 (0.15) | 171 | 0.66 (0.19) | 195 | 0.42 (0.19) |
| 11 to 13 | 283 | 0.42 (0.18) | 395 | 0.67 (0.20) | 324 | 0.42 (0.20) | |
| >13 | 178 | 0.43 (0.17) | 238 | 0.68 (0.19) | 176 | 0.43 (0.18) | |
| Menopause | Premenopausal | 235 | 0.43 (0.18) | 345 | 0.69 (0.18) | 256 | 0.44 (0.19) |
| Postmenopausal | 360 | 0.41 (0.16) | 471 | 0.66 (0.20) | 379 | 0.42 (0.18) | |
| Postmenopausal | |||||||
| Menopause at age ≥50 yrs. | No | 233 | 0.41 (0.16) | 319 | 0.67 (0.19) | 179 | 0.42 (0.17) |
| Yes | 109 | 0.40 (0.16) | 155 | 0.65 (0.21) | 140 | 0.40 (0.19) | |
| Hormone therapy use | Current | 142 | 0.39 (0.16) | 20 | 0.58 (0.20) | 155 | 0.40 (0.18) |
| Former | 65 | 0.41 (0.15) | 90 | 0.63 (0.18) | 76 | 0.41 (0.18) | |
| Never | 150 | 0.43 (0.16) | 356 | 0.67 (0.20) | 141 | 0.43 (0.18) | |
Abbreviations: 4-CBCS: 4-Corners Breast Cancer Study; MBCS: Mexico Breast Cancer Study; SFBCS: San Francisco Bay Area Breast Cancer Study; dx: diagnosis; BC: breast cancer; BMI: body mass index; AFFTP: Age at first full-term pregnancy.
Interactions of SNPs and environmental factors by Indigenous American ancestry
Assessment of the interaction between the three SNPs that had previously shown an interaction with genetic ancestry and the environmental risk factors showed a statistically significant interaction between the SNP rs13387042-2q35 (G/A) and hormone therapy use among postmenopausal women (P interaction 0.002) and a suggestive interaction with breastfeeding among premenopausal women (P interaction 0.01) (Table 3). We observed that the rs13387042-A variant was associated with increased breast cancer risk among postmenopausal women who had never used hormone therapy and among premenopausal women who breastfed more than 12 months. The strength of the interactions was not affected when other variables were included in the model and LRTs suggested that the models that included the rs13387042-hormone therapy or rs13387042-breastfeeding interaction terms fit the data better than the models that only included the GxAncestry interaction term (hormone therapy use: LRT P = 0.021; breastfeeding: LRT P = 0.028, Table 3). Estrogen receptor (ER) status information was available for 46% of the cases included in the present analysis. There were 720 patients with ER+ tumors and 257 patients with ER- tumors. Given that our analyses were based on stratification by ancestry and risk factors, further stratification by ER status greatly reduced the size of the groups being compared. Even though these exploratory ER status-specific results were inconclusive due to the small sample size, they suggest that the observed associations and interactions in the case/control analyses do not change for ER+ tumors (Supplementary Table S1).
Table 3.
Association between rs13387042 (2q35) and breast cancer risk, stratified by Indigenous American genetic ancestry, hormone therapy use, and breastfeeding in Hispanic women from the Breast Cancer Health Disparities Study
| N | Odds Ratio* | 95% CI | P value | P Interaction | P LRT** | ||
|---|---|---|---|---|---|---|---|
| Stratified by Indigenous American ancestry | |||||||
| Low | 557 | 1.10 | 0.87–1.39 | 0.441 | 0.051 | ||
| Intermediate | 3098 | 1.17 | 1.06–1.30 | 0.003 | |||
| High | 1078 | 1.47 | 1.17–1.83 | 0.001 | |||
| Stratified by hormone therapy use in postmenopausal women | |||||||
| Current | 575 | 0.94 | 0.74–1.20 | 0.644 | 0.002 | ||
| Former | 592 | 1.20 | 0.94–1.53 | 0.134 | |||
| Never | 1521 | 1.49 | 1.28–1.75 | <0.001 | |||
| Stratified by breastfeeding in premenopausal women | |||||||
| Never | 324 | 1.01 | 0.72–1.42 | 0.96 | 0.014 | ||
| ≤12 months | 923 | 1.19 | 0.98–1.45 | 0.076 | |||
| >12 months | 424 | 1.69 | 1.26–2.26 | <0.001 | |||
| Stratified by hormone therapy use and genetic ancestry in postmenopausal women | |||||||
| Current | Low | 113 | 1.10 | 0.64–1.87 | 0.733 | 0.373 | 0.021 |
| Intermediate | 439 | 0.90 | 0.67–1.20 | 0.473 | |||
| High | 22 | 0.05 | 0.00–2.41 | 0.128 | |||
| Former | Low | 80 | 0.57 | 0.27–1.21 | 0.144 | 0.016 | |
| Intermediate | 407 | 1.24 | 0.92–1.67 | 0.156 | |||
| High | 101 | 1.39 | 0.63–3.02 | 0.413 | |||
| Never | Low | 127 | 1.64 | 0.97–2.73 | 0.06 | 0.988 | |
| Intermediate | 929 | 1.29 | 1.05–1.57 | 0.015 | |||
| High | 465 | 1.72 | 1.21–2.42 | 0.002 | |||
| Stratified by breastfeeding and genetic ancestry in premenopausal women | |||||||
| Never | Low | 25 | 0.85 | 0.24–2.96 | 0.799 | 0.803 | 0.028 |
| Intermediate | 241 | 1.03 | 0.69–1.52 | 0.89 | |||
| High | 56 | 1.44 | 0.43–4.84 | 0.56 | |||
| ≤12 months | Low | 88 | 0.87 | 0.44–1.73 | 0.692 | 0.113 | |
| Intermediate | 542 | 1.1 | 0.86–1.41 | 0.463 | |||
| High | 293 | 1.38 | 0.91–2.11 | 0.130 | |||
| >12 months | Low | 47 | 1.52 | 0.60–3.82 | 0.377 | 0.917 | |
| Intermediate | 292 | 1.69 | 1.20–2.38 | 0.003 | |||
| High | 83 | 2.22 | 0.81–6.10 | 0.122 | |||
OR per additional A allele
P likelihood ratio test comparing the model with interaction between SNP and ancestry, adjusted for the environmental risk factor, to a model with SNP by ancestry and SNP by envionmental risk factor interaction.
After correcting for multiple testing, no statistically significant interactions were found for the seven SNPs that had not previously shown interactions with genetic ancestry (Supplementary Table S2). For suggestive GxE interactions (P<0.15) involving the three SNPs with previously observed GxAncestry interaction (rs13387042, rs17157903, and rs7696175), we conducted analyses stratified by genetic ancestry to evaluate if the observed GxE interaction affected the previously observed GxAncestry interaction and found no additional statistically significant results (Supplementary Table S3).
Discussion
Our results suggest that the previously reported interaction between a breast cancer risk variant at 2q35 (rs13387042) and genetic ancestry in Hispanics was partially due to the correlation between genetic ancestry and two environmental breast cancer risk factors that affect estrogen levels: use of menopausal hormone therapy and breastfeeding among premenopausal women. We found that the the rs13387042 derived allele (A) only increases breast cancer risk among postmenopausal women who have never been exposed to hormone therapy and among premenopausal women who have breastfed for more than 12 months.
An interaction between breast cancer susceptibility SNP rs13387042-2q35 and menopausal hormone therapy use has been previously reported(26). Nickels et. al. found weak evidence of interaction between the rs13387042 SNP and use of menopausal hormone therapy for combined estrogen/progesterone formulations (P interaction =2.4×10−3)(26). However, they reported a stronger association among current hormone therapy users compared to never users, while we observed an association among never users only. Two other studies that investigated the interaction between previously reported risk SNPs and never/ever use of hormone therapy(24, 29) did not find statistically significant interactions for the rs13387042 SNP (47, 48). A possible reason for the inter-study incosistencies could be heterogeneity in the definition and categorization of the hormone therapy use variable (e.g. estrogen only, estrogen/progesterone or not specified)(26).
The rs13387042 SNP is located in the short arm of chromosome 2 in an intergenic region. The derived allele A is associated with increased risk of breast cancer and is most common in African populations (77%), has lower frequencies in Europeans (51%) and Mexican Americans (41%), and is less common in Asians (12%) (frequencies from 1000 Genomes Project). The closest known genes are TNP1 (transition protein 1), IGFBP5 (insulin-like growth factor binding protein 5), IGFBP2 (insulin-like growth factor binding protein 2) and TNS1 (tensin 1/matrix- remodelling-associated protein 6)(12). Recent studies have shown that two polymorphisms that are in strong linkage disequilibrium (LD) with rs13387042 (rs6721996 and rs4442975, r2=0.92) are associated with expression of the IGFBP5 gene, with decreasing expression of IGFBP5 with increasing number of A alleles(49, 50). One of these studies was a fine-mapping effort that included functional analyses, and concluded that the rs4442975 SNP was the most likely functional variant(50). It has been reported that IGFBP5 is expressed in breast cancer and breast cancer cell lines, and it is produced by estrogen receptor (ER)-alpha positive tumors, which is concordant with the previous observation that the rs13387042 polymorphism is more strongly associated with risk of ER positive disease(12). IGFBP5 has been shown to inhibit cell proliferation via an insulin growth factor (IGF)-dependent mechanism(51). Adding to this, estrogen-induced transcriptional activity of ER-alpha is reduced by IGFBP5 expression independent of IGF(51). The observation that the rs13387042 SNP is associated with breast cancer risk only among women who have never used hormone therapy is counterintuitive and needs to be further explored. A possible explanation could be that in a low estrogenic environment (such as that of postmenopausal women who do not use hormone therapy or women who breastfeed for more than a year), the level of expression of IGFBP5 becomes crucial in limiting cell growth and proliferation, while in an estrogen-rich environment, the inhibitory effect of IGFBP5 might be overridden by other estrogen dependent regulatory mechanisms. We did not find strong evidence of heterogeneity of GxE interaction by tumor ER status, but the analysis was limited by the small size of the compared categories after ER status stratification.
We were not able to explain the previously reported gene by ancestry interactions for two SNPs (rs17157903 and rs7696175) and none of the remaining polymorphisms analyzed show statistically significant GxE interactions. We cannot dismiss the possibility that our study might have failed to detect interactions between these SNPs and non-genetic risk factors due to reduced power after accounting for multiple testing. In addition, we want to acknolwedge that the present analysis only included a small subset of all SNPs that have been reported to be associated with breast cancer risk to date (2–19), which was determined by the limited number of published associated polymorphisms at the time of platform design. Despite these limitations, our study is the largest study to date that tested the interaction between breast cancer GWAS-identified polymorphisms and non-genetic risk factors in Hispanic women.
In summary, in this study we present the results of a breast cancer GxE interaction analysis in Hispanic women. We found that the association between the rs13387042-2q35 polymorphism and breast cancer risk is modified by hormone therapy use among postmenopausal women and by breastfeeding among premenopausal women. The present analysis illustrates how genetic ancestry in admixed populations might not only reflect population differences in genetic predisposition, but also differences in environmental exposures that together with genetic factors can influence breast cancer risk. Future research should confirm the reported interactions as well as the negative results and properly account for the complex interactions between environment, genetics, and behavior in populations of mixed descent.
Supplementary Material
Acknowledgments
Funding: The Breast Cancer Health Disparities Study was funded by the National Cancer Institute [CA14002 to M.L. Slattery]. The San Francisco Bay Area Breast Cancer Study was supported by the National Cancer Institute [CA63446 and CA77305 to E.M. John], the U.S. Department of Defense [DAMD17-96-1-6071 to E.M. John] and the California Breast Cancer Research Program [7PB-0068 to E.M. John]. The collection of cancer incidence data used in this study was supported by the California Department of Public Health as part of the statewide cancer reporting program mandated by California Health and Safety Code Section 103885; the National Cancer Institute's Surveillance, Epidemiology and End Results Program under contract HHSN261201000036C awarded to the Cancer Prevention Institute of California; and the Centers for Disease Control and Prevention's National Program of Cancer Registries, under agreement #1U58 DP000807-01 awarded to the Public Health Institute. The 4-Corners Breast Cancer Study was funded by the National Cancer Institute [CA078682; CA078762; CA078552, and CA078802; N01-PC-67000 to Utah Cancer Registry], the State of Utah Department of Health, the New Mexico Tumor Registry, and the Arizona and Colorado cancer registries, funded by the Centers for Disease Control and Prevention National Program of Cancer Registries and additional state support. The Mexico Breast Cancer Study was funded by Consejo Nacional de Ciencia y Tecnología [SALUD-2002-C01-7462 to G. Torres-Mejía]. Work for this study was also supported by the National Cancer Institute [CA 160607 to L. Fejerman and CA120120 to E. Ziv], the Center for Aging in Diverse Communities (CADC) under the Resource Centers for Minority Aging Research program by the National Institute on Aging [P30-AG15272, pilot grant to L. Fejerman] and the National Cancer Institute grant from the Special Population Network program to University of Texas, San Antonio [Redes En Acción # U54CA153511, pilot grant to L. Fejerman]. The contents of this manuscript are solely the responsibility of the authors and do not necessarily represent the official view of the National Cancer Institute or endorsement by the State of California Department of Public Health, the National Cancer Institute, and the Centers for Disease Control and Prevention or their Contractors and Subcontractors.
Footnotes
Conflict of Interest: There are no conflict of interests to disclosed
References
- 1.Mavaddat N, Antoniou AC, Easton DF, Garcia-Closas M. Genetic susceptibility to breast cancer. Mol Oncol. 2010;4:174–191. doi: 10.1016/j.molonc.2010.04.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Ghoussaini M, Fletcher O, Michailidou K, Turnbull C, Schmidt MK, Dicks E, et al. Genome-wide association analysis identifies three new breast cancer susceptibility loci. Nat Genet. 2012;44:312–318. doi: 10.1038/ng.1049. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Thomas G, Jacobs KB, Kraft P, Yeager M, Wacholder S, Cox DG, 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. doi: 10.1038/ng.353. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Siddiq A, Couch FJ, Chen GK, Lindstrom S, Eccles D, Millikan RC, 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. doi: 10.1093/hmg/dds381. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Michailidou K, Hall P, Gonzalez-Neira A, Ghoussaini M, Dennis J, Milne RL, et al. Large-scale genotyping identifies 41 new loci associated with breast cancer risk. Nat Genet. 2013;45:353–361. 61e1–61e2. doi: 10.1038/ng.2563. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Hunter DJ, Kraft P, Jacobs KB, Cox DG, Yeager M, Hankinson SE, et al. A genome-wide association study identifies alleles in FGFR2 associated with risk of sporadic postmenopausal breast cancer. Nat Genet. 2007;39:870–874. doi: 10.1038/ng2075. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Haiman CA, Chen GK, Vachon CM, Canzian F, Dunning A, Millikan RC, et al. A common variant at the TERT-CLPTM1L locus is associated with estrogen receptor-negative breast cancer. Nat Genet. 2011;43:1210–1214. doi: 10.1038/ng.985. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Garcia-Closas M, Couch FJ, Lindstrom S, Michailidou K, Schmidt MK, Brook MN, et al. Genome-wide association studies identify four ER negative-specific breast cancer risk loci. Nat Genet. 2013;45:392–388. 8e1–8e2. doi: 10.1038/ng.2561. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Fletcher O, Johnson N, Orr N, Hosking FJ, Gibson LJ, Walker K, 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. doi: 10.1093/jnci/djq563. [DOI] [PubMed] [Google Scholar]
- 10.Easton DF, Pooley KA, Dunning AM, Pharoah PD, Thompson D, Ballinger DG, et al. Genome-wide association study identifies novel breast cancer susceptibility loci. Nature. 2007;447:1087–1093. doi: 10.1038/nature05887. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Stacey SN, Manolescu A, Sulem P, Thorlacius S, Gudjonsson SA, Jonsson GF, et al. Common variants on chromosome 5p12 confer susceptibility to estrogen receptor-positive breast cancer. Nat Genet. 2008;40:703–706. doi: 10.1038/ng.131. [DOI] [PubMed] [Google Scholar]
- 12.Stacey SN, Manolescu A, Sulem P, Rafnar T, Gudmundsson J, Gudjonsson SA, et al. Common variants on chromosomes 2q35 and 16q12 confer susceptibility to estrogen receptor-positive breast cancer. Nat Genet. 2007;39:865–869. doi: 10.1038/ng2064. [DOI] [PubMed] [Google Scholar]
- 13.Gold B, Kirchhoff T, Stefanov S, Lautenberger J, Viale A, Garber J, et al. Genome-wide association study provides evidence for a breast cancer risk locus at 6q22.33. Proc Natl Acad Sci U S A. 2008;105:4340–4345. doi: 10.1073/pnas.0800441105. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Turnbull C, Ahmed S, Morrison J, Pernet D, Renwick A, Maranian M, et al. Genome-wide association study identifies five new breast cancer susceptibility loci. Nat Genet. 2010;42:504–507. doi: 10.1038/ng.586. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Gaudet MM, Kuchenbaecker KB, Vijai J, Klein RJ, Kirchhoff T, McGuffog L, et al. Identification of a BRCA2-specific modifier locus at 6p24 related to breast cancer risk. PLoS Genet. 2013;9:e1003173. doi: 10.1371/journal.pgen.1003173. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Ahmed S, Thomas G, Ghoussaini M, Healey CS, Humphreys MK, Platte R, et al. Newly discovered breast cancer susceptibility loci on 3p24 and 17q23.2. Nat Genet. 2009;41:585–590. doi: 10.1038/ng.354. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Long J, Cai Q, Sung H, Shi J, Zhang B, Choi JY, et al. Genome-wide association study in east Asians identifies novel susceptibility loci for breast cancer. PLoS Genet. 2012;8:e1002532. doi: 10.1371/journal.pgen.1002532. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Long J, Cai Q, Shu XO, Qu S, Li C, Zheng Y, et al. Identification of a functional genetic variant at 16q12.1 for breast cancer risk: results from the Asia Breast Cancer Consortium. PLoS Genet. 2010;6:e1001002. doi: 10.1371/journal.pgen.1001002. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Fejerman L, Ahmadiyeh N, Hu D, Huntsman S, Beckman KB, Caswell JL, et al. Genome-wide association study of breast cancer in Latinas identifies novel protective variants on 6q25. Nature communications. 2014;5:5260. doi: 10.1038/ncomms6260. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Chen F, Chen GK, Stram DO, Millikan RC, Ambrosone CB, John EM, et al. A genome-wide association study of breast cancer in women of African ancestry. Hum Genet. 2013;132:39–48. doi: 10.1007/s00439-012-1214-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Song C, Chen GK, Millikan RC, Ambrosone CB, John EM, Bernstein L, et al. A genome-wide scan for breast cancer risk haplotypes among African American women. PLoS One. 2013;8:e57298. doi: 10.1371/journal.pone.0057298. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Warren Andersen S, Trentham-Dietz A, Gangnon RE, Hampton JM, Skinner HG, Engelman CD, et al. Breast cancer susceptibility loci in association with age at menarche, age at natural menopause and the reproductive lifespan. Cancer Epidemiol. 2014;38:62–65. doi: 10.1016/j.canep.2013.12.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Warren Andersen S, Trentham-Dietz A, Gangnon RE, Hampton JM, Figueroa JD, Skinner HG, et al. The associations between a polygenic score, reproductive and menstrual risk factors and breast cancer risk. Breast Cancer Res Treat. 2013;140:427–434. doi: 10.1007/s10549-013-2646-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Travis RC, Reeves GK, Green J, Bull D, Tipper SJ, Baker K, et al. Gene-environment interactions in 7610 women with breast cancer: prospective evidence from the Million Women Study. Lancet. 2010;375:2143–2151. doi: 10.1016/S0140-6736(10)60636-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Rebbeck TR, DeMichele A, Tran TV, Panossian S, Bunin GR, Troxel AB, et al. Hormone-dependent effects of FGFR2 and MAP3K1 in breast cancer susceptibility in a population-based sample of post-menopausal African-American and European-American women. Carcinogenesis. 2009;30:269–274. doi: 10.1093/carcin/bgn247. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Nickels S, Truong T, Hein R, Stevens K, Buck K, Behrens S, et al. Evidence of gene-environment interactions between common breast cancer susceptibility loci and established environmental risk factors. PLoS Genet. 2013;9:e1003284. doi: 10.1371/journal.pgen.1003284. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Milne RL, Gaudet MM, Spurdle AB, Fasching PA, Couch FJ, Benitez J, 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. doi: 10.1186/bcr2797. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Harlid S, Butt S, Ivarsson MI, Eyfjord JE, Lenner P, Manjer J, et al. Interactive effect of genetic susceptibility with height, body mass index, and hormone replacement therapy on the risk of breast cancer. BMC Womens Health. 2012;12:17. doi: 10.1186/1472-6874-12-17. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Campa D, Kaaks R, Le Marchand L, Haiman CA, Travis RC, Berg CD, 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. doi: 10.1093/jnci/djr265. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Butt S, Harlid S, Borgquist S, Ivarsson M, Landberg G, Dillner J, et al. Genetic predisposition, parity, age at first childbirth and risk for breast cancer. BMC Res Notes. 2012;5:414. doi: 10.1186/1756-0500-5-414. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Prentice RL, Huang Y, Hinds DA, Peters U, Pettinger M, Cox DR, 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. doi: 10.1158/1055-9965.EPI-09-0611. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Prentice RL, Huang Y, Hinds DA, Peters U, Cox DR, Beilharz E, et al. Variation in the FGFR2 gene and the effect of a low-fat dietary pattern on invasive breast cancer. Cancer Epidemiol Biomarkers Prev. 2010;19:74–79. doi: 10.1158/1055-9965.EPI-09-0663. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Kawase T, Matsuo K, Suzuki T, Hiraki A, Watanabe M, Iwata H, et al. FGFR2 intronic polymorphisms interact with reproductive risk factors of breast cancer: results of a case control study in Japan. Int J Cancer. 2009;125:1946–1952. doi: 10.1002/ijc.24505. [DOI] [PubMed] [Google Scholar]
- 34.Rudolph A, Milne RL, Truong T, Knight JA, Seibold P, Flesch-Janys D, et al. Investigation of gene-environment interactions between 47 newly identified breast cancer susceptibility loci and environmental risk factors. Int J Cancer. 2015;136:E685–E696. doi: 10.1002/ijc.29188. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Fejerman L, Stern MC, Ziv E, John EM, Torres-Mejia G, Hines LM, et al. Genetic ancestry modifies the association between genetic risk variants and breast cancer risk among Hispanic and non-Hispanic white women. Carcinogenesis. 2013;34:1787–1793. doi: 10.1093/carcin/bgt110. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Slattery ML, John EM, Torres-Mejia G, Lundgreen A, Herrick JS, Baumgartner KB, et al. Genetic variation in genes involved in hormones, inflammation and energetic factors and breast cancer risk in an admixed population. Carcinogenesis. 2012;33:1512–1521. doi: 10.1093/carcin/bgs163. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Slattery ML, Sweeney C, Edwards S, Herrick J, Baumgartner K, Wolff R, et al. Body size, weight change, fat distribution and breast cancer risk in Hispanic and non-Hispanic white women. Breast Cancer Res Treat. 2007;102:85–101. doi: 10.1007/s10549-006-9292-y. [DOI] [PubMed] [Google Scholar]
- 38.John EM, Horn-Ross PL, Koo J. Lifetime physical activity and breast cancer risk in a multiethnic population: the San Francisco Bay area breast cancer study. Cancer Epidemiol Biomarkers Prev. 2003;12:1143–1152. [PubMed] [Google Scholar]
- 39.John EM, Phipps AI, Davis A, Koo J. Migration history, acculturation, and breast cancer risk in Hispanic women. Cancer Epidemiol Biomarkers Prev. 2005;14:2905–2913. doi: 10.1158/1055-9965.EPI-05-0483. [DOI] [PubMed] [Google Scholar]
- 40.Angeles-Llerenas A, Ortega-Olvera C, Perez-Rodriguez E, Esparza-Cano JP, Lazcano-Ponce E, Romieu I, et al. Moderate physical activity and breast cancer risk: the effect of menopausal status. Cancer Causes Control. 2010;21:577–586. doi: 10.1007/s10552-009-9487-8. [DOI] [PubMed] [Google Scholar]
- 41.Pellatt AJ, Wolff RK, Torres-Mejia G, John EM, Herrick JS, Lundgreen A, et al. Telomere length, telomere-related genes, and breast cancer risk: the breast cancer health disparities study. Genes Chromosomes Cancer. 2013;52:595–609. doi: 10.1002/gcc.22056. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Slattery ML, Herrick JS, Torres-Mejia G, John EM, Giuliano AR, Hines LM, et al. Genetic variants in interleukin genes are associated with breast cancer risk and survival in a genetically admixed population: the Breast Cancer Health Disparities Study. Carcinogenesis. 2014 doi: 10.1093/carcin/bgu078. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Wang W, Ingles SA, Torres-Mejia G, Stern MC, Stanczyk FZ, Schwartz GG, et al. Genetic variants and non-genetic factors predict circulating vitamin D levels in Hispanic and non-Hispanic White women: the Breast Cancer Health Disparities Study. Int J Mol Epidemiol Genet. 2014;5:31–46. [PMC free article] [PubMed] [Google Scholar]
- 44.Slattery ML, Lundgreen A, Stern MC, Hines L, Wolff RK, Giuliano AR, et al. The influence of genetic ancestry and ethnicity on breast cancer survival associated with genetic variation in the TGF-beta-signaling pathway: The Breast Cancer Health Disparities Study. Cancer Causes Control. 2014;25:293–307. doi: 10.1007/s10552-013-0331-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Slattery ML, John EM, Torres-Mejia G, Lundgreen A, Lewinger JP, Stern MC, et al. Angiogenesis genes, dietary oxidative balance and breast cancer risk and progression: the Breast Cancer Health Disparities Study. Int J Cancer. 2014;134:629–644. doi: 10.1002/ijc.28377. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46.Fejerman L, Romieu I, John EM, Lazcano-Ponce E, Huntsman S, Beckman KB, et al. European ancestry is positively associated with breast cancer risk in Mexican women. Cancer Epidemiol Biomarkers Prev. 2010;19:1074–1082. doi: 10.1158/1055-9965.EPI-09-1193. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47.Flesch-Janys D, Slanger T, Mutschelknauss E, Kropp S, Obi N, Vettorazzi E, et al. Risk of different histological types of postmenopausal breast cancer by type and regimen of menopausal hormone therapy. Int J Cancer. 2008;123:933–941. doi: 10.1002/ijc.23655. [DOI] [PubMed] [Google Scholar]
- 48.Beral V. Breast cancer and hormone-replacement therapy in the Million Women Study. Lancet. 2003;362:419–427. doi: 10.1016/s0140-6736(03)14065-2. [DOI] [PubMed] [Google Scholar]
- 49.Li Q, Seo JH, Stranger B, McKenna A, Pe'er I, Laframboise T, et al. Integrative eQTL-based analyses reveal the biology of breast cancer risk loci. Cell. 2013;152:633–641. doi: 10.1016/j.cell.2012.12.034. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 50.Ghoussaini M, Edwards SL, Michailidou K, Nord S, Cowper-Sal Lari R, Desai K, et al. Evidence that breast cancer risk at the 2q35 locus is mediated through IGFBP5 regulation. Nature communications. 2014;4:4999. doi: 10.1038/ncomms5999. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 51.Hermani A, Shukla A, Medunjanin S, Werner H, Mayer D. Insulin-like growth factor binding protein-4 and-5 modulate ligand-dependent estrogen receptor-alpha activation in breast cancer cells in an IGF-independent manner. Cell Signal. 2013;25:1395–1402. doi: 10.1016/j.cellsig.2013.02.018. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.