Genome-wide association studies have reported that a polymorphism near the estrogen receptor gene (ESR1) (rs2046210) is associated with a risk of breast cancer, with the A allele conferring an increased risk. However, considering the controversial results from more recent replicated studies, we conducted a case-control study in an independent Chinese Han population and a meta-analysis to clarify the association of this polymorphism with breast cancer risk.
Method and Findings
A hospital-based case-control study including 461 cases and 537 controls from a Chinese Han population was conducted initially, and this study showed that the rs2046210 A allele was significantly associated with breast cancer risk, with an OR of 1.32 (95% CI = 1.10–1.59). Subsequently, a meta-analysis integrating the current study and previous publications with a total of 53,379 cases and 55,493 controls was performed to further confirm our findings. Similarly, a significant association between this polymorphism and breast cancer risk was also observed in the overall population especially among Asians, with ORs for per A allele of 1.14 (95% CI = 1.10–1.18) in the overall population and 1.27 (95% CI = 1.23–1.31) in the Asian population.
Our results provide strong evidence to support that the common polymorphism near the ESR1 gene, rs2046210, is associated with an increased risk of breast cancer in Asian and European populations but not in Africans, although the biological mechanisms need to be further investigated.
Citation: Guo H, Ming J, Liu C, Li Z, Zhang N, Cheng H, et al. (2012) A Common Polymorphism near the ESR1 Gene Is Associated with Risk of Breast Cancer: Evidence from a Case-Control Study and a Meta-Analysis. PLoS ONE 7(12): e52445. doi:10.1371/journal.pone.0052445
Editor: Xiao-Ping Miao, Huazhong University of Science and Technology, China
Received: September 1, 2012; Accepted: November 13, 2012; Published: December 18, 2012
Copyright: © 2012 Guo et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was supported by the National Natural Science Foundation of China (grants 30972935, 81001171 and 81202094) and Key Technologies R & D Program of Hubei Province (grant 2007AA302B07). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
According to global cancer statistics in 2008, breast cancer (BC) is the most common malignant tumor among women worldwide, accounting for 23% of new cancer cases and 14% of cancer deaths. In several developing countries, such as China, BC has surpassed cervical cancer and become the leading cause of cancer death among females . Although the underlying mechanism of BC pathogenesis is still unclear, accumulating evidence shows that development of BC is a multifactorial complex process influenced by multiple genetic variants and environmental factors . Given that only a few individuals receiving the same environmental exposure will develop BC, and that women with a family history of BC are at high risk for the cancer, it appears that genetic factors may play an important role in the etiology of BC. Previous studies have revealed that rare but high-penetrance mutations in BRCA1/2 and a few other inherited variants explain only up to 5% of overall BC incidence, whereas more common but low-penetrance susceptibility alleles may be responsible for a substantial fraction of BC –.
Recent genome-wide association studies (GWAS) have demonstrated numerous low-penetrance susceptibility loci were significantly associated with BC –. Among them, a common single nucleotide polymorphism (SNP) in the vicinity of the ESR1 gene was highlighted for its potentially biological plausibility in the development of breast carcinogenesis. ESR1 gene encodes estrogen receptor α (ERα), which stimulates proliferation and differentiation of mammary epithelial tissue through combining with estrogen, an established risk factor for BC . Because the biological roles of estrogen are achieved through a high-affinity combination with ERs, the genetic variants in ER genes have become the focus of molecular epidemiological studies on BC susceptibility , . rs2046210, which is located 180 kb upstream of the transcription initiation site of the first coding exon of the ESR1 gene, was firstly reported by Zheng et al. , to be associated with an increased risk of BC. However, several subsequent replication studies could not reach consistent results; for example, Stacey et al.  failed to validate the association in Europeans, and similarly, Campa et al.  were also unable to replicate the findings in Asians. Potential explanations for the discrepancy could be the modest effect of this SNP and the diverse genetic backgrounds of the different ethnic groups. Additionally, due to the “winner's curse” phenomenon, the replication studies were likely to be underpowered and possibly failed if the sample size calculations were based on the overestimated effect sizes generated from the initial study . Nevertheless, meta-analysis, a powerful tool that combines data to give the exponential increase in sample sizes, could resolve the discordances in genetic association studies . Thus, in this study, we carried out a case-control study to validate the association of rs2046210 and BC risk in the Han Chinese population along with a meta-analysis that integrated the current study and previous publications about this polymorphism, to derive a more accurate estimation of the association between this polymorphism and BC risk.
Materials and Methods
A total of 461 incident cases and 537 controls were enrolled between June 2009 and December 2011 from Union Hospital of Huazhong University of Science and Technology, Wuhan, China. All the cases were histopathologically confirmed with primary BC and none of them had received neo-adjuvant treatment. The controls were randomly selected from a health check-up program at the same hospital during the same period. The inclusion criteria for controls were that they had to be cancer-free individuals who were frequently age-matched (±5 years) to the BC cases. All subjects were unrelated ethnic Han Chinese from Wuhan and its surrounding region in the central region of China. Written informed consent was signed by each participant, and 2-ml peripheral blood samples were collected on recruitment. According to the NCCN (National Comprehensive Cancer Network) guidelines for BC , the criteria for determining menopause include any of the following: (1) prior bilateral oophorectomy; (2) age ≥60 y; (3) age<60 y and amenorrheic for 12 or more months in the absence of chemotherapy, tamoxifen, toremifene, or ovarian suppression and FSH and estradiol in the postmenopausal range; (4) if taking tamoxifen or toremifene, and age <60 y, then FSH and plasma estradiol level in postmenopausal ranges. This study was approved by the institutional review boards of Union Hospital of Huazhong University of Science and Technology and Tongji Medical College of Huazhong University of Science and Technology.
For each subject, genomic DNA was extracted from a 2-ml peripheral blood sample using the RelaxGene Blood System DP319-02 (Tiangen, Beijing, China) according to the manufacturer's instructions. The genotype of rs2046210 was carried out using the Taqman SNP Genotyping Assay (Applied Biosystems, Foster city, CA) on a 7900HT Fast Real-Time PCR System (Applied Biosystems, Foster city, CA). To assess the quality of genotyping, we conducted re-genotyping of a randomly selected 5% of the samples and obtained 100% concordance rate.
Differences in the distribution of demographic characteristics between the cases and controls were evaluated by using the χ2 test and t-test. The Hardy-Weinberg equilibrium (HWE) for genotypes in the controls was assessed by the goodness-of-fit χ2 test. An unconditional multivariate logistic regression model was used to estimate the associations between genotypes and BC risk by calculating the odds ratios (ORs) and 95% confidence intervals (CIs) after adjusting for age and menopausal status. To avoid the assumptions of genetic models, dominant, recessive and additive models for rs2046210 were also assessed. In addition, stratified analyses by menopausal status, estrogen receptor (ER) and progesterone receptor (PR) status were further carried out to evaluate the role of rs2046210 in BC risk. All statistical tests were two-sided and performed using the SPSS 12.0 computer program.
Meta-analysis of rs2046210 in association with BC risk
To further investigate the association between rs2046210 and BC risk, we carried out a meta-analysis combining previous publications and the current study. We searched PubMed, EMBASE, ISI Web of Science databases and CNKI (China National Knowledge Infrastructure) for literature published in any language up to June 2012 using the keywords combinations of “rs2046210 or 6q25.1” and “breast cancer, breast carcinoma or breast neoplasm”. The references of retrieved articles and reviews were also checked for missing information. The literature that was included needed to meet the following criteria: (1) the study evaluating the association between rs2046210 and BC risk; (2) providing data for calculating genotypic ORs with corresponding 95% CI; (3) the genotypes in control conforming to Hardy-Weinberg equilibrium (P>0. 05). Reviews, simple commentaries and case reports were excluded. If the studies had overlapping subjects, the one with the largest samples was finally included.
For each study, the following data were extracted: first author, year of publication, geographic location, ethnicity of the study population, study design, study method, control source, sample size, and frequencies of genotypes in cases and controls. The ORs were calculated for the risk allele A versus the wild allele G. Genotype AG versus GG, AA versus GG, dominant, recessive and additive models were recalculated from parts of the included studies because some research did not provide sufficient data. The between-study heterogeneity was assessed by Cochran's Q test and I2 statistics. Heterogeneity was considered significant at P<0.10 for the Q statistic . The I2 statistics was then used to evaluate heterogeneity quantitatively (I2<25%, low heterogeneity; I2 = 25–75%, moderate heterogeneity; I2>75%, high heterogeneity) . A fixed-effects model of the Mantel-Haenszel method was applied to pool data from studies if the heterogeneity was negligible based on a P value greater than 0.1 for Q statistics; otherwise, a random-effects model of the DerSimonian and Laird method was used . To explore sources of heterogeneity across studies, a meta-regression model was employed . The particular covariates for assessment of heterogeneity sources were: ethnicity (Asian, European and African), study design (GWA studies and replication), study method (case-control studies and nested case-control studies), sample size (≤2000 and >2000 subjects), source of control (population and hospital based controls). Stratified analysis was then conducted according to the potential sources of heterogeneity evaluated by meta-regression analysis. The subgroup meta-analyses stratified by ER and menopausal status were further performed. Additionally, sensitivity analysis was performed by omission of each study in turn to assess the influence of each study on the overall estimate . Cumulative meta-analysis was performed by assortment of publication times . Publication bias was assessed by a funnel plot and Eegger's test , . All statistical analyses were carried out in STATA 10.0, and all P values were two-sided with a significance level at 0.05.
In order to ensure the rigor of this current meta-analysis, we designed and reported it according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) statement and the checklist is shown in Table S1 (http://www.prisma-statement.org).
Results of case-control study
The characteristics of the cases and controls were listed in Table 1. A total of 461 BC cases and 537 frequency-matched controls were enrolled in this study. Mean age was 48.41 (±9.85) for cases and 49.04 (±12.45) for controls, and there was no significant difference between two groups (P = 0.369). The percentage of premenopausal women was 54.7% among the BC cases compared with 46.6% among controls, and the P value for the distribution of menopause status between the cases and controls was 0.011.
Association between SNP rs2046210 and BC risk.
The genotype data of rs2046210 in the cases and controls are shown in Table 2. Genotypes in the controls were consistent with the Hardy-Weinberg equilibrium (P>0.05). A multivariate logistic regression model demonstrated that individuals carrying the A allele, GA or AA genotypes presented a significantly elevated risk of BC compared with those carrying the G allele or GG genotypes after adjusting for age and menopausal status (A versus G: OR = 1.32, 95% CI = 1.10–1.59, P = 0.003; GA versus GG: OR = 1.34, 95% CI = 1.02–1.76, P = 0.036; AA versus GG: OR = 1.74, 95% CI = 1.17–2.57, P = 0.006). Meanwhile, under dominant, recessive, and additive models, significant associations were also found for SNP rs2046210, with ORs equal to 1.42(95% CI = 1.10–1.84, P = 0.008), 1.49(95% CI = 1.03–2.14, P = 0.034), and 1.32 (95% CI = 1.10–1.59, P = 0.003), respectively.
We then stratified the data according to menopausal status and ER status. The results demonstrated that rs2046210 was associated with an elevated risk of BC in an allelic model among both pre- and post-menopausal individuals. The positive association of this SNP with BC risk was also found for both ER positive and ER negative women with adjusted ORs equal to 1.27(P = 0.029) and 1.38(P = 0.009) respectively.
Result of meta-analyses
Characteristics of included studies.
As shown in Figure S1, 23 potentially relevant publications were identified through PubMed, EMBASE, ISI Web of Science and CNKI initially, of which 17 publications were judged to preliminarily meet the inclusion criteria mentioned above. Seven articles were excluded because the cases largely overlapped with the samples of previous studies –. The multicenter research reported by Cai et al.  contained samples that duplicated those in the research conducted by Han et al ; therefore the corresponding study with less case number was excluded. Finally, 10 previous publications –, – (Table 3) and the current study comprising 36 studies consisting of 53,379 cases and 55,493 controls were included in this meta-analysis based on our search strategy and eligibility criteria. Among them, the publication by Stacey et al.  provided only allelic OR value, and was thus only included in the pooled analysis for the allelic model of A VS. G. The study reported by Jiang et al.  did not provide the genotype of samples in detail, so we merely put it into the corresponding pooled analysis according to the data it provided.
Overall meta-analyses of rs2046210 in associated with BC risk.
As shown in Table 4, the P values for heterogeneity were less than 0.1 in all genetic models, therefore, ORs were pooled under a random-effects model. In the allelic model, A allele conferred a pooled OR of 1.14 (95% CI = 1.10–1.18, P<0.001) compared to G allele (Figure 1). Genotypic ORs of GA versus GG, AA versus GG, and a dominant model combined both crude and adjusted ORs because a study of Asians only provided adjusted ORs of the three models as mentioned previously , and the pooled ORs were 1.17 (95% CI = 1.11–1.24, P<0.001), 1.33 (95% CI = 1.24–1.44, P<0.001) and 1.21 (95% CI = 1.14–1.28, P<0.001) respectively. Significant associations between rs2046210 and BC risk were also observed in the recessive model (OR = 1.21, 95% CI = 1.15–1.28, P<0.001) and the additive model (OR = 1.15, 95% CI = 1.11–1.20, P<0.001) in this meta-analysis.
Meta-regression analyses and stratified analyses.
To investigate the potential sources of between-study heterogeneity under allelic model of A VS. G, meta-regression analyses were performed. A empty regression was initially run to estimate the baseline value for tau2 (tau2 = 0.0073), and then we conducted a series of univariate model by adding single covariates including ethnicity of population, study design, study method, sample size, and source of control. In the univariate analyses, we found that the tau2 value reduced to 0.0014 (adjusted R2 = 81.04%) in the model of ethnicity, suggesting that ethnicity could explain 81.04% of the heterogeneity across studies in this allelic model. Then the stratified analyses by ethnicity were further carried out (Table 4). In Asian and European populations, the polymorphism in all genetic models presented a significantly increased risk of BC; however, there was no obvious association between the SNP and BC risk in the African population in any genetic model (Figure 2). It demonstrated that the A variant played disparate roles in different ethnic populations. We found that the moderate heterogeneity still exited in the Europeans under allelic model (I2 = 47.8%, P = 0.013), therefore, the further meta-regression was carried out and it revealed that the source of control could explain 53.61% of heterogeneity. After excluding the multicenter research reported by Steven et al  that combined hospital- and population-based controls together, the heterogeneity was reduced apparently (I2 = 21.6%, P = 0.202).When we subsequently stratified the data by ER and menopausal status, the between-study heterogeneity was obvious, but it was reduced notably after further stratifying by ethnicity. The pooled ORs of A VS. G were 1.23(95% CI = 1.14–1.32, P<0.001) in the ER negative population and 1.12(95% CI = 1.04–1.20, P = 0.002) in the ER positive population. After comparing to cases with ER positive BC, the OR (95%CI) for ER negative BC was 1.11(95% CI = 1.06–1.15, P = 8.27×10−7) for allelic model of A VS.G, which indicated that the association was stronger for ER negative BC than ER positive BC. Meanwhile, the positive association of this SNP with BC risk was also found in both pre- and post-menopausal women (OR = 1.18, 95% CI = 1.13–1.24, P<0.001 and OR = 1.22, 95% CI = 1.10–1.36, P<0.001), however, no stronger association was found in post-menopausal cases by comparison with pre-menopausal counterparts (P = 0.706).
Sensitivity analyses and cumulative meta-analyses.
Since significant heterogeneity across studies existed in all genetic models of overall population and in allelic model of European population, we carried out sensitivity analyses to evaluate the effect of each study on the pooled estimate under a random-effects model by removing each study sequentially. As shown in Table 5 and Table S2, the pooled ORs were similar before and after deletion of each study. We also achieved similar results in other genetic models and no single study changed the OR values markedly, therefore, the current results are stable and credible.
Cumulative meta-analyses were carried out in all genetic models via an assortment of studies in chronologic order. As shown in Figure 3, the 95% CIs for the pooled ORs became increasingly narrower with each accumulation of more studies in all models, indicating that the precision of the estimation was progressively boosted by continually adding more samples.
A funnel plot (Figure S2) and Egger's test (all P values for Egger's test >0.05) reflected that there was no evidence of publication bias in any of the genetic models.
This study demonstrated a significant association between rs2046210 and an increased risk of BC in a Han Chinese population. The subsequent meta-analysis based on 36 studies consisting of 53,379 cases and 55,493 controls also confirmed the strong association under all genetic models in an overall population. To the best of our knowledge, this is the first meta-analysis seeking to clarify the association between this polymorphism and BC risk, and the sensitivity and cumulative analyses confirmed that the positive finding was stable and the precision of estimation was progressively boosted as more studies were involved. These results clearly revealed the role of this polymorphism, which is near the ESR1 gene, in BC susceptibility.
In the overall meta-analyses, all genetic models presented significant heterogeneity. However, the heterogeneity had been mostly explained by the ethnicity of study population according to the result of meta-regression analyses. After being stratified by ethnicity, it demonstrated that this polymorphism had a significant association with BC risk in Asians and only a weaker and unstable association in Europeans. Meanwhile it could not be validated in Africans. The strength of the association with rs2046210 varies greatly across ethnic groups. One probable reason is the considerable differences in genetic architecture across ethnic SNPs. Another plausible hypothesis suggests that rs2046210 is only a marker SNP of causative variants and resides in different linkage disequilibrium (LD) patterns among the three ethnic populations.
Intriguingly, in further analysis, we found that this association was more significant in ER negative than in ER positive BC. Two recent interesting studies ,  indicated that this polymorphism was associated with an increased risk of BC with BRCA1 mutation carriers, but not associated in BRCA2 mutation patients. Remarkably, accumulating evidence showed that the large majority of BRCA1 mutation carriers presented with ER negative tumors , which could partly explain why ER negative cases were accompanied by a stronger association. Additionally, recent studies in mice have revealed that the mammary stem cell compartment could be regulated by estrogen and progesterone through a paracrine signaling mechanism from ER positive cells to ER negative cells , . Thus, polymorphisms near the ESR1 locus could affect the occurrence and development of ER negative tumors through the paracrine pathway. In the stratified meta-analysis, we also found that rs2046210 was significantly associated with BC risk in both premenopausal and postmenopausal women for allelic model, which was kept in line with the result of our case-control study. However, there was no evidence showing that the association was stronger in post- than pre-menopausal women.
Considering the relative vicinity of rs2046210 to the ESR1 gene, it was speculated that the SNP itself or causal variants in LD with it might alter ESR1 gene expression, thus affecting the susceptibility to BC. However, the functional genomic analyses and in vitro functional experiments conducted by Cai et. al  provided no support for the potential involvement of this polymorphism in the regulation of ESR1. Although dozens of SNPs have been reported in high LD with this polymorphism, functional evaluations on them and their related genes were still warranted. Herein, we conjectured that this SNP might communicate with the ESR1 gene via a long-range chromatin loop. Nevertheless, it was just a postulation and needed to be confirmed by further longitudinal studies.
In conclusion, our case-control study and the subsequent meta-analysis effectively corroborated the impact of rs2046210 near the ESR1 gene on BC risk, and showed that the polymorphism had a larger effect on Asians than on Europeans or Africans. However, the function of this SNP is still unclear; future fine-mapping of the BC susceptibility loci tagged by rs2046210 is warranted and the underlying biological mechanism of this polymorphism still needs further investigation.
Flow diagram of the study selection procedure.
Funnel plot for publication bias test.
Sensitivity analysis of allelic model for Europeans.
Conceived and designed the experiments: TH HG JM. Performed the experiments: HG WS WW QZ PY RW YX XZ. Analyzed the data: HG JM. Contributed reagents/materials/analysis tools: XN CL ZL HC NZ NS DL LW. Wrote the paper: HG JM XN TH.
- 1. Jemal A, Bray F (2011) Center MM, Ferlay J, Ward E, et al (2011) Global cancer statistics. CA Cancer J Clin 61: 69–90. doi: 10.3322/caac.20107
- 2. Nathanson KL, Wooster R, Weber BL (2001) Breast cancer genetics: what we know and what we need. Nat Med 7: 552–556.
- 3. Easton DF (1999) How many more breast cancer predisposition genes are there? Breast Cancer Res 1: 14–17. doi: 10.1186/bcr6
- 4. Miki Y, Swensen J, Shattuck-Eidens D, Futreal PA, Harshman K, et al. (1994) A strong candidate for the breast and ovarian cancer susceptibility gene BRCA1. Science 266: 66–71. doi: 10.1126/science.7545954
- 5. Wooster R, Neuhausen SL, Mangion J, Quirk Y, Ford D, et al. (1994) Localization of a breast cancer susceptibility gene, BRCA2, to chromosome 13q12-13. Science 265: 2088–2090. doi: 10.1126/science.8091231
- 6. Hunter DJ, Kraft P, Jacobs KB, Cox DG, Yeager M, et al. (2007) A genome-wide association study identifies alleles in FGFR2 associated with risk of sporadic postmenopausal breast cancer. Nat Genet 39: 870–874. doi: 10.1038/ng2075
- 7. Gold B, Kirchhoff T, Stefanov S, Lautenberger J, Viale A, et al. (2008) Genome-wide association study provides evidence for a breast cancer risk locus at 6q22.33. Proc Natl Acad Sci U S A 105: 4340–4345. doi: 10.1073/pnas.0800441105
- 8. Kibriya MG, Jasmine F, Argos M, Andrulis IL, John EM, et al. (2009) A pilot genome-wide association study of early-onset breast cancer. Breast Cancer Res Treat 114: 463–477. doi: 10.1007/s10549-008-0039-9
- 9. Thomas G, Jacobs KB, Kraft P, Yeager M, Wacholder S, et al. (2009) A multistage genome-wide association study in breast cancer identifies two new risk alleles at 1p11.2 and 14q24.1 (RAD51L1). Nat Genet 41: 579–584. doi: 10.1038/ng.353
- 10. Zheng W, Long J, Gao YT, Li C, Zheng Y, et al. (2009) Genome-wide association study identifies a new breast cancer susceptibility locus at 6q25.1. Nat Genet 41: 324–328. doi: 10.1038/ng.318
- 11. Turnbull C, Ahmed S, Morrison J, Pernet D, Renwick A, et al. (2010) Genome-wide association study identifies five new breast cancer susceptibility loci. Nat Genet 42: 504–507. doi: 10.1038/ng.586
- 12. Cai Q, Long J, Lu W, Qu S, Wen W, et al. (2011) Genome-wide association study identifies breast cancer risk variant at 10q21.2: results from the Asia Breast Cancer Consortium. Hum Mol Genet 20: 4991–4999. doi: 10.1093/hmg/ddr405
- 13. Fletcher O, Johnson N, Orr N, Hosking FJ, Gibson LJ, et al. (2011) Novel breast cancer susceptibility locus at 9q31.2: results of a genome-wide association study. J Natl Cancer Inst 103: 425–435. doi: 10.1093/jnci/djq563
- 14. Kim HC, Lee JY, Sung H, Choi JY, Park SK, et al. (2012) 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 14: R56. doi: 10.1186/bcr3158
- 15. Long J, Cai Q, Sung H, Shi J, Zhang B, et al. (2012) Genome-wide association study in east Asians identifies novel susceptibility loci for breast cancer. PLoS Genet 8: e1002532. doi: 10.1371/journal.pgen.1002532
- 16. Shu XO, Long J, Lu W, Li C, Chen WY, et al. (2012) Novel genetic markers of breast cancer survival identified by a genome-wide association study. Cancer Res 72: 1182–1189. doi: 10.1158/0008-5472.can-11-2561
- 17. Kiyotani K, Mushiroda T, Tsunoda T, Morizono T, Hosono N, et al. (2012) A genome-wide association study identifies locus at 10q22 associated with clinical outcomes of adjuvant tamoxifen therapy for breast cancer patients in Japanese. Hum Mol Genet 21: 1665–1672. doi: 10.1093/hmg/ddr597
- 18. Clemons M, Goss P (2001) Estrogen and the risk of breast cancer. N Engl J Med 344: 276–285. doi: 10.1056/nejm200101253440407
- 19. Li N, Dong J, Hu Z, Shen H, Dai M (2010) Potentially functional polymorphisms in ESR1 and breast cancer risk: a meta-analysis. Breast Cancer Res Treat 121: 177–184. doi: 10.1007/s10549-009-0532-9
- 20. Cai Q, Shu XO, Jin F, Dai Q, Wen W, et al. (2003) Genetic polymorphisms in the estrogen receptor alpha gene and risk of breast cancer: results from the Shanghai Breast Cancer Study. Cancer Epidemiol Biomarkers Prev 12: 853–859.
- 21. Zheng W, Long J, Gao YT, Li C, Zheng Y, et al. (2009) Genome-wide association study identifies a new breast cancer susceptibility locus at 6q25.1. Nat Genet 41: 324–328. doi: 10.1038/ng.318
- 22. Stacey SN, Sulem P, Zanon C, Gudjonsson SA, Thorleifsson G, et al. (2010) Ancestry-shift refinement mapping of the C6orf97-ESR1 breast cancer susceptibility locus. PLoS Genet 6: e1001029. doi: 10.1371/journal.pgen.1001029
- 23. Campa D, Kaaks R, Le Marchand L, Haiman CA, Travis RC, et al. (2011) Interactions between genetic variants and breast cancer risk factors in the breast and prostate cancer cohort consortium. J Natl Cancer Inst 103: 1252–1263. doi: 10.1016/s0959-8049(12)70482-3
- 24. Zhong H, Prentice RL (2010) Correcting “winner's curse” in odds ratios from genomewide association findings for major complex human diseases. Genet Epidemiol 34: 78–91. doi: 10.1002/gepi.20437
- 25. Pabalan NA (2010) Meta-analysis in cancer genetics. Asian Pac J Cancer Prev 11: 33–38.
- 26. Carlson RW, Allred DC, Anderson BO, Burstein HJ, Edge SB (2012) NCCN clinical practice guidelines for breast cancer (version 1.2012) http://www.nccn.org.
- 27. Lau J, Ioannidis JP, Schmid CH (1997) Quantitative synthesis in systematic reviews. Ann Intern Med 127: 820–826. doi: 10.7326/0003-4819-127-9-199711010-00008
- 28. Higgins JP, Thompson SG, Deeks JJ, Altman DG (2003) Measuring inconsistency in meta-analyses. BMJ 327: 557–560. doi: 10.1136/bmj.327.7414.557
- 29. DerSimonian R, Laird N (1986) Meta-analysis in clinical trials. Control Clin Trials 7: 177–188. doi: 10.1016/0197-2456(86)90046-2
- 30. Thompson SG, Higgins JP (2002) How should meta-regression analyses be undertaken and interpreted? Stat Med 21: 1559–1573. doi: 10.1002/sim.1187
- 31. Thakkinstian A, McElduff P, D'Este C, Duffy D, Attia J (2005) A method for meta-analysis of molecular association studies. Stat Med 24: 1291–1306. doi: 10.1002/sim.2010
- 32. Mullen B MPBB (2001) Cumulative meta-analysis: A consideration of indicators of sufficiency and stability.: Personality and Social Psychology Bulletin. 1450–1462.
- 33. Begg CB, Mazumdar M (1994) Operating characteristics of a rank correlation test for publication bias. Biometrics 50: 1088–1101. doi: 10.2307/2533446
- 34. Egger M, Davey SG, Schneider M, Minder C (1997) Bias in meta-analysis detected by a simple, graphical test. BMJ 315: 629–634. doi: 10.1136/bmj.315.7109.629
- 35. Dai J, Hu Z, Jiang Y, Shen H, Dong J, et al. (2012) Breast cancer risk assessment with five independent genetic variants and two risk factors in Chinese women. Breast Cancer Res 14: R17. doi: 10.1186/bcr3101
- 36. Sueta A, Ito H, Kawase T, Hirose K, Hosono S, et al. (2012) A genetic risk predictor for breast cancer using a combination of low-penetrance polymorphisms in a Japanese population. Breast Cancer Res Treat 132: 711–721. doi: 10.1007/s10549-011-1904-5
- 37. Kim HC, Lee JY, Sung H, Choi JY, Park SK, et al. (2012) 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 14: R56. doi: 10.1186/bcr3158
- 38. Long J, Cai Q, Sung H, Shi J, Zhang B, et al. (2012) Genome-wide association study in east Asians identifies novel susceptibility loci for breast cancer. PLoS Genet 8: e1002532. doi: 10.1371/journal.pgen.1002532
- 39. Li G, Xiang YB, Courtney R, Cheng JR, Huang B, et al. (2011) Association of a single nucleotide polymorphism at 6q25.1,rs2046210, with endometrial cancer risk among Chinese women. Chin J Cancer 30: 138–143. doi: 10.5732/cjc.010.10516
- 40. Long J, Shu XO, Cai Q, Gao YT, Zheng Y, et al. (2010) Evaluation of breast cancer susceptibility loci in Chinese women. Cancer Epidemiol Biomarkers Prev 19: 2357–2365. doi: 10.1158/1055-9965.epi-10-0054
- 41. Zheng W, Wen W, Gao YT, Shyr Y, Zheng Y, et al. (2010) Genetic and clinical predictors for breast cancer risk assessment and stratification among Chinese women. J Natl Cancer Inst 102: 972–981. doi: 10.1093/jnci/djq170
- 42. Cai Q, Wen W, Qu S, Li G, Egan KM, et al. (2011) Replication and functional genomic analyses of the breast cancer susceptibility locus at 6q25.1 generalize its importance in women of chinese, Japanese, and European ancestry. Cancer Res 71: 1344–1355. doi: 10.1158/0008-5472.can-10-2733
- 43. Han J, Jiang T, Bai H, Gu H, Dong J, et al. (2011) Genetic variants of 6q25 and breast cancer susceptibility: a two-stage fine mapping study in a Chinese population. Breast Cancer Res Treat 129: 901–907. doi: 10.1007/s10549-011-1527-x
- 44. Stevens KN, Vachon CM, Lee AM, Slager S, Lesnick T, et al. (2011) Common breast cancer susceptibility loci are associated with triple-negative breast cancer. Cancer Res 71: 6240–6249. doi: 10.1158/0008-5472.can-11-1266
- 45. Mulligan AM, Couch FJ, Barrowdale D, Domchek SM, Eccles D, et al. (2011) Common breast cancer susceptibility alleles are associated with tumour subtypes in BRCA1 and BRCA2 mutation carriers: results from the Consortium of Investigators of Modifiers of BRCA1/2. Breast Cancer Res 13: R110. doi: 10.1186/bcr3052
- 46. Han W, Woo JH, Yu JH, Lee MJ, Moon HG, et al. (2011) Common genetic variants associated with breast cancer in Korean women and differential susceptibility according to intrinsic subtype. Cancer Epidemiol Biomarkers Prev 20: 793–798. doi: 10.1158/1055-9965.epi-10-1282
- 47. Jiang Y, Han J, Liu J, Zhang G, Wang L, et al. (2011) Risk of genome-wide association study newly identified genetic variants for breast cancer in Chinese women of Heilongjiang Province. Breast Cancer Res Treat 128: 251–257. doi: 10.1007/s10549-010-1327-8
- 48. Antoniou AC, Kartsonaki C, Sinilnikova OM, Soucy P, McGuffog L, et al. (2011) Common alleles at 6q25.1 and 1p11.2 are associated with breast cancer risk for BRCA1 and BRCA2 mutation carriers. Hum Mol Genet 20: 3304–3321.
- 49. Lakhani SR, Van De Vijver MJ, Jacquemier J, Anderson TJ, Osin PP, et al. (2002) The pathology of familial breast cancer: predictive value of immunohistochemical markers estrogen receptor, progesterone receptor, HER-2, and p53 in patients with mutations in BRCA1 and BRCA2. J Clin Oncol 20: 2310–2318. doi: 10.1200/jco.2002.09.023
- 50. Asselin-Labat ML, Vaillant F, Sheridan JM, Pal B, Wu D, et al. (2010) Control of mammary stem cell function by steroid hormone signalling. Nature 465: 798–802. doi: 10.1038/nature09027
- 51. Joshi PA, Jackson HW, Beristain AG, Di Grappa MA, Mote PA, et al. (2010) Progesterone induces adult mammary stem cell expansion. Nature 465: 803–807. doi: 10.1038/nature09091