Genome-wide-association studies (GWAS) have identified numerous single nucleotide polymorphisms (SNPs) that are associated with an increased risk of breast cancer. Most of these studies were conducted primarily in postmenopausal breast cancer patients. Therefore, we set out to assess whether or not these breast cancer variants are also associated with an elevated risk of breast cancer in young premenopausal patients.
In 451 women of European ancestry who had prospectively enrolled in a longitudinal cohort study for women diagnosed with breast cancer at or under age 40, we genotyped 44 SNPs that were previously associated with breast cancer risk. A control group was comprised of 1142 postmenopausal healthy women from the Nurses’ Health Study (NHS). We assessed if the frequencies of the adequately genotyped SNPs differed significantly (p≤0.05) between the cohort of young breast cancer patients and postmenopausal controls, and then we corrected for multiple testing.
Genotyping of the controls or cases was inadequate for comparisons between the groups for seven of the 44 SNPs. 9 of the remaining 37 were associated with breast cancer risk in young women with a p-value <0.05: rs10510102, rs1219648, rs13387042, rs1876206, rs2936870, rs2981579, rs3734805, rs3803662 and rs4973768. The directions of these associations were consistent with those in postmenopausal women. However, after correction for multiple testing (Benjamini Hochberg) none of the results remained statistically significant.
Citation: Rath M, Li Q, Li H, Lindström S, Miron A, Miron P, et al. (2019) Evaluation of significant genome-wide association studies risk — SNPs in young breast cancer patients. PLoS ONE 14(5): e0216997. https://doi.org/10.1371/journal.pone.0216997
Editor: Qingyi Wei, Duke Cancer Institute, UNITED STATES
Received: February 16, 2019; Accepted: May 2, 2019; Published: May 24, 2019
Copyright: © 2019 Rath 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.
Data Availability: Data are available from the Harvard Dataverse (https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/4TD8AS). A proposal should be submitted for review by the DF/HCC Breast Users Committee. More information on the users committee and its policies ca be found on the website: http://www.dfhcc.harvard.edu/research/research-programs/clinical-based-programs/breast-cancer/program-resources/dfhcc-breast-users-committee/policies/.
Funding: MR received funding from the German Research Foundation (Forschungsstipendium DFG), DI 1772/1-1. This work was also supported by the Tracy Starr Breast Cancer Research fund. 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.
Each year, more than 10 000 women are diagnosed with breast cancer before the age of 40 in the United States alone [1, 2], and breast cancer is the most common cause of cancer-related death in women of this age . For unknown reasons, young women are more likely to develop poorly differentiated tumors with low estrogen receptor expression [4–6]. Certain risk factors for premenopausal breast cancer have been shown to differ from those for postmenopausal breast cancer (e.g., thin body habitus increases risk of premenopausal breast cancer but reduces risk of postmenopausal breast cancer), but others (e.g., family history and deleterious BRCA mutations) are linked to risk during both periods of life [7–9].
While some cases of both premenopausal and postmenopausal breast cancer are explained by strong environmental exposures (e.g. mediastinal radiation) or mutations in cancer-predisposing genes such as BRCA and p53, the majority of women with breast cancer lack an obvious environmental trigger or a known genetic syndrome . Therefore, common genetic variants that may contribute to risk are of interest.
Recently, genome-wide association studies (GWAS) have identified numerous single nucleotide polymorphisms (SNPs) that are associated with an increased risk for the development of breast cancer in mostly postmenopausal patients [10–25]. Because most of these GWAS did not include a substantial number of women who were diagnosed with cancer while premenopausal, it has been unclear if the risk alleles discovered in postmenopausal women also predispose to breast cancer at a young age. Tapper and co-workers investigated 1001 women with early onset non-familial invasive breast cancer (≤ 40 years at diagnosis) and genotyped 206 single nucleotide polymorphisms (SNPs) across 30 candidate genes. This study confirmed previous associations between increased breast cancer risk and SNPs in CASP8, TOX3 and ESR1.
In order to investigate if known breast cancer genetic risk variants are also associated with premenopausally diagnosed breast cancer, we assessed the frequency of 44 previously identified SNPs known to be associated with risk of postmenopausal breast cancer in a cohort of 451 patients diagnosed with breast cancer at or under age 40. We included all SNPs that had been shown to be predictive of breast cancer risk at the time this study was planned [11, 12, 14, 15, 17, 20–28] We compared the allele frequencies in our cohort of young women with breast cancer to allele frequencies observed in women without evidence of breast cancer.
Material & methods
Young patients with breast cancer
The Young Women’s Breast Cancer Study is an ongoing prospective cohort study established to explore biological, medical, and psychosocial aspects of breast cancer in women diagnosed with breast cancer at or under 40 years of age (NCT01468246). Between November 2006 and December 13, 2012, 1463 women were invited to participate from eleven sites in Massachusetts, one site in Denver, Colorado, and one site in Toronto, Canada. Eligibility requirements included age 40 years or younger and diagnosis with stage 0–4 breast cancer less than six months prior to enrollment.
After 915 patients signed informed consent in person or by mail between 11/1/2006 and 12/13/2012, they received mailed surveys that included questions about sociodemographic information and medical history. In addition, medical record and central pathology review were used to obtain data on tumor stage, grade, ER/PR expression, and Her2/neu (human epidermal growth factor receptor 2) overexpression. Blood samples were collected at enrollment, one year after diagnosis, and four years after diagnosis. For the present study, only one blood sample per patient was genotyped.
Participants with non-invasive breast cancer (n = 32), as well as those with missing participant information (n = 31), were excluded from the study. In total, 451 patients with stage 1–4 breast cancer were eligible for inclusion in our analysis. This study was approved by the Institutional Review Board at Dana–Farber/Harvard Cancer Center as well as at other study sites.
Comparator genotype data came from 1142 controls who had no history of cancer and were participants in the Nurses’ Health Study (NHS). These were the same controls who were used in the “Cancer Genetic Markers of Susceptibility (CGEMS) Project” . All CGEMS data are available at dbGAP (Study accession number phs000147.v3.p1). Control blood samples were provided between 1989–1990, and only those not diagnosed with breast cancer during follow-up (until June 1, 2004) were included as controls. In the CGEMS project, 528.173 SNPs were genotyped with Illumina HumanHap550 and then imputed to HapMap 2. All control data were genotyped directly (n = 22) or imputed in our data (n = 15) (Table 1). All controls self-reported as postmenopausal and Caucasian (Southern European/Mediterranean, Scandinavian, other Caucasian); their inferred ancestry through genetic markers was consistent with this self-report.
DNA was extracted from patient whole blood samples using a Qiagen DNA extraction kit (QIAamp DNA Blood Mini kit) according to manufacturer instructions on a Qiacube instrument at the Dana-Farber Cancer Institute Breast Cancer SPORE CORE Laboratory.
All DNA samples were genotyped using the Sequenom platform. Sequenom probes and primers were purchased via Mass Array Typer 4.0.20 and MySequenom (www.mysequenom.com, Sequenom Inc). Quality control standards were followed for genotyping: 10% of the samples were genotyped twice and showed a concordance rate of 99.9%. Three SNPs were excluded from the analysis due to call rates of <95% and were replaced by proxies [proxy rs11041665 to rs3817198); proxy rs62391594 to rs6556756; proxy rs11628293 to rs999737].
Seven additional SNPs had to be excluded: rs418470 (proxy to rs1926657) and rs62391594 (proxy to rs6556756) because of missing control data; rs930395 (proxy to rs7716600) due to technical problems; rs1154865 because it had strand-ambiguous alleles (C/G); and rs10995194, rs11041665, rs2269336 due to low imputation quality score (r-sq<0.95) in the CGEMS control data. At three loci, SNPs were in Linkage Disequilibrium (LD) with each other (R2>0.9; R2>0.8; R2>0.68) and are highlighted in Table 2. The final data consisted of 37 SNPs tested in 451 cases and 1142 controls. We limited the analysis to European-ancestry patients (n = 451). All SNPs (cases and controls) were in Hardy-Weinberg equilibrium (p-value < = 0.01).
For comparison of genotype frequencies, a Chi-Square Test was used. Statistical analyses were performed using R-program (R 3.0.1 GUI, The R Foundation for statistical computing, S.Urbanek & H.-J. Bibiko). All results from the analysis with a p-value <0.05 were considered statistically significant. Correction for multiple testing was performed using the Benjamini-Hochberg method. The threshold for statistical significance based on the multiple tests performed is FDR<0.1. The chi-square test was able to detect a minimal difference in allele frequency of 8.3% with 80% power. The case-control comparison was allele-based. The association is evaluated using multivariate logistic regression, which correspond to the “additive model (odds ratio associated with per allele increase)”. We assessed three tumor biology-derived subgroups in our analysis: 1) patients with ER-positive, PR-positive and Her2-negative breast cancers (n = 193); 2) patients with Her2-positive breast cancers (n = 134); and 3) patients with ER-negative, PR-negative and Her2-negative (triple negative) breast cancers (n = 90). The 34 patients who had other types of tumors (20 with ER-positive, PR-negative, Her2-negative tumors, 6 with ER-negative, PR-positive, Her2-negative, and 8 with missing receptor information) were not included in any subgroup analysis. We also performed subgroup analyses focusing on patients with a known deleterious BRCA mutation (BRCA+; BRCA1 n = 27, BRCA2 n = 11)) and patients known not to carry a deleterious BRCA mutation (BRCA-) (n = 223). Nine patients were known to have an unclassified variant in one of the BRCA genes, 79 patients were not tested, and for 102 the BRCA-status data were unknown. Other subpopulations were described by age [≤25yrs (n = 10), 26-30yrs (n = 42), 31-35yrs (n = 113) and 36-40yrs (n = 286)]. We compared each subgroup to the same large control population (n = 1142).
We estimated the number of alternate alleles using the imputed probabilities as follow: P(G = 0) * 0 + P(G = 1) * 1 + P(G = 2) * 2 (0 = AA, 1 = AB, 2 = BB) and rounded the figures, if the results contained fractional parts.
Clinical and pathological data
DNA samples from 451 breast cancer patients with a median age at diagnosis of 37 years were genotyped. Please see Table 3 for a summary of the clinical and pathological characteristics of the patient cohort. In the control population of participants from the Nurses Health Study, the median age at the time of DNA collection was 66 years (mean: 65.7 years, range: 44–83 years).
The evaluation of 37 GWAS-SNPs revealed nine variants that differed significantly between the whole cohort of young breast cancer patients and postmenopausal controls: rs10510102, rs1219648, rs13387042, rs1876206, rs2936870 (proxy for rs2981575), rs2981579, rs3734805, rs3803662 and rs4973768. The directions of these associations were consistent with those in postmenopausal women.
In subgroup analyses, rs13387042 and rs2936870 were only associated with ER-positive, PR-positive, Her2-negative cancers, while rs10510102, rs2981579, rs3734805 were only associated with Her2-positive breast cancers. rs4973768 was associated with both ER-positive, PR-positive, Her2-negative breast cancers and Her2-positive breast cancers. Three SNPs did not appear to be associated with premenopausal breast cancer in any of the subgroups. Results from SNP analyses in the whole cohort as well as in the subgroups are shown in Tables 2 and 4 (Table 2 and Table 4).
After correction for multiple testing by Benjamini-Hochberg, none of the SNPs were found to be statistically significantly associated with breast cancer risk.
In order to elucidate if the allelic architecture in young women with breast cancer is similar to that in post-menopausal breast cancer, we tested the frequency of previously identified postmenopausal breast cancer risk-associated SNPs in a large cohort of young breast cancer patients. When our initially planned p value threshold of <0.05 was used, nine SNPs (rs10510102, rs1219648, rs13387042, rs1876206, rs2981579, rs3734805, rs3803662, rs4973768, proxy rs2936870) associated with breast cancer in postmenopausal women [11, 12, 16, 20, 22, 23] also appeared to be associated with breast cancer in young women, in the same direction as that observed in postmenopausal women. However, after correction for multiple testing (Benjamini Hochberg) none of the results remained statistically significant.
In the current study, the strongest association with breast cancer in young women was found for rs4973768. Importantly, this SNP was significantly associated with breast cancer in the overall patient cohort as well as in smaller subgroups based on tumor subtype (ER-positive/PR-positive/Her2-negative and Her2-positive breast cancers), BRCA status (BRCA negative), and age (31-40yrs).
rs4973768 lies in the untranslated region (3’UTR) of the sodium bicarbonate (Na+HCO3-) cotransporter NBCn1 (SLC4A7) . The Nashville Breast Health Study evaluated this SNP in 1511 cases with a mean age of 53.3 years (range 25–75 years) and identified an association with ER-positive breast cancer . Further, a significant association of SNP rs4973768 with increased breast cancer risk was replicated among different ethnicities in mixed age patients [32, 33]. Andersen et al. analyzed whether different SNPs previously identified in GWAS interact with one another and with reproductive and menstrual risk factors in association with breast cancer risk. Including over 1400 European-ancestry women with a median age of 54.5 years, this study confirmed the association of rs4973768 with breast cancer; however, modifications of menstrual and reproductive risk factors associations with breast cancer risk by a polygenic score were not observed . In addition to that, rs4973768 was significantly associated with breast cancer in 477 Chinese, thereby further corroborating the association of rs4973768 in different ethnicities . In contrast to our study, Antoniou and co-workers found an association of rs4973768 with an increased breast cancer risk in BRCA2 carriers , but we likely did not have enough BRCA2 carriers in our study (n = 11) to find such a link.
rs10510102, rs2981579 and rs3734805 were significantly associated with premenopausal breast cancer in the overall group and in the subgroup with Her2-positive breast cancers. Prior studies have described associations of rs10510102  and rs2981579 (FGFR2) [35–37] with breast cancer risk, but not specifically with Her2-positive breast cancers. FGFR2 (Fibroblast growth factor receptor 2) is a member of the tyrosine kinase gene superfamily  and is involved cell growth, invasiveness, motility and angiogenesis . Both rs13387042 and rs2936870 (proxy for rs2981575) were significantly associated with risk of premenopausal breast cancer in the overall group and in the Her2-negative subgroup.
rs3803662 on 16q12, located close to TOX3 (TNRC9, CAGF9) and LOC643714 , has been identified as a breast cancer risk allele in various GWAS [11, 16, 22–24] and linked to both ER-positive [22, 40–43] and ER-negative primarily postmenopausal breast cancer . Previous studies suggest that the rs3803662 risk allele may most strongly increase the likelihood of luminal A tumors in postmenopausal breast cancer, and that expression levels of TOX3 and/or LOC643714 might influence the progression of breast cancer . Within the current study of younger women, we now report an association of rs3803662 with breast cancer risk in the whole premenopausal patient cohort as well as in the subgroup of patients 36-40yrs, but not in the other subgroups. This is consistent with the prior findings of Tapper et al. 
Although the directions of these associations in our cohort were consistent with those observed in postmenopausal women, it has to be emphasized that none of the above mentioned SNPs remained significant after correction for multiple testing, perhaps due to our relatively small sample size. A larger study is needed to more definitively assess the relevance of these SNPs as causal factors in premenopausal breast cancer.
In addition to this limited power, which may have contributed to falsely negative results, our conclusions are limited by the fact that we did not evaluate all of the breast cancer predisposing variants that have now been discovered. For example, Michailidou et al.  identified SNPs at 41 new breast cancer susceptibility loci at genome-wide significance in 2013. In cases diagnosed at young age (<40 years), two loci rs2588809 at 14q24.1 (P = 0.001) and rs941764 at 14q32.12 (P = 0.007)) showed higher per-allele ORs. Both SNPs were newly published since we began our work, and therefore not included in our study. The newest breast cancer GWAS was recently published by Michailidou et al. .
In addition, earlier this year, Shi et al.  used a family-based design to analyze the relationship between breast cancer before age 50 and 77 GWAS-identified risk SNPs. They found 4 SNPs associated with a higher breast cancer risk, two of which, rs3803662 in TOX3 and rs2981579-A (FGFR2), are consistent with our findings. In our study, one of the SNPs identified to be important by Shi and colleagues, rs999737, had to be excluded due to call rates of <95%. It was replaced by its proxy, rs11628293, which did not appear to be significantly associated with risk of premenopausal breast cancer in our study. We did not assess the fourth SNP found by Shi et al. (rs12662670) because our analysis finished prior to the publication of their work.
A third limitation of this study is that different genotyping platforms were used for controls and patients, potentially introducing bias. In addition, it is possible that allele frequency differences between the control cohort and the premenopausal breast cancer cohort may have been related to age differences between the cohorts (i.e., if a gene predisposes to longevity for reasons other than a reduced risk of breast cancer, we might see a difference in the frequency of a SNP in or around that gene between the cases and controls in our study).
In conclusion, we found that nine SNPs previously associated with postmenopausal breast cancer risk might be associated with breast cancer risk in premenopausal women to some degree, but none of the results remained statistically significant after correction for multiple testing. This adds to the relatively scarce literature evaluating genetic predisposition to young-onset breast cancer [19, 35, 45, 47–50]. Future functional genomics analyses may help us better understand the causes of premenopausal breast cancer, and it will also be important to investigate potential interactions between genetic and environmental risk factors.
- 1. Society AC. Breast Cancer Facts & Figures 2007–2008. 2007–2008.
- 2. SEER Cancer Statistics Review 1975–2009 Breast [Internet]. 1975–2009. Available from: http://seer.cancer.gov/csr/1975_2009_pops09/results_merged/sect_04_breast.pdf.
- 3. Partridge AH, Hughes ME, Ottesen RA, Wong YN, Edge SB, Theriault RL, et al. The effect of age on delay in diagnosis and stage of breast cancer. Oncologist. 2012;17(6):775–82. Epub 2012/05/05. theoncologist.2011-0469 [pii] pmid:22554997; PubMed Central PMCID: PMC3380876.
- 4. Walker RA, Lees E, Webb MB, Dearing SJ. Breast carcinomas occurring in young women (< 35 years) are different. Br J Cancer. 1996;74(11):1796–800. Epub 1996/12/01. pmid:8956795; PubMed Central PMCID: PMC2077218.
- 5. Sidoni A, Cavaliere A, Bellezza G, Scheibel M, Bucciarelli E. Breast cancer in young women: clinicopathological features and biological specificity. Breast. 2003;12(4):247–50. Epub 2003/12/09. S096097760300095X [pii]. pmid:14659308.
- 6. Xiong Q, Valero V, Kau V, Kau SW, Taylor S, Smith TL, et al. Female patients with breast carcinoma age 30 years and younger have a poor prognosis: the M.D. Anderson Cancer Center experience. Cancer. 2001;92(10):2523–8. Epub 2001/12/18. [pii]. pmid:11745185.
- 7. Berstad P, Coates RJ, Bernstein L, Folger SG, Malone KE, Marchbanks PA, et al. A case-control study of body mass index and breast cancer risk in white and African-American women. Cancer Epidemiol Biomarkers Prev. 2010;19(6):1532–44. pmid:20501755; PubMed Central PMCID: PMCPMC2891096.
- 8. Ford D, Easton DF, Bishop DT, Narod SA, Goldgar DE. Risks of cancer in BRCA1-mutation carriers. Breast Cancer Linkage Consortium. Lancet. 1994;343(8899):692–5. pmid:7907678.
- 9. Renehan AG, Tyson M, Egger M, Heller RF, Zwahlen M. Body-mass index and incidence of cancer: a systematic review and meta-analysis of prospective observational studies. Lancet. 2008;371(9612):569–78. pmid:18280327.
- 10. Azzato EM, Pharoah PD, Harrington P, Easton DF, Greenberg D, Caporaso NE, et al. A genome-wide association study of prognosis in breast cancer. Cancer Epidemiol Biomarkers Prev. 2010;19(4):1140–3. Epub 2010/03/25. 1055-9965.EPI-10-0085 [pii] pmid:20332263; PubMed Central PMCID: PMC2852476.
- 11. 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(7148):1087–93. Epub 2007/05/29. nature05887 [pii] pmid:17529967; PubMed Central PMCID: PMC2714974.
- 12. 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(5):425–35. Epub 2011/01/26. djq563 [pii] pmid:21263130.
- 13. Gaudet MM, Kirchhoff T, Green T, Vijai J, Korn JM, Guiducci C, et al. Common genetic variants and modification of penetrance of BRCA2-associated breast cancer. PLoS Genet. 2010;6(10):e1001183. Epub 2010/11/10. pmid:21060860; PubMed Central PMCID: PMC2965747.
- 14. 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(11):4340–5. Epub 2008/03/11. 0800441105 [pii] pmid:18326623; PubMed Central PMCID: PMC2393811.
- 15. 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(7):870–4. Epub 2007/05/29. ng2075 [pii] pmid:17529973; PubMed Central PMCID: PMC3493132.
- 16. Li J, Humphreys K, Darabi H, Rosin G, Hannelius U, Heikkinen T, et al. A genome-wide association scan on estrogen receptor-negative breast cancer. Breast Cancer Res. 2010;12(6):R93. Epub 2010/11/11. bcr2772 [pii] pmid:21062454; PubMed Central PMCID: PMC3046434.
- 17. Li J, Humphreys K, Heikkinen T, Aittomaki K, Blomqvist C, Pharoah PD, et al. A combined analysis of genome-wide association studies in breast cancer. Breast Cancer Res Treat. 2011;126(3):717–27. Epub 2010/09/28. pmid:20872241.
- 18. Lindstrom S, Vachon CM, Li J, Varghese J, Thompson D, Warren R, et al. Common variants in ZNF365 are associated with both mammographic density and breast cancer risk. Nat Genet. 2011;43(3):185–7. Epub 2011/02/01. ng.760 [pii] pmid:21278746; PubMed Central PMCID: PMC3076615.
- 19. 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(4):353–61, 61e1-2. pmid:23535729; PubMed Central PMCID: PMCPMC3771688.
- 20. Murabito JM, Rosenberg CL, Finger D, Kreger BE, Levy D, Splansky GL, et al. A genome-wide association study of breast and prostate cancer in the NHLBI's Framingham Heart Study. BMC Med Genet. 2007;8 Suppl 1:S6. Epub 2007/10/16. 1471-2350-8-S1-S6 [pii] pmid:17903305; PubMed Central PMCID: PMC1995609.
- 21. Sehrawat B, Sridharan M, Ghosh S, Robson P, Cass CE, Mackey JR, et al. Potential novel candidate polymorphisms identified in genome-wide association study for breast cancer susceptibility. Hum Genet. 2011;130(4):529–37. Epub 2011/03/23. pmid:21424380; PubMed Central PMCID: PMC3178778.
- 22. 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(7):865–9. Epub 2007/05/29. ng2064 [pii] pmid:17529974.
- 23. 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(5):579–84. Epub 2009/03/31. ng.353 [pii] pmid:19330030; PubMed Central PMCID: PMC2928646.
- 24. 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(6):504–7. Epub 2010/05/11. ng.586 [pii] pmid:20453838; PubMed Central PMCID: PMC3632836.
- 25. Zheng W, Long J, Gao YT, Li C, Zheng Y, Xiang YB, et al. Genome-wide association study identifies a new breast cancer susceptibility locus at 6q25.1. Nat Genet. 2009;41(3):324–8. Epub 2009/02/17. ng.318 [pii] pmid:19219042; PubMed Central PMCID: PMC2754845.
- 26. 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(5):585–90. pmid:19330027; PubMed Central PMCID: PMCPMC2748125.
- 27. Antoniou AC, Wang X, Fredericksen ZS, McGuffog L, Tarrell R, Sinilnikova OM, et al. A locus on 19p13 modifies risk of breast cancer in BRCA1 mutation carriers and is associated with hormone receptor-negative breast cancer in the general population. Nat Genet. 2010;42(10):885–92. Epub 2010/09/21. ng.669 [pii] pmid:20852631; PubMed Central PMCID: PMC3130795.
- 28. 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(6):e1001002. Epub 2010/06/30. pmid:20585626; PubMed Central PMCID: PMC2891809.
- 29. dbGaP—Database of Genotypes and Phenotypes [Internet]. Available from: https://www.ncbi.nlm.nih.gov/gap.
- 30. Gorbatenko A, Olesen CW, Loebl N, Sigurdsson HH, Bianchi C, Pedraz-Cuesta E, et al. Oncogenic p95HER2 regulates Na+-HCO3- cotransporter NBCn1 mRNA stability in breast cancer cells via 3'UTR-dependent processes. Biochem J. 2016;473(21):4027–44. pmid:27609814.
- 31. Han MR, Deming-Halverson S, Cai Q, Wen W, Shrubsole MJ, Shu XO, et al. Evaluating 17 breast cancer susceptibility loci in the Nashville breast health study. Breast Cancer. 2015;22(5):544–51. pmid:24510657; PubMed Central PMCID: PMCPMC4127164.
- 32. Chen W, Song H, Zhong R, Zhu B, Guo H, Lou J, et al. Risk of GWAS-identified genetic variants for breast cancer in a Chinese population: a multiple interaction analysis. Breast Cancer Res Treat. 2013;142(3):637–44. pmid:24265035.
- 33. 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(2):427–34. pmid:23893088; PubMed Central PMCID: PMCPMC3799826.
- 34. Antoniou AC, Beesley J, McGuffog L, Sinilnikova OM, Healey S, Neuhausen SL, et al. Common breast cancer susceptibility alleles and the risk of breast cancer for BRCA1 and BRCA2 mutation carriers: implications for risk prediction. Cancer Res. 2010;70(23):9742–54. Epub 2010/12/02. 0008-5472.CAN-10-1907 [pii] pmid:21118973; PubMed Central PMCID: PMC2999830.
- 35. Shi M, O'Brien KM, Sandler DP, Taylor JA, Zaykin DV, Weinberg CR. Previous GWAS hits in relation to young-onset breast cancer. Breast Cancer Res Treat. 2017;161(2):333–44. pmid:27848153; PubMed Central PMCID: PMCPMC5226879.
- 36. Cui F, Wu D, Wang W, He X, Wang M. Variants of FGFR2 and their associations with breast cancer risk: a HUGE systematic review and meta-analysis. Breast Cancer Res Treat. 2016;155(2):313–35. pmid:26728143.
- 37. Barnholtz-Sloan JS, Shetty PB, Guan X, Nyante SJ, Luo J, Brennan DJ, et al. FGFR2 and other loci identified in genome-wide association studies are associated with breast cancer in African-American and younger women. Carcinogenesis. 2010;31(8):1417–23. Epub 2010/06/18. bgq128 [pii] pmid:20554749; PubMed Central PMCID: PMC2950798.
- 38. Wang H, Yang Z, Zhang H. Assessing interactions between the associations of fibroblast growth factor receptor 2 common genetic variants and hormone receptor status with breast cancer risk. Breast Cancer Res Treat. 2013;137(2):511–22. pmid:23184080.
- 39. Gudmundsdottir ET, Barkardottir RB, Arason A, Gunnarsson H, Amundadottir LT, Agnarsson BA, et al. The risk allele of SNP rs3803662 and the mRNA level of its closest genes TOX3 and LOC643714 predict adverse outcome for breast cancer patients. BMC Cancer. 2012;12:621. Epub 2012/12/29. 1471-2407-12-621 [pii] pmid:23270421; PubMed Central PMCID: PMC3553017.
- 40. Garcia-Closas M, Hall P, Nevanlinna H, Pooley K, Morrison J, Richesson DA, et al. Heterogeneity of breast cancer associations with five susceptibility loci by clinical and pathological characteristics. PLoS Genet. 2008;4(4):e1000054. Epub 2008/04/26. pmid:18437204; PubMed Central PMCID: PMC2291027.
- 41. Reeves GK, Travis RC, Green J, Bull D, Tipper S, Baker K, et al. Incidence of breast cancer and its subtypes in relation to individual and multiple low-penetrance genetic susceptibility loci. JAMA. 2010;304(4):426–34. Epub 2010/07/29. 304/4/426 [pii] pmid:20664043.
- 42. Milne RL, Benitez J, Nevanlinna H, Heikkinen T, Aittomaki K, Blomqvist C, et al. Risk of estrogen receptor-positive and -negative breast cancer and single-nucleotide polymorphism 2q35-rs13387042. J Natl Cancer Inst. 2009;101(14):1012–8. Epub 2009/07/02. djp167 [pii] pmid:19567422; PubMed Central PMCID: PMC2724850.
- 43. Broeks A, Schmidt MK, Sherman ME, Couch FJ, Hopper JL, Dite GS, et al. Low penetrance breast cancer susceptibility loci are associated with specific breast tumor subtypes: findings from the Breast Cancer Association Consortium. Hum Mol Genet. 2011;20(16):3289–303. Epub 2011/05/21. ddr228 [pii] pmid:21596841; PubMed Central PMCID: PMC3140824.
- 44. Stevens KN, Vachon CM, Lee AM, Slager S, Lesnick T, Olswold C, et al. Common breast cancer susceptibility loci are associated with triple-negative breast cancer. Cancer Res. 2011;71(19):6240–9. Epub 2011/08/17. 0008-5472.CAN-11-1266 [pii] pmid:21844186; PubMed Central PMCID: PMC3327299.
- 45. Tapper W, Hammond V, Gerty S, Ennis S, Simmonds P, Collins A, et al. The influence of genetic variation in 30 selected genes on the clinical characteristics of early onset breast cancer. Breast Cancer Res. 2008;10(6):R108. pmid:19094228; PubMed Central PMCID: PMCPMC2656905.
- 46. Michailidou K, Lindstrom S, Dennis J, Beesley J, Hui S, Kar S, et al. Association analysis identifies 65 new breast cancer risk loci. Nature. 2017;551(7678):92–4. pmid:29059683.
- 47. Kibriya MG, Jasmine F, Argos M, Andrulis IL, John EM, Chang-Claude J, et al. A pilot genome-wide association study of early-onset breast cancer. Breast Cancer Res Treat. 2009;114(3):463–77. Epub 2008/05/09. pmid:18463975.
- 48. Elematore I, Gonzalez-Hormazabal P, Reyes JM, Blanco R, Bravo T, Peralta O, et al. Association of genetic variants at TOX3, 2q35 and 8q24 with the risk of familial and early-onset breast cancer in a South-American population. Mol Biol Rep. 2014;41(6):3715–22. pmid:24532140.
- 49. Fu F, Wang C, Huang M, Song C, Lin S, Huang H. Polymorphisms in second intron of the FGFR2 gene are associated with the risk of early-onset breast cancer in Chinese Han women. Tohoku J Exp Med. 2012;226(3):221–9. pmid:22374580.
- 50. Jara L, Gonzalez-Hormazabal P, Cerceno K, Di Capua GA, Reyes JM, Blanco R, et al. Genetic variants in FGFR2 and MAP3K1 are associated with the risk of familial and early-onset breast cancer in a South-American population. Breast Cancer Res Treat. 2013;137(2):559–69. pmid:23225170.