Abstract
Background
Previous studies evaluating the association of vitamin D related genetic variants with breast cancer risk have produced inconsistent results.
Methods
We evaluated the association between breast cancer risk and of 559 SNPs in 12 vitamin D-related genes, including 6 genes associated with circulating 25(OH)D level identified by recent genome-wide association studies (GWAS) using directly observed and imputed GWAS genotyping data from 2,919 breast cancer cases and 2,323 controls recruited in the Shanghai Breast Cancer Study (SBCS).
Results
Of the studied SNPs, only rs12570116 in ACADSB, rs10902845 in C10orf88, rs4760658 in VDR, and rs6091822, rs8124792 and rs6097809 in CYP24A1 had a nominal association with breast cancer risk (P value <0.05 for all). None of these association persisted after adjustment for multiple comparisons. The most extensively studied SNPs including rs10735810, also known as rs2228570 (Fok1, VDR), rs1544410 (Bsm1, VDR), and rs2296241 (CYP24A1) were not associated with breast cancer risk. GWAS-identified genetic variants that were associated with 25(OH)D were also not related to breast cancer risk.
Conclusions
Our data suggest that genetic polymorphisms in vitamin D-related genes do not play a major role in breast cancer risk in Chinese women.
Impact
Although our study confirms previously documented breast cancer risk-factor associations, our null results suggest that common genetic variants in vitamin D genes and loci associated with control of vitamin D levels are not risk factors for breast cancer in Chinese women. Our data contributes to filling the gap in this field of research.
Keywords: breast cancer, risk, polymorphisms, vitamin D pathway genes, 25(OH)D, GWAS
Introduction
In recent years, the role of vitamin D in the etiology of breast cancer has been increasingly recognized, because of its importance in cell proliferation, apoptosis and differentiation in normal and malignant tumor cells (1, 2). Numerous epidemiologic studies have suggested that vitamin D status or circulating 25-hydroxyvitamin D [25(OH)D] level (1) and common variants that affect vitamin D production and signaling may play a role in the development of breast cancer (2, 3), however, the results are indefinite. No epidemiologic study has yet simultaneously evaluated the association between polymorphisms in vitamin D pathway genes (CYP27B1, CYP27A1, CYP24A1, GC, CYP3A4, CYP2J2, CYP2R1 and VDR) (2), as well as in novel genes associated with 25(OH)D level that have been identified by recent GWAS (NADSYN1, DHCR7, ACADSB, and C10orf88) (4, 5) and breast cancer risk. We comprehensively examined this association using data on over 5,000 women from the Shanghai Breast Cancer GWAS (SBC-GWAS).
Materials and Methods
Study population
The current study includes data from 5,242 Chinese women, aged 25–70 years, in the SBC-GWAS, which drew its data from women who participated in the Shanghai Breast Cancer Study (SBCS, Phases I and II), a population-based case-control study. Detailed methods for the SBCS and the SBC-GWAS are published elsewhere (6). This study was approved by all participating institutions, and participants provided written, informed consent.
SNP genotyping, selection, and imputation
Genotyping information was generated using the Affymetrix 6.0 as described in detail previously (6). The eight genes (CYP27B1, CYP27A1, CYP24A1, GC, CYP3A4, CYP2J2, CYP2R1 and VDR) evaluated in this study were selected based on their potential biologic role in vitamin D as determined by literature review (2, 4), as well as an informatics tool, the STRING database, version 8.3. Additionally, we included genes (NADSYN1, ACADSB, DHCR7, and C10orf88) that have been associated with 25(OH)D concentration, as identified by recent GWASs (4, 5). SNPs were selected within the region 10 kb upstream of the transcription start site and 10 kb downstream from the end of each gene. A total of 559 SNPs (175 directly observed and 384 imputed) in these 12 vitamin genes with a MAF ≥5% were included in the analyses.
Statistical analysis
Descriptive statistics and genome-wide analyses were conducted within each sample set and in aggregate using SAS Version 9.2 and PLINK, respectively, as described previously (6). Multivariate logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for associations between breast cancer risk and polymorphisms under an additive genetic model, controlling for age and education. P-values were not corrected for multiple tests (Table 1–2). Linkage disequilibrium (LD) was assessed by Haploview.
Table 1.
SNPs with a P value <0.1 in the genes of interest and breast cancer risk, the SBC-GWAS (2,919 cases and 2,323 controls)
|
Gene code SNPs |
Chr | Base position |
Effective allele and frequency |
Per allele OR (95% CIs)* |
P value | Data source |
|
|---|---|---|---|---|---|---|---|
| CYP2J2 | |||||||
| rs11572321 | 1 | 60134980 | A | 0.95 | 0.85 (0.70–1.01) | 0.077 | Imputed |
| rs11572307 | 1 | 60138925 | C | 0.05 | 1.18 (0.98–1.42) | 0.078 | Genotyped |
| rs11572305 | 1 | 60138974 | C | 0.95 | 0.85 (0.71–1.01) | 0.079 | Imputed |
| rs3738474 | 1 | 60154172 | T | 0.05 | 1.18 (0.98–1.41) | 0.072 | Genotyped |
| rs11572235 | 1 | 60156005 | A | 0.95 | 0.84 (0.70–1.01) | 0.065 | Imputed |
| rs11572227 | 1 | 60158049 | C | 0.05 | 1.18 (0.98–1.42) | 0.066 | Imputed |
| GC | |||||||
| rs12640179 | 4 | 72831551 | G | 0.16 | 1.10 (0.99–1.22) | 0.072 | Genotyped |
| rs1491709 | 4 | 72832430 | A | 0.17 | 1.09 (0.98–1.21) | 0.081 | Imputed |
| ACADSB | |||||||
| rs12570116 | 10 | 124766465 | C | 0.97 | 1.76 (1.18–2.62) | 0.004 | Imputed |
| C10orf88 | |||||||
| rs10902845 | 10 | 124673297 | C | 0.21 | 1.14 (1.01–1.30) | 0.039 | Imputed |
| VDR | |||||||
| rs4760658 | 12 | 46582753 | G | 0.02 | 0.71 (0.52–0.97) | 0.033 | Genotyped |
| rs2254210 | 12 | 46559981 | A | 0.29 | 1.08 (0.99–1.17) | 0.065 | Genotyped |
| rs11168292 | 12 | 46579872 | G | 0.01 | 0.74 (0.54–1.01) | 0.062 | Genotyped |
| rs4077869 | 12 | 46591911 | A | 0.95 | 0.84 (0.69–1.02) | 0.085 | Imputed |
| rs7302038 | 12 | 46593908 | A | 0.04 | 1.20 (0.99–1.46) | 0.058 | Genotyped |
| CYP24A1 | |||||||
| rs6091822 | 20 | 52195842 | G | 0.71 | 1.11 (1.01–1.21) | 0.022 | Imputed |
| rs8124792 | 20 | 52200214 | A | 0.30 | 0.89 (0.82–0.98) | 0.016 | Genotyped |
| rs6097805 | 20 | 52201903 | A | 0.38 | 1.07 (0.98–1.16) | 0.094 | Genotyped |
| rs6097807 | 20 | 52202862 | A | 0.37 | 1.07 (0.99–1.16) | 0.084 | Imputed |
| rs6097809 | 20 | 52206917 | C | 0.31 | 0.90 (0.83–0.98) | 0.021 | Genotyped |
| rs4809959 | 20 | 52219266 | A | 0.47 | 0.92 (0.83–1.01) | 0.094 | Imputed |
| rs2585424 | 20 | 52231986 | G | 0.79 | 1.09 (0.98–1.20) | 0.082 | Imputed |
Adjusted for age at interview and education
Note: P test for trend is from additive models of effect. SNPs associated with breast cancer risk (P value <0.05) are shown in bold, however, these P values were not significant after accounting for multiple comparisons (data not shown).
Table 2.
GWAS-identified SNPs associated with circulating vitamin D [25(OH)D] levels and proxy SNPs in relation to breast cancer risk in the SBC-GWAS (2,919 cases and 2,323 controls)
| GWAS SNP | Chr | Genomic position |
Gene location or nearest gene |
Effective allele and frequency |
Per allele OR (95% CIs)* |
P value |
Data source |
|
|---|---|---|---|---|---|---|---|---|
| rs17467825 a | 4 | 72824381 | GC | A | 0.69 | 0.98 (0.90–1.08) | 0.78 | Imputed |
| rs2282679 a,b | 4 | 72827247 | GC | G | 0.31 | 1.01 (0.92–1.10) | 0.82 | Imputed |
| rs3755967 a | 4 | 72828262 | GC | T | 0.31 | 1.01 (0.92–1.09) | 0.89 | Genotyped |
| rs2298850 a | 4 | 72833131 | GC | C | 0.31 | 1.01 (0.92–1.09) | 0.88 | Genotyped |
| rs7041 a, b | 4 | 72837198 | GC | A | 0.72 | 1.03 (0.94–1.13) | 0.44 | Imputed |
| rs1155563 a,b | 4 | 72862352 | GC | C | 0.40 | 1.02 (0.94–1.12) | 0.55 | Imputed |
| rs1993116 a,b | 11 | 14866810 | CYP2R1 | A | 0.35 | 0.98 (0.90–1.06) | 0.58 | Imputed |
| rs10500804 a | 11 | 14866849 | CYP2R1 | G | 0.37 | 1.00 (0.92–1.08) | 0.98 | Genotyped |
| rs12794714 | 11 | 14870151 | CYP2R1 | A | 0.37 | 0.99 (0.91–1.07) | 0.87 | Imputed |
| rs10741657 a,b | 11 | 14871454 | CYP2R1 | A | 0.35 | 0.97 (0.90–1.05) | 0.56 | Imputed |
| rs2060793 a,b | 11 | 14871886 | CYP2R1 | A | 0.35 | 0.98 (0.90–1.06) | 0.56 | Imputed |
| rs7944926 a | 11 | 70843273 | DHCR7/NADSYN1 | A | 0.54 | 0.94 (0.87–1.02) | 0.19 | Imputed |
| rs12785878 a,b | 11 | 70845097 | DHCR7/NADSYN1 | T | 0.46 | 1.02 (0.94–1.10) | 0.58 | Genotyped |
| rs4944957 a | 11 | 70845683 | DHCR7/NADSYN1 | G | 0.45 | 1.01 (0.93–1.09) | 0.79 | Genotyped |
| rs12800438 a | 11 | 70848651 | DHCR7/NADSYN1 | A | 0.45 | 1.04 (0.96–1.12) | 0.30 | Imputed |
| rs3794060 a | 11 | 70865327 | DHCR7/NADSYN1 | C | 0.54 | 0.95 (0.88–1.03) | 0.25 | Imputed |
| rs4945008 a | 11 | 70898896 | DHCR7/NADSYN1 | A | 0.55 | 0.95 (0.88–1.02) | 0.21 | Imputed |
| rs1790349 b | 11 | 70819998 | DHCR7/NADSYN1 | C | 0.29 | 0.97 (0.89–1.06) | 0.56 | Genotyped |
| rs3829251 b | 11 | 70872207 | NADSYN1 | A | 0.30 | 0.94 (0.86–1.02) | 0.16 | Imputed |
| rs10898193 | NADSYN1 | |||||||
| proxy for rs11234027 b | 11 | 70874731 | T | 0.30 | 0.94 (0.86–1.02) | 0.16 | Genotyped | |
| rs17104498 | C10orf88 | |||||||
| proxy for rs6599638 b | 10 | 124786160 | G | 0.12 | 0.96 (0.85–1.08) | 0.49 | Genotyped | |
| rs2762932 | CYP24A1 | |||||||
| proxy for rs6013897 a | 20 | 52201798 | C | 0.10 | 1.04 (0.91–1.18) | 0.53 | Genotyped | |
Adjusted for age at interview and education
The SUNLIGHT GWAS of 15 cohorts (UK, US, Canada, Netherlands, Sweden, and Finland)
Meta-analysis of five GWAS within five cohorts (European ancestry)
Results
The mean age was 50.7 for cases and 49.6 years for controls. As compared with controls, cases were more likely to have higher educational attainment, earlier age at menarche, later age at first birth and menopause, longer reproductive span, family history of breast cancer among first-degree relatives, and a higher waist-to-hip ratio (WHR), data not shown. Of the 559 SNPs in the 12 genes that we analyzed, only 6 SNPs (rs12570116 in ACADSB, rs10902845 in C10orf88, rs4760658 in VDR, and rs6091822, rs8124792 and rs6097809 in CYP24A1) were associated with breast cancer risk (P<0.05 for all, Table 1). However, these nominally significant associations were not significant after accounting for multiple testing. We also found no association between breast cancer risk and the most extensively studied genetic polymorphisms (2) including Fok1 (rs10735810, also known as rs2228570), Bsm1 (rs1544410), Taq1 (rs731236), and Apa1 (rs7975232) in VDR, and rs2296241 in CYP24A1 (data not shown). The 22 SNPs associated with 25(OH)D level identified by prior GWAS and none were associated with breast cancer risk in our population (Table 2). Further, in analyses stratified by menopausal status, physical activity, or dietary energy intake, no polymorphisms were associated with breast cancer risk (data not shown).
Discussion
To our knowledge, this is the first comprehensive examination of common genetic variations (559 SNPs) across 12 genes related to vitamin D and breast cancer risk. Overall, vitamin D pathway gene polymorphisms were not associated with breast cancer risk in our population. The suggestive association for 6 SNPs in ACADSB, C10orf88, VDR and CYP24A1 genes could be due to chance and need to be further investigated in other populations.
Among the vitamin D pathway genes, VDR SNPs (Apa1: rs7975232, Bsm1: rs1544410, Taq1: rs731236, and Fok1: rs10735810) have been investigated extensively in relation to cancer risk, particularly among European-ancestry populations (2, 3). However, none of these SNPs were associated with breast cancer risk in our study. In a meta-analysis of 21 case-control studies (2), and in a pooling analysis of 6 prospective studies (3) breast cancer risk was associated with the VDR Fok1 polymorphism, however, the Cancer Prevention Study (CPS) II Nutrition Cohort in the US did not find a relationship between any of these VDR SNPs and postmenopausal breast cancer risk (7). Prior studies showed that CYP24A1 was overexpressed in breast carcinoma (8). We also did not found that CYP24A1 polymorphisms were associated with breast cancer risk, consistent with the CPS II Nutrition Cohort (7).
In a recent GWAS from 5 cohorts among European ancestry populations in the US, several SNPs in GC, DHCR7/NADSYN, CYP2R1, and C10orf88 (in the vicinity of ACADSB) genes were associated with 25(OH)D level (4). Another GWAS (the Study of Underlying Genetic Determinants of Vitamin D and Highly Related Traits, SUNLIGHT) of 15 cohorts among 33,996 individuals of European ancestry found an association for other 3 SNPs in DHCR7/NADSYN1, GC, and CYP2R1 (5). None of these 25(OH)D associated gene variants were related to breast cancer risk in our population.
Strengths of this study include a large sample size, population-based design, comprehensive analysis of involving multiple genes, and control of potential confounding variables. A limitation of this study is that we have no direct measurements of circulating 25(OH)D level or vitamin D status. Additionally, our analysis was conducted among Chinese women, and the results may not be generalizable to other ethnic groups/populations.
In conclusion, this comprehensive survey provides no strong evidence for the hypothesis that genetic variants in a set of genes related to vitamin D level and metabolism play an independent role in breast cancer development among Chinese women.
Supplementary Material
Acknowledgments
The authors wish to thank the participants and research staff of the Shanghai Breast Cancer Study for their contributions and commitment to this project, and Bethanie Rammer for assistance with the preparation of this manuscript.
Financial Support: This research was supported by USPHS grants R01CA064277, R01CA090899, and R01CA124558. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Cancer Institute or the National Institutes of Health. Sample preparation and genotyping assays, using Affymetrix arrays, were conducted at the Survey and Biospecimen Shared Resource and the Vanderbilt Microarray Shared Resource, respectively, which are supported in part by the Vanderbilt-Ingram Cancer Center (P30CA68485).
Abbreviations
- 25(OH)D
25-hydroxyvitamin D
- LD
Linkage disequilibrium
- MAF
Minor allele frequency
- SNP
Single nucleotide polymorphism
- GWAS
Genome-wide association study
- SBCS
Shanghai Breast Cancer Study
- SBC-GWAS
Shanghai Breast Cancer-Genome Wide Association Study
- VDR
Vitamin D receptor gene
- CYP24A1
Vitamin D 24-hydroxylase
- CYP27A1
Vitamin D(3) 24-hydroxylase
- CYP27B1
25(OH)D-1-alpha hydroxylase
- CYP2R1
Cytochrome P450, family 2, subfamily R, polypeptide 1
- CYP3A4
Cytochrome P450, family 3, subfamily A, polypeptide 4
- CYP2J2
Cytochrome P450 arachidonic acid epoxygenase
- GC
Vitamin D binding protein
- ACADSB
acyl-Coenzyme A dehydrogenase
- C10orf88
the region of chromosome 10 harboring open-reading frame 88
- DHCR7
7-dehydrocholestrol reductase
- NADSYN1
Nicotinamide adenine dinucleotide (NAD) synthetase
Footnotes
Conflicts of Interest: No potential conflicts of interest.
References
- 1.Yin L, Grandi N, Raum E, et al. Meta-analysis: serum vitamin D and breast cancer risk. Eur J Cancer. 2010;46(12):2196–2205. doi: 10.1016/j.ejca.2010.03.037. [DOI] [PubMed] [Google Scholar]
- 2.Tang C, Chen N, Wu M, Yuan H, Du Y. Fok1 polymorphism of vitamin D receptor gene contributes to breast cancer susceptibility: a meta-analysis. Breast Cancer Res Treat. 2009;117(2):391–399. doi: 10.1007/s10549-008-0262-4. [DOI] [PubMed] [Google Scholar]
- 3.McKay JD, McCullough ML, Ziegler RG, et al. Vitamin D receptor polymorphisms and breast cancer risk: results from the National Cancer Institute Breast and Prostate Cancer Cohort Consortium. Cancer Epidemiol Biomarkers Prev. 2009;18(1):297–305. doi: 10.1158/1055-9965.EPI-08-0539. [DOI] [PubMed] [Google Scholar]
- 4.Ahn J, Yu K, Stolzenberg-Solomon R, et al. Genome-wide association study of circulating vitamin D levels. Hum Mol Genet. 2010;19(13):2739–2745. doi: 10.1093/hmg/ddq155. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Wang TJ, Zhang F, Richards JB, et al. Common genetic determinants of vitamin D insufficiency: a genome-wide association study. Lancet. 2010;376(9736):180–188. doi: 10.1016/S0140-6736(10)60588-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Long J, Shu XO, Cai Q, et al. Evaluation of breast cancer susceptibility loci in Chinese women. Cancer Epidemiol Biomarkers Prev. 2010;19(9):2357–2365. doi: 10.1158/1055-9965.EPI-10-0054. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.McCullough ML, Stevens VL, Diver WR, et al. Vitamin D pathway gene polymorphisms, diet, and risk of postmenopausal breast cancer: a nested case-control study. Breast Cancer Res. 2007;9(1):R9. doi: 10.1186/bcr1642. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Lopes N, Sousa B, Martins D, et al. Alterations in Vitamin D signalling and metabolic pathways in breast cancer progression: a study of VDR, CYP27B1 and CYP24A1 expression in benign and malignant breast lesions Vitamin D pathways unbalanced in breast lesions. BMC Cancer. 2010;10:483. doi: 10.1186/1471-2407-10-483. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.