Abstract
Background: Two recent genome-wide association studies (GWAS) identified SNPs in or near four genes related to circulating 25-hydroxyvitamin D [25(OH)D] concentration. To examine the hypothesized inverse relationship between vitamin D status and breast cancer, we studied the associations between SNPs in these genes and breast cancer risk in a large pooled study of 9,456 cases and 10,816 controls from six cohorts.
Methods: SNP markers localized to each of four genes (GC, CYP24A1, CYP2R1, and DHCR7) previously associated with 25(OH)D were genotyped and examined both individually and as a 4-SNP polygenic score. Logistic regression was used to estimate the associations between the genetic variants and risk of breast cancer.
Results: We found no association between any of the four SNPs or their polygenic score and breast cancer risk.
Conclusions: Our findings do not support an association between vitamin D status, as reflected by 25(OH)D–related genotypes, and breast cancer risk.
Impact: These findings may contribute to future meta-analyses and scientific review articles, and provide new data about the association between vitamin D–related genes and breast cancer. Cancer Epidemiol Biomarkers Prev; 24(3); 627–30. ©2014 AACR.
Introduction
A meta-analysis of prospective studies showed an inverse association between breast cancer and circulating 25(OH)D, an indicator of vitamin D status (1). Genome-wide association studies (GWAS) identified SNPs in or near four genes related to circulating 25(OH)D (2, 3): GC, encoding vitamin D binding protein, the major transporter of circulating vitamin D compounds; CYP24A1, encoding the cytochrome p450 24-hydroxylase that initiates intracellular catabolism of 25(OH)D and 1,25-dihydroxyvitamin D; CYP2R1, encoding the 25-hydroxylase that converts vitamin D to 25(OH)D in the liver; and DHCR7, encoding the enzyme that converts 7-dehydrocholesterol to cholesterol rather than vitamin D3 (2, 3). These four SNPs explain more variation in circulating 25(OH)D (5.2%) than a polygenic score including 9,000 SNPs (0.16% ref. 4).
The present study examines the four SNPs and their composite genetic score in relation to breast cancer in a large pooled analysis.
Materials and Methods
Details of the Breast and Prostate Cancer Cohort Consortium (BPC3) have been reported previously (5). Briefly, it is a consortium of breast cancer case–control sets nested in seven cohorts: the American Cancer Society Cancer Prevention Study II (CPS-II), the European Prospective Investigation into Cancer and Nutrition Cohort (EPIC), the Multiethnic Cohort (MEC), the Nurses' Health Study I and II (NHS and NHSII), the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO), and the Women's Health Study (WHS). This analysis was restricted to women of European ancestry (9,456 cases and 10,816 controls).
We chose SNPs identified in GWAS as associated with plasma/serum 25(OH)D concentrations (2, 3): rs2282679 (GC), rs10741657 (CYP2R1), rs12785878 (DHCR7), and rs6013897 (CYP24A1). Genotyping was conducted by Taqman or genome-wide scans as previously described (6).
Logistic regression was used to estimate odds ratios (OR) and 95% confidence intervals (CI) for breast cancer risk by individual SNPs and an unweighted polygenic score summing the number of lower 25(OH)D–associated alleles (i.e., “low vitamin D alleles”; ref. 6). Models were adjusted for cohort and baseline age (continuous). Further adjustment for additional baseline factors did not alter the associations: body mass index (BMI), height, history of diabetes, smoking status, alcohol consumption, age at menarche, menopausal status, age at menopause, use of menopausal hormone therapy or oral contraceptives, age at first live birth, and family history of breast cancer. Details on collection and harmonization of these data have been published previously (5, 6). Analyses stratifying by estrogen receptor (ER) and progesterone receptor (PR) status, cancer stage, menopausal status at diagnosis (postmenopausal at baseline or age 55+ at diagnosis vs. premenopausal or perimenopausal at baseline and age ≤51 at diagnosis), BMI, height, and cohort were conducted. SAS v9.3 was used.
Results
Characteristics of the cohorts have been published elsewhere (5). An examination of the four SNPs individually revealed no association with breast cancer and no heterogeneity across cohorts for any SNP (Table 1). We observed no differences in the association for any SNP across subgroups of stage, menopausal status at diagnosis, or PR status (data not shown), although women with two low vitamin D alleles for rs12785878 (DHCR7) appeared to be at lower risk of ER− breast cancer, consistent with a recessive genetic association (GG vs. TT: OR, 0.56; 95% CI, 0.32–0.97; P = 0.04). Consideration of multiple comparisons, however, rendered this finding not statistically significant (Bonferroni-corrected P value threshold = 0.00125). There was no statistically significant interaction with BMI or height after consideration of multiple comparisons. The polygenic score was also not associated with breast cancer risk (Fig. 1), and this did not vary by height, BMI, ER or PR status, stage, or menopausal status at diagnosis (data not shown).
Individual SNP associations with risk of breast cancer
| SNP | Gene | Frequency of low vitamin D allele | Low vitamin D alleles (n) | Cases (n) | Controls (n) | OR (95% CI)a | Pb | P for heterogeneity across cohortsc |
|---|---|---|---|---|---|---|---|---|
| rs2282679 | GC | 0.28 | ||||||
| TT | 0 | 4,531 | 5,100 | 1.0 (ref.) | ||||
| GT | 1 | 3,412 | 4,104 | 0.94 (0.88–1.00) | ||||
| GG | 2 | 713 | 781 | 1.03 (0.92–1.15) | 0.08 | |||
| Additive (per allele) | 0.98 (0.94–1.03) | 0.39 | 0.10 | |||||
| rs6013897 | CYP24A1 | 0.21 | ||||||
| TT | 0 | 5,759 | 6,551 | 1.0 (ref.) | ||||
| AT | 1 | 3,064 | 3,576 | 0.97 (0.91–1.03) | ||||
| AA | 2 | 447 | 491 | 1.03 (0.90–1.18) | 0.48 | |||
| Additive (per allele) | 0.99 (0.94–1.04) | 0.65 | 0.84 | |||||
| rs10741657 | CYP2R1 | 0.61 | ||||||
| AA | 0 | 1,301 | 1,486 | 1.0 (ref.) | ||||
| AG | 1 | 4,041 | 4,766 | 0.96 (0.88–1.05) | ||||
| GG | 2 | 3,276 | 3,708 | 1.00 (0.91–1.09) | 0.41 | |||
| Additive (per allele) | 1.01 (0.97–1.05) | 0.72 | 0.62 | |||||
| rs12785878 | DHCR7 | 0.27 | ||||||
| TT | 0 | 4,935 | 5,674 | 1.0 (ref.) | ||||
| GT | 1 | 3,620 | 4,052 | 1.03 (0.97–1.09) | ||||
| GG | 2 | 669 | 834 | 0.92 (0.83–1.03) | 0.16 | |||
| Additive (per allele) | 0.99 (0.95–1.03) | 0.60 | 0.13 |
| SNP | Gene | Frequency of low vitamin D allele | Low vitamin D alleles (n) | Cases (n) | Controls (n) | OR (95% CI)a | Pb | P for heterogeneity across cohortsc |
|---|---|---|---|---|---|---|---|---|
| rs2282679 | GC | 0.28 | ||||||
| TT | 0 | 4,531 | 5,100 | 1.0 (ref.) | ||||
| GT | 1 | 3,412 | 4,104 | 0.94 (0.88–1.00) | ||||
| GG | 2 | 713 | 781 | 1.03 (0.92–1.15) | 0.08 | |||
| Additive (per allele) | 0.98 (0.94–1.03) | 0.39 | 0.10 | |||||
| rs6013897 | CYP24A1 | 0.21 | ||||||
| TT | 0 | 5,759 | 6,551 | 1.0 (ref.) | ||||
| AT | 1 | 3,064 | 3,576 | 0.97 (0.91–1.03) | ||||
| AA | 2 | 447 | 491 | 1.03 (0.90–1.18) | 0.48 | |||
| Additive (per allele) | 0.99 (0.94–1.04) | 0.65 | 0.84 | |||||
| rs10741657 | CYP2R1 | 0.61 | ||||||
| AA | 0 | 1,301 | 1,486 | 1.0 (ref.) | ||||
| AG | 1 | 4,041 | 4,766 | 0.96 (0.88–1.05) | ||||
| GG | 2 | 3,276 | 3,708 | 1.00 (0.91–1.09) | 0.41 | |||
| Additive (per allele) | 1.01 (0.97–1.05) | 0.72 | 0.62 | |||||
| rs12785878 | DHCR7 | 0.27 | ||||||
| TT | 0 | 4,935 | 5,674 | 1.0 (ref.) | ||||
| GT | 1 | 3,620 | 4,052 | 1.03 (0.97–1.09) | ||||
| GG | 2 | 669 | 834 | 0.92 (0.83–1.03) | 0.16 | |||
| Additive (per allele) | 0.99 (0.95–1.03) | 0.60 | 0.13 |
aAdjusted for age at baseline (continuous) and study cohort.
bFor categorical analyses of genotype, the P value is a 2 degree of freedom test comparing a model containing indicator variables for genotype and covariates with a model containing only the covariates. For the per allele analyses, the P value is a 1 degree of freedom test comparing a model containing the number of alleles and covariates to a model containing only the covariates.
cThe P value for heterogeneity across cohorts is based on the test for interaction between cohort and the additive SNP variable.
Discussion
In this large, pooled analysis, we found no association between four SNPs related to vitamin D status (or the 4-SNP polygenic score) and breast cancer risk. Given that these SNPs explain approximately 5% of the 25(OH)D variation, if the OR of breast cancer per standard deviation increase of 25(OH)D were 0.83 or stronger, we should have 80% power to detect it (6). We observed no association with any of the breast cancer subtypes examined, with the possible exception of lower risk of ER− breast cancer among women with two low vitamin D alleles for rs12785878 near DHCR7, which encodes an enzyme that converts 7-dehydrocholesterol to cholesterol rather than to vitamin D3. As cholesterol is a precursor of sex steroids, including estrogens and androgens, a breast cancer association with this gene, if substantiated, may have its basis in steroid hormone, not vitamin D, metabolism (7). The finding may, however, be due to chance.
Few studies have examined these SNPs in relation to breast cancer risk. One found no association for a polygenic score of the same four genes, but increased risk among women with two low vitamin D alleles for rs6013897 in CYP24A1 (8). Our findings do not support an association between SNPs associated with vitamin D status and breast cancer risk, but the possible association of DHCR7 with ER− disease should be examined further.
Disclosure of Potential Conflicts of Interest
P. Kraft is a consultant/advisory board member for Merck. No potential conflicts of interest were disclosed by the other authors.
Disclaimer
The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. government.
Authors' Contributions
Conception and design: A.M. Mondul, I.M. Shui, K. Overvad, P.H. Peeters, E. Riboli, D.O. Stram, D. Trichopoulos, R. Tumino, E. Weiderpass, W. Willett, P. Kraft, D. Albanes
Development of methodology: D.O. Stram, R. Tumino, E. Weiderpass, W. Willett
Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): S.J. Weinstein, A. Agudo, C.D. Berg, A. Black, J.E. Buring, D.I. Chasman, C. Haiman, S.E. Hankinson, R.N. Hoover, D.J. Hunter, K.-T. Khaw, T. Kühn, M. Kvaskoff, L. Le Marchand, S. Lindström, M.L. McCullough, K. Overvad, P.H. Peeters, E. Riboli, P.M. Ridker, M. Sund, D. Trichopoulos, R. Tumino, E. Weiderpass, W. Willett, R.G. Ziegler, D. Albanes
Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): A.M. Mondul, I.M. Shui, K. Yu, K.K. Tsilidis, M.M. Gaudet, E. Weiderpass, W. Willett, P. Kraft, R.G. Ziegler, D. Albanes
Writing, review, and/or revision of the manuscript: A.M. Mondul, I.M. Shui, S.J. Weinstein, K.K. Tsilidis, A.D. Joshi, A. Agudo, A. Black, J.E. Buring, M.M. Gaudet, S.E. Hankinson, B.E. Henderson, R.N. Hoover, D.J. Hunter, K.-T. Khaw, T. Kühn, M. Kvaskoff, L. Le Marchand, S. Lindström, M.L. McCullough, K. Overvad, P.H. Peeters, E. Riboli, P.M. Ridker, D.O. Stram, M. Sund, D. Trichopoulos, E. Weiderpass, W. Willett, P. Kraft, R.G. Ziegler, D. Albanes
Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): S.J. Weinstein, K.-T. Khaw, T. Kühn, P.H. Peeters, P.M. Ridker, D. Trichopoulos, R. Tumino, E. Weiderpass, P. Kraft, D. Albanes
Study supervision: P.M. Ridker, D. Trichopoulos, R. Tumino, E. Weiderpass, P. Kraft, D. Albanes
Other (revised the final draft): R. Tumino
Grant Support
This work was supported by the NIH, National Cancer Institute cooperative agreements U01-CA98233-07 (to D.J. Hunter), U01-CA98710-06 (to M.J. Thun), U01-CA98216-06 (to E. Riboli and R. Kaaks), and U01-CA98758-07 (to B.E. Henderson), and the Intramural Research Program of NIH/National Cancer Institute, Division of Cancer Epidemiology and Genetics. I. M. Shui was supported by a National Research Service Award (T32 CA09001) from the National Cancer Institute, NIH.
The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
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