Abstract
Background: Calcium intake may reduce risk of colorectal cancer, but the mechanisms remain unclear. Studies of interaction between calcium intake and SNPs in calcium-related pathways have yielded inconsistent results.
Methods: To identify gene–calcium interactions, we tested interactions between approximately 2.7 million SNPs across the genome with self-reported calcium intake (from dietary or supplemental sources) in 9,006 colorectal cancer cases and 9,503 controls of European ancestry. To test for multiplicative interactions, we used multivariable logistic regression and defined statistical significance using the conventional genome-wide α = 5E−08.
Results: After accounting for multiple comparisons, there were no statistically significant SNP interactions with total, dietary, or supplemental calcium intake.
Conclusions: We found no evidence of SNP interactions with calcium intake for colorectal cancer risk in a large population of 18,509 individuals.
Impact: These results suggest that in genome-wide analysis common genetic variants do not strongly modify the association between calcium intake and colorectal cancer in European populations. Cancer Epidemiol Biomarkers Prev; 23(12); 2971–6. ©2014 AACR.
Introduction
Observational studies suggest that higher calcium intake may reduce risk of colorectal cancer (1, 2); however, the underlying mechanisms remain unclear (2). Gene–environment interaction (GxE) analysis can provide insight into disease pathways (3, 4). Studies of gene–calcium interactions for colorectal cancer have focused on SNPs in calcium-related pathways with limited success (5, 6).
The availability of genome-wide SNP data (7) now enables hypothesis-free GxE searches. This method recently identified a novel gene-processed meat interaction for colorectal cancer (3)—highlighting the potential of this approach to provide clues into disease etiology. Here, we tested interactions between approximately 2.7 million SNPs across the genome and calcium intake in 9,006 colorectal cancer cases and 9,503 controls.
Materials and Methods
We included 9,006 individuals with confirmed colorectal adenocarcinomas and 9,503 controls from the Colon Cancer Family Registry (CCFR) and the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO; refs. 3, 4, 7). We excluded participants missing all genotype or calcium data, or those of non-European ancestry. Participants gave informed written consent and studies were approved by their respective Institutional Review Boards.
Genotyping, quality control, and imputation procedures have been described previously (4, 7). Imputation to HapMap CEU was conducted using IMPUTE, BEAGLE, or MACH. In each study, SNPs were restricted on the basis of MAF>20/# samples and imputation accuracy (Rsq > 0.3). We tested approximately 2.7 million SNPs.
Data collection/harmonization procedures have been described previously (3, 4). Calcium intake at the reference time was assessed using food frequency questionnaires (FFQ) or diet history (DALS); intake (mg/d) was determined from calcium in foods (i.e., dietary) or supplements (single + multivitamins + antacids) when available. Total intake was calculated as dietary + supplemental calcium. For studies that entered supplement data as regular- versus nonuser (CCFR, OFCCR, and PMH-CCFR), regular use was assigned generic doses (1) of 500 mg/d, 500 mg/single pill, or 130 mg/multivitamin pill.
Multivariable logistic regression was used to estimate study-specific ORs and 95% confidence intervals (CI) for the association between calcium and colorectal cancer risk; study-specific estimates were combined using fixed-effects meta-analysis. We tested multiplicative GxE in each study using SNPxcalcium interaction terms, adjusting for age, sex, study center, total energy consumption, first 3 principal components of genetic ancestry, and SNP and calcium main effects. This was followed by meta-analysis across studies. Statistical significance was determined using the conventional genome-wide two-sided α = 5E−08 (3, 7). Heterogeneity was assessed using the Woolf test. We further explored interactions using the potentially more powerful Cocktail method (3). Statistical analyses used R, Version 2.15.1 or SAS, Version 9.3; power was estimated using Quanto, Version 1.2.4 (biostats.usc.edu/software).
Results
Higher total calcium intake was associated with reduced colorectal cancer risk (OR per quartile, 0.87; 95% CI, 0.84–0.89; Fig. 1A). Dietary and supplemental calcium were similarly associated with reduced risk (Fig. 1B and C). Total calcium results were similar after excluding studies that entered calcium data from only diet (DALS, DACHS, and PHS) or supplements (CCFR, OFCCR, and PMH-CCFR) (OR, 0.88; 95% CI, 0.84–0.92). Estimates were unchanged for right- versus left-sided cancers (data not shown). Supporting the validity of our calcium data, 2q21.3/MCM6/rs4988235 aka 13910 T>C, a marker of preserved lactase levels used in genetic tests of lactose intolerance (8), was associated with dietary (P = 1.1E−13), but not supplemental (P = 5.2E−01), intake.
Association between calcium intake and risk of colorectal cancer. Odds ratios (ORs) and 95% confidence intervals correspond to each quartile increase in A) total calcium intake (mg/d), B) dietary calcium intake (mg/d), and C) supplemental calcium intake (≥500 versus <500 mg/d). Total and dietary calcium intake were coded as sex- and study-specific quartiles based on cutoff points in controls, and modeled as an ordinal variable. Estimates adjusted for age (continuous), sex (F/M), study center (indicators), and energy consumption (continuous). CC, case–control; lower/upper, lower and upper bounds of 95% CI.
Association between calcium intake and risk of colorectal cancer. Odds ratios (ORs) and 95% confidence intervals correspond to each quartile increase in A) total calcium intake (mg/d), B) dietary calcium intake (mg/d), and C) supplemental calcium intake (≥500 versus <500 mg/d). Total and dietary calcium intake were coded as sex- and study-specific quartiles based on cutoff points in controls, and modeled as an ordinal variable. Estimates adjusted for age (continuous), sex (F/M), study center (indicators), and energy consumption (continuous). CC, case–control; lower/upper, lower and upper bounds of 95% CI.
There were no statistically significant SNP interactions with total, dietary, or supplemental calcium intake for colorectal cancer risk (Table 1). The strongest evidence of interaction was between 4q34.3/rs1028166 and supplemental calcium intake (OR interaction, 1.49; 95% CI, 1.27–1.74; Pinteraction = 7.3E−07). However, rs1028166 was located 669 kb from the nearest protein-coding gene (TENM3) and showed little evidence of interaction with total or dietary intake. The Cocktail approach (3) did not identify statistically significant interactions (data not shown).
SNP with smallest P for interaction with total, dietary, or supplemental calcium for colorectal cancer risk
| Interaction resultsb,c | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Calcium analysis | SNP with smallest P for interaction | Locus | Position (bp)a | Function class | Genetic region | Minor allele | Alt allele | MAF | Mean Rsq | Variable | OR-int (95% CI) | P | Phet |
| Total | rs1933755 | 6q23.1 | 130925767 | Intergenic | TMEM200A/EPB41L2 | C | T | 0.10 | 0.93 | Total | 0.84 (0.78–0.90) | 1.5E−06 | 4.1E−01 |
| Dietary | 0.86 (0.80–0.92) | 5.2E−05 | 3.2E−01 | ||||||||||
| Suppl | 0.81 (0.65–1.02) | 7.4E−02 | 5.5E−01 | ||||||||||
| Dietary | rs6855885 | 4q22.1 | 92039007 | Intronic | FAM190A | A | G | 0.50 | 0.94 | Total | 1.09 (1.04–1.13) | 9.0E−05 | 2.5E−01 |
| Dietary | 1.11 (1.06–1.16) | 1.9E−06 | 5.5E−01 | ||||||||||
| Suppl | 0.93 (0.81–1.07) | 3.0E−01 | 2.4E−01 | ||||||||||
| Supplemental | rs1028166 | 4q34.3 | 182813298 | Intergenic | AGA/TENM3 | G | A | 0.31 | 0.85 | Total | 1.07 (1.02–1.12) | 6.7E−03 | 3.8E−01 |
| Dietary | 1.02 (0.97–1.07) | 5.2E−01 | 7.4E−01 | ||||||||||
| Suppl | 1.49 (1.27–1.74) | 7.3E−07 | 2.9E−01 | ||||||||||
| Interaction resultsb,c | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Calcium analysis | SNP with smallest P for interaction | Locus | Position (bp)a | Function class | Genetic region | Minor allele | Alt allele | MAF | Mean Rsq | Variable | OR-int (95% CI) | P | Phet |
| Total | rs1933755 | 6q23.1 | 130925767 | Intergenic | TMEM200A/EPB41L2 | C | T | 0.10 | 0.93 | Total | 0.84 (0.78–0.90) | 1.5E−06 | 4.1E−01 |
| Dietary | 0.86 (0.80–0.92) | 5.2E−05 | 3.2E−01 | ||||||||||
| Suppl | 0.81 (0.65–1.02) | 7.4E−02 | 5.5E−01 | ||||||||||
| Dietary | rs6855885 | 4q22.1 | 92039007 | Intronic | FAM190A | A | G | 0.50 | 0.94 | Total | 1.09 (1.04–1.13) | 9.0E−05 | 2.5E−01 |
| Dietary | 1.11 (1.06–1.16) | 1.9E−06 | 5.5E−01 | ||||||||||
| Suppl | 0.93 (0.81–1.07) | 3.0E−01 | 2.4E−01 | ||||||||||
| Supplemental | rs1028166 | 4q34.3 | 182813298 | Intergenic | AGA/TENM3 | G | A | 0.31 | 0.85 | Total | 1.07 (1.02–1.12) | 6.7E−03 | 3.8E−01 |
| Dietary | 1.02 (0.97–1.07) | 5.2E−01 | 7.4E−01 | ||||||||||
| Suppl | 1.49 (1.27–1.74) | 7.3E−07 | 2.9E−01 | ||||||||||
Abbreviations: Alt, alternate; MAF, minor allele frequency; OR-int, odds ratio for interaction; Phet, P for heterogeneity across studies; Rsq, imputation Rsq.
aOn the basis of NCBI build 37 data.
bCorresponds to each additional copy of the minor allele (i.e., assuming additive genetic effects) and each quartile increase in calcium intake (total, dietary) or ≥500 versus <500 mg/d (supplemental). Genotyped SNPs were modeled as 0, 1, or 2 copies of the minor allele; imputed SNPs were modeled as the expected number of copies of the minor allele (the genotype “dosage”; refs. 3, 7).
cOn the basis of multivariable logistic regression adjusted for age (continuous), sex (F/M), study center (indicators), energy consumption (continuous), first 3 principal components of genetic ancestry (continuous), SNP main effect, and calcium main effect.
Discussion
In this large study, there were no statistically significant SNP interactions with total, dietary, or supplemental calcium intake. Candidate–gene studies of interaction between SNPs in calcium-related genes (e.g., CASR, VDR) and calcium intake have not reported consistent interactions (5, 6). Figueiredo and colleagues (9) investigated genome-wide SNP–calcium interactions for microsatellite-stable/microsatellite-instability low colorectal cancer. Consistent with our findings, they reported no statistically significant interactions in 1,191 cases and 990 controls.
Strengths of this study include the large sample size, comprehensive genetic data, and harmonization of calcium intake across 13 studies. However, misclassification of calcium intake may have attenuated associations, although calcium assessed by FFQ is reasonably accurate compared with diet records/24-hour recalls [correlations = 0.48–0.70 (ref. 1)], and we detected colorectal cancer associations with magnitudes comparable with previous studies (1, 2). For total calcium quartiles, at α = 5E−08, our study had >80% power to detect interaction ORs ≥ 1.33, 1.23, and 1.17 for SNPs with MAFs of 0.05, 0.10, and 0.20, respectively. We thus had adequate statistical power to detect modest interactions with common variants.
In summary, we did not observe evidence of SNP interactions with calcium intake. This suggests that individual common genetic variants do not strongly modify the association between calcium and colorectal cancer risk in European populations. Large studies with sequence data are needed to investigate interactions involving rare variants.
Disclosure of Potential Conflicts of Interest
D. Seminara is a consultant/advisory board member for Stanford University. No potential conflicts of interest were disclosed by the other authors.
Disclaimer
The content of this article does not necessarily reflect the views or policies of the National Cancer Institute or any of the collaborating centers in the CFRs, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government or the CFR.
Authors' Contributions
Conception and design: M. Du, X. Zhang, H. Brenner, J.L. Hopper, C.M. Hutter, D. Seminara, U. Peters, J. Chang-Claude
Development of methodology: J.L. Hopper, S. Jiao, U. Peters
Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): M. Du, M. Hoffmeister, R.E. Schoen, J.A. Baron, S.I. Berndt, H. Brenner, C.S. Carlson, G. Casey, A.T. Chan, D. Duggan, E.L. Giovannucci, J. Gong, R.B. Hayes, J.L. Hopper, T.J. Hudson, M.A. Jenkins, L.N. Kolonel, L. Le Marchand, P.A. Newcomb, A. Rudolph, M.D. Thornquist, E. White, B.W. Zanke, P.T. Campbell, M.L. Slattery, U. Peters, J. Chang-Claude, J.D. Potter
Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): M. Du, A.T. Chan, K.R. Curtis, D. Duggan, W.J. Gauderman, J. Gong, J.L. Hopper, L. Hsu, C.M. Hutter, S. Jiao, Y. Lin, C.M. Ulrich, K. Wu, P.T. Campbell, U. Peters
Writing, review, and/or revision of the manuscript: M. Du, X. Zhang, M. Hoffmeister, R.E. Schoen, J.A. Baron, S.I. Berndt, H. Brenner, G. Casey, A.T. Chan, K.R. Curtis, D. Duggan, W.J. Gauderman, E.L. Giovannucci, T.A. Harrison, B.E. Henderson, J.L. Hopper, L. Hsu, T.J. Hudson, C.M. Hutter, M.A. Jenkins, S. Jiao, J.M. Kocarnik, L.N. Kolonel, L. Le Marchand, P.A. Newcomb, A. Rudolph, D. Seminara, C.M. Ulrich, E. White, K. Wu, P.T. Campbell, M.L. Slattery, U. Peters, J. Chang-Claude, J.D. Potter
Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): M. Du, A.T. Chan, R.B. Hayes, A. Rudolph, B.W. Zanke, M.L. Slattery
Study supervision: M. Du, E.L. Giovannucci, M.A. Jenkins, M.D. Thornquist, U. Peters
Other (data harmonization): M.D. Thornquist
Acknowledgments
CCFR: The authors thank all participants, members, and investigators. For additional information see Newcomb, PA and colleagues Colon Cancer Family Registry: an international resource for studies of the genetic epidemiology of colon cancer. Cancer Epidemiol Biomarkers Prev. 2007 Nov;16(11):2331–2343.
DACHS: The authors thank all participants and cooperating clinicians, and Ute Handte-Daub, Renate Hettler-Jensen, Utz Benscheid, Muhabbet Celik, and Ursula Eilber for excellent technical assistance.
GECCO: The authors thank all those at the GECCO Coordinating Center for helping bring together the data and people that made this project possible. The authors also acknowledge COMPASS (Comprehensive Center for the Advancement of Scientific Strategies) at the Fred Hutchinson Cancer Research Center for their work harmonizing the GECCO epidemiologic dataset. The authors acknowledge Dave Duggan and team members at TGEN (Translational Genomics Research Institute), the Broad Institute, and the Génome Québec Innovation Center for genotyping DNA samples of cases and controls, and for scientific input for GECCO.
HPFS, NHS, and PHS: The authors thank Patrice Soule and Hardeep Ranu of the Dana Farber Harvard Cancer Center High-Throughput Polymorphism Core who assisted in the genotyping for NHS, HPFS, and PHS under the supervision of Dr. Immaculata Devivo and Dr. David Hunter, Qin (Carolyn) Guo and Lixue Zhu who assisted in programming for NHS and HPFS, and Haiyan Zhang who assisted in programming for the PHS. The authors thank the participants and staff of the Nurses' Health Study and the Health Professionals Follow-Up Study, for their valuable contributions as well as the following state cancer registries for their help: AL, AZ, AR, CA, CO, CT, DE, FL, GA, ID, IL, IN, IA, KY, LA, ME, MD, MA, MI, NE, NH, NJ, NY, NC, ND, OH, OK, OR, PA, RI, SC, TN, TX, VA, WA, WY. In addition, this study was approved by the Connecticut Department of Public Health (DPH) Human Investigations Committee. Certain data used in this publication were obtained from the DPH. The authors assume full responsibility for analyses and interpretation of these data.
PLCO: The authors thank Drs. Christine Berg and Philip Prorok, Division of Cancer Prevention, National Cancer Institute, the Screening Center investigators and staff or the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial, Tom Riley and staff, Information Management Services, Inc., Ms. Barbara O'Brien and staff, Westat, Inc., and Drs. Bill Kopp, Wen Shao, and staff, SAIC-Frederick. Most importantly, we acknowledge the study participants for their contributions to making this study possible. The statements contained herein are solely those of the authors and do not represent or imply concurrence or endorsement by NCI.
PMH: The authors would like to thank the study participants and staff of the Hormones and Colon Cancer study.
WHI: The authors thank the WHI investigators and staff for their dedication, and the study participants for making the program possible. A full listing of WHI investigators can be found at: http://www.whi.org/researchers/Documents%20%20Write%20a%20Paper/WHI%20Investigator%20Short%20List.pdf.
Grant Support
C.S. Carlson, K.R. Curtis, M. Du, J. Gong, T.A. Harrison, L. Hsu, C.M. Hutter, J.M. Kocarnik, S. Jiao, Y. Lin, U. Peters, M.D. Thornquist, and C.M. Ulrich are affiliated with GECCO, which is supported by the following grants from the National Cancer Institute, NIH, U.S. Department of Health and Human Services: U01 CA137088 and R01 CA059045.
L. Le Marchand is affiliated with COLO2&3, which is supported by the NIH (R01 CA60987).
J.A. Baron, G. Casey, J.L. Hopper, M.A. Jenkins, and P.A. Newcomb are affiliated with CCFR, which is supported by the NIH (RFA # CA-95-011) and through cooperative agreements with members of the Colon Cancer Family Registry and P.I.s. This genome-wide scan was supported by the National Cancer Institute, NIH by U01 CA122839. The following Colon CFR centers contributed data to this article and were supported by NIH: Australasian Colorectal Cancer Family Registry (U01 CA097735), Ontario Registry for Studies of Familial Colorectal Cancer (U01 CA074783), and Seattle Colorectal Cancer Family Registry (U01 CA074794).
H. Brenner, J. Chang-Claude, M. Hoffmeister, and A. Rudolph are affiliated with DACHS, which was supported by grants from the German Research Council (Deutsche Forschungsgemeinschaft, BR 1704/6-1, BR 1704/6-3, BR 1704/6-4 and CH 117/1-1), and the German Federal Ministry of Education and Research (01KH0404 and 01ER0814).
J.D. Potter and M.L. Slattery are affiliated with DALS, which was supported by the NIH (R01 CA48998 to M.L. Slattery). A.T. Chan, E.L. Giovannucci, K. Wu, and X. Zhang are affiliated with HPFS, NHS, and PHS. HPFS was supported by the NIH (P01 CA 055075, UM1 CA167552, R01 137178, and P50 CA 127003), NHS by the NIH (R01 CA137178, P01 CA 087969, and P50 CA 127003), and PHS by the NIH (R01 CA42182).
B.E. Henderson, L.N. Kolonel, and L. Le Marchand are affiliated with MEC, which is supported by the following grants from the NIH: R37 CA54281, P01 CA033619, and R01 CA63464.
T.J. Hudson and B.W. Zanke are affiliated with OFCCR, which is supported by the NIH, through funding allocated to the Ontario Registry for Studies of Familial Colorectal Cancer (U01 CA074783); see CCFR section above. Additional funding toward genetic analyses of OFCCR includes the Ontario Research Fund, the Canadian Institutes of Health Research, and the Ontario Institute for Cancer Research, through generous support from the Ontario Ministry of Research and Innovation.
S. I. Berndt, R.B. Hayes, and R.E. Schoen are affiliated with PLCO, which was supported by the Intramural Research Program of the Division of Cancer Epidemiology and Genetics and supported by contracts from the Division of Cancer Prevention, National Cancer Institute, NIH, DHHS. In addition, a subset of control samples were genotyped as part of the Cancer Genetic Markers of Susceptibility (CGEMS) Prostate Cancer GWAS (Yeager, M and colleagues Genome-wide association study of prostate cancer identifies a second risk locus at 8q24. Nat Genet. 2007 May;39(5):645-9), Colon CGEMS pancreatic cancer scan (PanScan; Amundadottir, L and colleagues Genome-wide association study identifies variants in the ABO locus associated with susceptibility to pancreatic cancer. Nat Genet. 2009 Sep;41(9):986-90, and Petersen, GM and colleagues A genome-wide association study identifies pancreatic cancer susceptibility loci on chromosomes 13q22.1, 1q32.1 and 5p15.33. Nat Genet. 2010 Mar;42(3):224-8), and the Lung Cancer and Smoking study (Landi MT, and colleagues A genome-wide association study of lung cancer identifies a region of chromosome 5p15 associated with risk for adenocarcinoma. Am J Hum Genet. 2009 Nov;85(5):679-91). The prostate and PanScan study datasets were accessed with appropriate approval through the dbGaP online resource (http://cgems.cancer.gov/data/) accession numbers phs000207.v1.p1 and phs000206.v3.p2, respectively, and the lung datasets were accessed from the dbGaP website (http://www.ncbi.nlm.nih.gov/gap) through accession number phs000093.v2.p2. Funding for the Lung Cancer and Smoking study was provided by National Institutes of Health (NIH), Genes, Environment, and Health Initiative (GEI) Z01 CP 010200, NIH U01 HG004446, and NIH GEI U01 HG 004438. For the lung study, the GENEVA Coordinating Center provided assistance with genotype cleaning and general study coordination, and the Johns Hopkins University Center for Inherited Disease Research conducted genotyping.
P.A. Newcomb is affiliated with PMH, which is supported by the NIH (R01 CA076366 to P.A. Newcomb). E. White is affiliated with VITAL, which is supported in part by the NIH (K05 CA154337) from the National Cancer Institute and Office of Dietary Supplements.
P.T. Campbell is at the American Cancer Society (ACS) and funded through ACS.
M. Du is supported by the National Cancer Institute, NIH (R25 CA94880).
D. Duggan is affiliated with TGEN and funded through a subaward with GECCO (R01 CA059045).
W.J. Gauderman is affiliated with Colorectal Transdisciplinary Study (CORECT), which is funded through U19 CA148107, and by the National Institutes of Environmental Health Sciences (ES020801).
D. Seminara is a Senior Scientist and Consortia Coordinator at the Epidemiology and Genetics Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, NIH.
The WHI program is funded by the National Heart, Lung, and Blood Institute, NIH, U.S. Department of Health and Human Services through contracts HHSN268201100046C, HHSN268201100001C, HHSN268201100002C, HHSN268201100003C, HHSN268201100004C, and HHSN271201100004C.
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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