Background: Red meat intake has been associated with risk of colorectal cancer, potentially mediated through heterocyclic amines. The metabolic efficiency of N-acetyltransferase 2 (NAT2) required for the metabolic activation of such amines is influenced by genetic variation. The interaction between red meat intake, NAT2 genotype, and colorectal cancer has been inconsistently reported.

Methods: We used pooled individual-level data from the Colon Cancer Family Registry and the Genetics and Epidemiology of Colorectal Cancer Consortium. Red meat intake was collected by each study. We inferred NAT2 phenotype based on polymorphism at rs1495741, highly predictive of enzyme activity. Interaction was assessed using multiplicative interaction terms in multivariate-adjusted models.

Results: From 11 studies, 8,290 colorectal cancer cases and 9,115 controls were included. The highest quartile of red meat intake was associated with increased risk of colorectal cancer compared with the lowest quartile [OR, 1.41; 95% confidence interval (CI), 1.29–1.55]. However, a significant association was observed only for studies with retrospective diet data, not for studies with diet prospectively assessed before cancer diagnosis. Combining all studies, high red meat intake was similarly associated with colorectal cancer in those with a rapid/intermediate NAT2 genotype (OR, 1.38; 95% CI, 1.20–1.59) as with a slow genotype (OR, 1.43; 95% CI, 1.28–1.61; P interaction = 0.9).

Conclusion: We found that high red meat intake was associated with increased risk of colorectal cancer only from retrospective case–control studies and not modified by NAT2 enzyme activity.

Impact: Our results suggest no interaction between NAT2 genotype and red meat intake in mediating risk of colorectal cancer. Cancer Epidemiol Biomarkers Prev; 24(1); 198–205. ©2014 AACR.

Colorectal cancer is a leading cause of morbidity and mortality. The NCI estimates there were 142,820 new cases and 50,830 deaths related to colorectal cancer in the United States in 2013 (1). The past few decades have witnessed a substantial increase in understanding of the mechanisms of colorectal carcinogenesis. Genome wide association studies (GWAS) have highlighted associations of several SNPs in the development of these cancers (2–8). Yet, known genetic variants explain only a fraction of the disease risk, suggesting contribution from the as yet unidentified genetic risk factors, environment, and gene–environment interactions (9).

The role of diet in the pathogenesis of colorectal cancer has been of particular substantial interest. Epidemiologic data support an association between greater intake of red meat and increased risk of colorectal cancer (10–16). However, the mechanism behind this association is not completely understood. One hypothesis relates to the formation of heterocyclic amines through the cooking process, and subsequent breakdown of these amines (13, 15–17). A key enzyme in the metabolic activation of heterocyclic amines is N-acetyltransferase 2 (NAT2; ref. 18). Common genetic variants in NAT2 are key determinants of enzyme activity, with individuals widely classified according to NAT2 phenotype as slow, intermediate, or rapid acetylators (19, 20).

Although some studies have suggested that individuals who are rapid acetylators exhibit a stronger association between red meat intake and colorectal cancer (12, 21–26), other studies have failed to confirm this association (27–31). This could in part be due to the small sample sizes of the replication studies, variation in assessment of red meat intake, incomplete adjustment for confounders, or different methods in estimating NAT2 enzymatic activity. However, as the mechanism behind the association between colorectal cancer and red meat intake is not completely understood, a large adequately powered study to examine a gene–environment interaction between NAT2 genotype and red meat intake might shed light on the carcinogenesis mechanism(s) and suggest potential avenues for disease prevention. Therefore, we used the strengths of a large international consortium of case–control and nested case–control studies within prospective cohorts to examine the potential interaction between NAT2 genotype and red meat intake, in relation to risk of colorectal cancer.

Study sample

This study included 8,290 cases of colorectal cancer and 9,115 controls from the Colon Cancer Family Registry (CCFR) and 10 studies within the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO). Details of the included studies are described in previous publications from this consortium (9, 32, 33). In brief, each study contributed colorectal cancer cases confirmed by review of medical records, pathology reports, or death certificates. All studies were approved by their respective institutional review boards. Six studies used a prospective nested case–control design, whereas five studies were retrospective case–control studies.

Genotyping and quality control

Informed consent was obtained from the participants to provide blood or buccal cells for genotyping. The genotyping platform varied between the different studies. Cases and controls from the Diet, Activity, and Lifestyle Study (DALS), Darmkrebs: Chancen der Verhutung durch Screening (DACHS), Prostate, Lung, Colorectal, and Ovarian (PLCO), Women's Health Initiative (WHI) Set 2, and VITamins And Lifestyle study (VITAL) were genotyped using the Illumina CytoSNP BeadChip platform. WHI Set 1 was genotyped using the Illumina 550K and 550K duo platforms; PLCO Set 1 was genotyped using Illumina 610K and 550K platforms, Ontario Familial Colorectal Cancer Registry (OFCCR) was genotyped using Affymetrix GeneChip Human Mapping 100K and 500K Array Set, Colon Cancer Family Registry (CCFR) using the Illumina 1M, 1Mduo, and 1M-Omni platforms, and Nurses' Health Study (NHS), Health Professionals Follow-up Study (HPFS), and Physicians' Health Study (PHS) were genotyped using the Illumina Human OmniExpress platform. Samples were excluded for call rates ≤ 97%, duplicates, unexpected relative pairs, gender discrepancy, heterozygosity, or being an outlier on principal component analysis.

Inferring NAT2 phenotype categories

NAT2 phenotype was inferred using a single tag SNP, rs1495741, on chromosome 8 (34). Information from this SNP alone is in strong agreement with that from a 7-SNP panel and infers the NAT2 slow phenotype with 99% sensitivity and 95% specificity (34). Furthermore, rs1495741 genotype correlates well with NAT2 activity in hepatocytes (34). For studies which did not directly measure rs1495741 (DALS2, PLCO2, WHI2, DACHS1, VITAL, OFCCR, PMH-CCFR), rs1495741 was imputed (mean imputation Rsq = 0.99, ranging from 0.97 to 1.00). The best genotype call was used to determine NAT2 phenotype. The GG, AG, and AA genotypes were classified as rapid, intermediate, and slow enzyme activity, respectively.

Red meat intake and other covariates

Total red meat intake from all participating studies was assessed as number of servings per day. Additional variables collected by the studies included referent age, sex, smoking status (ever or never), use of aspirin or NSAIDs (use at referent time), and body mass index (BMI; in kilogram/square meter) as a continuous variable. Additional dietary covariates were included based on association with colorectal cancer in prior studies and included total calcium intake, total folate intake, and number of servings per day of fruits or vegetables (9, 35–38). As previously described, a multistep harmonization process was used to combine data across the studies (9).

Statistical analysis

All statistical analyses were performed at the central GECCO coordinating center. In a minimally adjusted model, regression models adjusted for age, sex, study site, and the first three principal components from EIGENSTRAT to account for population substructure (39). The primary analysis was to estimate the interaction between NAT2 genotype, red meat intake, and risk of colorectal cancer. For this, we compared the NAT2 rapid (GG) or intermediate (AG) with the slow (AA) phenotype, as well as the NAT2 rapid/intermediate with the slow phenotype. We used study- and sex-specific quartiles of red meat intake modeled as indicator variables, with the lowest quartile of intake as the referent category. In sensitivity analyses, we examined the association with red meat intake when modeled as a dichotomous exposure (above or below study- and sex-specific medians) and as a continuous variable using study- and sex-specific quartiles taking on the values 1 to 4. The interaction between NAT2 activity and red meat intake was examined by stratifying subjects by inferred NAT2 enzyme activity into rapid/intermediate and slow categories. We tested the significance of multiplicative interaction using likelihood ratio tests comparing nested models with and without interaction terms between quartiles of red meat intake and NAT2 slow, intermediate, or rapid phenotype. We tested for the significance of additive interaction using the logistic regression methods outlined by Lundberg and colleagues (40) and Andersson and colleagues (41) to calculate the relative excess risk due to interaction (42) for red meat intake (above vs. below study- and sex-specific medians) and NAT2 phenotype (intermediate/rapid vs. slow). In a sensitivity analysis, we repeated the regression analysis using an extended model that additionally adjusted for the demographic and dietary covariates described above.

As the association of red meat with colorectal cancer reported in prior publications has generally appeared stronger in retrospective case–control studies compared with studies within prospective cohorts, we examined if the association between red meat, NAT2, and colorectal cancer varied by study design. We also estimated associations according to tumor location (proximal colon vs. distal colon or rectum). Tumor location was classified using International Classification of Diseases, 9th edition codes as proximal (153.0, 153.1, 153.4, 153.6) or distal (153.2, 153.3, 153.7, 154.0, 154.1) tumors. Two hundred and sixty cases could not be classified as one of the two locations.

Table 1 presents the baseline characteristics of the study sample according to case–control status. The mean age was 64 years and just over half were women. There was no difference in age or sex between colorectal cancer cases and controls. Consistent with previously reported associations, colorectal cancer cases had a greater BMI, were more likely to have smoked, and less likely to use aspirin or NSAIDs. Total calcium intake and the number of servings per day of fruits and vegetables were lower in cases compared with controls. The median daily intake of red meat across the studies was 0.64 servings per day (range, 0–8).

Main associations—red meat and NAT2

For the pooled analysis, adjusting for age, sex, and study site, higher intake of red meat was associated with an increased risk of colorectal cancer. Compared with the lowest quartile of red meat intake, the highest quartile was associated with an increased risk of colorectal cancer [adjusted OR, 1.41; 95% confidence interval (CI), 1.29–1.55; Table 2]. Estimates were attenuated after adjustment for smoking status, BMI, aspirin use, NSAID use, and dietary factors (OR for Q4 vs. Q1, 1.29; 95% CI, 1.15–1.44). Red meat intake modeled as a dichotomous or continuous variable similarly was associated with risk of colorectal cancer (data not shown). NAT2 enzyme activity inferred by genotype was not associated with risk of colorectal cancer. Compared with genotypes associated with slow acetylation, genotypes associated with intermediate or rapid acetylation were not associated with risk of colorectal cancer (OR, 1.04; 95% CI, 0.98–1.11; Table 2).

Interactions

Table 3 presents the association between red meat intake and risk of colorectal cancer according to NAT2 genotype. The association between the highest quartile of red meat intake and risk of colorectal cancer was similar for persons with the slow NAT2 genotype (OR, 1.43; 95% CI, 1.28–1.61) as for those with the intermediate or rapid genotype (OR, 1.38; 95% CI, 1.20–1.59). From the expanded model adjusting for demographic and dietary variables, the association between red meat intake and colorectal cancer was not modified by NAT2 genotype. There were no significant interactions on either the multiplicative (P = 0.99) or additive scale (P = 0.97).

Analysis by study design

As prior reports of the association of red meat and colorectal cancer based on the subjects in our analysis appeared to show a stronger association from retrospective case–control studies compared with prospective cohorts, we estimated associations stratified according to study design. From the analysis of 3,091 cases and 4,209 controls derived from case–control studies nested within prospective cohorts, the highest quartile of red meat intake was not significantly associated with risk of colorectal cancer (OR, 1.06; 95% CI, 0.93–1.22). In contrast, using the 5,199 cases and 4,906 controls from retrospective case–control studies, the highest quartile of red meat intake was associated with a significant risk of colorectal cancer (OR, 1.75; 95% CI, 1.55–1.98), and these risk estimates were significantly different (Fig. 1). We observed no significant interaction between inferred NAT2 phenotype, red meat intake, and colorectal cancer risk within either the retrospective case–control studies (P = 0.88) or those where diet was prospectively ascertained before cancer diagnosis (P = 0.64; Table 4).

Associations by tumor site

Stratifying by tumor site, higher red meat intake was associated with both proximal (OR for Q4 vs. Q1, 1.41; 95% CI, 1.25–1.59) and distal colorectal cancer (OR for Q4 vs. Q1, 1.50; 95% CI, 1.35–1.68). However, for both proximal and distal colorectal cancer, the association between red meat and cancer risk was similar in those with slow NAT2 phenotype compared with those with intermediate or rapid acetylation. Among those with slow NAT 2 phenotype, compared with individuals with the lowest quartile of intake, those in the highest quartile of intake had elevated risk of proximal (OR, 1.52; 95% CI, 1.30–1.78) or distal colorectal cancer (OR, 1.45; 95% CI, 1.26–1.67; Table 5). However, this elevated risk was similar in those with intermediate or rapid phenotype for either proximal (OR, 1.25; 95% CI, 1.03–1.51; P interaction = 0.59) or distal colorectal cancer (OR, 1.58; 95% CI, 1.33–1.87; P interaction = 0.90).

In this large international study, we observed that higher intake of red meat is associated with increased risk of colorectal cancer. However, this association was seen only in retrospective case–control studies and was not evident in the studies that prospectively assessed dietary data before cancer diagnosis. This association was similar for both proximal and distal colorectal cancer. Nonetheless, the association between red meat intake and colorectal cancer did not appear to differ according to underlying NAT2 genotype irrespective of study design.

Prior epidemiologic evidence supports the association between red meat intake and colorectal cancer (10–16, 43). A report from the American Institute of Cancer Research estimated a 29% increase in risk of colorectal cancer with every 100 g/day intake of red meat (44). In particular, cooking of red meat and the level of doneness associated with cooking have been associated with increased colorectal cancer risk (10–16, 43, 45). Of note, within this large pooled analysis of multiple study populations, the association between red meat intake and colorectal cancer was observed only in retrospective case–control studies. There are a few potential reasons for this apparently discrepant result. First, a true biologic association between red meat intake and colorectal cancer may be weak or nonexistent, with significant associations reported by retrospective case–control studies largely due to recall bias. Second, a true association between red meat intake and colorectal cancer may be mediated by recent intake. In general, the lag between the assessment of meat intake and incident colorectal cancer is typically prolonged in prospective cohorts. Moreover, most prospective cohorts did not update information on meat intake over follow-up, leading to misclassification of exposure and biasing associations toward the null. Nonetheless, a recent meta-analysis of prospective studies has concluded that red meat intake is associated with risk of colorectal cancer (46). Furthermore, selection bias that may occur in case–control studies should not influence the assessment of potential gene–environmental interactions (47).

One long-standing hypothesis linking red meat with cancer suggests that cooking meat at high temperatures results in the formation of heterocyclic amines (48). This process is mediated by several enzymes, perhaps most prominently NAT2, which metabolically activates heterocyclic amines to allow the formation of DNA adducts that subsequently cause DNA damage. Thus, interindividual variation in the activity of NAT2 may influence susceptibility to this exposure to heterocyclic amines. Although variation in NAT2 enzyme activity was first described in the context of neurotoxicity related to isoniazid use for tuberculosis (49), several genetic polymorphisms in the coding region of exon 2 of the NAT2 gene have been studied as modifiers of enzyme activity (19). Several different genetic panels have been used to classify NAT2 genotype and inferred phenotype, most commonly a 7-SNP panel that includes four SNPs that directly influence NAT2 activity and three SNPs that aid in the classification of the inferred phenotype. However, in two independent cohorts, Garcia-Closas and colleagues demonstrated that a single tag SNP (rs1495741) demonstrated strong correlation with the 7-SNP panel, with only rare misclassification for the intermediate phenotype and none for rapid or slow acetylators (34). The association between rs1495741 genotype and NAT2 phenotype was additionally validated by measuring NAT2 catalytic activity in cryopreserved human hepatocytes with strong correlation between measured activity and rs1495741 genotypes (34). Prior studies had also demonstrated that the rapid acetylator phenotype of NAT2 has been associated with a higher level of such DNA adducts compared with the slow acetylator phenotype (50).

Several studies have examined the interaction between NAT2 and meat intake on the risk of colorectal cancer. However, the results have been inconsistent. In a case–control study nested within the prospective Nurses' Health study, Chan and colleagues (12) demonstrated a 3-fold increase in risk of colorectal cancer with higher red meat intake among rapid but not slow acetylators. In contrast, Wang and colleagues identified no association between NAT2 and red meat intake on colorectal neoplasia (30). In the multiethnic cohort study, the strongest association between red meat intake and colorectal cancer risk was seen among the rapid NAT2 acetylators; however, this interaction was not statistically significant (31). Similarly, in the Ontario Cancer Registry, both red meat and well-done meat intake were associated with colorectal cancer, but this effect was independent of the NAT2 genotype (51). Several other published studies have either supported (21–26) or refuted this interaction (27–29). There are several possible reasons for the variation in our findings as well as those of prior studies. First, cohorts varied in their assessment and definition of red meat intake as well as availability of data on cooking methods and processing. Second, it is well recognized that the distribution of genetic polymorphisms for various xenobiotic metabolizing enzymes varies across ethnicity. Thus, an interaction observed in one ethnic group may not be consistently observed in other populations.

Our findings of the absence of an association of colorectal cancer with NAT 2 acetylation status across the range of average intake of red meat may suggest that heterocyclic amine exposure may not play a relevant role in colorectal cancer pathogenesis. However, it is still plausible that heterocyclic amines truly influence colorectal cancer risk but the range of variation in heterocyclic amine exposure associated with NAT2 acetylation status is narrow, resulting in low statistical power to detect a gene–environment interaction even within a sample size as large as the present study. Alternately, a threshold effect, rather than continuous dose response for exposure to heterocyclic amines may exist which we were not powered to detect. Finally, polymorphisms in other enzymes involved in heterocyclic amine metabolism may interact with NAT2 acetylation status and/or influence susceptibility to colorectal cancer.

There are considerable strengths to our study. First, our large collaboration of pooled studies resulted in a sample size substantially greater than most of the prior studies that have examined this association. Not only does this confer a greater statistical power to define gene–environment interactions, but it also allows for more robust and generalizable findings. Second, we were able to adjust for a spectrum of biologically important covariates, ensuring the independent significance of red meat intake.

We acknowledge several limitations to our study. First, we examined polymorphism in only one enzyme involved in the metabolic activation of heterocyclic amines. Polymorphisms in other enzymes in this pathway (NAT1, CYP1A1, CYP1A2, CYP1B1, AHR, and GSTM1) may modify the association between red meat intake and colorectal cancer. However, among the various polymorphisms examined, the largest body of evidence and the most heterogeneous results have been for the interaction between NAT2 and red meat intake. Furthermore, for some of the other enzymes, the association between genetic polymorphisms and enzyme activity is less well understood. Second, within the studies included in the consortium, there were differences in dietary methods of ascertainment of red meat intake. Some studies had much greater detail on food consumption, whereas others were more limited. Third, uniform information on cooking techniques was not available across all studies. Consequently, we were unable to specifically examine the association with well-done red meat intake that may have a stronger correlation with heterocyclic amine exposure. Another limitation is the referent time of dietary exposure varied from study to study and the most relevant time point associated with the disease process may not have been well captured in all studies. Finally, a subgroup of the entire cohort did not have full information on all relevant covariates and could not be included in our expanded model. However, as the magnitudes of the association between red meat intake and colorectal cancer in our main model and expanded regression model are comparable, we believe our results to be generalizable.

In conclusion, in the largest study to examine the association between red meat intake, NAT2 genotype, and colorectal cancer, we demonstrated that higher red meat intake is associated with increased risk of colorectal cancer primarily in retrospective but not prospective case–control studies. The effect was similar for both proximal colon cancer and distal colorectal cancer. However, irrespective of tumor site or study design, the association between red meat intake and colorectal cancer was independent of NAT2 genotype.

No potential conflicts of interest were disclosed.

The authors assume full responsibility for analyses and interpretation of these data. The content of this article does not necessarily reflect the views or policies of the NCI or any of the collaborating centers in the CCFRs, nor does mention of trade names, commercial products, or organizations imply endorsement by the US Government or the CFR.

Conception and design: A.N. Ananthakrishnan, M. Du, C.S. Fuchs, J.L Hopper, D. Seminara, U. Peters, A.T. Chan

Development of methodology: A.N. Ananthakrishnan, C.S. Fuchs, J.L Hopper, U. Peters, A.T. Chan

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): S.I. Berndt, H. Brenner, B.J. Caan, G. Casey, J. Chang-Claude, D. Duggan, C.S. Fuchs, S. Gallinger, E.L. Giovannucci, R.B. Hayes, M. Hoffmeister, J.L Hopper, M.A. Jenkins, J. Ma, P.A. Newcomb, S. Ogino, J.D. Potter, M.L. Slattery, M. Thornquist, E. White, U. Peters, A.T. Chan

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): A.N. Ananthakrishnan, M. Du, C.S. Fuchs, L. Hou, L. Hsu, P. Kraft, J. Ma, S. Ogino, K. Wu, U. Peters, A.T. Chan

Writing, review, and/or revision of the manuscript: A.N. Ananthakrishnan, M. Du, S.I. Berndt, H. Brenner, B.J. Caan, G. Casey, J. Chang-Claude, D. Duggan, C.S. Fuchs, E.L. Giovannucci, M. Hoffmeister, J.L Hopper, L. Hou, L. Hsu, M.A. Jenkins, J. Ma, H. Nan, P.A. Newcomb, S. Ogino, J.D. Potter, D. Seminara, M.L. Slattery, E. White, K. Wu, A.T. Chan

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): S. Gallinger, T.A. Harrison, R.B. Hayes, M. Hoffmeister, J.L Hopper, J. Ma, P.A. Newcomb, J.D. Potter, M. Thornquist, A.T. Chan

Study supervision: B.J. Caan, C.S. Fuchs, M. Hoffmeister, J.L Hopper, U. Peters, A.T. Chan

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.

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. They also 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, and 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.

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, the authors thank 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 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: https://www.whi.org/researchers/SitePages/WHI%20Investigators.aspx.

A. Ananthakrishnan is supported by a grant from the NIH (K23 DK097142). M. Du, T.A Harrison, L. Hsu, M. Thornquist, and U. Peters are affiliated with GECCO, which is supported by the following grants from the NCI, NIH and U.S. Department of Health and Human Services (U01 CA137088, R01 CA059045, R25 CA094880; to M. Du).

G. Casey, J.L. Hopper, M.A. Jenkins, and P.A. Newcomb are affiliated with CCFR, which is supported by the NIH (UM1 CA167551) and through cooperative agreements with members of the Colon Cancer Family Registry and Principal Investigators. This genome-wide scan was supported by the NCI, NIH by U01 CA122839 and R01 CA143237(to G. Casey). The following Colon CFR centers contributed data to this article and were supported by the NIH:

Australasian Colorectal Cancer Family Registry (U01/U24 CA097735), Ontario Registry for Studies of Familial Colorectal Cancer (U01/U24 CA074783). Seattle Colorectal Cancer Family Registry (U01/U24 CA074794).

H. Brenner, J. Chang-Claude, and M. Hoffmeister 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, C.S. Fuchs, K. Wu, E.L. Giovannucci, J. Ma, and S. Ogino are affiliated with HPFS, NHS, and PHS, which are supported by the NIH. HPFS is supported by the NIH (P01 CA 055075, UM1 CA167552, R01 CA137178, R01 CA151993, and P50 CA 127003), NHS by the NIH (R01 CA137178, R01 CA151993, P01 CA 087969, and P50 CA 127003,) and PHS by the NIH (R01 CA042182).

S. Gallinger is affiliated with the OFCCR, which is supported by funding from the NIH through funding allocated to the Ontario Registry for Studies of Familial Colorectal Cancer (U01 CA074783); see CCFR section above. As subset of ARCTIC, OFCCR is supported by a GL2 grant from the Ontario Research Fund, the Canadian Institutes of Health Research, and the Cancer Risk Evaluation (CaRE) Program grant from the Canadian Cancer Society Research Institute.

S.I. Berndt and R.B. Hayes are affiliated with the PLCO, which is supported by the Intramural Research Program of the Division of Cancer Epidemiology and Genetics and supported by contracts from the Division of Cancer Prevention, NCI, 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 and colleagues Nat Genet 2007;39:645–9), Colon CGEMS pancreatic cancer scan (PanScan; Amundadottir and colleagues Nat Genet 2009;41:986–90 and Petersen and colleagues Nat Genet 2010;42:224–8), and the Lung Cancer and Smoking study. 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 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 by the NIH (K05 CA154337).

D. Duggan is affiliated with TGEN and funded through a subaward with GECCO (R01 CA059045).

The WHI program is funded by the National Heart, Lung, and Blood Institute, National Institutes of Health, 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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