Genetic Variants in Epigenetic Pathways and Risks of Multiple Cancers in the GAME-ON Consortium

This article is featured in Highlights of This Issue, p. 807

Genetic and epigenetic alterations are hallmarks of cancer initiation and progression and can influence each other to work cooperatively (1). Dysfunction of epigenetic processes, such as DNA methylation, chromatin remodeling, and covalent histone modifications, can be as important in carcinogenesis as the change of the genetic material itself (2). Since the first studies that described the global hypomethylation of cancer genomes and the hypermethylation of the promoter sequence of mainly tumor suppressor genes, several “pan-cancer” DNA methylation patterns (patterns across multiple cancer types) have been identified (reviewed in ref. 3). The CpG island methylator phenotype (CIMP) was first described in colorectal cancer (4), and later similar patterns were observed in several other tumor types. Highlighting the interplay between genetic and epigenetic changes, CIMP subtypes usually present with characteristic genetic alterations. CIMP-H colorectal cancers are frequently characterized by BRAF mutations, whereas CIMP-L tumors tend to harbor KRAS mutations (5). Non-CIMP colorectal cancer, the B-CIMP–negative breast cancer, and the low methylated tumor group of serous ovarian cancers frequently acquire TP53 mutations (5–8).

Furthermore, somatic mutations in epigenetic regulatory genes that are either carcinogenic driver or passenger mutations are known to exist. Important mutations have been shown, for example, in DNMTs, IDH1, IDH2 and TETs (as important players of DNA methylation); in EZH2 and KDM1A (involved in histone modifications); and in ARID1A (participant of chromatin remodeling; reviewed in ref. 2). In addition, inherited genetic variants related to epigenetic regulatory processes were described in association with multiple cancers (9, 10). Given the fundamentality of epigenetic processes, germline variants in genes related to epigenetic pathways presumably have pleiotropic effects on the initiation of different cancers.

As part of the U.S. National Cancer Institute's Genetic Associations and Mechanisms in Oncology (GAME-ON) Network (http://epi.grants.cancer.gov/gameon/), we have previously shown the value of cross-cancer analyses in inflammation pathways (11).

An additional value is that our datasets include large numbers of cancer subtypes that were not studied in The Cancer Genome Atlas (TCGA). The current study was focused and approved by the GAME-ON consortium for the overall analyses of pleiotropy, where we aimed to identify cross-cancer associations of epigenetically related polymorphisms that advance our understanding of the role of epigenetics in cancer development. Given the central role of epigenetic processes in carcinogenesis, germline variants in genes related to epigenetic pathways show pleiotropic effects on the initiation of different cancers. Consequently, we investigated whether common polymorphisms in epigenetic genes are associated with risk of multiple cancer types (breast, colorectal, lung, ovarian, and prostate cancer) and their subtypes.

Within the GAME-ON Network, 32 studies from North America and Europe participated in this investigation (12–21). Studies included frequency matched cases and controls on at least age, and all subjects were of European descent based on ancestry analyses. The study characteristics are summarized in Table 1. In total, 51,724 cancer patients (breast, colorectal, lung, ovarian, and prostate with respective subtypes) and 52,001 controls were included in the analysis.

Genes (n = 634) involved in epigenetic processes were identified using GO and GeneCards databases by searching for the following keywords: DNA methylation, DNA demethylation, histone acetylation, deacetylation, methylation, demethylation, and other histone modification, chromatin remodeling, chromatin modification, and histones. The recent literature was also reviewed. After excluding genes on sex chromosomes and those not covered in all cancer sites, 555 genes were included in the analysis, which were categorized into one or more of epigenetic subpathways (Supplementary Table S1).

We analyzed all SNPs residing within 50 kb of the largest transcript for each gene (for databases, see Supplementary Table S2). Overall, 162,887 polymorphisms were included in the final analysis. In the combined dataset, the major alleles (according to dbSNP) were used as reference alleles.

Cancer sites were further divided into subtypes and for each cancer type and subtype, a fixed effect meta-analysis was conducted to combine results from individual studies (Table 1). This method used log-additive models adjusted for age, European principal components, and sex (where appropriate).

The beta values and SEs for each cancer or cancer subtype were then combined using the association analysis based on a subsets (ASSET) meta-analytic approach, which allows for disease heterogeneity and potential opposite directions of the same genetic variant on different cancer types (22). It searches for the most parsimonious grouping based on the test statistics using any of the five cancers or cancer subtypes simultaneously as the outcome variables. Overlapping subjects among cancer subtypes (e.g., overlapping cases and controls between overall lung cancer and its subtypes) and across cancer types [e.g., UK ovary and UK breast genome-wide association study (GWAS) both used controls from Wellcome Trust Case Control Consortium, (WTCCC)] were accounted for in the covariance matrix when estimating the SEs (11). The resulting P values were adjusted using false discovery rate (FDR) correction. Results with FDR q < 0.05 were considered statistically significant (Supplementary Table S3). All association analyses were performed in R (3.2.5).

The overall approach of the functional annotation is summarized in Supplementary Fig. S1. For each gene with more than five significant SNPs (FDR q < 0.05 in the ASSET meta-analysis), we selected tagSNPs to represent these regions in subsequent analysis. Specifically, a linkage disequilibrium (LD) map was prepared using the Haploview 4.2 software, and tagSNPs were identified with the tagger algorithm of Haploview using 1000 Genomes data (release 20130502). Variants with more than two alleles based on 1000 Genomes were excluded from LD mapping. As a result, we were able to investigate SNPs that were not covered in the original meta-analysis but potentially have functional effect on the genes in the region of interest.

To assess if any of the epigenetic subpathways shown in Supplementary Table S1 were enriched with genes containing significant associations with cancer types or subtypes, pathway analyses were conducted using the ALIGATOR algorithm of the SNPath R package.

The possible functional annotation of the tagSNPs and the region-representative SNPs [functional follow-up (FFU) SNPs] were then assigned using the FunciSNP R/Bioconductor package (23). Using the package, we identified all the corresponding SNPs of our tagSNPs using 50 kb searching window and r² > =0.8 as a linkage threshold. In the next step, FunciSNP package checks whether the corresponding SNPs or the tagSNPs show overlap with DNA segments with predicted functional importance. To annotate these biofeatures, we used the combined genome segmentation assessed by the ENCODE Project Consortium. These results represent ChIP-seq data for eight chromatin marks (H3K4me1, H3K4me2, H3K4me3, H3K9ac, H3K27ac, H3K27me3, H3K36me3, and H4K20me1), RNA Polymerase II and the CTCF transcription factor, as well as DNase-seq and FAIRE-seq data. These data are processed with ChromHMM and Segway software which segments the genome into seven disjoint segments based on their predicted functional role (24). As the goal of the study was to identify those polymorphisms that change the function of the epigenetic-related genes, we interpreted polymorphisms that overlap with a predicted transcribed region only, if they were in the gene of interest. We used the data available on Huvec, H1hesc, and Gm12878 cell lines. Unfortunately, comprehensive information for the genome segmentation track was not available for all cell lines of the respective cancer types. We thus decided to use data from normal cell lines. In addition, an ENCODE Uniform transcription factor binding site (TFBS) track was used, which encompasses data for 161 transcription factors from 91 cell types. Supplementary Table S4 summarizes the functional annotation of all SNPs, based on FunciSNP (SNPs that were annotated as not functional are not listed). Furthermore, the functionality of the ASSET-identified SNPs, as well as their corresponding SNPs, were annotated using RegulomeDB, version 1.1.

All software packages and databases that were used are listed in Supplementary Table S2.

The results of the original (individual study based) meta-analyses and the ASSET-based risk associations are summarized in Fig. 1. Ovarian cancer was associated with the largest number of variants (98), followed by prostate (70), lung (50), breast (46), and colorectal (10) cancers. Interestingly, all of the endometrioid ovarian cancer–specific SNPs also showed an association with overall prostate cancer risk. These polymorphisms were mainly located in the RUVBL1 gene regions. Variants in the flanking region of MORF4L1 on 15q25 were mainly associated with lung and ovarian cancer. Because of the proximity of MORF4L1 to CHRNA5, CHRNA3, and CHRNB4 genes and their well-known association with lung cancer, we excluded this region from further analysis (25–27). The number of remaining SNPs that were associated with lung cancer risk was 35 and with ovarian cancer was 83. Furthermore, variants in PHC3 (3q26) were solely associated with risk of overall prostate cancer and will not further be discussed.

When combining genes into epigenetic subpathways (see above), we observed no significant risk association with more than one cancer type or subtype (P values > 0.05), indicating that all pathways were similarly important for cancer risk.

Overall, 99 SNPs in 8 genes (excluding MORF4L1: 84 SNPs in 7 genes) showed significant associations (FDR q < 0.05) with risk of more than one cancer type (Supplementary Fig. S2A and S2B). Genes with associated SNPs were RUVBL1 (3q21), TET2 (4q24), L3MBTL3 (6q23), HDAC9 (7p21), BRCA2 (13q12), MORF4L1 (15q25), BABAM1 (19p13), and DPF1 (19q13); (Table 2; Supplementary Fig. S3). Previous GWAS-identified cancer risk associations in these and other genes located in these regions are listed in Supplementary Table S5.

The most pleiotropic genes were TET2, BABAM1, DPF1, and especially L3MBTL3 (Fig. 1; Table 2). Eleven variants in L3MBTL3 were associated with cancer risk, all with pleiotropic effects. The highest OR in this region was 1.06 [rs9388766: 95% confidence interval (CI) = 1.03–1.08; FDR q = 0.02), which was associated with risk of colorectal, overall breast, ESR1 [estrogen receptor (ER)]-negative breast, clear cell ovarian, and overall and aggressive prostate cancer. L3MBTL3 is a member of the putative Polycomb group (PcG) proteins. Two SNPs, rs9375694 and rs6569648, were previously identified as eQTLs (expression quantitative trait locus) for L3MBTL3 (RegulomeDB score: 1d and 1f, respectively; ref. 28). The variant allele of rs6899976 may also be functionally important, as it overlaps with CTCF-enriched regions in all cell lines as well as a TFBS. However, this variant has a RegulomeDB score of only 4.

TET2 (tet methylcytosine dioxygenase 2) at 4q24 encodes a protein catalyzing the conversion of methylcytosine to 5-hydroxymethylcytosine. Nine variants at this locus were significantly associated with risk of at least two cancer types, of which one variant (rs6825684) was associated with decreased risk of four cancers or subtypes—colorectal, overall prostate, and overall and endometrioid ovarian cancer (OR = 0.89; 95% CI = 0.85–0.93; FDR q = 0.02—and one polymorphism (rs62331150) showed a bidirectional effect. The variant allele of rs62331150 increased the risk of overall breast and serous ovarian cancer (OR: 1.09; 95% CI, 1.02–1.15; P = 0.009) and decreased the risk of clear cell ovarian and prostate cancer (OR = 0.91; 95% CI = 0.87–0.96; P = 0.0004) with a combined q value of 0.04 (Fig. 2). Most of the variants were positioned within TET2. The nonsynonymous rs34402524 was predicted to be deleterious (SIFT) and possibly damaging (PolyPhen). Among the ASSET identified and FFU SNPs, further functional annotation singled out polymorphisms with a possible functional role. rs62331150 (RegulomeDB score = 2b) overlaps with a transcription start site, a TFBS, and an enhancer region.

Thirty-three variants, all pleiotropic, showed an association with cancer susceptibility in the region containing BABAM1, a known ovarian and breast cancer locus. The strongest association was observed for rs4808076, which conferred a 14% increased risk of ESR1 (ER)-negative breast, serous ovarian, and squamous cell lung cancer (OR = 1.14; 95% CI = 1.10–1.19; FDR q = 6.87 × 10⁻⁵). Five variants decreased the risk of six cancer types and subtypes; overall prostate, overall breast, ESR1 (ER)-negative breast, squamous cell lung, overall, and serous ovarian cancer risk (strongest signal for rs8100241: OR = 0.95; 95% CI = 0.93–0.97, FDR q = 1.78 × 10⁻³). Besides BABAM1, the captured region (19p13) additionally contains ANKLE1, ABHD8, and USHBP (Supplementary Table S5). BABAM1 was selected for its involvement in chromatin modifications, namely ubiquitination as part of the BRCA1 A complex. The ASSET identified SNPs in this region were in LD with several variants that may play an important role in regulatory processes. The most important ones are shown in Table 3. Apart from the variants in regulatory regions, five SNPs were in coding sequences. Important features of these variants, as well as their SIFT (29) and PolyPhen (30) scores, are shown in Table 4.

DPF1 is part of the neuron-specific chromatin remodeling complex (nBAF complex). One variant (rs12611084) was significantly associated with endometrioid ovarian, ESR1 (ER)-negative breast, and overall and aggressive prostate cancer risk (OR = 0.93; 95% CI = 0.91–0.96; FDR q = 0.005), and one variant (rs8100395) additionally with lung adenocarcinoma (OR = 0.93; 95% CI = 0.90–0.96; FDR q = 7.2 × 10⁻³). Both variants were located upstream of DPF1, some were overlapping with other genes in this region, PPP1R14A and SPINT2, and were captured by one tagSNP in the FunciSNP analysis. Seven FFU SNPs showed a possible functional role, among them rs7250689, which was previously reported to be an eQTL for PPP1R14A (28). On the basis of RegulomeDB, rs8100395 and rs12611084 (both significant in the ASSET analysis) likely affect binding and additionally overlap with enhancer regions as well as TFBS and, in the case of rs8100395, overlaps with a CTCF-enriched region.

Overall, 27 polymorphisms in RUVBL1 were associated with risk of prostate and endometrioid ovarian cancer, whereas one SNP was additionally associated with colorectal cancer risk. The strongest association was observed for rs144609957, with increased risk of prostate and endometrioid ovarian cancer (OR = 1.13; 95% CI = 1.08–1.19; FDR q = 0.01). None of the SNPs had reached genome-wide significance in the original meta-analysis. RUVBL1 plays a role in chromatin organization. All associated SNPs belonged to the same LD block and were captured by one tagging SNP. Furthermore, FunciSNP analysis revealed seven variants that overlapped with multiple biofeatures (TFBS, weak enhancer region, and promoter flanking region). These variants also had low RegulomeDB scores, the lowest being 2b for rs9879865 and rs9879866, variants that likely affect binding.

We performed the first large-scale association study of variants in epigenetic-related genes and cancer risk utilizing the extensive genomic data on 51,724 cancer patients and 52,001 controls and identified eight epigenetic-related genes with pleiotropic effects on cancer risk.

Epigenetic disturbances are common drivers of carcinogenesis, yet, effects of germline variants and their potential pleiotropic mechanisms are not well understood. Thus, we investigated the risk association of SNPs related to epigenetic processes with multiple cancers. Using a subset-based meta-analysis, we were able to account for different subsets of cancer types and subtypes, even with contrasting risk associations.

The L3MBTL3 gene on 6q26 is a member of the putative PcG. It contains a methyl-lysine reader Malignant Brain Tumor (MBT) domain that is responsible for the recognition of the mono- and dimethylated lysines of H3 and H4 histone tails. MBT domain proteins are associated with gene expression repression, and their dysregulation has been shown to contribute to different diseases (31). In our analysis, two variants (rs9375694 and rs6569648), which were previously identified as eQTLs, were significantly associated with risk of prostate and breast cancer (and their subtypes), and to a lesser extent with risk of clear cell ovarian and colorectal cancer (28). Interestingly, previous GWAS identified an association of rs6569648 and rs6899976, both hits in our analysis, with height (32), and height is associated with risk of several cancers including breast, ovarian, prostate, and colorectal cancer (33). Our findings suggest the link between height and cancer risk may be vis-a-vis altered epigenetic processes, but this requires further investigations.

Several SNPs located in and around TET2 showed significant associations with risk of overall prostate, overall ovarian, endometrioid ovarian, overall breast, and colorectal cancer. Previous studies reported significant associations of variants at the TET2 locus, with risk of cancer including ovarian and breast cancer (9, 21, 34). A large number of functional variants were identified in this region forming multiple pleiotropic linkage blocks that support the role of TET2 and its germline variants in the development of multiple cancer types. Furthermore, an association between rs62331150 and TET2 gene expression in breast normal and tumor tissue was recently shown (9). The bidirectional association of the rs62331150 variant allele implies that the effect of TET2 genetic variation may be of a different nature for distinct cancers, increasing the risk of breast cancer but decreasing the risk of prostate cancer. Similar associations were observed for a group of highly linked polymorphisms, namely rs2007403, rs2047409, rs6533183, rs6839705, rs11097882, and rs13147502, confirming previous studies (21, 35)—however, with only one statistically significant risk direction.

Several functional variants were found at 19p13 with significant associations observed for risk of ESR1 (ER)-negative breast cancer, serous ovarian cancer, and squamous cell lung cancer, but also with overall ovarian, breast, and prostate cancer. BABAM1 is involved in chromatin modifications (ubiquitination), as part of the BRCA1 complex, and regulates the retention of BRCA1 at double-strand DNA breaks to maintain stability of this complex at the sites of DNA damage (36). Previous GWAS associated this region with breast (37) and ovarian cancer (10), with some of the SNPs showing triple-negative breast cancer specificity (38). However, to our knowledge, we are the first to describe an association with squamous cell lung cancer or overall prostate cancer risk. Demonstrating a limitation of our selective candidate gene approach, new evidence suggests that nearby 19p13 genes ANKLE1 and/or ABHD8, rather than BABAM1, may be the functional drivers in breast and ovarian cancer (Lawrenson and colleagues, Nat Comm in press). The complexity of this region requires detailed functional follow-up to disentangle the combined effect of individual variants and to understand their role in carcinogenesis.

DPF1 is part of the mSWI/SNF (also called BAF) chromatin remodeling complex with a central role in carcinogenesis (39). Mutations in DPF1 were seen in solid tumors (7). Furthermore, significant overexpression of DPF1 was observed in breast and squamous cell lung cancers (40). Our results also support a pleiotropic effect of DFP1 during carcinogenesis through potentially functional polymorphisms in this gene. However, as in each region of interest, we cannot exclude the potential relevance of the other genes in this region (PPP1R14A and SPINT2).

Polymorphisms in 3q21 were previously only observed in association with prostate cancer risk (41); however, our analysis has detected additional associations with endometrioid ovarian and colorectal cancer risk. RUVBL1 is a member of the INO80 family protein remodeling complex. It interacts with MYC and CTNNB1 (β-catenin), participates in many signal transduction pathways, and is overexpressed in many cancer types (42). We have identified several polymorphisms with seemingly strong functional impacts. Interestingly, a proportion of endometrioid ovarian and colorectal cancers arise from common etiologies associated with hereditary nonpolyposis cancer (HNPCC) or Lynch syndrome (43), and also show de novo promoter methylation silencing of DNA mismatch repair genes (44) and altered β-catenin signaling (45). RUVBL1 may represent novel susceptibility genes that further unify endometrioid ovarian and colorectal cancer development.

The major strength of this study is the large sample size of more than 100,000 subjects across five cancer types and their subtypes, some of which were not studied in TCGA. In addition, by searching the most parsimonious grouping based on the test statistics using any of the five cancers or cancer subtypes simultaneously as the outcome variables, the ASSET-subset–based meta-analysis (i) increased the power to detect associations, which may not have been detected in the individual analyses of the five cancer types; (ii) allowed estimation of associations with opposing effects; and (iii) provided new insights into pleiotropy that were not observed in the original analyses (22). Furthermore, the overlapping subjects (cases and controls) are accounted for during the analysis (11). Finally, our focused approached reduced the genome-wide multiple testing burden and allowed for examination of functionally grouped subsets of epigenetic-related genes (i.e., subpathways). We were thus able to confirm established and identify new risk genes, including TET2 and L3MBTL3.

Although the ORs that are discovered as pleiotropic across cancer types may be considered modest, there is potential clinical significance. First, the ORs for individual cancers may be higher than the summary OR. Second, the combination of several SNPs with low ORs may become relevant through creation of a risk score, and third, the association of SNPs with disease may be modified and, in some instances, strengthened by environmental factors.

Although our approach provides interesting insights into the pleiotropic effects of selected regions, it is limited with respect to the assignment of the identified predisposing variants to genes by chromosomal position rather than the actual cancer-initiating processes. Of note, several of the identified pleiotropic associations cannot clearly be linked to the selected epigenetic genes, as some of the regions additionally contain genes that were previously described for their effect on carcinogenesis.

Further investigations are required to elucidate the functional link between the identified pleiotropic variants and their impact on epigenetic processes, such as the potential effect of TET2 polymorphisms on DNA methylation. Indeed, our pathway-based selection of epigenetic-related genes overlooked the subtleties of complex gene networks, and most genes are involved in multiple biological processes. Finally, this dataset did not allow for the investigation of interactions with other genetic or environmental factors, which are undoubtedly of great importance.

In summary, using a unique, large dataset, we identified novel pleiotropic variants in epigenetic-related genes that are associated with susceptibility to multiple cancer types and subtypes. This study provides the basis for future studies investigating the impact of these variants, their causal relationship to epigenetic processes, and the mechanisms leading to carcinogenic pleiotropy.

No potential conflicts of interest were disclosed.

The authors assume full responsibility for analyses and interpretation of these data. The statements contained herein are solely those of the authors and do not represent or imply concurrence or endorsement by NCI. The content of this manuscript does not necessarily reflect the views or policies of the National Cancer Institute or any of the collaborating centers in the Colon Cancer Family Registry (CCFR), nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government or the CCFR.

Conception and design: R. Toth, D. Scherer, J.-P.J. Issa, S. Ogino, E.L. Goode, C.M. Ulrich

Development of methodology: L.E. Kelemen, R.J. Hung, C.M. Ulrich

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): A. Risch, A. Hazra, R.A. Eeles, X. Wu, Y. Ye, R.J. Hung, E.L. Goode

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): R. Toth, D. Scherer, L.E. Kelemen, A. Risch, Y. Balavarca, S. Ogino, R.J. Hung, C.M. Ulrich

Writing, review, and/or revision of the manuscript: R. Toth, D. Scherer, L.E. Kelemen, A. Risch, A. Hazra, Y. Balavarca, J.-P.J. Issa, V. Moreno, R.A. Eeles, S. Ogino, X. Wu, Y. Ye, R.J. Hung, E.L. Goode, C.M. Ulrich

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): X. Wu

Study supervision: C.M. Ulrich

NHS: We would like to acknowledge Patrice Soule and Hardeep Ranu of the Dana-Farber Harvard Cancer Center High-Throughput Polymorphism Core who assisted in the genotyping for NHS under the supervision of Dr. Immaculata De Vivo and Dr. David Hunter, Qin (Carolyn) Guo and Lixue Zhu who assisted in programming for NHS. We would like to 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.

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, Mr. Tom Riley and staff, Information Management Services, Inc., Ms. Barbara O'Brien and staff, Westat, Inc., and Drs. Bill Kopp, Wen Shao, and staff, and SAIC Frederick. Most importantly, we acknowledge the study participants for their contributions to making this study possible.

Discovery, Biology, and Risk of Inherited Variants in Breast Cancer (DRIVE): The DRIVE GAME-ON consortium (http://epi.grants.cancer.gov/gameon/) would like to thank the following key investigators (institution and location): Muriel Adank (VU University Medical Center, Amsterdam, the Netherlands), Habibul Ahsan (University of Chicago, Chicago, IL), Irene Andrulis (Mount Sinai Hospital, Toronto, Canada), Kristiina Aittomäki (University of Helsinki and Helsinki University Central Hospital, Helsinki, Finland), Lars Beckman (Institute for Quality and Efficieny in Health Care, Cologne, Germany), Carl Blomquist (University of Helsinki and Helsinki University Central Hospital, Helsinki, Finland), Federico Canzian (German Cancer Research Center, Heidelberg, Germany), Jenny Chang-Claude [Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Germany], Laura Crisponi (Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche, Cagliari, Italy), Kamila Czene (Karolinska Institut, Stockholm, Sweden), Norbert Dahmen (University of Mainz, Mainz, Germany), Isabel dos Santos Silva (London School of Hygiene and Tropical Medicine, London, United Kingdom), Olivia Fletcher (The Institute of Cancer Research, London, United Kingdom), Lorna Gibson (London School of Hygiene and Tropical Medicine, London, United Kingdom), Per Hall (Karolinska Institut, Stockholm, Sweden), HEBON (Hereditary Breast and Ovarian Cancer Research Group Netherlands), Rebecca Hein (Deutsches Krebsforschungszentrum, Heidelberg, Germany, and University of Cologne, Cologne, Germany), Albert Hofman (Erasmus Medical Center, Rotterdam, the Netherlands), John L. Hopper (Melbourne School of Population Health, University of Melbourne, Melbourne, Victoria, Australia), Astrid Irwanto (Genome Institute of Singapore, Singapore), Rudolf Kaaks (German Cancer Research Center, Heidelberg, Germany), Muhammad G. Kibriya (University of Chicago, Chicago, IL), Peter Lichtner (German Research Center for Environmental Health, Neuherberg, Germany), Jianjun Liu (Genome Institute of Singapore, Singapore), Enes Makalic (Melbourne School of Population Health, University of Melbourne, Melbourne, Victoria, Australia), Alfons Meindl (Technische Universität München, Munich, Germany), Hanne Meijers-Heijboer (VU University Medical Center, Amsterdam, the Netherlands), Bertram Müller-Myhsok (Max Planck Institute of Psychiatry, Munich, Germany), Taru A. Muranen (University of Helsinki and Helsinki University Central Hospital, Helsinki, Finland), Heli Nevanlinna (Univesity of Helsinki and Helsinki University Central Hospital, Helsinki, Finland), Julian Peto (London School of Hygiene and Tropical Medicine, London, United Kingdom), Ross L. Prentice (Fred Hutchinson Cancer Research Center, Seattle, WA), Nazneen Rahman (Institute of Cancer Research, Sutton, United Kingdom), Daniel F. Schmidt (Melbourne School of Population Health, University of Melbourne, Melbourne, Victoria, Australia), Rita K. Schmutzler (University of Cologne, Cologne, Germany), Melissa C. Southey (The University of Melbourne, Melbourne, Victoria, Australia), Clare Turnbull (Institute of Cancer Research, Sutton, United Kingdom), Andre G. Uitterlinden (Erasmus Medical Center, Rotterdam, the Netherlands), Rob B. van der Luijt (University Medical Center Utrecht, Utrecht, the Netherlands), Quinten Waisfisz (VU University Medical Center, Amsterdam, the Netherlands), Alice S. Whittemore (Stanford University, Stanford, CA), and Wei Zheng (Vanderbilt University, Nashville, TN).

FOCI: FOCI consortium would like to thank the following investigators for their contribution: Mike Birrer, Ann Chen, Julie Cunningham, Ed Iversen, John McLaughlin, Steven Narod, Harvey Risch, Jenny Permuth-Wey, Paul Pharoah, Simon Gayther, and Susan Ramus.

UK Ovarian Cancer GWAS: We thank all the individuals who took part in this study. We thank all the researchers, clinicians, and administrative staffs who have enabled the many studies contributing to this work. This study made use of data generated by the Wellcome Trust Case Control consortium with its project funding provided by the Wellcome Trust under award 076113. We thank the support of the UK National Institute for Health Research Biomedical Research Centres at the University of Cambridge and University College Hospital.

Colorectal Transdisciplinary Study (CORECT): We are incredibly grateful for the contributions of Dr. Brian Henderson and Dr. Roger Green over the course of this study and acknowledge them in memoriam.

Colon CFR: The Colon CFR graciously thanks the generous contributions of their study participants, dedication of study staff, and financial support from the U.S. National Cancer Institute, without which this important registry would not exist.

MCCS: This study was made possible by the contribution of many people, including the original investigators and the diligent team who recruited participants and continue to work on follow-up. We would also like to express our gratitude to the many thousands of Melbourne residents who took part in the study and provided blood samples.

This work was supported by TRICL (Transdisciplinary Research for Cancer of Lung) and International Lung Cancer Consortium (ILCCO): NIHU19 CA148127-01 (principal investigator: Amos), Canadian Cancer Society Research Institute (020214, principal investigator: R.J. Hung).

DRIVE: NIHU19 CA148065. CORECT (ColoRectal Transdisciplinary Study): NIHU19 CA148107; R01 CA81488, P30 CA014089.

ELLIPSE (Elucidating Loci in Prostate Cancer Susceptibility): This work was support by the GAME-ON U19 initiative for prostate cancer (ELLIPSE), U19 CA148537.

CRUK GWAS: This work was supported by the Canadian Institutes of Health Research, European Commission's Seventh Framework Programme grant agreement no. 223175 (HEALTH-F2-2009-223175), Cancer Research UK Grants C5047/A7357, C1287/A10118, C5047/A3354, C5047/A10692, C16913/A6135, and The NIH Cancer Post-Cancer GWAS initiative grant: No. 1 U19 CA 148537-01 (the GAME-ON initiative).

We would also like to thank the following for funding support: The Institute of Cancer Research and The Everyman Campaign, The Prostate Cancer Research Foundation, Prostate Research Campaign UK (now Prostate Action), The Orchid Cancer Appeal, The National Cancer Research Network UK, The National Cancer Research Institute (NCRI) UK. We are grateful for support of NIHR funding to the NIHR Biomedical Research Centre at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust. The Prostate Cancer Program of Cancer Council Victoria also acknowledges grant support from The National Health and Medical Research Council, Australia (126402, 209057, 251533, 396414, 450104, 504700, 504702, 504715, 623204, 940394, 614296), VicHealth, Cancer Council Victoria, The Prostate Cancer Foundation of Australia, The Whitten Foundation, PricewaterhouseCoopers, and Tattersall's. EAO, DMK, and EMK acknowledge the Intramural Program of the National Human Genome Research Institute for their support. CAPS GWAS study was supported by the Swedish Cancer Foundation (grant no. 09-0677, 11-484, 12-823), the Cancer Risk Prediction Center (CRisP; www.crispcenter.org), a Linneus Centre (contract ID 70867902) financed by the Swedish Research Council, and a Swedish Research Council (grant no. K2010-70X-20430-04-3, 2014-2269). The BPC3 was supported by the NIH, National Cancer Institute (cooperative agreements U01-CA98233 to D.J.H., U01-CA98710 to S.M.G., U01-CA98216 to E.R., and U01-CA98758 to B.E.H., and Intramural Research Program of NIH/National Cancer Institute, Division of Cancer Epidemiology and Genetics).

FOCI (Transdisciplinary Cancer Genetic Association and Interacting Studies): NIHU19 CA148112-01 (principal investigator: T.A. Sellers), R01-CA122443, P50-CA136393, P30-CA15083 (principal investigator: E.L. Goode), Cancer Research UK [C490/A8339, C490/A16561, C490/A10119, C490/A10124 (principal investigator: P. Pharoah)]. NHS by the NIH (P01 CA087969, UM1 CA186107, R01 CA151993, R35 CA197735 and P50 CA127003). The Colon CFR data collection was supported by grant UM1 CA167551, and the Colon CFR Illumina GWAS was supported by grants U01 CA122839 and R01 CA143237 to G. Casey from the National Cancer Institute, NIH.

PLCO: 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, Department of Health and Human Services. In addition, a subset of control samples were genotyped as part of the Cancer Genetic Markers of Susceptibility (CGEMS) Prostate Cancer GWAS (Yeager M, et al. Nat Genet. 2007;39(5):645–649), Colon CGEMS pancreatic cancer scan (PanScan; Amundadottir L, et al. Nat Genet. 2009;41(9):986–990 and Petersen GM, et al. Nat Genet. 2010;42(3):224–228), 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, NIHU01 HG004446, and NIHGEI 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.

This work was supported by the National Cancer Institute, National Institutes of Health, U.S. Department of Health and Human Services (U19 CA148107; R01 CA81488; P30 CA014089). MECC was supported by the National Cancer Institute, National Institutes of Health under R01 CA081488 and U19 CA148107. The Cancer Prevention Study-II Nutrition Cohort is funded by the American Cancer Society. The Colon CFR GWAS work was supported by funding from the NCI/NIH (U01 CA122839 and R01 CA143247). The Colon CFR participant recruitment and collection of data and biospecimens used in this study were supported by NCI/NIH (UM1 CA167551) and through cooperative agreements with the following Colon CFR centers: Australasian Colorectal Cancer Family Registry (NCI/NIH U01 CA074778 and U01/U24 CA097735),USC Consortium Colorectal Cancer Family Registry (NCI/NIH U01/U24 CA074799), Mayo Clinic Cooperative Family Registry for Colon Cancer Studies (NCI/NIH U01/U24 CA074800), Ontario Familial Colorectal Cancer Registry (NCI/NIH U01/U24 CA074783), Seattle Colorectal Cancer Family Registry (NCI/NIH U01/U24 CA074794), and University of Hawaii Colorectal Cancer Family Registry (NCI/NIH U01/U24 CA074806). Additional support for case ascertainment was provided from the Surveillance, Epidemiology and End Results (SEER) Program of the National Cancer Institute to Fred Hutchinson Cancer Research Center (Control Nos. N01-CN-67009 and N01-PC-35142, and Contract No. HHSN2612013000121), the Hawaii Department of Health (Control Nos. N01-PC-67001 and N01-PC-35137, and Contract No. HHSN26120100037C, and the California Department of Public Health (contracts HHSN261201000035C awarded to the University of Southern California, and the following state cancer registries: AZ, CO, MN, NC, NH, and by the Victoria Cancer Registry and Ontario Cancer Registry).The Kentucky Case Control study was supported by the following grants: 1) Clinical Investigator Award from Damon Runyon Cancer Research Foundation (CI-8) and 2) NCI R01CA136726. MCCS cohort recruitment was funded by VicHealth and Cancer Council Victoria. The MCCS was further supported by Australian NHMRC grants 509348, 209057, 251553 and 504711 and by infrastructure provided by Cancer Council Victoria. Cases and their vital status were ascertained through the Victorian Cancer Registry (VCR) and the Australian Institute of Health and Welfare (AIHW), including the National Death Index and the Australian Cancer Database. The Newfoundland Familial Colorectal Cancer Registry was supported by the Canadian Institutes of Health Research grant CRT-43821. This work was also supported by NCI U01 CA1817700 (to L. Li) and R01 CA144040 (to S. Markowitz).

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