Genome-Wide Gene–Diabetes and Gene–Obesity Interaction Scan in 8,255 Cases and 11,900 Controls from PanScan and PanC4 Consortia

Pancreatic cancer is the third leading cause of cancer-related death, accounting for more than 47,000 deaths each year in the United States (1). It is a highly lethal disease with a 5-year survival rate of 9% (2). Epidemiologic studies have shown that 20%–25% of pancreatic cancer cases are attributable to cigarette smoking (3). However, the incidence of pancreatic cancer has been rising slightly each year in the United States since 2002; this is unexpected given the decreasing prevalence of cigarette smoking, and may be due to the rising prevalence of obesity and diabetes. Accumulating evidence suggests that obesity and long-term type II diabetes are associated with increased risk of pancreatic cancer. For example, a pooled analysis of 14 cohort studies of body mass index (BMI) has shown that obesity (BMI ≥30 kg/m²) was associated with 47% [95% confidence interval (CI), 23%–75%] increased risk of pancreatic cancer (4). A meta-analysis of 23 cohort and case–control (CC) studies suggests that the association between BMI and pancreatic cancer is not linear (5). At least four meta-analyses of large datasets from cohort and CC studies have shown that long-term diabetes was associated with a 1.5- to 2-fold increased risk of pancreatic cancer (6–9). Because only a small portion of obese and diabetic individuals develop pancreatic cancer, understanding how genetic factors affect risk among those individuals could inform targeted interventions or screening. Identifying variants that are only associated with risk of cancer (or have stronger associations) among obese or diabetic individuals is of particular interest.

Genome-wide association studies (GWAS) conducted by the Pancreatic Cancer Cohort Consortium (PanScan) and Pancreatic Cancer Case Control Consortium (PanC4) have identified 21 genetic loci and chromosome regions significantly associated with the risk of pancreatic cancer (10–15). However, these findings explain limited heritability of the disease, that is, the established GWAS loci explain 2.1% of the heritability of pancreatic cancer in contrast to the estimated heritability of 36% from a large population-based twin study (13, 16). Beyond main effects, some genetic factors may contribute to the risk of pancreatic cancer only in the presence of specific risk factors for the disease such as obesity and diabetes, that is, gene–obesity/diabetes interaction, and broadly referred as gene–environment interaction (GxE) herein. Therefore, a genome-wide GxE scan may help find the missing heritability of pancreatic cancer. Several of the susceptibility genes identified by GWAS (NR5A2, PDX1, HNF1B, and HNF4G) are important for pancreas development (17). These genes are important components of the transcriptional networks governing embryonic pancreatic development and differentiation, as well as maintaining pancreatic homeostasis. Mutations in some of these genes are responsible for maturity onset diabetes of the young and common variants of these genes have been associated with BMI and risk of type 2 diabetes in GWAS (17). Therefore, in addition to their roles in regulating the development and function of the pancreas, these genes may contribute to pancreatic cancer, partially through an increased risk of obesity and diabetes. Whether these genes and other unidentified genes have an interactive action with obesity and diabetes in modifying the risk of pancreatic cancer is the focus of the current investigation.

We have previously performed GxE analyses at SNP/gene/pathway levels using GWAS data from 2,028 cases and 2,109 controls from PanScan I and II. No significant interactions at the SNP or gene levels were observed for diabetes or obesity. At the pathway level, NF-κB–mediated chemokine signaling and axonal guidance signaling pathway, respectively, were identified as the top pathways interacting with obesity and smoking in modifying the risk of pancreatic cancer (18, 19). These studies were limited by the small sample size, and underpowered for genome-wide GxE analysis (20). To address this limitation, we conducted the current analysis in a much larger combined dataset of PanScan I–III and PanC4 with 8,255 cases and 11,900 controls. We further leveraged recently developed, more powerful SNP-set/gene-based GxE tests (21, 22) to discover novel genetic variants that may modify the association between diabetics/obesity and pancreatic cancer.

This genome-wide GxE study includes 8,255 cases and 11,900 controls of European ancestry drawn from the PanScan and PanC4 consortia. Cases were patients with known or presumed primary pancreatic ductal adenocarcinoma (ICD-O-3 code C250–C259) and controls were free of pancreatic cancer. Individual studies were approved by the respective institutional review board following the institution's requirement. Written informed consent was obtained from each study participant. The approaches for data harmonization and meta-analysis were approved by the University of Texas MD Anderson Cancer Center Institutional Review Board (Houston, TX).

Genotype data were generated in four previously reported GWASs, that is, PanScan I, II, and III and PanC4, and the details of these studies have been described previously (10–13). Genotyping in PanScan I, II, and III was conducted at the Cancer Genomics Research Laboratory of the NCI of the National Institutes of Health (NIH) using the Illumina HumanHap550 Infinium II, Human 610-Quad, and OmniExpress series arrays, respectively. PanC4 employed the HumanOmniExpressExome-8v1 array. Because different genotyping platforms were used in these studies, missing genotypes were imputed using the University of Michigan imputation server (https://imputationserver.sph.umich.edu/index.html) with the Haplotype Reference Consortium (23) as the reference panel or IMPUTE2 with the 1000 Genomes Phase 3 as the reference panel (https://mathgen.stats.ox.ac.uk/impute/impute_v2.html). After imputation, SNPs that were identified by imputation only (not genotyped in any of the four GWASs), having minor allele frequency (MAF) ≤ 0.005, imputation quality score <0.3, or Hardy–Weinberg equilibrium test P < 1 × 10⁻⁶ in controls were excluded; a total of about 870,000 common SNPs to all four studies were included in this GxE analysis. The PanScan (I, II, and III) and PanC4 GWAS data are available through dbGaP (accession numbers phs000206.v5.p3 and phs000648.v1.p1, respectively).

The exposure variables considered in this GxE analysis were obesity (BMI ≥30 kg/m² vs. <30 kg/m²) and diabetes (diabetes with ≥3 years of duration vs. nondiabetes). Because diabetes could be a manifestation of occult pancreatic cancer, we excluded diabetes with a short duration (<3 years) for studies with diabetes duration information to control reverse causality. Covariates for adjustment included age (continuous), sex, study sites, and principal components accounting for population substructure. The distribution of demographics and risk factors of participants in each GWAS included in this analysis are summarized in Supplementary Table S1.

We applied CC, case-only (CO), and 2 degrees-of-freedom (2-df) joint-effect test (24) methods at the SNP level, and the “rareGE” method (21) at the gene level in the genome-wide GxE scan. The 2-df joint-effect test is more powerful in detecting a susceptible SNP in the presence of strong genetic main effect (SNP), strong interaction effect (SNPxE), or a combination of weak/moderate main and interaction effects (SNP + SNPxE). Thus, the joint-effect test is a useful complementary approach to CC, CO, and single-SNP marginal association analysis in identifying disease susceptible loci (20).

The PanScan I–III and PanC4 datasets were analyzed individually using the CC, CO, and joint-effect test at the SNP level. The “rareGE” method was used for gene-based GxE analysis. The summary statistics for each consortium were then subjected to meta-analysis.

To perform SNP-level analysis, we ran the logistic regression model as follows:

where Y is the disease status (1 for case; 0 for control); β₀ is the intercept; E is the exposure variable of interest (diabetes or obesity); SNP is the dosage of the genetic variant of interest, coded additively accounting for genotype imputation uncertainty (ranging from 0 to 2); and C is the vector of all covariates including age (continuous), sex, study indicators, principal components accounting for population substructures, and either diabetes or BMI [e.g., diabetes serves as the exposure of interest with BMI (continuous) included in the covariate vector]. For the CC study design, the null hypothesis to be tested H₀: |{{\rm{\beta }}_{{\rm{GE}}}}$| = 0. |{e^{{{\rm{\beta }}_{{\rm{GE}}}}}}$| was referred as the interaction OR.

Joint-effect analysis of SNP and SNPxE were run using the approach by Aschard and colleagues (25) by testing the null hypothesis H₀: |{\beta _G} = {\beta _{\curr GE}} = 0$|⁠, derived from model (A) with a 2-df |{\chi ^2}$| Wald test. For the CO study design, a logistic regression model was run in the case group only as follows:

where the coefficients in model (B) are denoted the same as those in model (A).

Gene regions were defined according to coordinates of the hg19 assembly, retrieved from the University of California, Santa Cruz (UCSC) Genome Browser (26). About 22,300 genes were downloaded from UCSC server, of which approximately 20,000 genes covering ≥2 GWAS genotyped SNPs were analyzed in this study.

We performed gene-based GxE analysis using the “rareGE” method (21) based on common SNPs (MAF ≥ 0.005, located within 20 kb upstream or downstream of a given gene). For a gene with p SNPs, the full model is as follows:

where |{{\rm{\beta }}_{{\rm{Gj}}}}$| and |{{\rm{\beta }}_{{\rm{GEj}}}}$| are the regression coefficients for the genetic main effect and GxE effect for the jth SNP, respectively.

Two tests were implemented in the “rareGE” R package: GxE test with genetic main effects estimated as random effects (P_Int) under the null hypothesis of no GxE, that is, |{H_0}\!\!:{\beta _{\curr GE1}} = {\beta _{\curr GE2}} = \ldots = {\beta _{\curr GEp}} = 0$|⁠, and a joint test of G and GxE (P_Joint) with |${H_0}\!\!:{\beta _{\curr G1}} = {\beta _{\curr G2}} = \ldots = {\beta _{\curr Gp}} =$| |0{\rm{\ and}}\ {\beta _{\curr GE1}} = {\beta _{\curr GE2}} = \ldots = {\beta _{\curr GEp}} = 0$|⁠, analogous to the 2-df SNP-level joint-effect test.

We applied a fixed-effects meta-analysis in METAL to combine SNP-level GxE results from the CC or CO method across individual consortia (27). Fisher meta-analysis was used to combine gene-level GxE P values from the rareGE method (28).

All tests were two sided. We consider P < 2.5 × 10⁻⁸ and P < 1.25 × 10⁻⁶ as genome-wide significant at the SNP and gene level, respectively (29), for each individual study and each meta-analysis, adjusted for 1 million SNPs, 20,000 genes, and two exposures of interest by the Bonferroni correction at family-wise error rate of 0.05. P < 5.0 × 10⁻² was considered as nominally significant for all analyses.

We used the QUANTO software (version 1.2.4; ref. 30) to perform power estimation for these GxE scans. With 8,255 cases and 11,900 controls, we had 80% power to detect an interaction OR of 1.5 and 1.6, respectively, for obesity (main effect OR = 1.2 with 20% prevalence in controls based on Supplementary Table S1) and diabetes (main effect OR = 1.7 with 10% prevalence in controls based on Supplementary Table S1) for an SNP with MAF of 20% at a significance level of 2.5 × 10⁻⁸ by the standard CC test.

First, we examined the GxE (obesity and diabetes) interactions at the SNP level using the CC, CO, and joint tests in each individual GWAS, followed by meta-analysis of the summary statistics. Supplementary Fig. S1 shows the quantile–quantile (Q–Q) plots for the CC and CO meta-analyses. There was no discernable abnormal behavior in the Q–Q plots for CC and CO study designs (genomic control λ ranged from 0.942 to 1.023). Q–Q plots also performed well for meta-analysis of joint-effect tests (λs: 0.94–1.045).

No signal at a genome-wide threshold of significance (P < 2.5 × 10⁻⁸) was detected in CC or CO analyses on interactions of genes with diabetes or obesity. Using the CC approach, four SNPs on chromosomes 10, 18, and 20 showed evidence of interactions with diabetes at near genome-wide significance (P < 1 × 10⁻⁶) and six SNPs on chromosomes 2, 7, 11, and 16 showed weaker evidence of interactions with obesity (P < 1 × 10⁻⁵; Table 1). By the CO approach, four SNPs on chromosomes 3 and 10 showed evidence of interactions with diabetes at near genome-wide significance (P < 1 × 10⁻⁶; Table 2). Of these, two SNPs (rs12255372 and rs7901695) were near TCF7L2 and in linkage disequilibrium (r² = 0.74 and 0.87, respectively) with the lead SNP from a recent GWAS of type 2 diabetes (rs7903146; P = 1 × 10⁻³⁴⁷; ref. 31). Thus, the CO signals for these two SNPs likely reflect violations of the gene–environment independence assumption rather than evidence for GxE. In addition, five SNPs on chromosomes 4, 8, 14, and 17 had possible interactions with obesity at P < 1 × 10⁻⁵ (Table 2). Further, no significant across-consortium heterogeneity was found for the meta-analysis results in Tables 1 and 2 (all heterogeneity test P > 0.05).

Meta-analysis of joint-effect tests for SNP and SNP × diabetes or SNP × obesity detected numerous genome-wide significant signals that are all located in the chromosome regions containing previously identified GWAS top hits (Supplementary Table S2). Conditional analysis adjusting for the GWAS top hits in each region resulted in null findings, indicating that joint-effect test signals were all driven by the strong main effects of the SNPs.

Possible interactions of nine genes with diabetes and three genes with obesity at a meta-analysis significance level of P < 1 × 10⁻⁴ in at least one of the interaction-only and joint tests are listed in Table 3. Among these genes, a significant (P < |1.25\ \times {10^{ - 6}}$|⁠) interaction of diabetes with FAM63A gene was observed in the meta-analysis (⁠|{P_{{\rm{Interaction}}}}$| = |1.2\ \times {10^{ - 6}}$|⁠, |{P_{{\rm{Joint}}}}$| = |4.2\ \times {10^{ - 7}}$|⁠; Table 3). The SNPs contributing to this gene are listed in Supplementary Table S3. No individual SNP of this gene showed a significant interaction with diabetes.

In this genome-wide gene–obesity/diabetes interaction study of pancreatic cancer, no significant departures from a log-linear odds model at the SNP level were identified by the CC or CO approaches. In the gene-based analysis, a significant interaction between variants in the FAM63A gene and diabetes was observed.

FAM63A, also known as MINDY-1 (MINDY lysine 48 deubiquitinase 1) is a member of an evolutionarily conserved and structurally distinct family of deubiquitinating enzymes (32), which specifically cleaves K48-linked poly-ubiquitin chain to regulate protein degradation. This distinct deubiquitinase class localizes to DNA lesions, where it plays an important role in genome stability pathways, functioning to prevent spontaneous DNA damage and to promote cellular survival in response to exogenous DNA damage (33). Previous GWASs have associated FAM63A or FAM63A homolog gene variants with the risk of primary rhegmatogenous retinal detachment (34) and chronic renal disease (35). Genetic analysis of a diabetes-prone mouse strain has revealed gene regions homologous to FAM63A contributing to diabetes susceptibility (36). Although the role of FAM63A in pancreatic cancer is unknown at present, the observed interaction with diabetes deserves further investigation.

Genome-wide GxE analysis has unique challenges compared with genetic main effects analysis in GWAS. First, GxE analysis requires a much larger sample size to detect a realistic interaction OR than does a GWAS scan for a comparable main effect OR (20, 37), largely explaining why few positive findings have been reported in GxE studies (38–40). For example, this GxE scan with 8,255 cases and 11,900 controls, even though about four times as large as our previous gene–obesity/diabetes interaction analysis (18), had 80% power to detect an interaction OR of 1.5 and 1.6, respectively, for obesity and diabetes for an SNP with MAF of 20% at a significance level of |2.5\ \times {10^{ - 8}}$| by the standard CC test; in contrast, the same sample size had 80% power to detect a genetic main effect OR of 1.18 at the same MAF and significance level. To boost the power for a given sample size, novel statistical and analytic methods have been proposed to leverage a priori biological knowledge in the form of genes, pathways, or other functional genomic annotations such as those derived from the ENCODE and NIH Epigenomics Roadmap projects (18, 19, 41). Second, exposure variability and measurement accuracy play a considerable role in determining the power and reproducibility of GxE studies (42, 43). Third, there is no single most powerful statistical method for either SNP or gene-level genome-wide GxE analysis due to the largely unknown patterns of GxE interaction signals and combinations of genetic main and GxE effects (20, 22). Therefore, we suggest that the GxE analysis should make use of multiple methods with complementary strengths, as used here and suggested by other investigators (44), to discover the missing heritability of pancreatic cancer (45).

This study identified a statistically significant interaction of diabetes with variants in FAM63A in gene-based GxE analysis, but no significant SNP-level GxE interactions with either diabetes or obesity. We note that the absence of interaction on the log-odds scale has potentially important implications for risk modeling, as it typically implies presence of interaction on the risk difference scale, sometimes referred to as “public health interaction” (46). Developing and validating a multifactorial risk model is beyond the scope of this article, but we note that our results lend support to the common assumption of additive log odds when combining genetic, clinical, and environmental risk factors to predict risk (47, 48).

This study has strengths and limitations. This is by far the largest GxE analysis in pancreatic cancer. Quality control was strictly performed in steps of genotyping, population structure definition, exposure measurement, and harmonization. Diabetes was defined as disease with ≥3-year duration, avoiding reverse causality. Along the same line, because it is common for patients with pancreatic cancer to experience severe weight loss (43), we avoided using body weight at or close to cancer diagnosis for cases when calculating the BMI. Following the state-of-the-art analysis strategies in large consortium-based GxE scans (49, 50), we only adjusted for a “minimum” set of covariates, including age, sex, study sites, and principal components accounting for population substructure, in the regression analysis. As shown by the well-behaved Q–Q plots in Supplementary Fig. S1, there was no indication of uncontrolled confounding effects. Finally, genome-wide significant thresholds based on the Bonferroni correction were applied to reduce false-positive discovery. Nevertheless, relatively small sample sizes curbed the power of the genome-wide GxE scan from CC and CO study designs. Despite this, the current GxE analysis discovered a novel susceptibility locus for pancreatic cancer using a gene-based GxE test, and may contribute to discovering the mechanism of diabetes-associated pancreatic cancer.

C. Fuchs reports other commercial research support from Agios, Bain Capital, Unum Therapeutics, CytomX Therapeutics, Daiichi Sankyo, Eli Lilly, Entrinsic Health, Evolveimmune Therapeutics, Genentech, Merck, and Taiho; has ownership interest (including patents) in CytomX Therapeutics, Entrinsic Health, and Evolveimmune Therapeutics; and reports other remuneration from Amylin Pharma. K. Ng reports receiving commercial research grants from Celgene and Revolution Medicines. No potential conflicts of interest were disclosed by other authors.

The authors assume full responsibility for analyses and interpretation of these data. Where authors are identified as personnel of the International Agency for Research on Cancer/World Health Organization, the authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy, or views of the International Agency for Research on Cancer/World Health Organization.

Conception and design: H. Tang, R.Z. Stolzenberg-Solomon, G.G. Giles, B. Bueno-de-Mesquita, S.J. Chanock, J.M. Gaziano, P. Hartge, M.H. Hassam, E.A. Holly, A. Tjønneland, L.T. Amundadottir, G.M. Petersen, A.P. Klein, D. Li, P. Kraft, P. Wei

Development of methodology: B. Bueno-de-Mesquita, P. Hartge, M.H. Hassam, E.A. Holly, M. Porta, D. Li, P. Kraft, P. Wei

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): R.Z. Stolzenberg-Solomon, A.A. Arslan, L.E.B. Freeman, P.M. Bracci, P. Brennan, F. Canzian, M. Du, S. Gallinger, G.G. Giles, C. Kooperberg, L.L. Marchand, R.E. Neale, X.-O. Shu, E. White, W. Zheng, D. Albanes, G. Andreotti, S.I. Berndt, B. Bueno-de-Mesquita, J.E. Buring, S.J. Chanock, C. Fuchs, J.M. Gaziano, M. Goggins, M.H. Hassam, E.A. Holly, R.N. Hoover, R.J. Hung, R.C. Kurtz, I.-M. Lee, N. Malats, R.L. Milne, I. Orlow, U. Peters, M. Porta, K.G. Rabe, N. Rothman, G. Scelo, H.D. Sesso, D.T. Silverman, I.M. Thompson Jr, A. Tjønneland, A. Trichopoulou, J. Wactawski-Wende, N. Wentzensen, L.R. Wilkens, H. Yu, A. Zeleniuch-Jacquotte, E.J. Jacobs, G.M. Petersen, B.M. Wolpin, H.A. Risch, A.P. Klein, D. Li

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): H. Tang, L. Jiang, P. Brennan, E. White, G. Andreotti, A. Babic, A. Blackford, E. Childs, M.H. Hassam, R.N. Hoover, R.L. Milne, K. Ng, L.T. Amundadottir, G.M. Petersen, H.A. Risch, D. Li, P. Kraft, P. Wei

Writing, review, and/or revision of the manuscript: H. Tang, L. Jiang, R.Z. Stolzenberg-Solomon, A.A. Arslan, L.E.B. Freeman, P.M. Bracci, P. Brennan, G.G. Giles, P.J. Goodman, C. Kooperberg, L.L. Marchand, R.E. Neale, X.-O. Shu, K. Visvanathan, E. White, W. Zheng, D. Albanes, G. Andreotti, A. Babic, W.R. Bamlet, S.I. Berndt, A. Blackford, B. Bueno-de-Mesquita, J.E. Buring, D. Campa, S.J. Chanock, E.J. Duell, C. Fuchs, J.M. Gaziano, M. Goggins, P. Hartge, M.H. Hassam, E.A. Holly, R.N. Hoover, R.J. Hung, I.-M. Lee, N. Malats, R.L. Milne, K. Ng, A.L. Oberg, I. Orlow, U. Peters, M. Porta, K.G. Rabe, G. Scelo, H.D. Sesso, D.T. Silverman, I.M. Thompson Jr, A. Tjønneland, A. Trichopoulou, J. Wactawski-Wende, N. Wentzensen, L.R. Wilkens, H. Yu, A. Zeleniuch-Jacquotte, L.T. Amundadottir, E.J. Jacobs, G.M. Petersen, B.M. Wolpin, H.A. Risch, N. Chatterjee, A.P. Klein, D. Li, P. Kraft, P. Wei

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): H. Tang, A.A. Arslan, L.E.B. Freeman, M. Du, S. Gallinger, G. Andreotti, W.R. Bamlet, A. Blackford, E.J. Duell, C. Fuchs, A.L. Oberg, G. Scelo, H.D. Sesso, I.M. Thompson Jr, N. Wentzensen, A.P. Klein, P. Kraft

Study supervision: M.H. Hassam, E.A. Holly, R.J. Hung, I.-M. Lee, J. Wactawski-Wende, D. Li, P. Kraft, P. Wei

Other (original principal investigator of one of the NIH/NCI-funded studies that is part of the consortium data that were used for this analysis): E.A. Holly

This work was supported by the NIH grants R01CA169122 (to P. Wei) and UH2CA191284 (to P. Kraft). The IARC/Central Europe study was supported by NIH grant R03 CA123546-02 and grants from the Ministry of Health of the Czech Republic (NR 9029-4/2006, NR9422-3, NR9998-3, and MH CZ-DRO-MMCI 00209805). The work at Johns Hopkins University was supported by the NCI grants P50CA062924 and R01CA97075. Additional support was provided by the Lustgarten Foundation, and Susan Wojcicki and Dennis Troper and the Sol Goldman Pancreas Cancer Research Center. This work was supported by an NCI grant R01 CA154823. The Mayo Clinic Biospecimen Resource for Pancreas Research study is supported by the Mayo Clinic SPORE in Pancreatic Cancer (P50 CA102701). The MD Anderson Cancer Center study was supported by the NIH grant R01CA98030 and a grant from the Khalifa Bin Zayed Al Nahyan Foundation. The Memorial Sloan Kettering Cancer Center Pancreatic Tumor Registry was supported by P30CA008748, the Geoffrey Beene Foundation, the Arnold and Arlene Goldstein Family Foundation, and the Society of MSKCC. The PACIFIC Study was supported by the NCI grant R01CA102765, and Kaiser Permanente and Group Health Cooperative. The Queensland Pancreatic Cancer Study was supported by a grant from the National Health and Medical Research Council of Australia (NHMRC; grant number 442302). R.E. Neale was supported by an NHMRC Senior Research Fellowship (#1060183). The UCSF pancreas study was supported by NIH-NCI grants (R01CA1009767, R01CA109767-S1, and R0CA059706) and the Joan Rombauer Pancreatic Cancer Fund. The Yale (CT) pancreas cancer study was supported by the NCI grant 5R01CA098870. The cooperation of 30 Connecticut hospitals, including Stamford Hospital, in allowing patient access, is gratefully acknowledged. The Connecticut Pancreas Cancer Study was approved by the State of Connecticut Department of Public Health Human Investigation Committee. Certain data used in that study were obtained from the Connecticut Tumor Registry in the Connecticut Department of Public Health. Studies included in PANDoRA were partly funded by: the Czech Science Foundation (No. P301/12/1734), the Internal Grant Agency of the Czech Ministry of Health (IGA NT 13 263); the Baden-Württemberg State Ministry of Research, Science and Arts (to Prof. H. Brenner), the Heidelberger EPZ-Pancobank (to Prof. M.W. Büchler and team: Prof. T. Hackert, Dr. N. A. Giese, Dr. Ch. Tjaden, E. Soyka, and M. Meinhardt; Heidelberger Stiftung Chirurgie and BMBF grant 01GS08114), the BMBH (to Prof. P. Schirmacher; BMBF grant 01EY1101), the “5 × 1000” voluntary contribution of the Italian Government, the Italian Ministry of Health (RC1203GA57, RC1303GA53, RC1303GA54, and RC1303GA50), the Italian Association for Research on Cancer (to Prof. A. Scarpa; AIRC n. 12182), the Italian Ministry of Research (to Prof. A. Scarpa; FIRB - RBAP10AHJB), the Italian FIMP-Ministry of Health (to Prof. A. Scarpa; 12 CUP_J33G13000210001), and by the National Institute for Health Research Liverpool Pancreas Biomedical Research Unit, United Kingdom. We would like to acknowledge the contribution of Dr Frederike Dijk and Prof. Oliver Busch (Academic Medical Center, Amsterdam, the Netherlands). The American Cancer Society funds the creation, maintenance, and updating of the Cancer Prevention Study II cohort. Cancer incidence data for CLUE were provided by the Maryland Cancer Registry, Center for Cancer Surveillance and Control, Department of Health and Mental Hygiene (Baltimore, MD; http://phpa.dhmh.maryland.gov/cancer, 410-767-4055). We acknowledge the State of Maryland, the Maryland Cigarette Restitution Fund, and the National Program of Cancer Registries of the Centers for Disease Control and Prevention for the funds that support the collection and availability of the cancer registry data. We thank all the CLUE participants. The Melbourne Collaborative Cohort Study (MCCS) recruitment was funded by VicHealth and Cancer Council Victoria. The MCCS was further supported by Australian NHMRC grants 209057 and 396414 and by infrastructure provided by Cancer Council Victoria. Cases and their vital status were ascertained through the Victorian Cancer Registry and the Australian Institute of Health and Welfare, including the National Death Index and the Australian Cancer Database. The NYU study (to A Zeleniuch-Jacquotte and A.A. Arslan) was funded by NIH R01 CA098661, UM1 CA182934, and center grants P30 CA016087 and P30 ES000260. The PANKRAS II Study in Spain was supported by research grants from Instituto de Salud Carlos III-FEDER, Spain; Fondo de Investigaciones Sanitarias (FIS; #PI13/00082 and #PI15/01573) and Red Temática de Investigación Cooperativa en Cáncer, Spain (#RD12/0036/0050); European Cooperation in Science and Technology (COST Action #BM1204: EU_Pancreas), Ministerio de Ciencia y Tecnología (CICYT SAF 2000-0097), Fondo de Investigación Sanitaria (95/0017), Madrid, Spain; Generalitat de Catalunya (CIRIT - SGR); and “Red temática de investigación cooperativa de centros en Cáncer” (C03/10), “Red temática de investigación cooperativa de centros en Epidemiología y salud pública” (C03/09), and CIBER de Epidemiología (CIBERESP), Madrid, Spain. The Physicians' Health Study was supported by research grants CA-097193, CA-34944, CA-40360, HL-26490, and HL-34595 from the NIH (Bethesda, MD). The Women's Health Study was supported by research grants CA-047988, HL-043851, HL-080467, and HL-099355 from the NIH (Bethesda, MD). Health Professionals Follow-up Study was supported by NIH grant UM1 CA167552 from the NCI (Bethesda, MD). Nurses' Health Study was supported by NIH grants UM1 CA186107, P01 CA87969, and R01 CA49449 from the NCI (Bethesda, MD). Additional support was provided from the Hale Center for Pancreatic Cancer Research, U01 CA21017 from the NCI (Bethesda, MD), and the United States Department of Defense CA130288, Lustgarten Foundation, Pancreatic Cancer Action Network, Noble Effort Fund, Peter R. Leavitt Family Fund, Wexler Family Fund, and Promises for Purple (to B.M. Wolpin). The WHI program was funded by the National Heart, Lung, and Blood Institute, NIH, U.S. Department of Health and Human Services through contracts HHSN268201600018C, HHSN268201600001C, HHSN268201600002C, HHSN268201600003C, and HHSN268201600004C. 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%20Long%20List.pdf.

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