Registry-based epidemiologic studies suggest associations between chronic inflammatory intestinal diseases and pancreatic ductal adenocarcinoma (PDAC). As genetic susceptibility contributes to a large proportion of chronic inflammatory intestinal diseases, we hypothesize that the genomic regions surrounding established genome-wide associated variants for these chronic inflammatory diseases are associated with PDAC. We examined the association between PDAC and genomic regions (±500 kb) surrounding established common susceptibility variants for ulcerative colitis, Crohn's disease, inflammatory bowel disease, celiac disease, chronic pancreatitis, and primary sclerosing cholangitis. We analyzed summary statistics from genome-wide association studies data for 8,384 cases and 11,955 controls of European descent from two large consortium studies using the summary data-based adaptive rank truncated product method to examine the overall association of combined genomic regions for each inflammatory disease group. Combined genomic susceptibility regions for ulcerative colitis, Crohn disease, inflammatory bowel disease, and chronic pancreatitis were associated with PDAC at P values < 0.05 (0.0040, 0.0057, 0.011, and 3.4 × 10−6, respectively). After excluding the 20 PDAC susceptibility regions (±500 kb) previously identified by GWAS, the genomic regions for ulcerative colitis, Crohn disease, and inflammatory bowel disease remained associated with PDAC (P = 0.0029, 0.0057, and 0.0098, respectively). Genomic regions for celiac disease (P = 0.22) and primary sclerosing cholangitis (P = 0.078) were not associated with PDAC. Our results support the hypothesis that genomic regions surrounding variants associated with inflammatory intestinal diseases, particularly, ulcerative colitis, Crohn disease, inflammatory bowel disease, and chronic pancreatitis are associated with PDAC.

Significance:

The joint effects of common variants in genomic regions containing susceptibility loci for inflammatory bowel disease and chronic pancreatitis are associated with PDAC and may provide insights to understanding pancreatic cancer etiology.

Pancreatic cancer, characterized by its increasing incidence and high fatality, is the third leading cause of cancer-related mortality in the United States (1). Pancreatic ductal adenocarcinoma (PDAC) is the most common type of pancreatic cancer (2). In addition to risk factors, such as cigarette smoking, excess weight, and diabetes, genetic susceptibility contributes to the disease with an estimated heritability of up to 21% (3).

Chronic inflammation plays an important role in PDAC pathogenesis. The most direct link between inflammation and pancreatic cancer is derived from known associations with chronic pancreatitis, with risks being particularly strong for hereditary pancreatitis (4). Swedish registry-based studies have identified significant excess incidence of PDAC in patients with ulcerative colitis (5), Crohn disease (6), and primary sclerosing cholangitis (7) compared with the general Swedish population, albeit based on a relatively small number of PDAC cases. Ulcerative colitis and Crohn disease are characterized as idiopathic, immune-mediated recurrent or chronic inflammatory disorders of the gastrointestinal tract and are collectively classified as inflammatory bowel disease. Primary sclerosing cholangitis is a chronic autoimmune disease with progressive inflammation and fibrosis in the intrahepatic and extrahepatic biliary ducts and has also been associated with inflammatory bowel disease (8). Celiac disease, an autoimmune disorder characterized by chronic inflammation in small intestine, has been variably associated with PDAC (9, 10). A limitation of registry-based studies and epidemiologic studies that rely on self-report is chronic inflammatory intestinal diseases may not be accurately ascertained (e.g., misdiagnosed with irritable bowel syndrome due to overlapping symptoms; refs. 11, 12), and thus affect risk estimates. Genetic susceptibility is known to contribute to each inflammatory disease mentioned above (13–16).

In this study, we examined the association between genomic regions surrounding common susceptibility variants identified from published genome-wide association studies (GWAS) of ulcerative colitis, Crohn disease, inflammatory bowel disease, celiac disease, chronic pancreatitis, and primary sclerosing cholangitis, and risk of PDAC using GWAS data from two large PDAC consortia (17–21). We hypothesize that combined genomic susceptibility regions for each chronic inflammatory intestinal disease group will be associated with PDAC. The statistical approach we employed considers the joint association of multiple genomic regions with PDAC risk (22), and thus has the potential to detect associations that could be overlooked by traditional single-marker approaches.

Study sample

Our study was based on 9,038 primary PDAC cases (ICD-O-3 code C250-C259) and 12,389 controls of European ancestry from four GWAS conducted in the Pancreatic Cancer Cohort Consortium (PanScan I, II, III) and the Pancreatic Cancer Case Control Consortium (PanC4; refs. 17–21). Participants with nonexocrine pancreatic tumors (histology types 8150, 8151, 8153, 8155, and 8240) were excluded because their etiologies are thought to be different. The details of the cases, controls, and study design have been described previously (17–21). The three PanScan GWAS included participants from 16 cohorts [Alpha-Tocopherol, Beta-Carotene Study (ATBC, Finland), Agricultural Health Study (US), Give us a Clue to Cancer and Heart Disease Study (CLUE II, US), Cancer Preventions Study II (CPS-II, US), European Prospective Investigation into Cancer and Nutrition Study (EPIC, which comprises cohorts from Denmark, France, Germany, Great Britain, Greece, Italy, the Netherlands, Spain, and Sweden), Health Professional Follow-Up Study (HPFS, US), Multiethnic Cohort (US), Melbourne (MCCS, Australia), Nurse's Health Study (NHS, US), New York University Women's Health Study (US), Physician's Health Study I (PHS, US), Prostate Lung Colorectal Ovarian Cancer Cohort (PLCO, US), Selenium and Vitamin E Cancer Prevention Trial (US), Vitamins and Lifestyle Cohort (US), Women Health Initiative Cohort (US), Women's Health Study (WHS, US)] from the NCI's cohort consortium, 9 case–control studies, and 1 case series (Gastrointestinal Cancer Clinic of Dana-Farber Cancer Institute, Boston, MA). PanC4 included 9 case–control studies. The case-control studies included in either PanScan II or PanC4 were the Central Europe study coordinated by IARC (hospital-based, EU), Johns Hopkins Hospital (clinic-based, US), Mayo Clinic (clinic-based, US), MD Anderson Cancer Center (hospital-based, US), Memorial Sloan Kettering Cancer Center (clinic-based, US), PANDoRA- pancreatic cancer case–control study (clinic-based, EU), PACIFIC Study of Group Health and Northern California Kaiser Permanente (HMO, US), QIMR Berghofer Medical Research Institute (population-based, Australia), Spanish Pancreatic Cancer Study (hospital-based, Spain), University of California San Francisco (population-based, US), University of Toronto (population-based, Canada), and Yale University (population-based hospitals, US; refs. 17–21). Controls for PanScan I and II and PanC4 were matched to cases by age, sex, self-reported race, area of residence (case-control studies), and/or smoking (HPFS, PHS, NHS, WHS cohorts only) and incidence density sampled within each respective cohort studies. PanScan III used previously genotyped controls, mostly from cohort studies (ATBC, CPS-II, EPIC, HPFS, MCCS, MEC, NHS, PLCO, WHI). To facilitate stratified analyses by study design (cohort vs. case–control) and evaluate potential survival bias, we excluded 654 cases and 434 controls from PanScan III studies because they did not have comparable controls or cases by study designs because PanScan III used previously genotyped controls (e.g., exclusion of cases from case-control or case series studies that used cohort controls). We only included participants of European ancestry to avoid confounding by population stratification. Our final analytic dataset included 8,384 (2,320 cohort, 6,064 case–control) PDAC cases and 11,955 (6,121 cohort, 5,834 case–control) controls.

Each participating study obtained written informed consent from participants and approval from their local Institutional Review Board. The NCI's Special Studies Institutional Review Board approved the consortia study.

GWAS summary statistics

Genotype imputation across the four study phases was based on the 1000 Genomes Project (phase III, v1) reference dataset (23) and IMPUTE2 (http://mathgen.stats.ox.ac.uk/impute/impute_v2.html; ref. 24) as described previously (21). Because of the large overlap of variants on the arrays (Illumina HumanHap550 Infinium II, Human 610-Quad) used for PanScan I and II, these studies were combined and jointly analyzed, whereas PanScan III (OmniExpress, Omni1M, Omni2.5M, and Omni5M) and PanC4 (Illumina HumanOmniExpressExome-8v1) were each analyzed separately. For quality control, single-nucleotide polymorphisms (SNP) with minor allele frequency (MAF) < 5% and low-quality imputation score (IMPUTE2 INFO score < 0.3) were excluded (21). For each GWAS phase, SNPTEST (http://mathgen.stats.ox.ac.uk/genetics_software/snptest/snptest.html; ref. 25) was used to perform association analysis and generate summary statistics based on probabilistic genotype values from IMPUTE2. The analysis was adjusted for age [10-year categories, (≤50, 51–60, 61–70, 71–80, and ≥81)], sex, top eigenvectors for each study phase, study (PanScan), and geographic region (PanScan III) as described previously (17–21). PanScan III was adjusted for geographic region (US, central and northern Europe, and southern Europe) because of the use of the previously genotyped controls that were not necessarily from the same study or region as the cases (20).

SNP selection

We identified “index-” SNPs associated with ulcerative colitis, Crohn disease, inflammatory bowel disease, celiac disease, chronic pancreatitis, and primary sclerosing cholangitis at genome-wide association level (P < 5 × 10−8) using the most recent and largest studies curated by NHGRI-EBI Catalog of published GWAS (https://www.ebi.ac.uk/gwas/) as of October 9, 2017 (Supplementary Table S1). As some GWAS had inflammatory bowel disease as an outcome alone, we defined inflammatory bowel disease as a unique disease group rather than combining GWAS regions for ulcerative colitis and Crohn disease. We reviewed the original GWAS publications and added additional SNPs that met the significance level criterion (26). For SNPs that were not genotyped or imputed in our dataset (i.e., genotyped on different platforms), we selected an alternative index-SNP in high linkage disequilibrium (LD, r2 ≥ 0.74) using LDlink (https://ldlink.nci.nih.gov; ref. 27). We then included genomic regions ±500 kb surrounding each index-SNP and applied LD filtering to highly correlated SNP pairs (r2 > 0.80) in each disease group. In total, our analysis included the following number of index-SNP–defined regions (SNPs in regions): 99 (728) for ulcerative colitis, 151 (997) for Crohn disease, 211 (1,403) for inflammatory bowel disease, 21 (148) for celiac disease, 11 (35) for chronic pancreatitis, and 15 (109) for primary sclerosing cholangitis. Fifty-five index-SNP–defined regions associated with inflammatory bowel disease did not overlap with ulcerative colitis or Crohn disease.

Statistical analysis

We first conducted a meta-analysis combining SNP-level summary statistics from the four GWAS using an inverse-variance fixed-effects model. To eliminate the effect of population stratification, the square root of the genomic inflation factors for each study phase (λ = 1.02, 1.02, 1.01, and 1.06 for PanC4, PanScan I and II case–control, PanScan I and II cohort, and PanScan III cohort, respectively) was used to rescale the standard error (SE) of the estimated log OR at each SNP before the meta-analysis. We then applied the summary data-based adaptive rank truncated product (sARTP) method (22) to the meta-analysis result. The genomic region surrounding an index-SNP was treated as a “gene,” and the disease-level analysis was the gene-set/pathway analysis by combining the signals across multiple regions defined by the index-SNPs for a given inflammatory intestinal disease group (22). The sARTP analysis selected up to five of the most significant outcome (PDAC)-associated SNPs within each index-SNP–defined region and adjusted for multiple comparisons through a resampling procedure. The disease-level sARTP adjusted for multiple comparisons for tests done around all considered index-SNP/regions within a disease group through a similar resampling procedure. One-hundred million resampling steps were used to estimate the P value of each index-SNP region and disease-level associations. A panel of 503 European subjects (population codes: CEU, TSI, FIN, GBR, IBS) in the 1000 Genomes Project (phase III, v1) was used in sARTP to estimate the LD between SNPs. We considered a disease-level P value less than or equal to 0.05 statistically significant. All statistical tests are two-sided.

We also conducted a sensitivity analysis excluding the 20 PDAC-associated risk signals identified by previous GWAS at 1q32.1 (NR5A2), 1p36.33 (NOC2L), 2p14 (ETAA1), 3q28 (TP63), 5p15.33 (CLPTM1L-TERT), 7p14.1 (SUGCT), 7q23.2 (LINC-PINT), 8q21.11 (HNF4G), 8q24.21 (MYC), 9q34.2 (ABO), 13q12.2 (PDX1), 13q22.1 (nongenic), 16q23.1 (BCAR1), 17q12 (HNF1B), 17q24.3 (LINC00673), 18q21.32 (GRP), and 22q12.1 (ZNRF3; refs. 17–21, 28) and genomic regions within ±500 kb (22).

LD score regression

To estimate the genetic correlation between the disease groups and PDAC, we performed LD score regression (29) using summary statistics (excluding MHC regions) from PDAC GWAS (21) and GWAS for ulcerative colitis, Crohn disease, inflammatory bowel disease, chronic pancreatitis, and primary sclerosing cholangitis with comparable HapMap3 genotype imputation (30–32). Celiac disease was excluded from the analysis because the originating study did not have imputed genotyping data.

Pairwise LD analysis, functional annotation, and expression quantitative trait locus analysis

We performed a pairwise LD analysis to evaluate haplotype patterns between alleles of the index-SNPs and sARTP-selected SNPs using LDlink (27). We conducted an exploratory analysis of expression quantitative trait locus (eQTL) data to assess the cis effects of the most statistically significant sARTP-selected SNPs (P ≤ 0.002) and their corresponding index-SNPs on gene expression in pancreas tissue and determined whether the same gene(s) were expressed in gastrointestinal tract, and whole blood tissues using data from the NIH Genotype-Tissue Expression (GTEx) v8 (33). We also examined the regulatory potential of these SNPs (and SNPs in LD) using experimental data and information from Ensembl (34), HaploReg v4.1 (35), and RegulomeDB v1.1 (36).

Data availability

The majority of the data that support the findings of this study are available in dbGAP at https://www.ncbi.nlm.nih.gov/gap/, reference number (phs000206.v5.p3 and phs000648.v1.p1). Biomedical research scientists from recognized research institutions can request data as bona fide researchers from dbGAP or by contacting the corresponding author.

Table 1 shows the characteristics of our study population by genotyping phase and study design. The age and sex distribution of cases compared with controls within each study phase (PanScan I and II, PanC4) was similar except for PanScan III, which used previously genotyped controls. Overall, the majority of cases were diagnosed after age 60 years (74.1%), 39.1% of the cases were diagnosed after age 70 years, and 54.3% of the cases were men. A higher proportion of the cases from the cohort studies were diagnosed at older ages (87.7% > 60 years and 53.0% > 70 years) compared with the case–control studies (68.9% > 60 years and 33.8% > 70 years). The proportion of female cases was greater among the cohort studies (51.8%), while the proportion of male cases was greater in the case–control studies (56.6%).

Table 1.

Number of cases and controls, case diagnosis age, control selection age, and sex distribution by study phase and study design from PanScan and PanC4 studies.a

Study phaseTotal
PanScan IPanScan IIPanScan IIIPanC4CohortCase–controlCombined
Case/controlCase/controlCase/controlCase/controlCase/controlCase/controlCase/control
Characteristic1,746/1,8121,768/1,841937/4,6513,933/3,6512,320/6,1216,064/5,8348,384/11,955
Case diagnosis age, >60 years, n (%) 1,419 (81.3) 1,168 (66.0) 880 (93.9) 2,744 (69.7) 2,034 (87.7) 4,177 (68.9) 6,211 (74.1) 
Ageb, years, n (%) 
 ≤50 63 (3.6)/44 (2.4) 157 (8.9)/181 (9.8) 6 (0.6)/104 (2.2) 357 (9.1)/389 (10.6) 44 (1.9)/125 (2.0) 539 (8.9)/593 (10.2) 583 (6.9)/718 (6.00) 
 51–60 264 (15.1)/226 (12.5) 443 (25.1)/422 (22.9) 51 (5.5)/687 (14.8) 832 (21.2)/910 (24.9) 242 (10.4)/849 (13.9) 1,348 (22.2)/1,396 (23.9) 1,590 (19.0)/2,245 (18.8) 
 61–70 673 (38.6)/724 (40.0) 605 (34.2)/599 (32.6) 256 (27.3)/2,450 (52.7) 1,401 (35.6)/1,225 (33.6) 805 (34.7)/3,053 (49.9) 2,130 (35.1)/1,945 (33.3) 2,935 (35.0)/4,998 (41.8) 
 ≥71 746 (42.7)/818 (45.1) 563 (31.8)/639 (34.7) 624 (66.6)/1,410 (30.3) 1,343 (34.1)/1,127 (30.9) 1,229 (53.0)/2,094 (34.2) 2,047 (33.8)/1,900 (32.6) 3,276 (39.1)/3,994 (33.4) 
Sex, n (%) 
 Male 892 (51.1)/925 (51.0) 945 (53.45)/965 (52.4) 433 (46.2)/3,422 (73.6) 2,280 (57.97)/2,038 (55.8) 1,118 (48.2)/4,154 (67.9) 3,432 (56.6)/3,196 (54.8) 4,550 (54.3)/7,350 (61.5) 
 Female 854 (48.9)/887 (49.0) 823 (46.55)/876 (47.6) 504 (53.8)/1,229 (26.4) 1,653 (42.03)/1,613 (44.2) 1,202 (51.8)/1,967 (32.1) 2,632 (43.4)/2,638 (45.2) 3,834 (45.7)/4,605 (38.5) 
Study phaseTotal
PanScan IPanScan IIPanScan IIIPanC4CohortCase–controlCombined
Case/controlCase/controlCase/controlCase/controlCase/controlCase/controlCase/control
Characteristic1,746/1,8121,768/1,841937/4,6513,933/3,6512,320/6,1216,064/5,8348,384/11,955
Case diagnosis age, >60 years, n (%) 1,419 (81.3) 1,168 (66.0) 880 (93.9) 2,744 (69.7) 2,034 (87.7) 4,177 (68.9) 6,211 (74.1) 
Ageb, years, n (%) 
 ≤50 63 (3.6)/44 (2.4) 157 (8.9)/181 (9.8) 6 (0.6)/104 (2.2) 357 (9.1)/389 (10.6) 44 (1.9)/125 (2.0) 539 (8.9)/593 (10.2) 583 (6.9)/718 (6.00) 
 51–60 264 (15.1)/226 (12.5) 443 (25.1)/422 (22.9) 51 (5.5)/687 (14.8) 832 (21.2)/910 (24.9) 242 (10.4)/849 (13.9) 1,348 (22.2)/1,396 (23.9) 1,590 (19.0)/2,245 (18.8) 
 61–70 673 (38.6)/724 (40.0) 605 (34.2)/599 (32.6) 256 (27.3)/2,450 (52.7) 1,401 (35.6)/1,225 (33.6) 805 (34.7)/3,053 (49.9) 2,130 (35.1)/1,945 (33.3) 2,935 (35.0)/4,998 (41.8) 
 ≥71 746 (42.7)/818 (45.1) 563 (31.8)/639 (34.7) 624 (66.6)/1,410 (30.3) 1,343 (34.1)/1,127 (30.9) 1,229 (53.0)/2,094 (34.2) 2,047 (33.8)/1,900 (32.6) 3,276 (39.1)/3,994 (33.4) 
Sex, n (%) 
 Male 892 (51.1)/925 (51.0) 945 (53.45)/965 (52.4) 433 (46.2)/3,422 (73.6) 2,280 (57.97)/2,038 (55.8) 1,118 (48.2)/4,154 (67.9) 3,432 (56.6)/3,196 (54.8) 4,550 (54.3)/7,350 (61.5) 
 Female 854 (48.9)/887 (49.0) 823 (46.55)/876 (47.6) 504 (53.8)/1,229 (26.4) 1,653 (42.03)/1,613 (44.2) 1,202 (51.8)/1,967 (32.1) 2,632 (43.4)/2,638 (45.2) 3,834 (45.7)/4,605 (38.5) 

aThe analysis (N = 8,384 cases and 11,955 controls) was restricted to participants of European ancestry from the PanScan and PanC4 consortia.

bAge at pancreatic cancer diagnosis or age when selected to be a control. PanScan III used previously genotyped controls.

Disease group and PDAC

Genetic susceptibility to the ulcerative colitis, Crohn disease, inflammatory bowel disease, and chronic pancreatitis groups (P = 0.0040, 0.0057, 0.011, and 3.4 × 10−6, respectively) was significantly associated with PDAC while that to the celiac disease and primary sclerosing cholangitis groups (P = 0.22 and 0.078) was not (Table 2). After excluding the previous PDAC GWAS risk signal regions, the associations for ulcerative colitis, Crohn disease, and inflammatory bowel disease remained (P = 0.0029, 0.0057, and 0.0098), but that for chronic pancreatitis did not (P = 0.073). Associations for ulcerative colitis, Crohn disease, inflammatory bowel disease, chronic pancreatitis, and primary sclerosing cholangitis (P = 0.022, 0.0069, 0.014, 3.2 × 10−6, and 0.018) were present in the case–control studies but not in the cohort studies (Supplementary Table S2).

Table 2.

Association of genetic susceptibility to inflammatory intestinal disease groups and risk of PDAC in participants of European ancestry from PanScan and PanC4 studies.a

Index-SNP–defined regionExcluding previous PDAC GWAS locic
Disease groupnSNPb, nPnSNP, nP
Ulcerative colitis 99 728 0.0040 96 716 0.0029 
Crohn disease 151 997 0.0057 151 997 0.0057 
Inflammatory bowel disease 211 1,403 0.011 208 1,393 0.0098 
Celiac disease 21 148 0.22 21 148 0.22 
Chronic pancreatitis 11 35 3.4 × 10−6 10 32 0.073 
Primary sclerosing cholangitis 15 109 0.078 15 109 0.078 
Index-SNP–defined regionExcluding previous PDAC GWAS locic
Disease groupnSNPb, nPnSNP, nP
Ulcerative colitis 99 728 0.0040 96 716 0.0029 
Crohn disease 151 997 0.0057 151 997 0.0057 
Inflammatory bowel disease 211 1,403 0.011 208 1,393 0.0098 
Celiac disease 21 148 0.22 21 148 0.22 
Chronic pancreatitis 11 35 3.4 × 10−6 10 32 0.073 
Primary sclerosing cholangitis 15 109 0.078 15 109 0.078 

aThe analysis (N = 8,384 cases and 11,955 controls) was adjusted for age, sex, study, geographic region, and the top eigenvectors for the PanScan studies and age, sex, and the top eigenvectors for the PanC4 studies.

bThese SNPs are within ± 500 kb genomic regions around index-SNPs and correlated with index-SNPs at r2 > 0.80.

cResults were obtained after excluding ± 500 kb genomic regions of 20 PDAC GWAS risk signals.

The PDAC associations for the index-SNP–defined regions, and for SNPs selected by sARTP within each region are shown in Supplementary Tables S3 and S4, respectively. The index-SNPs were highly correlated with their sARTP-selected SNPs, with LD r2 value ≥ 0.78 among all SNP pairs.

In total, 32 of 99 index-SNP–defined regions for ulcerative colitis, 28 of 151 regions for Crohn disease and 30 of 211 regions for inflammatory bowel disease were selected by sARTP and contributed to the associations between disease groups and PDAC (Table 3). There was some overlap of the regions across these disease groups. For example, 6 index-SNP–defined regions were in common for ulcerative colitis and Crohn disease (rs2872507, rs12946510, rs2413583, rs7517847, rs9868809, rs3766606; Fig. 1). The most significant index-SNP–defined regions associated with PDAC (P < 0.01) and corresponding gene (location) contributing to the associations included rs9858542 (BSN, 3p21.31; Crohn disease, inflammatory bowel disease), rs6651252 (LINC00824, 8q24.21; Crohn disease, inflammatory bowel disease), rs2872507 (intergenic variant, 17q21.1; ulcerative colitis, Crohn disease, inflammatory bowel disease), rs10883365 (LINC01475, 10q24.2; Crohn disease, inflammatory bowel disease), rs9988642 (IL23R, 1p31.3; Crohn disease, inflammatory bowel disease), rs4256018 (FERMT1, 20p12.3; inflammatory bowel disease), rs7809799 (KPNA7, 7q22.1; ulcerative colitis, inflammatory bowel disease), rs9822268 (APEH, 3p21.31; ulcerative colitis), rs4409764 (intergenic variant, 10q24.2; ulcerative colitis), rs80174646 (IL23R, 1p31.3; ulcerative colitis), and rs568617 (FIBP, 11q13.1; Crohn disease).

Table 3.

Expression quantitative trait loci (eQTL) for select index-SNPsa in normal tissues of pancreas, transverse colon, and small intestine from GTEx.b

GTEx pancreas (n = 305)GTEx colon-transverse (n = 368)GTEx small intestine-terminal ileum (n = 174)
Index-SNPLocationPositioncGener2deQTL genePEffect size (β)ePEffect size (β)PEffect size (β)
rs9858542f,g 3p21.31 49701983 BSN Ref. UBA7 2.4 × 10−9 0.29 1.2 × 10−12 0.26 5.5 × 10−6 0.17 
rs9822268h 3p21.31 49719729 APEH 1.00 UBA7 7.4 × 10−10 0.30 6.9 × 10−13 0.26 6.0 × 10−6 0.16 
rs3197999i 3p21.31 49721532 MST1 0.95 UBA7 2.7 × 10−9 0.29 1.2 × 10−12 0.26 3.9 × 10−6 0.17 
rs8055167j 16q23.1 75254889 CTRB1 — CTRB2 3.4 × 10−17 −0.50 — — — — 
   CRTB2         
rs2872507f,g,h 17q21.1 38040763 — — PGAP3 5.6 × 10−10 −0.23 2.8 × 10−8 −0.11 2.5 × 10−7 −0.16 
GTEx pancreas (n = 305)GTEx colon-transverse (n = 368)GTEx small intestine-terminal ileum (n = 174)
Index-SNPLocationPositioncGener2deQTL genePEffect size (β)ePEffect size (β)PEffect size (β)
rs9858542f,g 3p21.31 49701983 BSN Ref. UBA7 2.4 × 10−9 0.29 1.2 × 10−12 0.26 5.5 × 10−6 0.17 
rs9822268h 3p21.31 49719729 APEH 1.00 UBA7 7.4 × 10−10 0.30 6.9 × 10−13 0.26 6.0 × 10−6 0.16 
rs3197999i 3p21.31 49721532 MST1 0.95 UBA7 2.7 × 10−9 0.29 1.2 × 10−12 0.26 3.9 × 10−6 0.17 
rs8055167j 16q23.1 75254889 CTRB1 — CTRB2 3.4 × 10−17 −0.50 — — — — 
   CRTB2         
rs2872507f,g,h 17q21.1 38040763 — — PGAP3 5.6 × 10−10 −0.23 2.8 × 10−8 −0.11 2.5 × 10−7 −0.16 

Abbreviations: Chr., chromosome; TSS, transcription start site.

aOnly index-SNPs whose corresponding sARTP-selected SNPs show the strongest association with PDAC (P ≤ 0.002) are presented in the table.

bcis-acting eQTLs were examined by using ± 1 Mb cis-window around a gene's TSS.

cSNP position according to NCBI Human Genome Build 37.

dLD r2 values were obtained from NCI LDlink EUR population data.

eEffect size (β) denotes the directional effect for alternative allele relative to the reference allele. The magnitude has no direct biological interpretation.

fCrohn disease.

gInflammatory bowel disease.

hUlcerative colitis.

iPrimary sclerosing cholangitis.

jChronic pancreatitis.

Figure 1.

Index-SNP–defined regions contributing to the associations of Crohn disease and ulcerative colitis with PDAC. Index-SNP–defined regions associated with PDAC in ulcerative colitis (small circle) and Crohn disease (large circle). Circles highlighted in red are regions contributing to each disease group–PDAC association. Circles labeled with SNP IDs and locations represent index-SNP–defined regions that are in common between Crohn disease and ulcerative colitis. The analysis (N = 8,384 cases and 11,955 controls) is adjusted for age, sex, study, geographic region, and the top eigenvectors for the PanScan studies and age, sex, and the top eigenvectors for the PanC4 studies. All statistical tests are two-sided.

Figure 1.

Index-SNP–defined regions contributing to the associations of Crohn disease and ulcerative colitis with PDAC. Index-SNP–defined regions associated with PDAC in ulcerative colitis (small circle) and Crohn disease (large circle). Circles highlighted in red are regions contributing to each disease group–PDAC association. Circles labeled with SNP IDs and locations represent index-SNP–defined regions that are in common between Crohn disease and ulcerative colitis. The analysis (N = 8,384 cases and 11,955 controls) is adjusted for age, sex, study, geographic region, and the top eigenvectors for the PanScan studies and age, sex, and the top eigenvectors for the PanC4 studies. All statistical tests are two-sided.

Close modal

Five of 21 index-SNP–defined regions for celiac disease, 1 out of 11 regions for chronic pancreatitis, and 1 of 15 regions for primary sclerosing cholangitis contributed to the associations. The index-SNP–defined region for chronic pancreatitis that contributed to the chronic pancreatitis–PDAC association was rs8055167 (CTRB1, CTRB2, 16q23.1, P = 1.0 × 10−7). The index-SNP–defined region that contributed to the primary sclerosing cholangitis-PDAC association was rs3197999 (MST1, 3p21.31, P = 0.0028).

LD score regression

The genetic correlation rg of each disease group with PDAC was 0.08 (P = 0.42, SE = 0.10) for ulcerative colitis, −0.16 (P = 0.055, SE = 0.08) for Crohn disease, −0.07 (P = 0.39, SE = 0.08) for inflammatory bowel disease, 0.28 (P = 0.49, SE = 0.41) for chronic pancreatitis, and −0.13 (P = 0.25, SE = 0.12) for primary sclerosing cholangitis.

eQTL and functional annotation

The exploratory eQTL results for select index-SNPs and their corresponding sARTP-selected SNPs with the strongest evidence of association with PDAC are shown in Table 3 and Supplementary Table S5. Their functional annotations were shown in Supplementary Tables S6 and S7. In three regions defined by correlated index-SNPs (r2 ≥ 0.95) associated with ulcerative colitis, Crohn disease, inflammatory bowel disease, or primary sclerosing cholangitis, variants rs9858542-A, rs9822268-A, rs3197999-A were associated with increased UBA7 expression in normal tissues of pancreas, transverse colon, and small intestine (P ≤ 6.0 × 10−6). These SNPs were associated with increased UBA7 expression in esophageal mucosa, stomach, and whole blood (Supplementary Table S5). In the region defined by the chronic pancreatitis index-SNP rs8055167, variant rs8055167-C was associated with lower CTRB2 expression and increased CTRB1 expression in normal pancreatic tissue (P ≤ 2.0 × 10−6). In the region defined by ulcerative colitis/Crohn disease/inflammatory bowel disease index-SNP rs2872507, variant rs2872507-A was associated with decreased PGAP3 expression in normal pancreatic, transverse colon, and small intestine tissues (P ≤ 2.5 × 10−7). Similar eQTL effects were present in the esophagus–gastroesophageal junction, stomach, and whole blood.

Our analysis of GWAS data showed that the joint effects of common variants in genomic regions containing susceptibility loci for ulcerative colitis, Crohn disease, inflammatory bowel disease, and chronic pancreatitis were associated with PDAC. After excluding the previously established PDAC-associated regions, the genomic regions for ulcerative colitis, Crohn disease, and inflammatory bowel disease remained associated with PDAC, but the association for chronic pancreatitis was no longer significant. No significant associations were observed for genetic susceptibility to celiac disease or primary sclerosing cholangitis. Our results are consistent with the previous Swedish registry-based studies of patients previously diagnosed with ulcerative colitis and Crohn disease that found positive associations with PDAC compared with the general population (5, 6), and the known association between chronic pancreatitis and PDAC (4).

Although previous GWAS suggest overlap of genetic susceptibility between ulcerative colitis and Crohn disease (37), a connection with PDAC remains to be elucidated. In this study, we observed that multiple common regions between ulcerative colitis and Crohn disease are associated with PDAC, suggesting some shared underlying biology between the two inflammatory diseases that contributes to the risk of PDAC. Of note, index-SNP rs7517847 (ulcerative colitis, Crohn disease) resides in IL23R (Supplementary Table S3), which encodes a subunit of the receptor for IL23. The IL23 signaling pathway mediates intestinal inflammation (38) and promotes inflammation-induced tumorigenesis in colon in murine models (39). In addition, expression of IL23 was significantly higher in pancreatic tumor compared with normal adjacent tissue (40).

With regard to the three inflammatory bowel disease groups, susceptibility variants residing at loci 3p21.31 and 17q12-q21 are of interest due to their associations with PDAC (Supplementary Table S4), and significant cis-eQTL effects in pancreas, transverse colon and small intestine (Table 3). Index-SNPs rs9858542 (Crohn disease, inflammatory bowel disease), rs9822268 (ulcerative colitis), and rs3197999 (primary sclerosing cholangitis) reside at 3p21.31 (Supplementary Table S3), a region in which multiple susceptibility loci for chronic inflammatory intestinal diseases have been identified (26, 31, 37, 41). The three index SNPs and their sARTP-selected SNPs are eQTLs for UBA7. Previous studies suggested a tumor-suppressive effect of UBE1L (the protein encoded by UBA7) in both lung cancer and acute promyelocytic leukemia (42, 43). Index-SNP rs2872507 is an intergenic variant located at 17q12-q21, a locus found to be associated with many immune-mediated diseases (44). rs2872507 and its sARTP-selected SNPs are associated with decreased PGAP3 expression in pancreas, transverse colon, and small intestine. PGAP3 is frequently coamplified with the oncogene ERBB2 in breast and gastric cancer (45, 46), and cosilencing of PGAP3 with ERBB2 in vitro results in an additive inhibition of cell viability and induced apoptosis (47). In an agnostic pathway analysis, we previously identified PGAP3 as a top contributing gene associated with PDAC within the Nikolsky breast cancer chr17q11-q21 amplicon gene set with four SNPs associated with decreased PGAP3 expression (48). In a separate transcriptome-wide association study, genetically predicted expression of PGAP3 was found to be associated with PDAC risk (49). Our findings based on eQTL analysis are suggestive and further colocalization analysis may help identify a shared etiology between inflammatory bowel disease and PDAC.

Our sensitivity analyses suggest that the associations we observe between ulcerative colitis, Crohn's disease, inflammatory bowel disease groups and PDAC are not driven by previously identified PDAC GWAS loci. The chronic pancreatitis group, however, was no longer associated with PDAC. The association observed in the primary analysis was driven by the PDAC-associated SNP rs8051363, which is in close proximity to the PDAC risk signal rs7190458 (BCAR1/CTRB1/CTRB2) at 16q23.1 (20, 21).

Our analysis is based on regions surrounding established susceptibility variants from GWAS of each inflammatory intestinal disease. As a result, we may not be able to observe an association for disease groups with only few established GWAS loci (e.g., primary sclerosing cholangitis). In contrast to Mendelian randomization and polygenic risk scores that sum trait-associated single SNPs from GWAS weighted by their effect sizes to provide an overall measure of an individual's genetic risk to develop disease (50), our approach uses genomic regions surrounding established inflammatory intestinal disease SNPs from GWAS and selects only SNPs associated with PDAC in those regions to examine trait–disease associations. These aforementioned approaches rely on previous GWAS and may overlook a significant portion of heritability, given that heritability is often distributed over thousands of genetic variants with small effects in complex traits (29). To complement our current approach, we performed LD score regression that took the effect of all SNPs into account, regardless of their genome-wide significance level. The LD regression analyses did not show significant genome-wide shared heritability between inflammatory intestinal diseases and PDAC, which might be due to limited number of participants in the originating studies and lack of power to observe associations, particularly for pancreatitis and primary sclerosing cholangitis. Our results suggest that only selective regions surrounding established susceptibility variants for inflammatory intestinal diseases are associated with PDAC.

Strengths of the study include the large number of PDAC cases and controls, and our statistical approach using GWAS data. Our associations are based on genomic regions identified from GWAS of inflammatory intestinal diseases, which provides the opportunity to identify susceptibility to chronic intestinal inflammation, beyond that of questionnaires or registry data with variable accuracy. sARTP allowed us to discover disease group–PDAC associations that would not be detected by studies using traditional single-marker approaches. One limitation of the study is that although we highlighted individual PDAC-associated SNPs that contributed to the overall associations of inflammatory intestinal diseases with PDAC, none of them alone was statistically significant after correction for multiple comparisons. We observed more significant associations in the case–control studies compared with cohort studies. This is most likely due to the larger number of cases in the case–control compared with the cohort studies (n = 6,064 vs. n = 2,320), and thus increased power to detect associations. Alternatively, the differences might be related to selection bias of either the cases (e.g., referral bias, IBD-related, survival bias) or controls (e.g., participation bias) from the case–control studies particularly as many of the studies were clinic or hospital based. Finally, our study lacks clinical diagnosis of inflammatory intestinal diseases and information on important environmental factors (e.g., gluten exposure for celiac disease) that play a role in the etiology of these diseases.

In conclusion, our results support the hypothesis that variants within genomic regions surrounding susceptibility variants for ulcerative colitis, Crohn disease, inflammatory bowel disease, and chronic pancreatitis are associated with PDAC. Further investigations are warranted to replicate our findings and examine these associations in populations of non-European ancestry.

E.A. Platz reports grants from NCI during the conduct of the study; personal fees from Kaiser Permanente Northern California, Division of Research (scientific advisory committee) outside the submitted work. P. Bracci reports grants from NIH/NCI (data contributed were collected under an NCI-funded study) during the conduct of the study. M. Du reports grants from NCI, Geoffrey Beene Foundation, Arnold and Arlene Goldstein Family Foundation, and Society of MSKCC during the conduct of the study. G.G. Giles reports grants from National Health and Medical Research Council during the conduct of the study. R.E. Neale reports grants from National Health and Medical Research Council during the conduct of the study. P. Brennan reports grants from NCI (R03 CA123546-02) during the conduct of the study. J.E. Buring reports grants from NIH during the conduct of the study. C.S. Fuchs reports personal fees from Agios, Amylin Pharma, Bain Capital, CytomX Therapeutics, Daiichi-Sankyo, Eli Lilly, Entrinsic Health, Evolveimmune Therapeutics, Genentech, Merck, Taiho, and Unum Therapeutics outside the submitted work; in addition, C.S. Fuchs also serves as a Director for CytomX Therapeutics, owns unexercised stock options for CytomX and Entrinsic Health, is a cofounder of Evolveimmune Therapeutics, and has equity in this private company. C.S. Fuchs has also provided expert testimony for Amylin Pharmaceuticals and Eli Lilly. H. Kirsten reports grants from LIFE [H. Kirsten is funded by the Leipzig Research Center for Civilization Diseases (LIFE); LIFE is an organizational unit affiliated to the Medical Faculty of the University of Leipzig. LIFE is funded by means of the European Union, by the European Regional Development Fund (ERDF) and by funds of the Free State of Saxony within the framework of the excellence initiative] during the conduct of the study. I.-M. Lee reports grants from the NIH during the conduct of the study. R. Milne reports grants from NHMRC during the conduct of the study. K. Ng reports grants from Celgene, Revolution Medicines, Evergrande Group, Genentech, Gilead Sciences, Trovagene, grants and personal fees from Tarrex Biopharma, and personal fees from Bayer, Seattle Genetics, and Array Biopharma outside the submitted work. A.L. Oberg reports grants from NIH during the conduct of the study. J. Wactawski-Wende reports grants from NIH/NHLBI (funding for WHI) during the conduct of the study. A. Zeleniuch-Jacquotte reports grants from NIH/NCI during the conduct of the study. B.M. Wolpin reports grants and personal fees from Celgene and Eli Lilly, and personal fees from GRAIL and BiolineRx outside the submitted work. H.A. Risch reports grants from NIH during the conduct of the study. No potential conflicts of interest were disclosed by the other authors.

The content of the article is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. 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.

F. Yuan: Conceptualization, formal analysis, investigation, visualization, writing-original draft, writing-review and editing. R.J. Hung: Conceptualization, supervision, investigation, visualization, project administration, writing-review and editing. N. Walsh: Supervision, visualization, writing-review and editing. H. Zhang: Data curation, software, formal analysis, methodology, writing-review and editing. E.A. Platz: Supervision, writing-review and editing. W. Wheeler: Data curation, software, formal analysis, writing-review and editing. L. Song: Data curation, software, formal analysis, writing-review and editing. A.A. Arslan: Resources, supervision, funding acquisition, project administration, writing-review and editing. L.E. Beane Freeman: Resources, supervision, funding acquisition, project administration, writing-review and editing. P. Bracci: Resources, supervision, funding acquisition, project administration, writing-review and editing. F. Canzian: Resources, supervision, funding acquisition, project administration, writing-review and editing. M. Du: Resources, supervision, funding acquisition, project administration, writing-review and editing. S. Gallinger: Resources, supervision, funding acquisition, project administration, writing-review and editing. G.G. Giles: Resources, supervision, funding acquisition, project administration, writing-review and editing. P.J. Goodman: Resources, supervision, funding acquisition, project administration, writing-review and editing. C. Kooperberg: Resources, supervision, funding acquisition, project administration, writing-review and editing. L. Le Marchand: Resources, supervision, funding acquisition, project administration, writing-review and editing. R.E. Neale: Resources, supervision, funding acquisition, project administration, writing-review and editing. J. Rosendahl: Resources, supervision, project administration, writing-review and editing. G. Scelo: Resources, supervision, funding acquisition, project administration, writing-review and editing. X.-O. Shu: Resources, supervision, funding acquisition, project administration, writing-review and editing. K. Visvanathan: Resources, supervision, funding acquisition, project administration, writing-review and editing. E. White: Resources, supervision, funding acquisition, project administration, writing-review and editing. W. Zheng: Resources, supervision, funding acquisition, project administration, writing-review and editing. D. Albanes: Resources, project administration, writing-review and editing. P. Amiano: Resources, project administration, writing-review and editing. G. Andreotti: Resources, project administration, writing-review and editing. A. Babic: Resources, project administration, writing-review and editing. W.R. Bamlet: Resources, project administration, writing-review and editing. S.I. Berndt: Resources, project administration, writing-review and editing. P. Brennan: Resources, project administration, writing-review and editing. B. Bueno-de-Mesquita: Resources, project administration, writing-review and editing. J.E. Buring: Resources, project administration, writing-review and editing. P.T. Campbell: Resources, project administration, writing-review and editing. S.J. Chanock: Resources, project administration, writing-review and editing. C.S. Fuchs: Resources, project administration, writing-review and editing. J.M. Gaziano: Resources, project administration, writing-review and editing. M.G. Goggins: Resources, project administration, writing-review and editing. T. Hackert: Resources, project administration, writing-review and editing. P. Hartge: Resources, project administration, writing-review and editing. M.M. Hassan: Resources, project administration, writing-review and editing. E.A. Holly: Resources, project administration, writing-review and editing. R.N. Hoover: Resources, project administration, writing-review and editing. V. Katzke: Resources, project administration, writing-review and editing. H. Kirsten: Resources, project administration, writing-review and editing. R.C. Kurtz: Resources, project administration, writing-review and editing. I.-M. Lee: Resources, project administration, writing-review and editing. N. Malats: Resources, project administration, writing-review and editing. R.L. Milne: Resources, project administration, writing-review and editing. N. Murphy: Resources, project administration, writing-review and editing. K. Ng: Resources, project administration, writing-review and editing. A.L. Oberg: Resources, project administration, writing-review and editing. M. Porta: Resources, project administration, writing-review and editing. K.G. Rabe: Resources, project administration, writing-review and editing. F.X. Real: Resources, project administration, writing-review and editing. N. Rothman: Resources, project administration, writing-review and editing. H.D. Sesso: Resources, project administration, writing-review and editing. D.T. Silverman: Resources, project administration, writing-review and editing. I. Thompson: Resources, project administration, writing-review and editing. J. Wactawski-Wende: Resources, project administration, writing-review and editing. X. Wang: Resources, project administration, writing-review and editing. N. Wentzensen: Resources, project administration, writing-review and editing. L.R. Wilkens: Resources, project administration, writing-review and editing. H. Yu: Resources, project administration, writing-review and editing. A. Zeleniuch-Jacquotte: Resources, project administration. J. Shi: Supervision, methodology, project administration, writing-review and editing. E.J. Duell: Resources, supervision, funding acquisition, project administration, writing-review and editing. L.T. Amundadottir: Resources, supervision, funding acquisition, project administration, writing-review and editing. D. Li: Resources, supervision, funding acquisition, project administration, writing-review and editing. G.M. Petersen: Resources, supervision, funding acquisition, project administration, writing-review and editing. B.M. Wolpin: Resources, supervision, funding acquisition, project administration, writing-review and editing. H.A. Risch: Resources, supervision, funding acquisition, project administration, writing-review and editing. K. Yu: Conceptualization, software, supervision, investigation, methodology, project administration, writing-review and editing. A.P. Klein: Conceptualization, resources, supervision, funding acquisition, investigation, project administration, writing-review and editing. R. Stolzenberg-Solomon: Conceptualization, resources, data curation, supervision, funding acquisition, validation, investigation, writing-original draft, project administration, writing-review and editing.

This research is supported by the Intramural Research Program, Division of Cancer Epidemiology and Genetics, NCI, NIH. We thank Laurie Burdett, Aurelie Vogt, Belynda Hicks, Amy Hutchinson, Meredith Yeager, and other staff at the NCI's Division of Epidemiology and Genetics (DECG) Cancer Genomics Research Laboratory (CGR) for GWAS genotyping. The IARC/Central Europe study was supported by a grant from the NCI at the NIH (R03 CA123546-02) and grants from the Ministry of Health of the Czech Republic (NR 9029-4/2006, NR9422-3, NR9998-3, MH CZ-DRO-MMCI 00209805). The work at Johns Hopkins University was supported by the NCI grants P50CA062924, R01CA97075, and P30 CA006973. Additional support was provided by the Lustgarten Foundation, Susan Wojcicki and Dennis Troper, and the Sol Goldman Pancreas Cancer Research Center. This work was supported by RO1 CA154823 and federal funds from the NCI, NIH under contract number HHSN261200800001E. The Mayo Clinic Biospecimen Resource for Pancreas Research study is supported by the Mayo Clinic SPORE in Pancreatic Cancer (P50 CA102701). The Memorial Sloan Kettering Cancer Center Pancreatic Tumor Registry is supported by P30CA008748, the Geoffrey Beene Foundation, the Arnold and Arlene Goldstein Family Foundation, and the Society of MSKCC. We acknowledge the contribution of Dr. Irene Orlow to the study. 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 is supported by a NHMRC Senior Research Fellowship (#1060183). The UCSF pancreas study was supported by NIH-NCI grants (R01CA1009767, R01CA109767-S1, and R01CA059706) and the Joan Rombauer Pancreatic Cancer Fund. Collection of cancer incidence data was supported by the California Department of Public Health as part of the statewide cancer reporting program; the NCI's SEER Program under contract HSN261201000140C awarded to CPIC; and the CDC's National Program of Cancer Registries, under agreement #U58DP003862-01 awarded to the California Department of Public Health. The Yale (CT) pancreas cancer study was supported by NCI at the NIH (grant number 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. The authors assume full responsibility for analyses and interpretation of these data. 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-Wuürttemberg State Ministry of Research, Science and Arts (Prof. H. Brenner), the Heidelberger EPZ-Pancobank (Prof. M.W. Buüchler and team: Prof. T. Hackert, Dr. N.A. Giese, Dr. Ch. Tjaden, E. Soyka, M. Meinhardt; Heidelberger Stiftung Chirurgie and BMBF grant 01GS08114), the BMBH (Prof. P. Schirmacher; BMBF grant 01EY1101), the “5 × 1000” voluntary contribution of the Italian Government, the Italian Ministry of Health (RC1203GA57, RC1303GA53, RC1303GA54, RC1303GA50), the Italian Association for Research on Cancer (Prof. A. Scarpa; AIRC n. 12182), the Italian Ministry of Research (Prof. A. Scarpa; FIRB - RBAP10AHJB), the Italian FIMP-Ministry of Health (Prof. A. Scarpa; 12 CUP_J33G13000210001), and by the National Institute for Health Research Liverpool Pancreas Biomedical Research Unit, UK. We would like to acknowledge the contribution of Dr. Frederike Dijk and Prof. Oliver Busch (Academic Medical Center, Amsterdam, the Netherlands). Assistance with genotype data quality control was provided by Cecelia Laurie and Cathy Laurie at University of Washington Genetic Analysis Center. The American Cancer Society funds the creation, maintenance, and updating of the Cancer Prevention Study-II cohort. The authors express sincere appreciation to all Cancer Prevention Study-II participants, and to each member of the study and biospecimen management group. The authors would like to acknowledge the contribution to this study from central cancer registries supported through the Centers for Disease Control and Prevention's National Program of Cancer Registries and cancer registries supported by the NCI's Surveillance Epidemiology and End Results Program. 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 Kathy J. Helzlsouer for her contribution in CLUE. We also thank all the CLUE participants. The Melbourne Collaborative Cohort Study (MCCS) cohort recruitment was funded by VicHealth and Cancer Council Victoria. The MCCS was further augmented by Australian National Health and Medical Research Council grants 209057, 396414, and 1074383 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 Women's Health Study (AZJ and AAA) was funded by NIH 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); and 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); ‘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. The Physicians' Health Study was supported by research grants CA-097193, CA-34944, CA-40360, HL-26490, and HL-34595 from the NIH. The Women's Health Study was supported by research grants CA-047988, CA-182913, HL-043851, HL-080467, and HL-099355 from the NIH. Health Professionals Follow-up Study is supported by NIH grant UM1 CA167552 from the NCI. Nurses' Health Study is supported by NIH grants UM1 CA186107, P01 CA87969, and R01 CA49449 from the NCI. Multiethnic Cohort Study was supported by NIH grants CA63464, CA54281, CA098758, CA132839 and CA164973 from the NCI. Additional support from the Hale Center for Pancreatic Cancer Research, U01 CA21017 from the NCI, 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); Broman Family Fund for Pancreatic Cancer Research (to K. Ng); and K07 CA222159 from the NCI, and Bob Parsons Fellowship (to A. Babic). The WHI program is 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. SELECT is funded by the NCI of the NIH under Award Numbers U10 CA37429 (to C.D. Blanke), and UM1 CA182883 (to C.M. Tangen/I.M. Thompson). The authors thank the site investigators and staff and, most importantly, the participants from SELECT who donated their time to this trial. This study utilized the high-performance computational capabilities of the Biowulf Linux cluster at the NIH (https://hpc.nih.gov/systems/).The Genotype-Tissue Expression (GTEx) Project was supported by the Common Fund of the Office of the Director of the NIH, and by NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS. The data used for the analyses described in this manuscript were obtained from the GTEx Portal on September 24, 2019.

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