Genome-wide association meta-analysis identifies gp2 gene risk variants for pancreatic cancer

ABSTRACT Pancreatic cancer is the fourth leading cause of cancer-related deaths in Japan. To identify risk loci, we perform a meta-analysis of three genome-wide association studies

comprising 2,039 pancreatic cancer patients and 32,592 controls in the Japanese population. Here, we identify 3 (13q12.2, 13q22.1, and 16p12.3) genome-wide significant loci (_P_ < 5.0 × 

10−8), of which 16p12.3 has not been reported in the Western population. The lead single nucleotide polymorphism (SNP) at 16p12.3 is rs78193826 (odds ratio = 1.46, 95% confidence interval =

1.29-1.66, _P_ = 4.28 × 10−9), an Asian-specific, nonsynonymous _glycoprotein__ 2_ (_GP2_) gene variant. Associations between selected _GP2_ gene variants and pancreatic cancer are

replicated in 10,822 additional cases and controls of East Asian origin. Functional analyses using cell lines provide supporting evidence of the effect of rs78193826 on KRAS activity. These

findings suggest that _GP2_ gene variants are probably associated with pancreatic cancer susceptibility in populations of East Asian ancestry. SIMILAR CONTENT BEING VIEWED BY OTHERS ALLELIC

EFFECTS ON _KLHL17_ EXPRESSION UNDERLIE A PANCREATIC CANCER GENOME-WIDE ASSOCIATION SIGNAL AT CHR1P36.33 Article Open access 30 April 2025 PAN-CANCER AND CROSS-POPULATION GENOME-WIDE

ASSOCIATION STUDIES DISSECT SHARED GENETIC BACKGROUNDS UNDERLYING CARCINOGENESIS Article Open access 20 June 2023 PATHOGENIC GERMLINE VARIANTS IN CHINESE PANCREATIC ADENOCARCINOMA PATIENTS

Article Open access 05 March 2025 INTRODUCTION With 35,390 related deaths in 2018, pancreatic cancer is the fourth leading cause of cancer deaths in Japan, after lung, colorectal, and

stomach cancers1. The incidence and mortality rates of pancreatic cancer have increased steadily over the past decades, while those of other gastrointestinal cancers have shown decreasing

trends1. Despite the increasing burden levied by pancreatic cancer, few modifiable risk factors other than smoking and type 2 diabetes mellitus (T2D) have been identified, and the 5-year

survival proportions remain the worst (<10%) among major malignancies. Genome-wide association studies (GWASs) have increasingly revealed associations of pancreatic cancer susceptibility

with inherited genetic variations. Since the first GWAS, conducted by the PanScan consortium, identified common variants in the gene coding for the ABO blood group system in 20092, at least

23 genome-wide significant susceptibility loci have been linked to the risk of pancreatic cancer3. However, owing to the smaller sample sizes of the relevant GWASs, fewer loci have been

identified for pancreatic cancer than for other common cancers, including breast and colorectal cancers4,5. Furthermore, the risk variants identified to date explain approximately 13% of the

total heritability on the basis of GWAS-identified single-nucleotide polymorphisms (SNPs) in individuals of European ancestry6. These observations suggest that additional risk loci can be

identified by increasing the sample size, as evidenced by the trend in the numbers of variants reported by the PanScan and PanC4 consortia. It is also important to expand the GWASs to

populations of non-European ancestry because of differences in minor allele frequencies (MAFs) and patterns of linkage disequilibrium (LD) across diverse populations7. In fact, previous

GWASs focusing exclusively on populations of Eastern Asian ancestry led to the identification of additional susceptibility loci for breast and colorectal cancers8,9. The majority of the risk

loci for pancreatic cancer were discovered in the PanScan and PanC4 GWASs, which included populations of European ancestry. Only two GWASs have been conducted in East Asian populations: one

in China10 and one in Japan11. A total of eight risk loci (five genome-wide significant loci and three loci with suggestive evidence of association) have been identified for pancreatic

cancer, but these loci were not replicated in a previous study using samples from European populations12. Therefore, the role of common susceptibility loci in East Asian populations remains

uncertain and needs further exploration. To detect additional susceptibility loci for pancreatic cancer, we perform a meta-analysis combining all published and unpublished GWAS data in

Japan, followed by a replication study involving a Japanese population as well as other populations of East Asian origin. We identify 3 (13q12.2, 13q22.1, and 16p12.3) genome-wide

significant loci (_P_ < 5.0 × 10−8) and 4 suggestive loci (_P_ < 1.0 × 10−6) for the risk of pancreatic cancer. We replicate the associations between selected _GP2_ gene variants at

16p12.3 in 10,822 additional cases and controls of East Asian origin and further explore the functional impact of the top SNP rs78193826 of the _GP2_ gene. We also demonstrate pleiotropic

effects of the _GP2_ variants. Together, these findings indicate that _GP2_ gene variants are probably associated with pancreatic cancer susceptibility in populations of East Asian ancestry.

RESULTS GWAS META-ANALYSIS AND REPLICATION After imputation and quality control of individual subject genotype data, we performed a meta-analysis of 3 Japanese GWASs comprising data from

2039 pancreatic cancer patients and 32,592 controls for 7,914,378 SNPs (Supplementary Table 1 and Supplementary Data 1). Genomic control adjustment was not applied because there was little

evidence of genomic inflation (lambda = 1.02, Supplementary Fig. 1). We observed genome-wide significant _(P_ < 5.0 × 10−8) association signals at 3 loci (13q12.2, 13q22.1, and 16p12.3;

Fig. 1 and Table 1), for which the genes nearest the lead SNP were _PLUT_ (_PDX1-AS1_) and _PDX1_ on 13q12.2, _KLF5_ and _KLF12_ on 13q22.2, and _GP2_ on 16p12.3. In addition, 4 loci (1p13.2

(_WNT2B_), 2p12 (_CTNNA2_), 3p12.3 (_ROBO2_), and 9q34.2 (_ABO_)) showed suggestive evidence of associations (_P_ < 1.0 × 10−6, Supplementary Table 2). Among these risk loci, genome-wide

significant associations were observed for 10 SNPs at 16p12.3 (rs78193826, rs117267808, rs73541251, rs4609857, rs4544248, rs4632135, rs4420538, rs73541271, rs4383154, and rs4383153;

Supplementary Data 2). The odds ratio (ORs) for these variants ranged from 1.43 to 1.47, indicating generally stronger associations in this region than for GWAS associations for variants

identified in previously published GWASs. The lead SNP here is rs78193826, a nonsynonymous variant of the _GP2_ gene (Fig. 2). The risk increased by 46% per copy of the minor T allele (OR = 

1.46, 95% confidence interval (CI) = 1.29–1.66, _P_ = 4.28 × 10−9; Table 1). Regional association plots for the other loci are shown in Supplementary Fig. 2. According to the 1000 Genomes

Project Phase 3 database, rs78193826 is polymorphic, with the MAF ranging from 3.9% to 6.6% in Asian populations, compared with much lower MAFs (<0.1%) in other human populations

(Supplementary Data 2). LD maps of these 10 SNPs at 16p12.3 are shown in Supplementary Fig. 3. Complete LD between nine of these SNPs (all except rs4420538) was observed in the Japanese

population. Among the ten SNPs in this region, only rs4383153 had available association summary statistics in the previous PanScan and Panc4 consortia publications2,13, but this SNP was

negligible and not significantly associated with pancreatic cancer risk (Supplementary Table 4). The functional annotation results for the ten SNPs at 16p12.3 are shown in Supplementary Data

 2. The lead SNP rs78193826 was classified as “damaging” according to the Sifting Intolerant from Tolerant (SIFT) algorithm and as “possibly damaging” by Polymorphism Phenotyping v2

(PolyPhen-2). Moreover, the estimated Combined Annotation-Dependent Depletion (CADD) score was 20.3. For replication, we selected four SNPs (rs78193826, rs73541251, rs117267808, rs4632135)

that met either of the following criteria: (1) exonic SNP or (2) intronic SNP with a score of ≤3 according to the RegulomeDB database. We sought the replication of associations between these

4 SNPs and pancreatic cancer in populations of East Asian ancestry, including Japanese, Japanese American, and Chinese subjects, using an additional sample of 1926 cases and 8896 controls

drawn from 6 independent studies. All 4 SNPs were significantly associated with pancreatic cancer risk (_P_ < 0.05) in the combined replication analysis (Supplementary Table 3), with the

ORs for the lead SNP rs78193826 shown for each study cohort in Fig. 3. FUNCTIONAL CHARACTERIZATION OF THE _GP2_-CODING VARIANT We explored the functional impact of the identified coding

variant rs78193826 in the GP2-expressing pancreatic cancer cell line PaTu 8988s. The single-nucleotide change from G to A at codon 282 of the GP2 gene was induced using clustered regularly

interspaced short palindromic repeats (CRISPR)-Cas9-mediated homologous recombination, which enabled the generation of genome-edited PaTu 8988s cells (GP2_V282M PaTu 8988s, Fig. 4a). With

RNA-sequencing (RNA-seq) analysis of two separate clones established from wild-type (GP2_WT, serving as a control group) and genome-edited (GP2_V282M) PaTu 8988s cells, we demonstrated

consistently different gene expression patterns between GP2_WT and GP2_V282M cells (Fig. 4b, Supplementary Data 3). Gene set enrichment analysis (GSEA) using the collections of hallmark (H)

and oncogenic gene sets (C6) in MSigDB v6.214 yielded numerous significantly enriched gene sets for GP2_WT cells. Among them, gene sets related to _KRAS_-activating mutations appeared in

both the H and C6 collections (Supplementary Data 4 and 5). Because _KRAS_ mutation is the most frequently observed mutation and a critical initiating event in pancreatic cancer15, we

focused on these gene sets. Genes in HALLMARK_KRAS_SIGNALING_DN and KRAS.50_UP. V1_DN were significantly expressed at low levels in GP2_V282M PaTu 8988s cells (Fig. 4c). We recapitulated the

downregulation of three genes (_KLK7_, _BMPR1B_, and _KLK8_) in HALLMARK_KRAS_SIGNALLING_DN (Fig. 4d) in three independent GP2_V282M clones compared to GP2_WT clones by quantitative

real-time (qRT)-PCR, suggesting that the monoclonal findings were not due to selection bias (Fig. 4e). We also excluded the possibility of vector transfection–induced off-target effects or

non-specific effects on the expression of genes in specific pathways (Supplementary Figs. 9 and 10, Supplementary Note 1). PLEIOTROPIC EFFECTS OF _GP2_ VARIANTS Epidemiological studies have

consistently shown that longstanding T2D is associated with a mildly increased risk of pancreatic cancer16. Recently, 88 genetic variants were reported in a GWAS meta-analysis of T2D in the

Japanese population17. We found that the top 3 SNPs at 16p12.3 (rs78193826, rs117267808, and rs73541251) were also genome wide significantly associated with the risk of T2D in the latest

GWAS comprising 191,764 Japanese subjects (Supplementary Table 5). In addition, these 3 SNPs were significantly associated with hemoglobin A1c (HbA1c; _P_ < 1 × 10−4) and blood glucose

levels (_P_ < 0.01), which were included in other GWASs of quantitative traits in 42,790 and 93,146 Japanese subjects, respectively18. Among 82 T2D-related SNPs, rs117267808 at 16p12.3

(_GP2)_ and rs2290203 at 15q26.1 (_PRC1-AS1_) were significant after Bonferroni correction in our GWAS meta-analysis (_P_ < 0.0006, Supplementary Data 6). Among 15 blood glucose-related

SNPs, rs4581570 at 13q12.2 (_PDX1_) was significant after Bonferroni correction (_P_ < 0.0033, Supplementary Data 7). In addition, none of the 25 HbA1c-associated (Supplementary Data 8)

and 76 body mass index (BMI)-associated SNPs (Supplementary Data 9) were significant in our GWAS meta-analysis. MENDELIAN RANDOMIZATION (MR) ANALYSIS To examine whether the associations of

metabolic traits with pancreatic cancer are consistent with a systematic association, we performed a MR analysis with the inverse variance-weighted (IVW) and MR–Egger methods. Inconsistent

results were observed for T2D, with no significant association between the SNP index T2D and pancreatic cancer risk based on the IVW method (Fig. 5a). Although the MR–Egger analysis yielded

a significant association, the intercept differed significantly from zero (_P_ < 0.05, Supplementary Fig. 4a), suggesting the presence of horizontal pleiotropy. Applying the MR Pleiotropy

RESidual Sum and Outlier (MR-PRESSO) test enabled the detection of two outlying SNPs (rs117267808 at 16p12.3 (_GP2_) and rs2290203 at 15q26.1 (_PRC1-AS1_); Supplementary Data 6). After

correction for horizontal pleiotropy via outlier removal, the association remained nonsignificant in the IVW analysis (_β_ ± SE = 0.08 ± 0.06, _P_ = 0.16). In contrast, the HbA1c genetic

index level appeared to be related to an increased risk of pancreatic cancer, on the basis of significant results with both the IVW and MR–Egger methods (Fig. 5b and Supplementary Fig. 4b).

In addition, we found no significant associations between genetic index levels for the other two metabolic factors (blood glucose and BMI) and pancreatic cancer risk (Supplementary Fig. 5).

GENE-BASED GWAS To complement the SNP-based GWAS, we performed a gene-based GWAS using MAGMA19 (Supplementary Fig. 6). Among 17,581 genes, we confirmed the significant associations for _GP2_

and _WNT2B_ identified by the SNP-based GWAS. In addition, a significant association (Bonferroni-corrected _P_ = 2.84 × 10−6) for the gene _KRT8_ was observed (Supplementary Table 6 and

Supplementary Figs. 6 and 7), and this association was replicated in the combined PanScan I and PanScan II datasets (_P_ = 0.024)13. REPLICATION OF PANSCAN AND PANC4 CONSORTIA RISK LOCI We

also examined the previously published pancreatic cancer risk loci from the PanScan and PanC4 consortia20, noting that 5 of those 19 SNPs were statistically significant after Bonferroni

correction (_P_ < 0.0026, rs13303010 at 1p36.33, rs505922 at 9q34.2, rs9581943 at 13q12.2, rs7214041 at 17q24.3, and rs9543325 at 13q22.1) in our GWAS meta-analysis (Supplementary Data 

10). Notably, we confirmed the significant association (_P_ = 3.84 × 10−5) between rs505922 of the _ABO_ locus and pancreatic cancer risk. DISCUSSION The role of inherited common genetic

variations in pancreatic cancer susceptibility remains incompletely understood. We identified and replicated a risk locus at 16p12.3 by combining three GWAS datasets of East Asian

populations. Furthermore, we provided evidence that the identification of this locus can be attributed to the observed differences in the MAF of the lead SNP (rs78193826) at 16p12.3 and the

LD structure in this region across ethnic populations. Little overlap has been observed when risk loci reported from previous Chinese or Japanese GWASs are compared with those reported in

the PanScan GWASs2. By including more than twice the number of case patients than were included in previous Japanese or Chinese GWASs, as well as by using imputed SNP data, we replicated the

majority of significant loci discovered for pancreatic cancer in the PanScan and PanC4 consortia GWASs (Supplementary Data 10), including the well-established _ABO_ locus. Moreover, for

most variants, the direction and magnitude of the associations in our GWAS meta-analysis of Japanese subjects were consistent with those in populations of European ancestry. These findings

suggest that GWAS-identified variants at many loci are shared across ancestral groups and that lack of replication may be due to insufficient sample sizes in previous Chinese or Japanese

GWASs. The MAF of rs78193826 in cases varies considerably across populations in the replication cohorts when compared with that in control subjects. The main reason may be due to random

variation caused by a small sample size. As shown in Supplementary Table 3, the MAF was higher in controls than in cases in the Japan Public Health Center-based Prospective Study (JPHC),

generating an opposite direction in effect size from that observed in other replication cohorts. However, the overall positive association for rs78193826 was replicated in the analysis

combining all replication cohorts. Another possible reason is population stratification, but the Multiethnic Cohort Study (MEC) results were adjusted for the principal component. After

excluding the JPHC and MEC studies from the replication cohorts, we found no large variations in MAF in the cases. Several lines of evidence indicate that rs78193826, which lies within the

_GP2_ gene on 16p12.3, may be associated with pancreatic cancer risk. First, this variant is nonsynonymous; the nucleotide mutation from C to T causes an amino acid change from valine to

methionine, which could affect protein structure and function. Second, functional annotations in several databases consistently indicate that this variant is likely pathogenic. Third, the

observed differences in the MAF of rs78193826 as well as the LD structure across different ethnic populations provide indirect evidence supporting its role as a significant variant in the

Japanese population. The frequency of the minor T allele of rs78193826 is 0.1% in populations of European ancestry but 7% in the Japanese population. Given this apparent difference in the

MAF, rs78193826 could not have been identified in the PanScan GWASs, although the PanScan GWASs included a much larger sample size than our GWAS. Of the 10 SNPs in LD in this region, only

rs4383153 has association summary statistics available in PanScan publications; however, no significant associations were observed between this SNP and pancreatic cancer risk (Supplementary

Table 4). While complete LD between rs4383153 and rs78193826 was evident in the Japanese population, no LD data were available for these two SNPs in the European ancestry populations (1000

Genomes Project Phase 3 CEU). Genetic variations in the _GP2_ gene have been linked to several phenotypes in addition to pancreatic cancer. The SNP rs12597579, located ~60 kb downstream of

_GP2_, has been associated with BMI in a GWAS including East Asians21. However, rs12597579 was not in LD with rs78193826 (_r_2 = 0.003, calculated from Japanese samples in the 1000 Genomes

Project Phase 3), suggesting that rs12597579 may have functions different from those of rs78193826. Perhaps coincidently, the lead variant (rs117267808) in the _GP2_ gene identified in the

latest GWAS meta-analysis of T2D in the Japanese population was also identified in our GWAS meta-analysis of pancreatic cancer (Supplementary Table 5). Of the 82 T2D-related SNPs, 2 showed

significant associations with pancreatic cancer, suggesting that pancreatic cancer and T2D may share specific genetic susceptibility factors. The identified lead SNP (rs78193826) is in the

coding sequence for the GP2 protein, which is present on the inner surface of zymogen granules in pancreatic acinar cells22. GP2 is a glycosylated protein of ∼90 kDa that contains multiple

sites, such as an asparagine-linked glycosylation site, a zona pellucida domain, and a glycosylphosphatidylinositol linkage to the membrane22. During the secretory process, GP2 is cleaved

from the membrane and secreted into the pancreatic duct along with other digestive enzymes. The expression of GP2 is extremely high in normal pancreatic tissues compared with that in other

tissues (Supplementary Fig. 8)23. Furthermore, a previous RNA transcriptome analysis revealed that pancreatic tumor tissues have a decreased level of GP2 expression compared with adjacent

benign pancreatic tissues24. The functional characterization of GWAS-identified SNPs remains a challenge. We conducted a series of experiments to examine the possible functional impact of

the nonsynonymous lead SNP rs78193826 on global gene expression. RNA sequencing (RNA-seq) analysis revealed consistently differential gene expression patterns between genome-edited GP2_V282M

cells and control GP2_WT cells (Supplementary Data 3). Among many significantly enriched gene sets identified for GP2_WT cells in the GSEA, _KRAS_-related gene sets stood out because of the

well-known role of _KRAS_ mutation in pancreatic carcinogenesis (Supplementary Data 4 and 5)15. Interestingly, gene signatures related to _KRAS_-activating mutations

(HALLMARK_KRAS_SIGNALING_DN and KRAS.50_UP. V1_DN) in two distinct collections (H and C6) showed significant enrichment in GP2_WT cells. Collectively, these experimental findings suggest

that the functional relevance of rs78193826 may involve modulation of KRAS activity. Given that a very high frequency (>93%) of _KRAS_ mutations has been observed in pancreatic cancer

patients15, elucidating interactions among _GP2_ variants, _KRAS_ oncogenic mutations, and other potential effector genes would provide insights into pancreatic carcinogenesis. Previous

epidemiological studies have suggested that HbA1c levels, even in nondiabetic ranges, or changes in HbA1c levels in new-onset T2D are associated with pancreatic cancer risk25,26. Our MR

analysis of selected metabolic factors provided corroborating evidence that HbA1c genetic index levels may be associated with pancreatic cancer risk. This result was also partially

consistent with a previous MR analysis, in which T2D was not implicated but the genetic indices of BMI and fasting insulin were associated with pancreatic cancer27. However, it should be

noted that the MR results may be influenced by a few SNPs with relatively large effect sizes. To address this possibility, we further applied MR-PRESSO and detected two outlying SNPs

(Supplementary Data 6), one of which was the _GP2_ SNP. This finding indicated that the _GP2_ SNP may differ from other T2D-related SNPs in terms of the effect on pancreatic cancer risk. It

is likely that the null findings for the MR association for T2D reflect the phenotypic and genetic heterogeneity of T2D, but T2D may also be both a cause and consequence of pancreatic

cancer28. Additional studies are needed to further explore the associations of T2D and metabolic factors with pancreatic cancer using the best available genetic instruments in the Japanese

population. Three genome-wide significant genes (_GP2_, _WNT2B_, and _KRT8_) emerged in the gene-based GWAS. Among these genes, _WNT2B_ (1p13.1) showed suggestive evidence of association in

the previous PanScan and PanC4 GWAS6. KRT8 belongs to a group of intermediate-filament cytoskeletal proteins involved in maintaining epithelial structural integrity29. KRT8 is expressed in

both ductal and acinar single-layer epithelia, and mutations in the _KRT8_ gene have been linked to exocrine pancreatic disorders and liver disease30,31. In conclusion, our GWAS

meta-analysis identified a risk locus at chromosome 16p12.3 within the _GP2_ gene for pancreatic cancer in populations of East Asian ancestry. Functional analyses using cell lines provided

supporting evidence of the effect of the lead SNP rs78193826 of the _GP2_ gene on KRAS activity. Further fine mapping and functional characterization are needed to elucidate the associations

of common _GP2_ gene variants with pancreatic cancer susceptibility. Our findings also highlight genetic susceptibility factors shared between T2D and pancreatic cancer. METHODS STUDY

SAMPLES We performed a GWAS meta-analysis based on three Japanese studies: the Japan Pancreatic Cancer Research (JaPAN) consortium GWAS, the National Cancer Center (NCC) GWAS, and the

BioBank Japan (BBJ) GWAS. An overview of the characteristics of the study populations is provided in Supplementary Table 1. JAPAN CONSORTIUM GWAS Participants in this GWAS were drawn from

the JaPAN consortium32. Two case–control datasets were combined, resulting in data from a total of 945 pancreatic cancer patients and 3134 controls. The vast majority of patients were

diagnosed with primary adenocarcinoma of the exocrine pancreas (ICD-O-3 codes C250–C259). The first dataset included 622 pancreatic cancer patients who were recruited from January 2010 to

July 2014 at 5 participating hospitals in the Central Japan, Kanto, and Hokkaido regions. This multi-institutional case–control study collected questionnaire data on demographic and

lifestyle factors and 7-ml blood samples from the study participants. The second dataset included 323 patients with newly diagnosed pancreatic cancer and 3134 control subjects recruited for

an epidemiological research program at Aichi Cancer Center (HERPACC) between 2005 and 2012. All outpatients on their first visit to Aichi Cancer Center were invited to participate in

HERPACC. Those who agreed to participate completed a self-administered questionnaire and provided a 7-ml blood sample. After quality control, 943 cases and 3057 controls remained for the

subsequent analysis (Supplementary Data 1). None of the control subjects had a diagnosis of cancer by the time of recruitment. Written informed consent was obtained from all study

participants, and the study protocol was approved by the Ethical Review Board of Aichi Medical University, the Institutional Ethics Committee of Aichi Cancer Center, the Human Genome and

Gene Analysis Research Ethics Committee of Nagoya University, and the ethics committees of all participating hospitals. BIOBANK JAPAN GWAS Pancreatic cancer patient data were obtained from

the BBJ GWAS, which was launched in 2003 and collected DNA and clinical information from approximately 200,000 patients, including those with pancreatic cancer33. Overall, 422 pancreatic

cancer patients with available genotype data were recruited from 2003 to 2008. Clinical information was collected using a standardized questionnaire. This study was approved by the ethics

committees of the RIKEN Center for Integrative Medical Sciences. Controls were drawn from the participants in four population-based cohort studies in Japan: the Japan Multi-Institutional

Collaborative Cohort Study, the JPHC, the Tohoku Medical Megabank Project Organization, and the Iwate Tohoku Medical Megabank Organization (Supplementary Note 2). In total, 28,870 controls

who passed genotype data quality-control assessments were included in the study. In all participating cohort studies, informed consent was obtained from the participants by following the

protocols approved by the corresponding institutional ethics committees. Detailed descriptions of the BBJ and each cohort study are provided in Supplementary Note 2. NATIONAL CANCER CENTER

GWAS The case and control samples were derived from a previous pancreatic cancer GWAS11. Case subjects were 677 patients diagnosed with invasive pancreatic ductal adenocarcinoma at the NCC

Hospital, Tokyo, Japan. Controls consisted of 677 Japanese volunteers who participated in a health check-up program in Tokyo. After preimputation quality control, data from 674 cases and 674

controls remained for the subsequent analysis (Supplementary Data 1). This project was approved by the ethics committee of the NCC. QUALITY CONTROL AND GENOTYPE IMPUTATION Quality control

for samples and SNPs was performed based on the study-specific criteria. For the study that included data genotyped on two different platforms, we performed imputation using those SNPs that

were available from both genotyping platforms. Genotype data in each study were imputed separately based on the 1000 Genomes Project reference panel (Phase 3, all ethnicities). The phasing

was performed with the use of SHAPEIT (v2)34 or Eagle2 (v2)35, and the imputation was performed using minimac336 or IMPUTE (v2)37. Information on the study-specific genotyping, imputation,

and analysis tools is provided in Supplementary Data 1. After genotype imputation, quality control was applied to each study. SNPs with an imputation quality of _r_2 < 0.5 or an MAF of

<0.01 were excluded. SNPs that passed quality control in at least two cohorts were included in the meta-analysis. ASSOCIATION ANALYSIS FOR SNPS AND PANCREATIC CANCER The association of

pancreatic cancer with the SNP allele dose was estimated using logistic regression analysis with adjustment for the first two principal components. The association magnitudes and standard

errors were used in the subsequent meta-analysis. GWAS META-ANALYSIS We performed a meta-analysis of three pancreatic cancer GWASs (JaPAN, BBJ, and NCC). The association results for each SNP

across the studies were combined with the METAL software (v2011-03-25) in a fixed effects IVW meta-analysis. Heterogeneity in allelic associations was assessed using the _I_2 index. The

meta-analysis included 7,914,378 SNPs with genotype data available from at least two cohorts. A _P_ value threshold of 5 × 10−8 was used to establish a threshold for genome-wide

significance. We assessed the inflation of test statistics using the genomic control lambda. REPLICATION ANALYSIS We sought replication of the SNP associations in six additional studies

involving populations of East Asian origin, including Japanese, Japanese American, and Chinese individuals. In total, we assembled genotype data from 1926 cases and 8896 controls for the

replication analysis. Detailed information on the study descriptions, quality-control thresholds, and exclusion criteria for each replication cohort is provided in Supplementary Data 1 and

the Supplementary Note 3. The association between the SNP allele dose and pancreatic cancer risk in each replication cohort was estimated using logistic regression analysis with the

adjustment for study-specific covariates shown in Supplementary Data 1. For the lead SNP rs78193862, we also performed a fixed-effects IVW meta-analysis of SNP associations by combining all

six study sample sets included in the replication analysis. FUNCTIONAL ANNOTATION To prioritize the associated SNPs of the identified loci, we adopted a series of bioinformatic approaches to

collate functional annotations. We first used ANNOVAR38 to obtain an aggregate set of functional annotations—including gene locations and impacts of amino acid substitutions based on

prediction tools, such as SIFT, PolyPhen-2, and CADD—for SNPs with _P_ values <5 × 10–8 for pancreatic cancer. We also explored potential effects on gene regulation by annotating these

SNPs with information from the RegulomeDB database39. FUNCTIONAL CHARACTERIZATION We chose PaTu 8988s for the functional study, because it is the only pancreatic adenocarcinoma cell line

expressing _GP2_ according to the Cancer Cell Line Encyclopedia. PaTu 8988s cells were obtained from the Deutsche Sammlung von Mikroorganismen und Zellkulturen (DSMZ, Braunschweig, Germany)

and maintained with RPMI 1640 (Invitrogen) plus 10% fetal bovine serum at 37 °C in a 5% CO2 cell culture incubator. PaTu 8988s cells were genotyped for identity at BEX CO., LTD. and tested

routinely for mycoplasma contamination. GENERATION OF THE GP2_V282M PATU 8988S CELL LINE Mutation was induced through CRISPR-Cas9-mediated homologous recombination. The plasmid

pSpCas9(BB)-2A-GFP (PX458) (Addgene plasmid # 48138) was purchased from Addgene (Cambridge, MA)40. To generate a nucleotide mutation from G to A at codon 282 (synonymous with 285, 429, and

432; Supplementary Data 2) of the _GP2_ gene, we selected an single-guide RNA target with CHOPCHOP (https://chopchop.cbu.uib.no)41 and designed a DNA repair template with 45 bp homology

arms. Oligonucleotide pairs were annealed and ligated into the BbsI-linearized PX458 plasmid. Cells were transfected with the vector and the repair template using Lipofectamine 3000 (Life

Technologies) according to the manufacturer’s instructions. To obtain monoclonal clones, GFP-positive cells were sorted as single cells into 96-well plates using a BD FACSAria III cell

sorter (BD Biosciences) 48 h post-transfection. After 3 weeks of culture, cells were distributed into two 24-well plates, followed by Sanger sequencing-based genotyping. Mutation was also

confirmed by RNA-seq. A clone harboring the precise mutation was used for further analysis. All oligonucleotide primers were obtained from FASMAC (Kanagawa, Japan; Supplementary Table 7).

Control cell lines were obtained from clones without induced mutations at codon 282 by the same plasmid transfection. TRANSIENT TRANSFECTION EXPERIMENTS In all, 2 × 105 Patu-S cells plated

in 6-well plate were transfected with the empty vector alone or the sgGP2-cloned plasmid and the repair template using Lipofectamine 3000 (Life Technologies) according to the manufacturer’s

instructions. Total RNA was isolated 72 h post-transfection followed by cDNA synthesis and qRT-PCR. RNA ISOLATION AND CDNA SYNTHESIS Total RNA was isolated using the miRNeasy Mini Kit

(Qiagen) with DNase I (Qiagen) digestion according to the manufacturer’s instructions. cDNA was synthesized from total RNA using Superscript III (Invitrogen) and random primers (Invitrogen).

QUANTITATIVE REAL-TIME PCR qRT-PCR was performed using SYBR® Premix Ex Taq TM (TaKaRa) on an Applied Biosystems 7900HT Real-Time PCR System. All oligonucleotide primers were obtained from

FASMAC (Kanagawa, Japan; Supplementary Table 7). RNA-SEQUENCING RNA-seq was performed by GeneWiz Inc. (Saitama, Japan) in paired-end mode. RNA-seq reads were mapped to NCBI37 with TopHat2

and quantified to the human transcriptome (refGene) with GFOLD42. Gene expression was quantified in the form of RPKM (reads per kilobase of transcript, per million mapped reads). The

reference sequence and refGene GTF files were obtained from iGenomes. DIFFERENTIAL EXPRESSION ANALYSIS AND GSEA Differentially expressed genes were identified using GFOLD (v1.1)42 based on

comparisons between the GP2_V282M and GP2_WT groups. Significantly differentially expressed genes were defined as genes with empirical false discovery rates (FDR) < 0.10. The GSEA was

performed using the MSigDB v6.2 collections—H and C614. Genes with coding lengths <200 bp or <10 mean reads in either the GP2_V282M or GP2_WT groups were excluded. A rank list was

generated by ordering each gene according to the log2-fold-change value (log2fdc from GFOLD), which was calculated from the expression level ratio of GP2_V282M/GP2_WT. These rank lists were

used in a weighted, preranked GSEA. Sets of 1000 permutations of the genes were applied in the preranked GSEA performed with the above-described collections of gene sets. An FDR < 0.10

was considered significant for the GSEA analysis. MR ANALYSIS We performed MR analyses for the associations of pancreatic cancer with selected metabolic traits, including T2D, HbA1c, blood

glucose, and BMI. As genetic instruments for each trait, we selected genome-wide significant SNPs that had been reported in the three previously published GWAS meta-analyses involving

Japanese subjects17,18,43. For the two-sample MR analysis of T2D and pancreatic cancer, we did not exclude duplicate samples (15.5% found only in the controls) because retaining these

samples was unlikely to introduce substantial bias44. Data from 106 pancreatic cancer cases were excluded from the HbA1c GWAS, and the association magnitudes for the HbA1c-associated SNPs

were re-estimated. After the exclusion of SNPs on the X chromosome or SNPs without genotype data (Supplementary Data 6–9), the summary data were available for 82 T2D-related SNPs, 25

HbA1c-related SNPs, 15 blood glucose-related SNPs, and 76 BMI-related SNPs. The associations of these SNPs with pancreatic cancer risk were analyzed using the IVW and MR–Egger regression

methods. MR analysis was performed with the MendelianRandomization package45. Given that the presence of horizontal pleiotropy may violate MR assumptions, leading to invalid results, we

further applied the MR-PRESSO test to detect and correct for horizontal pleiotropic outliers46. GENE-BASED ANALYSIS SNP-based _P_ values for 7,914,378 SNPs were combined into gene-based _P_

values for 17,581 genes using the MAGMA software version 1.0618. SNP summary statistics (_P_ values) from the meta-analysis were used as input for MAGMA. In gene-based association

statistics, LD between SNPs was accounted for, and the _P_ value threshold for genome-wide significant associations was set at 2.84 × 10−6 (=0.05/17,581). The 1000 Genomes reference panel

(Phase 3, East Asian) was used to control for LD. We did not include any upstream/downstream regions around the genes in this analysis; only variants located between the first exon and the

last exon of a gene were used to calculate the gene-based _P_ values. The NCBI Gene database was used to define genomic intervals for protein-coding genes. To replicate the association

between _KRT8_ and pancreatic cancer, we applied SNP summary statistics from PanScan 1 and PanScan 2 (pha002889.1)13 to MAGMA. The MAF of the Haplotype Map (HapMap) project Phase 2 CEU

samples for each SNP was added to the summary statistics, because the pha002889.1 data did not include the MAFs. We excluded variants with call fractions <95% in either the case or

control data, Hardy–Weinberg equilibrium _P_ value <10−6 in the controls, or MAF < 0.01. The 1000 Genomes reference panel (Phase 3, European) was used to control for LD. The

significance level was set at _α_ = 0.05. REPORTING SUMMARY Further information on research design is available in the Nature Research Reporting Summary linked to this article. DATA

AVAILABILITY The summary GWAS statistics for this analysis are available on the website of the JaPAN consortium [http://www.aichi-med-u.ac.jp/JaPAN/current_initiatives-e.html]. The RNA-seq

data are available at the Gene Expression Omnibus under the following accession number: GSE147368. The other datasets generated during this study are available from the corresponding author

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complex traits anddiseases. _Nat. Genet._ 50, 693–698 (2018). Article  CAS  PubMed  PubMed Central  Google Scholar  Download references ACKNOWLEDGEMENTS We thank Mayuko Masuda, Kikuko Kaji,

Kazue Ando, Etsuko Ohara, Sumiyo Asakura, Keiko Hanai, and Yoko Mitsuda for assistance with data collection. We would like to express our gratefulness to the staff of BioBank Japan for their

outstanding assistance. This work was supported by the Ministry of Health, Labor, and Welfare of Japan (H21-11-1) and the Ministry of Education, Culture, Sports, Science, and Technology of

Japan (Nos. 16H06277, 17K09095, 17H04127, 26293145, 26253041, and 17015018). HERPACC, a part of the JaPAN consortium, was supported by Grants-in-Aid for Scientific Research for Priority

Areas of Cancer (No. 17015018) and Innovative Areas (No. 221S0001); JSPS KAKENHI grants (Nos. 26253041, 15H02524, 16H06277, and 18H03045) from the Japanese Ministry of Education, Science,

Sports, Culture and Technology; and a Grant-in-Aid for the Third Term Comprehensive 10-year Strategy for Cancer Control from the Ministry of Health, Labor and Welfare of Japan and the Cancer

Biobank Aichi. This study was partially supported by the BioBank Japan project and the Tohoku Medical Megabank project, which is supported by the Ministry of Education, Culture, Sports,

Sciences and Technology of Japan and the Japan Agency for Medical Research and Development. The JPHC Study has been supported by the National Cancer Center Research and Development Fund

(23-A31[toku], 26-A-2, and 29-A-4) since 2011 and was supported by a Grant-in-Aid for Cancer Research from the Ministry of Health, Labor and Welfare of Japan from 1989 to 2010. The

case–cohort study within the JPHC Study was supported by the National Cancer Center Research and Development Fund (28-A-19 and 31-A-18) and by Practical Research for Innovative Cancer

Control (JP16ck0106095h0003 and JP19ck0106266h0003) from the Japan Agency for Medical Research and Development, AMED. The J-MICC study was supported by Grants-in-Aid for Scientific Research

for Priority Areas of Cancer (No. 17015018) and Innovative Areas (No. 221S0001) and a JSPS KAKENHI grant (No. 16H06277) from the Japanese Ministry of Education, Science, Sports, Culture and

Technology. The JEPA study was supported by Grants-in-Aid for Scientific Research (S) and a JSPS KAKENHI grant (No. 15H05791) from the Japanese Ministry of Education, Science, Sports,

Culture and Technology. The Yale-Shanghai study was supported by NIH/NCI 5R01 CA114421. This work utilized the computational resources of the NIH HPC Biowulf cluster (http://hpc.nih.gov).

The MEC replication study was supported by NIH/NCI CA209798, and CA164973. AUTHOR INFORMATION Author notes * These authors contributed equally: Yingsong Lin, Masahiro Nakatochi, Yasuyuki

Hosono, Hidemi Ito. AUTHORS AND AFFILIATIONS * Department of Public Health, Aichi Medical University School of Medicine, Nagakute, Aichi, 480-1195, Japan Yingsong Lin, Chaochen Wang & 

Shogo Kikuchi * Division of Public Health Informatics, Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, Nagoya, 461-8673, Japan Masahiro Nakatochi *

Department of Nursing, Nagoya University Graduate School of Medicine, Nagoya, 461-8673, Japan Masahiro Nakatochi * Division of Molecular Therapeutics, Aichi Cancer Center Research Institute,

Nagoya, 464-8681, Japan Yasuyuki Hosono, Yuko Hayashi & Hiromichi Ebi * Division of Cancer Information and Control, Aichi Cancer Center Research Institute, Nagoya, 464-8681, Japan

Hidemi Ito * Department of Descriptive Epidemiology, Nagoya University Graduate School of Medicine, Nagoya, 466-8550, Japan Hidemi Ito * Laboratory for Statistical Analysis, RIKEN Center for

Integrative Medical Sciences, Yokohama, 230-0045, Japan Yoichiro Kamatani, Masato Akiyama, Kazuyoshi Ishigaki, Ken Suzuki & Yukinori Okada * Laboratory of Complex Trait Genomics,

Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, 108-8639, Japan Yoichiro Kamatani * Division of Cancer

Epidemiology and Prevention, Aichi Cancer Center Research Institute, Nagoya, 464-8681, Japan Akihito Inoko, Yumiko Kasugai & Keitaro Matsuo * Department of Pathology, Aichi Medical

University School of Medicine, Nagakute, 480-1195, Japan Akihito Inoko * Genetics Division, National Cancer Center Research Institute, Tokyo, 104-0045, Japan Hiromi Sakamoto & Teruhiko

Yoshida * Data Science Division, Data Coordinating Center, Department of Advanced Medicine, Nagoya University Hospital, Nagoya, 461-8673, Japan Fumie Kinoshita & Yumiko Kobayashi * Chiba

Cancer Center, Chiba, 260-8717, Japan Hiroshi Ishii * Department of Hepato-biliary-pancreatic Medicine, The Cancer Institute Hospital of Japanese Foundation for Cancer Research, Tokyo,

135-8550, Japan Masato Ozaka, Takashi Sasaki, Masato Matsuyama & Naoki Sasahira * Department of Gastroenterology, Hepatobiliary and Pancreatic Medical Oncology Division, Kanagawa Cancer

Center, Yokohama, 241-8515, Japan Manabu Morimoto, Satoshi Kobayashi, Taito Fukushima, Makoto Ueno & Shinichi Ohkawa * Department of Gastroenterology, Tokyo Metropolitan Hiroo Hospital,

Tokyo, 150-0013, Japan Naoto Egawa * Department of Gastroenterology, Tokyo Metropolitan Komagome Hospital, Tokyo, 113-8677, Japan Sawako Kuruma * Hokkaido Chitose College of Rehabilitation,

Hokkaido, 066-0055, Japan Mitsuru Mori * Division of Hepatology and Pancreatology, Aichi Medical University School of Medicine, Nagakute, 480-1195, Japan Haruhisa Nakao * Sapporo

Shirakabadai Hospital, Sapporo, 062-0052, Japan Yasushi Adachi * Department of Pediatrics, Hyogo College of Medicine, Nishinomiya, Hyogo, 663-8501, Japan Masumi Okuda * Department of

Infectious Diseases, Kyorin University School of Medicine, Tokyo, 181-8611, Japan Takako Osaki & Shigeru Kamiya * Department of Gastroenterology, Aichi Cancer Center Hospital, Nagoya,

464-8681, Japan Kazuo Hara * Department of Gastroenterological Surgery, Aichi Cancer Center Hospital, Nagoya, 464-8681, Japan Yasuhiro Shimizu * Department of Genetics and Cell Biology,

Research Institute for Radiation Biology and Medicine, Hiroshima University, Hiroshima, 734-8553, Japan Tatsuo Miyamoto * Department of Human Genetics, Tokushima University Graduate School

of Medicine, Tokushima, 770-8503, Japan Tomohiro Kohmoto * Division of Molecular Genetics, Aichi Cancer Center Research Institute, Nagoya, 464-8681, Japan Tomohiro Kohmoto & Issei Imoto

* Department of Cancer Epidemiology, Nagoya University Graduate School of Medicine, Nagoya, 466-8550, Japan Yumiko Kasugai & Keitaro Matsuo * Division of Molecular Pathology, Institute

of Medical Science, The University of Tokyo, Tokyo, 108-8639, Japan Yoshinori Murakami & Makoto Hirata * Department of Ophthalmology, Graduate School of Medical Sciences, Kyushu

University, Fukuoka, 812-8582, Japan Masato Akiyama * Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, 108-8639, Japan Koichi Matsuda * Department of Hepatobiliary and

Pancreatic Surgery, National Cancer Center Hospital, Tokyo, 104-0045, Japan Kazuaki Shimada * Department of Hepatobiliary and Pancreatic Oncology, National Cancer Center Hospital, Tokyo,

104-0045, Japan Takuji Okusaka * Center for Genomic Medicine, Graduate School of Medicine, Kyoto University, Kyoto, 606-8501, Japan Takahisa Kawaguchi, Meiko Takahashi & Fumihiko Matsuda

* Department of Epidemiology for Community Health and Medicine, Kyoto Prefectural University of Medicine, Kyoto, 602-8566, Japan Yoshiyuki Watanabe * Laboratory of Public Health, School of

Food and Nutritional Sciences, University of Shizuoka, Shizuoka, 422-8526, Japan Kiyonori Kuriki * Department of Public Health, Shiga University of Medical Science, Otsu, 520-2192, Japan Aya

Kadota * Department of Preventive Medicine, Nagoya University Graduate School of Medicine, Nagoya, 466-8550, Japan Rieko Okada & Kenji Wakai * Cancer Prevention Center, Chiba Cancer

Center Research Institute, Chiba, 260-8717, Japan Haruo Mikami * Department of International Island and Community Medicine, Kagoshima University Graduate School of Medical and Dental

Sciences, Kagoshima, 890-8544, Japan Toshiro Takezaki * Department of Public Health, Nagoya City University Graduate School of Medical Sciences, Nagoya, 467-8601, Japan Sadao Suzuki *

Division of Epidemiology, Center for Public Health Sciences, National Cancer Center, Tokyo, 104-0045, Japan Taiki Yamaji, Motoki Iwasaki, Norie Sawada & Atsushi Goto * Tohoku Medical

Megabank Organization, Tohoku University, Sendai, 980-8573, Japan Kengo Kinoshita, Nobuo Fuse & Fumiki Katsuoka * Iwate Tohoku Medical Megabank Organization, Iwate Medical University,

Iwate, 028-3694, Japan Atsushi Shimizu, Satoshi S. Nishizuka & Kozo Tanno * Department of Hygiene and Preventive Medicine, School of Medicine, Iwate Medicalm University, Iwate, 028-3694,

Japan Kozo Tanno * Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, 113-0033, Japan Ken Suzuki, Toshimasa Yamauchi & Takashi

Kadowaki * Laboratory for Endocrinology, Metabolism and Kidney Diseases, RIKEN Centre for Integrative Medical Sciences, Yokohama, 230-0045, Japan Ken Suzuki & Momoko Horikoshi *

Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, 565-0871, Japan Ken Suzuki & Yukinori Okada * Laboratory of Statistical Immunology, Immunology

Frontier Research Center (WPI-IFReC), Osaka University, Osaka, 565-0871, Japan Yukinori Okada * University of Hawaii Cancer Center, Honolulu, HI, 96813, USA Herbert Yu & Loic Le Marchand

* Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA Jun Zhong & 

Laufey T. Amundadottir * Department of Gastroenterological Surgery, Graduate School of Medicine, Osaka University, Osaka, 565-0871, Japan Yuichiro Doki & Hidetoshi Eguchi * Department of

Medical Data Science, Graduate School of Medicine, Osaka University, Osaka, 565-0871, Japan Hideshi Ishii * Department of Preventive Medicine, Keck School of Medicine, University of

Southern California, Los Angeless, CA, 90033, USA David Bogumil, Christopher A. Haiman & Veronica W. Setiawan * Norris Comprehensive Cancer Center, University of Southern California, Los

Angeles, CA, 90033, USA Christopher A. Haiman & Veronica W. Setiawan * Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, 812-8582,

Japan Masaki Mori * Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, CT, 06520, USA Harvey Risch * Center for Public Health Sciences, National Cancer

Center, Tokyo, 104-0045, Japan Shoichiro Tsugane * RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan Michiaki Kubo Authors * Yingsong Lin View author publications You

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Google Scholar * Keitaro Matsuo View author publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS Y.L., M.N., Y. Hosono, H. Ito, A.I., M.O., T.O, S. Kamiya,

and C.W. designed the study. Y.L., M.N., Y. Hosono, and Y. Kamatani wrote the manuscript. M.N., F.K., Y. Kobayashi., Y. Kamatani., M.A., K.I., and J.Z. performed the statistical analysis.

H.S., H.I., M.O., T.S., M. Matsuyama., N.S., M.M., S. Kobayashi, T.F., M.U., S.O., N.E., S. Kuruma, Mitsura Mori., H.N., Y.A., K.H., Y.S., Y.M., K. Matsuda, M. Hirata, K. Shimada, T.O.,

Y.W., K. Kuriki, A.K., R.O., H.M., T.T., S.S., K.W., T. Yamaji, M.I., N.S., A.G., S.T., K. Kinoshita, N.F., F.K., A.S., S.S.N., K.T., K. Suzuki, Y.O., M. Horikoshi, T. Yamauchi, T. Kadowaki,

H.Y., L.T.A., Y.D., H. Ishii, H.E., D.B., C.A.H., L.L.M., Masaki Mori, H.R., V.W.S., T. Yoshida, and M.K. contributed to data collection. Y. Hosono, A.I., T.M., Y. Hayashi, H.E., T.

Kohmoto, I.I., Y. Kasugai, M.K., T. Kawaguchi, M.T., and F.M. contributed to SNP genotyping and functional characterization. S. Kikuchi and K. Matsuo supervised the study. All authors

approved the final version of the manuscript. CORRESPONDING AUTHORS Correspondence to Yingsong Lin, Masahiro Nakatochi or Keitaro Matsuo. ETHICS DECLARATIONS COMPETING INTERESTS The authors

declare no competing interests. ADDITIONAL INFORMATION PEER REVIEW INFORMATION _Nature Communications_ thanks Paul Brennan and the other anonymous reviewer(s) for their contribution to the

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ARTICLE Lin, Y., Nakatochi, M., Hosono, Y. _et al._ Genome-wide association meta-analysis identifies _GP2_ gene risk variants for pancreatic cancer. _Nat Commun_ 11, 3175 (2020).

https://doi.org/10.1038/s41467-020-16711-w Download citation * Received: 10 September 2019 * Accepted: 15 May 2020 * Published: 24 June 2020 * DOI: https://doi.org/10.1038/s41467-020-16711-w

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