A multilayered post-GWAS assessment on genetic susceptibility to pancreatic cancer

Predictive Prioritization of Enhancers Associated with Pancreas Disease Risk

Genetic and epigenetic variations in regulatory enhancer elements increase susceptibility to a range of pathologies. Despite recent advances, linking enhancer elements to target genes and predicting transcriptional outcomes of enhancer dysfunction remain significant challenges. Using 3D chromatin conformation assays, we generated an extensive enhancer interaction dataset for the human pancreas, encompassing more than 20 donors and five major cell types, including both exocrine and endocrine compartments. We employed a network approach to parse chromatin interactions into enhancer-promoter tree models, facilitating a quantitative, genome-wide analysis of enhancer connectivity. With these tree models, we developed a machine learning algorithm to estimate the impact of enhancer perturbations on cell type-specific gene expression in the human pancreas. Orthogonal to our computational approach, we perturbed enhancer function in primary human pancreas cells using CRISPR interference and quantified the effects at the single-cell level through RNA FISH coupled with high-throughput imaging. Our enhancer tree models enabled the annotation of common germline risk variants associated with pancreas diseases, linking them to putative target genes in specific cell types. For pancreatic ductal adenocarcinoma, we found a stronger enrichment of disease susceptibility variants within acinar cell regulatory elements, despite ductal cells historically being assumed as the primary cell-of-origin. Our integrative approach-combining cell type-specific enhancer-promoter interaction mapping, computational models, and single-cell enhancer perturbation assays-produced a robust resource for studying the genetic basis of pancreas disorders.

A common CTRB misfolding variant associated with pancreatic cancer risk causes ER stress and inflammation in mice

Objective

Genome wide association studies have identified an exon 6 CTRB2 deletion variant that associates with increased risk of pancreatic cancer. To acquire evidence on its causal role, we developed a new mouse strain carrying an equivalent variant in Ctrb1 , the mouse orthologue of CTRB2 .

Design

We used CRISPR/Cas9 to introduce a 707bp deletion in Ctrb1 encompassing exon 6 ( Ctrb1 Δexon6 ). This mutation closely mimics the human deletion variant. Mice carrying the mutant allele were extensively profiled at 3 months to assess their phenotype.

Results

Ctrb1 Δexon6 mutant mice express a truncated CTRB1 that accumulates in the ER. The pancreas of homozygous mutant mice displays reduced chymotrypsin activity and total protein synthesis. The histological aspect of the pancreas is inconspicuous but ultrastructural analysis shows evidence of dramatic ER stress and cytoplasmic and nuclear inclusions. Transcriptomic analyses of the pancreas of mutant mice reveals acinar program down-regulation and increased activity of ER stress-related and inflammatory pathways. Heterozygous mice have an intermediate phenotype. Agr2 is one of the most up-regulated genes in mutant pancreata. Ctrb1 Δexon6 mice exhibit impaired recovery from acute caerulein-induced pancreatitis. Administration of TUDCA or sulindac partially alleviates the phenotype. A transcriptomic signature derived from the mutant pancreata is significantly enriched in normal human pancreas of CTRB2 exon 6 deletion variant carriers from the GTEx cohort.

Conclusions

This mouse strain provides formal evidence that the Ctrb1 Δexon6 variant causes ER stress and inflammation in vivo , providing an excellent model to understand its contribution to pancreatic ductal adenocarcinoma development and to identify preventive strategies.

SUMMARY BOX

What is already known about this subject?: - CTRB2 is one of the most abundant proteins produced by human pancreatic acinar cells. - A common exon 6 deletion variant in CTRB2 has been associated with an increased risk of pancreatic ductal adenocarcinoma. - Misfolding of digestive enzymes is associated with pancreatic pathology.What are the new findings?: - We developed a novel genetic model that recapitulates the human CTRB2 deletion variant in the mouse orthologue, Ctrb1 . - Truncated CTRB1 misfolds and accumulates in the ER; yet, mutant mice display a histologically normal pancreas at 3 months age.- CTRB1 and associated chaperones colocalize in the ER, the cytoplasm, and the nucleus of acinar cells.- Transcriptomics analysis reveals reduced activity of the acinar program and increased activity of pathways involved in ER stress, unfolded protein response, and inflammation.- Mutant mice are sensitized to pancreatic damage and do not recover properly from a mild caerulein-induced pancreatitis.- TUDCA administration partially relieves the ER stress in mutant mice.How might it impact on clinical practice in the foreseeable future?: - The new mouse model provides a tool to identify the mechanisms leading to increased pancreatic cancer risk in CTRB2 exon 6 carriers. - The findings suggest that drugs that cause ER stress relief and/or reduce inflammation might provide preventive opportunities.

Primary osteoarthritis chondrocyte map of chromatin conformation reveals novel candidate effector genes

OBJECTIVES

Osteoarthritis is a complex disease with a huge public health burden. Genome-wide association studies (GWAS) have identified hundreds of osteoarthritis-associated sequence variants, but the effector genes underpinning these signals remain largely elusive. Understanding chromosome organisation in three-dimensional (3D) space is essential for identifying long-range contacts between distant genomic features (e.g., between genes and regulatory elements), in a tissue-specific manner. Here, we generate the first whole genome chromosome conformation analysis (Hi-C) map of primary osteoarthritis chondrocytes and identify novel candidate effector genes for the disease.

METHODS

Primary chondrocytes collected from 8 patients with knee osteoarthritis underwent Hi-C analysis to link chromosomal structure to genomic sequence. The identified loops were then combined with osteoarthritis GWAS results and epigenomic data from primary knee osteoarthritis chondrocytes to identify variants involved in gene regulation via enhancer-promoter interactions.

RESULTS

We identified 345 genetic variants residing within chromatin loop anchors that are associated with 77 osteoarthritis GWAS signals. Ten of these variants reside directly in enhancer regions of 10 newly described active enhancer-promoter loops, identified with multiomics analysis of publicly available chromatin immunoprecipitation sequencing (ChIP-seq) and assay for transposase-accessible chromatin using sequencing (ATAC-seq) data from primary knee chondrocyte cells, pointing to two new candidate effector genes SPRY4 and PAPPA (pregnancy-associated plasma protein A) as well as further support for the gene SLC44A2 known to be involved in osteoarthritis. For example, PAPPA is directly associated with the turnover of insulin-like growth factor 1 (IGF-1) proteins, and IGF-1 is an important factor in the repair of damaged chondrocytes.

CONCLUSIONS

We have constructed the first Hi-C map of primary human chondrocytes and have made it available as a resource for the scientific community. By integrating 3D genomics with large-scale genetic association and epigenetic data, we identify novel candidate effector genes for osteoarthritis, which enhance our understanding of disease and can serve as putative high-value novel drug targets.

The relationship and clinical significance of lactylation modification in digestive system tumors

Lactylation, an emerging post-translational modification, plays a pivotal role in the initiation and progression of digestive system tumors. This study presents a comprehensive review of lactylation in digestive system tumors, underscoring its critical involvement in tumor development and progression. By focusing on metabolic reprogramming, modulation of the tumor microenvironment, and the molecular mechanisms regulating tumor progression, the potential of targeting lactylation as a therapeutic strategy is highlighted. The research reveals that lactylation participates in gene expression regulation and cell signaling by affecting the post-translational states of histones and non-histone proteins, thereby influencing metabolic pathways and immune evasion mechanisms in tumor cells. Furthermore, this study assesses the feasibility of lactylation as a therapeutic target, providing insights for clinical treatment of gastrointestinal cancers. Future research should concentrate on elucidating the mechanisms of lactylation, developing efficient lactylation inhibitors, and validating their therapeutic efficacy in clinical trials, which could transform current cancer treatment and immunotherapy approaches. In summary, this review emphasizes the crucial role of lactylation in tumorigenesis and progression through a detailed analysis of its molecular mechanisms and clinical significance.

SETDB1, an H3K9-specific methyltransferase: An attractive epigenetic target to combat cancer

SET domain bifurcated histone lysine methyltransferase 1 (SETDB1) is an important epigenetic regulator catalyzing histone H3 lysine 9 (H3K9) methylation, specifically di-/tri-methylation. This regulation promotes gene silencing through heterochromatin formation. Aberrant SETDB1 expression, and its oncogenic role is evident in many cancers. Thus, SETDB1 is a valid target with novel therapeutic benefits. In this review, we explore the structural and biochemical features of SETDB1, its regulatory mechanisms, and its role in various cancers. We also discuss recent discoveries in small molecules targeting SETDB1 and provide suggestions for future research.

PancanQTLv2.0: a comprehensive resource for expression quantitative trait loci across human cancers

Expression quantitative trait locus (eQTL) analysis is a powerful tool used to investigate genetic variations in complex diseases, including cancer. We previously developed a comprehensive database, PancanQTL, to characterize cancer eQTLs using The Cancer Genome Atlas (TCGA) dataset, and linked eQTLs with patient survival and GWAS risk variants. Here, we present an updated version, PancanQTLv2.0 (https://hanlaboratory.com/PancanQTLv2/), with advancements in fine-mapping causal variants for eQTLs, updating eQTLs overlapping with GWAS linkage disequilibrium regions and identifying eQTLs associated with drug response and immune infiltration. Through fine-mapping analysis, we identified 58 747 fine-mapped eQTLs credible sets, providing mechanic insights of gene regulation in cancer. We further integrated the latest GWAS Catalog and identified a total of 84 592 135 linkage associations between eQTLs and the existing GWAS loci, which represents a remarkable ∼50-fold increase compared to the previous version. Additionally, PancanQTLv2.0 uncovered 659516 associations between eQTLs and drug response and identified 146948 associations between eQTLs and immune cell abundance, providing potentially clinical utility of eQTLs in cancer therapy. PancanQTLv2.0 expanded the resources available for investigating gene expression regulation in human cancers, leading to advancements in cancer research and precision oncology.

The PENGUIN approach to reconstruct protein interactions at enhancer-promoter regions and its application to prostate cancer

We introduce Promoter-Enhancer-Guided Interaction Networks (PENGUIN), a method for studying protein-protein interaction (PPI) networks within enhancer-promoter interactions. PENGUIN integrates H3K27ac-HiChIP data with tissue-specific PPIs to define enhancer-promoter PPI networks (EPINs). We validated PENGUIN using cancer (LNCaP) and benign (LHSAR) prostate cell lines. Our analysis detected EPIN clusters enriched with the architectural protein CTCF, a regulator of enhancer-promoter interactions. CTCF presence was coupled with the prevalence of prostate cancer (PrCa) single nucleotide polymorphisms (SNPs) within the same EPIN clusters, suggesting functional implications in PrCa. Within the EPINs displaying enrichments in both CTCF and PrCa SNPs, we also show enrichment in oncogenes. We substantiated our identified SNPs through CRISPR/Cas9 knockout and RNAi screens experiments. Here we show that PENGUIN provides insights into the intricate interplay between enhancer-promoter interactions and PPI networks, which are crucial for identifying key genes and potential intervention targets. A dedicated server is available at https://penguin.life.bsc.es/ .

An Integrative Pancreatic Cancer Risk Prediction Model in the UK Biobank

Pancreatic cancer (PaCa) is a lethal cancer with an increasing incidence, highlighting the need for early prevention strategies. There is a lack of a comprehensive PaCa predictive model derived from large prospective cohorts. Therefore, we have developed an integrated PaCa risk prediction model for PaCa using data from the UK Biobank, incorporating lifestyle-related, genetic-related, and medical history-related variables for application in healthcare settings. We used a machine learning-based random forest approach and a traditional multivariable logistic regression method to develop a PaCa predictive model for different purposes. Additionally, we employed dynamic nomograms to visualize the probability of PaCa risk in the prediction model. The top five influential features in the random forest model were age, PRS, pancreatitis, DM, and smoking. The significant risk variables in the logistic regression model included male gender (OR = 1.17), age (OR = 1.10), non-O blood type (OR = 1.29), higher polygenic score (PRS) (Q5 vs. Q1, OR = 2.03), smoking (OR = 1.82), alcohol consumption (OR = 1.27), pancreatitis (OR = 3.99), diabetes (DM) (OR = 2.57), and gallbladder-related disease (OR = 2.07). The area under the receiver operating curve (AUC) of the logistic regression model is 0.78. Internal validation and calibration performed well in both models. Our integrative PaCa risk prediction model with the PRS effectively stratifies individuals at future risk of PaCa, aiding targeted prevention efforts and supporting community-based cancer prevention initiatives.

Cuproptosis-related lncRNA scoring system to predict the clinical outcome and immune landscape in pancreatic adenocarcinoma

Cuproptosis is a recently discovered novel programmed cell death pathway that differs from traditional programmed cell death and has an important role in cancer and immune regulation. Long noncoding RNA (lncRNA) is considered new potential prognostic biomarkers in pancreatic adenocarcinoma (PAAD). However, the prognostic role and immune landscape of cuproptosis-related lncRNA in PAAD remain unclear. The transcriptome and clinical data of PAAD were obtained from The Cancer Genome Atlas (TCGA) database. Cuproptosis-related lncRNA was identified using Pearson correlation analysis. The optimal lncRNA was screened by Cox and the Least Absolute Shrinkage and Selection Operator (LASSO) regression mode, and for the construction of risk scoring system. PAAD patients were divided into high- and low-risk groups according to the risk score. Clinicopathological parameter correlation analysis, univariate and multivariate Cox regression, time-dependent receiver operating characteristic (ROC) curves, and nomogram were performed to evaluate the model. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were used to explore differences in biological function between different risk groups. Single-sample gene set enrichment analysis (ssGSEA) and Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm were used to analyze the differences in tumor immune microenvironment (TIME) in different risk groups of PAAD. Additionally, the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm was used to predict immunotherapy response and identify potential immune beneficiaries. Immune checkpoints and tumor mutation burden (TMB) were also systematically analyzed. Finally, drug sensitivity analysis was used to explore the reactivity of different drugs in high- and low-risk groups to provide a reference for the selection of precise therapeutic drugs. Six cuproptosis-related lncRNAs (AL117335.1, AC044849.1, AL358944.1, ZNF236-DT, Z97832.2, and CASC8) were used to construct risk model. Survival analysis showed that overall survival and progression-free survival in the low-risk group were better than those in the high-risk group, and it is suitable for PAAD patients with different clinical characteristics. Univariate and multifactorial Cox regression analysis showed that risk score was an independent prognostic factor in PAAD patients. ROC analysis showed that the AUC values of the risk score in 1 year, 3 years and 5 years were 0.707,0.762 and 0.880, respectively. Nomogram showed that the total points of PAAD patients at 1 year, 3 years, and 5 years were 0.914,0.648, and 0.543. GO and KEGG analyses indicated that the differential genes in the high- and low-risk groups were associated with tumor proliferation and metastasis and immune regulatory pathway. Immune correlation analysis showed that the amount of pro-inflammatory cells, including CD8+ T cells, was significantly higher in the low-risk group than in the high-risk group, and the expression of immune checkpoint genes, including PD-1 and CTLA-4, was increased in the low-risk group. TIDE analysis suggests that patients in the low-risk group may benefit from immunotherapy. Finally, there was significant variability in multiple chemotherapeutic and targeted drugs across the risk groups, which informs our clinical drug selection. Our cuproptosis-related lncRNA scoring system (CRLss) could predict the clinical outcome and immune landscape of PAAD patients, identify the potential beneficiaries of immunotherapy, and provide a reference for precise therapeutic drug selection.

New proposal to address mediation analysis interrogations by using genetic variants as instrumental variables

The application of causal mediation analysis (CMA) considering the mediation effect of a third variable is increasing in epidemiological studies; however, this requires fitting strong assumptions on confounding bias. To address this limitation, we propose an extension of CMA combining it with Mendelian randomization (MRinCMA). We applied the new approach to analyse the causal effect of obesity and diabetes on pancreatic cancer, considering each factor as potential mediator. To check the performance of MRinCMA under several conditions/scenarios, we used it in different simulated data sets and compared it with structural equation models. For continuous variables, MRinCMA and structural equation models performed similarly, suggesting that both approaches are valid to obtain unbiased estimates. When noncontinuous variables were considered, MRinCMA presented, overall, lower bias than structural equation models. By applying MRinCMA, we did not find any evidence of causality of obesity or diabetes on pancreatic cancer. With this new methodology, researchers would be able to address CMA hypotheses by appropriately accounting for the confounding bias assumption regardless of the conditions used in their studies in different settings.

A novel genomic instability-derived lncRNA signature to predict prognosis and immune characteristics of pancreatic ductal adenocarcinoma

Background

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignant tumor of the digestive system. Its grim prognosis is mainly attributed to the lack of means for early diagnosis and poor response to treatments. Genomic instability is shown to be an important cancer feature and prognostic factor, and its pattern and extent may be associated with poor treatment outcomes in PDAC. Recently, it has been reported that long non-coding RNAs (lncRNAs) play a key role in maintaining genomic instability. However, the identification and clinical significance of genomic instability-related lncRNAs in PDAC have not been fully elucidated.

Methods

Genomic instability-derived lncRNA signature (GILncSig) was constructed based on the results of multiple regression analysis combined with genomic instability-associated lncRNAs and its predictive power was verified by the Kaplan-Meier method. And real-time quantitative polymerase chain reaction (qRT-PCR) was used for simple validation in human cancers and their adjacent non-cancerous tissues. In addition, the correlation between GILncSig and tumor microenvironment (TME) and epithelial-mesenchymal transition (EMT) was investigated by Pearson correlation analysis.

Results

The computational framework identified 206 lncRNAs associated with genomic instability in PDAC and was subsequently used to construct a genome instability-derived five lncRNA-based gene signature. Afterwards, we successfully validated its prognostic capacity in The Cancer Genome Atlas (TCGA) cohort. In addition, via careful examination of the transcriptome expression profile of PDAC patients, we discovered that GILncSig is associated with EMT and an adaptive immunity deficient immune profile within TME.

Conclusions

Our study established a genomic instability-associated lncRNAs-derived model (GILncSig) for prognosis prediction in patients with PDAC, and revealed the potential functional regulatory role of GILncSig.

Endoplasmic stress-inducing variants in CPB1 and CPA1 and risk of pancreatic cancer: A case-control study and meta-analysis

Gene variants that encode pancreatic enzymes with impaired secretion can induce pancreatic acinar endoplasmic reticulum (ER) stress, cellular injury and pancreatitis. The role of such variants in pancreatic cancer risk has received little attention. We compared the prevalence of ER stress-inducing variants in CPA1 and CPB1 in patients with pancreatic ductal adenocarcinoma (PDAC cases), enrolled in the National Familial Pancreas Tumor Registry, to their prevalence in noncancer controls in the Genome Aggregation Database (gnomAD). Variants of unknown significance were expressed and variants with reduced secretion assessed for ER stress induction. In vitro assessments were compared with software predictions of variant function. Protein variant software was used to assess variants found in only one gnomAD control ("n-of-one" variants). A meta-analysis of prior PDAC case/control studies was also performed. Of the 1385 patients with PDAC, 0.65% were found to harbor an ER stress-inducing variant in CPA1 or CPB1, compared to 0.17% of the 64 026 controls (odds ratio [OR]: 3.80 [1.92-7.51], P = .0001). ER stress-inducing variants in the CPA1 gene were identified in 4 of 1385 PDAC cases vs 77 of 64 026 gnomAD controls (OR: 2.4 [0.88-6.58], P = .087), and variants in CPB1 were detected in 5 of 1385 cases vs 33 of 64 026 controls (OR: 7.02 [2.74-18.01], P = .0001). Meta-analysis demonstrated strong associations for pancreatic cancer and ER-stress inducing variants for both CPA1 (OR: 3.65 [1.58-8.39], P < .023) and CPB1 (OR: 9.51 [3.46-26.15], P < .001). Rare variants in CPB1 and CPA1 that induce ER stress are associated with increased odds of developing pancreatic cancer.

Opportunity to improve livestock traits using 3D genomics

The advent of high-throughput chromosome conformation capture and sequencing (Hi-C) has enabled researchers to probe the 3D architecture of the mammalian genome in a genome-wide manner. Simultaneously, advances in epigenomic assays, such as chromatin immunoprecipitation and sequencing (ChIP-seq) and DNase-seq, have enabled researchers to study cis-regulatory interactions and chromatin accessibility across the same genome-wide scale. The use of these data has revealed many unique insights into gene regulation and disease pathomechanisms in several model organisms. With the advent of these high-throughput sequencing technologies, there has been an ever-increasing number of datasets available for study; however, this is often limited to model organisms. Livestock species play critical roles in the economies of developing and developed nations alike. Despite this, they are greatly underrepresented in the 3D genomics space; Hi-C and related technologies have the potential to revolutionise livestock breeding by enabling a more comprehensive understanding of how production traits are controlled. The growth in human and model organism Hi-C data has seen a surge in the availability of computational tools for use in 3D genomics, with some tools using machine learning techniques to predict features and improve dataset quality. In this review, we provide an overview of the 3D genome and discuss the status of 3D genomics in livestock before delving into advancing the field by drawing inspiration from research in human and mouse. We end by offering future directions for livestock research in the field of 3D genomics.

UEG position paper on pancreatic cancer. Bringing pancreatic cancer to the 21st century: Prevent, detect, and treat the disease earlier and better

BACKGROUND

Pancreatic ductal adenocarcinoma is the deadliest cancer worldwide with a 98% loss-of-life expectancy and a 30% increase in the disability-adjusted life years during the last decade in Europe. The disease cannot be effectively prevented nor being early detected. When diagnosed, 80% of patients have tumors that are in incurable stages, while for those who undergo surgery, 80% of patients will present with local or distant metastasis. Importantly, chemotherapies are far from being effective.

OBJECTIVE

Pancreatic cancer represents a great challenge and, at the same time, a huge opportunity for advancing our understanding on the basis of the disease, the molecular profiles, that would lead to develop tools for early detection and effective treatments, thus, boosting patient survival.

RESULTS

Research on pancreatic cancer has being receiving little or minimal funds from European funding bodies. UEG is calling for public-private partnerships that would effectively fund research on pancreatic cancer.

CONCLUSION

This would increase our understanding of this disease and better treatment, through pan-European efforts that take advantage of the strong academic European research landscape on pancreatic cancer, and the contribution by the industry of all sizes.

Association of Genetic Variants Affecting microRNAs and Pancreatic Cancer Risk

Genetic factors play an important role in the susceptibility to pancreatic cancer (PC). However, established loci explain a small proportion of genetic heritability for PC; therefore, more progress is needed to find the missing ones. We aimed at identifying single nucleotide polymorphisms (SNPs) affecting PC risk through effects on micro-RNA (miRNA) function. We searched in silico the genome for SNPs in miRNA seed sequences or 3 prime untranslated regions (3'UTRs) of miRNA target genes. Genome-wide association data of PC cases and controls from the Pancreatic Cancer Cohort (PanScan) Consortium and the Pancreatic Cancer Case-Control (PanC4) Consortium were re-analyzed for discovery, and genotyping data from two additional consortia (PanGenEU and PANDoRA) were used for replication, for a total of 14,062 cases and 11,261 controls. None of the SNPs reached genome-wide significance in the meta-analysis, but for three of them the associations were in the same direction in all the study populations and showed lower value of p in the meta-analyses than in the discovery phase. Specifically, rs7985480 was consistently associated with PC risk (OR = 1.12, 95% CI 1.07-1.17, p = 3.03 × 10-6 in the meta-analysis). This SNP is in linkage disequilibrium (LD) with rs2274048, which modulates binding of various miRNAs to the 3'UTR of UCHL3, a gene involved in PC progression. In conclusion, our results expand the knowledge of the genetic PC risk through miRNA-related SNPs and show the usefulness of functional prioritization to identify genetic polymorphisms associated with PC risk.

Associations between pancreatic expression quantitative traits and risk of pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is among the most lethal cancers. Its poor prognosis is predominantly due to the fact that most patients remain asymptomatic until the disease reaches an advanced stage, alongside the lack of early markers and screening strategies. A better understanding of PDAC risk factors is essential for the identification of groups at high risk in the population. Genome-wide association studies (GWAS) have been a powerful tool for detecting genetic variants associated with complex traits, including pancreatic cancer. By exploiting functional and GWAS data, we investigated the associations between polymorphisms affecting gene function in the pancreas (expression quantitative trait loci, eQTLs) and PDAC risk. In a two-phase approach, we analysed 13 713 PDAC cases and 43 784 controls and identified a genome-wide significant association between the A allele of the rs2035875 polymorphism and increased PDAC risk (P = 7.14 × 10-10). This allele is known to be associated with increased expression in the pancreas of the keratin genes KRT8 and KRT18, whose increased levels have been reported to correlate with various tumour cell characteristics. Additionally, the A allele of the rs789744 variant was associated with decreased risk of developing PDAC (P = 3.56 × 10-6). This single nucleotide polymorphism is situated in the SRGAP1 gene and the A allele is associated with higher expression of the gene, which in turn inactivates the cyclin-dependent protein 42 (CDC42) gene expression, thus decreasing the risk of PDAC. In conclusion, we present here a functional-based novel PDAC risk locus and an additional strong candidate supported by significant associations and plausible biological mechanisms.