Identification of Recessively Inherited Genetic Variants Potentially Linked to Pancreatic Cancer Risk

Although 21 pancreatic cancer susceptibility loci have been identified in individuals of European ancestry through genome-wide association studies (GWASs), much of the heritability of pancreatic cancer risk remains unidentified. A recessive genetic model could be a powerful tool for identifying additional risk variants. To discover recessively inherited pancreatic cancer risk loci, we performed a re-analysis of the largest pancreatic cancer GWAS, the Pancreatic Cancer Cohort Consortium (PanScan) and the Pancreatic Cancer Case-Control Consortium (PanC4), including 8,769 cases and 7,055 controls of European ancestry. Six single nucleotide polymorphisms (SNPs) showed associations with pancreatic cancer risk according to a recessive model of inheritance. We replicated these variants in 3,212 cases and 3,470 controls collected from the PANcreatic Disease ReseArch (PANDoRA) consortium. The results of the meta-analyses confirmed that rs4626538 (7q32.2), rs7008921 (8p23.2) and rs147904962 (17q21.31) showed specific recessive effects (p<10(-5)) compared with the additive effects (p>10(-3)), although none of the six SNPs reached the conventional threshold for genome-wide significance (p < 5x10(-8)). Additional bioinformatic analysis explored the functional annotations of the SNPs and indicated a possible relationship between rs36018702 and expression of the BCL2L11 and BUB1 genes, which are known to be involved in pancreatic biology. Our findings, while not conclusive, indicate the importance of considering non-additive genetic models when performing GWAS analysis. The SNPs associated with pancreatic cancer in this study could be used for further meta-analysis for recessive association of SNPs and pancreatic cancer risk and might be a useful addiction to improve the performance of polygenic risk scores.

Automated summary

In this study, a re-analysis of pancreatic cancer GWAS data identified six SNPs associated with pancreatic cancer risk under a recessive genetic model. The results suggest the importance of considering non-additive genetic models in GWAS analysis and provide potential candidates for further meta-analysis to improve polygenic risk scores for pancreatic cancer. Additional bioinformatic analysis revealed a possible relationship between specific SNPs and genes known to be involved in pancreatic biology.