Unidentified genetic variants influence pancreatic cancer risk: an analysis of polygenic susceptibility in the PanScan study

Genome-wide association (GWA) studies have identified several pancreatic cancer (PanCa) susceptibility loci. Methods for assessment of polygenic susceptibility can be employed to detect the collective effect of additional association signals for PanCa. Using data on 492,651 autosomal single nucleotide polymorphisms (SNPs) from the PanScan GWA study (2,857 cases, 2,967 controls), we employed polygenic risk score (PRS) cross-validation (CV) methods to (a) confirm the existence of unidentified association signals, (b) assess the predictive value of PRSs, and (c) assess evidence for polygenic effects in specific genomic locations (genic vs. intergenic). After excluding SNPs in known PanCa susceptibility regions, we constructed PRS models using a training GWA dataset and then tested the model in an independent testing dataset using fourfold CV. We also employed a "power-replication" approach, where power to detect SNP associations was calculated using a training dataset, and power was tested for association with "replication status" in a testing dataset. PRS scores constructed using ≥ 10% of genome-wide SNPs showed significant association with PanCa (P< 0.05) across the majority of CV analyses. Associations were stronger for PRSs restricted to genic SNPs compared to intergenic PRSs. The power-replications approach produced weaker associations that were not significant when restricting to SNPs with low pairwise linkage disequilibrium, whereas PRS results were robust to such restrictions. Although the PRS approach will not dramatically improve PanCa prediction, it provides strong evidence for unidentified association signals for PanCa. Our results suggest that focusing association studies on genic regions and conducting larger GWA studies can reveal additional PanCa susceptibility loci.