METHODS: We used logistic regression adjusted for age, education and area to examine associations between reproductive factors and GBC risk, using 790 cases of histologically confirmed GBC and group-matched 1726 visitor controls. We tested the interaction of these associations by genetic variants known to increase the risk of GBC.

RESULTS: Parity was strongly positively associated with GBC risk: each additional pregnancy was associated with an ∼25% higher risk {odds ratio [OR] 1.26 [95% confidence interval (95% CI) 1.17-1.37]}. After controlling for parity, GBC risk was weakly positively associated with later age of menarche [postmenopausal women, OR 1.11 (95% CI 1.00-1.22) per year], earlier menopause [OR 1.03 (95% CI 1.00-1.06) per year] and shorter reproductive lifespan [OR 1.04 (95% CI 1.01-1.07) per year], but there was little evidence of an association with breastfeeding duration or years since last pregnancy. Risk alleles of single-nucleotide polymorphisms in the ABCB4 and ABCB1 genetic regions had a multiplicative effect on the association with parity, but did not interact with other reproductive factors.

METHODS: We conducted a gene-environment interaction (GxE) analysis including 8,255 cases and 11,900 controls from four pancreatic cancer genome-wide association study (GWAS) datasets (Pancreatic Cancer Cohort Consortium I-III and Pancreatic Cancer Case Control Consortium). Obesity (body mass index ≥30 kg/m2) and diabetes (duration ≥3 years) were the environmental variables of interest. Approximately 870,000 SNPs (minor allele frequency ≥0.005, genotyped in at least one dataset) were analyzed. Case-control (CC), case-only (CO), and joint-effect test methods were used for SNP-level GxE analysis. As a complementary approach, gene-based GxE analysis was also performed. Age, sex, study site, and principal components accounting for population substructure were included as covariates. Meta-analysis was applied to combine individual GWAS summary statistics.

RESULTS: No genome-wide significant interactions (departures from a log-additive odds model) with diabetes or obesity were detected at the SNP level by the CC or CO approaches. The joint-effect test detected numerous genome-wide significant GxE signals in the GWAS main effects top hit regions, but the significance diminished after adjusting for the GWAS top hits. In the gene-based analysis, a significant interaction of diabetes with variants in the FAM63A (family with sequence similarity 63 member A) gene (significance threshold P < 1.25 × 10-6) was observed in the meta-analysis (P GxE = 1.2 ×10-6, P Joint = 4.2 ×10-7).

CONCLUSIONS: This analysis did not find significant GxE interactions at the SNP level but found one significant interaction with diabetes at the gene level. A larger sample size might unveil additional genetic factors via GxE scans.