ABSTRACT
It is of great scientific interest to identify interactions between genetic variants and environmental exposures that may modify the risk of complex diseases. However, larger sample sizes are usually required to detect gene-by-environment interaction (G × E) than required to detect genetic main association effects. To boost the statistical power and improve the understanding of the underlying molecular mechanisms, we incorporate functional genomics information, specifically, expression quantitative trait loci (eQTLs), into a data-adaptive G × E test, called aGEw. This test adaptively chooses the best eQTL weights from multiple tissues and provides an extra layer of weighting at the genetic variant level. Extensive simulations show that the aGEw test can control the Type 1 error rate, and the power is resilient to the inclusion of neutral variants and noninformative external weights. We applied the proposed aGEw test to the Pancreatic Cancer Case-Control Consortium (discovery cohort of 3,585 cases and 3,482 controls) and the PanScan II genome-wide association study data (replication cohort of 2,021 cases and 2,105 controls) with smoking as the exposure of interest. Two novel putative smoking-related pancreatic cancer susceptibility genes, TRIP10 and KDM3A, were identified. The aGEw test is implemented in an R package aGE.
Subject(s)
Gene Expression Regulation , Gene-Environment Interaction , Genetic Predisposition to Disease , Genome-Wide Association Study , Pancreatic Neoplasms/genetics , Quantitative Trait Loci/genetics , Case-Control Studies , Cohort Studies , Computer Simulation , Data Interpretation, Statistical , Humans , Models, Genetic , Polymorphism, Single Nucleotide/genetics , Smoking/geneticsABSTRACT
We conducted a case-control exome-wide association study to discover germline variants in coding regions that affect risk for pancreatic cancer, combining data from 5 studies. We analyzed exome and genome sequencing data from 437 patients with pancreatic cancer (cases) and 1922 individuals not known to have cancer (controls). In the primary analysis, BRCA2 had the strongest enrichment for rare inactivating variants (17/437 cases vs 3/1922 controls) (P = 3.27x10-6; exome-wide statistical significance threshold P < 2.5x10-6). Cases had more rare inactivating variants in DNA repair genes than controls, even after excluding 13 genes known to predispose to pancreatic cancer (adjusted odds ratio, 1.35; P = .045). At the suggestive threshold (P < .001), 6 genes were enriched for rare damaging variants (UHMK1, AP1G2, DNTA, CHST6, FGFR3, and EPHA1) and 7 genes had associations with pancreatic cancer risk, based on the sequence-kernel association test. We confirmed variants in BRCA2 as the most common high-penetrant genetic factor associated with pancreatic cancer and we also identified candidate pancreatic cancer genes. Large collaborations and novel approaches are needed to overcome the genetic heterogeneity of pancreatic cancer predisposition.
Subject(s)
Biomarkers, Tumor/genetics , Exome Sequencing , Exome , Genetic Variation , Pancreatic Neoplasms/genetics , BRCA2 Protein/genetics , Case-Control Studies , Genetic Heterogeneity , Genetic Predisposition to Disease , Genome-Wide Association Study , Humans , Odds Ratio , Pancreatic Neoplasms/diagnosis , Phenotype , Risk Assessment , Risk FactorsABSTRACT
In a previous study, alpha-1-antitrypsin (A1AT) deficiency alleles were found to be over represented among individuals with microsatellite unstable (MSI-high) colorectal cancers, and this was most significant in former or current smokers. We evaluated this association in a larger case-control study, stratified by microsatellite instability phenotypes. Concordant with prior observations, gender (female) and smoking history were positively associated with colorectal cancers having an MSI-high phenotype. No difference in frequency of A1AT deficiency alleles was found between cases and controls, irrespective of the MSI subtype.