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Gene-by-Environment Interactions in Pancreatic Cancer: Implications for Prevention.

ABSTRACT: Pancreatic cancer (PC) has been estimated to have higher incidence and correspondingly higher mortality rates in more developed regions worldwide. Overall, the age-adjusted incidence rate is 4.9/10(5) and age-adjusted mortality rate is at 4.8/10(5). We review here our current knowledge of modifiable risk factors (cigarette smoking, obesity, diet, and alcohol) for PC, genetic variants implicated by genome-wide association studies, possible genetic interactions with risk factors, and prevention strategies to provide future research directions that may further our understanding of this complex disease. Cigarette smoking is consistently associated with a two-fold increased PC risk. PC associations with dietary intake have been largely inconsistent, with the potential exception of certain unsaturated fatty acids decreasing risk and well-done red meat or meat mutagens increasing risk. There is strong evidence to support that obesity (and related measures) increase risk of PC. Only the heaviest alcohol drinkers seem to be at an increased risk of PC. Currently, key prevention strategies include avoiding tobacco and excessive alcohol consumption and adopting a healthy lifestyle. Screening technologies and PC chemoprevention are likely to become more sophisticated, but may only apply to those at high risk. Risk stratification may be improved by taking into account gene environment interactions. Research on these modifiable risk factors is key to reducing the incidence of PC and understanding who in the population can be considered high risk.

SUBMITTER: Jansen RJ

PROVIDER: S-EPMC4445433 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:"Quantile-dependent expressivity" occurs when the effect size of a genetic variant depends upon whether the phenotype (e.g., leptin) is high or low relative to its distribution. Leptin concentrations are strongly related to adiposity, whose heritability is quantile dependent. Whether inheritance of leptin concentrations is quantile dependent, and whether this explains the greater heritability in women than men in accordance with their greater adiposity, and explains other gene-environment interactions, remains to be determined. Therefore, leptin and leptin receptor concentrations from 3068 siblings in 1133 sibships from the Framingham Heart Study Third Generation Cohort were analyzed. Free leptin index (FLI) was calculated as the ratio of leptin to soluble leptin receptor concentrations. Full-sib (βFS) regression slopes were robustly estimated by quantile regression with nonparametric significance assigned from 1000 bootstrap samples. The analyses showed βFS increased significantly with increasing percentiles of the offspring's age- and sex-adjusted leptin distribution (Plinear = 0.0001), which was accelerated at the higher concentrations (Pquadratic = 0.0003). βFS at the 90th percentile (0.418 ± 0.066) was 4.7-fold greater than at the 10th percentile (0.089 ± 0.032, Pdifference = 3.6 × 10-6). Consistent with quantile-dependent expressivity, the βFS was greater in female sibs, which was attributable to their higher leptin concentrations. Reported gene-environment interactions involving adiposity and LEP, LEPR, MnSOD, PPARγ, PPARγ2, and IRS-1 polymorphisms were consistent with quantile-dependent expressivity of leptin concentrations. βFS for leptin receptor concentrations and free leptin index also increased significantly with increasing percentiles of their distributions (Plinear = 0.04 and Plinear = 8.5 × 10-6, respectively). In conclusion, inherited genetic and shared environmental effects on leptin concentrations were quantile dependent, which likely explains male-female differences in heritability and some gene-environment interactions.

Project description:BackgroundThe potential public health benefits of targeting environmental interventions by genotype depend on the environmental and genetic contributions to the variance of common diseases, and the magnitude of any gene-environment interaction. In the absence of prior knowledge of all risk factors, twin, family and environmental data may help to define the potential limits of these benefits in a given population. However, a general methodology to analyze twin data is required because of the potential importance of gene-gene interactions (epistasis), gene-environment interactions, and conditions that break the 'equal environments' assumption for monozygotic and dizygotic twins.MethodA new model for gene-gene and gene-environment interactions is developed that abandons the assumptions of the classical twin study, including Fisher's (1918) assumption that genes act as risk factors for common traits in a manner necessarily dominated by an additive polygenic term. Provided there are no confounders, the model can be used to implement a top-down approach to quantifying the potential utility of genetic prediction and prevention, using twin, family and environmental data. The results describe a solution space for each disease or trait, which may or may not include the classical twin study result. Each point in the solution space corresponds to a different model of genotypic risk and gene-environment interaction.ConclusionThe results show that the potential for reducing the incidence of common diseases using environmental interventions targeted by genotype may be limited, except in special cases. The model also confirms that the importance of an individual's genotype in determining their risk of complex diseases tends to be exaggerated by the classical twin studies method, owing to the 'equal environments' assumption and the assumption of no gene-environment interaction. In addition, if phenotypes are genetically robust, because of epistasis, a largely environmental explanation for shared sibling risk is plausible, even if the classical heritability is high. The results therefore highlight the possibility--previously rejected on the basis of twin study results--that inherited genetic variants are important in determining risk only for the relatively rare familial forms of diseases such as breast cancer. If so, genetic models of familial aggregation may be incorrect and the hunt for additional susceptibility genes could be largely fruitless.

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Gene-by-Environment Interactions in Pancreatic Cancer: Implications for Prevention.

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