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Complex diseases are multifactorial traits caused by both genetic andenvironmental factors. They represent the most part of human diseases andinclude those with largest prevalence and mortality (cancer, heart disease,obesity, etc.). Despite of a large amount of information that have beencollected about both genetic and environmental risk factors, there arerelatively few examples of studies on their interactions in epidemiologicalliterature. One reason can be the incomplete knowledge of the power ofstatistical methods designed to search for risk factors and their interactionsin this data sets. An improving in this direction would lead to a betterunderstanding and description of gene-environment interaction. To this aim, apossible strategy is to challenge the different statistical methods againstdata sets where the underlying phenomenon is completely known and fullycontrollable, like for example simulated ones. We present a mathematicalapproach that models gene-environment interactions. By this method it ispossible to generate simulated populations having gene-environment interactionsof any form. We implemented a simple version of this model in aGene-Environment iNteraction Simulator (GENS), a tool designed to simulatecase-control data sets where a one gene-one environment interaction influencesthe disease risk. The main effort has been to allow user to describecharacteristics of population by using standard epidemiological measures and toimplement constraints to make the simulator behavior biologically meaningful.

A novel approach to simulate gene-environment interactions in complex diseases