  Complex diseases are multifactorial traits caused by both genetic and
environmental factors. They represent the most part of human diseases and
include those with largest prevalence and mortality (cancer, heart disease,
obesity, etc.). Despite of a large amount of information that have been
collected about both genetic and environmental risk factors, there are
relatively few examples of studies on their interactions in epidemiological
literature. One reason can be the incomplete knowledge of the power of
statistical methods designed to search for risk factors and their interactions
in this data sets. An improving in this direction would lead to a better
understanding and description of gene-environment interaction. To this aim, a
possible strategy is to challenge the different statistical methods against
data sets where the underlying phenomenon is completely known and fully
controllable, like for example simulated ones. We present a mathematical
approach that models gene-environment interactions. By this method it is
possible to generate simulated populations having gene-environment interactions
of any form. We implemented a simple version of this model in a
Gene-Environment iNteraction Simulator (GENS), a tool designed to simulate
case-control data sets where a one gene-one environment interaction influences
the disease risk. The main effort has been to allow user to describe
characteristics of population by using standard epidemiological measures and to
implement constraints to make the simulator behavior biologically meaningful.
