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. 2012 Jun 14:13:132.
doi: 10.1186/1471-2105-13-132.

Simulating gene-gene and gene-environment interactions in complex diseases: Gene-Environment iNteraction Simulator 2

Michele Pinelli  1 , Giovanni Scala, Roberto Amato, Sergio Cocozza, Gennaro Miele
Affiliations

Simulating gene-gene and gene-environment interactions in complex diseases: Gene-Environment iNteraction Simulator 2

Michele Pinelli et al. BMC Bioinformatics. .

Abstract

Background: The analysis of complex diseases is an important problem in human genetics. Because multifactoriality is expected to play a pivotal role, many studies are currently focused on collecting information on the genetic and environmental factors that potentially influence these diseases. However, there is still a lack of efficient and thoroughly tested statistical models that can be used to identify implicated features and their interactions. Simulations using large biologically realistic data sets with known gene-gene and gene-environment interactions that influence the risk of a complex disease are a convenient and useful way to assess the performance of statistical methods.

Results: The Gene-Environment iNteraction Simulator 2 (GENS2) simulates interactions among two genetic and one environmental factor and also allows for epistatic interactions. GENS2 is based on data with realistic patterns of linkage disequilibrium, and imposes no limitations either on the number of individuals to be simulated or on number of non-predisposing genetic/environmental factors to be considered. The GENS2 tool is able to simulate gene-environment and gene-gene interactions. To make the Simulator more intuitive, the input parameters are expressed as standard epidemiological quantities. GENS2 is written in Python language and takes advantage of operators and modules provided by the simuPOP simulation environment. It can be used through a graphical or a command-line interface and is freely available from http://sourceforge.net/projects/gensim. The software is released under the GNU General Public License version 3.0.

Conclusions: Data produced by GENS2 can be used as a benchmark for evaluating statistical tools designed for the identification of gene-gene and gene-environment interactions.

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Figures

Figure 1
Figure 1
GENS2 work flow. Chart of the steps that were used to simulate a complex disease in a population using the simuPOP and GENS2 systems.
Figure 2
Figure 2
Example of application of epistasis. Disease penetrance for combined genotypes before (left panel) and after (right panel) the application of an epistasis model with an increment of 20% of the risk associated with the (CC-TT) composed genotype. The x- and y- axes plot the reported genotypes of the two DPLs; the z-axis plots the risk associated with each combined genotype.
Figure 3
Figure 3
Association test for the case of epistatic interaction. The population comprised 5,000 cases and 5,000 controls. Two DPLs with no marginal risk (RR=1), an epistatic interaction ( + 20%penetrance for the (3,3) combined genotype) and an additive G×E model (odds ratio (OR)=1.2) were used. The two DPLs are in two distinct genomic regions (Chr 8: 117,948,182 - 119,256,695 in yellow; Chr 10: 114,408,939 - 115,256,799 in cyan). In the upper panel, the Manhattan plot shows the significance of the association (−log10(p-value)) of each marker when tested individually (each dot represents a different marker). The red dashed line represents the significance threshold (0.05 after Bonferroni correction) and the green dashed lines mark the position of DPLs. In the middle panel, the r2 of each marker with the DPL in the same region is shown. In the bottom panel, the significance of the association for each 2-loci interaction (grey scale, nonsignificant; red scale, significant at a 0.05 level after Bonferroni correction) is shown.
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