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

• Published in: BMC bioinformatics Vol. 13; no. 1; p. 132
• Main Authors: Pinelli, Michele, Scala, Giovanni, Amato, Roberto, Cocozza, Sergio, Miele, Gennaro
• Format: Journal Article
• Language: English
• Published: London BioMed Central 14.06.2012; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Algorithms; Analysis; Bioinformatics; Biomedical and Life Sciences; Complex disease; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Computer graphics; Computer programs; Computer Simulation; Data processing; Disease; Disease - genetics; Environment; Environmental factors; Epidemiology; Epistasis; Gene loci; Gene-Environment Interaction; Genes; Genetic; Genetics; Genome-wide association study; Genomics; Genotype-environment interactions; Humans; Language; Life Sciences; Linkage Disequilibrium; Mathematical models; Mathematical programming; Microarrays; Mortality; Performance assessment; Population; Programming languages; Risk Factors; Sequence analysis (applications); SimuPOP; Software; Statistical analysis; Statistical methods; Statistical models; Statistics; Studies; Computer simulation; Genome-wide association study; Linkage disequilibrium; Genomics; Gene-environment interaction; Genetic; Genetics; Population; Epistasis; Complex disease; SimuPOP
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/1471-2105-13-132

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.