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

BackgroundThe 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.ResultsThe 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.ConclusionsData produced by GENS2 can be used as a benchmark for evaluating statistical tools designed for the identification of gene-gene and gene-environment interactions.

[1]  Marek Kimmel,et al.  Forward-Time Simulations of Human Populations with Complex Diseases , 2007, PLoS genetics.

[2]  S. Kardia,et al.  Synergistic polymorphisms of beta1- and alpha2C-adrenergic receptors and the risk of congestive heart failure. , 2002, The New England journal of medicine.

[3]  D. Allison,et al.  Detection of gene x gene interactions in genome-wide association studies of human population data. , 2007, Human heredity.

[4]  Michele Pinelli,et al.  A novel approach to simulate gene-environment interactions in complex diseases , 2009, BMC Bioinformatics.

[5]  P. Donnelly,et al.  New models of collaboration in genome-wide association studies: the Genetic Association Information Network , 2007, Nature Genetics.

[6]  Matthew Reimherr,et al.  You've Gotta Be Lucky: Coverage and the Elusive Gene–Gene Interaction , 2011, Annals of human genetics.

[7]  Bo Peng.  Simulating gene-environment interactions in complex human diseases , 2010, Genome Medicine.

[8]  P. Chanda,et al.  The challenge of detecting epistasis (G×G Interactions): Genetic Analysis Workshop 16 , 2009, Genetic epidemiology.

[9]  Peter Donnelly,et al.  HAPGEN2: simulation of multiple disease SNPs , 2011, Bioinform..

[10]  Bonnie R. Joubert,et al.  Detecting gene‐environment interactions in genome‐wide association data , 2009, Genetic epidemiology.

[11]  I. Rosenberg,et al.  Relation between folate status, a common mutation in methylenetetrahydrofolate reductase, and plasma homocysteine concentrations. , 1996, Circulation.

[12]  Philippe Amouyel,et al.  Deletion polymorphism in the gene for angiotensin-converting enzyme is a potent risk factor for myocardial infarction , 1992, Nature.

[13]  D. Hunter.  Gene–environment interactions in human diseases , 2005, Nature Reviews Genetics.

[14]  E. Lander,et al.  Meta-analysis of genetic association studies supports a contribution of common variants to susceptibility to common disease , 2003, Nature Genetics.

[15]  Bo Peng,et al.  Forward-time simulation of realistic samples for genome-wide association studies , 2010, BMC Bioinformatics.

[16]  Jason H. Moore,et al.  STUDENTJAMA. The challenges of whole-genome approaches to common diseases. , 2004, JAMA.

[17]  D. Postma,et al.  Gene-gene interaction in asthma: IL4RA and IL13 in a Dutch population with asthma. , 2002, American journal of human genetics.

[18]  S. Kardia,et al.  Synergistic Polymorphisms of β1- and α2C-Adrenergic Receptors and the Risk of Congestive Heart Failure , 2002 .

[19]  Judy H. Cho,et al.  Finding the missing heritability of complex diseases , 2009, Nature.

[20]  Jing Li,et al.  Generating samples for association studies based on HapMap data , 2008, BMC Bioinformatics.

[21]  D E Weeks,et al.  Polygenic disease: methods for mapping complex disease traits. , 1995, Trends in genetics : TIG.