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

BACKGROUND: Complex diseases are multifactorial traits caused by both genetic and environmental factors. They represent the major part of human diseases and include those with largest prevalence and mortality (cancer, heart disease, obesity, etc.). Despite a large amount of information that has been...

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Autores principales: Amato, Roberto, Pinelli, Michele, D'Andrea, Daniel, Miele, Gennaro, Nicodemi, Mario, Raiconi, Giancarlo, Cocozza, Sergio
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2824681/
https://www.ncbi.nlm.nih.gov/pubmed/20051127
http://dx.doi.org/10.1186/1471-2105-11-8
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author Amato, Roberto
Pinelli, Michele
D'Andrea, Daniel
Miele, Gennaro
Nicodemi, Mario
Raiconi, Giancarlo
Cocozza, Sergio
author_facet Amato, Roberto
Pinelli, Michele
D'Andrea, Daniel
Miele, Gennaro
Nicodemi, Mario
Raiconi, Giancarlo
Cocozza, Sergio
author_sort Amato, Roberto
collection PubMed
description BACKGROUND: Complex diseases are multifactorial traits caused by both genetic and environmental factors. They represent the major part of human diseases and include those with largest prevalence and mortality (cancer, heart disease, obesity, etc.). Despite a large amount of information that has been collected about both genetic and environmental risk factors, there are 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 these data sets. An improvement in this direction would lead to a better understanding and description of gene-environment interactions. 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, for example simulated ones. RESULTS: 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, involving any number of genetic and environmental factors and also allowing non-linear interactions as epistasis. In particular, 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 aim has been to allow the input of population characteristics by using standard epidemiological measures and to implement constraints to make the simulator behaviour biologically meaningful. CONCLUSIONS: By the multi-logistic model implemented in GENS it is possible to simulate case-control samples of complex disease where gene-environment interactions influence the disease risk. The user has full control of the main characteristics of the simulated population and a Monte Carlo process allows random variability. A knowledge-based approach reduces the complexity of the mathematical model by using reasonable biological constraints and makes the simulation more understandable in biological terms. Simulated data sets can be used for the assessment of novel statistical methods or for the evaluation of the statistical power when designing a study.
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spelling pubmed-28246812010-02-19 A novel approach to simulate gene-environment interactions in complex diseases Amato, Roberto Pinelli, Michele D'Andrea, Daniel Miele, Gennaro Nicodemi, Mario Raiconi, Giancarlo Cocozza, Sergio BMC Bioinformatics Methodology article BACKGROUND: Complex diseases are multifactorial traits caused by both genetic and environmental factors. They represent the major part of human diseases and include those with largest prevalence and mortality (cancer, heart disease, obesity, etc.). Despite a large amount of information that has been collected about both genetic and environmental risk factors, there are 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 these data sets. An improvement in this direction would lead to a better understanding and description of gene-environment interactions. 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, for example simulated ones. RESULTS: 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, involving any number of genetic and environmental factors and also allowing non-linear interactions as epistasis. In particular, 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 aim has been to allow the input of population characteristics by using standard epidemiological measures and to implement constraints to make the simulator behaviour biologically meaningful. CONCLUSIONS: By the multi-logistic model implemented in GENS it is possible to simulate case-control samples of complex disease where gene-environment interactions influence the disease risk. The user has full control of the main characteristics of the simulated population and a Monte Carlo process allows random variability. A knowledge-based approach reduces the complexity of the mathematical model by using reasonable biological constraints and makes the simulation more understandable in biological terms. Simulated data sets can be used for the assessment of novel statistical methods or for the evaluation of the statistical power when designing a study. BioMed Central 2010-01-05 /pmc/articles/PMC2824681/ /pubmed/20051127 http://dx.doi.org/10.1186/1471-2105-11-8 Text en Copyright ©2010 Amato et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Methodology article
Amato, Roberto
Pinelli, Michele
D'Andrea, Daniel
Miele, Gennaro
Nicodemi, Mario
Raiconi, Giancarlo
Cocozza, Sergio
A novel approach to simulate gene-environment interactions in complex diseases
title A novel approach to simulate gene-environment interactions in complex diseases
title_full A novel approach to simulate gene-environment interactions in complex diseases
title_fullStr A novel approach to simulate gene-environment interactions in complex diseases
title_full_unstemmed A novel approach to simulate gene-environment interactions in complex diseases
title_short A novel approach to simulate gene-environment interactions in complex diseases
title_sort novel approach to simulate gene-environment interactions in complex diseases
topic Methodology article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2824681/
https://www.ncbi.nlm.nih.gov/pubmed/20051127
http://dx.doi.org/10.1186/1471-2105-11-8
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