A novel approach to simulate gene-environment interactions in complex diseases.
A novel approach to simulate gene-environment interactions in complex diseases.
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DOI：
10.1186/1471-2105-11-8
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发表时间：
2010-01-05
影响因子：
3
通讯作者：
Cocozza S
中科院分区：
文献类型：
--
作者：
Amato R;Pinelli M;D'Andrea D;Miele G;Nicodemi M;Raiconi G;Cocozza S
复杂性疾病是由遗传和环境因素引起的多因素特征。它们是人类疾病的主要部分，包括发病率和死亡率最高的疾病（癌症、心脏病、肥胖症等）。尽管已经收集了大量关于遗传和环境风险因素的信息，但在流行病学文献中很少有关于它们相互作用的研究实例。原因之一可能是对旨在搜索这些数据集中的风险因素及其相互作用的统计方法的能力的不完全了解。在这方面的改进将导致更好地理解和描述基因-环境相互作用。为此，一个可能的战略是挑战不同的统计方法对数据集的基本现象是完全已知的和完全可控的，例如模拟的。我们提出了一种数学方法，模型基因与环境的相互作用。通过这种方法，可以产生具有任何形式的基因-环境相互作用的模拟种群，涉及任何数量的遗传和环境因素，并且还允许非线性相互作用作为上位性。特别是，我们在基因-环境交互作用模拟器（GENS）中实现了该模型的一个简单版本，该工具旨在模拟病例对照数据集，其中一个基因-一个环境交互作用影响疾病风险。其主要目的是通过使用标准流行病学措施输入人口特征，并实施限制，使模拟器行为具有生物意义。通过在GENS中实现的多逻辑模型，可以模拟基因-环境相互作用影响疾病风险的复杂疾病的病例对照样本。用户可以完全控制模拟人群的主要特征，蒙特卡罗过程允许随机变化。基于知识的方法通过使用合理的生物学约束降低了数学模型的复杂性，并使模拟在生物学方面更容易理解。模拟数据集可用于评估新的统计方法或在设计研究时评估统计功效。
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. 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. 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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影响因子：
9.8
作者：
Ritchie, MD;Hahn, LW;Moore, JH
通讯作者：
Moore, JH
影响因子：
2.1
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Blangero, J;Williams, JT;Almasy, L
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Almasy, L
影响因子：
2.1
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OTT, J
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1.4
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影响因子：
4.5
作者：
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