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首页> 外文会议>Bioinformatics, 2009. OCCBIO '09 >Modeling Genetic and Environmental Factors in Biological Systems Using Structural Equation Modeling: An Application to Energy Balance
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Modeling Genetic and Environmental Factors in Biological Systems Using Structural Equation Modeling: An Application to Energy Balance

机译：使用结构方程模型对生物系统中的遗传和环境因素进行建模：在能量平衡中的应用

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摘要

To improve our understanding of the role(s) that genes and environmental factors play in a complex disease, we need statistical approaches that model multiple factors simultaneously in a hierarchical manner that aims to reflect the underlying biological system(s). We present an approach that models genes as latent constructs, defined by multiple variants (single nucleotide polymorphisms, SNPs) within each gene, using the multivariate statistical framework of structural equation modeling (SEM) to model multiple, putative genetic and environmental factors involved in energy imbalance (dasiaobesitypsila) using subjects from a colon polyp case-control study. We found that modeling constructs for the leptin receptor (LEPR) gene (defined by SNPs rs1137100, rs1137101, rs1805096, rs6588147) and the fat mass-and-obesity-associated (FTO) gene (defined by SNPs rs9939609, rs1421085, rs8044769) together with demographic (age, race, gender), physical activity, diet and sleep variables increased the strength of the association (betastd=-0.13 plusmn 0.06; p=0.03) between the FTO and obesity constructs compared to that observed in a reduced model with only the FTO and LEPR constructs and demographic variables (betastd=-0.05 plusmn 0.03; p=0.08). Several indirect paths, including an association between the LEPR and physical activity constructs (betastd=-0.15 plusmn 0.04; p=0.01), were found. Interestingly, removing FTO revealed a marginal association between the LEPR and obesity constructs (betastd=0.24 plusmn 0.14; p=0.09), which was not present when FTO was in the model. These results illustrate the importance of modeling multiple relevant genes and other factors in the same model, which is a major strength of this approach. Moreover, our latent gene construct approach exploits the correlation structure between SNPs while capturing overall effects of variation in that gene, which will enable better utilization of candidate gene and genome--wide SNP array data.
机译：为了提高我们对复杂疾病中基因和环境因素发挥作用的作用的理解，我们需要统计方法，以旨在反映潜在的生物系统的分层方式同时模拟多种因素。我们提出了一种模拟基因作为潜在构建体的方法，这些方法由每个基因内的多变体（单核苷酸多态性，SNP）定义，使用结构方程模型（SEM）的多变量统计框架来模拟能量涉及的多元化遗传和环境因素不平衡（DasiaobesityPsila）使用来自冒号息肉盒控制研究的受试者。我们发现瘦素受体（LEPR）基因的建模构建体（由SNPS RS1137100，RS1137101，RS1805096，RS6588147）和脂肪质量相关（FTO）基因（由SNPS RS9939609，RS1421085，RS8044769定义）一起定义通过人口统计（年龄，种族，性别），体育活动，饮食和睡眠变量增加了FTO和肥胖构建之间的关联强度（Beta std = - 0.13plymn 0.06; p = 0.03）在减少模型中观察到的是，只有FTO和LEPR构造和人口变量（β STD = - 0.05 PLYMN 0.03; P = 0.08）。发现了几条间接路径，包括LEPR和物理活性构建体之间的关联（β std = - 0.15plymn 0.04; p = 0.01）。有趣的是，去除FTO揭示了LEPR和肥胖构建体之间的边际关联（β std = 0.24 plusmn 0.14; p = 0.09），当FTO在模型中时不存在。这些结果说明了在同一模型中建模多种相关基因和其他因素的重要性，这是这种方法的主要优点。此外，我们的潜在基因构建方法利用SNP之间的相关结构，同时捕获该基因变异的整体影响，这将能够更好地利用候选基因和基因组 - -Wide SNP阵列数据。

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