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首页> 外文期刊>BMC Genetics >Retrospective analysis of main and interaction effects in genetic association studies of human complex traits
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Retrospective analysis of main and interaction effects in genetic association studies of human complex traits

机译：人类复杂性状遗传关联研究中主要作用和相互作用的回顾性分析

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Background The etiology of multifactorial human diseases involves complex interactions between numerous environmental factors and alleles of many genes. Efficient statistical tools are demanded in identifying the genetic and environmental variants that affect the risk of disease development. This paper introduces a retrospective polytomous logistic regression model to measure both the main and interaction effects in genetic association studies of human discrete and continuous complex traits. In this model, combinations of genotypes at two interacting loci or of environmental exposure and genotypes at one locus are treated as nominal outcomes of which the proportions are modeled as a function of the disease trait assigning both main and interaction effects and with no assumption of normality in the trait distribution. Performance of our method in detecting interaction effect is compared with that of the case-only model. Results Results from our simulation study indicate that our retrospective model exhibits high power in capturing even relatively small effect with reasonable sample sizes. Application of our method to data from an association study on the catalase -262C/T promoter polymorphism and aging phenotypes detected significant main and interaction effects for age-group and allele T on individual's cognitive functioning and produced consistent results in estimating the interaction effect as compared with the popular case-only model. Conclusion The retrospective polytomous logistic regression model can be used as a convenient tool for assessing both main and interaction effects in genetic association studies of human multifactorial diseases involving genetic and non-genetic factors as well as categorical or continuous traits.
机译：背景技术人类多因素疾病的病因涉及许多环境因素与许多基因的等位基因之间的复杂相互作用。需要有效的统计工具来确定影响疾病发展风险的遗传和环境变异。本文介绍了一种回顾性的多因素逻辑回归模型，用于测量人类离散和连续复杂性状的遗传关联研究中的主要作用和相互作用。在该模型中，两个相互作用位点的基因型或环境暴露与一个位点的基因型的组合被视为名义结果，其比例被建模为疾病特征的函数，既分配主要作用又相互作用，并且没有正常假设在特征分布中。将我们的方法在检测交互效果方面的性能与仅案例模型的性能进行了比较。结果我们的模拟研究结果表明，我们的回顾性模型在以合理的样本量捕获相对较小的影响方面也表现出很高的威力。我们的方法在过氧化氢酶-262C / T启动子多态性与衰老表型的关联研究数据中的应用，检测到年龄组和等位基因T对个体的认知功能具有重要的主要作用和相互作用，并在估计相互作用效果方面取得了一致的结果流行的仅案例模式。结论回顾性多因素逻辑回归模型可作为一种方便的工具，用于评估涉及遗传和非遗传因素以及分类或连续性状的人类多因素疾病的遗传关联研究中的主要作用和相互作用。

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