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首页> 外文期刊>Annals of Human Genetics >Application of multi-locus analytical methods to identify interacting loci in case-control studies.
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Application of multi-locus analytical methods to identify interacting loci in case-control studies.

机译：多位点分析方法在病例对照研究中确定相互作用位点的应用。

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To identify interacting loci in genetic epidemiological studies the application of multi-locus methods of analysis is warranted. Several more advanced classification methods have been developed in the past years, including multiple logistic regression, sum statistics, logic regression, and the multifactor dimensionality reduction method. The objective of our study was to apply these four multi-locus methods to simulated case-control datasets that included a variety of underlying statistical two-locus interaction models, in order to compare the methods and evaluate their strengths and weaknesses. The results showed that the ability to identify the interacting loci was generally good for the sum statistic method, the logic regression and MDR. The performance of the logistic regression was more dependent on the underlying model and multiple comparison adjustment procedure. However, identification of the interacting loci in a model with two two-locus interactions of common disease alleles with relatively small effects was impaired in all methods. Several practical and methodological issues that can be considered in the application of these methods, and that may warrant further research, are identified and discussed.
机译：为了在遗传流行病学研究中识别相互作用的基因座，需要使用多基因座分析方法。过去几年中，已经开发了几种更高级的分类方法，包括多元逻辑回归，总和统计，逻辑回归和多因素降维方法。我们的研究目标是将这四种多场所方法应用于包含各种潜在统计两场所交互模型的模拟病例对照数据集，以便比较这些方法并评估其优缺点。结果表明，识别相互作用基因座的能力通常对于总和统计方法，逻辑回归和MDR具有良好的优势。逻辑回归的性能更多地取决于基础模型和多重比较调整程序。但是，在所有方法中，对具有相对较小影响的常见疾病等位基因的两个两基因座相互作用的模型中相互作用位点的鉴定都是不利的。确定并讨论了在应用这些方法时可以考虑的一些实际问题和方法论问题，这些问题可能需要进一步研究。

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