A Comparison of Analytical Methods for Genetic Association Studies
A Comparison of Analytical Methods for Genetic Association Studies
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DOI：
10.1002/gepi.20345
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发表时间：
2008-12-01
影响因子：
2.1
通讯作者：
Ritchie, Marylyn D.
中科院分区：
文献类型：
--
作者：
Motsinger-Reif, Alison A.;Reif, David M.;Ritchie, Marylyn D.
在过去的十年中，遗传信息的爆炸为遗传关联研究提出了分析挑战。随着每个个体检测的遗传变量数量的增加，在分析过程中必须进行变量选择和统计建模任务。虽然这些任务可以单独执行，但要选择有效建模数据的有意义的变量，就必须将它们耦合起来。由于研究中表型的复杂性和复杂的潜在遗传病因学，这一挑战更加严峻。为了解决这个问题，已经开发了许多新的方法。在目前的研究中，我们比较了六种分析方法的性能，以检测一系列遗传模型中的主效应和基因-基因相互作用。比较了多因素降维、语法进化神经网络、随机森林、聚焦交互测试框架、逐步logistic回归和显式logistic回归。正如人们所期望的那样，每个方法的相对成功是依赖于上下文的。本研究展示了每种方法的优点和缺点，并说明了持续方法开发的重要性。Genet.流行病学32：767-778，2008. (C)2008 Wiley-Liss，Inc.
The explosion of genetic information over the last decade presents an analytical challenge for genetic association Studies. As the number of genetic variables examined per individual increases, both variable selection and statistical modeling tasks must be performed during analysis. While these tasks Could be performed separately, coupling them is necessary to select meaningful variables that effectively model the data. This challenge is heightened due to the complex nature of the phenotypes under Study and the complex underlying genetic etiologies. To address this problem, a number of novel methods have been developed. In the current study, we compare the performance of six analytical approaches to detect both main effects and gene-gene interactions in a range of genetic models. Multifactor dimensionality reduction, grammatical evolution neural networks, random forests, focused interaction testing framework, step-wise logistic regression, and explicit logistic regression were compared. As one might expect, the relative Success of each method is context dependent. This study demonstrates the strengths and weaknesses of each method and illustrates the importance of continued methods development. Genet. Epidemiol. 32:767-778, 2008. (C) 2008 Wiley-Liss, Inc.