A comparison of methods sensitive to interactions with small main effects.
A comparison of methods sensitive to interactions with small main effects.
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DOI：
10.1002/gepi.21622
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发表时间：
2012-05
影响因子：
2.1
通讯作者：
Culverhouse, Robert C.
中科院分区：
文献类型：
--
作者：
Culverhouse, Robert C.
关键词：
许多遗传变异已被成功地确定为复杂的性状，但这些遗传因素只占一小部分的预测方差，由于遗传因素。这导致了对其他方法的兴趣增加，以解释表型的“缺失”遗传贡献，包括联合基因-基因或基因-环境分析。已经提出了各种分析方法。然而，它们很少被系统地比较。为了便于进行这种比较，多因素重复性降低（MDR）的开发人员模拟了96个双位点模型中每个模型的100个数据重复，显示出来自任一位点的边际效应可以忽略不计（6个基本遗传模型中每个模型有16个变异）。基于二分表型的遗传模型具有不同的次要等位基因频率和与基因型相关的2至8个不同的风险水平。基本模型进行了修改，包括“噪音”的组合缺失的数据，基因分型错误，遗传异质性，和表型。本研究比较了三种方法的性能设计敏感的联合效应（MDR，支持向量机（SVM），和限制分区方法（RPM））对这些模拟数据。在这些测试中，对于6类遗传模型中的每一类，RPM始终优于其他两种方法。相比之下，其他两种方法之间的比较结果喜忧参半。当真实模型只有几个分离良好的风险类别时，MDR优于SVM;而SVM在更复杂的模型上优于MDR。在这些方法中，只有MDR具有开发良好的用户界面。
Numerous genetic variants have been successfully identified for complex traits, yet these genetic factors only account for a modest portion of the predicted variance due to genetic factors. This has led to increased interest in other approaches to account for the “missing” genetic contributions to phenotype, including joint gene-gene or gene-environment analysis. A variety of methods for such analysis have been advocated. However, they have seldom been compared systematically. To facilitate such comparisons, the developers of the Multifactor Dimensionality Reduction (MDR) simulated 100 data replicates for each of 96 two-locus models displaying negligible marginal effects from either locus (16 variations on each of 6 basic genetic models). The genetic models, based on a dichotomous phenotype, had varying minor allele frequencies and from 2 to 8 distinct risk levels associated with genotype. The basic models were modified to include “noise” from combinations of missing data, genotyping error, genetic heterogeneity, and phenocopies. This study compares the performance of three methods designed to be sensitive to joint effects (MDR, Support Vector Machines (SVM), and the Restricted Partition Method (RPM)) on these simulated data. In these tests, the RPM consistently outperformed the other two methods for each of the 6 classes of genetic models. In contrast, the comparison between other two methods had mixed results. The MDR outperformed the SVM when the true model had only a few, well-separated risk classes; while the SVM outperformed the MDR on more complicated models. Of these methods, only MDR has a well-developed user interface.
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影响因子：
1.8
作者：
Culverhouse, R.
通讯作者：
Culverhouse, R.
影响因子：
2.1
作者：
Martin, ER;Ritchie, MD;Moore, JH
通讯作者：
Moore, JH
影响因子：
9.8
作者：
Culverhouse, R;Suarez, BK;Reich, T
通讯作者：
Reich, T
影响因子：
9.8
作者：
Ritchie, MD;Hahn, LW;Moore, JH
通讯作者：
Moore, JH
影响因子：
30.8
作者：
Anholt, RRH;Dilda, CL;Mackay, TFC
通讯作者：
Mackay, TFC