An application of conditional logistic regression and multifactor dimensionality reduction for detecting gene-gene interactions on risk of myocardial infarction: the importance of model validation.
An application of conditional logistic regression and multifactor dimensionality reduction for detecting gene-gene interactions on risk of myocardial infarction: the importance of model validation.
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DOI：
10.1186/1471-2105-5-49
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发表时间：
2004-04-30
影响因子：
3
通讯作者：
Moore JH
中科院分区：
文献类型：
--
作者：
Coffey CS;Hebert PR;Ritchie MD;Krumholz HM;Gaziano JM;Ridker PM;Brown NJ;Vaughan DE;Moore JH
为了研究血管紧张素转换酶（ACE）插入/缺失、纤溶酶原激活物抑制剂-1 (PAI-1) 4G/5G和组织纤溶酶原激活物（t-PA）插入/缺失基因多态性对心肌梗死风险的相互作用，使用来自医师健康研究的343对匹配病例对照的数据。我们使用条件逻辑回归和多因素降维（MDR）方法检查数据。MDR方法的一个优点是它为验证提供了一个内部预测误差。我们总结了我们对模型验证的内部预测误差的使用。两种方法的总体结果是一致的，都表明ACE I/D和PAI-1 4G/5G多态性之间存在相互作用。然而，使用十倍交叉验证，最终MDR模型的46%预测误差并不显著低于偶然预期。最初观察到的重要相互作用不能验证，可能表示类型I错误。随着数据驱动的分析方法不断发展并用于检查复杂的遗传相互作用，强调模型验证将变得越来越重要，以确保重大影响代表真实的关系，而不是偶然的发现。
To examine interactions among the angiotensin converting enzyme (ACE) insertion/deletion, plasminogen activator inhibitor-1 (PAI-1) 4G/5G, and tissue plasminogen activator (t-PA) insertion/deletion gene polymorphisms on risk of myocardial infarction using data from 343 matched case-control pairs from the Physicians Health Study. We examined the data using both conditional logistic regression and the multifactor dimensionality reduction (MDR) method. One advantage of the MDR method is that it provides an internal prediction error for validation. We summarize our use of this internal prediction error for model validation. The overall results for the two methods were consistent, with both suggesting an interaction between the ACE I/D and PAI-1 4G/5G polymorphisms. However, using ten-fold cross validation, the 46% prediction error for the final MDR model was not significantly lower than that expected by chance. The significant interaction initially observed does not validate and may represent a type I error. As data-driven analytic methods continue to be developed and used to examine complex genetic interactions, it will become increasingly important to stress model validation in order to ensure that significant effects represent true relationships rather than chance findings.
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影响因子：
1.8
作者：
Moore, JH
通讯作者：
Moore, JH
影响因子：
9.8
作者：
Ritchie, MD;Hahn, LW;Moore, JH
通讯作者：
Moore, JH
DOI：
10.1073/pnas.91.9.3662
发表时间：
1994-04-26
影响因子：
11.1
作者：
RUIZ, J;BLANCHE, H;FROGUEL, P
通讯作者：
FROGUEL, P
DOI：
10.1161/01.atv.11.1.183
发表时间：
1991-01-01
期刊：
ARTERIOSCLEROSIS AND THROMBOSIS
影响因子：
--
作者：
DAWSON, S;HAMSTEN, A;HUMPHRIES, S
通讯作者：
HUMPHRIES, S
影响因子：
5.8
作者：
Hahn, LW;Ritchie, MD;Moore, JH
通讯作者：
Moore, JH