Power and type I error results for a bias-correction approach recently shown to provide accurate odds ratios of genetic variants for the secondary phenotypes associated with primary diseases.
Power and type I error results for a bias-correction approach recently shown to provide accurate odds ratios of genetic variants for the secondary phenotypes associated with primary diseases.
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DOI：
10.1002/gepi.20611
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发表时间：
2011-11
影响因子：
2.1
通讯作者：
Shete, Sanjay
中科院分区：
文献类型：
--
作者：
Wang, Jian;Shete, Sanjay
关键词：
我们最近提出了一种偏倚校正方法，以评估与继发表型相关的遗传变异的比值比（OR）的准确估计，其中继发表型与原发疾病相关，基于为研究原发疾病而收集的原始病例对照数据。如本文所述，我们进一步研究了所提出方法的I类错误概率和功效，并将结果与logistic回归分析（调整或不调整原发疾病状态）所得结果进行了比较。我们进行了一项模拟研究的基础上，频率匹配的病例对照研究，就次要表型的利益。我们检查了从偏倚校正方法获得的校正OR的自然对数的经验分布，发现它在零假设下呈正态分布。在模拟研究结果的基础上，我们发现，调整或不调整原发疾病状态的逻辑回归方法在检测继发表型相关变异和高度膨胀的I型错误概率方面具有较低的功效，而我们的方法在识别SNP-继发表型关联方面更强大，并且具有更好的控制I型错误概率。
We recently proposed a bias correction approach to evaluate accurate estimation of the odds ratio (OR) of genetic variants associated with a secondary phenotype, in which the secondary phenotype is associated with the primary disease, based on the original case-control data collected for the purpose of studying the primary disease. As reported in this communication, we further investigated the type I error probabilities and powers of the proposed approach, and compared the results to those obtained from logistic regression analysis (with or without adjustment for the primary disease status). We performed a simulation study based on a frequency-matching case-control study with respect to the secondary phenotype of interest. We examined the empirical distribution of the natural logarithm of the corrected OR obtained from the bias correction approach and found it to be normally distributed under the null hypothesis. On the basis of the simulation study results, we found that the logistic regression approaches that adjust or do not adjust for the primary disease status had low power for detecting secondary phenotype associated variants and highly inflated type I error probabilities, whereas our approach was more powerful for identifying the SNP-secondary phenotype associations and had better-controlled type I error probabilities.
影响因子：
2.1
作者：
Monsees, Genevieve M.;Tamimi, Rulla M.;Kraft, Peter
通讯作者：
Kraft, Peter
DOI：
10.1126/science.1156409
发表时间：
2008-11-07
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Altshuler D;Daly MJ;Lander ES
通讯作者：
Lander ES
影响因子：
2.1
作者：
Wang, Jian;Shete, Sanjay
通讯作者：
Shete, Sanjay