Estimating odds ratios in genome scans: An approximate conditional likelihood approach
Estimating odds ratios in genome scans: An approximate conditional likelihood approach
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DOI：
10.1016/j.ajhg.2008.03.002
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发表时间：
2008-05-01
影响因子：
9.8
通讯作者：
Wright, Fred A.
中科院分区：
文献类型：
--
作者：
Ghosh, Arpita;Zou, Fei;Wright, Fred A.
在现代全基因组扫描中，使用严格的阈值来控制全基因组测试误差会扭曲估计过程，产生估计的效应大小，其平均值可能远远大于真实的效应大小。我们介绍了一种方法，根据估计的遗传效应和标准误差报告的标准统计软件，以纠正这种偏见的病例对照关联研究。我们的方法是广泛适用的，是更容易实现比竞争的方法，并可能经常被应用到已发表的研究，而无需访问原始数据。我们通过对一系列遗传模型、次要等位基因频率和遗传效应大小的广泛模拟来评估我们的方法的性能。与朴素估计过程相比，我们的方法减少了偏差和均方误差，特别是对于适度的效应大小。我们还开发了一个原则性的方法来构建置信区间的遗传效应，承认条件的统计意义。我们的方法是在特定的上下文中描述的比值比和逻辑建模，但更广泛的适用。最近公布的数据集的应用程序表明，我们的方法现代基因组扫描的相关性。
In modern whole-genome scans, the use of stringent thresholds to control the genome-wide testing error distorts the estimation process, producing estimated effect sizes that may be on average far greater in magnitude than the true effect sizes. We introduce a method, based on the estimate of genetic effect and its standard error as reported by standard statistical software, to correct for this bias in case-control association studies. Our approach is widely applicable, is far easier to implement than competing approaches, and may often be applied to published studies without access to the original data. We evaluate the performance of our approach via extensive simulations for a range of genetic models, minor allele frequencies, and genetic effect sizes. Compared to the naive estimation procedure, our approach reduces the bias and the mean squared error, especially for modest effect sizes. We also develop a principled method to construct confidence intervals for the genetic effect that acknowledges the conditioning on statistical significance. Our approach is described in the specific context of odds ratios and logistic modeling but is more widely applicable. Application to recently published data sets demonstrates the relevance of our approach to modern genome scans.