Interpreting meta-analyses of genome-wide association studies.
Interpreting meta-analyses of genome-wide association studies.
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DOI：
10.1371/journal.pgen.1002555
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发表时间：
2012
期刊：
影响因子：
4.5
通讯作者：
Eskin E
中科院分区：
文献类型：
--
作者：
Han B;Eskin E
荟萃分析是一种越来越受欢迎的工具，用于将多个全基因组关联研究结合在一个分析中，以确定具有小效应量的关联。荟萃分析中研究之间的效应量可能不同，这些差异或异质性可能由许多因素引起。如果在荟萃分析的结果中观察到异质性，解释异质性的原因很重要，因为正确的解释可以更好地理解疾病和更有效的重复研究设计。然而，解释异质性结果是困难的。检查研究的关联p值的标准方法不能有效预测每个研究中是否存在效应。在本文中，我们提出了一个框架，便于解释的荟萃分析的结果。我们的框架基于一个新的统计数据，该统计数据代表了每个研究中存在效果的后验概率，该概率是利用交叉研究信息估计的。对真实的数据的模拟和应用表明，我们的框架可以有效地分离预测有效果的研究，预测没有效果的研究，以及动力不足的模糊研究。除了有助于解释，新的框架还允许我们开发一个新的联想测试程序，考虑到存在的效果。全基因组关联研究是识别与疾病相关的遗传变异的有效手段。虽然许多相关的基因座已被确定，这些基因座只占一小部分的遗传贡献的疾病。剩下的贡献可能是由效应量非常小的基因座所占，效应量非常小，以至于需要数万个样本来识别它们。由于收集如此大的样本进行研究的成本很高，一个实际的替代方案是将多个独立的研究结合在一个称为荟萃分析的分析中。然而，许多因素，如遗传或环境因素，在荟萃分析中结合的研究之间可能存在差异。这些因素可能导致因果变量的效应大小在研究之间存在差异，这种现象称为异质性。如果荟萃分析的数据存在异质性，解释荟萃分析结果是一项重要而困难的任务。在本文中，我们提出了一种方法，有助于这种解释，除了一个新的关联测试程序，是强大的异质性存在时。
Meta-analysis is an increasingly popular tool for combining multiple genome-wide association studies in a single analysis to identify associations with small effect sizes. The effect sizes between studies in a meta-analysis may differ and these differences, or heterogeneity, can be caused by many factors. If heterogeneity is observed in the results of a meta-analysis, interpreting the cause of heterogeneity is important because the correct interpretation can lead to a better understanding of the disease and a more effective design of a replication study. However, interpreting heterogeneous results is difficult. The standard approach of examining the association p-values of the studies does not effectively predict if the effect exists in each study. In this paper, we propose a framework facilitating the interpretation of the results of a meta-analysis. Our framework is based on a new statistic representing the posterior probability that the effect exists in each study, which is estimated utilizing cross-study information. Simulations and application to the real data show that our framework can effectively segregate the studies predicted to have an effect, the studies predicted to not have an effect, and the ambiguous studies that are underpowered. In addition to helping interpretation, the new framework also allows us to develop a new association testing procedure taking into account the existence of effect. Genome-wide association studies are an effective means of identifying genetic variants that are associated with diseases. Although many associated loci have been identified, those loci account for only a small fraction of the genetic contribution to the disease. The remaining contribution may be accounted by loci with very small effect sizes, so small that tens of thousands of samples are needed to identify them. Since it is costly to conduct a study collecting such a large sample, a practical alternative is to combine multiple independent studies in a single analysis called meta-analysis. However, many factors, such as genetic or environmental factors, can differ between the studies combined in a meta-analysis. These factors can cause the effect size of the causal variant to differ between the studies, a phenomenon called heterogeneity. If heterogeneity exists in the data of a meta-analysis, interpreting the meta-analysis results is an important but difficult task. In this paper, we propose a method that helps such interpretation, in addition to a new association testing procedure that is powerful when heterogeneity exists.
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影响因子：
30.8
作者：
通讯作者：
--
DOI：
10.1056/nejmra0808700
发表时间：
2009-04-23
期刊：
The New England journal of medicine
影响因子：
--
作者：
Hardy J;Singleton A
通讯作者：
Singleton A
影响因子：
30.8
作者：
Helgadottir, A;Manolescu, A;Stefansson, K
通讯作者：
Stefansson, K
DOI：
10.1016/0197-2456(86)90046-2
发表时间：
1986-09-01
期刊：
CONTROLLED CLINICAL TRIALS
影响因子：
--
作者：
DERSIMONIAN, R;LAIRD, N
通讯作者：
LAIRD, N
影响因子：
3.5
作者：
de Bakker, Paul I. W.;Ferreira, Manuel A. R.;Voight, Benjamin F.
通讯作者：
Voight, Benjamin F.