Association Tests of Multiple Phenotypes: ATeMP.
Association Tests of Multiple Phenotypes: ATeMP.
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多种表型的关联测试：ATeMP
DOI：
10.1371/journal.pone.0140348
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发表时间：
2015
期刊：
影响因子：
3.7
通讯作者：
Zhang H
中科院分区：
文献类型：
--
作者：
Guo X;Li Y;Ding X;He M;Wang X;Zhang H
多表型的联合分析在全基因组关联研究（GWASs）中受到越来越多的关注，特别是对测量同一潜在复杂人类疾病的多种中间表型的分析。其中一种多变量方法MultiPhen （O ' Reilly et al. 2012）采用比例赔率模型对多个表型进行基因型回归，从而忽略了表型分布。尽管MultiPhen具有灵活性，但其特性和性能尚不清楚，特别是当表型分布是非正态分布时。事实上，在统计文献中众所周知，当解释变量包含测量误差时，估计是衰减的。在这项研究中，我们首先建立了MultiPhen和广义Kendall tau关联测试之间的等效关系，揭示了为什么MultiPhen可以很好地用于多种表型的联合关联分析。通过等效性，我们发现当表型异常时，MultiPhen可能失去功率。为了保持功率，我们提出了两种解决方案（atp -rn和atp -or）来改进MultiPhen，并通过广泛的模拟研究和广州双眼研究的真实案例研究证明了它们的有效性。
Joint analysis of multiple phenotypes has gained growing attention in genome-wide association studies (GWASs), especially for the analysis of multiple intermediate phenotypes which measure the same underlying complex human disorder. One of the multivariate methods, MultiPhen (O’ Reilly et al. 2012), employs the proportional odds model to regress a genotype on multiple phenotypes, hence ignoring the phenotypic distributions. Despite the flexibilities of MultiPhen, the properties and performance of MultiPhen are not well understood, especially when the phenotypic distributions are non-normal. In fact, it is well known in the statistical literature that the estimation is attenuated when the explanatory variables contain measurement errors. In this study, we first established an equivalence relationship between MultiPhen and the generalized Kendall tau association test, shedding light on why MultiPhen can perform well for joint association analysis of multiple phenotypes. Through the equivalence, we show that MultiPhen may lose power when the phenotypes are non-normal. To maintain the power, we propose two solutions (ATeMP-rn and ATeMP-or) to improve MultiPhen, and demonstrate their effectiveness through extensive simulation studies and a real case study from the Guangzhou Twin Eye Study.