Identifying Pleiotropic Genes in Genome-Wide Association Studies for Multivariate Phenotypes with Mixed Measurement Scales.
Identifying Pleiotropic Genes in Genome-Wide Association Studies for Multivariate Phenotypes with Mixed Measurement Scales.
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DOI：
10.1371/journal.pone.0169893
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发表时间：
2017
期刊：
影响因子：
3.7
通讯作者：
Buu A
中科院分区：
文献类型：
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作者：
Yang JJ;Williams LK;Buu A
我们提出了一个多变量全基因组关联测试混合连续，二进制和有序表型。利用潜在反应模型估计不同测量尺度下表型之间的相关性，从而有效估计零假设下Fisher组合统计量的经验分布。仿真研究表明，本文提出的相关估计方法具有较高的精度。更重要的是，我们的方法保守地估计了检验统计量的方差，从而控制了第一类错误率。仿真还表明，在控制I型误差的同时，所提出的测试保持在非常接近于基于已知潜在表型的理想分析的水平上。相比之下，传统的方法——对所有观察到的表型进行二分类或将它们视为连续变量——可能会降低功率或使用不适合数据的线性回归模型。此外，对成瘾研究：遗传与环境（SAGE）数据库的统计分析表明，对多种表型进行多变量测试可以增加识别标记的能力，否则，使用边际测试可能无法选择这些标记。该方法还提供了一种新的方法来分析Fagerström尼古丁依赖测试作为全基因组关联研究中的多变量表型。
We propose a multivariate genome-wide association test for mixed continuous, binary, and ordinal phenotypes. A latent response model is used to estimate the correlation between phenotypes with different measurement scales so that the empirical distribution of the Fisher’s combination statistic under the null hypothesis is estimated efficiently. The simulation study shows that our proposed correlation estimation methods have high levels of accuracy. More importantly, our approach conservatively estimates the variance of the test statistic so that the type I error rate is controlled. The simulation also shows that the proposed test maintains the power at the level very close to that of the ideal analysis based on known latent phenotypes while controlling the type I error. In contrast, conventional approaches–dichotomizing all observed phenotypes or treating them as continuous variables–could either reduce the power or employ a linear regression model unfit for the data. Furthermore, the statistical analysis on the database of the Study of Addiction: Genetics and Environment (SAGE) demonstrates that conducting a multivariate test on multiple phenotypes can increase the power of identifying markers that may not be, otherwise, chosen using marginal tests. The proposed method also offers a new approach to analyzing the Fagerström Test for Nicotine Dependence as multivariate phenotypes in genome-wide association studies.