Wang Kai
Department of Biostatistics, University of Iowa, Iowa City, Iowa, United States of America.
PLoS One. 2014 Sep 15;9(9):e106918. doi: 10.1371/journal.pone.0106918. eCollection 2014.
Complex disorders are typically characterized by multiple phenotypes. Analyzing these phenotypes jointly is expected to be more powerful than dealing with one of them at a time. A recent approach (O'Reilly et al. 2012) is to regress the genotype at a SNP marker on multiple phenotypes and apply the proportional odds model. In the current research, we introduce an explicit expression for the score test statistic and its non-centrality parameter that determines its power. Same simulation studies as those reported in Galesloot et al. (2014) were conducted to assess its performance. We demonstrate by theoretical arguments and simulation studies that, despite its potential usefulness for multiple phenotypes, the proportional odds model method can be less powerful than regular methods for univariate traits. We also introduce an implementation of the proposed score statistic in an R package named iGasso.
复杂疾病通常具有多种表型特征。联合分析这些表型有望比一次处理其中一个表型更具效力。一种最新方法(O'Reilly等人,2012年)是在多个表型的单核苷酸多态性(SNP)标记处对基因型进行回归分析,并应用比例优势模型。在当前研究中,我们给出了得分检验统计量及其非中心参数的显式表达式,该非中心参数决定了其效力。我们进行了与Galesloot等人(2014年)报告中相同的模拟研究,以评估其性能。我们通过理论论证和模拟研究表明,尽管比例优势模型方法对多种表型可能有用,但其效力可能低于针对单变量性状的常规方法。我们还在一个名为iGasso的R包中引入了所提出的得分统计量的实现方法。