In genetic association studies, mixed effects models have been widely used in detecting the pleiotropy effects which occur when one gene affects multiple phenotype traits. In particular, bivariate mixed effects models are useful for describing the association of a gene with a continuous trait and a binary trait. However, such models are inadequate to feature the data with response mismeasurement, a characteristic that is often overlooked. It has been well studied that in univariate settings, ignorance of mismeasurement in variables usually results in biased estimation. In this paper, we consider the setting with a bivariate outcome vector which contains a continuous component and a binary component both subject to mismeasurement. We propose an induced likelihood approach and an EM algorithm method to handle measurement error in continuous response and misclassification in binary response simultaneously. Simulation studies confirm that the proposed methods successfully remove the bias induced from the response mismeasurement.

中文翻译：

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具有双变量混合反应的遗传关联研究容易受到测量误差和分类错误的影响。

在遗传关联研究中，混合效应模型已被广泛用于检测一种基因影响多种表型性状时发生的多效性效应。特别地，双变量混合效应模型可用于描述具有连续性状和二元性状的基因的关联。但是，这样的模型不足以将数据带有错误的响应特征，而这一特征经常被忽略。众所周知，在单变量环境中，对变量错误测量的无知通常会导致估计偏差。在本文中，我们考虑具有双变量结果向量的设置，该向量包含一个连续分量和一个二进制分量，这两个分量都容易测量错误。我们提出了一种诱导似然方法和一种EM算法方法来同时处理连续响应中的测量误差和二进制响应中的错误分类。仿真研究证实，所提出的方法成功地消除了因响应错误而引起的偏差。