Pleiotropy, the effect of one variant on multiple traits, is widespread in complex diseases. Joint analysis of multiple traits can improve statistical power to detect genetic variants and uncover the underlying genetic mechanism. Currently, a large number of existing methods target one common variant or only rare variants. Increasing evidence shows that complex diseases are caused by common and rare variants. Here we propose a region-based method to test both rare and common variant associated multiple traits based on variable reduction method (abbreviated as MULVR). However, in the presence of noise traits, the MULVR method may lose power, so we propose the MULVR-O method, which jointly analyses the optimal number of traits associated with genetic variants by the MULVR method, to guard against the effect of noise traits. Extensive simulation studies show that our proposed method (MULVR-O) is applied to not only multiple quantitative traits but also qualitative traits, and is more powerful than several other comparison methods in most scenarios. An application to the two genes (SHBG and CHRM3) and two phenotypes (systolic blood pressure and diastolic blood pressure) from the GAW19 dataset illustrates that our proposed methods (MULVR and MULVR-O) are feasible and efficient as a region-based method.

中文翻译：

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基于变量归约法的具有多个性状的稀有和常见变异的关联分析。

多效性是一种变体对多种性状的影响，在复杂疾病中广泛存在。多个特征的联合分析可以提高统计能力，以检测遗传变异并揭示潜在的遗传机制。当前，大量现有方法针对一种常见变体或仅罕见变体。越来越多的证据表明，复杂的疾病是由常见和罕见的变异引起的。在这里，我们提出了一种基于区域的方法，基于可变约简方法（简称为MULVR）来测试稀有和常见变异相关的多个性状。但是，在存在噪声性状的情况下，MULVR方法可能会失去功能，因此我们建议使用MULVR-O方法，该方法通过MULVR方法共同分析与遗传变异相关的最佳性状数量，以防止噪声性状的影响。大量的仿真研究表明，我们提出的方法（MULVR-O）不仅适用于多个定量性状，而且还适用于定性性状，并且在大多数情况下比其他几种比较方法更有效。对来自GAW19数据集的两个基因（SHBG和CHRM3）和两个表型（收缩压和舒张压）的应用表明，我们提出的方法（MULVR和MULVR-O）作为基于区域的方法既可行又有效。