In genetic association analysis, several relevant phenotypes or multivariate traits with different types of components are usually collected to study complex or multifactorial diseases. Over the past few years, jointly testing for association between multivariate traits and multiple genetic variants has become more popular because it can increase statistical power to identify causal genes in pedigree- or population-based studies. However, most of the existing methods mainly focus on testing genetic variants associated with multiple continuous phenotypes. In this investigation, we develop a framework for identifying the pleiotropic effects of genetic variants on multivariate traits by using collapsing and kernel methods with pedigree- or population-structured data. The proposed framework is applicable to the burden test, the kernel test, and the omnibus test for autosomes and the X chromosome. The proposed multivariate trait association methods can accommodate continuous phenotypes or binary phenotypes and further can adjust for covariates. Simulation studies show that the performance of our methods is satisfactory with respect to the empirical type I error rates and power rates in comparison with the existing methods.

中文翻译：

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使用折叠和核方法将多元性状与遗传变异相关联，并进行基于谱系或种群的研究

在遗传关联分析中，通常收集具有不同类型成分的几种相关表型或多变量性状来研究复杂或多因素疾病。在过去几年中，联合测试多变量性状和多个遗传变异之间的关联变得越来越流行，因为它可以提高统计能力，以在基于谱系或基于人群的研究中识别因果基因。然而，大多数现有方法主要侧重于测试与多个连续表型相关的遗传变异。在这项调查中，我们开发了一个框架，通过使用具有谱系或种群结构数据的折叠和核方法来识别遗传变异对多元性状的多效性影响。提出的框架适用于负担测试、内核测试、以及常染色体和 X 染色体的综合测试。所提出的多元性状关联方法可以适应连续表型或二元表型，并且可以进一步调整协变量。模拟研究表明，与现有方法相比，我们的方法在经验 I 类错误率和功率率方面的性能令人满意。