In genetic association studies of rare variants, the low power of association tests is one of the main challenges. In this study, we propose a new single-marker association test called C-JAMP (Copula-based Joint Analysis of Multiple Phenotypes), which is based on a joint model of multiple phenotypes given genetic markers and other covariates. We evaluated its performance and compared its empirical type I error and power with existing univariate and multivariate single-marker and multi-marker rare-variant tests in extensive simulation studies. C-JAMP yielded unbiased genetic effect estimates and valid type I errors with an adjusted test statistic. When strongly dependent traits were jointly analyzed, C-JAMP had the highest power in all scenarios except when a high percentage of variants were causal with moderate/small effect sizes. When traits with weak or moderate dependence were analyzed, whether C-JAMP or competing approaches had higher power depended on the effect size. When C-JAMP was applied with a misspecified copula function, it still achieved high power in some of the scenarios considered. In a real-data application, we analyzed sequencing data using C-JAMP and performed the first genome-wide association studies of high-molecular-weight and medium-molecular-weight adiponectin plasma concentrations. C-JAMP identified 20 rare variants with p-values smaller than 10-5 , while all other tests resulted in the identification of fewer variants with higher p-values. In summary, the results indicate that C-JAMP is a powerful, flexible, and robust method for association studies, and we identified novel candidate markers for adiponectin. C-JAMP is implemented as an R package and freely available from https://cran.r-project.org/package=CJAMP.

中文翻译：

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强大的稀有变异关联测试，基于多种表型的基于copula的联合分析。

在稀有变体的遗传关联研究中，关联测试的低功耗是主要挑战之一。在这项研究中，我们提出了一种新的单标记关联测试，称为C-JAMP（基于Copula的多种表型联合分析），该测试基于给定遗传标记和其他协变量的多种表型的联合模型。我们评估了它的性能，并在广泛的模拟研究中将其经验I类错误和功效与现有的单变量和多变量单标记和多标记稀有变量测试进行了比较。C-JAMP产生了无偏见的遗传效应估计值和有效的I型错误，其中检验统计数据进行了调整。当联合分析强烈依赖的性状时，在所有情况下，C-JAMP的功效最高，除非因高/中等效应大小而导致变异百分率较高。当分析具有弱或中度依赖的性状时，C-JAMP或竞争方法是否具有更高的功效取决于效果的大小。当将C-JAMP应用到具有错误指定的copula功能时，在某些考虑的场景中，它仍然可以达到很高的功率。在实际数据应用中，我们使用C-JAMP分析了测序数据，并进行了高分子量和中等分子量脂联素血浆浓度的全基因组关联研究。C-JAMP鉴定了20个p值小于10-5的稀有变异体，而所有其他测试均导致鉴定出了具有较高p值的较少变异体。总而言之，结果表明C-JAMP是一种强大的，灵活的，健壮的关联研究方法，并且我们确定了脂联素的新型候选标记。