Molecular Epidemiology Research Group, Max Delbrück Center (MDC) for Molecular Medicine in the Helmholtz Association, Berlin, Germany.
Digital Health and Machine Learning Research Group, Hasso Plattner Institute for Digital Engineering, Potsdam, Germany.
Genet Epidemiol. 2020 Jan;44(1):26-40. doi: 10.1002/gepi.22265. Epub 2019 Nov 15.
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 , 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.
在罕见变异的遗传关联研究中，关联测试的低功效是主要挑战之一。在这项研究中，我们提出了一种新的单标记关联测试方法，称为 C-JAMP（基于 Copula 的多种表型联合分析），它基于给定遗传标记和其他协变量的多种表型的联合模型。我们在广泛的模拟研究中评估了它的性能，并将其经验性 I 型错误率和功效与现有的单变量和多变量单标记和多标记罕见变异测试进行了比较。C-JAMP 产生了无偏的遗传效应估计值和有效的调整后的检验统计量的 I 型错误率。当强烈依赖的性状被联合分析时，C-JAMP 在所有情况下（除了当高比例的变异具有中等/小效应大小且具有因果关系时）都具有最高的功效。当分析具有弱或中等依赖性的性状时，C-JAMP 或竞争方法是否具有更高的功效取决于效应大小。当 C-JAMP 与指定的 Copula 函数一起使用时，它在考虑的一些情况下仍能获得高功效。在实际数据应用中，我们使用 C-JAMP 分析了测序数据，并首次对高分子量和中分子量脂联素血浆浓度进行了全基因组关联研究。C-JAMP 鉴定了 20 个 p 值小于 10 的罕见变异，而所有其他测试都导致鉴定了具有更高 p 值的较少变异。总之，结果表明 C-JAMP 是一种强大、灵活和稳健的关联研究方法，我们确定了脂联素的新候选标记。C-JAMP 作为一个 R 包实现，可从 https://cran.r-project.org/package=CJAMP 免费获得。
