通过半参数多水平混合模型对基于集合的遗传关联推断进行有效检验和效应量估计：应用于冠状动脉疾病的全基因组关联研究
Efficient testing and effect size estimation for set-based genetic association inference via semiparametric multilevel mixture modeling: Application to a genome-wide association study of coronary artery disease
摘要：
在遗传关联研究中，具有极小等位基因频率的罕见变异在复杂性状中起着至关重要的作用，并且开发了联合评估单核苷酸多态性（SNPs）组的影响的基于集合的测试方法以提高关联测试的功效。然而，由于等位基因频率非常小，这些测试的功效仍然受到严重限制，并且精确估计单个SNP的效应大小基本上是不可能的。在这篇文章中，我们提供了一个有效的基于集合的推理框架，解决了两个重要的问题，同时基于贝叶斯半参数多级混合模型。我们建议使用多层次的层次模型，将集特定的效果和变量特定的效果的变化，并应用最佳发现程序（ODP），实现了最大的整体功率在多个显着性检验。此外，我们提供了贝叶斯最优的“基于集合”的估计的经验分布的效果大小。通过应用于冠状动脉疾病（CAD）的全基因组关联研究，并通过模拟研究，证明了所提出的方法的效率。这些结果表明，CAD可能存在许多具有大效应量的罕见变异，ODP检测到的显著集的数量远大于现有方法。
英文摘要：
In genetic association studies, rare variants with extremely small allele frequency play a crucial role in complex traits, and the set-based testing methods that jointly assess the effects of groups of single nucleotide polymorphisms (SNPs) were developed to improve powers for the association tests. However, the powers of these tests are still severely limited due to the extremely small allele frequency, and precise estimations for the effect sizes of individual SNPs are substantially impossible. In this article, we provide an efficient set-based inference framework that addresses the two important issues simultaneously based on a Bayesian semiparametric multilevel mixture model. We propose to use the multilevel hierarchical model that incorporate the variations in set-specific effects and variant-specific effects, and to apply the optimal discovery procedure (ODP) that achieves the largest overall power in multiple significance testing. In addition, we provide Bayesian optimal "set-based" estimator of the empirical distribution of effect sizes. Efficiency of the proposed methods is demonstrated through application to a genome-wide association study of coronary artery disease (CAD), and through simulation studies. These results suggested there could be a lot of rare variants with large effect sizes for CAD, and the number of significant sets detected by the ODP was much greater than those by existing methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
