Testing Genetic Pleiotropy with GWAS Summary Statistics for Marginal and Conditional Analyses
Testing Genetic Pleiotropy with GWAS Summary Statistics for Marginal and Conditional Analyses
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DOI：
10.1534/genetics.117.300347
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发表时间：
2017-12-01
期刊：
影响因子：
3.3
通讯作者：
Pan, Wei
中科院分区：
文献类型：
--
作者：
Deng, Yangqing;Pan, Wei
人们对测试遗传多效性越来越感兴趣，这是当一个单一的遗传变异影响多个性状。已经提出了几种方法;然而，这些方法具有一些局限性。首先，所有提出的方法都是基于使用个体水平的基因型和表型数据;相比之下，出于逻辑和其他原因，单变量SNP-性状关联的汇总统计通常仅基于荟萃或大型分析的大型全基因组关联研究（GWAS）数据。其次，现有的测试是基于边缘多效性，这不能区分直接和间接的关联，一个单一的遗传变异与多个性状之间的相关性，由于性状。因此，考虑条件分析是有用的，在条件分析中，一个子集的特征被调整为另一个子集的特征。例如，尽管他汀类药物治疗显著降低了低密度脂蛋白胆固醇（LDL），但一些患者仍然保持较高的残余心血管风险，对于这些患者，降低甘油三酯（TG）水平可能有帮助。为此目的，为了鉴定新的治疗靶点，在调整LDL和TG后鉴定对LDL和TG具有多效性效应的遗传变体将是有用的;否则，由边缘模型检测到的遗传变体的多效性效应可能仅仅是由于其仅与LDL相关，考虑到两种类型的脂质之间的众所周知的相关性。在这里，我们开发了一个新的多效性测试程序，仅基于GWAS汇总统计量，可以应用于边际分析和条件分析。虽然主要的技术发展是基于已公布的unionintersection测试方法，但需要注意指定条件模型，以避免无效的统计估计和推断。除了先前使用的似然比检验，我们还建议使用广义估计方程下的工作独立性模型的鲁棒性推断。我们提供了基于模拟和真实的数据的数值示例，包括分别基于类似于100，000和类似于189，000样本的两个大型脂质GWAS汇总关联数据集，以证明边缘分析和条件分析之间的差异，以及我们新方法的有效性。
There is growing interest in testing genetic pleiotropy, which is when a single genetic variant influences multiple traits. Several methods have been proposed; however, these methods have some limitations. First, all the proposed methods are based on the use of individual-level genotype and phenotype data; in contrast, for logistical, and other, reasons, summary statistics of univariate SNP-trait associations are typically only available based on meta-or mega-analyzed large genome-wide association study (GWAS) data. Second, existing tests are based on marginal pleiotropy, which cannot distinguish between direct and indirect associations of a single genetic variant with multiple traits due to correlations among the traits. Hence, it is useful to consider conditional analysis, in which a subset of traits is adjusted for another subset of traits. For example, in spite of substantial lowering of low-density lipoprotein cholesterol (LDL) with statin therapy, some patients still maintain high residual cardiovascular risk, and, for these patients, it might be helpful to reduce their triglyceride (TG) level. For this purpose, in order to identify new therapeutic targets, it would be useful to identify genetic variants with pleiotropic effects on LDL and TG after adjusting the latter for LDL; otherwise, a pleiotropic effect of a genetic variant detected by a marginal model could simply be due to its association with LDL only, given the well-known correlation between the two types of lipids. Here, we develop a new pleiotropy testing procedure based only on GWAS summary statistics that can be applied for both marginal analysis and conditional analysis. Although the main technical development is based on published unionintersection testing methods, care is needed in specifying conditional models to avoid invalid statistical estimation and inference. In addition to the previously used likelihood ratio test, we also propose using generalized estimating equations under the working independence model for robust inference. We provide numerical examples based on both simulated and real data, including two large lipid GWAS summary association datasets based on similar to 100,000 and similar to 189,000 samples, respectively, to demonstrate the difference between marginal and conditional analyses, as well as the effectiveness of our new approach.