Evaluation of a Two-Stage Approach in Trans-Ethnic Meta-Analysis in Genome-Wide Association Studies
Evaluation of a Two-Stage Approach in Trans-Ethnic Meta-Analysis in Genome-Wide Association Studies
复制标题
DOI：
10.1002/gepi.21963
复制
发表时间：
2016-05-01
影响因子：
2.1
通讯作者：
Liu, Ching-Ti
中科院分区：
文献类型：
--
作者：
Hong, Jaeyoung;Lunetta, Kathryn L.;Liu, Ching-Ti
全基因组关联研究（GWAS）的荟萃分析在检测人类疾病的潜在基因位点方面取得了巨大的成功。然而，将来自不同种族人群的GWAS结果纳入meta分析仍然具有挑战性，因为研究之间可能存在异质性。传统的固定效应（FE）或随机效应（RE）方法可能不适合汇总多民族GWAS结果，因为它们违反了跨研究的均匀效应假设（FE）或检测信号的功率低（RE）。最近提出的三种方法，即改进的RE （RE- he）模型、二元效应（BE）模型和贝叶斯方法（跨民族协会meta分析[MANTRA]），在对跨民族GWAS结果进行meta分析时，显示出比FE和RE方法更强大的功效，同时纳入了效应的异质性。我们提出了一种两阶段的方法来解释跨种族荟萃分析中的异质性，在荟萃分析之前，我们将具有特定队列祖先信息的研究聚类。我们将其与无先验聚类（粗）方法进行比较，在广泛的模拟研究中评估了这两种策略的I型误差和功率，以调查两阶段方法是否比粗方法提供了任何改进。我们发现两阶段方法和所有五种方法（FE， RE, RE- he， BE， MANTRA）的粗糙方法提供了良好控制的I型误差。然而，与相应的粗方法相比，两阶段方法显示BE和RE-HE的功效增加，而MANTRA和FE的功效相似，特别是当多种族GWAS结果存在异质性时。这些结果表明，两阶段方法中的先验聚类可以作为荟萃分析中有效和高效的中间步骤，以解释多民族异质性。
Meta-analysis of genome-wide association studies (GWAS) has achieved great success in detecting loci underlying human diseases. Incorporating GWAS results from diverse ethnic populations for meta-analysis, however, remains challenging because of the possible heterogeneity across studies. Conventional fixed-effects (FE) or random-effects (RE) methods may not be most suitable to aggregate multiethnic GWAS results because of violation of the homogeneous effect assumption across studies (FE) or low power to detect signals (RE). Three recently proposed methods, modified RE (RE-HE) model, binary-effects (BE) model and a Bayesian approach (Meta-analysis of Transethnic Association [MANTRA]), show increased power over FE and RE methods while incorporating heterogeneity of effects when meta-analyzing trans-ethnic GWAS results. We propose a two-stage approach to account for heterogeneity in trans-ethnic meta-analysis in which we clustered studies with cohort-specific ancestry information prior to meta-analysis. We compare this to a no-prior-clustering (crude) approach, evaluating type I error and power of these two strategies, in an extensive simulation study to investigate whether the two-stage approach offers any improvements over the crude approach. We find that the two-stage approach and the crude approach for all five methods (FE, RE, RE-HE, BE, MANTRA) provide well-controlled type I error. However, the two-stage approach shows increased power for BE and RE-HE, and similar power for MANTRA and FE compared to their corresponding crude approach, especially when there is heterogeneity across the multiethnic GWAS results. These results suggest that prior clustering in the two-stage approach can be an effective and efficient intermediate step in meta-analysis to account for the multiethnic heterogeneity.