Population Genomics Program, Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ontario, Canada.
Eur J Hum Genet. 2012 Nov;20(11):1174-81. doi: 10.1038/ejhg.2012.75. Epub 2012 May 30.
In multi-cohort genetic association studies or meta-analysis, associations of genetic variants with complex traits across cohorts may be heterogeneous because of genuine genetic diversity or differential biases or errors. To detect the associations of genes with heterogeneous associations across cohorts, new global fixed-effect (FE) and random-effects (RE) meta-analytic methods have been recently proposed. These global methods had improved power over both traditional FE and RE methods under heterogeneity in limited simulation scenarios and data application, but their usefulness in a wide range of practical situations is not clear. We assessed the performance of these methods for both binary and quantitative traits in extensive simulations and applied them to a multi-cohort association study. We found that these new approaches have higher power to detect mostly the very small to small associations of common genetic variants when associations are highly heterogeneous across cohorts. They worked well when both the underlying and assumed genetic models are either multiplicative or dominant. But, they offered no clear advantage for less common variants unless heterogeneity was substantial. In conclusion, these new meta-analytic methods can be used to detect the association of genetic variants with high heterogeneity, which can then be subjected to further exploration, in multi-cohort association studies and meta-analyses.
在多队列遗传关联研究或荟萃分析中，由于真正的遗传多样性或不同的偏差或误差，遗传变异与队列间复杂性状的关联可能存在异质性。为了检测基因与跨队列异质关联的关联，最近提出了新的全局固定效应（FE）和随机效应（RE）荟萃分析方法。在有限的模拟场景和数据应用中，这些全局方法在异质性下的表现优于传统的 FE 和 RE 方法，但在广泛的实际情况下的有用性尚不清楚。我们在广泛的模拟中评估了这些方法对二分类和定量性状的性能，并将其应用于多队列关联研究。我们发现，当队列间的关联高度异质时，这些新方法在检测常见遗传变异的小到中等关联方面具有更高的效能。当潜在和假设的遗传模型都是乘法或显性时，这些方法效果良好。但是，除非异质性很大，否则对于不太常见的变异体，它们没有明显的优势。总之，这些新的荟萃分析方法可用于检测遗传变异与高度异质性的关联，然后可以在多队列关联研究和荟萃分析中进一步探索这些关联。
