Abstract
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.
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Acknowledgements
JB would like to acknowledge funding from the Natural Sciences and Engineering Research Council of Canada (NSERC) and Canadian Institutes of Health Research (CIHR) (grant number 84392). JB holds the John D Cameron Endowed Chair in the Genetic Determinants of Chronic Diseases at McMaster University. We are grateful to two anonymous reviewers whose comments were very helpful and improved our manuscript. We would also like to thank Dr Russell de Souza for helpful comments.
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Neupane, B., Loeb, M., Anand, S. et al. Meta-analysis of genetic association studies under heterogeneity. Eur J Hum Genet 20, 1174–1181 (2012). https://doi.org/10.1038/ejhg.2012.75
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DOI: https://doi.org/10.1038/ejhg.2012.75
