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On individual genome-wide association studies and their meta-analysis

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Abstract

Individual genome-wide association (GWA) studies and their meta-analyses represent two approaches for identifying genetic loci associated with complex diseases/traits. Inconsistent findings and non-replicability between individual GWA studies and meta-analyses are commonly observed, hence posing the critical question as to how to interpret their respective results properly. In this study, we performed a series of simulation studies to investigate and compare the statistical properties of the two approaches. Our results show that (1) as expected, meta-analysis of larger sample size is more powerful than individual GWA studies under the ideal setting of population homogeneity among individual studies; (2) under the realistic setting of heterogeneity among individual studies, detection of heterogeneity is usually difficult and meta-analysis (even with the random-effects model) may introduce elevated false positive and/or negative rates; (3) despite relatively small sample size, well-designed individual GWA study has the capacity to identify novel loci for complex traits; (4) replicability between meta-analysis and independent individual studies or between independent meta-analyses is limited, and thus inconsistent findings are not unexpected.

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Acknowledgments

The study was partially supported by grants from National Natural Science Foundation of China project (31100902), grants from NIH (P50AR055081, R01AG026564, R01AR050496, RC2DE020756, R01AR057049 and R03TW008221) and Edward G. Schlieder Endowed to Tulane University, and benefited from support of Shanghai Leading Academic Discipline Project (S30501) and young teacher startup fund from University of Shanghai for Science and Technology (slg11018 and slg11019). We thank the reviewers for their constructive comments and suggestions. The authors have declared that no competing interests exist.

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  1. Center of System Biomedical Sciences, University of Shanghai for Science and Technology, Shanghai, 200093, People’s Republic of China

    Yu-Fang Pei, Lei Zhang & Hong-Wen Deng

  2. Center for Bioinformatics and Genomics, Department of Biostatistics and Bioinformatics, School of Public Health and Tropical Medicine, Tulane University, 1440 Canal St., Suite 2001, New Orleans, LA, 70112, USA

    Yu-Fang Pei, Lei Zhang, Yu-Ping Wang & Hong-Wen Deng

  3. Department of Basic Medical Science, University of Missouri-Kansas City, Kansas City, MO, 64108, USA

    Christopher J. Papasian

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  1. Yu-Fang Pei
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Pei, YF., Zhang, L., Papasian, C.J. et al. On individual genome-wide association studies and their meta-analysis. Hum Genet 133, 265–279 (2014). https://doi.org/10.1007/s00439-013-1366-4

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