期刊
HUMAN GENETICS
卷 133, 期 3, 页码 265-279出版社
SPRINGER
DOI: 10.1007/s00439-013-1366-4
关键词
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资金
- National Natural Science Foundation of China [31100902]
- NIH [P50AR055081, R01AG026564, R01AR050496, RC2DE020756, R01AR057049, R03TW008221]
- Shanghai Leading Academic Discipline Project [S30501]
- University of Shanghai for Science and Technology [slg11018, slg11019]
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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