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| Content Provider | Springer Nature Link |
|---|---|
| Author | Pei, Yu Fang Zhang, Lei Papasian, Christopher J. Wang, Yu Ping Deng, Hong Wen |
| Copyright Year | 2013 |
| 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. |
| Starting Page | 265 |
| Ending Page | 279 |
| Page Count | 15 |
| File Format | |
| ISSN | 03406717 |
| Journal | Human Genetics |
| Volume Number | 133 |
| Issue Number | 3 |
| e-ISSN | 14321203 |
| Language | English |
| Publisher | Springer Berlin Heidelberg |
| Publisher Date | 2013-10-11 |
| Publisher Place | Berlin, Heidelberg |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Human Genetics Molecular Medicine Gene Function Metabolic Diseases |
| Content Type | Text |
| Resource Type | Article |
| Subject | Genetics Genetics (clinical) |
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