Journal
AMERICAN JOURNAL OF HUMAN GENETICS
Volume 85, Issue 6, Pages 862-872Publisher
CELL PRESS
DOI: 10.1016/j.ajhg.2009.11.001
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Funding
- NCI NIH HHS [R01 CA082659] Funding Source: Medline
- NIMH NIH HHS [R01 MH085520, R01 MH077139, U01 MH085520, R01 MH074027] Funding Source: Medline
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Data from multiple genome-wide association studies are often analyzed together for the purposes of combining information from several studies of the same disease or comparing results across different disorders. We provide a valid and efficient approach to such meta-analysis, allowing for overlapping study subjects. The available data may contain individual participant records or only meta-analytic summary results. Simulation studies demonstrate that failure to account for overlapping subjects can greatly inflate type I error when combining results from multiple studies of the same disease and can drastically reduce power when comparing results across different disorders. In addition, the proposed approach can be substantially more powerful than the simple approach of splitting the overlapping subjects among studies, especially for comparing results across different disorders. The advantages of the new approach are illustrated with empirical data from two sets of genome-wide association studies.
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