Meta-Analysis of Genome-wide Association Studies with Overlapping Subjects

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...

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Bibliographic Details
Published inAmerican journal of human genetics Vol. 85; no. 6; pp. 862 - 872
Main Authors Lin, Dan-Yu, Sullivan, Patrick F.
Format Journal Article
LanguageEnglish
Published Cambridge, MA Elsevier Inc 11.12.2009
Cell Press
Elsevier
Subjects
Alleles
Arthritis, Rheumatoid - genetics
Biological and medical sciences
Case-Control Studies
Comparative analysis
Computer Simulation
Data Interpretation, Statistical
Diabetes Mellitus, Type 1 - genetics
Fundamental and applied biological sciences. Psychology
Gene Frequency
General aspects. Genetic counseling
Genetics of eukaryotes. Biological and molecular evolution
Genome-Wide Association Study
Genomics
Genotype
Humans
Medical genetics
Medical sciences
Meta-analysis
Meta-Analysis as Topic
Models, Statistical
Molecular and cellular biology
Odds Ratio
Reproducibility of Results
Schizophrenia - genetics
Simulation
Human
Genetics
Association
Genome
Metaanalysis
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Summary: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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ISSN:0002-9297
1537-6605
1537-6605
DOI:10.1016/j.ajhg.2009.11.001