Document Detail


Meta-analysis of genetic association studies under heterogeneity.
MedLine Citation:
PMID:  22643179     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
In multi-cohort genetic association studies or meta-analysis, associations of genetic variants with complex traits across cohorts may be heterogeneous because of genuine genetic diversity or differential biases or errors. To detect the associations of genes with heterogeneous associations across cohorts, new global fixed-effect (FE) and random-effects (RE) meta-analytic methods have been recently proposed. These global methods had improved power over both traditional FE and RE methods under heterogeneity in limited simulation scenarios and data application, but their usefulness in a wide range of practical situations is not clear. We assessed the performance of these methods for both binary and quantitative traits in extensive simulations and applied them to a multi-cohort association study. We found that these new approaches have higher power to detect mostly the very small to small associations of common genetic variants when associations are highly heterogeneous across cohorts. They worked well when both the underlying and assumed genetic models are either multiplicative or dominant. But, they offered no clear advantage for less common variants unless heterogeneity was substantial. In conclusion, these new meta-analytic methods can be used to detect the association of genetic variants with high heterogeneity, which can then be subjected to further exploration, in multi-cohort association studies and meta-analyses.
Authors:
Binod Neupane; Mark Loeb; Sonia S Anand; Joseph Beyene
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Publication Detail:
Type:  Journal Article; Meta-Analysis; Research Support, Non-U.S. Gov't     Date:  2012-05-30
Journal Detail:
Title:  European journal of human genetics : EJHG     Volume:  20     ISSN:  1476-5438     ISO Abbreviation:  Eur. J. Hum. Genet.     Publication Date:  2012 Nov 
Date Detail:
Created Date:  2012-10-18     Completed Date:  2013-03-19     Revised Date:  2013-11-06    
Medline Journal Info:
Nlm Unique ID:  9302235     Medline TA:  Eur J Hum Genet     Country:  England    
Other Details:
Languages:  eng     Pagination:  1174-81     Citation Subset:  IM    
Affiliation:
Population Genomics Program, Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ontario, Canada.
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MeSH Terms
Descriptor/Qualifier:
Genetic Heterogeneity*
Genome, Human
Genome-Wide Association Study*
Humans
Models, Theoretical
Polymorphism, Single Nucleotide
Grant Support
ID/Acronym/Agency:
84392//Canadian Institutes of Health Research
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