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Human Genetics

, Volume 123, Issue 1, pp 1–14 | Cite as

Methods for meta-analysis in genetic association studies: a review of their potential and pitfalls

  • Fotini K. Kavvoura
  • John P. A. Ioannidis
Review

Abstract

Meta-analysis offers the opportunity to combine evidence from retrospectively accumulated or prospectively generated data. Meta-analyses may provide summary estimates and can help in detecting and addressing potential inconsistency between the combined datasets. Application of meta-analysis in genetic associations presents considerable potential and several pitfalls. In this review, we present basic principles of meta-analytic methods, adapted for human genome epidemiology. We describe issues that arise in the retrospective or the prospective collection of relevant data through various sources, common traps to consider in the appraisal of evidence and potential biases that may interfere. We describe the relative merits and caveats for common methods used to trace inconsistency across studies along with possible reasons for non-replication of proposed associations. Different statistical models may be employed to combine data and some common misconceptions may arise in the process. Several meta-analysis diagnostics are often applied or misapplied in the literature, and we comment on their use and limitations. An alternative to overcome limitations arising from retrospective combination of data from published studies is to create networks of research teams working in the same field and perform collaborative meta-analyses of individual participant data, ideally on a prospective basis. We discuss the advantages and the challenges inherent in such collaborative approaches. Meta-analysis can be a useful tool in dissecting the genetics of complex diseases and traits, provided its methods are properly applied and interpreted.

Keywords

Genetic Association Study Massive Testing Summary Effect Selective Reporting Bias Human Genome Epidemiology 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgment

Dr. Kavvoura is supported by a PENED grant co-financed by the European Union-European Social Fund (75%) and the Greek Ministry of Development-General Secretariat of Research and Technology (25%).

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Copyright information

© Springer-Verlag 2007

Authors and Affiliations

  1. 1.Clinical and Molecular Epidemiology Unit, Department of Hygiene and EpidemiologyUniversity of Ioannina School of MedicineIoanninaGreece
  2. 2.Biomedical Research InstituteFoundation for Research and Technology-HellasIoanninaGreece
  3. 3.Institute for Clinical Research and Health Policy Studies, Department of Medicine, Tufts-New England Medical CenterTufts University School of MedicineBostonUSA

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