This article appears in the following Human Molecular Genetics issue: Association Studies [View the issue table of contents]
Practical aspects of imputation-driven meta-analysis of genome-wide association studies
1 Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School-Partners Healthcare Systems Center for Genetics and Genomics, Boston, MA 02115, USA 2 Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA 3 Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA 02114, USA 4 Harvard-MIT Division of Health Sciences and Technology, Boston, MA 02115, USA 5 Division of Rheumatology, Immunology, and Allergy, Brigham and Women's Hospital, Boston, MA 02115, USA
* To whom correspondence should be addressed at: Brigham and Women's Hospital, New Research Building, Suite 168, 77 Avenue Louis Pasteur, Boston, MA 02115, USA. Tel: +1 6175254452; Fax: +1 6175255722; Email: pdebakker{at}rics.bwh.harvard.edu
Received September 2, 2008; Accepted September 5, 2008
Motivated by the overwhelming success of genome-wide association studies, droves of researchers are working vigorously to exchange and to combine genetic data to expediently discover genetic risk factors for common human traits. The primary tools that fuel these new efforts are imputation, allowing researchers who have collected data on a diversity of genotype platforms to share data in a uniformly exchangeable format, and meta-analysis for pooling statistical support for a genotype–phenotype association. As many groups are forming collaborations to engage in these efforts, this review collects a series of guidelines, practical detail and learned experiences from a variety of individuals who have contributed to the subject.
Present address: Genetic Epidemiology, Queensland Institute of Medical Research, Queensland, Australia.
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