Practical aspects of imputation-driven meta-analysis of genome-wide association studies.
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.
de Bakker
Paul I W
PI
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. pdebakker@rics.bwh.harvard.edu
Ferreira
Manuel A R
MA
Jia
Xiaoming
X
Neale
Benjamin M
BM
Raychaudhuri
Soumya
S
Voight
Benjamin F
BF
D003627:Data Interpretation, Statistical; D020022:Genetic Predisposition to Disease; D015894:Genome, Human / Q000235:genetics; D055106:Genome-Wide Association Study / Q000379:methods* / Q000592:standards; D006801:Humans; D015201:Meta-Analysis as Topic; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide