D Vuckovic1, P Gasparini2, N Soranzo3, V Iotchkova4. 1. Department of Medical, Surgical and Health Sciences, University of Trieste, 34100 Trieste, Italy. 2. Department of Medical, Surgical and Health Sciences, University of Trieste, 34100 Trieste, Italy, Medical Genetics, Institute for Maternal and Child Health IRCCS "Burlo Garofolo", 34100 Trieste, Italy. 3. Human Genetics, Wellcome Trust Sanger Institute, Genome Campus, Hinxton CB10 1HH, Department of Haematology, University of Cambridge, Cambridge CB2 0AH and. 4. Human Genetics, Wellcome Trust Sanger Institute, Genome Campus, Hinxton CB10 1HH, EMBL-EBI, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK.
Abstract
UNLABELLED: As new methods for multivariate analysis of genome wide association studies become available, it is important to be able to combine results from different cohorts in a meta-analysis. The R package MultiMeta provides an implementation of the inverse-variance-based method for meta-analysis, generalized to an n-dimensional setting. AVAILABILITY AND IMPLEMENTATION: The R package MultiMeta can be downloaded from CRAN. CONTACT: dragana.vuckovic@burlo.trieste.it; vi1@sanger.ac.uk SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
UNLABELLED: As new methods for multivariate analysis of genome wide association studies become available, it is important to be able to combine results from different cohorts in a meta-analysis. The R package MultiMeta provides an implementation of the inverse-variance-based method for meta-analysis, generalized to an n-dimensional setting. AVAILABILITY AND IMPLEMENTATION: The R package MultiMeta can be downloaded from CRAN. CONTACT: dragana.vuckovic@burlo.trieste.it; vi1@sanger.ac.uk SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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