METAL: Fast and efficient meta-analysis of genomewide association scans Public Deposited

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Creator
  • Li, Yun
    • Affiliation: Gillings School of Global Public Health, Department of Biostatistics
    • Other Affiliation: Department of Biostatistics, University of Michigan, 1420 Washington Heights, Ann Arbor, 48109
  • Abecasis, Goncalo R.
    • Affiliation: Gillings School of Global Public Health, Department of Biostatistics
    • Other Affiliation: Department of Biostatistics, University of Michigan, 1420 Washington Heights, Ann Arbor, 48109
  • Willer, Cristen J.
    • Affiliation: Gillings School of Global Public Health, Department of Biostatistics
    • Other Affiliation: Department of Biostatistics, University of Michigan, 1420 Washington Heights, Ann Arbor, 48109
Abstract
  • "Summary: METAL provides a computationally efficient tool for meta-analysis of genome-wide association scans, which is a commonly used approach for improving power complex traits gene mapping studies. METAL provides a rich scripting interface and implements efficient memory management to allow analyses of very large data sets and to support a variety of input file formats. Availability and implementation: METAL, including source code, documentation, examples, and executables, is available at http://www.sph.umich.edu/csg/abecasis/metal/"
Date of publication
DOI
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Resource type
  • Article
Rights statement
  • In Copyright
Journal title
  • Bioinformatics
Journal volume
  • 26
Journal issue
  • 17
Page start
  • 2190
Page end
  • 2191
Language
  • English
Version
  • Postprint
ISSN
  • 1460-2059
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