BOLT-REML specifications

Unique identifier:
OMICS_13777
Interface:
Command line interface
Input data:
A genotype
Output data:
A tab-delimited with many fields
License:
GNU General Public License version 3.0, MIT License
Version:
2.2
Maintained:
Yes
Software type:
Package/Module
Restrictions to use:
None
Input format:
BED, BIM, FAM
Operating system:
Unix/Linux
Computer skills:
Advanced
Stability:
Stable

versioning

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BOLT-REML distribution

download

BOLT-REML support

Maintainer

  • Alkes Price <>

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Credits

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Publications

Institution(s)

Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA; The Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia; School of Environmental and Rural Science, University of New England, Armidale, New South Wales, Australia; Department of Psychiatry and Human Genetics, Virginia Institute of Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA; MRC Centre for Neuropsychiatric Genetics and Genomics, Institute of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK

Funding source(s)

This work was supported by US National Institutes of Health (grants n°R01HG006399 and R01MH101244); US National Institutes of Health fellowship (grant n°F32HG007805); the Fannie and John Hertz Foundation; Members of the Schizophrenia Working Group of the Psychiatric Genomics Consortium; the Genetic Cluster Computer; the Netherlands Scientific Organization (grant n°NWO 480-05-003 PI); the Dutch Brain Foundation; the VU University Amsterdam and the Orchestra High Performance Compute Cluster at Harvard Medical School (grant n°NCRR 1S10RR028832-01).

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