MTAG statistics

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MTAG specifications


Unique identifier OMICS_26022
Alternative name Multi-Trait Analysis of GWAS
Software type Application/Script
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux
License GNU General Public License version 3.0
Computer skills Advanced
Version 0.9.0
Stability Stable
numpy, scipy, pandas, argparse, bitarray, joblib
Maintained Yes



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  • person_outline Benjamin Neale <>
  • person_outline Patrick Turley <>
  • person_outline David Cesarini <>
  • person_outline Daniel Benjamin <>

Additional information

Publication for Multi-Trait Analysis of GWAS

MTAG in publications

PMCID: 5717338
PMID: 29093210
DOI: 10.1098/rsob.170125

[…] lmm analysis (see §2.6) []. multimeta generalizes single-trait inverse variance weighted meta-analysis to allow each variant to have a vector of effect estimates (one for each trait included)., the multi-trait analysis of gwas (mtag) tool takes a slightly different approach to the above-mentioned methods: in a generalization of inverse variance weighted meta-analysis, it incorporates effect […]

PMCID: 5482506
PMID: 28598966
DOI: 10.1371/journal.pgen.1006836

[…] in more robust and generalizable estimation. besides the methods discussed above, we have also compared the performance of proposed joint models with a recently developed multi-trait analysis tool (mtag []). following the polygenic prediction section in their biorxiv preprint (page 8), we first applied mtag to gwas summary statistics to get the multi-trait adjusted p values and effect sizes […]

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MTAG institution(s)
Broad Institute, Cambridge, MA, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Cambridge, MA, USA; [etc.]
MTAG funding source(s)
Supported by the Social Science Genetic Association Consortium (SSGAC), the Ragnar Söderberg Foundation (E9/11 E42/15), the Swedish Research Council (421-2013-1061), The Jan Wallander and Tom Hedelius Foundation, an ERC Consolidator Grant (647648 EdGe), the Pershing Square Fund of the Foundations of Human Behavior, the National Science Foundation’s Graduate Research Fellowship Program (DGE 1144083), and the NIA/NIH through grants P01-AG005842, P01-AG005842-20S2, P30-AG012810, and T32-AG000186-23 and R01-AG042568-02 to the University of Southern California.

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