A mini subset of the MAGENTA software, reimplemented in Python. The minimgnt enables users to calculate the corrected "gene association score" from a GWAS result, according to MAGENTA's method.

For the full functionality of MAGENTA (i.e. GSEA analysis), try MAGENTApy, an experimental complete port of MAGENTA to Python. (Note: MAGENTApy is still under beta.)

./minimgnt.py score_filename [--out OUT] [-j CPUS] [--not-remove-HLA] [--remove-NA] [--no-rsid]

• score_filename : GWAS SNP score filename.

  • [columns]
    1. rsID (optional with --no-rsid flag.)
    2. chromosome
    3. bp
    4. z-score (optional)
    5. p-value

• --out OUT : output filename prefix. (default: minimgnt)

• -j/--cpus CPUS : a number of cpus used for computation. (default: 1)

• --not-remove-HLA : do not remove genes in HLA region from a result. (default: False)

• --remove-NA : remove genes with NA score from the output. (default: False)

• --no-rsid : use this flag when a score file doesn't contain a rsID column. (default: False)

  • This file format corresponds to Input file #1 of the original MAGENTA.

• --HLA-start HLA_START : start position (bp) of HLA region in chr6. (default: 25,000,000)

• --HLA-end HLA_END : end position (bp) of HLA region in chr6. (default: 35,000,000)

• --boundary-upstream BOUNDR_UPSTR : added distance (bp) upstream to gene's start position. (default: 110,000)

• --boundary-downstream BOUNDR_DOWNSTR : added distance (bp) downstream to gene's end position. (default: 40,000)

All pre-installed reference data are located at ./minimgnt/data.

• AllHumanGeneChrPosStrandNames_RefSeq_hg19_072111.txt
  • The data were retrieved from RefSeq ver. 48 (released at July 10, 2011), using the same dataset as the MAGENTA software.
  • [columns]
    1. chromosome
    2. txStart (bp)
    3. txEnd (bp)
    4. strand (1: forward, 2: reverse)
    5. name
• AllHumanMiRNAChrPosStrandNames_miRBase_hg19_062413.txt
  • The data were retrieved from miRBase release 20 (released at June 24, 2013).
  • [columns] same as the above.
• CEU_HapMap_pruned_SNPs_ChrNumPos_hg19_072111.txt
  • The LD information used for correcting "gene association score" is based on the CEU HapMap dataset.
  • For other ethnic samples, you can calculate similar properties using the appropriate HapMap dataset as described in Segrè et al. (2010).
  • [columns]
    1. chromosome
    2. position (bp)
• hotspot_boundaries_b37_hg19_072111.txt
  • [columns]
    1. chromosome
    2. start (bp)
    3. end (bp)

git clone https://github.com/mkanai/minimgnt

• numpy
• scipy
• pandas
• six
• argparse
• futures

To install these requirements,

[sudo] pip install -r requirements.txt

• The original MAGENTA was written by Ayellet Segre, Mark Daly, and David Altshuler of The Broad Institute.
  • Ayellet V. Segrè, DIAGRAM Consortium, MAGIC investigators, Leif Groop, Vamsi K. Mootha, Mark J. Daly, and David Altshuler (2010). Common Inherited Variation in Mitochondrial Genes is not Enriched for Associations with Type 2 Diabetes or Related Glycemic Traits. PLoS Genetics Aug 12;6(8). pii: e1001058.
• This minimgnt (miniMAGENTA) was written by Masahiro Kanai, reimplementing the calculation of "gene associatino score" feature in Python.