Command Line

Required Input Files

--bfile arg

plink .bed/.bim/.fam file prefix containing SNPs

--numeric-file arg

inbix format matrix of individuals (rows) by transcripts (columns)

Optional Commands

--dcvar-pfilter

Enable p-value filtering

--dcvar-pfilter-value arg

P-value filter value

--dcvar-pfilter-type arg

P-value filter type {bon|fdr}

--var-model arg

dom, rec, hom

Help

--help

Displays this screen

Example Usage


./dcVar --bfile plinkBfile --numeric-file transcripts.num --out example


HapMap3 dcVar Analysis

Full annotated output from the HapMap3 dcVar analysis described in Lareau et al. The first and third columns show the ILMN probe IDs that were differentially correlated
when conditioning on the variant in column 5. Gene symbols were annotated to the probe IDs in columns 2 and 4. The interaction score from STRING (string-db.org) for the two genes is listed in column 6 as well a genome-wide association annotation from NHGRI (https://www.genome.gov/26525384) from general population studies in column 7.
Probes that did not readily map to a particular gene ID, gene pairs that lacked an interaction score from STRING or variants that did not carry a GWAS annotation were
labeled with “#NA#” in their respective columns.

• dcVar-HapMap-annotated.txt.zip
  

Tutorial

• dcVar Tutorial

Using the R Version

• Download dcVar.R, and use your favorite editor to set the various files and run options. Use the source("dcVar.R") command or an IDE like RStudio to run dcVar with your settings.

Installating the C++ Version

• Download dcVar-1.0 and unzip the file.
• Install Armadillo library if not already installed.
• Build the dcVar executable program with the command:
  make -f Makefile.dcVar or make -f Makefile.dcVar.osx for Mac OSX.
• Install the dcVar executable program to /usr/local/bin with the command: sudo make -f Makefile.dcVar install.