In order to discover quantitative trait loci (QTLs), multi-dimensional genomic datasets combining DNA-seq and ChiP-/RNA-seq require methods that rapidly correlate tens of thousands of molecular phenotypes with millions of genetic variants while appropriately controlling for multiple testing. FastQTL implements a popular cis-QTL mapping strategy in a user- and cluster-friendly tool. FastQTL also proposes an efficient permutation procedure to control for multiple testing.

References:

• Halit Ongen, Alfonso Buil, Andrew Anand Brown, Emmanouil T. Dermitzakis and Olivier Delaneau, "Fast and efficient QTL mapper for thousands of molecular phenotypes", Bioinformatics, 32(10), 2016, 1479–1485

• FastQTL SourceForge page

• Module Name: FastQTL (see the modules page for more information)
• Fast; with a permutation scheme relying on Beta approximation.
• Flexible; association testing is implemented w/o qualitative/quantitative covariates.
• User-friendly; only standard file formats are used and easy-to-use options implemented.
• Cluster-friendly; parallelization is easy to set up for large deployment on compute clusters.
• Unusual environment variables set
  • FASTQTL_HOME  installation directory
  • FASTQTL_BIN       executable directory
  • FASTQTL_SRC      source code directory
  • FASTQTL_TEST    sample data directory
  Interactive job
  Interactive jobs should be used for debugging, graphics, or applications that cannot be run as batch jobs.

  Allocate an interactive session and run the program. Sample session:

  [user@biowulf]$ sinteractive --mem=4g
  [user@@cn3316 ~]$ module load FastQTL
  [user@@cn3316 ~]$ cp ${FASTQTL_TEST}/* . 
  [user@@cn3316 ~]$ fastQTL -V genotypes.vcf.gz -B  phenotypes.bed.gz -O res -L res.log --chunk 1 10 
  Fast QTL
    * Authors : Olivier DELANEAU, Halit ONGEN, Alfonso BUIL & Manolis DERMITZAKIS
    * Contact : olivier.delaneau@gmail.com
    * Webpage : http://fastqtl.sourceforge.net/
    * Version : v2.0
  
  Perform nominal analysis (used to get raw p-values of association)
    * Using p-value threshold = 1.0000000000
    * Random number generator is seeded with 1525965118
    * Considering variants within 1e+06 bp of the MPs
    * Chunk processed 1 / 10
  
  Scanning phenotype data in [phenotypes.bed.gz]
    * 364 phenotypes
  
  Reading phenotype data in [phenotypes.bed.gz]
    * region = 22:17517460-20748405
    * 373 samples included
    * 45 phenotypes included
  Reading genotype data in [genotypes.vcf.gz] in VCF format
    * region = 22:16517460-21748405
    * 373 samples included
    * 18602 sites included
  
  Imputing missing genotypes
  
  Imputing missing phenotypes
  
  Initialize covariate 
  
  Processing gene [ENSG00000237438.1]
    * Number of variants in cis = 7966
    * Progress = 2.2%
  
  Processing gene [ENSG00000177663.8]
    * Number of variants in cis = 8165
    * Progress = 4.4%
  
  Processing gene [ENSG00000183307.3]
    * Number of variants in cis = 8279
    * Progress = 6.7%
  
  Processing gene [ENSG00000069998.8]
    * Number of variants in cis = 8466
    * Progress = 8.9%
  ...
  ...
  Processing gene [ENSG00000206176.5]
    * Number of variants in cis = 7031
    * Progress = 93.3%
  
  Processing gene [ENSG00000196622.5]
    * Number of variants in cis = 7212
    * Progress = 95.6%
  
  Processing gene [ENSG00000161133.12]
    * Number of variants in cis = 6103
    * Progress = 97.8%
  
  Processing gene [ENSG00000185252.13]
    * Number of variants in cis = 6090
    * Progress = 100.0%
  
  Running time: 97 seconds
  

  Batch job
  Most jobs should be run as batch jobs.

  Create a batch input file (e.g. FastQTL.sh). For example:

  #!/bin/bash
  module load FastQTL
  fastQTL -V genotypes.vcf.gz -B  phenotypes.bed.gz -O res -L res.log --chunk 7 10
  

  Submit this job using the Slurm sbatch command.

  sbatch [--cpus-per-task=#] [--mem=#] FastQTL.sh