tensorQTL is a GPU-based QTL mapper, enabling ~200-300 fold faster cis- and trans-QTL mapping compared to CPU-based implementations.
For cis-QTL mapping, beta-approximated empirical p-values are computed as implemented in FastQTL (Ongen et al., 2016).
Run the following commands to install tensorQTL:
$ git clone git@github.com:broadinstitute/tensorqtl.git
$ cd tensorqtl
# set up virtual environment and install
$ virtualenv venv
$ source venv/bin/activate
(venv)$ pip install -r install/requirements.txt .
tensorQTL requires an environment configured with a GPU. Instructions for setting up a virtual machine on Google Cloud Platform are provided here.
tensorQTL requires three input files: genotypes, phenotypes, and covariates. Phenotypes must be provided in BED format (phenotypes x samples), and covariates as a text file (covariates x samples). Both are in the format used by FastQTL. Genotypes must currently be in PLINK format, and can be converted as follows:
plink2 --make-bed \
--output-chr chrM \
--vcf ${plink_prefix_path}.vcf.gz \
--out ${plink_prefix_path}
For examples illustrating cis- and trans-QTL mapping, please see tensorqtl_examples.ipynb.
tensorQTL can be run from the command line. For options, see tensorqtl.py --help.
Phenotype-level summary statistics with empirical p-values:
python3 -m tensorqtl ${plink_prefix_path} ${expression_bed} ${prefix} --covariates ${covariates_file} --mode cis
All variant-phenotype associations:
python3 -m tensorqtl ${plink_prefix_path} ${expression_bed} ${prefix} --covariates ${covariates_file} --mode cis_nominal
This will generate a parquet file for each chromosome. The output can easily be read using pandas:
import pandas as pd
df = pd.read_parquet(file_name)
Conditionally independent cis-QTL (as described in GTEx Consortium, 2017):
python3 -m tensorqtl ${plink_prefix_path} ${expression_bed} ${prefix} --covariates ${covariates_file} --cis_results ${cis_results_file} --mode cis_independent
python3 -m tensorqtl ${plink_prefix_path} ${expression_bed} ${prefix} --covariates ${covariates_file} --mode trans
For trans-QTL mapping, tensorQTL generates sparse output by default (associations with p-value < 1e-5). cis-associations are filtered out. The output is written in parquet format, with four columns: phenotype_id, variant_id, pval, maf