tensorQTL is a GPU-based QTL mapper, enabling ~200-300 fold faster cis- and trans-QTL mapping compared to CPU-based implementations.
If you use tensorQTL in your research, please cite the following paper: Taylor-Weiner, Aguet, et al., bioRxiv, 2019.
Empirical beta-approximated p-values are computed as described in FastQTL (Ongen et al., 2016).
Run the following commands to install tensorQTL:
$ git clone [email protected]: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.
This section describes how to run tensorQTL from the command line. For a full list of options, run
python3 -m tensorqtl --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. These files can 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 in parquet format, with four columns: phenotype_id, variant_id, pval, maf.
TensorQTL can also be run as a module to more efficiently run multiple analyses:
import pandas as pd
import tensorqtl
from tensorqtl import genotypeio
Load phenotypes and covariates:
phenotype_df, phenotype_pos_df = tensorqtl.read_phenotype_bed(phenotype_bed_file)
covariates_df = pd.read_csv(covariates_file, sep='\t', index_col=0).T # samples x covariates
Genotypes can be loaded as follows, where plink_prefix_path is the path to the VCF in PLINK format:
# for VCFs with hard GT calls only, specify type as np.int8 to save memory:
pr = genotypeio.PlinkReader(plink_prefix_path, dtype=np.int8)
# load all genotypes into a pd.DataFrame
genotype_df = pd.DataFrame(pr.get_all_genotypes(), index=pr.bim['snp'], columns=pr.fam['iid'])
To save memory when using genotypes for a subset of samples, specify the samples as follows (this is not strictly necessary, since tensorQTL will select the relevant samples from genotype_df otherwise):
pr = genotypeio.PlinkReader(plink_prefix_path, select_samples=phenotype_df.columns, dtype=np.int8)
cis_df = tensorqtl.map_cis(pr, phenotype_df, phenotype_pos_df, covariates_df, genotype_df=genotype_df)
tensorqtl.calculate_qvalues(qtl_df, qvalue_lambda=0.85)
trans_df = tensorqtl.map_trans(genotype_df, phenotype_df, covariates_df,
return_sparse=True, batch_size=50000)
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