tensorQTL is a GPU-enabled QTL mapper, achieving ~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., Genome Biol., 2019.
Empirical beta-approximated p-values are computed as described in Ongen et al., Bioinformatics, 2016.
You can install tensorQTL using pip:
pip3 install tensorqtl
or directly from this repository:
$ 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 .
To use PLINK 2 binary files (pgen/pvar/psam), pgenlib must be installed:
git clone [email protected]:chrchang/plink-ng.git
cd plink-ng/2.0/Python/
python3 setup.py build_ext
python3 setup.py install
tensorQTL requires an environment configured with a GPU for optimal performance, but can also be run on a CPU. Instructions for setting up a virtual machine on Google Cloud Platform are provided here.
Three inputs are required for QTL analyses with tensorQTL: genotypes, phenotypes, and covariates.
-
Phenotypes must be provided in BED format, with a single header line starting with
#and the first four columns corresponding to:chr,start,end,phenotype_id, with the remaining columns corresponding to samples (the identifiers must match those in the genotype input). The BED file should specify the center of the cis-window (usually the TSS), withstart == end-1. A function for generating a BED template from a gene annotation in GTF format is available in pyqtl (io.gtf_to_tss_bed). -
Covariates can be provided as a tab-delimited text file (covariates x samples) or dataframe (samples x covariates), with row and column headers.
-
Genotypes must be in PLINK format, which can be generated from a VCF as follows:
plink2 --make-bed \ --output-chr chrM \ --vcf ${plink_prefix_path}.vcf.gz \ --out ${plink_prefix_path}If using PLINK 1.9 or earlier, add the
--keep-allele-orderflag.Alternatively, the genotypes can be provided as a dataframe (genotypes x samples).
The examples notebook below contains examples of all input files. The input formats for phenotypes and covariates are identical to those used by FastQTL.
For examples illustrating cis- and trans-QTL mapping, please see tensorqtl_examples.ipynb.
This section describes how to run the different modes of tensorQTL, both from the command line and within Python. For a full list of options, run
python3 -m tensorqtl --help
This section is only relevant when running tensorQTL in Python. The following imports are required:
import pandas as pd
import tensorqtl
from tensorqtl import genotypeio, cis, trans
Phenotypes and covariates can be loaded as follows:
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 (excluding .bed/.bim/.fam extensions):
pr = genotypeio.PlinkReader(plink_prefix_path)
# load genotypes and variants into data frames
genotype_df = pr.load_genotypes()
variant_df = pr.bim.set_index('snp')[['chrom', 'pos']]
To save memory when using genotypes for a subset of samples, a subset of samples can be loaded (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)
This is the main mode for cis-QTL mapping. It generates phenotype-level summary statistics with empirical p-values, enabling calculation of genome-wide FDR. In Python:
cis_df = cis.map_cis(genotype_df, variant_df, phenotype_df, phenotype_pos_df, covariates_df)
tensorqtl.calculate_qvalues(cis_df, qvalue_lambda=0.85)
Shell command:
python3 -m tensorqtl ${plink_prefix_path} ${expression_bed} ${prefix} \
--covariates ${covariates_file} \
--mode cis
${prefix} specifies the output file name.
In Python:
cis.map_nominal(genotype_df, variant_df, phenotype_df, phenotype_pos_df,
prefix, covariates_df, output_dir='.')
Shell command:
python3 -m tensorqtl ${plink_prefix_path} ${expression_bed} ${prefix} \
--covariates ${covariates_file} \
--mode cis_nominal
The results are written to a parquet file for each chromosome. These files can be read using pandas:
df = pd.read_parquet(file_name)
This mode maps conditionally independent cis-QTLs using the stepwise regression procedure described in GTEx Consortium, 2017. The output from the permutation step (see map_cis above) is required.
In Python:
indep_df = cis.map_independent(genotype_df, variant_df, cis_df,
phenotype_df, phenotype_pos_df, covariates_df)
Shell command:
python3 -m tensorqtl ${plink_prefix_path} ${expression_bed} ${prefix} \
--covariates ${covariates_file} \
--cis_output ${prefix}.cis_qtl.txt.gz \
--mode cis_independent
Instead of mapping the standard linear model (p ~ g), this mode includes an interaction term (p ~ g + i + gi) and returns full summary statistics for the model. The interaction term is a tab-delimited text file or dataframe mapping sample ID to interaction value(s) (if multiple interactions are used, the file must include a header with variable names). With the run_eigenmt=True option, eigenMT-adjusted p-values are computed.
In Python:
cis.map_nominal(genotype_df, variant_df, phenotype_df, phenotype_pos_df, prefix,
covariates_df=covariates_df,
interaction_df=interaction_df, maf_threshold_interaction=0.05,
run_eigenmt=True, output_dir='.', write_top=True, write_stats=True)
The input options write_top and write_stats control whether the top association per phenotype and full summary statistics, respectively, are written to file.
Shell command:
python3 -m tensorqtl ${plink_prefix_path} ${expression_bed} ${prefix} \
--covariates ${covariates_file} \
--interaction ${interactions_file} \
--best_only \
--mode cis_nominal
The option --best_only disables output of full summary statistics.
Full summary statistics are saved as parquet files for each chromosome, in ${output_dir}/${prefix}.cis_qtl_pairs.${chr}.parquet, and the top association for each phenotype is saved to ${output_dir}/${prefix}.cis_qtl_top_assoc.txt.gz. In these files, the columns b_g, b_g_se, pval_g are the effect size, standard error, and p-value of g in the model, with matching columns for i and gi. In the *.cis_qtl_top_assoc.txt.gz file, tests_emt is the effective number of independent variants in the cis-window estimated with eigenMT, i.e., based on the eigenvalue decomposition of the regularized genotype correlation matrix (Davis et al., AJHG, 2016). pval_emt = pval_gi * tests_emt, and pval_adj_bh are the Benjamini-Hochberg adjusted p-values corresponding to pval_emt.
This mode computes nominal associations between all phenotypes and genotypes. 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. In Python:
trans_df = trans.map_trans(genotype_df, phenotype_df, covariates_df,
return_sparse=True, pval_threshold=1e-5, maf_threshold=0.05,
batch_size=20000)
# remove cis-associations
trans_df = trans.filter_cis(trans_df, phenotype_pos_df.T.to_dict(), variant_df, window=5000000)
Shell command:
python3 -m tensorqtl ${plink_prefix_path} ${expression_bed} ${prefix} \
--covariates ${covariates_file} \
--mode trans
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