Important information
Versions up to and including 1.2, suffer from a bug in reading missing genotypes in VCF/BCF files. This bug affects variants with a DS field in their genotype's FORMAT and have a missing genotype (DS field is .) in one of the samples, in which case genotypes for all the samples are set to missing, effectively removing this variant from the analyses. Affected modes: cis, correct, gwas, pca, rep, trans, rtc-union
Please use the man pages for documentation. The previous documentation is still available but is now OUTDATED, and will be removed in the future.
General information
QTLtools is a tool set for molecular QTL discovery and analysis. It allows to go from the raw sequence data to collection of molecular Quantitative Trait Loci (QTLs) in few easy-to-perform steps. For the moment, QTLtools can perform the following tasks:
- bamstat; to check the quality of some sequence data by counting the number of reads mapped to the reference genome and falling within a reference annotation.
- mbv; to ensure good match between the sequence and genotype data. This is useful to detect sample mislabeling, contamination or PCR amplification biases.
- quan; to quantify exon and gene expression levels given a reference annotation such as GENCODE.
- pca; to perform Principal Component Analysis (PCA) on genotype or molecular phenotype data.
- cis; to map molecular Quantitative Trait Loci (mQTLs) in cis (i.e. proximal to the phenotype). This mode extends FastQTL by including two additional options: (i) to study groups of phenotypes such as genes containing multiple exons or (ii) to perform conditional analysis in order to discover multiple mQTLs per phenotype
- trans; to map QTLs in trans. This can be done either using a full permutation scheme or with a much quicker approximation.
- fdensity; to measure the density of functional annotations around the QTL locations. Useful to look at the degree of overlap between QTLs and functional annotations.
- fenrich; to measure how QTLs are enriched in any given functional annotations.
- rtc; to co-localize collections of QTLs with collection of GWAS hits.
- rtc-union; to find the union of QTLs from independent datasets. If there was a QTL in a given recombination interval in one dataset, then find the best QTL (may or may not be genome-wide significant) in the same recombination interval in all other datasets.
- correct; to correct data for technical covariates.
- extract; to extract all the data from multiple files into one flat file.
- ase; to measure allele specific expression.
- rep; to replicate QTLs in an independent dataset.
- gwas; to correlate all genotypes with all phenotypes
QTLtools has been implemented in C++ and has some nice properties:
- It is fast; a lot of efforts have been made to make most of the analyses fast to perform. It notably includes a permutation scheme to map QTLs in cis and in trans that shows very good performance compared to state of the arts methods.
- It is User-friendly; all functional modes have been designed to minimize the work needed to set them up. Usually, a single command line does the trick.
- It is Cluster-friendly; all functional modes have been designed to be easily parallelized on a compute cluster.
QTLtools is mostly developed by :
- Dr Olivier DELANEAU, University of Geneva, olivier.delaneau[at]unige.ch
- Dr Halit ONGEN, University of Geneva, halit.ongen[at]unige.ch
- Prof. Manolis DERMITZAKIS, University of Geneva, Emmanouil.Dermitzakis[at]unige.ch
For questions and/or bug reports concerning QTLtools, please contact:
- Dr Olivier DELANEAU, University of Geneva, olivier.delaneau[at]unige.ch
- Dr Halit ONGEN, University of Geneva, halit.ongen[at]unige.ch
When QTLtools is used, please cite one of the following papers:
- mode mbv: Fort A., Panousis N. I., Garieri M., et al. MBV: a method to solve sample mislabeling and detect technical bias in large combined genotype and sequencing assay datasets, Bioinformatics 33(12), 1895 2017. <https://doi.org/10.1093/bioinformatics/btx074>
- mode rtc and rtc-union: Ongen H, Brown A. A., Delaneau O., et al. Estimating the causal tissues for complex traits and diseases. Nat Genet. 2017;49(12):1676-1683. doi:10.1038/ng.3981 <https://doi.org/10.1038/ng.3981>
- one of the other modes: Delaneau O., Ongen H., Brown A. A., et al. A complete tool set for molecular QTL discovery and analysis. Nat Commun 8, 15452 (2017). <https://doi.org/10.1038/ncomms15452>