QTLtools - A complete tool set for molecular QTL discovery and analysis
QTLtools [
MODE] [
OPTIONS]
QTLtools is a complete tool set for molecular QTL discovery and analysis that is
fast, user and cluster friendly. QTLtools performs multiple key tasks such as
checking the quality of the sequence data, checking that sequence and genotype
data match, quantifying and stratifying individuals using molecular
phenotypes, discovering proximal or distal molQTLs and integrating them with
functional annotations or GWAS data, and analyzing allele specific expression.
It utilizes HTSlib <
http://www.htslib.org/> to quickly and efficiently
handle common genomics files types like VCF, BCF, BAM, SAM, CRAM, BED, and
GTF, and the Eigen C++ library <
http://eigen.tuxfamily.org/> for fast
linear algebra.
- bamstat
-
QTLtools bamstat --bam
[in.sam|in.bam|in.cram] --bed
annotation.bed.gz --out output.txt [OPTIONS]
Calculate basic QC metrics for BAM/SAM.
- mbv
-
QTLtools mbv --bam
[in.sam|in.bam|in.cram] --vcf
[in.vcf|in.vcf.gz|in.bcf] --out
output.txt [OPTIONS]
Match BAM to VCF
- pca
-
QTLtools pca --vcf
[in.vcf|in.vcf.gz|in.bcf] | --bed
in.bed.gz --out output.txt [OPTIONS]
Calculate principal components for a BED/VCF/BCF/CRAM file.
- correct
-
QTLtools correct --vcf
[in.vcf|in.vcf.gz|in.bcf] | --bed
in.bed.gz --cov covariates.txt | --normal
--out output.txt [OPTIONS]
Covariate correction of a BED or a VCF file.
- cis
-
QTLtools cis --vcf
[in.vcf|in.vcf.gz|in.bcf|in.bed.gz]
--bed quantifications.bed.gz [--nominal float
| --permute integer | --mapping in.txt]
--out output.txt [OPTIONS]
cis QTL analysis.
- trans
-
QTLtools trans --vcf
[in.vcf|in.vcf.gz|in.bcf|in.bed.gz]
--bed quantifications.bed.gz [--nominal |
--permute | --sample integer | --adjust
in.txt] --out output.txt [OPTIONS]
trans QTL analysis.
- fenrich
-
QTLtools fenrich --qtl significanty_genes.bed
--tss gene_tss.bed --bed TFs.encode.bed.gz
--out output.txt [OPTIONS]
Functional enrichment for QTLs.
- fdensity
-
QTLtools fdensity --qtl
significanty_genes.bed --bed TFs.encode.bed.gz
--out output.txt [OPTIONS]
Functional density around QTLs.
- genrich
-
QTLtools genrich --qtl significanty_genes.bed
--tss gene_tss.bed --vcf 1000kg.vcf
--gwas gwas_hits.bed --out output.txt
[OPTIONS]
GWAS enrichment for QTLs. This mode is deprecated and not supported, use rtc
instead.
- rtc
-
QTLtools rtc --vcf
[in.vcf|in.vcf.gz|in.bcf|in.bed.gz]
--bed quantifications.bed.gz --hotspots
hotspots_b37_hg19.bed [--gwas-cis | --gwas-trans |
--mergeQTL-cis | --mergeQTL-trans] variants_external.txt
qtls_in_this_dataset.txt --out output.txt
[OPTIONS]
Regulatory Trait Concordance score analysis to test if two colocalizing
variants are due to the same functional effect.
- rtc-union
-
QTLtools rtc-union --vcf [
in.vcf|in.vcf.gz| in.bcf|in.bed.gz] ...
--bed quantifications.bed.gz ... --hotspots
hotspots_b37_hg19.bed --results qtl_results_files.txt
... [OPTIONS]
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.
- extract
-
QTLtools extract [--vcf --bed --cov]
relevant_file --out output_prefix [OPTIONS]
Data extraction mode. Extract all the data from the provided files into one
flat file.
- quan
-
QTLtools quan --bam
[in.sam|in.bam|in.cram] --gtf
gene_annotation.gtf --out-prefix output
[OPTIONS]
Quantify gene and exon expression from RNAseq.
- ase
-
QTLtools ase --bam
[in.sam|in.bam|in.cram] --vcf
[in.vcf|in.vcf.gz|in.bcf] --ind
sample_name_in_vcf --mapq integer --out
output.txt [OPTIONS]
Measure allele specific expression from RNAseq at transcribed heterozygous
SNPs
- rep
-
QTLtools rep --bed quantifications.bed.gz
--vcf [in.vcf|in.vcf.gz|in.bcf] --qtl
qtls_external.txt --out output.txt [OPTIONS]
Replicate QTL associations in an independent dataset
- gwas
-
QTLtools gwas --vcf
[in.vcf|in.vcf.gz|in.bcf|in.bed.gz]
--bed quantifications.bed.gz --out output.txt
[OPTIONS]
GWAS tests. Correlate all genotypes with all phenotypes.
QTLtools can read gzip, bgzip, and bzip2 files, and can output gzip and bzip2
files. This is dependent on the input and output files' extension. E.g --out
output.txt.gz will write a gzipped file.
The following are common options that are used in all of the modes. Some of
these will not apply to certain modes.
- --help
- Produces a description of options for a given mode.
-
--seed integer
- Random seed for analyses that utilizes randomness. Useful
for generating replicable results. Default=15112011.
-
--log file
- Dump screen output to this file.
- --silent
- Disable screen output.
-
--exclude-samples file
- List of samples to exclude. One sample name per line.
-
--include-samples file
- List of samples to include. One sample name per line.
-
--exclude-sites file
- List of variants to exclude. One variant ID per line.
-
--include-sites file
- List of variants to include. One variant ID per line.
-
--exclude-positions file
- List of positions to exclude from genotypes. One chr
position per line (separated by a space).
-
--include-positions file
- List of positions to include from genotypes. One chr
position per line (separated by a space).
-
--exclude-phenotypes file
- List of phenotypes to exclude. One phenotype ID per
line.
-
--include-phenotypes file
- List of phenotypes to include. One phenotype ID per
line.
-
--exclude-covariates file
- List of covariates to exclude. One covariate name per
line.
-
--include-covariates file
- List of covariates to include. One covariate name per line.
- .bcf|.vcf|.vcf.gz
- These files are used for genotype data. The official VCF
specification is described at
<https://samtools.github.io/hts-specs/VCFv4.2.pdf>. The VCF/BCF
files used with QTLtools must satisfy this spec's requirements. BCF files
must be indexed with bcftools index in.bcf
<http://samtools.github.io/bcftools/bcftools.html>. VCF files should
be compressed by bgzip <http://www.htslib.org/doc/bgzip.html>
and indexed with tabix -p vcf in.vcf.gz
<http://www.htslib.org/doc/tabix.html>.
- .bed|.bed.gz
- These files are used for phenotype data, and in certain
modes they can also be used with the --vcf option, which can be used to
correlate two molecular phenotypes. The format used for QTLtools is a
custom UCSC BED format
<https://genome.ucsc.edu/FAQ/FAQformat.html#format1>, which has 6
annotation columns followed by sample columns. The header line must exist,
and must begin with a # and columns must be tab separated. THIS IS A
DIFFERENT FILE FORMAT THAN THE ONE USED FOR FASTQTL, THUS FASTQTL BED
FILES ARE INCOMPATIBLE WITH QTLTOOLS. Phenotype BED files must be
compressed by bgzip <http://www.htslib.org/doc/bgzip.html>
and indexed with tabix -p bed in.bed.gz
<http://www.htslib.org/doc/tabix.html>. Missing values must be
coded as NA. Following is an example BED file:
#chr start end pid gid strand sample1 sample2
1 9999 10000 exon1 gene1 + 15 234
1 9999 10000 exon2 gene1 + 11 134
1 19999 20000 exon1 gene2 - 154 284
1 19999 20000 exon2 gene2 - 112 301
BED file's annotation columns' descriptions:
| 1 |
Phenotype chromosome [string] |
| 2 |
Start position of the phenotype [integer,
0-based] |
| 3 |
End position of the phenotype [integer, 1-based] |
| 4 |
Phenotype ID [string] |
| 5 |
Phenotype group ID or any type of info about the phenotype
[string] |
| 6 |
Phenotype strand [+/-] |
- .bam|.sam|.cram
- These files are used for sequence data. The official SAM
specification is described at
<https://samtools.github.io/hts-specs/SAMv1.pdf>. The SAM/BAM/CRAM
files used with QTLtools must satisfy this spec's requirements.
SAM/BAM/CRAM files must be indexed with samtools index
in.bam <http://www.htslib.org/doc/samtools.html>.
- .gtf
- These files are used for gene annotation. The file
specification is described at
<https://www.ensembl.org/info/website/upload/gff.html>. The GTF
files used must comply with this spec, and should have the gene_id,
transcript_id, gene_name, gene_type, and trnascript_type attributes. We
recommend using gene annotations from GENCODE
<https://www.gencodegenes.org/>.
- covariate files
- The covariate file contains the covariate data in simple
text format. The missing values should be encoded as NA. Both
quantitative and qualitative covariates are supported. Quantitative
covariates are assumed when only numeric values are provided. Qualitative
covariates are assumed when only non-numeric values are provided. In
practice, qualitative covariates with F factors are converted in F-1
binary covariates. Following is an example a covariate file:
id sample1 sample2 sample3
PC1 -0.02 0.14 0.16
PC2 0.01 0.11 0.10
PC3 0.03 0.05 0.07
COV A B C
- include/exclude files
- The various
--{include,exclude}-{sites,samples,phenotypes,covariates} options require
a simple text file which lists the IDs of the desired type, one ID per
line. The include options will result in running the analyses only in this
subset of IDs, whereas exclude options will remove these IDs from the
analyses. The IDs for --{include,exclude}-sites refer to the 3rd column in
VCF/BCF files, --{include,exclude}-covariates refer to the 1st column in
COV files, --{include,exclude}-phenotyps refer to the 4th column in BED
files and when --grp-best option is used to the 5th column. The
--include-positions and --exclude-positions options require a text file
which lists the chromosomes and positions (separated by a space) of
genotypes to be excluded or included. One position per line.
- o
- BED files' start position is 0-based, whereas the
end position is 1-based. Positions in all other files used in
QTLtools are 1-based. All positions provided as option arguments
and filters, even the ones referring to BED files, must be 1-based.
1-based means the first base of the sequence has the position 1, whereas
in 0-based the first position is 0.
- o
- Make sure the chromosome names are the same across all
files. If some files have e.g. chr1 and another has 1 as a chromosome name
then these will be considered different chromosomes.
- o
- BED files used for FastQTL
<http://fastqtl.sourceforge.net/> are not directly compatible with
QTLtools. To convert a FastQTL BED file to the format used in QTLtools you
need to add 2 columns after the 4th column.
- o
- The quan mode in version 1.2 and above is not compatible
with the quantifications generated by the previous versions. This due to
bug fixes and slight adjustments to the way we quantify. Do not mix
quantifications generated by earlier versions of QTLtools with
quantifications from version 1.2 and above, as this will create a bias
in your dataset.
- o
- Make sure you index all your genotype, phenotype, and
sequence files.
- o
- Use BCF and BAM files for the best performance.
exons.50percent.chr22.bed.gz <
http://jungle.unige.ch/QTLtools_examples/exons.50percent.chr22.bed.gz>
exons.50percent.chr22.bed.gz.tbi <
http://jungle.unige.ch/QTLtools_examples/exons.50percent.chr22.bed.gz.tbi>
gencode.v19.annotation.chr22.gtf.gz <
http://jungle.unige.ch/QTLtools_examples/gencode.v19.annotation.chr22.gtf.gz>
gencode.v19.exon.chr22.bed.gz <
http://jungle.unige.ch/QTLtools_examples/gencode.v19.exon.chr22.bed.gz>
genes.50percent.chr22.bed.gz <
http://jungle.unige.ch/QTLtools_examples/genes.50percent.chr22.bed.gz>
genes.50percent.chr22.bed.gz.tbi <
http://jungle.unige.ch/QTLtools_examples/genes.50percent.chr22.bed.gz.tbi>
genes.covariates.pc50.txt.gz <
http://jungle.unige.ch/QTLtools_examples/genes.covariates.pc50.txt.gz>
genes.simulated.chr22.bed.gz <
http://jungle.unige.ch/QTLtools_examples/genes.simulated.chr22.bed.gz>
genes.simulated.chr22.bed.gz.tbi <
http://jungle.unige.ch/QTLtools_examples/genes.simulated.chr22.bed.gz.tbi>
genotypes.chr22.vcf.gz <
http://jungle.unige.ch/QTLtools_examples/genotypes.chr22.vcf.gz>
genotypes.chr22.vcf.gz.tbi <
http://jungle.unige.ch/QTLtools_examples/genotypes.chr22.vcf.gz.tbi>
GWAS.b37.txt <
http://jungle.unige.ch/QTLtools_examples/GWAS.b37.txt>
HG00381.chr22.bam <
http://jungle.unige.ch/QTLtools_examples/HG00381.chr22.bam>
HG00381.chr22.bam.bai <
http://jungle.unige.ch/QTLtools_examples/HG00381.chr22.bam.bai>
hotspots_b37_hg19.bed <
http://jungle.unige.ch/QTLtools_examples/hotspots_b37_hg19.bed>
results.genes.full.txt.gz <
http://jungle.unige.ch/QTLtools_examples/results.genes.full.txt.gz>
TFs.encode.bed.gz <
http://jungle.unige.ch/QTLtools_examples/TFs.encode.bed.gz>
QTLtools-bamstat(1),
QTLtools-mbv(1),
QTLtools-pca(1),
QTLtools-correct(1),
QTLtools-cis(1),
QTLtools-trans(1),
QTLtools-fenrich(1),
QTLtools-fdensity(1),
QTLtools-rtc(1),
QTLtools-rtc-union(1),
QTLtools-extract(1),
QTLtools-quan(1),
QTLtools-ase(1),
QTLtools-rep(1),
QTLtools-gwas(1)
QTLtools website: <
https://qtltools.github.io/qtltools>
- o
- 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 submit bugs to <
https://github.com/qtltools/qtltools>
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>
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>
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>
Olivier Delaneau (
[email protected]), Halit Ongen
(
[email protected])