Provided by: qtltools_1.3.1+dfsg-4_amd64 
NAME
QTLtools trans - trans QTL analysis
SYNOPSIS
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]
DESCRIPTION
This mode maps trans (distal) quantitative trait loci (QTLs) that affect the phenotypes,
using linear regression. The method is detailed in
<https://www.nature.com/articles/ncomms15452>. We first regress out the provided
covariates from the phenotype data, followed by running the linear regression between the
phenotype residuals and the genotype. If --normal and --cov are provided at the same
time, then the residuals after the covariate correction are rank normal transformed. It
incorporates an efficient permutation scheme. You can run a nominal pass (--nominal)
listing all genotype-phenotype associations below a certain threshold, a permutation pass
(--permute or --sample no_genes_to_sample) to empirically characterize the null
distribution of associations, or adjust the nominal p-values based on permutations
(--adjust).
In the full permutation scheme (--permute) we permute all phenotypes using the same random
number sequence to preserve the correlation structure. By doing so, the only association
we actually break in the data is between the genotype and the phenotype data. Then, we
proceed with a standard association scan identical to the one used in the nominal pass.
In practice, we repeat this for 100 permutations of the phenotype data. Subsequently, we
can proceed with FDR correction by ranking all the nominal p-values in ascending order and
by counting how many p-values in the permuted data sets are smaller. This provides an FDR
estimate: if we have 500 p-values in the permuted data sets that are smaller than the
100th smallest nominal p-value, we can then assume that the FDR for the 100 first
associations is around 5% (=500/(100 × 100)).
To enable fast screening in trans, we also designed an approximation of the method
described just above based on what we already do in cis. To make it possible, we assume
that the phenotypes are independent and normally distributed (which can be enforced with
--normal). The idea is that since all phenotypes are normally distributed, effectively
they are the same, and also the cis region removed from each phenotype is so small
compared to rest of the genome that its phenotype specific impact is negligible. Hence
the number of and the correlation amongst variants for each phenotype is approximately the
same, and each phenotype is approximately the same; thus we can run permutations with a
small number of phenotypes rather then all, which drastically decreases the computational
burden and the null distribution generated can be applied to all phenotypes. The
implementation draws from the null by permuting some randomly chosen phenotypes, testing
for associations with all variants in trans and storing the smallest p-value. When we
repeat this many times (typically 1000), effectively building a null distribution of the
strongest associations for a single phenotype. We then make it continuous by fitting a
beta distribution as we do in cis and use it to adjust every nominal p-value coming from
the initial pass for the number of variants being tested. To correct for the number of
phenotypes being tested, we estimate FDR as we do in cis; that is from the best adjusted
p-values per phenotype (one per phenotype). This also gives an adjusted p-value threshold
that we use to identify all phenotype-variant pairs that are whole-genome significant. In
our experiments, this approach gives similar results to the full permutation scheme both
in term of FDR estimates and number of discoveries, while running faster.
Since linear regressions assumes normally distributed data, we highly recommend using the
--normal option to rank normal transform the phenotype quantifications in order to avoid
false positive associations due to outliers. If you are using the approximate permutation
scheme (--sample) you MUST use the --normal option or make sure that your phenotypes are
normally distributed.
OPTIONS
--vcf [in.vcf|in.bcf|in.vcf.gz|in.bed.gz]
Genotypes in VCF/BCF format, or another molecular phenotype in BED format. If
there is a DS field in the genotype FORMAT of a variant (dosage of the genotype
calculated from genotype probabilities, e.g. after imputation), then this is used
as the genotype. If there is only the GT field in the genotype FORMAT then this is
used and it is converted to a dosage. REQUIRED.
--bed quantifications.bed.gz
Molecular phenotype quantifications in BED format. REQUIRED.
--out output.txt
Output file. REQUIRED.
--cov covariates.txt
Covariates to correct the phenotype data with.
--normal
Rank normal transform the phenotype data so that each phenotype is normally
distributed. RECOMMENDED.
--window integer
Size of the cis window to remove flanking each phenotype's start position.
DEFAULT=5000000.
--threshold float
P-value threshold below which hits are reported. Give 1.0 to print everything,
which may generate a huge file. When --adjust is provided, this threshold applies
to the adjusted p-values. DEFAULT=1e-5.
--bins integer
Number of bins to use to categorize all p-values above --threshold. DEFAULT=1000.
--nominal
Calculate the nominal p-value for the genotype-phenotype associations and print out
the ones that pass the provided threshold. Mutually exclusive with --permute,
--sample and --adjust.
--permute
Permute all phenotypes together, once. For multiple permutations you need to
change the random seed using --seed for each permutation. Mutually exclusive with
--nominal, --sample and --adjust.
--sample integer
Permute randomly chosen phenotypes integer times. Mutually exclusive with
--nominal, --permute, --adjust, and --chunk.
--adjust filename
Test and adjust p-values using the null distribution in filename. Mutually
exclusive with --nominal, --permute, and --sample.
--chunk integer1 integer2
For parallelization. Divide the data into integer2 number of chunks and process
chunk number integer1. Minimum number of chunks has to be at least the same number
of chromosomes in the --bed file.
OUTPUT FILES
.hits.txt.gz
Space separated results output file detailing the variant-phenotype pairs that pass the
threshold with the following columns:
1 The phenotype ID
2 The phenotype chromosome
3 Start position of the phenotype
4 The variant ID
5 The variant chromosome
6 The start position of the variant
7 The nominal p-value of the association between the variant and the phenotype.
8 The adjusted p-value of the association between the variant and the phenotype.
Requires --adjust
9 Correlation coefficient
.best.txt.gz
Space separated output file listing the most significant variant per phenotype.
1 The phenotype ID
2 The adjusted p-value of the association between the variant and the phenotype.
Requires --adjust
3 The nominal p-value of the association between the variant and the phenotype.
4 The variant ID
.bins.txt.gz
Space separated output file containing the binning of all hits with a p-value below the
specified --threshold.
1 The index of the bin
2 The lower bound of the correlation coefficient for this bin
3 The upper bound of the correlation coefficient for this bin
4 The upper bound of the p-value for this bin
5 The lower bound of the p-value for this bin
FULL PERMUTATION ANALYSIS EXAMPLE
1 Run a nominal analysis, rank normal transforming the phenotypes and outputting all
associations with a p-value below 1e-5:
QTLtools trans --vcf genotypes.chr22.vcf.gz --bed genes.simulated.chr22.bed.gz --nominal
--normal --out trans.nominal
2 Run a full permutation analysis with 100 jobs on a compute cluster, run the following
making sure that you change the seed for each permutation iteration (qsub needs to be
changed to the job submission system used [bsub, psub, etc...])
for j in $(seq 1 100); do
echo "QTLtools trans --vcf genotypes.chr22.vcf.gz --bed genes.simulated.chr22.bed.gz
--permute --normal --out trans.perm$j.txt --seed $j" | qsub
done
APPROXIMATE PERMUTATION ANALYSIS EXAMPLE
1 Build the null distribution randomly selecting 1000 phenotypes, and rank normal
transforming the phenotypes:
QTLtools trans --vcf genotypes.chr22.vcf.gz --bed genes.simulated.chr22.bed.gz --sample
1000 --normal --out trans.sample
2 Run the nominal pass adjusting the p-values with the given null distribution, rank
normal transforming the phenotypes, and printing out associations with an adjusted p-
value less than 0.1:
QTLtools trans --vcf genotypes.chr22.vcf.gz --bed genes.simulated.chr22.bed.gz --adjust
trans.sample.best.txt.gz --threshold 0.1 --normal --out trans.adjust
SEE ALSO
QTLtools(1)
QTLtools website: <https://qtltools.github.io/qtltools>
BUGS
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.
Please submit bugs to <https://github.com/qtltools/qtltools>
CITATION
Delaneau, O., Ongen, H., Brown, A. et al. A complete tool set for molecular QTL discovery
and analysis. Nat Commun 8, 15452 (2017). <https://doi.org/10.1038/ncomms15452>
AUTHORS
Halit Ongen (halitongen@gmail.com), Olivier Delaneau (olivier.delaneau@gmail.com)