Ubuntu Manpage: QTLtools fenrich - Functional enrichment of molecular QTLs

Provided by: qtltools_1.3.1+dfsg-2build2_amd64

NAME

       QTLtools fenrich - Functional enrichment of molecular QTLs

SYNOPSIS

       QTLtools fenrich --qtl significanty_genes.bed --tss gene_tss.bed --bed TFs.encode.bed.gz --out output.txt
       [OPTIONS]

DESCRIPTION

This mode allows assessing whether a set of QTLs fall within some functional annotations more often than
what is expected by chance. The method is detailed in <https://www.nature.com/articles/ncomms15452>.
Here, we mean by chance is what is expected given the non-uniform distributions of molQTLs and functional
annotations around the genomic positions of the molecular phenotypes. To do so, we first enumerate all
the functional annotations located nearby a given molecular phenotype. In practice, for X phenotypes
being quantified, we have X lists of annotations. And, for the subset Y of those having a significant
molQTL, we count how often the Y molQTLs overlap the annotations in the corresponding lists: this gives
the observed overlap frequency fobs(Y) between molQTLs and functional annotations. Then, we permute the
lists of functional annotations across the phenotypes (e.g, phenotype A may be assigned the list of
annotations coming from phenotype B) and for each permuted data set, we count how often the Y molQTLs do
overlap the newly assigned functional annotations: this gives the expected overlap frequency fexp(Y)
between molQTLs and functional annotations. By doing this permutation scheme, we keep the distribution
of functional annotations and molQTLs around molecular phenotypes unchanged. Now that we have the
observed and expected overlap frequencies, we use a fisher test to assess how fobs(Y) and fexp(Y) differ.
This gives an odd ratio estimate and a two-sided p-value which basically tells us first if there is and
enrichment or depletion, and second how significant this is.

OPTIONS

       --qtl in.bed
              List of QTLs of interest in BED format.  REQUIRED.

       --bed functional_annotation.bed.gz
              Functional annotations in BED format.  REQUIRED.

       --tss genes.bed
              List of positions of all phenotypes you mapped QTLs for, in BED format.  REQUIRED.

       --out output.txt
              Output file.  REQUIRED.

       --permute integer
              Number of permutation to run.  DEFAULT=1000

INPUT FILES

       --qtl file
        List of QTLs of interest.  An example:

        1    15210     15211     1_15211   ENSG00000227232.4   -
        1    735984    735985    1_735985  ENSG00000177757.1   +
        1    735984    735985    1_735985  ENSG00000240453.1   -
        1    739527    739528    1_739528  ENSG00000237491.4   +

        The column definitions are:

        1   The variant chromosome
        2   The variant's start position (0-based)
        3   The variant's end position (1-based)
        4   The variant ID
        5   The phenotype ID
        6   The phenotype's strand. (not used)

       --bed file
        List of annotations in BED format.  An example:

        1    254874    265487
        1    730984    735985
        1    734984    736585
        1    739527    748528

        The column definitions are:

        1   Chromosome
        2   Start position (0-based)
        3   End position (1-based)

       --tss file
        List of positions of all phenotypes you mapped QTLs for.  An example:

        1    29369     29370     ENSG00000227232.4   1_15211   -
        1    135894    135895    ENSG00000268903.1   1_985446  -
        1    137964    137965    ENSG00000269981.1   1_1118728 -
        1    317719    317720    ENSG00000237094.7   1_15211   +

        The column definitions are:

        1   Phenotype's chromosome
        2   The start position of the phenotype (0-based)
        3   The end position of the phenotype (1-based)
        4   The phenotype ID
        5   Top variant (not used)
        6   The phenotype's strand

OUTPUT FILE

       --out file
        Space separated results output file detailing the enrichment with the following columns:

        1   The observed number of QTLs falling within the functional annotations
        2   The total number of QTLs
        3   The  mean  expected  number  of  QTLs  falling  within  the  functional annotations (across multiple
            permutations)
        4   The standard deviation of the expected number of QTLs  falling  within  the  functional  annotations
            (across multiple permutations)
        5   The empirical p-value
        6   Lower bound of the 95% confidence interval of the odds ratio
        7   The odds ratio
        8   Upper bound of the 95% confidence interval of the odds ratio

EXAMPLE

       1 You need to prepare a BED file containing the positions of the QTLs of interest.  To do so, extract all
         significant hits at a given FDR threshold (e.g. 5%), and then transform the significant QTL list into a
         BED file:

         Rscript ./script/qtltools_runFDR_cis.R results.genes.full.txt.gz 0.05 results.genes
         cat  results.genes.significant.txt  |  awk  '{ print $9, $10-1, $11, $8, $1, $5 }' | tr ' ' '\t' | sort
         -k1,1V -k2,2g > results.genes.significant.bed

       2 Prepare a BED file containing the positions of all phenotypes you mapped QTLs for:

         zcat results.genes.full.txt.gz | awk '{ print $2, $3-1, $4, $1, $8, $5 }' | tr ' ' '\t' |  sort  -k1,1V
         -k2,2g > results.genes.quantified.bed

       3 Run the enrichment analysis:

         QTLtools   fenrich   --qtl   results.genes.significant.bed   --tss  results.genes.quantified.bed  --bed
         TFs.encode.bed.gz --out enrichment.QTL.in.TF.txt

BUGS

       o 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

       Olivier Delaneau (olivier.delaneau@gmail.com), Halit Ongen (halitongen@gmail.com)