garfield.run {garfield} | R Documentation

## GARFIELD enrichment analysis function

### Description

`garfield.run` is used to perform greedy pruning of variants from a genome-wide association study, calculate fold enrichment and test its significance at a given genome-wide significance threshold.

### Usage

    garfield.run(out.file, data.dir, trait, run.option = "complete",
        chrs = c(1:22, "X"), exclude = c(895, 975, 976, 977, 978, 979, 98),
        nperm = 100000, thresh = c(0.1, 0.01, 0.001, 1e-04, 1e-05, 1e-06,
        1e-07, 1e-08), pt_thresh = c(1e-05, 1e-06, 1e-07, 1e-08),
        maf.bins = 5, tags.bins = 5, tss.bins = 5, prep.file = "",
        optim_mode = 1, minit = 100, thresh_perm = 1e-04)

### Arguments

`out.file` | Prefix for output file. Full garfield analysis creates out.file.prep and out.file.perm files from prunning and annotation step and permutation for significance testing step, respectively. These steps can additionally be run separately - for more information see run.option flag.
`data.dir` | Path to annotation and p-value files. The directory must contain "annotation","maftssd","pval" and "tags" subdirectories with per chromosome files of input data. See `details` section for further information.
`trait` | GWAS phenotype name. This must match a folder name in the data.dir folder. See details for more information.
`run.option` | an object specifying which part of the analysis to run. Valid options are `complete`, `prep` and `perm`, where `prep` denotes the preparation step (pruning and annotation of variants), `perm` denotes the permutation step (calculating fold enrichment and its significance) and `complete` executes both the preparation and permutation steps.
`chrs` | A vector of the chromosomes for which to run the enrichment analysis. `chrs` can have all or subsets of values from `c(1:22,'X')`.
`exclude` | A numeric vector of indeces of annotations for which LD tags should not be used for annotation of pruned variants. Value of -1 denotes using LD for annotation for all features.
`nperm` | A numeric value of the number of permutations to be performed.
`thresh` | A numeric vector of genome-wide significance thresholds to be used for fold enrichment calculation.
`pt_thresh` | A numeric vector of genome-wide significance thresholds to be used for calculating the significance of the observed fold enrichment. All values must be contained in the `thresh` vector.
`maf.bins` | A numeric value denoting the number of bins for the minor allele frequency matching during permutation testing. Must be greater or equal to 1.
`tags.bins` | A numeric value denoting the number of bins for the number of LD tags (r2>=0.8) matching during permutation testing. Must be greater or equal to 1.
`tss.bins` | A numeric value denoting the number of bins for the distance to nearest transcription start site matching during permutation testing. Must be greater or equal to 1.
`prep.file` | File from pruning stage of algorithm. Only required if using the 'run.option'=perm flag.
`optim_mode` | A binary flag denoting whether to run fast version of method (1) or general version (0), where the fast version checks if significance of a given enrichment would still be possible to be obtained after 'minit' number of iterations and terminated permutations if not.
`minit` | An integer value for the minimum number of permutations to be performed before checking if `thresh_perm` condition can still be met. Only used if 'optim_mode'=1.
`thresh_perm` | After 'minit' number of permutations, at each iteration check if EmpPval can still reach 'thresh_perm' value. If not terminate permutations and output obtained results at that stage. Only used if 'optim_mode'=1.

### Details

Output files: out.file.prep contains the genomic positions of pruned variants, p-values for association with the trait of interest, number of LD tags (r2>0.8), MAF, distance to the nearest TSS and binary representation of annotation information (with LD-tagging r2>0.8). out.file.perm contains enrichment analysis results for each annotation, where PThresh is the GWAS p-value threshold used for analysis, FE denotes the fold enrichment statistic (equals -1 if no sufficient data was available for the FE calculation), EmpPval shows the empirical p-value of enrichment (equals -1 if FE is calculated but significance of enrichment analysis is not run at that threshold), NAnnotThresh - the number of variants at the threshold which are annotated with the given feature, NAnnot - the total number of annotated variants, NThresh - the total number of variants at that threshold and N - the total number of pruned variants. The remaining columns show additional information on the annotations used for analysis.

Data directory: data.dir, should point to a location containing "annotation","maftssd","pval" and "tags" subdirectories, where (i) the "pval" folder contains subfolders with trait names, which in turn contain per chromosome space separated files with genomic position in the first column and p-value in the second. They should be named chr1, chr2, etc, and be numerically sorted with respect to genomic position; (ii) "annotation" should contain per chromosome space separated files with position in the first column and annotations in stacked binary format in the second. The files should be named chr1, chr2, etc and be sorted numerically according to position. Additionally the directory should contain a link_file.txt file that links the annotations to relevant information about them; (iii) "maftssd" should contain per chromosome space separated files with position in the first column, minor allele frequency in the second and distance to the nearest TSS in the third. The files should be named chr1, chr2, etc and be sorted numerically according to position; (iv) "tags" should contain two subfolders named r01 and r08, which in turn contain per chromosome space separated files with variant position in the first column and comma separated positions of all variants with r2>0.1 or 0.8, respectively with the variant in the first column. The files should again be named chr1, chr2, etc and be sorted numerically according to position. For further information on the data.dir structure, please see http://www.ebi.ac.uk/birney-srv/GARFIELD/documentation/GARFIELD.pdf

Pre-computed LD (European samples - UK10K sequence data), MAF, TSS distance, p-value files for two example traits (Crohn's Disease from the IBD Consortium and Height from the GIANT consortium) and annotation files for 1005 GENCODE, ENCODE and Roadmap Epigenomics annotations can be downloaded from http://www.ebi.ac.uk/birney-srv/GARFIELD/package/garfield-data.tar.gz. Note the data is 5.9Gb in compressed format and needs to be uncompressed prior to analysis (83Gb). Variant genomic position (build 37) is used as an identifier in all data files.

### Value

No value is produced, instead output files are generated. See Details and 'out.file' for more information.

### Author(s)

Sandro Morganella <email: sm22@sanger.ac.uk>

Maintainer: Valentina Iotchkova <email: vi1@sanger.ac.uk>

### References

Valentina Iotchkova, Graham Ritchie, Matthias Geihs, Sandro Morganella, Josine Min, Klaudia Walter, Nicholas Timpson, UK10K Consortium, Ian Dunham, Ewan Birney and Nicole Soranzo. GARFIELD - GWAS Analysis of Regulatory or Functional Information Enrichment with LD correction. In preparation

### See Also

`garfield.plot`, `garfield`

### Examples

    garfield.run("tmp", data.dir=system.file("extdata",package = "garfield"),
        trait="trait",run.option = "prep", chrs = c(22),
        exclude = c(895, 975, 976, 977, 978, 979, 98))

    garfield.run("tmp", data.dir=system.file("extdata",package = "garfield"),
        run.option = "perm", nperm = 1000, thresh = c(0.001, 1e-04, 1e-05),
        pt_thresh = c(1e-04, 1e-05), maf.bins = 2, tags.bins = 3, tss.bins = 3,
        prep.file = "tmp.prep", optim_mode = TRUE, minit = 100, thresh_perm = 0.05)

    if (file.exists("tmp.perm")){
        perm = read.table("tmp.perm", header=TRUE)
        head(perm)
    } else { print("Error: tmp.perm does not exist!") }

    ###### To get the sample data for enrichment analysis in European samples
    ###### execute the following - note this can take a long time to run and
    ###### needs a substantial disk space (see Details)
    #
    # download data and decompress
    # system("wget http://www.ebi.ac.uk/birney-srv/GARFIELD/package/
    # garfield-data.tar.gz")
    # system("tar -zxvf garfield-data.tar.gz")
    #
    # if downloaded in current working directory use the following to execute
    # garfield, otherwise please change data.dir location
    # garfield.run("cd-meta.output", data.dir="garfield-data", trait="cd-meta",
    # run.option = "prep", chrs = c(1:22), exclude = c(895, 975, 976, 977, 978,
    # 979, 980))
    #
    # garfield.run("cd-meta.output", data.dir="garfield-data", run.option = "perm",
    # nperm = 100000, thresh = c(0.1,0.01,0.001, 1e-04, 1e-05, 1e-06, 1e-07, 1e-08),
    # pt_thresh = c(1e-05, 1e-06, 1e-07, 1e-08), maf.bins = 5, tags.bins = 5,
    # tss.bins = 5, prep.file = "cd-meta.output.prep", optim_mode = TRUE,
    # minit = 100, thresh_perm = 0.0001)
    #
    # garfield.plot("cd-meta.output.perm", num_perm = 100000,
    # output_prefix = "cd-meta.output", plot_title = "Crohn's Disease",
    # filter = 10, tr = -log10(0.05/498))

[Package garfield version 1.20.0 Index]