Created: 2014-05-19 15:38
Updated: 2018-11-19 18:15
License: other



Swiss is a tool for pruning association scan results from a GWAS or sequencing study, and identifying regions near or in LD with previously reported GWAS signals.

Swiss implements the following procedure:

  • Prune a list of variants using LD or distance, keeping the best variant by p-value (very similar to PLINK's method.)
  • Identify which of the pruned variants are near, or in LD, with previously reported GWAS signals

Swiss supports two main formats:

  • A tab-delimited file of association results with the usual columns (CHROM, POS, SNP, PVAL)
  • An EPACTS multi-assoc file containing association p-values across a number of traits


Both tabix and plink should be somewhere on your $PATH ideally, or alternatively you must specify their locations in the config file. Use swiss --list-files to find the config file.


The latest package tarballs are here:

Version Date Install
1.0.0 08/30/2018 pip install git+


1.0.0 - 08/30/2018

Bug fixes:

  • Fixed an issue with VCFs that misuse the FILTER column. Swiss now checks if "PASS" occurs anywhere within the FILTER column, and if it does, the variant is assumed to be OK to use. Previously, Swiss expected the column simply to contain "PASS" and nothing else.

New features:

  • Support for GRCh38. EBI GWAS catalog and 1000G phase 3 genotypes in GRCh38 coordinates are both now available. Use swiss --download-data to grab the latest files. You can also now use swiss-create-data to generate new up-to-date GWAS catalogs for both GRCh37 and GRCh38.

    Note: if you previously customized your install by copying the default swiss.yaml to ~/.config/, you will need to repeat this process again to see the new LD sources (or just copy them over from the bottom of the file.)

  • Header rows beginning with "##" are now skipped in association files

  • Paths to files being used for calculating LD will now be shown in log

Backward incompatible changes:

  • 1000G phase 1 LD files have been removed since they are superseded by 1000G phase 3

1.0.0b7 - 03/03/2018

Bug fixes:

  • Fix issue when installing latest version of bx-python requiring python-lzo which does not install nicely. There is now a requirements.txt with versions pinned.

1.0.0b6 - 03/03/2018

Bug fixes:

  • Fix PLINK version detection

New features:

  • Allow passing arguments through to PLINK, use --plink-args. For example: --plink-args '--double-id --vcf-half-call missing'. You must quote the arguments to be passed through or the shell will expand them.

1.0.0b5 - 10/03/2017

Slight change in versioning scheme to more closely follow semver.

Bug fixes:

  • Previously swiss would not include the top independent variants themselves when looking for LD buddies that exist in the GWAS catalog. These would only have been picked up in the near-gwas scan and not the ld-gwas scan. Now they will correctly appear in both places. (GH #6)
  • Deprecation of pandas.DataFrame.sort -> sort_values
  • Updated NCBI URLs for swiss-create-data (thank you Daniele Di Domizio)

New features:

  • Better accounting/printing of what is happening during GWAS catalog parsing
  • Allow using existing SNP history and RsMergeArch when using swiss-create-data
  • Better display of LD (and distance) clumping settings currently in use

1.0b4 - 01/17/2016

Bug fixes:

  • Indels with very long alleles are now supported, previously they could not be used for calculating LD due to allele length limitation in PLINK

New features:

  • Include 1000G phase 3 (hg19/GRCh37) (re-run swiss --download-data)
  • Issue template for github

1.0b3 - 12/26/2016

Bug fixes:

  • Unicode error when parsing catalog

1.0b2 - 11/30/2016

New features:

  • Script to create GWAS catalog without waiting for data releases swiss-create-data - see Generate GWAS catalog for more info

1.0b1 - 11/27/2016

This version has backwards incompatible changes with the previous 0.x releases.

New features:

  • Support for indel and other types of variants

  • Much improved speed in calculating LD

  • New option --list-files will now show the current config file and data files in use

  • New option --download-data to automatically download/update when new supporting data (GWAS catalog, LD files, etc.) are available

Backwards incompatible changes:

  • Swiss is installed now as a python package, instead of a standalone directory. Some files have shifted around in locations. Use --list-files to find installed locations.

  • Swiss requires PLINK 1.9 or greater now to compute LD. It must exist on your $PATH, or the path must be set in the config file (see next).

  • Config file is no longer stored relative to the swiss root directory, but rather within the package directory. To override, you can copy the default config file to ~/.config/swiss.yaml and modify it. Use swiss --list-files to find the default config file.

  • Option --snp-col is now --variant-col. The default is "MARKER_ID". Variants in your association results file must contain both ref and alt alleles. This needs to be specified either 1) in the variant column, as EPACTS style IDs (chr:pos_ref/alt), or 2) there must be CHR, POS, REF, and ALT columns in the file.

  • The default GWAS catalog has been renamed from nhgri to ebi. Use --gwas-cat ebi to specify this catalog. It is currently only available for hg19/GRCh37, but the hg38 version will be generated soon.

  • The GWAS catalog now only contains a LOG_PVAL, rather than P_VALUE column. LOG_PVAL is -log10(p-value). As a result, .ld-gwas and .near-gwas files will have a GWAS_LOG_PVAL column, rather than the prior p-value based column.

0.9.5 - 02/18/2016

  • Update NHGRI GWAS catalog

0.9.4 - 12/4/2014

  • Fixes a potential installation issue on Debian where virtualenv would not install pip and setuptools


1. Install swiss

You can install directly from the tarball as a regular python package:

# Install globally
pip install git+

# Install in ~/.local/ instead
pip install --user git+

If you don't have administrator privileges on your machine, you can install into your home directory by adding --user. This causes pip to install packages into ~/.local/lib/python2.7/site-packages/, and binaries/scripts into ~/.local/bin/. In this case, you will want to make sure ~/.local/bin/ is in your $PATH (export PATH="/home/<user>/.local/bin:$PATH").

An alternative would be to install into a virtualenv, to keep swiss encapsulated away from your main python packages:

virtualenv swiss
source swiss/bin/activate
pip install git+
swiss --help

If you're using anaconda/miniconda, and prefer to use conda environments rather than virtualenv, you could do:

conda create -n swiss
source activate swiss
pip install git+
swiss --help

2. Install required dependencies

Swiss requires these two programs to function:

Make sure both are installed and somewhere on your $PATH.

Alternatively, you can create a user config (follow instructions by swiss --list-files) and use this to specify the paths to the plink and tabix binaries.

3. Download supporting data files (optional)

If you're planning to run swiss with your own GWAS catalog and LD files, you can skip this step. Otherwise, after installing (above), you can download all supporting data by doing:

swiss --download-data

This tries to install data into your user data directory (typically ~/.local/share/swiss on nix systems). If you want to use a different directory, copy the config file (follow instructions from swiss --list-files) and change the data_dir parameter.


Simple example

swiss --assoc my_file.txt --ld-clump --clump-p 5e-08 --out my_results

Genome build

You should always specify which genome build you're working in by using --build. By default, the build is hg19.

Additionally, if you specify your own GWAS catalog, or VCF files for calculating LD, you should verify that the positions for these match the genome build of your association results.

Association result formats

Simple format

The simplest format looks like your typical association results:

1 10 A G 1:10_A/G 5e-08
3 400 C T 3:400_C/T 1e-09

You can specify the delimiter with --delim and the names of the columns with --variant-col, --chrom-col, --pos-col, --pval-col. The defaults are listed below under options.

The "variant" column ideally is all EPACTS-formatted IDs (chr:pos_ref/alt). If they are not, then you must have a CHR, POS, REF, and ALT column so that these types of IDs can be constructed.

If you're analyzing multiple files, 1 per trait, you may want to tell swiss the name of your trait using --trait <trait>. This will include a TRAIT column in your output, which can be useful for joining results together later.

The file can be gzipped.

EPACTS multi-assoc format

Additionally, you can tell Swiss that your file is an EPACTS multi-assoc file with the --multi-assoc flag. This type of file looks like the following:

1 15903 15903 1:15903_G/GC 8448 14459.66 1 0/3/8445 0.1442 0.5 0.195 0.659 0.128
1 19190 19191 1:19190_GC/G 8448 98.23 1 8448/0/0 0.00581 0.703 0.266 0.588 -0.379
1 20316 20317 1:20316_GA/G 8448 120.46 1 8448/0/0 0.00713 0.714 -0.512 0.645 0.644
1 30967 30970 1:30967_CCCA/C 8448 47.35 1 8448/0/0 0.0028 0.322 3.15 0.296 3.32
1 51972 51975 1:51972_GGAC/G 8448 268.34 1 8448/0/0 0.01588 0.673 0.301 0.866 -0.121
1 53138 53140 1:53138_TAA/T 8448 402.05 1 8448/0/0 0.0238 0.368 -0.768 0.905 -0.103
1 54421 54421 1:54421_A/G 8448 422.81 1 8448/0/0 0.02502 0.367 -0.776 0.98 -0.0215
1 66221 66221 1:66221_A/AT 8448 338.19 1 8448/0/0 0.02002 0.0378 1.24 0.211 0.747
1 66222 66223 1:66222_TA/T 8448 298.81 1 8448/0/0 0.01769 0.0653 1.13 0.314 0.615

There are a set of columns (.P, .B) for each trait that was analyzed. The file is tab-delimited, and gzipped.

Example command line:

swiss --assoc results.epacts.gz --multi-assoc --out my_results

By default, swiss will try to run on every single trait given in the file. However, if you only wish to look at a single trait, you can use --trait instead:

swiss --assoc results.epacts.gz --multi-assoc --out my_results --trait TRAIT1

If you're running on a machine with multiple CPU cores, you can ask swiss to do multiple traits from the multi-assoc file at the same time by telling it how many to run with -T <num of parallel jobs>. Please remember these run on the same machine, and not on the cluster - do not overwhelm the machine!

LD sources

Swiss comes with a few built-in sources of LD information:

swiss --list-ld-sources

Build      LD Sources
-----      ----------
hg19       1000G_2012-03_AFR, 1000G_2012-03_AMR, 1000G_2012-03_ASN, 1000G_2012-03_EUR, GOT2D_2011-11

You can select different sources to use when LD pruning results, and when looking for GWAS catalog variants in LD. For example, you may wish to use your own genotypes for pruning (since they will cover all of your markers), but when looking for GWAS catalog variants in LD, it may be better to use a reference panel such as GoT2D for better coverage of your novel variants + known GWAS variants.

  • For the pruning step, use: --ld-clump-source <name>.
  • For the GWAS catalog LD lookup step, use: --ld-gwas-source <name>.

Both options can be the same (and in fact, if you only specify one of them, it assumes you meant to use that source for both.)

You can always provide a VCF directly to use instead of selecting a built-in one:

swiss --ld-clump-source /path/to/vcf.gz

If you have multiple VCF files split up across chromosomes, you can specify a .json file that maps chromosomes to VCF files:

swiss --ld-clump-source /path/to/vcfmap.json

Where the vcfmap.json file looks like:

  "1": "/net/got2d/cfuchsb/T2Dgo/paper/data/2657/GoT2D.chr1.final_integrated_snps_indels_sv_beagle_thunder.qc.vcf.gz",
  "10": "/net/got2d/cfuchsb/T2Dgo/paper/data/2657/GoT2D.chr10.final_integrated_snps_indels_sv_beagle_thunder.qc.vcf.gz",
  "11": "/net/got2d/cfuchsb/T2Dgo/paper/data/2657/GoT2D.chr11.final_integrated_snps_indels_sv_beagle_thunder.qc.vcf.gz",
  "12": "/net/got2d/cfuchsb/T2Dgo/paper/data/2657/GoT2D.chr12.final_integrated_snps_indels_sv_beagle_thunder.qc.vcf.gz",
  "13": "/net/got2d/cfuchsb/T2Dgo/paper/data/2657/GoT2D.chr13.final_integrated_snps_indels_sv_beagle_thunder.qc.vcf.gz",
  "14": "/net/got2d/cfuchsb/T2Dgo/paper/data/2657/GoT2D.chr14.final_integrated_snps_indels_sv_beagle_thunder.qc.vcf.gz",
  "15": "/net/got2d/cfuchsb/T2Dgo/paper/data/2657/GoT2D.chr15.final_integrated_snps_indels_sv_beagle_thunder.qc.vcf.gz",
  "16": "/net/got2d/cfuchsb/T2Dgo/paper/data/2657/GoT2D.chr16.final_integrated_snps_indels_sv_beagle_thunder.qc.vcf.gz",
  "17": "/net/got2d/cfuchsb/T2Dgo/paper/data/2657/GoT2D.chr17.final_integrated_snps_indels_sv_beagle_thunder.qc.vcf.gz",
  "18": "/net/got2d/cfuchsb/T2Dgo/paper/data/2657/GoT2D.chr18.final_integrated_snps_indels_sv_beagle_thunder.qc.vcf.gz",
  "19": "/net/got2d/cfuchsb/T2Dgo/paper/data/2657/GoT2D.chr19.final_integrated_snps_indels_sv_beagle_thunder.qc.vcf.gz",
  "2": "/net/got2d/cfuchsb/T2Dgo/paper/data/2657/GoT2D.chr2.final_integrated_snps_indels_sv_beagle_thunder.qc.vcf.gz",
  "20": "/net/got2d/cfuchsb/T2Dgo/paper/data/2657/GoT2D.chr20.final_integrated_snps_indels_sv_beagle_thunder.qc.vcf.gz",
  "21": "/net/got2d/cfuchsb/T2Dgo/paper/data/2657/GoT2D.chr21.final_integrated_snps_indels_sv_beagle_thunder.qc.vcf.gz",
  "22": "/net/got2d/cfuchsb/T2Dgo/paper/data/2657/GoT2D.chr22.final_integrated_snps_indels_sv_beagle_thunder.qc.vcf.gz",
  "3": "/net/got2d/cfuchsb/T2Dgo/paper/data/2657/GoT2D.chr3.final_integrated_snps_indels_sv_beagle_thunder.qc.vcf.gz",
  "4": "/net/got2d/cfuchsb/T2Dgo/paper/data/2657/GoT2D.chr4.final_integrated_snps_indels_sv_beagle_thunder.qc.vcf.gz",
  "5": "/net/got2d/cfuchsb/T2Dgo/paper/data/2657/GoT2D.chr5.final_integrated_snps_indels_sv_beagle_thunder.qc.vcf.gz",
  "6": "/net/got2d/cfuchsb/T2Dgo/paper/data/2657/GoT2D.chr6.final_integrated_snps_indels_sv_beagle_thunder.qc.vcf.gz",
  "7": "/net/got2d/cfuchsb/T2Dgo/paper/data/2657/GoT2D.chr7.final_integrated_snps_indels_sv_beagle_thunder.qc.vcf.gz",
  "8": "/net/got2d/cfuchsb/T2Dgo/paper/data/2657/GoT2D.chr8.final_integrated_snps_indels_sv_beagle_thunder.qc.vcf.gz",
  "9": "/net/got2d/cfuchsb/T2Dgo/paper/data/2657/GoT2D.chr9.final_integrated_snps_indels_sv_beagle_thunder.qc.vcf.gz",
  "X": "/net/got2d/cfuchsb/T2Dgo/paper/data/2657/GoT2D.chrX.final_integrated_snps_indels_sv_beagle_thunder.qc.vcf.gz"

JSON format is a little fussy, so be careful. Make sure to use double quotes like above.

Filtering results

If you provided an imputation quality column in your association results (specified with --rsq-col), swiss can remove variants below a certain threshold using --rsq-filter <threshold>.

Clumping options

LD based clumping

Swiss can clump your association results using LD. The result being that only the best variants by p-value are kept first, and the remaining variants in LD with it are dropped.

swiss --ld-clump --ld-clump-source GOT2D_2011-11 --clump-ld-thresh 0.8 --clump-p 4e-09

In the example above, variants in LD (r2) > 0.8 with the top variant per region are removed, and only variants with a p-value < 4e-09 are considered at all.

Distance based clumping

Similarly, you can prune based on distance. The best variants by p-value are retained, the remaining variants within X distance are dropped, and this process is continued until no variants remain to be considered.

swiss --dist-clump --clump-dist 250000

In the example, variants within 250kb of the best p-value variant are removed, and so forth.

GWAS catalogs

Swiss supports two types of GWAS catalogs: built-in ones that come with the program, and user-supplied catalogs.

The built-in catalogs can be found by doing:

swiss --list-gwas-cats

Build      Catalog
-----      -------
hg19       ebi

Then you can select the catalog to use by --gwas-cat fusion, for example. Build is selected with --build hg19.

The fusion catalog is an internal one maintained by our group here.

If you'd like a list of traits contained in a particular catalog:

swiss --list-gwas-traits

Available traits for GWAS catalog 'fusion':



Amino acids clumped

3-(4-hydroxyphenyl)lactate/ alpha-hydroxyisovalerate
3-phenylpropionate (hydrocinnamate)
4-acetamidobutanoate/ X-03056

You can also specify your own GWAS catalog by giving a filename instead of a codename for the catalog, like: --gwas-cat /path/to/my/

The GWAS catalog format looks like the following (tab-delimited):

rs964184 11:116648917_G/C 11 116648917 G C Vitamin E levels Vitamin E levels 11.1
rs2108622 19:15990431_C/T 19 15990431 C T Vitamin E levels Vitamin E levels 10
rs11057830 12:125307053_G/A 12 125307053 G A Vitamin E levels Vitamin E levels 8.1
rs3130573 6:31106268_A/G 6 31106268 A G Systemic sclerosis Systemic sclerosis 9.22
rs6457617 6:32663851_C/T 6 32663851 C T Systemic sclerosis Systemic sclerosis 36.7

It can contain additional columns, for example you may have citations along with each hit or other supporting information:

rs964184 11:116648917_G/C 11:116648917 11 116648917 G C Vitamin E levels Vitamin E levels 11.1 Major JM et al. Hum Mol Genet G 0.15 ZNF259,APOA5,BUD13 0.04
rs2108622 19:15990431_C/T 19:15990431 19 15990431 C T Vitamin E levels Vitamin E levels 10 Major JM et al. Hum Mol Genet T 0.21 CYP4F2 0.03
rs11057830 12:125307053_G/A 12:125307053 12 125307053 G A Vitamin E levels Vitamin E levels 8.1 Major JM et al. Hum Mol Genet A 0.15 SCARB1 0.03
rs3130573 6:31106268_A/G 6:31106268 6 31106268 A G Systemic sclerosis Systemic sclerosis 9.22 Allanore Y et al. PLoS Genet G 0.32 PSORS1C1 1.25
rs6457617 6:32663851_C/T 6:32663851 6 32663851 C T Systemic sclerosis Systemic sclerosis 36.7 Allanore Y et al. PLoS Genet T 0.5 HLA,DQB1 1.61

The extra columns will be included with the output from Swiss.

GWAS catalog lookups

After LD or distance based clumping, Swiss will look for GWAS catalog hits that are near, or in LD, with your clumped/top variants. It does both and generates two files, one for each:

  • - file contains GWAS catalog variants that were in LD with your top variants after clumping
  • - contains GWAS catalogs near your top variants by distance

You can control the LD threshold using --gwas-cat-ld <threshold> and distance threshold using --gwas-cat-dist <threshold>.

Swiss normally only includes columns from the GWAS catalog (as well as a few relevant columns from your association results) in these files. If you want to include additional columns from your assoc file:

swiss --assoc my_assoc.txt --include-cols "RSQ,EFF_AL,EFF_FREQ"

Output from Swiss

Swiss generates the two GWAS catalog lookup files (listed above), and a third .clump file containing your top variants after clumping. The files are named starting with a prefix given by --out, for example:

swiss --assoc my_assoc.txt --ld-clump --out prefix

Will create:

  • prefix.clump

The .clump file looks like this:

#CHROM BEG END MARKER_ID PVALUE BETA MRSQ TRAIT ld_with ld_with_values failed_clump
11 60784275 60784275 11:60784275_G/A 4.47E-08 -0.0992 0.98842 otPUFA pass
11 60786289 60786289 11:60786289_C/T 3.64E-10 -0.307 0.93654 otPUFA pass
11 60859791 60859791 11:60859791_C/T_rs175133 9.51E-11 0.118 0.99901 otPUFA 11:60899767_A/G_exm915580,11:60853986_A/G,11:60859624_A/C_SNP11-60616200 0.40,0.60,0.61 pass
11 60866519 60866519 11:60866519_A/ACCCAG 1.49E-11 -0.246 0.94861 otPUFA fail

The ld_with column gives a comma separated list of variants that were pruned away (if LD clumping was used.) The r2 values are given for each variant (in the same order) in the ld_with_values column.

If a variant failed LD calculation for some reason (not present in the VCF file, variant was an indel, etc.) the failed_clump column will say fail. The program will also generate a warning while running.

The and files are very similar (removing some columns for brevity):

15:58683366_A/G 15:58683366 TotFA rs4775041 15:58674695 0.54800787 LIPC Lipids HDL 3.20E-20
15:58683366_A/G 15:58683366 TotFA rs4775041 15:58674695 0.54800787 LIPC Lipids TG 1.60E-08
15:58683366_A/G 15:58683366 TotFA rs10468017 15:58678512 0.636239711 LIPC Lipids HDL 8.00E-23
15:58683366_A/G 15:58683366 TotFA rs1532085 15:58683366 1 LIPC Lipids HDL 1.00E-188
  • ASSOC_MARKER: Variant from your clumped association results (the top hit.)
  • ASSOC_CHRPOS: CHR:POS naming for the variant
  • ASSOC_TRAIT: Either taken from the multi-assoc file, or specified with --trait.
  • GWAS_SNP: The GWAS catalog variant that your ASSOC_MARKER is in LD with.
  • ASSOC_GWAS_LD: The r2 between the GWAS_SNP and the ASSOC_MARKER.
  • GWAS_PHENO: The phenotype associated with the GWAS_SNP according to the GWAS catalog.
  • GWAS_P_VALUE: P-value reported in the GWAS catalog.

The file has ASSOC_GWAS_DIST instead of ASSOC_GWAS_LD, and denotes the distance between the ASSOC_MARKER and the GWAS_SNP.

Common command-lines used

swiss --assoc example.multiassoc.epacts.gz --multi-assoc \
--build hg19 --ld-clump-source /net/snowwhite/home/welchr/projects/FFA/metsim_got2d_exomechip.json \
--ld-gwas-source /net/snowwhite/home/welchr/projects/FFA/metsim_got2d_exomechip.json \
--gwas-cat nhgri --ld-clump --clump-p 5e-08 --out example

The command above will:

  • Run on an EPACTS multiassoc file (and do all traits. To do a single trait, use --trait).
  • Use LD clumping to prune variants, and use VCF files specified by metsim_got2d_exomechip.json to do it
  • Remove any variant with p-value > 5e-08
  • Use the NHGRI GWAS catalog for looking up GWAS variants in LD with top signals
  • Again use the VCFs specified by metsim_got2d_exomechip.json to find GWAS variants in LD with top signals

swiss --assoc --delim tab --chrom-col CHROM --pos-col POS --pval-col PVAL --snp-col SNP \
--rsq-col RSQ --rsq-filter 0.3 \
--build hg19 --ld-clump-source 1000G_2012-03_EUR --ld-gwas-source 1000G_2012-03_EUR \
--gwas-cat nhgri --dist-clump --clump-p 5e-08 --clump-dist 500000 --out example

The command above will:

  • Run on a simple tab-delimited format of GWAS association results, specifying the column names directly
  • Filter variants on imputation quality 0.3
  • Clump results using distance of 500kb, and also remove variants with p > 5e-08
  • Use 1000G EUR to both LD clump AND find GWAS variants in LD with top signals

Generate GWAS catalog

Instead of waiting for data releases from swiss --download-data (which contain a GWAS catalog from EBI), you can generate your own up to date catalog with the swiss-create-data script.

Note that this script downloads some rather large files from NCBI, in order to translate GWAS catalog variants into CHR/POS/REF/ALT.

The process takes roughly an hour or two depending on your internet connection.

To generate a new catalog:

swiss-create-data --genome-build GRCh37p13 --dbsnp-build b147

This will create two files:

-rw-r----- 1 user user  22G Nov 30 18:39 GRCh37p13_b147.sqlite
-rw-r----- 1 user user 1.7M Nov 30 18:39

The first file is a SQLite database created from the downloaded NCBI dbSNP VCF. The second file is the processed GWAS catalog that can be used by swiss.

To use the catalog, you can either provide the path to it directly by using --gwas-cat /path/to/, or you can modify the config file (see swiss --list-files) and add an entry for it there.


usage: swiss [options]

  -h, --help
    show this help message and exit

    Show the locations of files in use by swiss.
    Default value is: False

    Download pre-formatted and compiled data (LD, GWAS catalogs, etc.)
    Default value is: False

  --assoc <string>
    [Required] Association results file.

    Designate that the results file is in EPACTS multi-assoc format.
    Default value is: False

  --trait <string>
    Description of phenotype for association results file. E.g. 'HDL' or 'T2D'

  --delim <string>
    Association results delimiter.
    Default value is: tab

  --build <string>
    Genome build your association results are anchored to.
    Default value is: hg19

  --variant-col <string>
    Variant column name in results file.
    Default value is: MARKER_ID

  --pval-col <string>
    P-value column name in results file.
    Default value is: PVALUE

  --chrom-col <string>
    Chromosome column name in results file.
    Default value is: CHR

  --pos-col <string>
    Position column name in results file.
    Default value is: POS

  --rsq-col <string>
    Imputation quality column name.
    Default value is: RSQ

  --trait-col <string>
    Trait column name. Can be omitted, in which case the value of --trait will be added as a column.
    Default value is: None

  --rsq-filter <string>
    Remove variants below this imputation quality.
    Default value is: None

  --filter <string>
    Give a general filter string to filter variants.
    Default value is: None

  --out <string>
    Prefix for output files.
    Default value is: swiss_output

    Clump association results by LD.
    Default value is: False

  --clump-p <string>
    P-value threshold for LD and distance based clumping.
    Default value is: 5e-08

  --clump-ld-thresh <float>
    LD threshold for clumping.
    Default value is: 0.2

  --clump-ld-dist <int>
    Distance from each significant result to calculate LD.
    Default value is: 1000000

    Clump association results by distance.
    Default value is: False

  --clump-dist <int>
    Distance threshold to use for clumping based on distance.
    Default value is: 250000

  --ld-clump-source <string>
    Name of pre-configured LD source, or a VCF file from which to compute LD.
    Default value is: 1000G_2012-03_EUR

    Print a list of available LD sources for each genome build.
    Default value is: False

  --gwas-cat <string>
    GWAS catalog to use.
    Default value is: ebi

  --ld-gwas-source <string>
    Name of pre-configured LD source or VCF file to use when calculating LD with GWAS variants.
    Default value is: 1000G_2012-03_EUR

    Give a listing of all valid GWAS catalogs and their descriptions.
    Default value is: False

    List all of the available traits in a selected GWAS catalog.
    Default value is: False

    List all of the available groupings of traits in a selected GWAS catalog.
    Default value is: False

  --gwas-cat-p <float>
    P-value threshold for GWAS catalog variants.
    Default value is: 5e-08

  --gwas-cat-ld <float>
    LD threshold for considering a GWAS catalog variant in LD.
    Default value is: 0.1

  --gwas-cat-dist <int>
    Distance threshold for considering a GWAS catalog variant 'nearby'.
    Default value is: 250000

  --include-cols <string>
    List of columns to merge in from association results (grouped by variant.)
    Default value is: None

    Perform the check of whether the GWAS catalog has variants that are not in your --ld-gwas-source.
    Default value is: False

    Skip the step of looking for GWAS hits in LD with top variants after clumping.
    Default value is: False

  --cache <string>
    Prefix for LD cache.
    Default value is: ld_cache

  -T, --threads <int>
    Number of parallel jobs to run. Only works with --multi-assoc currently.
    Default value is: 1

    Print version and exit.
    Default value is: False


The latest human genome build (hg38) is not yet supported.


Copyright (C) 2014 Ryan Welch, The University of Michigan

Swiss is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

Swiss is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see

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