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:
Swiss supports two main formats:
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 version is:
| Version | Date | Install |
|---|---|---|
| 1.1.1 | 10/31/2019 | pip install git+https://github.com/welchr/swiss.git@v1.1.1 |
Please see the changelog for a list of recent bug fixes and new features.
You can install directly from the tarball as a regular python package:
# Install globally
pip install git+https://github.com/welchr/swiss.git@v1.0b4
# Install in ~/.local/ instead
pip install --user git+https://github.com/welchr/swiss.git@v1.0b4If 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+https://github.com/welchr/swiss.git@v1.0b4
swiss --helpIf 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+https://github.com/welchr/swiss.git@v1.0b4
swiss --helpSwiss 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.
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-dataThis 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.
swiss --assoc my_file.txt --ld-clump --clump-p 5e-08 --out my_resultsYou 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.
The simplest format looks like your typical association results:
| CHR | POS | REF | ALT | MARKER_ID | PVALUE |
|---|---|---|---|---|---|
| 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.
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:
| #CHROM | BEG | END | MARKER_ID | NS | AC | CALLRATE | GENOCNT | MAF | TRAIT1.P | TRAIT1.B | TRAIT2.P | TRAIT2.B |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 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_resultsBy 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 TRAIT1If 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!
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_2011-11You 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.
--ld-clump-source <name>.--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.gzIf 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.jsonWhere 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.
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>.
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-09In 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.
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 250000In the example, variants within 250kb of the best p-value variant are removed, and so forth.
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 ebiThen 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':
APOA1B
------
ApoA1
ApoB
ApoB/ApoA1
Amino acids clumped
-------------------
2-aminobutyrate
2-hydroxyisobutyrate
3-(4-hydroxyphenyl)lactate
3-(4-hydroxyphenyl)lactate/ alpha-hydroxyisovalerate
3-phenylpropionate (hydrocinnamate)
4-acetamidobutanoate/ X-03056
5-oxoprolineYou can also specify your own GWAS catalog by giving a filename instead of a codename for the catalog, like: --gwas-cat /path/to/my/gwascat.tab.
The GWAS catalog format looks like the following (tab-delimited):
| VARIANT | EPACTS | CHR | POS | REF | ALT | GROUP | PHENO | LOG_PVAL |
|---|---|---|---|---|---|---|---|---|
| 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:
| VARIANT | EPACTS | CHRPOS | CHR | POS | REF | ALT | PHENO | GROUP | LOG_PVAL | CITATION | RISK_ALLELE | RISK_AL_FREQ | GENE_LABEL | OR_BETA |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 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.
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:
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"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 prefixWill create:
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 .ld-gwas.tab and .near-gwas.tab files are very similar (removing some columns for brevity):
| ASSOC_MARKER | ASSOC_CHRPOS | ASSOC_TRAIT | GWAS_SNP | GWAS_CHRPOS | ASSOC_GWAS_LD | GWAS_GENE_LABEL | GWAS_Group | GWAS_PHENO | GWAS_P_VALUE |
|---|---|---|---|---|---|---|---|---|---|
| 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 |
--trait.The .near-gwas.tab file has ASSOC_GWAS_DIST instead of ASSOC_GWAS_LD, and denotes the distance between the ASSOC_MARKER and the GWAS_SNP.
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 exampleThe command above will:
--trait).swiss --assoc my_results.tab --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 exampleThe command above will:
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 b147This 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 gwascat_ebi_GRCh37p13.tabThe 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/gwascat_ebi_GRCh37p13.tab, 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
--list-files
Show the locations of files in use by swiss.
Default value is: False
--download-data
Download pre-formatted and compiled data (LD, GWAS catalogs, etc.)
Default value is: False
--assoc <string>
[Required] Association results file.
--multi-assoc
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
--ld-clump
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
--dist-clump
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
--list-ld-sources
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
--list-gwas-cats
Give a listing of all valid GWAS catalogs and their descriptions.
Default value is: False
--list-gwas-traits
List all of the available traits in a selected GWAS catalog.
Default value is: False
--list-gwas-trait-groups
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
--do-overlap-check
Perform the check of whether the GWAS catalog has variants that are not in your --ld-gwas-source.
Default value is: False
--skip-gwas
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
--version
Print version and exit.
Default value is: FalseThe latest human genome build (hg38) is not yet supported.
1.1.1 - 10/31/2019
Bug fixes:
swiss-create-data caused by invalid unicode characters in rsIDs from GWAS catalog1.1.0 - 10/24/2019
Bug fixes:
Fixed an issue where VCFs with chromosomes specified as 'chr#' instead of simply '#' would cause swiss to send no output to PLINK, which produced a red herring "File read failure" message.
Existence of tabix index was not previously checked
P-values exceeding double precision were not properly handled and would result in p-value of 0.0 in result file
New features:
--logp-col option to denote which column contains log10 p-values. Note that this is exactly log10(p-value), and not -log10. The reason for this is that the most popular meta-analysis program METAL outputs log10(p) when using the LOGPVALUE ON option.1.0.0 - 08/30/2018
Bug fixes:
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:
1.0.0b7 - 03/03/2018
Bug fixes:
1.0.0b6 - 03/03/2018
Bug fixes:
New features:
--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:
near-gwas scan and not the ld-gwas scan. Now they will correctly appear in both places. (GH #6)pandas.DataFrame.sort -> sort_valuesNew features:
1.0b4 - 01/17/2016
Bug fixes:
New features:
swiss --download-data)1.0b3 - 12/26/2016
Bug fixes:
1.0b2 - 11/30/2016
New features:
swiss-create-data - see Generate GWAS catalog for more info1.0b1 - 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
0.9.4 - 12/4/2014
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 http://www.gnu.org/licenses/.