For a list of available instances of PheWeb, navigate here. For a walk-through demo see here. If you have questions or comments, check out our Google Group.
If you use the PheWeb code base for your work, please cite our paper:
Gagliano Taliun, S.A., VandeHaar, P. et al. Exploring and visualizing large-scale genetic associations by using PheWeb. Nat Genet 52, 550–552 (2020).
If this is broken, open an issue on github and hopefully I can help.
pip3 install pheweb- If that doesn't work, follow the detailed install instructions.
mkdir ~/my-new-pheweb && cd ~/my-new-pheweb
This directory will store all the files pheweb makes for your dataset. All pheweb ... commands should be run in this directory.
Make config.py in this directory. In it, either set hg_build_number = 19 or hg_build_number = 38. Other options you can set are listed here.
You need one file for each phenotype. Most common GWAS file formats should work. Here are the requirements:
- It needs a header row.
- Columns can be delimited by tabs, spaces, or commas.
- It needs a column for the reference allele (which must always match the bases on the reference genome that you specified with
hg_build_number) and a column for the alternate allele. If you have aMARKER_IDcolumn like1:234_C/G, that's okay too. If you have an allele1 and allele2, and sometimes one or the other is the reference, then you'll need to modify your files. - It can be gzipped if you want.
- Variants must be sorted by chromosome and position, with chromosomes in the order [1-22,X,Y,MT].
The file must have columns for:
| column description | name | other allowed column names | allowed values |
|---|---|---|---|
| chromosome | chrom |
#chrom, chr |
1-22, X, Y, M, MT, chr1, etc |
| position | pos |
beg, begin, bp |
integer |
| reference allele | ref |
reference |
must match reference genome |
| alternate allele | alt |
alternate |
anything |
| p-value | pval |
pvalue, p, p.value |
number in [0,1] |
You may also have columns for:
| column description | name | other allowed column names | allowed values |
|---|---|---|---|
| minor allele frequency | maf |
number in (0,0.5] | |
| allele frequency (of alternate allele) | af |
a1freq, frq |
number in (0,1) |
| AF among cases | case_af |
af.cases |
number in (0,1) |
| AF among controls | control_af |
af.controls |
number in (0,1) |
| allele count | ac |
integer | |
| effect size (of alternate allele) | beta |
number | |
| standard error of effect size | sebeta |
se |
number |
| odds ratio (of alternate allele) | or |
number | |
| R2 | r2 |
number | |
| number of samples | num_samples |
ns, n |
integer, must be the same for every variant in its phenotype |
| number of controls | num_controls |
ns.ctrl, n_controls |
integer, must be the same for every variant in its phenotype |
| number of cases | num_cases |
ns.case, n_cases |
integer, must be the same for every variant in its phenotype |
Column names are case-insensitive. If your file has a different column name, set field_aliases = {"column_name": "field_name"} in config.py. For example, field_aliases = {'P_BOLT_LMM_INF': 'pval', 'NSAMPLES': 'num_samples'}.
Any field can be null if it is one of ['', '.', 'NA', 'N/A', 'n/a', 'nan', '-nan', 'NaN', '-NaN', 'null', 'NULL']. If a required field is null, the variant gets dropped.
If your pval is log10 (like in REGENIE output), then set these variables in config.py: pval_is_neglog10 = True and field_aliases = {'LOGP':'pval'}.
Inside of your data directory, you need a file named pheno-list.json that looks like this:
[
{
"assoc_files": ["/home/peter/data/ear-length.gz"],
"phenocode": "ear-length"
},
{
"assoc_files": ["/home/peter/data/a1c.X.gz","/home/peter/data/a1c.autosomal.gz"],
"phenocode": "A1C"
}
]Each phenotype needs assoc_files (a list of paths to association files) and phenocode (a string representing your phenotype that is used in filenames and URLs, comprised of [A-Za-z0-9_~-]).
If you want, you can also include:
phenostring(string): a name for the phenotype. Shown in tables and tooltips and page headers.category(string): groups together phenotypes in the PheWAS plot. Shown in tables and tooltips.num_cases,num_controls, and/ornum_samples(number): if your input data only hasACorMAC, this will be used to calculatedAForMAF. Shown in tooltips. If your input data has correctly-named columns for these, the commandpheweb phenolist read-info-from-association-fileswill add them into your existingpheno-list.json.- anything else you want, but you'll have to modify templates to use it.
You can use a csv by running:
pheweb phenolist import-phenolist "/path/to/pheno-list.csv"
or you can make one from scratch by running:
pheweb phenolist glob --star-is-phenocode "/home/peter/data/*.gz"
You can see other methods here.
Run pheweb process.
To distribute jobs across a cluster, follow these instructions.
To include VEP annotations, follow these instructions.
If something breaks and you can't understand the error message or it's something that PheWeb should support by default, open an issue on github or email me.
Run pheweb serve --open.
That command should either open a browser to your new PheWeb, or it should give you a URL that you can open in your browser to access your new PheWeb. If it doesn't, follow the directions for hosting a PheWeb and accessing it from your browser.
To run pheweb through systemd, see sample file here. To use Apache2 or Nginx, see instructions here. To require login via OAuth, see instructions here. To track page views with Google Analytics, see instructions here. To reduce storage use, see instructions here. To customize page contents, see instructions here.
PheWeb can display genetic correlations generated by another tool.
To use this feature, set show_correlations = True in config.py and place the output of the rg pipeline as pheno-correlations.txt in the same folder as pheno-list.json.
To hide the button for downloading summary stats, add download_pheno_sumstats = "secret" and SECRET_KEY = "your random string" in config.py. That will make a secret page (printed to the console when you start the server) to share summary stats.
To hide the button for downloading top hits and phenotypes, add download_top_hits = "hide" and download_phenotypes = "hide" respectively.
To allow dynamically filtering the manhattan plot, run pheweb best-of-pheno and set show_manhattan_filter_button=True in config.py.
See instructions here.
See documentation about the files in generated-by-pheweb/ here.
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