pandasgwas 1.2.2

A Python package for easy retrieval of GWAS Catalog data

pandasGWAS: a Python package for easy retrieval of GWAS Catalog data

Cite this work

Cao, T., Li, A. & Huang, Y. pandasGWAS: a Python package for easy retrieval of GWAS catalog data. BMC Genomics 24, 238 (2023). https://doi.org/10.1186/s12864-023-09340-2

News

Starting from V1.2.0, pandasGWAS upgraded the version supported by Python to 3.11.
Starting from V0.99.18, pandasGWAS can cache API requests in memory.
Starting from V0.99.14, pandasGWAS can retrieve the summary statistics of the GWAS Catalog.

Installation

pip install pandasgwas

Example

Get studies related to triple-negative breast cancer:

from pandasgwas import get_studies
studies = get_studies(efo_trait = 'triple-negative breast cancer')
studies.studies[0:4]
#                  initialSampleSize                    gxe    gxg   snpCount  qualifier  imputed  pooled studyDesignComment  accessionId   fullPvalueSet  userRequested            platforms                                ancestries                                   genotypingTechnologies                             replicationSampleSize                                diseaseTrait.trait                 publicationInfo.pubmedId publicationInfo.publicationDate publicationInfo.publication               publicationInfo.title                publicationInfo.author.fullname publicationInfo.author.orcid
#0  1,529 European ancestry cases, 3,399 European ...  False  False        NaN    None     True     False        None           GCST002305      False          False      [{'manufacturer': 'Illumina'}]  [{'type': 'replication', 'numberOfIndividuals'...  [{'genotypingTechnology': 'Genome-wide genotyp...  2,148 European ancestry cases, 1,309 European ...  Breast cancer (estrogen-receptor negative, pro...         24325915                    2013-12-09                    Carcinogenesis      Genome-wide association study identifies 25 kn...           Purrington KS              0000-0002-5710-1692    
#1  8,602 European ancestry triple negative cases,...  False  False  9.700e+06       ~     True     False        None           GCST010100      False           True      [{'manufacturer': 'Illumina'}]  [{'type': 'initial', 'numberOfIndividuals': 11...  [{'genotypingTechnology': 'Genome-wide genotyp...                                                 NA  Breast cancer (estrogen-receptor negative, pro...         32424353                    2020-05-18                         Nat Genet      Genome-wide association study identifies 32 no...                 Zhang H                             None    
#2                5,631 European ancestry individuals  False  False  1.000e+07    None     True     False        None         GCST90029052      False          False                                  []  [{'type': 'initial', 'numberOfIndividuals': 56...  [{'genotypingTechnology': 'Genome-wide genotyp...                                                 NA  15-year breast cancer-specific survival (ER ne...         34407845                    2021-08-18                 Breast Cancer Res      Association of germline genetic variants with ...                 Morra A                             None

Find associated variants with study GCST002305:

from pandasgwas import get_variants
variants = get_variants(study_id='GCST002305')
variants.variants[['rsId', 'functionalClass']]
#      rsId      functionalClass   
# 0   rs4245739  3_prime_UTR_variant
# 1   rs2363956     missense_variant
# 2  rs10069690       intron_variant
# 3   rs3757318       intron_variant
# 4  rs10771399   intergenic_variant

Aggregate queried results using mathematical symbols. In addition to using the plus sign(+), the package can also use other symbols(-, &, |, ^) to perform corresponding set operations on data objects of the same type.

from pandasgwas.get_studies import get_studies
study1=get_studies(reported_trait='Suicide risk')
study2=get_studies(reported_trait="Dupuytren's disease")
study3=get_studies(reported_trait="Triglycerides")
study4=get_studies(reported_trait="Retinal vascular caliber")
study5=get_studies(reported_trait="Non-small cell lung cancer (survival)")
all_studies=study1+study2+study3+study4+study5

Summary statistics

It’s important to note that the data available on the FTP and REST API out of sync. The FTP is updated nightly with any newly ingested data. Currently, we’re unable to release more data to the REST API as it’s undergoing a complete redevelopment to help us cope with the tremendous growth in summary statistics data.

Due to the above description on the official website, pandasGWAS has established a programming interface to query summary statistics data based on FTP data.
An example to get started is as follows:

from pandasgwas.summary_statistics import search, browser, download, parse
#Search the index based on PubMed_id, study_accession_id, and EFO_trait_id. The indexed results will be returned as a DataFrame.
search_DF = search(PubMed_id='27918534', study_accession_id='GCST003966')
#Based on the index results, view the data directory on the browser.
browser(search_DF)
#Based on index results, download summary statistics data in $Home/pandasgwas_home.
download(search_DF)
#Based on the index results, load the data from $Home/pandasgwas_home and convert it into a DataFrame. 
df = parse(search_DF)

Dependencies

python: 3.11
pandas: 1.5.3
requests: 2.31.0
progressbar2: 4.2.0

Documentation

See pandasGWAS Documentation

Licensing information

Source code

MIT License

Data from NHGRI-EBI GWAS Catalog

The NHGRI-EBI GWAS Catalog and all its contents are available under the general Terms of Use for EMBL-EBI Services. Summary statistics are made available under CC0 unless otherwise stated.

Development environment

OS: Windows10 Professional
IDE: PyCharm 2022.1 (Community Edition)

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