PheWAS analysis on large-scale biobank data with PheTK.

Autor:	Tran TC; National Human Genome Research Institute, National Institutes of Health, Bethesda, MD., Schlueter DJ; National Human Genome Research Institute, National Institutes of Health, Bethesda, MD.; University of Toronto, Ontario Canada., Zeng C; National Human Genome Research Institute, National Institutes of Health, Bethesda, MD., Mo H; National Human Genome Research Institute, National Institutes of Health, Bethesda, MD., Carroll RJ; Vanderbilt University School of Medicine, Nashville, TN., Denny JC; National Human Genome Research Institute, National Institutes of Health, Bethesda, MD.; All of Us Research Program, National Institutes of Health, Bethesda, MD.
Jazyk:	angličtina
Zdroj:	Bioinformatics (Oxford, England) [Bioinformatics] 2024 Dec 09. Date of Electronic Publication: 2024 Dec 09.
DOI:	10.1093/bioinformatics/btae719
Abstrakt:	Summary: With the rapid growth of genetic data linked to electronic health record data in huge cohorts, large-scale phenome-wide association study (PheWAS) have become powerful discovery tools in biomedical research. PheWAS is an analysis method to study phenotype associations utilizing longitudinal electronic health record (EHR) data. Previous PheWAS packages were developed mostly with smaller data sets and with earlier PheWAS approaches. PheTK was designed to simplify analysis and efficiently handle biobank-scale data. PheTK uses multithreading and supports a full PheWAS workflow including extraction of data from OMOP databases and Hail matrix tables as well as PheWAS analysis for both phecode version 1.2 and phecodeX. Benchmarking results showed PheTK took 64% less time than the R PheWAS package to complete the same workflow. PheTK can be run locally or on cloud platforms such as the All of Us Researcher Workbench (All of Us) or the UK Biobank (UKB) Research Analysis Platform (RAP). Availability and Implementation: The PheTK package is freely available on the Python Package Index, on GitHub under GNU General Public License (GPL-3) at https://github.com/nhgritctran/PheTK, and on Zenodo, DOI 10.5281/zenodo.14217954, at https://doi.org/10.5281/zenodo.14217954. PheTK is implemented in Python and platform independent. Supplementary Information: Supplementary data are available at Bioinformatics online. (Published by Oxford University Press 2024.)
Databáze:	MEDLINE
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