Identifying Datasets for Cross-Study Analysis in dbGaP using PhenX

Identifying relevant studies and harmonizing datasets are major hurdles for data reuse. Common Data Elements (CDEs) can help identify comparable study datasets and reduce the burden of retrospective data harmonization, but they have not been required, historically. The collaborative team at PhenX an...

Full description

Saved in:
Bibliographic Details
Published in:Scientific data 2022-09, Vol.9 (1), p.532-532, Article 532
Main Authors: Pan, Huaqin, Bakalov, Vesselina, Cox, Lisa, Engle, Michelle L., Erickson, Stephen W., Feolo, Michael, Guo, Yuelong, Huggins, Wayne, Hwang, Stephen, Kimura, Masato, Krzyzanowski, Michelle, Levy, Josh, Phillips, Michael, Qin, Ying, Williams, David, Ramos, Erin M., Hamilton, Carol M.
Format: Article
Language:English
Subjects:
692/308/174
692/308/2056
Browsing
Data Collection
Datasets
Datasets as Topic
Humanities and Social Sciences
multidisciplinary
Retrospective Studies
Science
Science (multidisciplinary)
Variables
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Identifying relevant studies and harmonizing datasets are major hurdles for data reuse. Common Data Elements (CDEs) can help identify comparable study datasets and reduce the burden of retrospective data harmonization, but they have not been required, historically. The collaborative team at PhenX and dbGaP developed an approach to use PhenX variables as a set of CDEs to link phenotypic data and identify comparable studies in dbGaP. Variables were identified as either comparable or related, based on the data collection mode used to harmonize data across mapped datasets. We further added a CDE data field in the dbGaP data submission packet to indicate use of PhenX and annotate linkages in the future. Some 13,653 dbGaP variables from 521 studies were linked through PhenX variable mapping. These variable linkages have been made accessible for browsing and searching in the repository through dbGaP CDE-faceted search filter and the PhenX variable search tool. New features in dbGaP and PhenX enable investigators to identify variable linkages among dbGaP studies and reveal opportunities for cross-study analysis.
ISSN:2052-4463
2052-4463
DOI:10.1038/s41597-022-01660-4