RTI uses cookies to offer you the best experience online. By clicking “accept” on this website, you opt in and you agree to the use of cookies. If you would like to know more about how RTI uses cookies and how to manage them please view our Privacy Policy here. You can “opt out” or change your mind by visiting: http://optout.aboutads.info/. Click “accept” to agree.
Identifying datasets for cross-study analysis in dbGaP using PhenX
Pan, H., Bakalov, V., Cox, L., Engle, M. L., Erickson, S. W., Feolo, M., Guo, Y., Huggins, W., Hwang, S., Kimura, M., Krzyzanowski, M., Levy, J., Phillips, M., Qin, Y., Williams, D., Ramos, E. M., & Hamilton, C. M. (2022). Identifying datasets for cross-study analysis in dbGaP using PhenX. Scientific data, 9(1), Article 532. https://doi.org/10.1038/s41597-022-01660-4
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.
