GenoPheno: cataloging large-scale phenotypic and next-generation sequencing data within human datasets

Abstract

Precision medicine promises to revolutionize treatment, shifting therapeutic approaches from the classical one-size-fits-all to those more tailored to the patient's individual genomic profile, lifestyle and environmental exposures. Yet, to advance precision medicine's main objective-ensuring the optimum diagnosis, treatment and prognosis for each individual-investigators need access to large-scale clinical and genomic data repositories. Despite the vast proliferation of these datasets, locating and obtaining access to many remains a challenge. We sought to provide an overview of available patient-level datasets that contain both genotypic data, obtained by next-generation sequencing, and phenotypic data-and to create a dynamic, online catalog for consultation, contribution and revision by the research community. Datasets included in this review conform to six specific inclusion parameters that are: (i) contain data from more than 500 human subjects; (ii) contain both genotypic and phenotypic data from the same subjects; (iii) include whole genome sequencing or whole exome sequencing data; (iv) include at least 100 recorded phenotypic variables per subject; (v) accessible through a website or collaboration with investigators and (vi) make access information available in English. Using these criteria, we identified 30 datasets, reviewed them and provided results in the release version of a catalog, which is publicly available through a dynamic Web application and on GitHub. Users can review as well as contribute new datasets for inclusion (Web: https://avillachlab.shinyapps.io/genophenocatalog/; GitHub: https://github.com/hms-dbmi/GenoPheno-CatalogShiny).

Keywords: Large-scale datasets; biobanks; catalog; next-generation sequencing data; phenotypic data; precision medicine.

Figure 1

Curation and usage of the catalog. After locating rich datasets containing both phenotypic and genomic data from the same patients, the descriptive information is extracted from each dataset, submitted through the Shiny App form and displayed in an interactive Shiny app, where users can run dynamic searches.

Figure 2

New submission and validation process. Users first search for the information available in the current version of the catalog. Those interested in updating or adding new information can edit the CSV file in GitHub. New information will be reviewed and the accepted changes in the CSV file in GitHub will automatically update the Web Shiny app.