Using PhenX measures to identify opportunities for cross-study analysis

RTI International, Research Triangle Park, NC 27709, USA.
Human Mutation (Impact Factor: 5.14). 05/2012; 33(5):849-57. DOI: 10.1002/humu.22074
Source: PubMed


The PhenX Toolkit provides researchers with recommended, well-established, low-burden measures suitable for human subject research. The database of Genotypes and Phenotypes (dbGaP) is the data repository for a variety of studies funded by the National Institutes of Health, including genome-wide association studies. The dbGaP requires that investigators provide a data dictionary of study variables as part of the data submission process. Thus, dbGaP is a unique resource that can help investigators identify studies that share the same or similar variables. As a proof of concept, variables from 16 studies deposited in dbGaP were mapped to PhenX measures. Soon, investigators will be able to search dbGaP using PhenX variable identifiers and find comparable and related variables in these 16 studies. To enhance effective data exchange, PhenX measures, protocols, and variables were modeled in Logical Observation Identifiers Names and Codes (LOINC®). PhenX domains and measures are also represented in the Cancer Data Standards Registry and Repository (caDSR). Associating PhenX measures with existing standards (LOINC® and caDSR) and mapping to dbGaP study variables extends the utility of these measures by revealing new opportunities for cross-study analysis.
Hum Mutat 33:849–857, 2012. Published 2012 Wiley Periodicals,Inc.†

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Available from: Heather Junkins, May 06, 2015
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    • "To overcome these obstacles, a number of innovative solutions and tools have been proposed. They aim to facilitate the recognition of research contributions, help motivate researchers and the administrators of biobanks to develop and endorse solid data sharing plans, and train junior researchers on responsible data sharing and reuse practices (Harris et al. 2012; Pan et al. 2012). However, these solutions are primarily developed and used only within a few well-connected groups and are not universally applied. "
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    ABSTRACT: Fostering data sharing is a scientific and ethical imperative. Health gains can be achieved more comprehensively and quickly by combining large, information-rich datasets from across conventionally siloed disciplines and geographic areas. While collaboration for data sharing is increasingly embraced by policymakers and the international biomedical community, we lack a common ethical and legal framework to connect regulators, funders, consortia, and research projects so as to facilitate genomic and clinical data linkage, global science collaboration, and responsible research conduct. Governance tools can be used to responsibly steer the sharing of data for proper stewardship of research discovery, genomics research resources, and their clinical applications. In this article, we propose that an international code of conduct be designed to enable global genomic and clinical data sharing for biomedical research. To give this proposed code universal application and accountability, however, we propose to position it within a human rights framework. This proposition is not without precedent: international treaties have long recognized that everyone has a right to the benefits of scientific progress and its applications, and a right to the protection of the moral and material interests resulting from scientific productions. It is time to apply these twin rights to internationally collaborative genomic and clinical data sharing.
    Human Genetics 02/2014; 133(7). DOI:10.1007/s00439-014-1432-6 · 4.82 Impact Factor
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    • "The Phenotype Finder IN Data Resources (PFINDR) initiative, put forth by the National Heart, Lung, and Blood Institute (NHLBI), aims to make various phenotype data available for GWAS related investigations. Challenges associated with non-standardized phenotype variables generated in different research institutions are widely recognized [6]. The eMERGE (Electronic medical Records and Genomics) Network [7], funded by the National Human Genome Research Institute (NHGRI), is another project dealing with the use of phenotypes collected in the electronic medical record to support GWAS. "
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    ABSTRACT: The database of Genotypes and Phenotypes (dbGaP) contains various types of data generated from genome-wide association studies (GWAS). These data can be used to facilitate novel scientific discoveries and to reduce cost and time for exploratory research. However, idiosyncrasies and inconsistencies in phenotype variable names are a major barrier to reusing these data. We addressed these challenges in standardizing phenotype variables by formalizing their descriptions using Clinical Element Models (CEM). Designed to represent clinical data, CEMs were highly expressive and thus were able to represent a majority (77.5%) of the 215 phenotype variable descriptions. However, their high expressivity also made it difficult to directly apply them to research data such as phenotype variables in dbGaP. Our study suggested that simplification of the template models makes it more straightforward to formally represent the key semantics of phenotype variables.
    PLoS ONE 09/2013; 8(9):e76384. DOI:10.1371/journal.pone.0076384 · 3.23 Impact Factor
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    • "Whereas in ''controls'', or individuals where the drug showed expected efficacy or no toxic adverse event, they may not be as detailed in their recall of other drugs, environmental, or diet exposures because they have no need to. There are a number of epidemiological survey techniques used to control this issue, which can protect from these biases (Lash and Ahern 2012; Pathak et al. 2011; Stover et al. 2010; Hamilton et al. 2011; Pan et al. 2012; Hendershot et al. 2011). Another limitation, which is also true of any retrospective clinical trial or biobank as well, is population stratification . "
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    ABSTRACT: Pharmacogenomics is emerging as a popular type of study for human genetics in recent years. This is primarily due to the many success stories and high potential for translation to clinical practice. In this review, the strengths and limitations of pharmacogenomics are discussed as well as the primary epidemiologic, clinical trial, and in vitro study designs implemented. A brief discussion of molecular and analytic approaches will be reviewed. Finally, several examples of bench-to-bedside clinical implementations of pharmacogenetic traits will be described. Pharmacogenomics continues to grow in popularity because of the important genetic associations identified that drive the possibility of precision medicine.
    Human Genetics 08/2012; 131(10):1615-26. DOI:10.1007/s00439-012-1221-z · 4.82 Impact Factor
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