PhenX: A toolkit for interdisciplinary genetics research

Cornell University, Ithaca, New York, USA.
Current opinion in lipidology (Impact Factor: 5.66). 04/2010; 21(2):136-40. DOI: 10.1097/MOL.0b013e3283377395
Source: PubMed


To highlight standard PhenX (consensus measures for Phenotypes and eXposures) measures for nutrition, dietary supplements, and cardiovascular disease research and to demonstrate how these and other PhenX measures can be used to further interdisciplinary genetics research.
PhenX addresses the need for standard measures in large-scale genomic research studies by providing investigators with high-priority, well established, low-burden measurement protocols in a web-based toolkit ( Cardiovascular and Nutrition and Dietary Supplements are just 2 of 21 research domains and accompanying measures included in the PhenX Toolkit.
Genome-wide association studies (GWAS) provide promise for the identification of genomic markers associated with different disease phenotypes, but require replication to validate results. Cross-study comparisons typically increase statistical power and are required to understand the roles of comorbid conditions and environmental factors in the progression of disease. However, the lack of comparable phenotypic, environmental, and risk factor data forces investigators to infer and to compare metadata rather than directly combining data from different studies. PhenX measures provide a common currency for collecting data, thereby greatly facilitating cross-study analysis and increasing statistical power for identification of associations between genotypes, phenotypes, and exposures.

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    • "In order to do this effectively, a number of data sets need to be leveraged, which means that relationships between those datasets need to be made explicit so that the data may be harmonized. One goal of this effort is to enable new analyses of complex behaviors and systems [1] [2] thereby allowing researchers and policymakers to explore hypotheses about possible correlations between intervention strategies and possible health outcomes, such as the Statewide Health Information Network of New York (SHIN-NY). "
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    ABSTRACT: Increasingly, experts and interested laypeople are turning to the explosion of online data to form and explore hypotheses about relationships between public health intervention strategies and their possible impacts. We have engaged in a multi-year collaboration to use and design semantic techniques and tools to support the current and next generation of these explorations. We introduce a tool, qb.js, to enable access to multidimensional statistical data in ways that allow non-specialists to explore and create specific visualizations of that data. We focus on explorations of health data - in particular aimed at helping to support the formation and analysis of hypotheses about public health intervention strategies and their correlation with health-related behavior changes. We used qb.js to formulate and explore the hypothesis that youth tobacco access laws have consistent, measurable impacts on the rate of change in cigarette smoking among high school students over time. While focused in this instance on one particular intervention strategy (i.e., limiting youth access to tobacco), this analytics platform may be used for a wide range of correlational analyses. To address this hypothesis, we converted population science data on tobacco-related policy and behavior from Impacteen to a Resource Description framework (RDF) representation that was annotated with the RDF Data Cube vocabulary. A Semantic Data Dictionary enabled mapping between the original datasets and the RDF representation. This allowed for the creation and publication of data visualizations using qb.js. The RDF Data Cube representation made it possible to discover a significant downward effect from the introduction of nine youth tobacco access laws on the rate of change in smoking prevalence among high school-aged youth.
    System Sciences (HICSS), 2013 46th Hawaii International Conference on; 01/2013
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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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    • "Instead, it embraces methods of knowledge generation which prepare us to ask questions we may not yet be able to formulate. Extensive work has already been undertaken (Knoppers et al. 2008) to reWne the design (Burton et al. 2009; ISBER 2005; Moore et al. 2011; Wallace et al. 2009), management (Borugian et al. 2010; Litton et al. 2003; Peakman and Elliott 2008; Yuille et al. 2008) and harmonisation (Fortier et al. 2010; Stover et al. 2010; Wichmann et al. 2011) of biobanking platforms and to promote and facilitate liberal data access (P3G Consortium et al. 2009; Wolfson et al. 2010). However, biobanks and data are not ends in themselves. "
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    ABSTRACT: The promise of science lies in expectations of its benefits to societies and is matched by expectations of the realisation of the significant public investment in that science. In this paper, we undertake a methodological analysis of the science of biobanking and a sociological analysis of translational research in relation to biobanking. Part of global and local endeavours to translate raw biomedical evidence into practice, biobanks aim to provide a platform for generating new scientific knowledge to inform development of new policies, systems and interventions to enhance the public's health. Effectively translating scientific knowledge into routine practice, however, involves more than good science. Although biobanks undoubtedly provide a fundamental resource for both clinical and public health practice, their potentiating ontology--that their outputs are perpetually a promise of scientific knowledge generation--renders translation rather less straightforward than drug discovery and treatment implementation. Biobanking science, therefore, provides a perfect counterpoint against which to test the bounds of translational research. We argue that translational research is a contextual and cumulative process: one that is necessarily dynamic and interactive and involves multiple actors. We propose a new multidimensional model of translational research which enables us to imagine a new paradigm: one that takes us from bench to bedside to backyard and beyond, that is, attentive to the social and political context of translational science, and is cognisant of all the players in that process be they researchers, health professionals, policy makers, industry representatives, members of the public or research participants, amongst others.
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