There is enormous potential for genome-wide association (GWA) studies to lead to advances in biomedical and clinical research and in translational medicine. By establishing a consensus set of measures for GWA studies, this project will increase the collective impact of these studies and lead to a better understanding of the function of the human genome and its role in health and disease. GWA studies provide a unique opportunity to identify relationships between genotype and phenotype. This genetic information can provide clues to the etiology of the disease, thus leading to the development of more effective methods of risk stratification and suggesting new targets for the development of therapeutics and prophylactics. However, a major impediment to realizing the full potential of GWA studies has been the lack of consistent measures within and across biomedical domains. The proposed work will result in the selection of high-priority phenotypic and environmental exposure measures for up to 20 biomedical domains. Effective consensus building is vital to the success of this project. Domains will be prioritized and selected according to a set of criteria that will be established at the outset of the project. Criteria for domain selection may include the expected impact of a GWA study on biomedical research in that domain, known or hypothesized environmental effects on gene expression, potential for translation to clinical research, and overall effect on global health. Working groups of domain experts will reach consensus on 10 to 15 high-priority measures for each domain. The resulting cumulative GWA Tool Set will consist of up to 300 clearly specified measures, a relevant subset of which can be selected for any specific GWA study. Efforts will be made to ensure that the chosen measures will respect and build on existing standards and that common data elements will be defined. The GWA Tool Set will provide supplementary components that will be helpful to researchers who are planning a GWA study, including core bioinformatics support. Input from the wide range of biomedical domain experts who will be gathered for consensus building will be used to develop a preliminary specification for a GWA study data analysis environment. A web portal will facilitate communication between and among groups throughout the project, providing reference materials to domain experts and an open forum to facilitate discussions and consensus building. A web-based survey will be implemented to efficiently expand the consensus building process and help attain broad acceptance in the biomedical research community. A strategy is proposed for managing the challenging meeting logistics required for an effective consensus building process. In addition, we present a multifaceted approach to disseminating the results of this project that includes the use of the Internet and submission of presentations and publications to a variety of contributors to the consensus building process.
| Hendershot, Tabitha; Pan, Huaqin; Haines, Jonathan et al. (2015) Using the PhenX Toolkit to Add Standard Measures to a Study. Curr Protoc Hum Genet 86:1.21.1-17 |
| Conway, Kevin P; Vullo, Genevieve C; Kennedy, Ashley P et al. (2014) Data compatibility in the addiction sciences: an examination of measure commonality. Drug Alcohol Depend 141:153-8 |
| McCarty, Catherine A; Huggins, Wayne; Aiello, Allison E et al. (2014) PhenX RISING: real world implementation and sharing of PhenX measures. BMC Med Genomics 7:16 |
| McCarty, Catherine A; Berg, Richard; Rottscheit, Carla M et al. (2014) Validation of PhenX measures in the personalized medicine research project for use in gene/environment studies. BMC Med Genomics 7:3 |
| Haskell, William L; Troiano, Richard P; Hammond, Jane A et al. (2012) Physical activity and physical fitness: standardizing assessment with the PhenX Toolkit. Am J Prev Med 42:486-92 |
| Whitehead, Nedra S; Hammond, Jane A; Williams, Michelle A et al. (2012) The PhenX Toolkit pregnancy and birth collections. Ann Epidemiol 22:753-8 |
| Pan, Huaqin; Tryka, Kimberly A; Vreeman, Daniel J et al. (2012) Using PhenX measures to identify opportunities for cross-study analysis. Hum Mutat 33:849-57 |
| Hendershot, Tabitha; Pan, Huaqin; Haines, Jonathan et al. (2011) Using the PhenX Toolkit to Add Standard Measures to a Study. Curr Protoc Hum Genet Chapter 1:Unit1.21 |
| Hamilton, Carol M; Strader, Lisa C; Pratt, Joseph G et al. (2011) The PhenX Toolkit: get the most from your measures. Am J Epidemiol 174:253-60 |
| Schad, Peter A; Mobley, Lee Rivers; Hamilton, Carol M (2011) Building a biomedical cyberinfrastructure for collaborative research. Am J Prev Med 40:S144-50 |
Showing the most recent 10 out of 12 publications
