Environment-Wide Association Study (Ewas) for Type 2 Diabetes in the Marshfield Personalized Medicine Research Project Biobank

Big Data as a Driver for Clinical Decision Support Systems: A Learning Health Systems Perspective

“…This data model leverages standard terminologies and coding systems for healthcare (including ICD, SNOMED, CPT, HCPSC, and LOINC) to enable interoperability with and responsiveness to evolving data standards. Examples of applications to chronic diseases include the use of PCORNet (McGlynn et al, 2014) to create a common data model for patients affected by metabolic diseases, or of eMERGE to secondary data analysis for personalized medicine and phenotype definition in Type 2 Diabetes (Yazdanpanah et al, 2013;Hall et al, 2014).…”

Informatics and Data Analytics to Support Exposome-Based Discovery for Public Health

“…Such a tool kit provides standard measures for association studies, including the name of the exposure (e.g., cigarette smoke), related exposures that may be of interest, and protocols for an exposure's measurement in a study. For example, a recent EWAS of type 2 diabetes (17) successfully ascertained self-reported indicators of exposure by implementing the measurements cataloged in PhenX. Although the current focus of PhenX is to disseminate self-reported standard instruments to assess exposure, this resource can be expanded to include quantitative markers of the exposome.…”

Phenome-Wide Association Studies: Embracing Complexity for Discovery

“…Environment-wide association studies explore the association between a comprehensive range of environmental exposures and disease outcome (much like PheWAS), identifying key environmental exposures for further study. This method has been demonstrated using laboratory [157][158][159][160] and survey [12] measures. Dietary-wide association studies are another example exploring the association between dietary exposures and phenotypes in a high-throughput fashion, again identifying key dietary exposures for further research in G × E contexts [161] , as well as being able to incorporate potential environmental exposures into high-throughput PheWAS analyses.…”

“…Headway is being made in collecting more high-throughput environmental exposure data, and environmental exposure data are now being collected at an unprecedented rate. Examples include the use of PhenX variables, such as those being collected by the Marshfield Personalized Medicine Project (Marshfield PMRP) in order to link exposures and harmonized measures to electronic health record (EHR) data [7][8][9][10][11][12] . Another example is the expanding collection of environmental exposures, such as monitoring activity levels using geographic information systems (GIS), global positioning system (GPS) technology, and accelerometers [13][14][15] .…”