Storing and interpreting genomic information in widely deployed electronic health record systems

Ury, Andrew G.

doi:10.1038/gim.2013.111

Cited by 15 publications

(13 citation statements)

References 47 publications

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“…Recently, the electronic MEdical Records and GEnomics (eMERGE) network outlined challenges and opportunities for integrating genetic data into an electronic health records (De Jager et al, 2009) system. One issue identified was the automated interpretation of genetic data (Gottesman et al, 2013; Kho et al, 2013; Marsolo and Spooner, 2013; Ury, 2013). The sheer size of genomic data provides many interpretative challenges, particularly in the age of whole genome sequencing with billions of variant base pairs, many of which are de novo .…”

Section: Introductionmentioning

confidence: 99%

Simple, standardized incorporation of genetic risk into non-genetic risk prediction tools for complex traits: coronary heart disease as an example

et al. 2014

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Purpose: Genetic risk assessment is becoming an important component of clinical decision-making. Genetic Risk Scores (GRSs) allow the composite assessment of genetic risk in complex traits. A technically and clinically pertinent question is how to most easily and effectively combine a GRS with an assessment of clinical risk derived from established non-genetic risk factors as well as to clearly present this information to patient and health care providers.Materials and Methods: We illustrate a means to combine a GRS with an independent assessment of clinical risk using a log-link function. We apply the method to the prediction of coronary heart disease (CHD) in the Atherosclerosis Risk in Communities (ARIC) cohort. We evaluate different constructions based on metrics of effect change, discrimination, and calibration.Results: The addition of a GRS to a clinical risk score (CRS) improves both discrimination and calibration for CHD in ARIC. Results are similar regardless of whether external vs. internal coefficients are used for the CRS, risk factor single nucleotide polymorphisms (SNPs) are included in the GRS, or subjects with diabetes at baseline are excluded. We outline how to report the construction and the performance of a GRS using our method and illustrate a means to present genetic risk information to subjects and/or their health care provider.Conclusion: The proposed method facilitates the standardized incorporation of a GRS in risk assessment.

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Section: Introductionmentioning

confidence: 99%

Simple, standardized incorporation of genetic risk into non-genetic risk prediction tools for complex traits: coronary heart disease as an example

et al. 2014

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“…However, because the set of known pathological variants may change over time, this approach may lead to the inclusion of false positive and the exclusion of false negative variants. Thus, an alternative solution is to archive raw data in separate repositories easily accessible when necessary [158]. …”

Section: Case Studiesmentioning

confidence: 99%

–Omic and Electronic Health Record Big Data Analytics for Precision Medicine

Wu¹,

Cheng²,

Kaddi³

et al. 2017

IEEE Trans. Biomed. Eng.

230

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Objective Rapid advances of high-throughput technologies and wide adoption of electronic health records (EHRs) have led to fast accumulation of -omic and EHR data. These voluminous complex data contain abundant information for precision medicine, and big data analytics can extract such knowledge to improve the quality of health care. Methods In this article, we present -omic and EHR data characteristics, associated challenges, and data analytics including data pre-processing, mining, and modeling. Results To demonstrate how big data analytics enables precision medicine, we provide two case studies, including identifying disease biomarkers from multi-omic data and incorporating -omic information into EHR. Conclusion Big data analytics is able to address –omic and EHR data challenges for paradigm shift towards precision medicine. Significance Big data analytics makes sense of –omic and EHR data to improve healthcare outcome. It has long lasting societal impact.

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“…72 The analyses must be delivered at the point of care via the EHR system as part of normal clinician workflow, not through a separate system. 73 Ideally, this would be accomplished with the use of rules-based decision support system standards currently being deployed such as the Clinical Quality Framework.…”

Section: Data Analytics and Visualizationmentioning

confidence: 99%

Merging Electronic Health Record Data and Genomics for Cardiovascular Research

Hall¹,

Ryan²,

Bray³

et al. 2016

Circ Cardiovasc Genet

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The process of scientific discovery is rapidly evolving. The funding climate has influenced a favorable shift in scientific discovery toward the use of existing resources such as the electronic health record. The electronic health record enables long-term outlooks on human health and disease, in conjunction with multidimensional phenotypes that include laboratory data, images, vital signs, and other clinical information. Initial work has confirmed the utility of the electronic health record for understanding mechanisms and patterns of variability in disease susceptibility, disease evolution, and drug responses. The addition of biobanks and genomic data to the information contained in the electronic health record has been demonstrated. The purpose of this statement is to discuss the current challenges in and the potential for merging electronic health record data and genomics for cardiovascular research.

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Storing and interpreting genomic information in widely deployed electronic health record systems

Cited by 15 publications

References 47 publications

Simple, standardized incorporation of genetic risk into non-genetic risk prediction tools for complex traits: coronary heart disease as an example

Simple, standardized incorporation of genetic risk into non-genetic risk prediction tools for complex traits: coronary heart disease as an example

–Omic and Electronic Health Record Big Data Analytics for Precision Medicine

Merging Electronic Health Record Data and Genomics for Cardiovascular Research

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