Electronic Medical Records for Genetic Research: Results of the eMERGE Consortium

Kho, Abel N.; Pacheco, Jennifer A.; Peissig, Peggy L.; Rasmussen, Luke V.; Newton, Katherine M.; Weston, Noah; Crane, Paul K.; Pathak, Jyotishman; Chute, Christopher G.; Bielinski, Suzette J.; Kullo, Iftikhar J.; Li, Rongling; Chisholm, Rex L.; Denny, Joshua C.

doi:10.1126/scitranslmed.3001807

Cited by 293 publications

(218 citation statements)

References 47 publications

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“…9,38 We identified 15 chromosomal loci associated with at least one RBC trait, including 11 loci associated with RBC traits in prior cohort studies. 4,5,7 Notably, genes present in 3 of the 4 loci newly identified in individuals of European ancestry (THRB, PTPLAD1, CDT1) and in 6 of the 11 replicated loci (KLF1, ALDH8A1, CCND3, SPTA1, FBXO7, TFR2/EPO) are implicated in erythroid differentiation and regulation of cell cycle in hematopoietic stem cells.…”

Section: Discussionmentioning

confidence: 99%

Genetic Loci Implicated in Erythroid Differentiation and Cell Cycle Regulation Are Associated With Red Blood Cell Traits

Ding

Shameer

Jouni

et al. 2012

Mayo Clinic Proceedings

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Objective: To identify common genetic variants influencing red blood cell (RBC) traits. Patients and Methods: We performed a genomewide association study from June 2008 through July 2011 of hemoglobin, hematocrit, RBC count, mean corpuscular volume, mean corpuscular hemoglobin, and mean corpuscular hemoglobin concentration in 12,486 patients of European ancestry from the electronic MEdical Records and Genomics (eMERGE) network. We developed an electronic medical record-based algorithm that included individuals who had RBC measurements obtained for clinical care and excluded values measured in the setting of hematopoietic disorders, comorbid conditions, or medications known to affect RBC production or a recent history of blood loss. Results: We identified 4 new genetic loci and replicated 11 loci previously reported to be associated with one or more RBC traits in individuals of European ancestry. Notably, genes present in 3 of the 4 newly identified loci (THRB, PTPLAD1, CDT1) and in 6 of the 11 replicated loci (KLF1, ALDH8A1, CCND3, SPTA1, FBXO7, TFR2/EPO) are implicated in erythroid differentiation and regulation of cell cycle in hematopoietic stem cells. Conclusion: Genes in the erythroid differentiation and cell cycle regulation pathways influence interindividual variation in RBC indices. Our results provide insights into the molecular basis underlying variation in RBC traits.

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Section: Discussionmentioning

confidence: 99%

Genetic Loci Implicated in Erythroid Differentiation and Cell Cycle Regulation Are Associated With Red Blood Cell Traits

Ding

Shameer

Jouni

et al. 2012

Mayo Clinic Proceedings

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“…Because the main purpose of the hospital information system 10 years ago in Korea was the implementation of four less hospital drudgery (paperless, chartless, slipless, and filmless), the system focused on only digitizing clinical data from handwritten papers. After a decade, with the arrival of the big data era, we face the limitation of free text data in current EMR, i.e., inconsistent meanings for equivalent abbreviations, and the incompleteness of data [14].…”

Section: Clinical Datamentioning

confidence: 99%

Status and Direction of Healthcare Data in Korea for Artificial Intelligence

Park

Shin

2017

Hanyang Med Rev

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Predicting temporal patterns across various domains poses significant challenges due to their nuanced and often nonlinear trajectories. To address this challenge, prediction frameworks have been continuously refined, employing data-driven statistical methods, mathematical models, and machine learning. Recently, as one of the challenging systems, shared transport systems such as public bicycles have gained prominence due to urban constraints and environmental concerns. Predicting rental and return patterns at bicycle stations remains a formidable task due to the system's openness and imbalanced usage patterns across stations. In this study, we propose a deep learning framework to predict rental and return patterns by leveraging cartogram approaches. The cartogram approach facilitates the prediction of demand for newly installed stations with no training data as well as long-period prediction, which has not been achieved before. We apply this method to public bicycle rentaland-return data in Seoul, South Korea, employing a spatial-temporal convolutional graph attention network. Our improved architecture incorporates batch attention and modified node feature updates for better prediction accuracy across different time scales. We demonstrate the effectiveness of our framework in predicting temporal patterns and its potential applications.

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“…At the level of patient care, integration of genotyping order template and/or genotype result into a robust system of electronic medical record (EMR) with popup action alert and order templates for actionable pharmacogenomic tests to be used by physicians will be necessary [113,114]. At the level of research, the health information technology would enable organizational management of all research data and accessibility by the EMR [115][116][117][118]. Both the patient care-and research-level informatics should incorporate updated information when available and be linked to other health informatics such as billing, clinical laboratory, and clinical trials within the healthcare facility.…”

Section: The Multifacet Process Of Clinicalmentioning

confidence: 99%

Scientific Challenges and Implementation Barriers to Translation of Pharmacogenomics in Clinical Practice

Lam¹

2014

Omics in Clinical Practice

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The mapping of the human genome and subsequent advancements in genetic technology had provided clinicians and scientists an understanding of the genetic basis of altered drug pharmacokinetics and pharmacodynamics, as well as some examples of applying genomic data in clinical practice. This has raised the public expectation that predicting patients' responses to drug therapy is now possible in every therapeutic area, and personalized drug therapy would come sooner than later. However, debate continues among most stakeholders involved in drug development and clinical decision-making on whether pharmacogenomic biomarkers should be used in patient assessment, as well as when and in whom to use the biomarker-based diagnostic tests. Currently, most would agree that achieving the goal of personalized therapy remains years, if not decades, away. Realistic application of genomic findings and technologies in clinical practice and drug development require addressing multiple logistics and challenges that go beyond discovery of gene variants and/or completion of prospective controlled clinical trials. The goal of personalized medicine can only be achieved when all stakeholders in the field work together, with willingness to accept occasional paradigm change in their current approach.

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Electronic Medical Records for Genetic Research: Results of the eMERGE Consortium

Cited by 293 publications

References 47 publications

Genetic Loci Implicated in Erythroid Differentiation and Cell Cycle Regulation Are Associated With Red Blood Cell Traits

Genetic Loci Implicated in Erythroid Differentiation and Cell Cycle Regulation Are Associated With Red Blood Cell Traits

Status and Direction of Healthcare Data in Korea for Artificial Intelligence

Scientific Challenges and Implementation Barriers to Translation of Pharmacogenomics in Clinical Practice

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