Literature DB >> 32073412

Using the electronic health record for genomics research.

Maya S Safarova1, Iftikhar J Kullo.   

Abstract

PURPOSE OF REVIEW: Although primarily designed for medical documentation and billing purposes, the electronic health record (EHR) has significant potential for translational research. In this article, we provide an overview of the use of the EHR for genomics research with a focus on heritable lipid disorders. RECENT
FINDINGS: Linking the EHR to genomic data enables repurposing of vast phenotype data for genomic discovery. EHR data can be used to study the genetic basis of common and rare disorders, identify subphenotypes of diseases, assess pathogenicity of novel genomic variants, investigate pleiotropy, and rapidly assemble cohorts for genomic medicine clinical trials. EHR-based discovery can inform clinical practice; examples include use of polygenic risk scores for assessing disease risk and use of phenotype data to interpret rare variants. Despite limitations such as missing data, variable use of standards and poor interoperablility between disparate systems, the EHR is a powerful resource for genomic research.
SUMMARY: When linked to genomic data, the EHR can be leveraged for genomic discovery, which in turn can inform clinical care, exemplifying the virtuous cycle of a learning healthcare system.

Entities:  

Mesh:

Year:  2020        PMID: 32073412      PMCID: PMC9229554          DOI: 10.1097/MOL.0000000000000662

Source DB:  PubMed          Journal:  Curr Opin Lipidol        ISSN: 0957-9672            Impact factor:   4.616


  58 in total

Review 1.  Machine Learning in Medicine.

Authors:  Alvin Rajkomar; Jeffrey Dean; Isaac Kohane
Journal:  N Engl J Med       Date:  2019-04-04       Impact factor: 91.245

Review 2.  Clinical use of current polygenic risk scores may exacerbate health disparities.

Authors:  Alicia R Martin; Masahiro Kanai; Yoichiro Kamatani; Yukinori Okada; Benjamin M Neale; Mark J Daly
Journal:  Nat Genet       Date:  2019-03-29       Impact factor: 38.330

3.  Validation of electronic medical record-based phenotyping algorithms: results and lessons learned from the eMERGE network.

Authors:  Katherine M Newton; Peggy L Peissig; Abel Ngo Kho; Suzette J Bielinski; Richard L Berg; Vidhu Choudhary; Melissa Basford; Christopher G Chute; Iftikhar J Kullo; Rongling Li; Jennifer A Pacheco; Luke V Rasmussen; Leslie Spangler; Joshua C Denny
Journal:  J Am Med Inform Assoc       Date:  2013-03-26       Impact factor: 4.497

Review 4.  Mining electronic health records: towards better research applications and clinical care.

Authors:  Peter B Jensen; Lars J Jensen; Søren Brunak
Journal:  Nat Rev Genet       Date:  2012-05-02       Impact factor: 53.242

5.  Merging Electronic Health Record Data and Genomics for Cardiovascular Research: A Science Advisory From the American Heart Association.

Authors:  Jennifer L Hall; John J Ryan; Bruce E Bray; Candice Brown; David Lanfear; L Kristin Newby; Mary V Relling; Neil J Risch; Dan M Roden; Stanley Y Shaw; James E Tcheng; Jessica Tenenbaum; Thomas N Wang; William S Weintraub
Journal:  Circ Cardiovasc Genet       Date:  2016-03-14

6.  Association of Arrhythmia-Related Genetic Variants With Phenotypes Documented in Electronic Medical Records.

Authors:  Sara L Van Driest; Quinn S Wells; Sarah Stallings; William S Bush; Adam Gordon; Deborah A Nickerson; Jerry H Kim; David R Crosslin; Gail P Jarvik; David S Carrell; James D Ralston; Eric B Larson; Suzette J Bielinski; Janet E Olson; Zi Ye; Iftikhar J Kullo; Noura S Abul-Husn; Stuart A Scott; Erwin Bottinger; Berta Almoguera; John Connolly; Rosetta Chiavacci; Hakon Hakonarson; Laura J Rasmussen-Torvik; Vivian Pan; Stephen D Persell; Maureen Smith; Rex L Chisholm; Terrie E Kitchner; Max M He; Murray H Brilliant; John R Wallace; Kimberly F Doheny; M Benjamin Shoemaker; Rongling Li; Teri A Manolio; Thomas E Callis; Daniela Macaya; Marc S Williams; David Carey; Jamie D Kapplinger; Michael J Ackerman; Marylyn D Ritchie; Joshua C Denny; Dan M Roden
Journal:  JAMA       Date:  2016-01-05       Impact factor: 56.272

7.  Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data.

Authors:  Joshua C Denny; Lisa Bastarache; Marylyn D Ritchie; Robert J Carroll; Raquel Zink; Jonathan D Mosley; Julie R Field; Jill M Pulley; Andrea H Ramirez; Erica Bowton; Melissa A Basford; David S Carrell; Peggy L Peissig; Abel N Kho; Jennifer A Pacheco; Luke V Rasmussen; David R Crosslin; Paul K Crane; Jyotishman Pathak; Suzette J Bielinski; Sarah A Pendergrass; Hua Xu; Lucia A Hindorff; Rongling Li; Teri A Manolio; Christopher G Chute; Rex L Chisholm; Eric B Larson; Gail P Jarvik; Murray H Brilliant; Catherine A McCarty; Iftikhar J Kullo; Jonathan L Haines; Dana C Crawford; Daniel R Masys; Dan M Roden
Journal:  Nat Biotechnol       Date:  2013-12       Impact factor: 54.908

8.  Precision screening for familial hypercholesterolaemia: a machine learning study applied to electronic health encounter data.

Authors:  Kelly D Myers; Joshua W Knowles; David Staszak; Michael D Shapiro; William Howard; Mrinal Yadava; David Zuzick; Latoya Williamson; Nigam H Shah; Juan M Banda; Joe Leader; William C Cromwell; Ed Trautman; Michael F Murray; Seth J Baum; Seth Myers; Samuel S Gidding; Katherine Wilemon; Daniel J Rader
Journal:  Lancet Digit Health       Date:  2019-10-21

9.  A large electronic-health-record-based genome-wide study of serum lipids.

Authors:  Thomas J Hoffmann; Elizabeth Theusch; Tanushree Haldar; Dilrini K Ranatunga; Eric Jorgenson; Marisa W Medina; Mark N Kvale; Pui-Yan Kwok; Catherine Schaefer; Ronald M Krauss; Carlos Iribarren; Neil Risch
Journal:  Nat Genet       Date:  2018-03-05       Impact factor: 38.330

10.  Exploring Gaps of Family History Documentation in EHR for Precision Medicine -A Case Study of Familial Hypercholesterolemia Ascertainment.

Authors:  Saeed Mehrabi; Yanshan Wang; Donna Ihrke; Hongfang Liu
Journal:  AMIA Jt Summits Transl Sci Proc       Date:  2016-07-20
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  2 in total

1.  An Implementation Science Framework to Develop a Clinical Decision Support Tool for Familial Hypercholesterolemia.

Authors:  Hana Bangash; Laurie Pencille; Justin H Gundelach; Ahmed Makkawy; Joseph Sutton; Lenae Makkawy; Ozan Dikilitas; Stephen Kopecky; Robert Freimuth; Pedro J Caraballo; Iftikhar J Kullo
Journal:  J Pers Med       Date:  2020-07-23

2.  Prediction and risk stratification from hospital discharge records based on Hierarchical sLDA.

Authors:  Guanglei Yu; Linlin Zhang; Ying Zhang; Jiaqi Zhou; Tao Zhang; Xuehua Bi
Journal:  BMC Med Inform Decis Mak       Date:  2022-01-15       Impact factor: 2.796

  2 in total

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