Literature DB >> 18716918

Association of chromosome 9p21 SNPs with cardiovascular phenotypes in morbid obesity using electronic health record data.

G Craig Wood1, Christopher D Still, Xin Chu, Meghan Susek, Robert Erdman, Christina Hartman, Stephanie Yeager, Mary Ann Blosky, Wanda Krum, David J Carey, Kimberly A Skelding, Peter Benotti, Walter F Stewart, Glenn S Gerhard.   

Abstract

Genomic medicine research requires substantial time and resources to obtain phenotype data. The electronic health record offers potential efficiencies in addressing these temporal and economic challenges, but few studies have explored the feasibility of using such data for genetics research. The main objective of this study was to determine the association of two genetic variants located on chromosome 9p21 conferring susceptibility to coronary heart disease and type 2 diabetes with a variety of clinical phenotypes derived from the electronic health record in a population of morbidly obese patients. Data on more than 100 clinical measures including diagnoses, laboratory values, and medications were extracted from the electronic health records of a total of 709 morbidly obese (body mass index (BMI) >/= 40 kg/m(2)) patients. Two common single nucleotide polymorphisms located at chromosome 9p21 recently linked to coronary heart disease and type 2 diabetes (McPherson et al. Science 316:1488-1491, 2007; Saxena et al. Science 316:1331-1336, 2007; Scott et al. Science 316:1341-1345, 2007) were genotyped to assess statistical association with clinical phenotypes. Neither the type 2 diabetes variant nor the coronary heart disease variant was related to any expected clinical phenotype, although high-risk type 2 diabetes/coronary heart disease compound genotypes were associated with several coronary heart disease phenotypes. Electronic health records may be efficient sources of data for validation studies of genetic associations.

Entities:  

Year:  2008        PMID: 18716918      PMCID: PMC2518660          DOI: 10.1007/s11568-008-9023-z

Source DB:  PubMed          Journal:  Genomic Med        ISSN: 1871-7934


  21 in total

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Authors:  Krish Thiru; Alan Hassey; Frank Sullivan
Journal:  BMJ       Date:  2003-05-17

2.  A common variant on chromosome 9p21 affects the risk of myocardial infarction.

Authors:  Anna Helgadottir; Gudmar Thorleifsson; Andrei Manolescu; Solveig Gretarsdottir; Thorarinn Blondal; Aslaug Jonasdottir; Adalbjorg Jonasdottir; Asgeir Sigurdsson; Adam Baker; Arnar Palsson; Gisli Masson; Daniel F Gudbjartsson; Kristinn P Magnusson; Karl Andersen; Allan I Levey; Valgerdur M Backman; Sigurborg Matthiasdottir; Thorbjorg Jonsdottir; Stefan Palsson; Helga Einarsdottir; Steinunn Gunnarsdottir; Arnaldur Gylfason; Viola Vaccarino; W Craig Hooper; Muredach P Reilly; Christopher B Granger; Harland Austin; Daniel J Rader; Svati H Shah; Arshed A Quyyumi; Jeffrey R Gulcher; Gudmundur Thorgeirsson; Unnur Thorsteinsdottir; Augustine Kong; Kari Stefansson
Journal:  Science       Date:  2007-05-03       Impact factor: 47.728

3.  Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels.

Authors:  Richa Saxena; Benjamin F Voight; Valeriya Lyssenko; Noël P Burtt; Paul I W de Bakker; Hong Chen; Jeffrey J Roix; Sekar Kathiresan; Joel N Hirschhorn; Mark J Daly; Thomas E Hughes; Leif Groop; David Altshuler; Peter Almgren; Jose C Florez; Joanne Meyer; Kristin Ardlie; Kristina Bengtsson Boström; Bo Isomaa; Guillaume Lettre; Ulf Lindblad; Helen N Lyon; Olle Melander; Christopher Newton-Cheh; Peter Nilsson; Marju Orho-Melander; Lennart Råstam; Elizabeth K Speliotes; Marja-Riitta Taskinen; Tiinamaija Tuomi; Candace Guiducci; Anna Berglund; Joyce Carlson; Lauren Gianniny; Rachel Hackett; Liselotte Hall; Johan Holmkvist; Esa Laurila; Marketa Sjögren; Maria Sterner; Aarti Surti; Margareta Svensson; Malin Svensson; Ryan Tewhey; Brendan Blumenstiel; Melissa Parkin; Matthew Defelice; Rachel Barry; Wendy Brodeur; Jody Camarata; Nancy Chia; Mary Fava; John Gibbons; Bob Handsaker; Claire Healy; Kieu Nguyen; Casey Gates; Carrie Sougnez; Diane Gage; Marcia Nizzari; Stacey B Gabriel; Gung-Wei Chirn; Qicheng Ma; Hemang Parikh; Delwood Richardson; Darrell Ricke; Shaun Purcell
Journal:  Science       Date:  2007-04-26       Impact factor: 47.728

4.  Outcomes of preoperative weight loss in high-risk patients undergoing gastric bypass surgery.

Authors:  Christopher D Still; Peter Benotti; G Craig Wood; Glenn S Gerhard; Anthony Petrick; Mary Reed; William Strodel
Journal:  Arch Surg       Date:  2007-10

5.  Electronic health records should support clinical research.

Authors:  John Powell; Iain Buchan
Journal:  J Med Internet Res       Date:  2005-03-14       Impact factor: 5.428

6.  Replication of genome-wide association signals in UK samples reveals risk loci for type 2 diabetes.

Authors:  Eleftheria Zeggini; Michael N Weedon; Cecilia M Lindgren; Timothy M Frayling; Katherine S Elliott; Hana Lango; Nicholas J Timpson; John R B Perry; Nigel W Rayner; Rachel M Freathy; Jeffrey C Barrett; Beverley Shields; Andrew P Morris; Sian Ellard; Christopher J Groves; Lorna W Harries; Jonathan L Marchini; Katharine R Owen; Beatrice Knight; Lon R Cardon; Mark Walker; Graham A Hitman; Andrew D Morris; Alex S F Doney; Mark I McCarthy; Andrew T Hattersley
Journal:  Science       Date:  2007-04-26       Impact factor: 47.728

7.  Framingham Heart Study 100K project: genome-wide associations for cardiovascular disease outcomes.

Authors:  Martin G Larson; Larry D Atwood; Emelia J Benjamin; L Adrienne Cupples; Ralph B D'Agostino; Caroline S Fox; Diddahally R Govindaraju; Chao-Yu Guo; Nancy L Heard-Costa; Shih-Jen Hwang; Joanne M Murabito; Christopher Newton-Cheh; Christopher J O'Donnell; Sudha Seshadri; Ramachandran S Vasan; Thomas J Wang; Philip A Wolf; Daniel Levy
Journal:  BMC Med Genet       Date:  2007-09-19       Impact factor: 2.103

8.  The Framingham Heart Study 100K SNP genome-wide association study resource: overview of 17 phenotype working group reports.

Authors:  L Adrienne Cupples; Heather T Arruda; Emelia J Benjamin; Ralph B D'Agostino; Serkalem Demissie; Anita L DeStefano; Josée Dupuis; Kathleen M Falls; Caroline S Fox; Daniel J Gottlieb; Diddahally R Govindaraju; Chao-Yu Guo; Nancy L Heard-Costa; Shih-Jen Hwang; Sekar Kathiresan; Douglas P Kiel; Jason M Laramie; Martin G Larson; Daniel Levy; Chun-Yu Liu; Kathryn L Lunetta; Matthew D Mailman; Alisa K Manning; James B Meigs; Joanne M Murabito; Christopher Newton-Cheh; George T O'Connor; Christopher J O'Donnell; Mona Pandey; Sudha Seshadri; Ramachandran S Vasan; Zhen Y Wang; Jemma B Wilk; Philip A Wolf; Qiong Yang; Larry D Atwood
Journal:  BMC Med Genet       Date:  2007       Impact factor: 2.103

9.  Genome-wide association study for subclinical atherosclerosis in major arterial territories in the NHLBI's Framingham Heart Study.

Authors:  Christopher J O'Donnell; L Adrienne Cupples; Ralph B D'Agostino; Caroline S Fox; Udo Hoffmann; Shih-Jen Hwang; Erik Ingellson; Chunyu Liu; Joanne M Murabito; Joseph F Polak; Philip A Wolf; Serkalem Demissie
Journal:  BMC Med Genet       Date:  2007-09-19       Impact factor: 2.103

10.  Power to detect risk alleles using genome-wide tag SNP panels.

Authors:  Michael A Eberle; Pauline C Ng; Kenneth Kuhn; Lixin Zhou; Daniel A Peiffer; Luana Galver; Karine A Viaud-Martinez; Cynthia Taylor Lawley; Kevin L Gunderson; Richard Shen; Sarah S Murray
Journal:  PLoS Genet       Date:  2007-08-22       Impact factor: 5.917

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  14 in total

1.  Using Electronic Health Records To Generate Phenotypes For Research.

Authors:  Sarah A Pendergrass; Dana C Crawford
Journal:  Curr Protoc Hum Genet       Date:  2018-12-05

2.  Robust replication of genotype-phenotype associations across multiple diseases in an electronic medical record.

Authors:  Marylyn D Ritchie; Joshua C Denny; Dana C Crawford; Andrea H Ramirez; Justin B Weiner; Jill M Pulley; Melissa A Basford; Kristin Brown-Gentry; Jeffrey R Balser; Daniel R Masys; Jonathan L Haines; Dan M Roden
Journal:  Am J Hum Genet       Date:  2010-04-01       Impact factor: 11.025

3.  The electronic health record as a primary source of clinical phenotype for genetic epidemiological studies.

Authors:  Yoshiji Yamada
Journal:  Genomic Med       Date:  2008-07-01

4.  The VA Hypertension Primary Care Longitudinal Cohort: Electronic medical records in the post-genomic era.

Authors:  Rany M Salem; Braj Pandey; Erin Richard; Maple M Fung; Erin P Garcia; Victoria H Brophy; Nicholas J Schork; Daniel T O'Connor; Vibha Bhatnagar
Journal:  Health Informatics J       Date:  2010-12       Impact factor: 2.681

5.  Characterization of Statin Low-Density Lipoprotein Cholesterol Dose-Response Using Electronic Health Records in a Large Population-Based Cohort.

Authors:  Akinyemi Oni-Orisan; Thomas J Hoffmann; Dilrini Ranatunga; Marisa W Medina; Eric Jorgenson; Catherine Schaefer; Ronald M Krauss; Carlos Iribarren; Neil Risch
Journal:  Circ Genom Precis Med       Date:  2018-09

6.  Association of an INSIG2 obesity allele with cardiovascular phenotypes is gender and age dependent.

Authors:  Kimberly A Skelding; Glenn S Gerhard; Helen Vlachos; Faith Selzer; Sheryl F Kelsey; Xin Chu; Robert Erdman; David O Williams; Kevin E Kip
Journal:  BMC Cardiovasc Disord       Date:  2010-09-29       Impact factor: 2.298

7.  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

8.  Chapter 13: Mining electronic health records in the genomics era.

Authors:  Joshua C Denny
Journal:  PLoS Comput Biol       Date:  2012-12-27       Impact factor: 4.475

9.  Extracting research-quality phenotypes from electronic health records to support precision medicine.

Authors:  Wei-Qi Wei; Joshua C Denny
Journal:  Genome Med       Date:  2015-04-30       Impact factor: 11.117

10.  Disproportionate Contributions of Select Genomic Compartments and Cell Types to Genetic Risk for Coronary Artery Disease.

Authors:  Hong-Hee Won; Pradeep Natarajan; Amanda Dobbyn; Daniel M Jordan; Panos Roussos; Kasper Lage; Soumya Raychaudhuri; Eli Stahl; Ron Do
Journal:  PLoS Genet       Date:  2015-10-28       Impact factor: 5.917

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