Enhancing the Power of Genetic Association Studies Through the Use of Silver Standard Cases Derived from Electronic Medical Records

Overview

Journal PLoS One

Specialties General Medicine
Science

Date 2013 Jun 14

PMID 23762230

Citations 14

Authors

Andrew McDavid

Paul K Crane

Katherine M Newton

David R Crosslin

Wayne McCormick

Noah Weston

Kelly Ehrlich

Eugene Hart

Robert Harrison

Walter A Kukull

Carla Rottscheit

Peggy Peissig

Elisha Stefanski

Catherine A McCarty

Rebecca Lynn Zuvich

Marylyn D Ritchie

Jonathan L Haines

Joshua C Denny

Gerard D Schellenberg

Mariza de Andrade

Iftikhar Kullo

Rongling Li

Daniel Mirel

Andrew Crenshaw

James D Bowen

Ge Li

Debby Tsuang

Susan McCurry

Linda Teri

Eric B Larson

Gail P Jarvik

Chris S Carlson

Affiliations

Soon will be listed here.

Abstract

The feasibility of using imperfectly phenotyped "silver standard" samples identified from electronic medical record diagnoses is considered in genetic association studies when these samples might be combined with an existing set of samples phenotyped with a gold standard technique. An analytic expression is derived for the power of a chi-square test of independence using either research-quality case/control samples alone, or augmented with silver standard data. The subset of the parameter space where inclusion of silver standard samples increases statistical power is identified. A case study of dementia subjects identified from electronic medical records from the Electronic Medical Records and Genomics (eMERGE) network, combined with subjects from two studies specifically targeting dementia, verifies these results.

Citing Articles

Predicting functional effects of ion channel variants using new phenotypic machine learning methods.

Bosselmann C, Hedrich U, Lerche H, Pfeifer N PLoS Comput Biol. 2023; 19(3):e1010959.

PMID: 36877742 PMC: 10019634. DOI: 10.1371/journal.pcbi.1010959.

Smartphone-Based VO2max Measurement With Heart Snapshot in Clinical and Real-world Settings With a Diverse Population: Validation Study.

Webster D, Tummalacherla M, Higgins M, Wing D, Ashley E, Kelly V JMIR Mhealth Uhealth. 2021; 9(6):e26006.

PMID: 34085945 PMC: 8214186. DOI: 10.2196/26006.

GWAS and Beyond: Using Omics Approaches to Interpret SNP Associations.

Chen H, Petty L, Bush W, Naj A, Below J Curr Genet Med Rep. 2020; 7(1):30-40.

PMID: 33312764 PMC: 7731888. DOI: 10.1007/s40142-019-0159-z.

The eMERGE genotype set of 83,717 subjects imputed to ~40 million variants genome wide and association with the herpes zoster medical record phenotype.

Stanaway I, Hall T, Rosenthal E, Palmer M, Naranbhai V, Knevel R Genet Epidemiol. 2018; 43(1):63-81.

PMID: 30298529 PMC: 6375696. DOI: 10.1002/gepi.22167.

Diagnosed dementia and the risk of motor vehicle crash among older drivers.

Fraade-Blanar L, Hansen R, Chan K, Sears J, Thompson H, Crane P Accid Anal Prev. 2018; 113:47-53.

PMID: 29407668 PMC: 5869102. DOI: 10.1016/j.aap.2017.12.021.

References

Naj A, Jun G, Beecham G, Wang L, Vardarajan B, Buros J . Common variants at MS4A4/MS4A6E, CD2AP, CD33 and EPHA1 are associated with late-onset Alzheimer's disease. Nat Genet. 2011; 43(5):436-41. PMC: 3090745. DOI: 10.1038/ng.801. View

Moskvina V, Craddock N, Holmans P, Owen M, ODonovan M . Effects of differential genotyping error rate on the type I error probability of case-control studies. Hum Hered. 2006; 61(1):55-64. DOI: 10.1159/000092553. View

Abecasis G, Altshuler D, Auton A, Brooks L, Durbin R, Gibbs R . A map of human genome variation from population-scale sequencing. Nature. 2010; 467(7319):1061-73. PMC: 3042601. DOI: 10.1038/nature09534. View

Hirschhorn J, Lohmueller K, Byrne E, Hirschhorn K . A comprehensive review of genetic association studies. Genet Med. 2002; 4(2):45-61. DOI: 10.1097/00125817-200203000-00002. View

Teng E, Hasegawa K, Homma A, Imai Y, Larson E, Graves A . The Cognitive Abilities Screening Instrument (CASI): a practical test for cross-cultural epidemiological studies of dementia. Int Psychogeriatr. 1994; 6(1):45-58; discussion 62. DOI: 10.1017/s1041610294001602. View

Londono D, Haynes C, De La Vega F, Finch S, Gordon D . A cost-effective statistical method to correct for differential genotype misclassification when performing case-control genetic association. Hum Hered. 2010; 70(2):102-8. DOI: 10.1159/000314470. View

Larson E, Wang L, Bowen J, McCormick W, Teri L, Crane P . Exercise is associated with reduced risk for incident dementia among persons 65 years of age and older. Ann Intern Med. 2006; 144(2):73-81. DOI: 10.7326/0003-4819-144-2-200601170-00004. View

Turner S, Armstrong L, Bradford Y, Carlson C, Crawford D, Crenshaw A . Quality control procedures for genome-wide association studies. Curr Protoc Hum Genet. 2011; Chapter 1:Unit1.19. PMC: 3066182. DOI: 10.1002/0471142905.hg0119s68. View

Edwards B, Haynes C, Levenstien M, Finch S, Gordon D . Power and sample size calculations in the presence of phenotype errors for case/control genetic association studies. BMC Genet. 2005; 6:18. PMC: 1131899. DOI: 10.1186/1471-2156-6-18. View

10.

Barral S, Haynes C, Stone M, Gordon D . LRTae: improving statistical power for genetic association with case/control data when phenotype and/or genotype misclassification errors are present. BMC Genet. 2006; 7:24. PMC: 1471798. DOI: 10.1186/1471-2156-7-24. View

11.

McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan E . Clinical diagnosis of Alzheimer's disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer's Disease. Neurology. 1984; 34(7):939-44. DOI: 10.1212/wnl.34.7.939. View

12.

Roden D, Pulley J, Basford M, Bernard G, Clayton E, Balser J . Development of a large-scale de-identified DNA biobank to enable personalized medicine. Clin Pharmacol Ther. 2008; 84(3):362-9. PMC: 3763939. DOI: 10.1038/clpt.2008.89. View

13.

McCarty C, Wilke R, Giampietro P, Wesbrook S, Caldwell M . Marshfield Clinic Personalized Medicine Research Project (PMRP): design, methods and recruitment for a large population-based biobank. Per Med. 2018; 2(1):49-79. DOI: 10.1517/17410541.2.1.49. View

14.

MOTE V, Anderson R . AN INVESTIGATION OF THE EFFECT OF MISCLASSIFICATION ON THE PROPERTIES OF CHI-2-TESTS IN THE ANALYSIS OF CATEGORICAL DATA. Biometrika. 1965; 52:95-109. View

15.

McCarty C, Chisholm R, Chute C, Kullo I, Jarvik G, Larson E . The eMERGE Network: a consortium of biorepositories linked to electronic medical records data for conducting genomic studies. BMC Med Genomics. 2011; 4:13. PMC: 3038887. DOI: 10.1186/1755-8794-4-13. View

16.

Hollingworth P, Harold D, Sims R, Gerrish A, Lambert J, Carrasquillo M . Common variants at ABCA7, MS4A6A/MS4A4E, EPHA1, CD33 and CD2AP are associated with Alzheimer's disease. Nat Genet. 2011; 43(5):429-35. PMC: 3084173. DOI: 10.1038/ng.803. View

17.

Peltonen L, Altshuler D, de Bakker P, Deloukas P, Gabriel S, Gwilliam R . Integrating common and rare genetic variation in diverse human populations. Nature. 2010; 467(7311):52-8. PMC: 3173859. DOI: 10.1038/nature09298. View

18.

Ghebranious N, Mukesh B, Giampietro P, Glurich I, Mickel S, Waring S . A pilot study of gene/gene and gene/environment interactions in Alzheimer disease. Clin Med Res. 2010; 9(1):17-25. PMC: 3064752. DOI: 10.3121/cmr.2010.894. View

19.

Kullo I, Fan J, Pathak J, Savova G, Ali Z, Chute C . Leveraging informatics for genetic studies: use of the electronic medical record to enable a genome-wide association study of peripheral arterial disease. J Am Med Inform Assoc. 2010; 17(5):568-74. PMC: 2995686. DOI: 10.1136/jamia.2010.004366. View

20.

Kho A, Pacheco J, Peissig P, Rasmussen L, Newton K, Weston N . Electronic medical records for genetic research: results of the eMERGE consortium. Sci Transl Med. 2011; 3(79):79re1. PMC: 3690272. DOI: 10.1126/scitranslmed.3001807. View