Improving the power of genetic association tests with imperfect phenotype derived from electronic medical records

Human Genetics

Volumn 133, Issue 11, 2014, Pages 1369-1382

  Sinnott, Jennifer A ^a   Dai, Wei ^a   Liao, Katherine P ^b   Shaw, Stanley Y ^c   Ananthakrishnan, Ashwin N ^c   Gainer, Vivian S ^d   Karlson, Elizabeth W ^b   Churchill, Susanne ^e   Szolovits, Peter ^f   Murphy, Shawn ^c,d   Kohane, Isaac ^e,g   Plenge, Robert ^h   Cai, Tianxi ^a  

^a HARVARD SCHOOL OF PUBLIC HEALTH   (United States)

^b BRIGHAM AND WOMEN S HOSPITAL   (United States)

^c MASSACHUSETTS GENERAL HOSPITAL   (United States)

^d PARTNERS HEALTHCARE   (United States)

^e I2b2 National Center for Biomedical Computing   (United States)

^f MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^g Harvard Medical School Center for Biomedical Informatics   (United States)

^h MERCK RESEARCH LABORATORIES   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURACY; ALGORITHM; ARTICLE; COHORT ANALYSIS; COMPUTER PROGRAM; CONTROLLED STUDY; ELECTRONIC MEDICAL RECORD; GENE FREQUENCY; GENETIC ASSOCIATION; GENETIC RISK; GENOTYPE; HOSPITAL BASED CASE CONTROL STUDY; HUMAN; PHENOTYPE; RHEUMATOID ARTHRITIS; ARTHRITIS, RHEUMATOID; BIOLOGICAL MODEL; CASE CONTROL STUDY; CLASSIFICATION; COMPUTER SIMULATION; EPIDEMIOLOGY; GENETICS; MEDICAL AUDIT; PREVALENCE; PROCEDURES; SAMPLE SIZE; UNITED STATES;

ALGORITHMS; ARTHRITIS, RHEUMATOID; CASE-CONTROL STUDIES; COHORT STUDIES; COMPUTER SIMULATION; ELECTRONIC HEALTH RECORDS; GENETIC ASSOCIATION STUDIES; GENETIC RESEARCH; GENOTYPE; HUMANS; MEDICAL AUDIT; MODELS, GENETIC; PHENOTYPE; PREVALENCE; SAMPLE SIZE; SOFTWARE; UNITED STATES;

EID: 84919418380     PISSN: 03406717     EISSN: 14321203     Source Type: Journal    
DOI: 10.1007/s00439-014-1466-9     Document Type: Article

References (31)

1. Ananthakrishnan AN, Cai T, Savova G, Cheng SC, Chen P, Perez RG, Gainer VS, Murphy SN, Szolovits P, Xia Z et al (2013) Improving case definition of Crohn’s disease and ulcerative colitis in electronic medical records using natural language processing: a novel informatics approach. Inflam Bowel Dis 19(7):1411–1420
2. Breslow NE, Day NE et al (1980) Statistical methods in cancer research. The analysis of case–control studies, vol 1. Distributed for IARC by WHO, Geneva
3. Brinkman B, Huizinga T, Kurban S, Van der Velde E, Schreuder G, Hazes J, Breedveld F, Verweij C (1997) Tumour necrosis factor alpha gene polymorphisms in rheumatoid arthritis: association with susceptibility to, or severity of, disease? Rheumatology 36(5):516–521
4. Carroll RJ, Ruppert D, Stefanski LA, Crainiceanu CM (2012a) Measurement error in nonlinear models: a modern perspective. CRC Press
5. Carroll RJ, Thompson WK, Eyler AE, Mandelin AM, Cai T, Zink RM, Pacheco JA, Boomershine CS, Lasko TA, Xu H et al (2012b) Portability of an algorithm to identify rheumatoid arthritis in electronic health records. J Am Med Inf Assoc 19(e1):e162–e169
6. Denny J, Ritchie M, Basford M, Pulley J, Bastarache L, Brown-Gentry K, Wang D, Masys D, Roden D, Crawford D (2010) Phewas: demonstrating the feasibility of a phenome-wide scan to discover gene-disease associations. Bioinformatics 26(9):1205–1210
7. Denny JC, Crawford DC, Ritchie MD, Bielinski SJ, Basford MA, Bradford Y, Chai HS, Bastarache L, Zuvich R, Peissig P et al (2011) Variants near foxe1 are associated with hypothyroidism and other thyroid conditions: using electronic medical records for genome-and phenome-wide studies. Am J Hum Genet 89(4):529–542
8. Denny JC, Bastarache L, Ritchie MD, Carroll RJ, Zink R, Mosley JD, Field JR et al (2013) Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data. Nat Biotechnol 31(12):1102–1111
9. Gabriel SE (1994) The sensitivity and specificity of computerized databases for the diagnosis of rheumatoid arthritis. Arthritis Rheum 37(6):821–823
10. Gonzalez-Gay MA, Garcia-Porrua C, Hajeer AH (2002) Influence of human leukocyte antigen-DRB1 on the susceptibility and severity of rheumatoid arthritis. Semin Arthritis Rheum 31(6):355–360
11. Gordon D, Finch SJ, Nothnagel M (2002) Power and sample size calculations for case–control genetic association tests when errors are present: application to single nucleotide polymorphisms. Hum Hered 54(1):22–33
12. Kastbom A, Verma D, Eriksson P, Skogh T, Wingren G, Söderkvist P (2008) Genetic variation in proteins of the cryopyrin inflammasome influences susceptibility and severity of rheumatoid arthritis (the swedish tira project). Rheumatology 47(4):415–417
13. Katz J, Barrett J, Liang M, Bacon A, Kaplan H, Kieval R, Lindsey S, Roberts W, Sheff D, Spencer R et al (1997) Sensitivity and positive predictive value of medicare part b physician claims for rheumatologic diagnoses and procedures. Arthritis Rheum 40(9):1594–1600
14. Kho A, Hayes M, Rasmussen-Torvik L, Pacheco J, Thompson W, Armstrong L, Denny J, Peissig P, Miller A, Wei W et al (2012) Use of diverse electronic medical record systems to identify genetic risk for type 2 diabetes within a genome-wide association study. J Am Med Inf Assoc 19(2):212–218
15. Kohane I (2011) Using electronic health records to drive discovery in disease genomics. Nat Rev Genet 12(6):417–428
16. Kullback S (1959) Information theory and statistics. Wiley, New York
17. Kurreeman F, Liao K, Chibnik L, Hickey B, Stahl E, Gainer V, Li G, Bry L, Mahan S, Ardlie K et al (2011) Genetic basis of autoantibody positive and negative rheumatoid arthritis risk in a multi-ethnic cohort derived from electronic health records. Am J Hum Genet 88(1):57–69
18. Liao K, Cai T, Gainer V, Goryachev S, Zeng-treitler Q, Raychaudhuri S, Szolovits P, Churchill S, Murphy S, Kohane I et al (2010) Electronic medical records for discovery research in rheumatoid arthritis. Arthritis Care Res 62(8):1120–1127
19. Magder LS, Hughes JP (1997) Logistic regression when the outcome is measured with uncertainty. Am J Epidemiol 146(2):195–203
20. McCarthy MI, Abecasis GR, Cardon LR, Goldstein DB, Little J, Ioannidis JP, Hirschhorn JN (2008) Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nat Rev Genet 9(5):356–369
21. McDavid A, Crane PK, Newton KM, Crosslin DR, McCormick W, Weston N, Ehrlich K, Hart E, Harrison R, Kukull WA et al (2013) Enhancing the power of genetic association studies through the use of silver standard cases derived from electronic medical records. PLoS One 6(6):e63481
22. Neuhaus JM (1999) Bias and efficiency loss due to misclassified responses in binary regression. Biometrika 86(4):843–855
23. Newton KM, Peissig PL, Kho AN, Bielinski SJ, Berg RL, Choudhary V, Basford M, Chute CG, Kullo IJ, Li R, Pacheco JA, Rasmussen LV, Spangler L, Denny JC (2013) Validation of electronic medical record-based phenotyping algorithms: results and lessons learned from the eMERGE network. J Am Med Inf Assoc 20(e1):e147–e154
24. Perlis R, Iosifescu D, Castro V, Murphy S, Gainer V, Minnier J, Cai T, Goryachev S, Zeng Q, Gallagher P et al (2011) Using electronic medical records to enable large-scale studies in psychiatry: treatment resistant depression as a model. Psychol Med 1(1):1–10
25. Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D (2006) Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 38(8):904–909
26. R Development Core Team (2009) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. ISBN 3-900051-07-0, http://www.R-project.org.
27. Ritchie M, Denny J, Crawford D, Ramirez A, Weiner J, Pulley J, Basford M, Brown-Gentry K, Balser J, Masys D et al (2010) Robust replication of genotype–phenotype associations across multiple diseases in an electronic medical record. Am J Hum Genet 86(4):560–572
28. Singh J, Holmgren A, Noorbaloochi S (2004) Accuracy of veterans administration databases for a diagnosis of rheumatoid arthritis. Arthritis Care Res 51(6):952–957
29. Stahl EA, Raychaudhuri S, Remmers EF, Xie G, Eyre S, Thomson BP, Li Y, Kurreeman FA, Zhernakova A, Hinks A et al (2010) Genome-wide association study meta-analysis identifies seven new rheumatoid arthritis risk loci. Nat Genet 42(6):508–514
30. Weyand CM, Hicok KC, Conn DL, Goronzy JJ (1992) The influence of hla-drb1 genes on disease severity in rheumatoid arthritis. Ann Intern Med 117(10):801–806
31. Wilke R, Xu H, Denny J, Roden D, Krauss R, McCarty C, Davis R, Skaar T, Lamba J, Savova G (2011) The emerging role of electronic medical records in pharmacogenomics. Clin Pharmacol Therapeut 89(3):379–386