title: Improving the power of genetic association tests with imperfect phenotype derived from electronic medical records.
abstract: To reduce costs and improve clinical relevance of genetic studies, there has been increasing interest in performing such studies in hospital-based cohorts by linking phenotypes extracted from electronic medical records (EMRs) to genotypes assessed in routinely collected medical samples. A fundamental difficulty in implementing such studies is extracting accurate information about disease outcomes and important clinical covariates from large numbers of EMRs. Recently, numerous algorithms have been developed to infer phenotypes by combining information from multiple structured and unstructured variables extracted from EMRs. Although these algorithms are quite accurate, they typically do not provide perfect classification due to the difficulty in inferring meaning from the text. Some algorithms can produce for each patient a probability that the patient is a disease case. This probability can be thresholded to define case-control status, and this estimated case-control status has been used to replicate known genetic associations in EMR-based studies. However, using the estimated disease status in place of true disease status results in outcome misclassification, which can diminish test power and bias odds ratio estimates. We propose to instead directly model the algorithm-derived probability of being a case. We demonstrate how our approach improves test power and effect estimation in simulation studies, and we describe its performance in a study of rheumatoid arthritis. Our work provides an easily implemented solution to a major practical challenge that arises in the use of EMR data, which can facilitate the use of EMR infrastructure for more powerful, cost-effective, and diverse genetic studies.
authors: Department of Biostatistics, Harvard School of Public Health, Boston, MA, 02115, USA, jas953@mail.harvard.edu.
authors: Jennifer A
authors: JA
authors: Sinnott
authors: Wei
authors: W
authors: Dai
authors: Katherine P
authors: KP
authors: Liao
authors: Stanley Y
authors: SY
authors: Shaw
authors: Ashwin N
authors: AN
authors: Ananthakrishnan
authors: Vivian S
authors: VS
authors: Gainer
authors: Elizabeth W
authors: EW
authors: Karlson
authors: Susanne
authors: S
authors: Churchill
authors: Peter
authors: P
authors: Szolovits
authors: Shawn
authors: S
authors: Murphy
authors: Isaac
authors: I
authors: Kohane
authors: Robert
authors: R
authors: Plenge
authors: Tianxi
authors: T
authors: Cai
mesh_terms: D000465:Algorithms; D001172:Arthritis, Rheumatoid / Q000145:classification / Q000453:epidemiology / Q000235:genetics*; D016022:Case-Control Studies; D015331:Cohort Studies; D003198:Computer Simulation; D057286:Electronic Health Records; D056726:Genetic Association Studies / Q000379:methods*; D036281:Genetic Research; D005838:Genotype; D006801:Humans; D008485:Medical Audit; D008957:Models, Genetic; D010641:Phenotype; D015995:Prevalence; D018401:Sample Size; D012984:Software; D014481:United States / Q000453:epidemiology