Improving the Power of Genetic Association Tests with Imperfect Phenotype Derived from Electronic Medical Records

• Activity Number: 514
• Type: Contributed
• Date/Time: Wednesday, August 6, 2014 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Jennifer Sinnott*+ and Wei Dai and Katherine P. Liao and Stanley Y. Shaw and Ashwin Ananthakrishnan and Vivian S. Gainer and Elizabeth W. Karlson and Susanne Churchill and Peter Szolovits and Shawn Murphy and Isaac Kohane and Robert Plenge and Tianxi Cai
• Companies: Harvard and Harvard and Brigham & Women's Hospital and Massachusetts General Hospital and Massachusetts General Hospital and Partners Healthcare and Brigham & Women's Hospital and i2b2 and Massachusetts Institute of Technology and Partners Healthcare and Harvard Medical School and Merck and Harvard
• Keywords: Case-Control Studies ; Electronic Health Records ; Electronic Medical Records ; Genetic association studies ; Outcome Misclassification

Abstract

To reduce costs and improve clinical relevance of genetic studies, there is 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 difficulty in implementing such studies is extracting accurate information about phenotypes from large numbers of EMRs. Algorithms are being developed to infer phenotype information, but they typically do not provide perfect classification, instead producing a probability that a patient is a disease case. The probability can be thresholded to define estimated case-control status, but using this in place of true disease status can diminish test power and bias odds ratio estimates. We propose instead to model directly the algorithm-derived probability of being a case. We demonstrate how our approach improves test power and effect estimation in simulations and 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.