Informed Conditioning on Environmental Covariates Increases Power in Case-Control Association Studies

• Activity Number: 211
• Type: Invited
• Date/Time: Monday, August 5, 2013 : 2:00 PM to 3:50 PM
• Sponsor: International Indian Statistical Association
• Author(s): Noah Aaron Zaitlen*+ and Sara Lindstrom and Bogdan Pasaniuc and Marilyn Cornelis and Giulio Genovese and Samuela Pollack and Benjamin Voight and Peter Kraft and Nick Patterson and Alkes L. Price
• Companies: UCSF and HSPH and UCLA and HSPH and HMS and HSPH and University of Pennsylvania and HSPH and Broad Institute and Harvard School of Public Health
• Keywords: ascertainment ; GWAS ; covariates ; conditioning ; epidemiology ; liability threshold

Abstract

Genetic case-control association studies often include data on clinical covariates, such as body mass index (BMI), smoking status, or age, that may modify the underlying genetic risk of case or control samples. An unanswered question is how to use this information to maximize statistical power in case-control studies that ascertain individuals on the basis of phenotype (case-control ascertainment) or phenotype and clinical covariates (case-control-covariate ascertainment). We show that an informed conditioning approach, based on the liability threshold model with parameters informed by external epidemiological information, fully accounts for disease prevalence and non-random ascertainment of phenotype as well as covariates and provides a substantial increase in power while maintaining a properly controlled false-positive rate. Our method outperforms standard case-control association tests with or without covariates, tests of gene x covariate interaction, and previously proposed tests for dealing with covariates in ascertained data, with especially large improvements in the case of case-control-covariate ascertainment.