Case-Control Studies of Genetic and Environmental Factors with Error in Measurement of Environmental Factors

• Activity Number: 411
• Type: Topic Contributed
• Date/Time: Wednesday, August 5, 2009 : 8:30 AM to 10:20 AM
• Sponsor: Biometrics Section
• Author(s): Iryna Lobach*+ and Raymond J. Carroll and Christine Spinka and Mitchell H. Gail and Nilanjan Chatterjee
• Companies: New York University School of Medicine and Texas A&M University and University of Missouri and National Cancer Institute and National Cancer Institute
• Keywords: Gene-environment interactions ; Semiparametric methods ; Errors in variables ; Inferences in measurement error models ; Profile likelihood ; Likelihood-ratio tests in misspecified models

Abstract

It is widely believed that risks of many complex diseases are determined by genetic susceptibilities, including environmental exposures, and their interaction. Chatterjee and Carroll (2005) have recently developed an efficient retrospective maximum-likelihood method for analysis of case-control studies that exploits an assumption of gene-environment independence and leaves the distribution of the environmental covariates to be completely nonparametric. We generalize the semiparametric maximum-likelihood approach to situations when some of the environmental covariates are measured with error, and allow genetic information to be missing on some subjects, e.g., unphased haplotypes. Profile likelihood techniques and an EM algorithm are developed, resulting in a relatively simple procedure for parameter estimation. We prove consistency and derive the resulting asymptotic covariance matrix of