Association Analysis of Untyped SNPs: Maximum Likelihood and Imputation Methods

• Activity Number: 100
• Type: Invited
• Date/Time: Monday, August 2, 2010 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Danyu Lin*+
• Companies: The University of North Carolina at Chapel Hill
• Keywords: Association studies ; Genotype ; Haplotype ; Semiparametric efficiency ; Single nucleotide polymorphisms ; Untyped SNPs

Abstract

A General Framework for Studying Genetic Effects and Gene-Environment Interactions With Incomplete Genetic Data

Missing data arise in genetic association studies when genotypes are unknown or when haplotypes are of direct interest. We provide a general likelihood-based framework for making inference on genetic effects and gene-environment interactions with such missing data. We allow genetic and environmental variables to be correlated while leaving the distribution of environmental variables completely unspecified. The maximum likelihood estimators are shown to be consistent, asymptotically normal, and asymptotically efficient. EM algorithms are developed to implement the corresponding inference procedures. Illustration with a genome-wide association study of lung cancer is provided.