This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 100
Type: Invited
Date/Time: Monday, August 2, 2010 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics in Epidemiology
Abstract - #306245
Title: Association Analysis of Untyped SNPs: Maximum Likelihood and Imputation Methods
Author(s): Danyu Lin*+
Companies: The University of North Carolina at Chapel Hill
Address: 3101E McGavran-Greenberg Hall, CB#7420, Chapel Hill, NC, 27599-7420,
Keywords: Association studies ; Genotype ; Haplotype ; Semiparametric efficiency ; Single nucleotide polymorphisms ; Untyped SNPs

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.

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