American Statistical Association
New York City
Metropolitan Area Chapter

Mailman School of Public Health
Columbia University
Department of Biostatistics Colloquium



A GENERAL FRAMEWORK FOR ESTIMATING GENETIC EFFECTS
AND GENE-ENVIRONMENT INTERACTIONS WITH MISSING DATA

by

Prof. Danyu Lin
Department of Statistics
University of North Carolina-Chapel Hill


Abstract

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 estimating 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. We consider three major study designs --- cross-sectional, case-control, and cohort designs --- and construct appropriate likelihood functions for all common phenotypes (e.g., case-control status, quantitative traits, and potentially censored ages at onset of disease). The likelihood functions involve both finite- and infinite-dimensional parameters. The maximum likelihood estimators are shown to be consistent, asymptotically normal, and asymptotically efficient. Fast and stable numerical algorithms are developed to implement the corresponding inference procedures. Extensive simulation studies demonstrate that the proposed inferential and numerical methods perform well in practical settings. Applications to two genome-wide association studies are provided.


Date: Thursday, April 2, 2009
Time: 4:00 - 5:00 P.M.
Location: Mailman School of Public Health
Department of Biostatistics
722 West 168th Street
Biostatistics Computer Lab
6th Floor - Room 656
New York, New York

RESERVATIONS ARE NOT REQUIRED

Refreshments will be served at 3:30 P.M. in the
Biostatistics Conference Room (R627).


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