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Abstract Details
Activity Number:
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452
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 3, 2011 : 8:30 AM to 10:20 AM
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Sponsor:
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Biometrics Section
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Abstract - #301422 |
Title:
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Testing for the Effect of a Genetic Pathway in Longitudinal/Clustered Data Using Kernel Machine Regression
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Author(s):
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Arnab Maity*+ and Stacey Alexeeff
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Companies:
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North Carolina State University and Harvard University
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Address:
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Department of Statistics , Raleigh, NC, 27695,
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Keywords:
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Longitudinal/clustered data ;
Kernel Machine regression ;
Score test ;
Gene-gene interaction
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Abstract:
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There is a growing scientific interest to test for genetic effects on disease by considering a set of genes that may be on the same biological pathway. Genes within a pathway may interact in functional ways to influence the progression of disease. Kernel machine regression has been introduced as a way to model a pathway effect, either parametrically or nonparametrically. We consider kernel machine regression for the testing of a genetic pathway effect in the longitudinal/clustered data setting, where a continuous disease outcome is measured repeatedly, possibly over time, for each subject. We develop a score-based test statistic for testing the effect of the genetic pathway accounting for the within subject correlation in the outcome variable. In addition, we present a simulation study to investigate the power of the test for different correlation structures, and we compare its performance with the test without accounting for correlation.
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