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Activity Number: 186
Type: Contributed
Date/Time: Monday, August 4, 2014 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract #313637
Title: Identifying Genetic Variants for Addiction via Propensity Score Adjusted Generalized Kendall's Tau
Author(s): Yuan Jiang*+ and Ni Li and Heping Zhang
Companies: Oregon State University and Hainan Normal University and Yale
Keywords: Addiction ; Comorbidity ; Genome-wide association studies ; Inverse probability weighting ; Propensity Score ; Substance dependence
Abstract:

Identifying replicable genetic variants for addiction has been extremely challenging. Besides the common difficulties with genome-wide association studies, environmental factors are known to be critical to addiction, and comorbidity is widely observed. Although parametric methods have been developed, difficulties arise when the traits are multivariate. Recent nonparametric development includes U-statistics to measure the phenotype-genotype association weighted by a similarity score of covariates. However, it is not clear how to optimize the similarity score. Therefore, we propose a semiparametric method to measure the association adjusted by covariates. In our approach, the nonparametric U-statistic is adjusted by parametric estimates of propensity scores using the idea of inverse probability weighting. The new measurement is shown to be asymptotically unbiased under our null hypothesis while the previous non-weighted and weighted ones are not. Simulation results show that our test improves power as opposed to the existing methods. Finally, we apply our proposed test to the Study of Addiction: Genetics and Environment to identify genetic variants for addiction.


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