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June 2014 Gene-level pharmacogenetic analysis on survival outcomes using gene-trait similarity regression
Jung-Ying Tzeng, Wenbin Lu, Fang-Chi Hsu
Ann. Appl. Stat. 8(2): 1232-1255 (June 2014). DOI: 10.1214/14-AOAS735


Gene/pathway-based methods are drawing significant attention due to their usefulness in detecting rare and common variants that affect disease susceptibility. The biological mechanism of drug responses indicates that a gene-based analysis has even greater potential in pharmacogenetics. Motivated by a study from the Vitamin Intervention for Stroke Prevention (VISP) trial, we develop a gene-trait similarity regression for survival analysis to assess the effect of a gene or pathway on time-to-event outcomes. The similarity regression has a general framework that covers a range of survival models, such as the proportional hazards model and the proportional odds model. The inference procedure developed under the proportional hazards model is robust against model misspecification. We derive the equivalence between the similarity survival regression and a random effects model, which further unifies the current variance component-based methods. We demonstrate the effectiveness of the proposed method through simulation studies. In addition, we apply the method to the VISP trial data to identify the genes that exhibit an association with the risk of a recurrent stroke. The TCN2 gene was found to be associated with the recurrent stroke risk in the low-dose arm. This gene may impact recurrent stroke risk in response to cofactor therapy.


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Jung-Ying Tzeng. Wenbin Lu. Fang-Chi Hsu. "Gene-level pharmacogenetic analysis on survival outcomes using gene-trait similarity regression." Ann. Appl. Stat. 8 (2) 1232 - 1255, June 2014.


Published: June 2014
First available in Project Euclid: 1 July 2014

zbMATH: 06333794
MathSciNet: MR3262552
Digital Object Identifier: 10.1214/14-AOAS735

Keywords: association study , gene/pathway , pharmacogenetics , proportional hazards model , proportional odds model , similarity regression , survival data

Rights: Copyright © 2014 Institute of Mathematical Statistics

Vol.8 • No. 2 • June 2014
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