Abstract 2350: Efficient survival multifactor dimensionality reduction method for genome-wide association study

Abstract

Abstract The problem of identifying associations between SNPs and case-control outcome has been studied extensively and a number of new techniques have been developed. Little progress has been made, however in the analysis of SNP effect in relation to censored survival data. We present an extension of the two class multifactor dimensionality reduction (MDR) algorithm that enables detection and characterization of epistatic SNP-SNP interactions in the context of survival outcome. The proposed an Efficient Survival MDR (ES-MDR) method handles censored data by modifying MDR's constructive induction algorithm to use logrank Test. We applied ES-MDR to genetic data of over 470,000 SNPs from the OncoArray Consortium. We use onset age of lung cancer and case-control (n=27,312) status as the survival outcome and divided data into training and testing sets. We also adjust for subject's age, gender and smoking status. From training set, we identified strong association from SNPs in BRCA1 and IL17RC genes with lung cancer onset age. This result is validated in the testing set. Citation Format: Jiang Gui, Xuemei Ji, Christopher I. Amos. Efficient survival multifactor dimensionality reduction method for genome-wide association study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2350.