Abstract LB-441: The genome wide gene-environment interaction analysis for asbestos exposure on lung cancer susceptibility

Abstract

Abstract Asbestos exposure is a known risk factor for lung cancer. Although recent genome wide association studies (GWAS) have identified a few novel loci for lung cancer risk, little is known about genome wide gene-environment interactions and lung cancer risk. To determine genetic variations involved in the interactions of gene-asbestos exposure on lung cancer risk, we conducted genome wide gene-environment interaction analyses at levels of single nucleotide polymorphisms (SNPs), genes and pathways, using the Texas lung cancer GWAS dataset. This dataset included 307,870 SNPs from 1,154 lung cancer cases and 1,137 cancer-free controls. The initial SNP-level p values for interactions between genetic variants and self-reported asbestos exposure were estimated by unconditional logistic regression models adjusted for age, sex, smoking status and pack-years. The p value for the most significant SNP rs12706823 was 1.37 × 10–4 and did not reach the genome-wide statistical significance. Using a versatile gene-based test approach to estimate the interactions at the gene level, we found the top significant gene was RNF121and was located on 11q13.4 (p value = 4 × 10–4). Interestingly, most of the genes with top significant p values were located in 11q13. When we used an improved gene set enrichment analysis approach (i-GSEA), however, we found a total of 249 pathways containing 16,079 genes, of which the Fas signaling pathway was the most significant pathway, significantly involved in the interactions between genetic variations and self-reported asbestos exposure (nominal p value < 0.001; false discovery rate (FDR) < 0.05). We believe our analysis is the first to comprehensively describe the genome wide gene-asbestos exposure interaction on lung cancer risk at the levels of SNPs, genes and pathways. Our findings suggested that the current designed GWAS may have a limited power to detect gene-environment interactions at the SNP or gene levels, but the pathway-based approach may be more powerful in performing genome wide gene-environment analyses. (This study was supported by NIH grants R01 ES 11740, R01CA 131274, R01CA055769, R01CA127219, R01CA121197 and P30CA016672) Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr LB-441. doi:10.1158/1538-7445.AM2011-LB-441