An integrated analysis of genome-wide DNA methylation and genetic variants underlying etoposide-induced cytotoxicity in European and African populations
Genetic variations among individuals account for a large portion of variability in drug response. The underlying mechanism of the variability is still not known, but it is expected to comprise of a wide range of genetic factors that interact and communicate with each other. Here, we present an integrated genome-wide approach to uncover the interactions among genetic factors that can explain some of the inter-individual variation in drug response. The International HapMap consortium generated genotyping data on human lymphoblastoid cell lines of (Center d’Etude du Polymorphisme Humain population - CEU) European descent and (Yoruba population - YRI) African descent. Using genome-wide analysis, Huang et al. identified SNPs that are associated with etoposide, a chemotherapeutic drug, response on the cell lines. Using the same lymphoblastoid cell lines, Fraser et al. generated genome-wide methylation profiles for gene promoter regions. We evaluated associations between candidate SNPs generated by Huang et al and genome-wide methylation sites. The analysis identified a set of methylation sites that are associated with etoposide related SNPs. Using the set of methylation sites and the candidate SNPs, we built an integrated model to explain etoposide response observed in CEU and YRI cell lines. This integrated method can be extended to combine any number of genomics data types to explain many phenotypes of interest.
KeywordsMethylation Level Drug Response Methylation Site Interactive Relationship Candidate SNPs
Unable to display preview. Download preview PDF.
- 8.Sinha, B.K., Haim, N., Dusre, L., Kerrigan, D., Pommier, Y.: DNA strand breaks produced by etoposide (VP-16,213) in sensitive and resistant human breast tumor cells: implications for the mechanism of action. Cancer Res. 48, 5096–5100 (1988)Google Scholar
- 10.Ratain, M.J., et al.: Acute nonlymphocytic leukemia following etoposide and cisplatin combination chemotherapy for advanced non-small-cell carcinoma of the lung. Blood 70, 1412–1417 (1987)Google Scholar
- 11.Thomson.Micromedex. Drug Inf. Heal. Care Prof. 24th edn. vol. 1, p. 1326 (2004)Google Scholar
- 12.Holzinger, E., Buchanan, C.: Initialization Parameter Sweep in ATHENA: Optimizing Neural Networks for Detecting Gene-Gene Interactions in the Presence of Small Main Effects. In: Proc. 12th …, pp. 203–210 (2010). doi: 10.1145/1830483.1830519.Initialization
- 13.Holzinger, E.R., Dudek, S.M., Torstenson, E.C., Ritchie, M.D.: ATHENA Optimization : The Effect of Initial Parameter Settings across Different Genetic Models, pp. 48–58 (2011)Google Scholar
- 15.Holzinger, E.R., et al:. Comparison of Methods for Meta-dimensional Data Analysis Using in Silico and Biological Data Sets, pp. 134–143Google Scholar
- 16.Holzinger, E.R., et al.: ATHENA: a tool for meta-dimensional analysis applied to genotypes and gene expression data to predict HDL cholesterol levels. In: Pac. Symp. Biocomput., pp. 385–96 (2013). http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3587764&tool=pmcentrez&rendertype=abstract
- 17.Skapura, D.M.: Building neural networks (1995). http://dl.acm.org/citation.cfm?id=217718
- 18.Koza, J.R., Rice, J.P.: Genetic generation of both the weights and architecture for a neural network. In: IJCNN 1991, Seattle Int. Jt. Conf. Neural Networks ii, pp. 397–404. IEEE (1991)Google Scholar
- 25.Jiang, Y., et al.: Germline PTPRD Mutations in Ewing Sarcoma: Biologic and Clinical Implications. Oncotarget 4, 884–889 (2013)Google Scholar