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.

[1]  Y. Pommier,et al.  DNA strand breaks produced by etoposide (VP-16,213) in sensitive and resistant human breast tumor cells: implications for the mechanism of action. , 1988, Cancer research.

[2]  Raymond K. Auerbach,et al.  An Integrated Encyclopedia of DNA Elements in the Human Genome , 2012, Nature.

[3]  Marylyn D. Ritchie,et al.  ATHENA: A Tool for Meta-Dimensional Analysis Applied to Genotypes and Gene Expression Data to Predict HDL Cholesterol Levels , 2012, Pacific Symposium on Biocomputing.

[4]  R. Kurzrock,et al.  Germline PTPRD Mutations in Ewing Sarcoma: Biologic and Clinical Implications , 2013, Oncotarget.

[5]  Marylyn D. Ritchie,et al.  Comparison of Methods for Meta-dimensional Data Analysis Using in Silico and Biological Data Sets , 2012, EvoBIO.

[6]  M. Olivier.  A haplotype map of the human genome , 2003, Nature.

[7]  S. Gabriel,et al.  Whole-exome sequencing identifies mutated c12orf57 in recessive corpus callosum hypoplasia. , 2013, American journal of human genetics.

[8]  Shiwei Duan,et al.  Identification of genetic variants contributing to cisplatin-induced cytotoxicity by use of a genomewide approach. , 2007, American journal of human genetics.

[9]  T. Wienker,et al.  A newly recognized autosomal recessive syndrome affecting neurologic function and vision , 2013, American journal of medical genetics. Part A.

[10]  J. Witte,et al.  Fine‐mapping of prostate cancer aggressiveness loci on chromosome 7q22–35 , 2011, The Prostate.

[11]  M. Peters,et al.  Systematic identification of trans eQTLs as putative drivers of known disease associations , 2013, Nature Genetics.

[12]  M. Segal,et al.  DNA topoisomerase II in therapy-related acute promyelocytic leukemia. , 2005, The New England journal of medicine.

[13]  John R. Koza,et al.  Genetic generation of both the weights and architecture for a neural network , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.

[14]  Marylyn D. Ritchie,et al.  ATHENA: A knowledge-based hybrid backpropagation-grammatical evolution neural network algorithm for discovering epistasis among quantitative trait Loci , 2010, BioData Mining.

[15]  M. Eileen Dolan,et al.  Chemotherapeutic drug susceptibility associated SNPs are enriched in expression quantitative trait loci , 2010, Proceedings of the National Academy of Sciences.

[16]  P. Ferrara,et al.  Protocadherin 15 (PCDH15): a new secreted isoform and a potential marker for NK/T cell lymphomas , 2006, Oncogene.

[17]  M. Eileen Dolan,et al.  A genome-wide approach to identify genetic variants that contribute to etoposide-induced cytotoxicity , 2007, Proceedings of the National Academy of Sciences.

[18]  Marylyn D. Ritchie,et al.  ATHENA Optimization: The Effect of Initial Parameter Settings across Different Genetic Models , 2011, EvoBio.

[19]  Marylyn D. Ritchie,et al.  Initialization parameter sweep in ATHENA: optimizing neural networks for detecting gene-gene interactions in the presence of small main effects , 2010, GECCO '10.

[20]  David M. Skapura,et al.  Building neural networks , 1995 .

[21]  Data production leads,et al.  An integrated encyclopedia of DNA elements in the human genome , 2012 .

[22]  Ryan D. Morin,et al.  Mutational evolution in a lobular breast tumour profiled at single nucleotide resolution , 2009, Nature.

[23]  Soma Das,et al.  Genetic variants contributing to daunorubicin-induced cytotoxicity. , 2008, Cancer research.

[24]  Shiwei Duan,et al.  Genetic variants associated with carboplatin-induced cytotoxicity in cell lines derived from Africans , 2008, Molecular Cancer Therapeutics.

[25]  Hunter B. Fraser,et al.  Population-specificity of human DNA methylation , 2012, Genome Biology.