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Modeling Host-Cancer Genetic Interactions with Multilocus Sequence Data | OMICS International | Abstract
ISSN: 0974-7230

Journal of Computer Science & Systems Biology
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Research Article

Modeling Host-Cancer Genetic Interactions with Multilocus Sequence Data

Yao Li1 and Rongling Wu1,2*
1Department of Statistics, University of Florida, Gainesville, FL 32611, USA
2Departments of Public Health Sciences and Statistics, Pennsylvania State University, Hershey, PA 17033
Corresponding Author : Dr. Rongling Wu
Department of Statistics
University of Florida
Gainesville, FL 32611 USA
Email  : [email protected]
Received January 06, 2009; Accepted February 04, 2009; Published February 05, 2009
Citation: Yao L, Rongling W (2009) Modeling Host-Cancer Genetic Interactions with Multilocus Sequence Data. J Comput Sci Syst Biol 2:024-043. doi:10.4172/jcsb.1000015
Copyright: © 2009 Yao L, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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Cancer susceptibility may be controlled not only by host genes and mutated genes in cancer cells, but also by the epistatic interactions between genes from the host and cancer genomes. We derive a novel statistical model for cancer gene identification by integrating the gene mutation hypothesis of cancer formation into the mixturemodel framework. Within this framework, genetic interactions of DNA sequences (or haplotypes) between host and cancer genes responsible for cancer risk are defined in terms of quantitative genetic principle. Our model was founded on a commonly used genetic association design in which a random sample of patients is drawn from a natural human population. Each patient is typed for single nucleotide polymorphisms (SNPs) on normal and cancer cells and measured for cancer susceptibility. The model is formulated within the maximum likelihood context and implemented with the EM algorithm, allowing the estimation of both population and quantitative genetic parameters. The model provides a general procedure for testing the distribution of haplotypes constructed by SNPs from host and cancer genes and the linkage disequilibria of different orders among the SNPs. The model also formulates a series of testable hypotheses about the effects of host genes, cancer genes, and their interactions on cancer susceptibility. We carried out simulation studies to examine the statistical properties of the model. The implications of this model for cancer gene identification are discussed.


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