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Published July 1, 2010 | Published
Journal Article Open

Modeling the Aneuploidy Control of Cancer


Background: Aneuploidy has long been recognized to be associated with cancer. A growing body of evidence suggests that tumorigenesis, the formation of new tumors, can be attributed to some extent to errors occurring at the mitotic checkpoint, a major cell cycle control mechanism that acts to prevent chromosome missegregation. However, so far no statistical model has been available quantify the role aneuploidy plays in determining cancer. Methods: We develop a statistical model for testing the association between aneuploidy loci and cancer risk in a genome-wide association study. The model incorporates quantitative genetic principles into a mixture-model framework in which various genetic effects, including additive, dominant, imprinting, and their interactions, are estimated by implementing the EM algorithm. Results: Under the new model, a series of hypotheses tests are formulated to explain the pattern of the genetic control of cancer through aneuploid loci. Simulation studies were performed to investigate the statistical behavior of the model. Conclusions: The model will provide a tool for estimating the effects of genetic loci on aneuploidy abnormality in genome-wide studies of cancer cells.

Additional Information

© 2010 Li et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Received: 30 August 2009; Accepted: 1 July 2010; Published: 1 July 2010. We thank Dr. Justo Lorenzo Bermejo, Dr. George Heinze, Dr. Marek Kimmel, and Dr. Elizabeth Petty for their constructive comments which help to improve the manuscript. This work is supported by joint grant DMS/ NIGMS-0540745 and a Penn State Cancer Institute Seed Grant. Authors' contributions: YL derived the equations and performed simulation studies. AB led and performed simulation studies. LW participated in simulation studies. ZW participated in simulation studies. GC interpreted the biological relevance of the model. RW conceived of the model and coordinated its design and simulation test. All authors read and approved the final manuscript.

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