Document Detail

Application of Multidimensional Selective Item Response Regression Model for Studying Multiple Gene Methylation in SV40 Oncogenic Pathways.
MedLine Citation:
PMID:  19830254     Owner:  NLM     Status:  Publisher    
Alteration of gene methylation patterns has been reported to be involved in the early onsets of many human malignancies. Many exogenous risk factors, such as cigarette smoke, dietary additives, chemical exposures, radiation, and biologic agents including viral infection, are involved in the methylation pathways of cancers. We propose a multidimensional selective item response regression model to describe and test how a risk factor may alter molecular pathways involving aberrant methylation of multiple genes in oncogenesis. Our modeling framework is built on an item response model for multivariate dichotomous responses of high dimension, such as aberrant methylation of multiple tumor-suppressor genes, but we allow risk factors such as SV40 viral infection to alter the distribution of the latent factors that subsequently affect the outcome of cancer. We postulate empirical identification conditions under our model formulation. Moreover, we do not prespecify the links between the multiple dichotomous methylation responses and the latent factors, but rather conduct specification searches with a genetic algorithm to discover the links. Parameter estimation through maximum likelihood and specification searches in models with multidimensional latent factors for multivariate binary responses have become practical only recently, due to modern statistical computing development. We illustrate our proposal with the biological finding that simultaneous methylation of multiple tumor-suppressor genes is associated with the presence of SV40 viral sequences and with the cancer status of lymphoma/leukemia.We are able to test whether the data are consistent with the causal hypothesis that SV40 induces aberrant methylation of multiple genes in its oncogenic pathways. At the same time, we are able to evaluate the role of SV40 in the methylation pathway and to determine whether the methylation pathway is responsible for the development of leukemia/lymphoma.
Haiqun Lin; Ziding Feng; Yan Yu; Yingye Zheng; Narayan Shivapurkar; Adi F Gazdar
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Publication Detail:
Journal Detail:
Title:  Journal of the American Statistical Association     Volume:  103     ISSN:  0162-1459     ISO Abbreviation:  J Am Stat Assoc     Publication Date:  2008 Mar 
Date Detail:
Created Date:  2009-10-15     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  01510020R     Medline TA:  J Am Stat Assoc     Country:  -    
Other Details:
Languages:  ENG     Pagination:  201-211     Citation Subset:  -    
Division of Biostatistics, Yale University School of Medicine, New Haven, CT 06520 ( ).
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Grant Support
U01 CA084971-08//NCI NIH HHS

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