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Grant Details

Grant Number: 5R03CA150140-02 Interpret this number
Primary Investigator: Wang, Shuang
Organization: Columbia University Health Sciences
Project Title: Statistical Methods for DNA Methylation Data
Fiscal Year: 2011
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Abstract

DESCRIPTION (provided by applicant): Statistical Methods for DNA Methylation Data Abstract The overall objective of this study is to develop novel statistical methods and software to study how DNA methylation profiles associated with cancers. DNA methylation is a molecular modification of DNA that is important for normal organism development. Genes that are rich in CpG dinucleotides are usually not methylated in normal tissues, but are frequently hypermethylated in cancer. This is often associated with gene silencing and is an important mechanism for the inactivation of tumor suppressor genes. Studies have also suggested that methylation profiles differ between cancers arising in different organs and between different cancer histologies from the same organ. For example, different DNA methylation profiles are found in different subtypes of leukemia and lung cancer. With the rapid development in array technologies, high-throughput arrays with DNA mathylation measures on the genome-wide level have become widely available. There is a great need for development of novel statistical models to evaluate complex DNA methylation data generated with high-throughput platforms. The specific objectives of this project are: (1) to develop novel models for the distribution of methylation proportions to select differentially methylated loci between cancer and normal subjects; (2) to propose a new classification method that differentiates tumor subtypes using DNA methylation profiles; (3) to develop computer software packages that implement methods developed in specific aims 1-2. The proposed methods will be applied to an existing data with tumor samples/normal tissue samples and an ongoing methylation study the PI is collaborating. We believe the proposed methods will significantly improve current and future efforts in understanding the significance of DNA methylation profiles in cancers. PUBLIC HEALTH RELEVANCE: Abstract narrative To develop a series of novel and powerful statistical methods to study DNA methylation profiles. The proposed methods will significantly improve current and future efforts in understanding the significance of DNA methylation profiles in cancers.

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Publications

Semiparametric tests for identifying differentially methylated loci with case-control designs using Illumina arrays.
Authors: Chen Y. , Ning Y. , Hong C. , Wang S. .
Source: Genetic epidemiology, 2014 Jan; 38(1), p. 42-50.
EPub date: 2013-12-03.
PMID: 24301455
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Network-based regularization for matched case-control analysis of high-dimensional DNA methylation data.
Authors: Sun H. , Wang S. .
Source: Statistics in medicine, 2013-05-30; 32(12), p. 2127-39.
EPub date: 2012-12-05.
PMID: 23212810
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Predictors and consequences of global DNA methylation in cord blood and at three years.
Authors: Herbstman J.B. , Wang S. , Perera F.P. , Lederman S.A. , Vishnevetsky J. , Rundle A.G. , Hoepner L.A. , Qu L. , Tang D. .
Source: PloS one, 2013; 8(9), p. e72824.
EPub date: 2013-09-04.
PMID: 24023780
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Exploring genome-wide DNA methylation profiles altered in hepatocellular carcinoma using Infinium HumanMethylation 450 BeadChips.
Authors: Shen J. , Wang S. , Zhang Y.J. , Wu H.C. , Kibriya M.G. , Jasmine F. , Ahsan H. , Wu D.P. , Siegel A.B. , Remotti H. , et al. .
Source: Epigenetics, 2013 Jan; 8(1), p. 34-43.
EPub date: 2012-12-03.
PMID: 23208076
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Genome-wide aberrant DNA methylation of microRNA host genes in hepatocellular carcinoma.
Authors: Shen J. , Wang S. , Zhang Y.J. , Kappil M.A. , Chen Wu H. , Kibriya M.G. , Wang Q. , Jasmine F. , Ahsan H. , Lee P.H. , et al. .
Source: Epigenetics, 2012 Nov; 7(11), p. 1230-7.
EPub date: 2012-09-13.
PMID: 22976466
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Genome-wide DNA methylation profiles in hepatocellular carcinoma.
Authors: Shen J. , Wang S. , Zhang Y.J. , Kappil M. , Wu H.C. , Kibriya M.G. , Wang Q. , Jasmine F. , Ahsan H. , Lee P.H. , et al. .
Source: Hepatology (Baltimore, Md.), 2012 Jun; 55(6), p. 1799-808.
EPub date: 2012-04-24.
PMID: 22234943
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Penalized logistic regression for high-dimensional DNA methylation data with case-control studies.
Authors: Sun H. , Wang S. .
Source: Bioinformatics (Oxford, England), 2012-05-15; 28(10), p. 1368-75.
EPub date: 2012-03-30.
PMID: 22467913
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Method to detect differentially methylated loci with case-control designs using Illumina arrays.
Authors: Wang S. .
Source: Genetic epidemiology, 2011 Nov; 35(7), p. 686-94.
EPub date: 2011-08-04.
PMID: 21818777
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Methods for detecting interactions between imprinted genes and environmental exposures using birth cohort designs with mother-offspring pairs.
Authors: Wang S. , Yu Z. , Miller R.L. , Tang D. , Perera F.P. .
Source: Human heredity, 2011; 71(3), p. 196-208.
EPub date: 2011-07-20.
PMID: 21778739
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