Grant Number:	5R01CA097346-04 Interpret this number
Primary Investigator:	Siegmund, Kimberly
Organization:	University Of Southern California
Project Title:	Statistical Models in Epigenomics
Fiscal Year:	2005

The primary objective of this proposal is to develop statistical models for the analysis of DNA methylation data. Questions that are currently posed for gene expression profiles, such as how to classify samples based on cell-wide gene expression patterns or how to classify genes with unknown function, based on their expression patterns across samples, may be similarly posed for DNA methylation patterns. Our focus is on the discovery of new subgroups based on patterns of DNA methylation. We view DNA methylation patterns as an intermediate variable along a pathway from exposures to outcomes and take a flexible hierarchical modeling approach that can incorporate measured and unmeasured covariates. Our aims are motivated by ongoing and planned future studies in the Department of Preventive Medicine and Norris Cancer Center at the University of Southern California. Specifically, we propose to: 1. Develop model-based class discovery methods (cluster analysis/unsupervised learning approaches) using quantitative measures of DNA methylation, adjusting for locus-specific and sample-specific covariate effects. a. Analysis of paired samples of tumor tissue and normal tissue taken from the margin of the tumor. b. Extend model to allow for multiple tumors per subject. c. Extend model to a two-dimensional cluster analysis of samples and loci. 2. Develop models for characterizing methylation patterns as an intermediate variable by modeling a) the association between risk factors and methylation pattern clusters, and b) the association between methylation pattern clusters and outcome. 3. Apply methods to studies of DNA methylation in colorectacl adenomas and lung cancer.




Identifying Differential Transcription Factor Binding In Chip-seq
Authors: Wu D.Y. , Bittencourt D. , Stallcup M.R. , Siegmund K.D. .
Source: Frontiers In Genetics, 2015; 6, p. 169.
PMID: 25972895
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Ancestral Inference In Tumors: How Much Can We Know?
Authors: Zhao J. , Siegmund K.D. , Shibata D. , Marjoram P. .
Source: Journal Of Theoretical Biology, 2014-10-21 00:00:00.0; 359, p. 136-45.
PMID: 24907673
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A Panel Of Three Markers Hyper- And Hypomethylated In Urine Sediments Accurately Predicts Bladder Cancer Recurrence
Authors: Su S.F. , de Castro Abreu A.L. , Chihara Y. , Tsai Y. , Andreu-Vieyra C. , Daneshmand S. , Skinner E.C. , Jones P.A. , Siegmund K.D. , Liang G. .
Source: Clinical Cancer Research : An Official Journal Of The American Association For Cancer Research, 2014-04-01 00:00:00.0; 20(7), p. 1978-89.
PMID: 24691641
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Non-specific Filtering Of Beta-distributed Data
Authors: Wang X. , Laird P.W. , Hinoue T. , Groshen S. , Siegmund K.D. .
Source: Bmc Bioinformatics, 2014; 15, p. 199.
PMID: 24943962
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Low-level Processing Of Illumina Infinium Dna Methylation Beadarrays
Authors: Triche T.J. , Weisenberger D.J. , Van Den Berg D. , Laird P.W. , Siegmund K.D. .
Source: Nucleic Acids Research, 2013 Apr; 41(7), p. e90.
PMID: 23476028
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Dna Methylation In The Arginase-nitric Oxide Synthase Pathway Is Associated With Exhaled Nitric Oxide In Children With Asthma
Authors: Breton C.V. , Byun H.M. , Wang X. , Salam M.T. , Siegmund K. , Gilliland F.D. .
Source: American Journal Of Respiratory And Critical Care Medicine, 2011-07-15 00:00:00.0; 184(2), p. 191-7.
PMID: 21512169
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Modeling Measurement Error In Tumor Characterization Studies
Authors: Rakovski C. , Weisenberger D.J. , Marjoram P. , Laird P.W. , Siegmund K.D. .
Source: Bmc Bioinformatics, 2011-07-13 00:00:00.0; 12, p. 284.
PMID: 21752297
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Statistical Approaches For The Analysis Of Dna Methylation Microarray Data
Authors: Siegmund K.D. .
Source: Human Genetics, 2011 Jun; 129(6), p. 585-95.
PMID: 21519831
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Dna Methylation Changes In Atypical Adenomatous Hyperplasia, Adenocarcinoma In Situ, And Lung Adenocarcinoma
Authors: Selamat S.A. , Galler J.S. , Joshi A.D. , Fyfe M.N. , Campan M. , Siegmund K.D. , Kerr K.M. , Laird-Offringa I.A. .
Source: Plos One, 2011; 6(6), p. e21443.
PMID: 21731750
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High Dna Methylation Pattern Intratumoral Diversity Implies Weak Selection In Many Human Colorectal Cancers
Authors: Siegmund K.D. , Marjoram P. , Tavaré S. , Shibata D. .
Source: Plos One, 2011; 6(6), p. e21657.
PMID: 21738754
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Unique Dna Methylation Patterns Distinguish Noninvasive And Invasive Urothelial Cancers And Establish An Epigenetic Field Defect In Premalignant Tissue
Authors: Wolff E.M. , Chihara Y. , Pan F. , Weisenberger D.J. , Siegmund K.D. , Sugano K. , Kawashima K. , Laird P.W. , Jones P.A. , Liang G. .
Source: Cancer Research, 2010-10-15 00:00:00.0; 70(20), p. 8169-78.
PMID: 20841482
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Hormone Therapy, Dna Methylation And Colon Cancer
Authors: Wu A.H. , Siegmund K.D. , Long T.I. , Cozen W. , Wan P. , Tseng C.C. , Shibata D. , Laird P.W. .
Source: Carcinogenesis, 2010 Jun; 31(6), p. 1060-7.
PMID: 20064828
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Using Dna Methylation Patterns To Infer Tumor Ancestry
Authors: Hong Y.J. , Marjoram P. , Shibata D. , Siegmund K.D. .
Source: Plos One, 2010; 5(8), p. e12002.
PMID: 20711251
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Modeling Dna Methylation In A Population Of Cancer Cells
Authors: Siegmund K.D. , Marjoram P. , Shibata D. .
Source: Statistical Applications In Genetics And Molecular Biology, 2008; 7(1), p. Article 18.
PMID: 18597664
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Statistical Methods For Evaluating Dna Methylation As A Marker For Early Detection Or Prognosis
Authors: Alonzo T.A. , Siegmund K.D. .
Source: Disease Markers, 2007; 23(1-2), p. 113-20.
PMID: 17325431
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Modeling Exposures For Dna Methylation Profiles
Authors: Siegmund K.D. , Levine A.J. , Chang J. , Laird P.W. .
Source: Cancer Epidemiology, Biomarkers & Prevention : A Publication Of The American Association For Cancer Research, Cosponsored By The American Society Of Preventive Oncology, 2006 Mar; 15(3), p. 567-72.
PMID: 16537717
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Cluster Analysis For Dna Methylation Profiles Having A Detection Threshold
Authors: Marjoram P. , Chang J. , Laird P.W. , Siegmund K.D. .
Source: Bmc Bioinformatics, 2006; 7, p. 361.
PMID: 16872497
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A Comparison Of Cluster Analysis Methods Using Dna Methylation Data
Authors: Siegmund K.D. , Laird P.W. , Laird-Offringa I.A. .
Source: Bioinformatics (oxford, England), 2004-08-12 00:00:00.0; 20(12), p. 1896-904.
PMID: 15044245
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