|Grant Number:||5R01CA074841-09 Interpret this number|
|Primary Investigator:||Kooperberg, Charles|
|Organization:||Fred Hutchinson Cancer Research Center|
|Project Title:||Adaptive Function Estimation for Genomic Data|
DESCRIPTION (provided by applicant): The publication of the sequence of the human genome and breakthroughs in the high throughput technologies for single nucleotide polymorphism (SNP) genotyping, gene expression, and protein measurements have offered new opportunities for the study of genome complexity. New technologies are generating large amounts of high dimensional data at an astounding speed. Relative to the high dimension of the data the number of independent samples is often rather small, either because the techniques are too expensive, or because it is hard to obtain enough independent biological samples. Clearly, the development of new statistical techniques is required for the extraction of useful biological information from such data. Adaptive regression methods, which combine variable selection and nonlinear modeling, are well suited for many of these problems. The aim of this proposal is to develop and enhance these methods to address the practical problems that arise directly from several collaborative projects. In particular we focus on association studies with SNP and microarray data. For SNP association studies we plan to make use of Logic Regression. This methodology combines mostly binary predictors using rules of Boolean algebra. The proposed developments include new techniques to deal with haplotype data, new approaches to model selection that scale up to high-dimensional problems, and computational techniques that make it feasible to deal with large data sets. For the analysis of microarray association studies we plan to use polynomial splines, an approach that combines nonlinear functions of predictors and low-order interactions. Gene expression measurements usually have a large variance, and measurements for different genes are often highly correlated. This, combined with the high dimensionality, makes regularization a necessity. Therefore, another focus of this proposal is to develop methods for combining predictors or models to regularize the model selection process. In addition, we plan to develop methods to improve inference for polynomial spline methodologies.
Widespread Collaboration Of Isw2 And Sin3-rpd3 Chromatin Remodeling Complexes In Transcriptional Repression
Authors: Fazzio T.G. , Kooperberg C. , Goldmark J.P. , Neal C. , Basom R. , Delrow J. , Tsukiyama T. .
Source: Molecular And Cellular Biology, 2001 Oct; 21(19), p. 6450-60.
Estimating The Statistical Significance Of Gene Expression Changes Observed With Oligonucleotide Arrays
Authors: Strand A.D. , Olson J.M. , Kooperberg C. .
Source: Human Molecular Genetics, 2002-09-15 00:00:00.0; 11(19), p. 2207-21.
Yeast Isw1p Forms Two Separable Complexes In Vivo
Authors: Vary J.C. , Gangaraju V.K. , Qin J. , Landel C.C. , Kooperberg C. , Bartholomew B. , Tsukiyama T. .
Source: Molecular And Cellular Biology, 2003 Jan; 23(1), p. 80-91.
Directed Indices For Exploring Gene Expression Data
Authors: LeBlanc M. , Kooperberg C. , Grogan T.M. , Miller T.P. .
Source: Bioinformatics (oxford, England), 2003-04-12 00:00:00.0; 19(6), p. 686-93.
Combining Biomarkers To Detect Disease With Application To Prostate Cancer
Authors: Etzioni R. , Kooperberg C. , Pepe M. , Smith R. , Gann P.H. .
Source: Biostatistics (oxford, England), 2003 Oct; 4(4), p. 523-38.
The Histone Modification Pattern Of Active Genes Revealed Through Genome-wide Chromatin Analysis Of A Higher Eukaryote
Authors: Schübeler D. , MacAlpine D.M. , Scalzo D. , Wirbelauer C. , Kooperberg C. , van Leeuwen F. , Gottschling D.E. , O'Neill L.P. , Turner B.M. , Delrow J. , et al. .
Source: Genes & Development, 2004-06-01 00:00:00.0; 18(11), p. 1263-71.
Prostate-specific Antigen And Free Prostate-specific Antigen In The Early Detection Of Prostate Cancer: Do Combination Tests Improve Detection?
Authors: Etzioni R. , Falcon S. , Gann P.H. , Kooperberg C.L. , Penson D.F. , Stampfer M.J. .
Source: Cancer Epidemiology, Biomarkers & Prevention : A Publication Of The American Association For Cancer Research, Cosponsored By The American Society Of Preventive Oncology, 2004 Oct; 13(10), p. 1640-5.
Global And Gene-specific Analyses Show Distinct Roles For Myod And Myog At A Common Set Of Promoters
Authors: Cao Y. , Kumar R.M. , Penn B.H. , Berkes C.A. , Kooperberg C. , Boyer L.A. , Young R.A. , Tapscott S.J. .
Source: The Embo Journal, 2006-02-08 00:00:00.0; 25(3), p. 502-11.
Regional And Cellular Gene Expression Changes In Human Huntington's Disease Brain
Authors: Hodges A. , Strand A.D. , Aragaki A.K. , Kuhn A. , Sengstag T. , Hughes G. , Elliston L.A. , Hartog C. , Goldstein D.R. , Thu D. , et al. .
Source: Human Molecular Genetics, 2006-03-15 00:00:00.0; 15(6), p. 965-77.
Logic Regression For Analysis Of The Association Between Genetic Variation In The Renin-angiotensin System And Myocardial Infarction Or Stroke
Authors: Kooperberg C. , Bis J.C. , Marciante K.D. , Heckbert S.R. , Lumley T. , Psaty B.M. .
Source: American Journal Of Epidemiology, 2007-02-01 00:00:00.0; 165(3), p. 334-43.
Conservation Of Regional Gene Expression In Mouse And Human Brain
Authors: Strand A.D. , Aragaki A.K. , Baquet Z.C. , Hodges A. , Cunningham P. , Holmans P. , Jones K.R. , Jones L. , Kooperberg C. , Olson J.M. .
Source: Plos Genetics, 2007-04-20 00:00:00.0; 3(4), p. e59.
Renin-angiotensin System Haplotypes And The Risk Of Myocardial Infarction And Stroke In Pharmacologically Treated Hypertensive Patients
Authors: Marciante K.D. , Bis J.C. , Rieder M.J. , Reiner A.P. , Lumley T. , Monks S.A. , Kooperberg C. , Carlson C. , Heckbert S.R. , Psaty B.M. .
Source: American Journal Of Epidemiology, 2007-07-01 00:00:00.0; 166(1), p. 19-27.
Expression Profiling Of Huntington's Disease Models Suggests That Brain-derived Neurotrophic Factor Depletion Plays A Major Role In Striatal Degeneration
Authors: Strand A.D. , Baquet Z.C. , Aragaki A.K. , Holmans P. , Yang L. , Cleren C. , Beal M.F. , Jones L. , Kooperberg C. , Olson J.M. , et al. .
Source: The Journal Of Neuroscience : The Official Journal Of The Society For Neuroscience, 2007-10-24 00:00:00.0; 27(43), p. 11758-68.
Share: An Adaptive Algorithm To Select The Most Informative Set Of Snps For Candidate Genetic Association
Authors: Dai J.Y. , Leblanc M. , Smith N.L. , Psaty B. , Kooperberg C. .
Source: Biostatistics (oxford, England), 2009 Oct; 10(4), p. 680-93.
Risk Prediction Using Genome-wide Association Studies
Authors: Kooperberg C. , LeBlanc M. , Obenchain V. .
Source: Genetic Epidemiology, 2010 Nov; 34(7), p. 643-52.