|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.
Risk prediction using genome-wide association studies.
Authors: Kooperberg C, LeBlanc M, Obenchain V
Source: Genet Epidemiol, 2010 Nov;34(7), p. 643-52.
SHARE: an adaptive algorithm to select the most informative set of SNPs for candidate genetic association.
Authors: Dai JY, Leblanc M, Smith NL, Psaty B, Kooperberg C
Source: Biostatistics, 2009 Oct;10(4), p. 680-93.
EPub date: 2009 Jul 15.
Expression profiling of Huntington's disease models suggests that brain-derived neurotrophic factor depletion plays a major role in striatal degeneration.
Authors: Strand AD, Baquet ZC, Aragaki AK, Holmans P, Yang L, Cleren C, Beal MF, Jones L, Kooperberg C, Olson JM, Jones KR
Source: J Neurosci, 2007 Oct 24;27(43), p. 11758-68.
Renin-angiotensin system haplotypes and the risk of myocardial infarction and stroke in pharmacologically treated hypertensive patients.
Authors: Marciante KD, Bis JC, Rieder MJ, Reiner AP, Lumley T, Monks SA, Kooperberg C, Carlson C, Heckbert SR, Psaty BM
Source: Am J Epidemiol, 2007 Jul 1;166(1), p. 19-27.
EPub date: 2007 May 22.
Conservation of regional gene expression in mouse and human brain.
Authors: Strand AD, Aragaki AK, Baquet ZC, Hodges A, Cunningham P, Holmans P, Jones KR, Jones L, Kooperberg C, Olson JM
Source: PLoS Genet, 2007 Apr 20;3(4), p. e59.
Logic regression for analysis of the association between genetic variation in the renin-angiotensin system and myocardial infarction or stroke.
Authors: Kooperberg C, Bis JC, Marciante KD, Heckbert SR, Lumley T, Psaty BM
Source: Am J Epidemiol, 2007 Feb 1;165(3), p. 334-43.
EPub date: 2006 Nov 2.
Regional and cellular gene expression changes in human Huntington's disease brain.
Authors: Hodges A, Strand AD, Aragaki AK, Kuhn A, Sengstag T, Hughes G, Elliston LA, Hartog C, Goldstein DR, Thu D, Hollingsworth ZR, Collin F, Synek B, Holmans PA, Young AB, Wexler NS, Delorenzi M, Kooperberg C, Augood SJ, Faull RL, Olson JM, Jones L, Luthi-Carter R
Source: Hum Mol Genet, 2006 Mar 15;15(6), p. 965-77.
EPub date: 2006 Feb 8.
Global and gene-specific analyses show distinct roles for Myod and Myog at a common set of promoters.
Authors: Cao Y, Kumar RM, Penn BH, Berkes CA, Kooperberg C, Boyer LA, Young RA, Tapscott SJ
Source: EMBO J, 2006 Feb 8;25(3), p. 502-11.
EPub date: 2006 Jan 26.
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 PH, Kooperberg CL, Penson DF, Stampfer MJ
Source: Cancer Epidemiol Biomarkers Prev, 2004 Oct;13(10), p. 1640-5.
The histone modification pattern of active genes revealed through genome-wide chromatin analysis of a higher eukaryote.
Authors: Schübeler D, MacAlpine DM, Scalzo D, Wirbelauer C, Kooperberg C, van Leeuwen F, Gottschling DE, O'Neill LP, Turner BM, Delrow J, Bell SP, Groudine M
Source: Genes Dev, 2004 Jun 1;18(11), p. 1263-71.
Combining biomarkers to detect disease with application to prostate cancer.
Authors: Etzioni R, Kooperberg C, Pepe M, Smith R, Gann PH
Source: Biostatistics, 2003 Oct;4(4), p. 523-38.
Directed indices for exploring gene expression data.
Authors: LeBlanc M, Kooperberg C, Grogan TM, Miller TP
Source: Bioinformatics, 2003 Apr 12;19(6), p. 686-93.
Yeast Isw1p forms two separable complexes in vivo.
Authors: Vary JC Jr, Gangaraju VK, Qin J, Landel CC, Kooperberg C, Bartholomew B, Tsukiyama T
Source: Mol Cell Biol, 2003 Jan;23(1), p. 80-91.
Estimating the statistical significance of gene expression changes observed with oligonucleotide arrays.
Authors: Strand AD, Olson JM, Kooperberg C
Source: Hum Mol Genet, 2002 Sep 15;11(19), p. 2207-21.
Widespread collaboration of Isw2 and Sin3-Rpd3 chromatin remodeling complexes in transcriptional repression.
Authors: Fazzio TG, Kooperberg C, Goldmark JP, Neal C, Basom R, Delrow J, Tsukiyama T
Source: Mol Cell Biol, 2001 Oct;21(19), p. 6450-60.