title: A Bayesian Approach for Graph-constrained Estimation for High-dimensional Regression.
abstract: Many different biological processes are represented by network graphs such as regulatory networks, metabolic pathways, and protein-protein interaction networks. Since genes that are linked on the networks usually have biologically similar functions, the linked genes form molecular modules to affect the clinical phenotypes/outcomes. Similarly, in large-scale genetic association studies, many SNPs are in high linkage disequilibrium (LD), which can also be summarized as a LD graph. In order to incorporate the graph information into regression analysis with high dimensional genomic data as predictors, we introduce a Bayesian approach for graph-constrained estimation (Bayesian GRACE) and regularization, which controls the amount of regularization for sparsity and smoothness of the regression coefficients. The Bayesian estimation with their posterior distributions can provide credible intervals for the estimates of the regression coefficients along with standard errors. The deviance information criterion (DIC) is applied for model assessment and tuning parameter selection. The performance of the proposed Bayesian approach is evaluated through simulation studies and is compared with Bayesian Lasso and Bayesian Elastic-net procedures. We demonstrate our method in an analysis of data from a case-control genome-wide association study of neuroblastoma using a weighted LD graph.
authors.affiliation: Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA 19104, USA.
authors.forename: Hokeun
authors.initials: H
authors.lastname: Sun
authors.affiliation: Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA 19104, USA.
authors.forename: Hongzhe
authors.initials: H
authors.lastname: Li
keywords: Bayesian Lasso; DIC; Laplacian matrix; biological network; high dimensional data
references.citation: N Engl J Med. 2008 Jun 12;358(24):2585-93
references.pmid: 18463370
references.citation: Ann Stat. 2009 Jan 1;37(5A):2178-2201
references.pmid: 19784398
references.citation: Ann Appl Stat. 2010 Sep 1;4(3):1498-1516
references.pmid: 22916087
references.citation: Bioinformatics. 2008 May 1;24(9):1175-82
references.pmid: 18310618
references.citation: J Dairy Sci. 2003 May;86(5):1837-44
references.pmid: 12778595
references.citation: Biostatistics. 2001 Dec;2(4):485-500
references.pmid: 12933638
references.citation: Comput Stat Data Anal. 2009 Mar 15;53(5):1850-1860
references.pmid: 20161286
references.citation: Biostatistics. 2010 Jan;11(1):139-50
references.pmid: 19822692
references.citation: Genetics. 2008 Jun;179(2):1045-55
references.pmid: 18505874
references.citation: J Dairy Sci. 2003 May;86(5):1821-7
references.pmid: 12778593
references.citation: Nature. 2008 Oct 16;455(7215):930-5
references.pmid: 18724359