Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/12117
Title: Genomic Biomarkers for Depression: Feature-Specific and Joint Biomarkers
Authors: TILAHUN ESHETE, Abel 
LIN, Dan 
SHKEDY, Ziv 
GEYS, Helena 
ALONSO ABAD, Ariel 
Peeters, Pieter
Talloen, Willem
Drinkenburg, Wilhelmus
Goehlmann, Hinrich
Gorden, Evian
BIJNENS, Luc 
MOLENBERGHS, Geert 
Issue Date: 2010
Publisher: AMER STATISTICAL ASSOC
Source: STATISTICS IN BIOPHARMACEUTICAL RESEARCH, 2(3). p. 419-434
Abstract: Recently, preclinical microarray experiments have become increasingly common laboratory tools to investigate the activity of thousands of genes simultaneously and their response to a certain treatment (Amaratunga and Cabrera 2004). In some experiments, in addition to the gene expressions, other responses are also available. In such situations, the primary question of interest is to identify whether or not the gene expressions can serve as biomarkers for the responses. In addition to gene expressions, metabolites are potential biomarkers for some responses as well. In the present study, we focus on the identification of genomic biomarkers, based on gene and metabolite expression for depression. One measure of the level of depression is the Hamilton Depression Scale (HDS or HAMD) which is a test measuring the severity of depressive symptoms in individuals. The data for this study are a result of a clinical trial in which both HAMD and gene/metabolites expression were measured. We use three modeling approaches commonly used in the surrogate marker validation theory to select and evaluate a set of genes and metabolites as possible biomarkers for depression, as measured by the HAMD score. In addition to gene and metabolite specific biomarkers, we use supervised principal components analysis and supervised partial least squares regression technique to construct a joint biomarker that uses information from all genes/metabolites in the array.
Notes: [Tilahun, A; Lin, D; Shkedy, Z; Alonso, A; Molenberghs, G] Univ Hasselt, I BioStat, Diepenbeek, Belgium [Tilahun, A] Harvard Univ, Sch Publ Hlth, Dept Biostat, Ctr Biostat Aids Res, Cambridge, MA 02138 USA [Geys, H; Peeters, P; Talloen, W; Drinkenburg, W; Gohlmann, H; Bijnens, L] Johnson & Johnson Pharmaceut Res & Dev, Beerse, Belgium [Gorden, E] Brain Resource Co, Ultimo, NSW 2007, Australia [Molenberghs, G] Katholieke Univ Leuven, I BioStat, Louvain, Belgium
Keywords: Genes; HAMD score; Joint modeling; Metabolites; Microarray experiments; Partial least squares; Supervised principal component analysis;genes; HAMD score; joint modeling; metabolites; microarray experiments; partial least squares; supervised principal component analysis
Document URI: http://hdl.handle.net/1942/12117
ISSN: 1946-6315
e-ISSN: 1946-6315
DOI: 10.1198/sbr.2009.08091
ISI #: 000292680500013
Rights: (c) American Statistical Association Statistics in Biopharmaceutical Research
Category: A1
Type: Journal Contribution
Validations: ecoom 2012
Appears in Collections:Research publications

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