eprintid: 29937
rev_number: 24
userid: 5901
dir: disk0/00/02/99/37
datestamp: 2016-11-01 18:32:36
lastmod: 2021-03-10 06:25:31
status_changed: 2016-11-01 18:32:36
type: article
metadata_visibility: show
eprint_status: archive
creators_name: Wang, X
creators_name: Lin, Y
creators_name: Song, C
creators_name: Sibille, E
creators_name: Tseng, GC
creators_email: xbw1@pitt.edu
creators_email: yal14@pitt.edu
creators_email: 
creators_email: 
creators_email: 
creators_id: XBW1
creators_id: YAL14
creators_id: 
creators_id: 
creators_id: 
title: Detecting disease-associated genes with confounding variable adjustment and the impact on genomic meta-analysis: With application to major depressive disorder
ispublished: pub
divisions: sch_gsph_biostatistics
divisions: sch_gsph_humangenetics
divisions: sch_med_Computational_Systems_Biology
divisions: sch_med_Psychiatry
full_text_status: public
abstract: Background: Detecting candidate markers in transcriptomic studies often encounters difficulties in complex diseases, particularly when overall signals are weak and sample size is small. Covariates including demographic, clinical and technical variables are often confounded with the underlying disease effects, which further hampers accurate biomarker detection. Our motivating example came from an analysis of five microarray studies in major depressive disorder (MDD), a heterogeneous psychiatric illness with mostly uncharacterized genetic mechanisms.Results: We applied a random intercept model to account for confounding variables and case-control paired design. A variable selection scheme was developed to determine the effective confounders in each gene. Meta-analysis methods were used to integrate information from five studies and post hoc analyses enhanced biological interpretations. Simulations and application results showed that the adjustment for confounding variables and meta-analysis improved detection of biomarkers and associated pathways.Conclusions: The proposed framework simultaneously considers correction for confounding variables, selection of effective confounders, random effects from paired design and integration by meta-analysis. The approach improved disease-related biomarker and pathway detection, which greatly enhanced understanding of MDD neurobiology. The statistical framework can be applied to similar experimental design encountered in other complex and heterogeneous diseases. © 2012 Wang et al; licensee BioMed Central Ltd.
date: 2012-03-29
date_type: published
publication: BMC Bioinformatics
volume: 13
number: 1
refereed: TRUE
id_number: 10.1186/1471-2105-13-52
citation:   Wang, X and Lin, Y and Song, C and Sibille, E and Tseng, GC  (2012) Detecting disease-associated genes with confounding variable adjustment and the impact on genomic meta-analysis: With application to major depressive disorder.  BMC Bioinformatics, 13 (1).       
document_url: http://d-scholarship-dev.library.pitt.edu/29937/1/art%253A10.1186%252F1471-2105-13-52.pdf
document_url: http://d-scholarship-dev.library.pitt.edu/29937/7/licence.txt