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Detecting disease-associated genes with confounding variable adjustment and the impact on genomic meta-analysis: With application to major depressive disorder

UNSPECIFIED (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).

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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.


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Details

Item Type: Article
Status: Published
Date: 29 March 2012
Date Type: Publication
Journal or Publication Title: BMC Bioinformatics
Volume: 13
Number: 1
DOI or Unique Handle: 10.1186/1471-2105-13-52
Schools and Programs: Graduate School of Public Health > Biostatistics
Graduate School of Public Health > Human Genetics
School of Medicine > Computational and Systems Biology
School of Medicine > Psychiatry
Refereed: Yes
Date Deposited: 01 Nov 2016 18:32
Last Modified: 08 Jan 2019 16:55
URI: http://d-scholarship.pitt.edu/id/eprint/29937

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