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Biostatistics Advance Access published online on July 27, 2005
Biostatistics, doi:10.1093/biostatistics/kxi045
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© The Author 2005. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oupjournals.org.
Received November 22, 2002
Revised July 20, 2005
Accepted July 20, 2005

Article

Statistical modelling in case-control real-time RT-PCR assays, for identification of differentially expressed genes in schizophrenia

Rolf Sundberg 1*, Anja Castensson 2, and Elena Jazin 2

1 Mathematical Statistics, Stockholm University, SE-106 91 Stockholm, Sweden
2 Evolutionary Biology, Uppsala University, SE-752 36 Uppsala, Sweden

* To whom correspondence should be addressed.
Rolf Sundberg, E-mail: rolfs{at}math.su.se


  Abstract

Aspects of experimental design, statistical modelling, and statistical inference in case-control real-time RT-PCR assays are discussed. The background is mRNA expression data from an investigation of genes previously suggested to be schizophrenia-related. Real-time RT-PCR allows large samples of individuals. However, with more individuals than positions per plate, incomplete designs are required. A basic multivariate (for several genes jointly) random-effects analysis of covariance (MRANCOVA) model, incorporating heterogeneity both between and within individuals, is formulated. The use of reference genes to form additional regressors is found to be highly efficient. Because regressions between and within individuals are usually different, it is important first to average over the intra-individual replicates, This has consequences for the influence of plate effects. Topics also discussed are testing for a significant mean disease effect, differential co-regulation, and the difficulty of identifying genes affected in only a subgroup of cases.

Keywords: ANCOVA; BIBD; cDNA; differential co-regulation; house-keeping genes; mRNA; MRANCOVA; quantitative PCR; RANCOVA; reference genes; Subgroup; TaqMan.
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