Abstract
BackgroundReference genes with stable expression are required to normalize expression differences of target genes in qPCR experiments. Several procedures and companion software have been proposed to find the most stable genes. Model based procedures are attractive because they provide a solid statistical framework. NormFinder, a widely used software, uses a model based method. The pairwise comparison procedure implemented in GeNorm is a simpler procedure but one of the most extensively used. In the present work a statistical approach based in Maximum Likelihood estimation under mixed models was tested and compared with NormFinder and geNorm softwares. Sixteen candidate genes were tested in whole blood samples from control and heat stressed sheep.ResultsA model including gene and treatment as fixed effects, sample (animal), gene by treatment, gene by sample and treatment by sample interactions as random effects with heteroskedastic residual variance in gene by treatment levels was selected using goodness of fit and predictive ability criteria among a variety of models. Mean Square Error obtained under the selected model was used as indicator of gene expression stability. Genes top and bottom ranked by the three approaches were similar; however, notable differences for the best pair of genes selected for each method and the remaining genes of the rankings were shown. Differences among the expression values of normalized targets for each statistical approach were also found.ConclusionsOptimal statistical properties of Maximum Likelihood estimation joined to mixed model flexibility allow for more accurate estimation of expression stability of genes under many different situations. Accurate selection of reference genes has a direct impact over the normalized expression values of a given target gene. This may be critical when the aim of the study is to compare expression rate differences among samples under different environmental conditions, tissues, cell types or genotypes. To select reference genes not only statistical but also functional and biological criteria should be considered. Under the method here proposed SDHA/MDH1 have arisen as the best set of reference genes to be used in qPCR assays to study heat shock in ovine blood samples.
Highlights
Reference genes with stable expression are required to normalize expression differences of target genes in qPCR experiments
Some efforts have been made to determine the best way to estimate expression stability of candidate reference genes. Concerning the former, the Pair-Wise comparison method employed by geNorm [4], the most used software in the establishment of RGs, considers that all the samples belong to one group, and the estimate of the expression stability ignores differences in gene expression level and gene expression variability across groups
We propose a Maximum Likelihood (ML) Mixed model based approach to estimate the expression stability of 16 candidate RGs taking the heat stress response in the ovine species as example
Summary
Reference genes with stable expression are required to normalize expression differences of target genes in qPCR experiments. Andersen et al [5] proposed a model-based approach to identify RGs, by means of the NormFinder Visual Basic application for Microsoft Excel This method estimates the intra- and inter-group variances for each gene and calculates a stability value by combining both sources of variation. Another model-based procedure is the method proposed by Szabo et al [6] which uses the intraand inter-gene variation across groups but in a more solid statistical framework In this case, the stability criterion is the Mean Square Error (MSE), a measure of variability around an intended value, the overall expression level of a gene. A model based procedure using mixed models and optimal statistical methods, such as Maximum Likelihood, to estimate inter- and intra-group variances would be desirable
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