Optimal use of statistical methods to validate reference gene stability in longitudinal studies

Multiple statistical approaches have been proposed to validate reference genes in qPCR assays. However, conflicting results from these statistical methods pose a major hurdle in the choice of the best reference genes. Indeed, as their respective approaches to calculating reference gene stability is different, their suitability has to be tested for a given experimental setting. In this study, the stability of 10 candidate reference genes (Actb, Gapdh, Tbp, Sdha, Pgk1, Ppia, Rpl13a, Hsp60, Mrpl10, Rps26) was assessed using four common statistical approaches (GeNorm, NormFinder, Coefficient of Variation analysis and Pairwise {Delta}Ct method) in a longitudinal setting. We used the development of the cerebellum and the spinal cord of mice as a model to assess the suitability of these statistical methods for reference gene validation. GeNorm and the Pairwise {Delta}Ct were found to be ill suited due to a fundamental assumption in their stability calculations. Whereas, NormFinder and Coefficient of Variation analysis fare better provided they are used complementarily. We therefore devised a workflow combining these two methods for validating reference genes in developmental studies. This workflow proves to be more robust than any of the methods used individually.