Selecting reference genes in RT-qPCR based on equivalence tests: a network based approach

• Published in: Scientific reports Vol. 9; no. 1; pp. 16231 - 8
• Main Authors: Curis, Emmanuel, Nepost, Calypso, Grillault Laroche, Diane, Courtin, Cindie, Laplanche, Jean-Louis, Etain, Bruno, Marie-Claire, Cynthia
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 07.11.2019; Nature Publishing Group
• Subjects: 38; 631/114/2415; 631/1647/2017/2003; Adult; Biochemistry, Molecular Biology; Bipolar disorder; Bipolar Disorder - genetics; Cell Line; Female; Gene expression; Gene Expression Profiling; Genetics; Genomics; Human genetics; Human health and pathology; Humanities and Social Sciences; Humans; Life Sciences; Lymphoblastoid cell lines; Male; multidisciplinary; Psychiatrics and mental health; Real-Time Polymerase Chain Reaction - standards; Reference Standards; Reverse transcription; Ribonucleic acid; RNA; Science; Science (multidisciplinary)
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-019-52217-2

Because quantitative reverse transcription PCR (RT-qPCR) gene expression data are compositional, amounts of quantified RNAs must be normalized using reference genes. However, the two most used methods to select reference genes (NormFinder and geNorm) ignore the compositional nature of RT-qPCR data, and often lead to different results making reliable reference genes selection difficult. We propose a method, based on all pairwise equivalence tests on ratio of gene expressions, to select genes that are stable enough to be used as reference genes among a set a candidate genes. This statistical procedure controls the error of selecting an inappropriate gene. Application to 30 candidate reference genes commonly used in human studies, assessed by RT-qPCR in RNA samples from lymphoblastoid cell lines of 14 control subjects and 26 patients with bipolar disorder, allowed to select 7 reference genes. This selection was consistent with geNorm’s ranking, less with NormFinder’s ranking. Our results provide an important fundamental basis for reference genes identification using sound statistics taking into account the compositional nature of RT-qPCR data. The method, implemented in the SARP.compo package for R (available on the CRAN), can be used more generally to prove that a set of genes shares a common expression pattern.