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The choice of reference gene affects statistical efficiency in quantitative PCR data analysis.

ABSTRACT: Quantitative polymerase chain reaction (qPCR), a highly sensitive method of measuring gene expression, is widely used in biomedical research. To produce reliable results, it is essential to use stably expressed reference genes (RGs) for data normalization so that sample-to-sample variation can be controlled. In this study, we examine the effect of different RGs on statistical efficiency by analyzing a qPCR data set that contains 12 target genes and 3 RGs. Our results show that choosing the most stably expressed RG for data normalization does not guarantee reduced variance or improved statistical efficiency. We also provide a formula for determining when data normalization will improve statistical efficiency and hence increase the power of statistical tests in data analysis.

SUBMITTER: Guo Y

PROVIDER: S-EPMC4102495 | biostudies-literature | 2013 Oct

REPOSITORIES: biostudies-literature

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The choice of reference gene affects statistical efficiency in quantitative PCR data analysis.

Guo Yi Y Pennell Michael L ML Pearl Dennis K DK Knobloch Thomas J TJ Fernandez Soledad S Weghorst Christopher M CM

BioTechniques 20131001 4

Quantitative polymerase chain reaction (qPCR), a highly sensitive method of measuring gene expression, is widely used in biomedical research. To produce reliable results, it is essential to use stably expressed reference genes (RGs) for data normalization so that sample-to-sample variation can be controlled. In this study, we examine the effect of different RGs on statistical efficiency by analyzing a qPCR data set that contains 12 target genes and 3 RGs. Our results show that choosing the most ...[more]

PMID: 24107253

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Project description:BackgroundQuantitative real-time polymerase chain reaction (RT-qPCR) is the key platform for the quantitative analysis of gene expression in a wide range of experimental systems and conditions. However, the accuracy and reproducibility of gene expression quantification via RT-qPCR is entirely dependent on the identification of reliable reference genes for data normalisation. Green foxtail (Setaria viridis) has recently been proposed as a potential experimental model for the study of C4 photosynthesis and is closely related to many economically important crop species of the Panicoideae subfamily of grasses, including Zea mays (maize), Sorghum bicolor (sorghum) and Sacchurum officinarum (sugarcane). Setaria viridis (Accession 10) possesses a number of key traits as an experimental model, namely; (i) a small sized, sequenced and well annotated genome; (ii) short stature and generation time; (iii) prolific seed production, and; (iv) is amendable to Agrobacterium tumefaciens-mediated transformation. There is currently however, a lack of reference gene expression information for Setaria viridis (S. viridis). We therefore aimed to identify a cohort of suitable S. viridis reference genes for accurate and reliable normalisation of S. viridis RT-qPCR expression data.ResultsEleven putative candidate reference genes were identified and examined across thirteen different S. viridis tissues. Of these, the geNorm and NormFinder analysis software identified SERINE/THERONINE-PROTEIN PHOSPHATASE 2A (PP2A), 5'-ADENYLYLSULFATE REDUCTASE 6 (ASPR6) and DUAL SPECIFICITY PHOSPHATASE (DUSP) as the most suitable combination of reference genes for the accurate and reliable normalisation of S. viridis RT-qPCR expression data. To demonstrate the suitability of the three selected reference genes, PP2A, ASPR6 and DUSP, were used to normalise the expression of CINNAMYL ALCOHOL DEHYDROGENASE (CAD) genes across the same tissues.ConclusionsThis approach readily demonstrated the suitably of the three selected reference genes for the accurate and reliable normalisation of S. viridis RT-qPCR expression data. Further, the work reported here forms a highly useful platform for future gene expression quantification in S. viridis and can also be potentially directly translatable to other closely related and agronomically important C4 crop species.

Dataset Information

The choice of reference gene affects statistical efficiency in quantitative PCR data analysis.

Publications

The choice of reference gene affects statistical efficiency in quantitative PCR data analysis.

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