Evidence map›Paper›PMID 21849053›Full record

ArticleBMC molecular biology2011

Use of Maximum Likelihood-Mixed Models to select stable reference genes: a case of heat stress response in sheep.

Magdalena Serrano, Natalia Moreno-Sánchez, Carmen González, Ane Marcos-Carcavilla, Mario Van Poucke, Jorge H Calvo, Judit Salces, Jaime Cubero, María J Carabaño

Abstract read
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Article in BMC molecular biology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

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0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Magdalena SerranoDepartamento de Mejora Genética Animal, INIA, Ctra, de la Coruña km 7,5, Madrid, Spain. malena@inia.es
Natalia Moreno-Sánchez
Carmen González
Ane Marcos-Carcavilla
Mario Van Poucke
Jorge H Calvo
Judit Salces
Jaime Cubero
María J Carabaño

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Likelihood FunctionsAnimalsGene ExpressionHeat-Shock ResponseMalePolymerase Chain ReactionSheep

Identifiers

PMID21849053
PMCPMC3175163

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.