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PLoS One. 2014 Oct 24;9(10):e111239. doi: 10.1371/journal.pone.0111239. eCollection 2014.

Impact of temporal variation on design and analysis of mouse knockout phenotyping studies.

Author information

1
Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, United Kingdom.
2
German Mouse Clinic - Institute of Experimental Genetics, Helmholtz Zentrum München, Neuherberg, Germany; Experimental Genetics, Technische Universität München, Freising-Weihenstephan, Germany; German Center for Diabetes Research, Neuherberg, Germany.
3
Institut Clinique de la Souris, Université de Strasbourg, Illkirch, France.
4
The Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, Oxfordshire, United Kingdom.

Abstract

A significant challenge facing high-throughput phenotyping of in-vivo knockout mice is ensuring phenotype calls are robust and reliable. Central to this problem is selecting an appropriate statistical analysis that models both the experimental design (the workflow and the way control mice are selected for comparison with knockout animals) and the sources of variation. Recently we proposed a mixed model suitable for small batch-oriented studies, where controls are not phenotyped concurrently with mutants. Here we evaluate this method both for its sensitivity to detect phenotypic effects and to control false positives, across a range of workflows used at mouse phenotyping centers. We found the sensitivity and control of false positives depend on the workflow. We show that the phenotypes in control mice fluctuate unexpectedly between batches and this can cause the false positive rate of phenotype calls to be inflated when only a small number of batches are tested, when the effect of knockout becomes confounded with temporal fluctuations in control mice. This effect was observed in both behavioural and physiological assays. Based on this analysis, we recommend two approaches (workflow and accompanying control strategy) and associated analyses, which would be robust, for use in high-throughput phenotyping pipelines. Our results show the importance in modelling all sources of variability in high-throughput phenotyping studies.

PMID:
25343444
PMCID:
PMC4208881
DOI:
10.1371/journal.pone.0111239
[Indexed for MEDLINE]
Free PMC Article

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