Evidence map›Paper›PMID 25343444›Full record

ArticlePloS one2014

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

Natasha A Karp, Anneliese O Speak, Jacqueline K White, David J Adams, Martin Hrabé de Angelis, Yann Hérault, Richard F Mott

Abstract read
In one paragraph

Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
35citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

35 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. A consensus score to combine inferences from multiple centres.Mammalian genome : official journal of the International Mammalian Genome Society · 2023
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  8. A reaction norm perspective on reproducibility.Theory in biosciences = Theorie in den Biowissenschaften · 2021
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Natasha A KarpWellcome Trust Sanger Institute, Hinxton, Cambridgeshire, United Kingdom.
Anneliese O SpeakWellcome Trust Sanger Institute, Hinxton, Cambridgeshire, United Kingdom.
Jacqueline K WhiteWellcome Trust Sanger Institute, Hinxton, Cambridgeshire, United Kingdom.
David J AdamsWellcome Trust Sanger Institute, Hinxton, Cambridgeshire, United Kingdom.
Martin Hrabé de AngelisGerman 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.
Yann HéraultInstitut Clinique de la Souris, Université de Strasbourg, Illkirch, France.
Richard F MottThe Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, Oxfordshire, United Kingdom.

Funding

Mouse Phenotyping Informatics Infrastruture - MP12U54HG006370 · NHGRI · EUROPEAN MOLECULAR BIOLOGY LABORATORY · PI BROWN, STEVE, FLICEK, PAUL · 2011 to 2015
$11.4M
Cancer Research UK 13031Medical Research Council MR/L007428/1NHGRI NIH HHS 1 U54 HG006370-01NHGRI NIH HHS U54 HG006370Wellcome TrustWellcome Trust 083573/Z/07/ZWellcome Trust 090532/Z/09/ZWellcome Trust WT098051
6 · The paper itself

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.

Indexed as

Research DesignAnimalsChloridesComputer SimulationMice, KnockoutPhenotypeReproducibility of ResultsTime FactorsChlorides

Identifiers

PMID25343444
PMCPMC4208881

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.