Impact of temporal variation on design and analysis of mouse knockout phenotyping studies.
Impact of temporal variation on design and analysis of mouse knockout phenotyping studies.
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DOI：
10.1371/journal.pone.0111239
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发表时间：
2014
期刊：
影响因子：
3.7
通讯作者：
Mott RF
中科院分区：
文献类型：
--
作者：
Karp NA;Speak AO;White JK;Adams DJ;Hrabé de Angelis M;Hérault Y;Mott RF
体内敲除小鼠的高通量表型分型面临的一个重大挑战是确保表型调用是稳健和可靠的。这个问题的核心是选择一个适当的统计分析，既模拟实验设计（工作流程和方式对照小鼠被选择与基因敲除动物进行比较）和变异的来源。最近，我们提出了一个混合模型，适合小批量的研究，控制不表型突变体同时。在这里，我们评估了这种方法在小鼠表型中心使用的一系列工作流程中检测表型效应和控制假阳性的灵敏度。我们发现假阳性的灵敏度和控制取决于工作流程。我们发现，对照小鼠的表型在批次之间意外波动，这可能会导致表型调用的假阳性率膨胀时，只有少量的批次进行测试，当敲除的效果变得混淆与对照小鼠的时间波动。在行为和生理试验中都观察到这种效果。基于这种分析，我们推荐两种方法（工作流程和伴随的控制策略）和相关分析，这将是强大的，用于高通量表型分析管道。我们的研究结果显示了在高通量表型研究中建模所有可变性来源的重要性。
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.
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影响因子：
48
作者：
Richter, S. Helene;Garner, Joseph P.;Wuerbel, Hanno
通讯作者：
Wuerbel, Hanno
影响因子：
64.8
作者：
Skarnes WC;Rosen B;West AP;Koutsourakis M;Bushell W;Iyer V;Mujica AO;Thomas M;Harrow J;Cox T;Jackson D;Severin J;Biggs P;Fu J;Nefedov M;de Jong PJ;Stewart AF;Bradley A
通讯作者：
Bradley A
影响因子：
3.7
作者：
Karp NA;Melvin D;Sanger Mouse Genetics Project;Mott RF
通讯作者：
Mott RF
影响因子：
14.9
作者：
Ringwald M;Iyer V;Mason JC;Stone KR;Tadepally HD;Kadin JA;Bult CJ;Eppig JT;Oakley DJ;Briois S;Stupka E;Maselli V;Smedley D;Liu S;Hansen J;Baldock R;Hicks GG;Skarnes WC
通讯作者：
Skarnes WC
影响因子：
64.5
作者：
White JK;Gerdin AK;Karp NA;Ryder E;Buljan M;Bussell JN;Salisbury J;Clare S;Ingham NJ;Podrini C;Houghton R;Estabel J;Bottomley JR;Melvin DG;Sunter D;Adams NC;Sanger Institute Mouse Genetics Project;Tannahill D;Logan DW;Macarthur DG;Flint J;Mahajan VB;Tsang SH;Smyth I;Watt FM;Skarnes WC;Dougan G;Adams DJ;Ramirez-Solis R;Bradley A;Steel KP
通讯作者：
Steel KP