Genetics of Colitis Susceptibility in IL-10-Deficient Mice: Backcross versus F2 Results Contrasted by Principal Component Analysis

Mähler, Michael; Most, Claudia; Schmidtke, Sybille; Sundberg, John P.; Li, Renhua; Hedrich, Hans J.; Churchill, Gary A.

doi:10.1006/geno.2002.6840

Cited by 56 publications

(60 citation statements)

References 48 publications

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“…The main effect found by our approach is the same as in Mähler et al (2002). However, we also detected an epistatic effect between markers on chromosomes 4 and 7 that considerably improves the fit of the model to the data.…”

Section: Resultssupporting

confidence: 67%

“…When using the rBIC on the other hand, we get one main effect on chromosome 5 (D5Mit205). This effect is different from the one that was suggested by Mähler et al (2002). This difference is explained in Figure 6, which shows the relationship between regular t-test statistics and Wilcoxon rank statistics, for each of the considered 11 markers.…”

Section: Resultsmentioning

confidence: 56%

“…However, the ranking of markers according to the Wilcoxon statistic differs from the ranking due to t-test results. In particular marker 9, identified by Mähler et al (2002) and having the largest absolute value of the t-statistic, has a smaller value of the Wilcoxon rank statistic than marker 3, identified by rBIC. The respective P-values of the Wilcoxon test are 0.0089 for marker 9 and 0.0026 for marker 3, which supports the choice of marker 3.…”

Section: Resultsmentioning

confidence: 87%

“…Mähler et al (2002) report one main effect on chromosome 12 for the trait MidPC1 (D12Mit214) and one suggestive QTL for CecumPC1 on chromosome 13. They do not detect any interactions.…”

Section: Resultsmentioning

confidence: 92%

“…Real data analysis: To verify the performance of the rBIC in the case of real data, we reanalyze a data set from Mähler et al (2002). The data concern colitis susceptibility strains that carry a deficient IL-10 gene that is important in limiting the immune response against intestinal antigens.…”

Section: Resultsmentioning

confidence: 99%

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Locating Multiple Interacting Quantitative Trait Loci Using Rank-Based Model Selection

et al. 2007

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In previous work, a modified version of the Bayesian information criterion (mBIC) was proposed to locate multiple interacting quantitative trait loci (QTL). Simulation studies and real data analysis demonstrate good properties of the mBIC in situations where the error distribution is approximately normal. However, as with other standard techniques of QTL mapping, the performance of the mBIC strongly deteriorates when the trait distribution is heavy tailed or when the data contain a significant proportion of outliers. In the present article, we propose a suitable robust version of the mBIC that is based on ranks. We investigate the properties of the resulting method on the basis of theoretical calculations, computer simulations, and a real data analysis. Our simulation results show that for the sample sizes typically used in QTL mapping, the methods based on ranks are almost as efficient as standard techniques when the data are normal and are much better when the data come from some heavy-tailed distribution or include a proportion of outliers. (2006). The most difficult part in constructing an appropriate regression model is the decision on the number of its components (i.e., the QTL number). This decision is particularly important in cases where the trait is influenced by some linked QTL. In such situations, the estimated QTL locations may depend substantially on the number of QTL in the model. When the size of the model is underestimated, for example, two linked QTL may be easily represented as one putative QTL in the middle between two real QTL locations. The opposite situation occurs when the size of the model is overestimated, leading to the incorrect identification of spurious QTL.Since the size of the chosen model depends on the level of significance used for including or deleting its components, the choice of the corresponding threshold value may substantially influence the results of the analysis. The corresponding problem exists also in the framework of Bayesian statistics, where the final estimates of the QTL locations depend on the prior distribution of the QTL number. In the setting of classical statistics, a systematic approach for the comparison of different models is provided by model selection criteria. Different model selection criteria serve different purposes. If the purpose of the study is the choice of markers for marker-assisted selection, then one should consider the criteria aiming at minimizing the prediction error, like, e.g., the Akaike information criterion (Akaike 1974). Note that the prediction does not suffer much from including several markers closely linked to a QTL. However, in the case when the purpose of the study is to identify real QTL, then consistent criteria, like, e.g., the Bayesian information criterion (BIC) (Schwarz 1978), seem to be a better choice.The classical model selection criteria were developed on the basis of asymptotic arguments and assuming that the sample size is large in comparison to the size of the analyzed models. This assumption is no longer...

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Section: Resultssupporting

confidence: 67%

Section: Resultsmentioning

confidence: 56%

Section: Resultsmentioning

confidence: 87%

“…Mähler et al (2002) report one main effect on chromosome 12 for the trait MidPC1 (D12Mit214) and one suggestive QTL for CecumPC1 on chromosome 13. They do not detect any interactions.…”

Section: Resultsmentioning

confidence: 92%

Section: Resultsmentioning

confidence: 99%

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Locating Multiple Interacting Quantitative Trait Loci Using Rank-Based Model Selection

et al. 2007

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Host Response to Bacterial Homeostasis

Zeißig

Blumberg

2010

Inflammatory Bowel Disease

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IL‐10 Receptor Neutralization–Induced Colitis in Mice: A Comprehensive Guide

et al. 2021

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Interleukin‐10 (IL‐10) and its receptor (IL‐10R) have been foremost targets to understand inflammatory bowel disease (IBD) pathogenesis. For the past several decades, IL‐10‐deficient (Il10‐/‐) mice were considered one of the best models to study immune‐mediated colitis. Several physiologic limitations with this model, e.g., delayed and varied disease onset, have hindered investigators in testing new clinical therapies for IBD. In this article, we provide comprehensive guidance for using anti‐IL‐10R monoclonal antibody (αIL‐10R mAb) neutralization as a superior alternative model to study IBD. This article describes the feasibility of using αIL‐10R mAb to induce chronic colitis (within 4 weeks), perform time‐dependent mechanistic studies, and assess the efficacy of IBD therapeutics. This article also delineates protocols for in‐house assays to critically assess colitis and associated inflammatory parameters. Overall, we underscore αIL‐10R mAb neutralization as a relevant immune‐mediated murine colitis model to study human Crohn's disease. © 2021 Wiley Periodicals LLC. Basic Protocol 1: Induction of chronic colitis in mice via αIL‐10R mAb neutralization Basic Protocol 2: Biochemical evaluation of αIL‐10R mAb neutralization–induced chronic colitis Support Protocol 1: Stool analysis and scoring Support Protocol 2: Swiss roll method

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Genetics of Colitis Susceptibility in IL-10-Deficient Mice: Backcross versus F2 Results Contrasted by Principal Component Analysis

Cited by 56 publications

References 48 publications

Locating Multiple Interacting Quantitative Trait Loci Using Rank-Based Model Selection

Locating Multiple Interacting Quantitative Trait Loci Using Rank-Based Model Selection

Host Response to Bacterial Homeostasis

IL‐10 Receptor Neutralization–Induced Colitis in Mice: A Comprehensive Guide

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