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Background and Environment Affect Phenotype

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A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions
15:30

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Published on: August 5, 2020

Power matters in closing the phenotyping gap.

Carola W Meyer1, Ralf Elvert, André Scherag

  • 1Faculty of Biology, Philipps-Universität Marburg, Karl-von-Frisch-Str. 8, Marburg, Germany. meyerc@staff.uni-marburg.de

Die Naturwissenschaften
|January 12, 2007
PubMed
Summary

Standardized mouse phenotyping can miss subtle gene-function links due to high variability in control mice. Researchers should account for this intrastrain variability to improve the detection of novel phenotypes.

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Area of Science:

  • Physiology and Metabolism
  • Genetics and Genomics
  • Animal Models

Background:

  • Standardized mouse phenotyping platforms like the German Mouse Clinic (GMC) are crucial for identifying gene-function relationships.
  • Phenotyping involves comparing wild-type control mice with mutant or transgenic littermates to detect biological differences.

Purpose of the Study:

  • To assess the effectiveness of standardized phenotyping in detecting biologically relevant differences in mice from various sources.
  • To analyze quantitative metabolic data for statistical power considerations in mouse phenotyping.

Main Methods:

  • Analysis of quantitative metabolic data (body mass, energy intake, energy metabolized) from wild-type C57BL/6 (B6) mice from different sources.
  • Statistical power considerations based on observed intrastrain variability.

Main Results:

  • Significant variability was observed in metabolic parameters among wild-type B6 mice from different suppliers.
  • This background noise can obscure subtle phenotypes in mutant or transgenic mice.
  • Phenotypic differences may not be consistently reproducible across different environments or suppliers.

Conclusions:

  • Researchers must consider intrastrain variability in study planning for mouse phenotyping.
  • Advanced hierarchical analyses can improve the detectability of novel phenotypes.
  • Accounting for variability enhances the reliability of gene-function discoveries through phenotypic screening.