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Meyer, C.W.* ; Elvert, R. ; Scherag, A.* ; Ehrhardt, N. ; Gailus-Durner, V. ; Fuchs, H. ; Schäfer, H.* ; Hrabě de Angelis, M. ; Klingenspor, M.*

Power matters in closing the phenotyping gap.

Naturwissenschaften 94, 401-406 (2007)
DOI PMC
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
Much of our understanding of physiology and metabolism is derived from investigating mouse mutants and transgenic mice, and open-access platforms for standardized mouse phenotyping such as the German Mouse Clinic (GMC) are currently viewed as one powerful tool for identifying novel gene-function relationships. Phenotyping or phenotypic screening involves the comparison of wild-type control mice with their mutant or transgenic littermates. In our study, we explored the extent to which standardized phenotyping will succeed in detecting biologically relevant phenotypic differences in mice generated and provided by different collaborators. We analyzed quantitative metabolic data (body mass, energy intake, and energy metabolized) collected at the GMC under the current workflow, and used them for statistical power considerations. Our results demonstrate that there is substantial variability in these parameters among lines of wild-type C57BL/6 (B6) mice from different sources. Given this variable background noise in mice that serve as controls, subtle phenotypes in mutant or transgenic littermates may be overlooked. Furthermore, a phenotype observed in one cohort of a mutant line may not be reproducible (to the same extent) in mice coming from a different environment or supplier. In the light of these constraints, we encourage researchers to incorporate information on intrastrain variability into future study planning, or to perform advanced hierarchical analyses. Both will ultimately improve the detectability of novel phenotypes by phenotypic screening.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Phenotyping; Mouse mutants; Statistical power
Sprache englisch
Veröffentlichungsjahr 2007
HGF-Berichtsjahr 0
ISSN (print) / ISBN 0028-1042
e-ISSN 1432-1904
Quellenangaben Band: 94, Heft: 5, Seiten: 401-406 Artikelnummer: , Supplement: ,
Verlag Springer
Begutachtungsstatus Peer reviewed
POF Topic(s) 30201 - Metabolic Health
Forschungsfeld(er) Genetics and Epidemiology
PSP-Element(e) G-500600-001
PubMed ID 17216184
Scopus ID 34247335031
Erfassungsdatum 2007-05-29