Mammalian Genome

, Volume 26, Issue 7–8, pp 348–354 | Cite as

Within-strain variation in behavior differs consistently between common inbred strains of mice

  • Maarten LoosEmail author
  • Bastijn Koopmans
  • Emmeke Aarts
  • Gregoire Maroteaux
  • Sophie van der Sluis
  • Neuro-BSIK Mouse Phenomics Consortium
  • Matthijs Verhage
  • August B. Smit


Genetic and environmental factors interact throughout life and give rise to individual differences, i.e., individuality. The diversifying effect of environmental factors is counteracted by genetic mechanisms to yield persistence of specific features (robustness). Here, we compared robustness between cohorts of isogenic mice of eight different commonly used strains by analyzing to what extent environmental variation contributed to individuality in each of the eight genotypes, using a previously published dataset. Behavior was assessed in the home-cage, providing control over environmental factors, to reveal within-strain variability in numerous spontaneous behaviors. Indeed, despite standardization and in line with previous studies, substantial variability among mice of the same inbred strain was observed. Strikingly, across a multidimensional set of 115 behavioral parameters, several strains consistently ranked high in within-strain variability (DBA/2J, 129S1/Sv A/J and NOD/LtJ), whereas other strains ranked low (C57BL/6J and BALB/c). Strain rankings of within-strain variability in behavior were confirmed in an independent, previously published behavioral dataset using conventional behavioral tests administered to different mice from the same breeding colonies. Together, these show that genetically inbred mouse strains consistently differ in phenotypic robustness against environmental variation, suggesting that genetic factors contribute to variation in robustness.


Inbred Strain Strain Difference Behavioral Phenotype Video Tracking Average Variability 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We thank Rolinka van der Loo for operating the PhenoTyper systems and Ruud Wijnands for assistance, Noldus Information Technology for supplying software free of charge and hardware at cost price, and Ben Loke, Cecilia Herrera, Raymond de Heer, and Willem van der Veer for development of hardware, software, and test scripts. This work was supported by Agentschap NL (NeuroBSIK Mouse Phenomics Consortium, BSIK03053), the Netherlands Organization for Scientific Research (NWO/MaGW: VIDI-452-12-014 to S.v.d.S). The authors declare no conflict of interest. M.L. and B.K. are full time employees of Sylics (Synaptologics BV), a private, VU University spin-off company that offers mouse phenotyping services using AHCODA™. A.B.S. and M.V. participate in a holding that owns Sylics shares and have received consulting fees from Sylics.

Supplementary material

335_2015_9578_MOESM1_ESM.pdf (91 kb)
Supplementary material 1 (PDF 91 kb)
335_2015_9578_MOESM2_ESM.pdf (187 kb)
Supplementary material 2 (PDF 186 kb)


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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Maarten Loos
    • 1
    • 2
    Email author
  • Bastijn Koopmans
    • 1
  • Emmeke Aarts
    • 3
  • Gregoire Maroteaux
    • 3
  • Sophie van der Sluis
    • 4
  • Neuro-BSIK Mouse Phenomics Consortium
  • Matthijs Verhage
    • 3
  • August B. Smit
    • 2
  1. 1.Sylics (Synaptologics BV)AmsterdamThe Netherlands
  2. 2.Department of Molecular and Cellular Neurobiology, Center for Neurogenomics and Cognitive Research (CNCR), Neuroscience Campus AmsterdamVU University AmsterdamAmsterdamThe Netherlands
  3. 3.Department of Functional Genomics, Center for Neurogenomics and Cognitive Research (CNCR), Neuroscience Campus AmsterdamVU University AmsterdamAmsterdamThe Netherlands
  4. 4.Department of Clinical Genetics, Section Complex Trait Genetics, VU Medical CenterVU UniversityAmsterdamThe Netherlands

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