Animal Characteristics

Which animal models are chosen for an experiment should be decided by which best answer a specific scientific question, however, differences in the characteristics of the animals used (e.g. strain and substrain, sex, age, baseline weight) can also have a large influence on both the results and the analysis methods used to analyse those results.


 Strain and substrain

The choice of strain or substrain should be based on the specific research question. Each strain, and in some cases substrain, has unique characteristics, such as fecundity, susceptibility to specific diseases or sensitivity to certain pharmaceuticals. These need to be considered to choose a strain appropriate for the experiment.

An additional consideration is whether to use inbred or outbred animals. Outbred strains with the same name can vary between different stocks or vendors both genetically and in their response to tests or treatments. Inbred strains have less variability than outbred, and are generally better defined both genetically and phenotypically.

To make results more generalisable, a factorial experimental design involving several different inbred strains can be used without increasing overall animal numbers. More information on strains and substrains can be found in Crabbe et al, 1999, Crawley et al, 1997, Festing, 2014.

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Regulators, funders and publishers are pushing researchers, across all stages of the research pipeline, to study both sexes. Frequently, the misconception that female mice are intrinsically more variable is used as justification for only including one sex. Empirical evidence does not support this view. A meta-analysis comparing the variance seen with male and female mice, where mice were tested without control of the oestrus cycle, found that variability was not significantly greater in females for any endpoint but was for males for several traits.

Using male and female animals in an experiment enables the findings to be generalised to the whole population. Sex differences exist in disease prevalence, symptoms and progression, as well as in treatment efficacy and side effects. A large scale study with rodents found that sex frequently modified the treatment effect. Studies that only examine one sex need to carefully justify this design decision.

When studying both sexes, a factorial design (for example, with two independent variables of interest where sex is one and the treatment/intervention is another) is recommended. This enables the treatment effect to be estimated from both sexes simultaneously, increasing statistical power. With this design, the researcher can also statistically test whether the treatment effect depends on the sex. If sex differences are not the main interest of the study, power the study for the effect size of the main independent variable of interest (e.g. drug treatment), and have half of the animals in each group be male and half female. If the sex of the animals leads to a large difference in the response to the intervention, this will be detectable without doubling animal numbers. If the study is specifically interested in sex differences in relation to the treatment/intervention, power the study for the interaction expected between sex and treatment/intervention. This will require more animals.

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When deciding what age of animal to use in an experiment, the biology being studied must be considered. Different organs and systems reach developmental maturity at different times and the precise age that is most relevant for the research question needs to be considered carefully. For example, using juvenile animals of an Alzheimer’s model could limit the applicability of study results, depending on the Alzheimer’s model used, and whether the study is focused on early- or late-onset Alzheimer’s. For more information about how the age of animals can affect results see Jackson et al, 2017.

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Genetic modifications

When genetically modified animals are being used to model human disease it is important they recapitulate the aspects of the disease relevant to the specific scientific question. Considerations should also be made for the method of genetic modification used to create the model (chances of off-target effects, or transgene copy number effects), the genetic background the modifications are made in, and what has been done to verify both the precise genetic change and the resulting phenotype.

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Representing animal characteristics in the EDA

The animal characteristics node in an EDA diagram contains further information about the animals that will be used in the experiment. Distinct animal characteristics nodes are used for distinct sets of animal characteristics, which can be entered in the properties of each node.

Details of species, strain and substrain, sex, age or developmental stage, and weight are important, as outlined in the ARRIVE guidelines item on Experimental animals.

When designing your experiment in the EDA, more information can be found in the Help Centre.

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References and further reading

CLAYTON, J. A. and COLLINS, F. S. 2014. Policy: NIH to balance sex in cell and animal studies. Nature News, 509, 282. doi: 10.1038/509282a

CRABBE, J. C., WAHLSTEN, D. and DUDEK, B. C. 1999. Genetics of mouse behavior: interactions with laboratory environment. Science, 284, 1670-2. doi: 10.1126/science.284.5420.1670

CRAWLEY, J. N., BELKNAP, J. K., COLLINS, A., et al. 1997. Behavioral phenotypes of inbred mouse strains: implications and recommendations for molecular studies. Psychopharmacology (Berl), 132, 107-24. doi: 10.1007/s002130050327

DOCHERTY, J. R., STANFORD, S. C., PANATTIERI, R. A., et al. 2019. Sex: A change in our guidelines to authors to ensure that this is no longer an ignored experimental variable. Br J Pharmacol, 176, 4081-4086. doi: 10.1111/bph.14761

FESTING, M. F. 2010. Inbred strains should replace outbred stocks in toxicology, safety testing, and drug development. Toxicol Pathol, 38, 681-90. doi: 10.1177/0192623310373776

FESTING, M. F. 2014. Evidence should trump intuition by preferring inbred strains to outbred stocks in preclinical research. ILAR J, 55, 399-404. doi: 10.1093/ilar/ilu036

JACKSON, S. J., ANDREWS, N., BALL, D., et al. 2017. Does age matter? The impact of rodent age on study outcomes. Laboratory Animals, 51, 160-169. doi: 10.1177/0023677216653984

JANKOWSKY, J. L. and ZHENG, H. 2017. Practical considerations for choosing a mouse model of Alzheimer's disease. Mol Neurodegener, 12, 89. doi: 10.1186/s13024-017-0231-7

KARP, N. A., MASON, J., BEAUDET, A. L., et al. 2017. Prevalence of sexual dimorphism in mammalian phenotypic traits. Nature Communications, 8, 15475. doi: 10.1038/ncomms15475

KARP, N. A. and REAVEY, N. 2018. Sex bias in preclinical research and an exploration of how to change the status quo. Br J Pharmacol, 176, 4107-4118. doi: 10.1111/bph.14539

MCCARTHY, M. M. 2015. Incorporating Sex as a Variable in Preclinical Neuropsychiatric Research. Schizophr Bull, 41, 1016-20. doi: 10.1093/schbul/sbv077

PRENDERGAST, B. J., ONISHI, K. G. and ZUCKER, I. 2014. Female mice liberated for inclusion in neuroscience and biomedical research. Neurosci Biobehav Rev, 40, 1-5. doi: 10.1016/j.neubiorev.2014.01.001

RIPPON, G., JORDAN-YOUNG, R., KAISER, A., et al. 2017. Journal of neuroscience research policy on addressing sex as a biological variable: Comments, clarifications, and elaborations. J Neurosci Res, 95, 1357-1359. doi: 10.1002/jnr.24045

YOON, D. Y., MANSUKHANI, N. A., STUBBS, V. C., et al. 2014. Sex bias exists in basic science and translational surgical research. Surgery, 156, 508-16. doi: 10.1016/j.surg.2014.07.001