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 the experimental design used, the results and the analysis methods used to analyse those results.
- Strain and substrain
- Genetic modifications
- Representing animal characteristics in the EDA
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.
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 in males for several traits.
Using male and female animals in an experiment enables the findings to be generalised to the whole population and hence improves the external validity of the experiment. Sex differences exist in disease prevalence, symptoms and progression, as well as in treatment efficacy and side effects. Large scale studies with rodents frequently find that sex modifies the treatment effect. Researchers who only examine one sex need to justify this design decision carefully.
When an experiment includes both male and female animals, you must consider how to take sex into account in the randomisation. For example, separately randomising males and females to the experimental groups ensures the researcher has control over the proportion of male/female animals in each group and this can prevent the introduction of sex bias in the experiment. However, how to calculate the sample size and the exact analysis method both depend on the purpose of the experiment. With regards to the analysis, sex can be used either as a blocking factor or a factor of interest, depending on the purpose of the experiment (see below).
This approach is appropriate when the purpose of the study is to determine the overall effect of an intervention, and sex differences are not of direct interest. While the overall response may be different in males and females (e.g. the response is stronger in males) you have no reason to believe that the intervention has different effects in males and females (e.g. the direction of the response is different, or the treatment only has an effect in one sex).
Including sex as a blocking factor in the experimental design and statistical analysis allows the variability introduced by sex (i.e. caused by the overall difference in response between sexes) to be taken into account. This increases the ability to detect the real effect of the intervention (or treatment) without increasing the total number of animals used in the experiment. The sample size in each experimental group should be determined using a power calculation based on a biologically relevant effect size for the main independent variable of interest (e.g. drug treatment) with each group consisting of both male and female animals (ideally half and half).
When sex is included as a blocking factor and the treatment/intervention is the only independent variable of interest, the statistical analysis performed should be a one-way ANOVA with a blocking factor. This analysis will allow researchers to test for the effect of the intervention (e.g. if there is an overall difference between drug-treated and control animals) but will not allow for a test to see if males and females respond differently to the intervention (i.e. it will not generate a p value for the interaction between treatment and sex). Summary/descriptive statistics for each sex can be plotted separately (e.g. a boxplot) to see if the sex of the animals leads to a large difference (i.e. visible on the graph) in the response to the intervention. Observing a difference in the treatment responses between sexes suggests it would be worth considering including sex as an independent variable of interest in future experiments. This will require a suitable sample size to allow a comparison of treatments to be made separately for males and females (see below).
This approach is appropriate if you want to investigate whether the effect of the intervention depends on sex. The experimental design can be a factorial design. If the intervention is the only independent variable of interest in the experiment other than sex, it can be analysed using a two-way ANOVA with two factors of interest: sex and treatment/intervention.
If you are planning post-hoc comparisons to test the effect of the intervention or treatment in females and males separately, then males and females at different treatment levels are considered different experimental groups (e.g. if the treatment is a drug there is one male and one female group for each dose). The sample size for each group should be determined using a power calculation based on a biologically relevant effect size for the intervention or treatment (further guidance on how to choose the appropriate power calculation can be found here). This may imply using twice the number of animals compared to a single-sex experiment or an experiment using sex as a blocking factor. It is worth noting that this approach is more sensitive (i.e. the statistical power is higher) than using males and females in separate experiments and analysing the male and female data separately.
If you are not planning post-hoc comparisons to test the effect of the treatment in males and in females separately, the sample size should be determined using a power calculation for a two-way ANOVA (seek help from a statistician if unsure). Note that the experiment will only be able to test whether there is an interaction between sex and the intervention (i.e. does the effect of the treatment/intervention depend on sex?), it will not necessarily be sensitive enough (i.e. will not have enough animals per group) to test reliably the effect of the intervention/treatment in females and males separately.
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.
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.
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.
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