Effects of environmental and physiological covariates on sex differences in unconditioned and conditioned anxiety and fear in a large sample of genetically heterogeneous (N/Nih-HS) rats

Regina López-Aumatell,Adolf Tobeña,Jonathan Flint,Martina Johannesson,Gloria Blázquez,Toni Cañete,Alberto Fernández-Teruel,Esther Martínez-Membrives,Elia Vicens-Costa,Carme Mont-Cardona

doi:10.1186/1744-9081-7-48

Regina López-Aumatell, Adolf Tobeña + Show 8 more

Open Access

https://doi.org/10.1186/1744-9081-7-48

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Abstract

Physiological and environmental variables, or covariates, can account for an important portion of the variability observed in behavioural/physiological results from different laboratories even when using the same type of animals and phenotyping procedures. We present the results of a behavioural study with a sample of 1456 genetically heterogeneous N/Nih-HS rats, including males and females, which are part of a larger genome-wide fine-mapping QTL (Quantitative Trait Loci) study. N/Nih-HS rats have been derived from 8 inbred strains and provide very small distance between genetic recombinations, which makes them a unique tool for fine-mapping QTL studies. The behavioural test battery comprised the elevated zero-maze test for anxiety, novel-cage (open-field like) activity, two-way active avoidance acquisition (related to conditioned anxiety) and context-conditioned freezing (i.e. classically conditioned fear). Using factorial analyses of variance (ANOVAs) we aimed to analyse sex differences in anxiety and fear in this N/Nih-HS rat sample, as well as to assess the effects of (and interactions with) other independent factors, such as batch, season, coat colour and experimenter. Body weight was taken as a quantitative covariate and analysed by covariance analysis (ANCOVA). Obliquely-rotated factor analyses were also performed separately for each sex, in order to evaluate associations among the most relevant variables from each behavioural test and the common dimensions (i.e. factors) underlying the different behavioural responses. ANOVA analyses showed a consistent pattern of sex effects, with females showing less signs of anxiety and fear than males across all tests. There were also significant main effects of batch, season, colour and experimenter on almost all behavioural variables, as well as "sex × batch", "sex × season" and "sex × experimenter" interactions. Body weight showed significant effects in the ANCOVAs of most behavioural measures, but sex effects were still present in spite of (and after controlling for) these "body weight" effects. Factor analyses of relevant variables from each test showed a two-fold factor structure in both sexes, with the first factor mainly representing anxiety and conditioned fear in males, while in females the first factor was dominated by loadings of activity measures. Thus, besides showing consistent sex differences in anxiety-, fear- and activity-related responses in N/Nih-HS rats, the present study shows that females' behaviour is predominantly influenced by activity while males are more influenced by anxiety. Moreover, the results point out that, besides "sex" effects, physiological variables such as colour and body weight, and environmental factors as batch/season or "experimenter", have to be taken into account in both behavioural and quantitative genetic studies because of their demonstrated influences on phenotypic outcomes.

Highlights

Valdar et al [6,7] study constituted a landmark in the field of quantitative genetics of complex traits, both because its powerful methodological foundations allowing the simultaneous detection and genome-wide fine-mapping of QTLs and because it showed that gene-by-environment interaction effects were even more frequent and larger than the main genetic effects, both on behavioural and on physiological/biological phenotypes
An important concern regarding the aforementioned fine-mapping genetic studies is the type of animals, i.e. the level of genetic recombination
Flint and co-workers [7,11] have demonstrated that simultaneous detection and fine mapping of QTLs is possible by using large samples of genetically heterogeneous animal stocks

Summary

Introduction

Valdar et al [6,7] study constituted a landmark in the field of quantitative genetics of complex traits, both because its powerful methodological foundations allowing the simultaneous detection and genome-wide fine-mapping of QTLs and because it showed that gene-by-environment interaction effects were even more frequent and larger than the main genetic effects, both on behavioural and on physiological/biological phenotypes. These results have pointed out the need of mapping the QTLs responsible for these gene-by-environment interactions when aiming to fully understand the underlying mechanisms of the observed phenotypes [6,7,10]. Flint and co-workers [7,11] have demonstrated that simultaneous detection and fine mapping of QTLs is possible by using large samples of genetically heterogeneous animal stocks (see [12])

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