Genotype-environment interactions in mouse behavior: a way out of the problem

Abstract

In behavior genetics, behavioral patterns of mouse genotypes, such as inbred strains, crosses, and knockouts, are characterized and compared to associate them with particular gene loci. Such genotype differences, however, are usually established in single-laboratory experiments, and questions have been raised regarding the replicability of the results in other laboratories. A recent multilaboratory experiment found significant laboratory effects and genotype x laboratory interactions even after rigorous standardization, raising the concern that results are idiosyncratic to a particular laboratory. This finding may be regarded by some critics as a serious shortcoming in behavior genetics. A different strategy is offered here: (i) recognize that even after investing much effort in identifying and eliminating causes for laboratory differences, genotype x laboratory interaction is an unavoidable fact of life. (ii) Incorporate this understanding into the statistical analysis of multilaboratory experiments using the mixed model. Such a statistical approach sets a higher benchmark for finding significant genotype differences. (iii) Develop behavioral assays and endpoints that are able to discriminate genetic differences even over the background of the interaction. (iv) Use the publicly available multilaboratory results in single-laboratory experiments. We use software-based strategy for exploring exploration (see) to analyze the open-field behavior in eight genotypes across three laboratories. Our results demonstrate that replicable behavioral measures can be practically established. Even though we address the replicability problem in behavioral genetics, our strategy is also applicable in other areas where concern about replicability has been raised.

Fig. 2.

Group means of distance traveled (Upper in cm) and segment acceleration (Lower, in cm/s per s, log-transformed). Each genotype was measured in three laboratories: National Institute of Drug Abuse (NIDA), Maryland Psychiatric Research Center (MPRC), and Tel Aviv University (TAU). Small vertical bars on the right represent the SD of GxL interaction and the within-group (individual animal) variability.

Fig. 1.

Principles of see analysis: the time series of the animal's location in the arena is automatically tracked and smoothed. (A) The path in a 3D representation of X, Y, and time (in data points) is shown. (B) The distribution of speed peaks (dark line) is used to parse the data into segments: slow local movements (lingering, L, in red) and progression (P, in green). (C) The data can be treated as a string of these discrete units. (D and E) The path plot and speed profile of two progression segments (P1 and P2) are separated by one lingering episode (L1). The typical properties of these units are used to quantify the behavior. For example, the segment acceleration endpoint (Fig. 2 Lower) is estimated by dividing the segment's speed peak by its duration, i.e., the aspect ratio of a segment in the speed profile.

Fig. 3.

The proportion of variance attributed to each factor for all endpoints by using MM is shown. Endpoints are sorted by their proportion of genotypic variance. Genotypic differences for all endpoints are significant when using MM. Asterisks indicate interaction effects that were found to be significant according to the traditionally used FM at a level of 5%.