Behavioral genetics

Spatial and Visual Performance and Learning in Mice

Inbred mice (C57BL/6J and A/J) received training in a water maze with free spatial (right-left) or visual (black-white) choices followed by spatial or visual learning. C57BL/6J mice showed spatial preferences and learned the spatial task faster than the visual task. A/J mice evidenced no preferences and learned neither task.

Insensitivity of the analysis of variance to heredity-environment interaction

It makes sense to attribute a definite percentage of variation in some measure of behavior to variation in heredity only if the effects of heredity and environment are truly additive. Additivity is often tested by examining the interaction effect in a two-way analysis of variance (ANOVA) or its equivalent multiple regression model. If this effect is not statistically significant at the α = 0.05 level, it is common practice in certain fields (e.g., human behavior genetics) to conclude that the two factors really are additive and then to use linear models, which assume additivity. Comparing several simple models of nonadditive, interactive relationships between heredity and environment, however, reveals that ANOVA often fails to detect nonadditivity because it has much less power in tests of interaction than in tests of main effects. Likewise, the sample sizes needed to detect real interactions are substantially greater than those needed to detect main effects. Data transformations that reduce interaction effects also change drastically the properties ofthe causal model and may conceal theoretically interesting and practically useful relationships. If the goal ofpartitioning variance among mutually exclusive causes and calculating “heritability” coefficients is abandoned, interactive relationships can be examined more seriously and can enhance our understanding of the ways living things develop.

Gerald E. McClearn: complexity in behavior genetics

A festskrift in honor of the career contributions of Gerald E. McClearn was held in State College, Pennsylvania in May 2009. A selection of papers presented at that celebration is included in this issue of Behavior Genetics. These papers illustrate contemporary progress in research areas that have been chosen to reflect key aspects of Jerry's career accomplishments.

Genetic analysis of water maze discrimination learning for Mus musculus: polygenes and albinism

Recombinant inbred strains, their progenitor strains and reciprocal F1 hybrids were given thirty spatial discrimination learning trials in the water maze. The pattern of RI strains in relation to the reciprocal F1 hybrids and the progenitor strains, and differences among strains, suggested that different sets of genes affect the various components of water maze discrimination learning. There was no evidence that the albino gene influenced either inferior or superior performance. Pigmented strains performed as well as, or better than, some albino strains, and albino strain BALB/cBy differed significantly from albino strains CXBG and CXBI. The two reciprocal F1 hybrids differed from blocks 2 and 3 of discrimination trials when swimming time was used a dependent variable. Thus, it appears that performance of the F1 hybrids was influenced by paternal effects, maternal effects, or paternal maternal interactions.

Maze learning: a genetic investigation in the mouse

Recombinant inbred strains, their progenitor strains, and their reciprocal F1 hybrids were tested for maze learning in the Lashley III maze and in the Y-water maze. The resulting pattern of strain distribution suggested that the genetic model provided by the RI strains is based on at least two, and possibly many more, loci. There was no evidence of maternal effects or heterosis. Although the BALB/cBy strain represented an extremely high scoring strain for the Lashley maze and a low scoring strain for the Y-maze, presence of albino recombinant inbred strains intermediate to extreme strains and nonsignificantly different from pigmented recombinant inbred strains suggested that the albino gene was not responsible for the observed performance differences.