Document type: Editorial published in Journal of Animal Breeding and Genetics
Author: Piter Bijma
Preview: A social genetic effect (SGE) refers to the effect of an individual's genotype on the phenotypic traits of its social partners. Such effects often work via behavioural interactions. Empirical work on SGE has included laying hens, pigs, mink, Eucalyptus trees, quail and cod. Studies on laying hens, quail and mink show strong evidence of substantial SGE for traits relating to behavioural interactions. Estimation of SGE is more challenging in pigs, because the number of groups is smaller, and there is often more confounding of groups with environmental factors. Hence, genetic analysis of SGE in pig populations requires careful model comparison and validation. Nevertheless, analyses of large populations of Topigs Norsvin show convincing evidence of SGE for growth rate and feed intake, but not for backfat and far depth. A one-generation selection contrast for SGE on growth rate in pigs, performed by Camerlink et al. furthermore suggests that pigs selected for favourable SGE show better social behaviour. Because the social environment is a very important component of animal welfare, improvement of SGE should be an integral component of strategies to improve the well-being of our animals. On the one hand, the trend to larger groups with more behavioural freedom for the animals increases the importance of good social behaviour. On the other hand, however, it severely complicates the estimation of SGE. In small groups, such as traditional battery cages in laying hens, the number of social partners of an individual is limited, so that SGE can be teased out statistically based on the covariance between the phenotypes of the group mates of relatives. When data consist of a few large groups, however, this is impossible, and more information is needed on who interacts with whom.
The next step in the genetic improvement of SGE, therefore, will have to come from the automated detection of behavioural interactions between individuals, with the help of sensors and AI. We need to know who interacts with whom, how often and the consequences of each interaction. The availability of such data would not only greatly advance the breeding for SGE, but could also considerably increase our understanding of animal behaviour. Most importantly, it would allow transforming the study of animal behaviour into a quantitative field, with explicit quantitative models, potentially including genetic terms that provide testable quantitative predictions.
