February 12, 2007
Scientists find method to pick non-competitive animals, improve productionWEST LAFAYETTE, Ind. -
Scientists from Purdue University, the Netherlands and England designed mathematical equations based on traits to choose animals that are more congenial in groups, said William Muir, a Purdue Department of Animal Sciences geneticist. The new method is a tool that may contribute both to animal well-being and to securing the world's future food supply, including possibly permitting more animals to be domesticated, Muir said.
The tool makes it possible to design selective breeding programs to effectively reduce competitive interactions in livestock, he said. The method also aids in predicting how social interactions impact the natural evolution of species.
Muir and his colleagues write about the tool and its effectiveness in two papers published in the current issue of the journal Genetics. The journal's cover highlights the work with a photograph that Muir took of various colorful fish species interacting in a simulated ecosystem at the Monterey, Calif., aquarium.
"There is an inherited part of the associations among animals that has profound effects on performance," Muir said. "It's called competition. Animals compete for food, space, territory and mates."
In the first of the two papers, Muir and his colleagues explain the tool they developed to determine inherited traits that contribute to interactions among both individual animals and groups. The second paper refines the methodology and validates it by applying the tool to a flock of chickens.
In previous research, Muir showed that choosing less aggressive animals from a group for breeding purposes increases productivity. In the latest research, the scientists show that aggressiveness and all other traits affecting social interactions are inherited and can be estimated. They also found that by using the new tool they were able to confirm two-thirds more inherited trait variations that impact social interactions than could be identified with classical selection analysis.
"Now we have a tool to explain how species in nature evolved in response to each other," Muir said. "It can be applied across species and can tell us how social interactions developed in the past and will develop in the future between individuals and among various animal species.
"This is important because the most stable ecosystems are those that have multiple species that cohabitate. Natural selection is nature's way of keeping the ecosystem in balance."
Muir previously proved that animals living in groups and bred to be more passive sustain fewer injuries and are more productive than animals bred naturally. For instance, chickens bred to be less aggressive don't engage in as much pecking, which often causes severe injury and even death. The energy that animals used for negative behavior or to avoid such activities is then transferred to production.
"This selection methodology is a roadmap to improving the breeding of domesticated animals," Muir said. "The tool also could allow us to domesticate more species as readily available food sources, such as cannibalistic shellfish and game fish."
The other researchers on these studies are Piter Bijma, Esther Ellen and Johan Van Arendonk of Wageningen University, The Netherlands, and Jason Wolf of the University of Manchester in Manchester, England.This research is part of the regional project Advanced Technologies for the Genetic Improvement of Poultry.
Writer: Susan A. Steeves, (765) 496-7481, firstname.lastname@example.org
Source: William Muir, (765) 494-8032, email@example.com
Related Web sites:
Wageningen University: https://www.wau.wageningen-ur.nl/welcome.html
University of Manchester: https://www.manchester.ac.uk/
A publication-quality photo is available at https://news.uns.purdue.edu/images/+2007/muir-selection.jpg
Multilevel Selection 1: Quantitative Genetics
Piter Bijma, William M. Muir and Johan A. M. Van Arendonk
Interaction among individuals is universal, both in animals and in plants, and substantially affects evolution of natural populations and responses to artificial selection in agriculture. Although quantitative genetics has successfully been applied to many traits, it does not provide a general theory accounting for interaction among individuals and selection acting on multiple levels. Consequently, current quantitative genetic theory fails to explain why some traits do not respond to selection among individuals, but respond greatly to selection among groups. Understanding the full impacts of heritable interactions on the outcomes of selection requires a quantitative genetic framework including all levels of selection and relatedness. Here we present such a framework and provide expressions for the response to selection. Results show that interaction among individuals may create substantial heritable variation, which is hidden to classical analyses. Selection acting on higher levels of organization captures this hidden variation and, therefore, always yields positive response, whereas individual selection may yield response in the opposite direction. Our work provides testable predictions of response to multilevel selection and reduces to classical theory in the absence of interaction. Statistical methodology provided elsewhere enables empirical application of our work to both natural and domestic populations.
Multilevel Selection 2:
Piter Bijma, William M. Muir, Esther Ellen, Jason B. Wolf and Johan A. M. Van Arendonk*
Interactions among individuals are universal, both in animals and in plants and in natural as well as domestic populations. Understanding the consequences of these interactions for the evolution of populations by either natural or artificial selection requires knowledge of the heritable components underlying them. Here we present statistical methodology to estimate the genetic parameters determining response to multilevel selection of traits affected by interactions among individuals in general populations. We apply these methods to obtain estimates of genetic parameters for survival days in a population of layer chickens with high mortality due to pecking behavior. We find that heritable variation is threefold greater than that obtained from classical analyses, meaning that two-thirds of the full heritable variation is hidden to classical analysis due to social interactions. As a consequence, predicted responses to multilevel selection applied to this population are threefold greater than classical predictions. This work, combined with the quantitative genetic theory for response to multilevel selection presented in an accompanying article in this issue, enables the design of selection programs to effectively reduce competitive interactions in livestock and plants and the prediction of the effects of social interactions on evolution in natural populations undergoing multilevel selection.
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