Emerging technologies and new statistical methods offer novel approaches to breed livestock that are resilient to disease, with potential significant industry cost savings, according to an announcement from The Roslin Institute in the U.K.
Improved statistical methods to quantify how animals respond to infection, and to what extent this is controlled by genes that give rise to these traits, pave the way towards selective breeding for optimum disease resilience, according to scientists from Roslin and pig breeding company PIC.
Their approach could help limit the cost of infectious diseases in livestock production, the announcement said.
The Roslin/PIC team developed mathematical models to estimate the economic value of disease resilience, i.e., how an animal’s productivity is affected by exposure to infection. The models take into account resistance to disease — how well an animal’s immune response can limit the amount of harmful organisms that infect it — as well as tolerance, which is an animal’s ability to limit the damage caused by infection, Roslin said.
The team used data from a previous study of piglets infected by porcine reproductive and respiratory syndrome.
According to their calculations, the value of selective breeding based on resistance and tolerance during infectious conditions was more than three times the value of breeding based on production traits in disease-free conditions, demonstrating the high economic value of disease resilience.
"Managing infectious disease continues to place a heavy toll on livestock producers and the animals they raise. By applying new statistical methods to big data from genomic and automated recording technologies, we can breed livestock that are more resilient to infection," Roslin professor Andrea Doeschl-Wilson said.
Using data capture or technologies that determine an animal’s response to infection based on its genes could be used to mitigate trade-offs between traits that contribute to resilience, the team found.
Monitoring the level of infection in an animal over time, in addition to tracking production traits, could be used to estimate the influence of resistance and tolerance on its response to infection, the researchers said.
Knowledge of genes that affect resistance and tolerance can be applied in two ways, they suggested.
Selective breeding may be based on groups of genes that, in combination, have a relatively large positive effect on both resistance or tolerance, or by identifying genes that confer complete resistance or complete tolerance to disease. However, this may not be possible without new technologies such as genome editing, researchers added.
An alternative option would be to study automated data capture of livestock to determine how much an animal is affected by infection and how well and how quickly it recovers based on parameters such as milk yield or feed intake.
The study was published in Genetics Selection Evolution.