Breeding organisations participating in Breed4Food conduct their business internationally and provide animals to farms located around the world in biotypes which differ in altitude, temperature, feed quality, health status, and human intervention/attention. Designing next generation selection strategies asks for animals which are resilient to challenges, which are able to keep up production even if the environment is temporary or structurally adverse.
The next generation of animals should be more robust. Improved health of laying hens, broilers, pigs and dairy cattle is one of the objectives. Intestinal health is becoming more important when the use of antibiotics will be reduced substantially. The genetic selection will reduce or completely eliminate the need for use of antibiotics for production and thereby reduce the risk of drug resistance.
We aim to identify families of animals which are more robust to challenges, be resistant to some infectious agents or tolerant for other infectious agents and for climate and feed factors.
Interesting traits could be components of the innate immune systems associated with high/low immunologic responders which may be measured in blood/milk, but also specific metabolic indicator traits of microbiota . Furthermore “Natural Antibodies” (Nab) could potentially be defined as a parameter of humoral innate immunity at the effector level. A prerequisite for these indicator traits to qualify as a selection tool / marker is that they are heritable, quantifiable, stable at specific time points and locations and linked to genetic variation in disease resistance.
The objective of the programme Intestinal health and Disease resistance in cattle, pig and poultry is to develop and to validate tools/phenotypes/technologies for genetic improvement in robustness of animals. This will contribute to improvement of health and welfare of animals, reduction in losses due to environmental challenges, improvement of longevity and reduction in use of antibiotics/drugs.
- Develop innovative statistical models for the genetic analysis of data to determine the genetic background of disease related phenotypes (including biomarkers) collected in challenge experiments or field data.
- Account for the complex nature of disease related phenotypes and the lack of detailed information on the magnitudes of challenges.