Why breed disease-resilient livestock, and how?

Abstract

Background Fighting and controlling epidemic and endemic diseases represents a considerable cost to livestock production. Much research is dedicated to breeding disease resilient livestock, but this is not yet a common objective in practical breeding programs. In this paper, we investigate how future breeding programs may benefit from recent research on disease resilience. Main body We define disease resilience in terms of its component traits resistance (R: the ability of a host animal to limit within-host pathogen load (PL)) and tolerance (T: the ability of an infected host to limit the damage caused by a given PL), and model the host's production performance as a reaction norm on PL, depending on R and T. Based on this, we derive equations for the economic values of resilience and its component traits. A case study on porcine respiratory and reproductive syndrome (PRRS) in pigs illustrates that the economic value of increasing production in infectious conditions through selection for R and T can be more than three times higher than by selection for production in disease-free conditions. Although this reaction norm model of resilience is helpful for quantifying its relationship to its component traits, its parameters are difficult and expensive to quantify. We consider the consequences of ignoring R and T in breeding programs that measure resilience as production in infectious conditions with unknown PL—particularly, the risk that the genetic correlation between R and T is unfavourable (antagonistic) and that a trade-off between them neutralizes the resilience improvement. We describe four approaches to avoid such antagonisms: (1) by producing sufficient PL records to estimate this correlation and check for antagonisms—if found, continue routine PL recording, and if not found, shift to cheaper proxies for PL; (2) by selection on quantitative trait loci (QTL) known to influence both R and T in favourable ways; (3) by rapidly modifying towards near-complete resistance or tolerance, (4) by re-defining resilience as the animal's capacity to resist (or recover from) the perturbation caused by an infection, measured as temporal deviations of production traits in within-host longitudinal data series. Conclusions All four alternatives offer promising options for genetic improvement of disease resilience, and most rely on technological and methodological developments and innovation in automated data generation.

Validation of resilience indicators by estimating genetic correlations among daughter groups and with yield responses to a heat wave and disturbances at herd level

“…LnVar and r auto summarize response to all disturbances throughout the entire lactation or multiple lactations (Poppe et al, 2021), which explains the higher heritability of LnVar and r auto . Another disadvantage of using response traits to only a single disturbance per cow, is that for some types of disturbances or extremely severe disturbances the best approach may be to decrease milk yield severely instead of only by a little, to preserve health and resilience later in life (Friggens et al, 2017;Knap and Doeschl-Wilson, 2020). In those cases, cows may be classified as resilient if they did not show a response but showed reduced resilience in the remaining lactation or later lactations.…”

Unraveling the genetic variability of host resilience to endo and ectoparasites under natural infestation, in Nellore cattle

“…The environmental pathogen load is a crucial component of resilience that must be considered [8]. Thus, we used the median counts of TICK, GIN, and EIM in each cohort as the environmental gradient, that is formed by the combination of animals raised in the same paddock (contemporary groups) on the same period of the year (ME).…”

Digital Phenotyping in Livestock Farming

“…Diseases and dramatic natural environmental changes are two factors in farming that are nearly impossible to contain. These factors create a great economic burden and reduce production, but the solution exists within livestock [15]. Their resilience and robustness in the face of these challenges are heritable to some degree and can prepare a population to better face unknown challenges [2].…”

“…The method of phenotyping with a specific focus can also influence inaccurate conclusions and actions that lead to trade-offs. In the agricultural field, these trade-offs are most commonly in favor of production but frequently decrease livestock resilience [15].…”