Copyright © 2016. . © 2016 Knol, Nielsen and Knap

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.

Introduction: Litter size in pigs has increased steadily since 1990. Because of unfavorable genetic correlations with piglet mortality, breeding goals should include survival traits next to litter size. Unbalanced breeding programs that neglect this requirement have produced increased mortality levels, attracting negative public attention. Balanced breeding does not have this disadvantage, but the general public is largely unaware of this.Methods: We present long-term time trends as realized in commercial breeding. The data includes (i) phenotypes of litter size, piglet birth weight, and piglet mortality, as used in routine breeding value estimation; and (ii) the genomic Best Linear Unbiased Prediction (gBLUP) estimated breeding values thus obtained. Piglet mortality (2001–2022) and birth weight (2009–2022) phenotypes were related to litter size by recording year. Estimated breeding values (EBVs) for the mortality traits were regressed on those for litter size by birth year (2012–2022).Results: Average litter size is very weakly correlated to the mortality (R² ≤ 0.06) and birth weight (0.07 ≤ R² ≤ 0.26) traits, and those correlations are unfavorable (antagonistic) within each year. However, all traits analyzed here show favorable simultaneous phenotypic and genetic trends over time: the antagonisms are neutralized by balanced breeding. Above the annual mean litter size level, farrowing and lactation mortality rates increased with increasing litter size in every year (unfavorable), but the annual intercepts and the slopes decreased from 2001 to 2022 (favorable). Average litter birth weight decreased with litter size in every year (unfavorable), but the annual intercepts increased and the slopes decreased from 2009 to 2022 (favorable). The within-litter birth weight variation coefficient increased with litter size in every year (unfavorable), but the annual intercepts decreased from 2009 to 2022 (favorable). The proportion of low birth weights (i.e.,< 0.9 kg) for a given litter size is decreasing over time, and the critical birth weight level (below which lactation mortality increases strongly) is clearly population dependent and changes over time too.Discussion: The increases in litter size and piglet survival rates due to balanced breeding policies lead to reduced total numbers of dead piglets (i.e., per country, per year) coinciding with a certain pig production volume (i.e., with a certain total number of weaned piglets).

Feed incurs most of the cost of aquaculture production, so feed efficiency improvement is of great importance. Our aim is to use work done in pigs to formulate a logical framework for assessing the most useful component traits influencing feed intake and efficiency in farmed fish – either to identify traits that can together be used for genetic improvement of feed efficiency, or as substitute traits for feed intake recording. Improvement of gross feed efficiency in growing fish can be accomplished by selection for increased growth rate. However, the correlation of growth with feed efficiency is typically only modest, and hence there is room for further improvement of feed efficiency through methods other than growth selection. Based on a literature review we propose that the most effective additional methods are selection for reduced body lipid content and for reduced residual feed intake (RFI). Both methods require more or less sophisticated recording equipment; in particular, the estimation of RFI requires recording of feed intake (DFI) which is a challenge. In mammals and birds, both these approaches have been effective, and despite the high costs of daily feed intake recording, the RFI approach can be cost-efficient because maintenance requirements are high and therefore RFI variation covers a large part of DFI variance. Maintenance requirements of fish are lower and therefore RFI variation covers a smaller part of DFI variance. Moreover, accurate high-volume routine individual DFI recording is much more challenging in fish than in mammals or birds. Hence selection for reduced body fat content is likely a more effective (and certainly more cost-efficient) way to improve FCR in fish than selection for reduced RFI. As long as body fat content is dealt with as an explicit selection criterion, the only valid reason for DFI recording would be the requirement of RFI reduction. So, if RFI reduction is not required, there would be no need for the expense and effort of individual DFI recording – and in fish breeding that would be a very desirable situation. Solid evidence for these propositions is still scarce, and their generality still needs to be confirmed.

The feed efficiency of growing pigs has been a matter of serious commercial and scientific interest since at least 1970, but early recording technology made it difficult to produce accurate feed intake data at the individual level. Since electronic feeders were introduced, the pig breeding industry has been making good genetic improvement in feed conversion ratio (FCR) but this has been mainly due to genetic improvement of growth and body composition traits. More than one third of the variation in feed intake is due to processes that are independent of growth and body composition, mainly body maintenance processes such as basal metabolism, protein turnover, thermoregulation, physical activity, immune and other coping functions, nutrient digestion and absorption efficiency. We give an example of how genetic variation in basal metabolism may be generated by electron leakage through the mitochondrial membrane. This considerable (and up to now insufficiently exploited) variation can be accessed through the trait residual feed intake (RFI: feed intake, statistically adjusted for growth and body composition). In routine breeding value estimation systems, this is catered for by including feed intake (rather than FCR) in the breeding goal and in the multi-trait BLUP evaluation. We give examples of how selection for growth and body composition traits and RFI leads to genetic change in feed intake and from there in FCR, in four real-life breeding populations, and show that genetic improvement of FCR is a function of genetic improvement of those underlying traits. Improving the efficiency of any system often leads to a higher sensitivity to extraneous challenges; this also holds for the growing pig. An important element of a breeding program that focuses on genetic improvement of feed efficiency is therefore the proper monitoring and control of side effects in other traits, most notably robustness and quality traits. And because many of the body maintenance processes are strongly influenced by the production environment, the data used for breeding value estimation of RFI should be recorded in commercial conditions.

Improvement of feed efficiency in pigs is of great economical and environmental interest and contributes to use limited resources efficiently to feed the world population. Genome scans for feed efficiency traits are of importance to reveal the underlying biological causes and increase the rate of genetic gain. The aim of this study was to determine the genomic architecture of feed efficiency measured by residual energy intake (REI), in association with production, feed conversion ratio (FCR) and nitrogen excretion traits through the identification of quantitative trait loci (QTL) at different stages of growth using a three generation full-sib design population which originated from a cross between Pietrain and a commercial dam line.