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Why breed disease-resilient livestock, and how?

Genetics selection evolution (Paris), 2020-10, Vol.52 (1), p.60-60, Article 60 [Peer Reviewed Journal]

COPYRIGHT 2020 BioMed Central Ltd. ;2020. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. ;Distributed under a Creative Commons Attribution 4.0 International License ;The Author(s) 2020 ;ISSN: 1297-9686 ;ISSN: 0999-193X ;EISSN: 1297-9686 ;DOI: 10.1186/s12711-020-00580-4 ;PMID: 33054713

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  • Title:
    Why breed disease-resilient livestock, and how?
  • Author: Knap, Pieter W ; Doeschl-Wilson, Andrea
  • Subjects: Animal diseases ; Animals ; Breeding ; Breeding - methods ; Case studies ; Costs ; Disease ; Disease control ; Disease Resistance ; Economic conditions ; Epidemics ; Fighting ; Gene mapping ; Genomics - methods ; Hogs ; Husbandry ; Immune system ; Infections ; Life Sciences ; Livestock ; Livestock - genetics ; Livestock - immunology ; Medical research ; Microbial drug resistance ; Mortality ; Pathogens ; Perturbation ; Quantitative genetics ; Quantitative trait loci ; Quantitative Trait, Heritable ; Resilience ; Review ; Swine
  • Is Part Of: Genetics selection evolution (Paris), 2020-10, Vol.52 (1), p.60-60, Article 60
  • Description: 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. 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. 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.
  • Publisher: France: BioMed Central Ltd
  • Language: English;German
  • Identifier: ISSN: 1297-9686
    ISSN: 0999-193X
    EISSN: 1297-9686
    DOI: 10.1186/s12711-020-00580-4
    PMID: 33054713
  • Source: Hyper Article en Ligne (HAL) (Open Access)
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