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      Enhancing genetic disease control by selecting for lower host infectivity and susceptibility

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          Abstract

          Infectious diseases have a huge impact on animal health, production and welfare, and human health. Understanding the role of host genetics in disease spread is important for developing disease control strategies that efficiently reduce infection incidence and risk of epidemics. While heritable variation in disease susceptibility has been targeted in livestock breeding, emerging evidence suggests that there is additional genetic variation in host infectivity, but the potential benefits of including infectivity into selection schemes are currently unknown. A Susceptible-Infected-Recovered epidemiological model incorporating polygenic genetic variation in both susceptibility and infectivity was combined with quantitative genetics selection theory to assess the non-linear impact of genetic selection on field measures of epidemic risk and severity. Response to 20 generations of selection was calculated in large simulated populations, exploring schemes differing in accuracy and intensity. Assuming moderate genetic variation in both traits, 50% selection on susceptibility required seven generations to reduce the basic reproductive number R 0 from 7.64 to the critical threshold of <1, below which epidemics die out. Adding infectivity in the selection objective accelerated the decline towards R 0 < 1, to 3 generations. Our results show that although genetic selection on susceptibility reduces disease risk and prevalence, the additional gain from selection on infectivity accelerates disease eradication and reduces more efficiently the risk of new outbreaks, while it alleviates delays generated by unfavourable correlations. In conclusion, host infectivity was found to be an important trait to target in future genetic studies and breeding schemes, to help reducing the occurrence and impact of epidemics.

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          Mapping genes for complex traits in domestic animals and their use in breeding programmes.

          Genome-wide panels of SNPs have recently been used in domestic animal species to map and identify genes for many traits and to select genetically desirable livestock. This has led to the discovery of the causal genes and mutations for several single-gene traits but not for complex traits. However, the genetic merit of animals can still be estimated by genomic selection, which uses genome-wide SNP panels as markers and statistical methods that capture the effects of large numbers of SNPs simultaneously. This approach is expected to double the rate of genetic improvement per year in many livestock systems.
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            Heterogeneities in the transmission of infectious agents: implications for the design of control programs.

            From an analysis of the distributions of measures of transmission rates among hosts, we identify an empirical relationship suggesting that, typically, 20% of the host population contributes at least 80% of the net transmission potential, as measured by the basic reproduction number, R0. This is an example of a statistical pattern known as the 20/80 rule. The rule applies to a variety of disease systems, including vector-borne parasites and sexually transmitted pathogens. The rule implies that control programs targeted at the "core" 20% group are potentially highly effective and, conversely, that programs that fail to reach all of this group will be much less effective than expected in reducing levels of infection in the population as a whole.
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              Heterogeneities in the transmission of infectious agents: Implications for the design of control programs

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                Author and article information

                Contributors
                +0131 6519221 , Smaragda.Tsairidou@roslin.ed.ac.uk
                Journal
                Heredity (Edinb)
                Heredity (Edinb)
                Heredity
                Springer International Publishing (Cham )
                0018-067X
                1365-2540
                16 January 2019
                16 January 2019
                June 2019
                : 122
                : 6
                : 742-758
                Affiliations
                [1 ]ISNI 0000 0004 1936 7988, GRID grid.4305.2, The Roslin Institute and R(D)SVS, , University of Edinburgh, ; Edinburgh, EH25 9RG UK
                [2 ]ISNI 0000 0004 1937 0722, GRID grid.11899.38, Institute of Mathematical and Computer Sciences, , University of São Paulo, ; São Paulo, Brazil
                Author information
                http://orcid.org/0000-0003-0792-9421
                Article
                176
                10.1038/s41437-018-0176-9
                6781107
                30651590
                07ab4f79-45cf-454b-9b1a-4817b0da914c
                © The Author(s) 2019

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 5 July 2018
                : 13 December 2018
                : 14 December 2018
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100000268, RCUK | Biotechnology and Biological Sciences Research Council (BBSRC);
                Award ID: BB/J004235/1 (ISP1)
                Award ID: BB/P013740/1 (ISP2)
                Award Recipient :
                Funded by: European Union FP7 project FISHBOOST (KBBE - 7-613611) (ST and OA)
                Funded by: European Union FP7 project FISHBOOST (KBBE - 7-613611) Biotechnology and Biological Sciences Research Council Institute Strategic Programme grants BB/J004235/1 (ISP1) and BB/P013740/1 (ISP2)
                Categories
                Article
                Custom metadata
                © The Genetics Society 2019

                Human biology
                animal breeding,agriculture
                Human biology
                animal breeding, agriculture

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