Elsevier

Aquaculture

Volume 511, 15 September 2019, 734207
Aquaculture

Novel insights into the genetic relationship between growth and disease resistance in an aquaculture strain of Coho salmon (Oncorhynchus kisutch)

https://doi.org/10.1016/j.aquaculture.2019.734207Get rights and content

Highlights

  • Significant genetic variation for early growth rate and during an infection with P. salmonis in coho salmon

  • First time that bacterial load has been assessed as a resistance phenotype in aquaculture

  • Selective breeding for early growth rate is expected to simultaneously increase P. salmonis resistance and harvest weight

Abstract

Breeding for disease resistance has become a highly desirable strategy for mitigating infectious disease problems in aquaculture. However, knowledge of the genetic relationship between resistance and other economically important traits, such as growth, is important to assess prior to including disease resistance into the breeding goal. Our study assessed the genetic correlations between growth and survival traits in a large bacterial infection challenge experiment. A population of 2606 coho salmon individuals from 107 full-sibling families were challenged with the bacteria Piscirickettsia salmonis. Growth was measured as average daily gain prior (ADG0) and during (ADGi) the experimental infection and as harvest weight (HW). Resistance was measured as Survival time (ST) and binary survival (BS). Furthermore, individual measures of bacterial load (BL) were assessed as new resistance phenotypes and to provide an indication of genetic variation in tolerance in salmonid species. Resistant families showed lower bacterial load than those susceptible to P. salmonis. Furthermore, some surviving fish belonging to resistant families, were considered as bacterial-free because their bacterial load was below the detection threshold. Adding logBL as a covariate into the models for growth under infection and survival indicated significant genetic variation in tolerance.

Significant moderate heritabilities were estimated for ADG0 (0.30 ± 0.05), HW (0.38 ± 0.03), and for the survival traits ST (0.16 ± 0.03) and BS (0.18 ± 0.03). In contrast, heritabilities for ADGi and log-transformed BL were low (0.07 ± 0.02 (significant) and 0.04 ± 0.03, respectively), although these increased to moderate significant levels (0.20 ± 0.09 and 0.12 ± 0.05, respectively) when traits were assessed in survivors only. Significant favorable genetic correlations were found between ADG0 and ADGi (0.40 ± 0.16), HW (0.64 ± 0.09), and with resistance as ST (0.43 ± 0.18), indicating that fish with higher genetic growth rate early on and prior to infection not only tend to maintain their genetic growth advantage until harvest, but also tend to grow faster and survive longer during infection. Although a significant unfavorable correlation (−0.50 ± 0.13) between HW and ST was found, this value decreased to −0.35 ± 0.20 using uncensored data from non-survivors only. Similarly, no robust unfavorable genetic correlations between ADG0 and LogBL, or ADG0 and any of the other traits considered in this study, was identified. These results suggest that selective breeding for early growth, in the current coho salmon population, would be expected to simultaneously increase survival time and growth performance during an infection with Piscirickettsia salmonis, without negatively impacting on pathogen burden.

Introduction

Chile is the main producer of farmed coho salmon (Oncorhynchus kisutch) globally, with approximately 90% of the total production (FAO, 2016). In common with other intensive production systems, the health status of farmed coho salmon is a major concern for profitability and animal welfare. One of the main diseases affecting the Chilean coho salmon industry is Salmon Rickettsial Syndrome (SRS), caused by Piscirickettsia salmonis, a facultative intracellular bacteria which was first isolated in Chile (Cvitanich et al., 1991). SRS is responsible for great economic losses, either directly through mortality, or indirectly through treatment costs or reduction on fish performance. During the first half of 2016, P. salmonis was responsible for 53% of mortalities attributed to infectious disease in farmed coho salmon (Sernapesca, 2016). Current strategies to control SRS, such as vaccines and antibiotics, have not been fully effective in tackling this disease under field conditions (Rozas and Enríquez, 2014).

Selective breeding for improved resistance to infectious diseases is a feasible and potentially more sustainable strategy for the long-term control of disease outbreaks in both livestock and aquaculture species (Bishop and Woolliams, 2014). To date, salmonid breeding programs typically use disease challenge testing of relatives of the selection candidates to enable breeding values estimations and genetic improvement of disease resistance (Gjedrem, 2012; Ødegård et al., 2011a). Accordingly, there is an increasing number of studies aimed at quantifying and dissecting the host genetic variation for disease resistance by measuring survival after exposure to diverse infectious pathogens (Houston et al., 2008; Ødegård et al., 2011a; Yáñez et al., 2014a; Yáñez and Martinez, 2010). Previous studies have demonstrated significant genetic variation for resistance to Piscirickettsia salmonis in Atlantic salmon (Salmo salar), rainbow trout (Oncorhynchus mykiss) and coho salmon using disease challenge data, with heritability estimates ranging from 0.11 to 0.62 (Barría et al., 2018; Correa et al., 2015; Yáñez et al., 2016, Yáñez et al., 2014b, Yáñez et al., 2013; Yoshida et al., 2017).

From an economic perspective, growth, SRS resistance and flesh color are key traits to be included into the breeding goal of Chilean salmon (Neira et al., 2014). Before including all these traits simultaneously into the breeding objective, knowledge of the genetic correlations between traits is needed. A positively correlated response to selection between growth at harvest and flesh color has been reported in a Chilean coho salmon breeding population (Dufflocq et al., 2016). Moreover, although (Yáñez et al., 2014b) estimated a genetic correlation not significantly different from zero between SRS resistance (defined as day of death) and body weight in Atlantic salmon, the same authors reported a negative genetic correlation of −0.50 ± 0.13 between SRS resistance (defined as day of death) and harvest weight in coho salmon (Yáñez et al., 2016). However, little is known about the relationship between growth prior to and during infection and SRS resistance, therefore better estimates are needed.

In aquaculture, disease resistance is commonly defined using host survival data (measured as binary or day of death) after being exposed to an infection, either in an experimental challenge (Houston et al., 2010; Ødegård et al., 2011a; Vallejo et al., 2016; Yáñez et al., 2016, Yáñez et al., 2013) or under field conditions (Houston et al., 2008). However, this definition captures two different host response mechanisms to infections under potential genetic regulation, i.e. (i) the ability of the host to restrict pathogen invasion or replication (best described by within-host pathogen load) and (ii) the ability of an infected host (with a given pathogen load) to survive the infection (Lough et al., 2015). Studies that include both types of mechanisms often refer to the first trait as ‘resistance’ and to the second trait as ‘tolerance or endurance’ (Doeschl-Wilson and Kyriazakis, 2012; Mazé-guilmo et al., 2014; Ødegård et al., 2011b; Restif and Koella, 2004; Roy and Kirchner, 2000; Saura et al., 2019). Clearly, resistance and tolerance /endurance contribute both positively to an individual's ability to survive an infection. However, at the population level, tolerant individuals that tend to harbor and can cope with high pathogen load are undesirable, as these would be expected to shed more infectious material and thus to cause a higher disease threat to individuals in the same contact group (Lipschutz-Powell et al., 2012). Thus, in order to minimize disease prevalence and mortality due to disease in a population, a fuller understanding of the relationship between within-host pathogen load and mortality is required. Nonetheless, measures of individual pathogen load are rarely available in large scale genetic studies, especially in aquaculture. In the present study we aimed to overcome this shortcoming by quantifying bacterial load as a measurement of disease resistance in coho salmon families with contrasting mortality rate (i.e. most susceptible and most resistant families) after infection with P. salmonis. We then evaluated the phenotypic association and genetic correlation of this trait with SRS survival traits and growth rate.

The aim of the current study was to provide a better understanding of the genetic relationship between growth and SRS resistance traits in coho salmon. Specifically, the objectives were (i) to quantify the level of genetic variation for growth both prior to and during an infection with P. salmonis, and for harvest weight, in a coho salmon breeding population, (ii) to assess the genetic correlations between these growth traits and survival traits in a large SRS infection challenge experiment, and (iii) quantify the level of genetic variation in individual bacterial loads as an alternative phenotype for disease resistance, and their relationship with survival and growth traits.

Section snippets

Breeding population

The study was performed on a coho salmon (Oncorhynchus kisutch) population from the 2012 year-class, belonging to a genetic improvement program established in 1997. This breeding program is owned by Pesquera Antares and is managed by Aquainnovo (Puerto Montt, Chile). The breeding population consists of two sub-populations, depending on the spawning year. Both sub-populations have been selected for eight generations for harvest weight (HW) using best linear unbiased prediction (BLUP). Summary

SRS experimental challenge

Typical clinical signs and pathological lesions associated with a P. salmonis infection were observed after IP injection. These signs included inappetence, lethargy and pale gills (Rozas and Enríquez, 2014). Mortality began on day 10 post IP injection. Dead individuals showed a swollen kidney, splenomegaly and yellowish liver tone (typical symptoms of SRS infection; (Rozas and Enríquez, 2014)). During the 50 days of challenge, the three replicate tanks reached a cumulative mortality of 35.6,

Discussion

Prior to include P. salmonis resistance as a breeding goal it is necessary to determine if the trait is heritable and its potential association with other economically important traits, such as growth. The current dataset comprises animals that were exposed to P. salmonis using an experimental challenge, and previously analyzed by Yáñez et al., 2016. However, these authors only focused on the genetic association between harvest weight and day of death as a measure of resistance.

The current work

Conclusion

The current study showed the presence of significant genetic variation for average daily gain in an early stage of a coho salmon life cycle. This genetic variation decreased during infection by the facultative intracellular bacteria Piscirickettsia salmonis, and a moderate positive genetic correlation between growth prior and during infection was observed. We identified that early growth is positive genetically correlated with P. salmonis resistance measured as day of death and with harvest

Ethics approval and consent to participate

Experimental challenge and sample procedures were approved by the Comité de Bioética Animal from the Facultad de Ciencias Veterinarias y Pecuarias, Universidad de Chile (Certificate N08-2015).

Consent for publication

Not applicable.

Availability of data and materials

The dataset used during the current study is commercially sensitive and could be available from the corresponding author on reasonable request.

Competing interests

The authors declare that they have no competing interests

Funding

This work has been conceived on the frame of the grant FONDEF NEWTON-PICARTE, funded by CONICYT (Government of Chile) and the Newton fund The British Council (Government of United Kingdom). ADW's and RH's contributions were funded by RCUK-CONICYT grant (BB/N024044/1) and Institute Strategic Funding Grants to The Roslin Institute (BBS/E/D/20002172, BBS/E/D/30002275 and BBS/E/D/10002070).

Author's contribution

JY and JL conceived the experiment and provided data for analysis. AB performed the analysis. AW and RH helped to optimize the analysis. AB, AW, RH, and JY interpreted the results. AB and AW drafted the manuscript. All authors improved the writing, read and approved the final manuscript.

Acknowledgements

AB want to acknowledge to the National Commission of Scientific and Technologic Research (CONICYT) for the funding through the National PhD Funding Program.

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