Quantitative genetic basis for resistance to Caligus rogercresseyi sea lice in a breeding population of Atlantic salmon (Salmo salar)

Lhorente, Jean Paul; Gallardo, José; Villanueva, Beatriz; Araya, Angélica M.; Torrealba, Débora; Toledo, Ximena E.; Neira, Roberto

doi:10.1016/j.aquaculture.2011.10.046

Cited by 35 publications

(29 citation statements)

References 22 publications

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“…The stages of nauplii (n1-2) and the stage of copepods (infectious stage) are planktonic stages. The four stages of chalimus (1-4) are sessile stages and the adult is a mobile stage 5 .…”

Section: Introductionmentioning

confidence: 99%

“…Recent studies have estimated significant low to moderate genetic variation for resistance to C. rogercresseyi , with heritability values ranging between 0.12 and 0.32 in Atlantic salmon when resistance is defined as the number of parasites fixed to all the fins 5,7,8 . Similarly, when sea lice resistance is defined as the logarithm of the parasite load density, heritability values range between 0.13 and 0.33 in Atlantic salmon 9,10 .…”

Section: Introductionmentioning

confidence: 99%

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Genome-scale comparative analysis for host resistance against sea lice between Atlantic salmon and rainbow trout

Cáceres

Barría

Christensen

et al. 2019

Preprint

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“…The stages of nauplii (n1-2) and the stage of copepods (infectious stage) are planktonic stages. The four stages of chalimus (1-4) are sessile stages and the adult is a mobile stage 5 .…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Genome-scale comparative analysis for host resistance against sea lice between Atlantic salmon and rainbow trout

Cáceres

Barría

Christensen

et al. 2019

Preprint

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“…In recent years, selective breeding for disease resistance has received increasing importance in aquaculture breeding programmes worldwide (Gitterle et al, 2006;Guy et al, 2009;Krishna et al, 2011;Kube et al, 2012;Lhorente et al, 2012;Liu et al, 2016;Mahapatra et al, 2009;Moss et al, 2013;Ødegård, Olesen, Gjerde, & Klemetsdal, 2006;Ødegård et al, 2010;Ødegård et al, 2011;Taylor et al, 2009;Wang et al, 2014;Xu et al, 2010;Yáñez & Martínez, 2010;Yáñez et al, 2013). Genetic variation in the rate of survival has been estimated using different trait definition by cross-sectional and longitudinal models (Gitterle et al, 2006;Liu et al, 2016;Mahapatra et al, 2009;Ødegård et al, 2006;Ødegård et al, 2011;Taylor et al, 2009;Yáñez et al, 2013; (Taylor et al, 2009) 15, 0.24, 0.18, 0.11, 0.41 and 0.25, respectively (Yáñez et al, 2013).…”

Section: Discussionmentioning

confidence: 99%

“…The economic importance of selective breeding for disease resistance in aquaculture species has led to the estimation of genetic parameters, aiming for genetic improvement. In previous studies, resistance was measured from survival rates after experimental infections (Gitterle Ødegård, Gjerde, Rye, & Salte, 2006;Guy, Bishop, Woolliams, & Brotherston, 2009;Krishna et al, 2011;Kube, Taylor, & Elliott, 2012;Lhorente, Gallardo, Villanueva, & Araya, 2012;Liu et al, 2016;Mahapatra et al, 2009;Moss, Moss, & Lotz, 2013;Ødegård, Baranski, Gjerde, & Gjedrem, 2011;Ødegård, Sommer, & Praebel, 2010;Wang et al, 2014;Xu, Chen, & Zhang, 2010;Yáñez & Martínez, 2010;Yáñez et al, 2013). Statistical models for analysis of survival data (e.g., proportional hazard frailty model and threshold model) can be longitudinal or cross-sectional (Yáñez et al, 2013).…”

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confidence: 99%

Genetic parameters for resistance against Vibrio anguillarum in turbot Scophthalmus maximus

Wang

2019

Journal of Fish Diseases

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Genetic parameters for resistance to Vibrio anguillarum in Scophthalmus maximus were estimated using three different statistical models. Data were recorded from an experimental infection performed on 2,400 individuals from 30 full‐sib groups. Cross‐sectional linear model and cross‐sectional threshold probit model were used to analyse the test‐period survival; the cross‐sectional threshold logit models were used to analyse the test‐day survival. The heritability values estimated by cross‐sectional linear model (CSL), cross‐sectional threshold (probit) model (THRp) and cross‐sectional threshold (logit) model (THRl) were 0.202 ± 0.101, 0.296 ± 0.168 and 0.110 ± 0.023, respectively. The correlation coefficients between the full‐sib families’ estimated breeding values (EBVs) based on these three models were over 0.993. An almost identical ranking of families was generated using any of these models. Accuracy of selection using CSL, THRp and THRl models was 0.783, 0.789 and 0.801, respectively. Accuracy of selection based on the THRl model was higher than that based on CSL and THRp models.

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“…Selective breeding for C. semilaevis strains with high disease resistance is one approach for long‐term disease control (Xu et al., ; Du et al., ). Research on selective breeding for disease resistance has been conducted for a range of different diseases in cultured species, such as the Pacific white shrimp ( Litopenaeus vannamei ) (Krishna et al., ; Moss et al., ), Atlantic cod ( Gadus morhua ) (Ødegård et al., ), Atlantic salmon ( Salmo salar L.) (Guy et al., ; Kube et al., ; Lhorente et al., ), and olive flounder ( Paralichthys olivaceus ) (Xu et al., ; Wang et al., ). Selective breeding for improved disease resistance (Bishop and Mackenzie, ) in fish is commonly based on a challenge test, in which fish are infected with a target pathogen, with the goal of showing significant genetic variation in survival indicating that improvement by selection is possible (Fjalestad et al., ).…”

Section: Introductionmentioning

confidence: 99%

Estimation of genetic parameters for disease-resistance traits in Cynoglossus semilaevis (Günther, 1873)

Liu

Wang

et al. 2016

J. Appl. Ichthyol.

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The objective of this study was to estimate genetic parameters for three traits based on 28 Cynoglossus semilaevis families approximately 6 months of age (at least 5 cm total length), including trait_1 (survival of 26 families, 3434 individuals in total subjected to challenge tests with Edwardsiella tarda), trait_2 (survival of 20 families, 2016 individuals in total subjected to challenge tests with Vibrio anguillarum) and trait_3 (survival of 27 families, 9340 individuals tagged at circa 180 days of age and reared in indoor ponds for circa another 5 months). The result showed that there were large differences in the survival of the families after challenge (11.11-65.31% for E. tarda and 9.18-70.54% for V. anguillarum). Additionally, the survival of families reared in indoor ponds was also different, varying from 21.00% to 73.67%. Heritabilities of the three traits varied from 0.14 to 0.26, as estimated by the linear model (LM) and the threshold model (TM). The trait_1 heritabilities (0.26 and 0.19 estimated by LM and TM) were higher than those of the others (0.20 and 0.23 estimated by LM, 0.14 and 0.19 estimated by TM). The estimates of heritabilities using LM were consistently higher than those of TM in this study. There were significant medium genetic correlations of 0.44 and 0.42 between trait_1 and trait_2 obtained from LM and TM (P < 0.05). However, very low and non-significant genetic correlations of trait_1 and trait_3 (À0.10 for LM, À0.05 for TM), as well as those of trait_2 and trait_3 (0.05 for LM, 0.04 for TM) were obtained. Therefore, indirect selection for trait_1 and trait_2 was effective, but almost ineffectual for trait_1 and trait_3 as well as trait_2 and trait_3. Otherwise, there was no significant difference in the predictive abilities of LM and TM. Two families resistant to both Edwardsiella tarda and Vibrio anguillarum were selected plus one family resistant to both Vibrio anguillarum and naturally infected by unknown pathogens through family selection. As there was very low and non-significant genetic correlation of trait_3 and trait_1 as well as trait_2, superior strains are anticipated with the ability to resist two or more kinds of diseases, through the crossing of families selected for the three traits described above. The results support the hypothesis that genetic variation exists for disease survival, which could be used to design a breeding program for selecting strains of Cynoglossus semilaevis with high disease resistance.

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Quantitative genetic basis for resistance to Caligus rogercresseyi sea lice in a breeding population of Atlantic salmon (Salmo salar)

Cited by 35 publications

References 22 publications

Genome-scale comparative analysis for host resistance against sea lice between Atlantic salmon and rainbow trout

Genome-scale comparative analysis for host resistance against sea lice between Atlantic salmon and rainbow trout

Genetic parameters for resistance against Vibrio anguillarum in turbot Scophthalmus maximus

Estimation of genetic parameters for disease-resistance traits in Cynoglossus semilaevis (Günther, 1873)

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