Genetic trends in growth, sexual maturity and skeletal deformations, and rate of inbreeding in a breeding programme for rainbow trout (Oncorhynchus mykiss)

Kause, Antti; Ritola, Ossi; Paananen, Tuija; Wahlroos, Heli; Mäntysaari, Esa

doi:10.1016/j.aquaculture.2005.02.023

Cited by 118 publications

(126 citation statements)

References 29 publications

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“…It is well established that individuals with initially different growth or life-history trait levels may be differently exposed to infections, parasites and production diseases (Arendt, 1997 ;Rauw et al, 1998 ;Moghadam et al, 2001;Kause et al, 2005), confounding the cause-and-effect relation between pathogen burden and reduction in host performance in field datasets.…”

Section: (I) Random Regression Modelsmentioning

confidence: 99%

Genetic analysis of tolerance to infections using random regressions: a simulation study

Kause

2011

Genet. Res.

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SummaryTolerance to infections is the ability of a host to limit the impact of a given pathogen burden on host performance. This simulation study demonstrated the merit of using random regressions to estimate unbiased genetic variances for tolerance slope and its genetic correlations with other traits, which could not be obtained using the previously implemented statistical methods. Genetic variance in tolerance was estimated as genetic variance in regression slopes of host performance along an increasing pathogen burden level. Random regressions combined with covariance functions allowed genetic variance for host performance to be estimated at any point along the pathogen burden trajectory, providing a novel means to analyse infection-induced changes in genetic variation of host performance. Yet, the results implied that decreasing family size as well as a non-zero environmental or genetic correlation between initial host performance before infection and pathogen burden led to biased estimates for tolerance genetic variance. In both cases, genetic correlation between tolerance slope and host performance in a pathogen-free environment became artificially negative, implying a genetic trade-off when it did not exist. Moreover, recording a normally distributed pathogen burden as a threshold trait is not a realistic way of obtaining unbiased estimates for tolerance genetic variance. The results show that random regressions are suitable for the genetic analysis of tolerance, given suitable data structure collected either under field or experimental conditions.

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Section: (I) Random Regression Modelsmentioning

confidence: 99%

Genetic analysis of tolerance to infections using random regressions: a simulation study

Kause

2011

Genet. Res.

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“…During the fingerling period, the total number of fish within each year class was 16 169-50 962. During grow-out in each year class, fish were kept either in the freshwater nucleus station (range : 4459-13 643 fish/year class), or sent to one or two sea test stations (range : 1456-5165 fish/year class, (Kause et al, 2005). Parental fish were mated at the Tervo freshwater nucleus station during April-June.…”

Section: Methodsmentioning

confidence: 99%

“…All fish were fed commercial fish feed pellets throughout the rearing cycle. The rearing procedure is detailed by Kause et al (2005). The individual grow-out survival of fish from tagging to the end of the second growing season (fingerling period+grow-out season) was determined in May at freshwater (mean weight of fish= 964 g) and in late summer-autumn (July-December) at the sea stations (mean weight of fish=1095 g).…”

Section: (B) Juvenile Fingerling Periodmentioning

confidence: 99%

“…The body weight traits included were: (1) ' JuvBW ' -individual body weight of fingerlings recorded at tagging (n=189 299 fish); (2) 'FreshwBW ' -individual body weight recorded at the end of the grow-out period in freshwater in April-June (n=58 724) and (3) 'SeaBW ' -individual body weight recorded at the end of grow-out period in sea in October-April (n=41 678). Genetic analysis of body weights has been reported previously (Kause et al, 2003(Kause et al, , 2005.…”

Section: (V) Accounting For Selection Biasmentioning

confidence: 99%

“…These results provide evidence of the extent to which (1) survival is heritable and (2) survival traits within and across life cycle stages share genetic architecture. (Kause et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

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Genetic architecture of rainbow trout survival from egg to adult

Vehviläinen

Kause

Koskinen³

et al. 2010

Genet. Res.

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SummarySurvival from birth to a reproductive adult is a challenge that only robust individuals resistant to a variety of mortality factors will overcome. To assess whether survival traits share genetic architecture throughout the life cycle, we estimated genetic correlations for survival within fingerling stage, and across egg, fingerling and grow-out stages in farmed rainbow trout. Genetic parameters of survival at three life cycle stages were estimated for 249 166 individuals originating from ten year classes of a pedigreed population. Despite being an important fitness component, survival traits harboured significant but modest amount of genetic variation (h 2 =0 . 07-0 . 27). Weak associations between survival during egg-fry and fingerling periods, between early and late fingerling periods (r G =0 . 30) and generally low genetic correlations between fingerling and grow-out survival (mean r G =0 . 06) suggested that life-stage specific survival traits are best regarded as separate traits. However, in the sub-set of data with detailed time of death records, positive genetic correlations between early and late fingerling survival (r G =0 . 89) showed that during certain years the best genotypes in the early period were also among the best in the late period. That survival across fingerling period can be genetically the same, trait was indicated also by only slightly higher heritability (h 2 =0 . 15) estimated with the survival analysis of time to death during fingerling period compared to the analysis treating fingerling survival as a binary character (h 2 =0 . 11). The results imply that (1) inherited resistance against unknown mortality factors exists, but (2) ranking of genotypes changes across life stages.

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Comparison of Index Selection, BLUP, MAS, and Whole Genome Selection

Bao

2010

Next Generation Sequencing and Whole Genome Selection in Aquaculture

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Genetic trends in growth, sexual maturity and skeletal deformations, and rate of inbreeding in a breeding programme for rainbow trout (Oncorhynchus mykiss)

Cited by 118 publications

References 29 publications

Genetic analysis of tolerance to infections using random regressions: a simulation study

Genetic analysis of tolerance to infections using random regressions: a simulation study

Genetic architecture of rainbow trout survival from egg to adult

Comparison of Index Selection, BLUP, MAS, and Whole Genome Selection

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