Bovenhuis, H.

Kause, A.

Kettune-Praebel, A.

Koskinen, H.

Paananen, T.

Vehvilainen, H.

English

Wageningen Yield 

Comparison Of Direct And Indirect Selection For 
Rainbow Trout Sea Grow-out Survival 
Harri Vehviläinen*, Antti Kause*,†, Anne Kettunen Præbel *, Heikki 
Koskinen‡ & Tuija Paananen‡ 
Introduction  
Survival of farmed fish during grow-out is important economic, environmental and animal 
health issue. General assumption has been that traits under strong natural selection harbour 
little genetic variation. However, in our previous studies (Vehviläinen et al. (2008); (2009); 
(2010)) we have established that 1) rainbow trout survival during grow-out period has low, 
but significant heritability (0.08-0.17), 2) genetic correlations between survival in generation 
and test-station specific cohorts are not stable, 3) life-stage (egg, fingerling, grow-out) 
specific survival traits are best regarded as separate traits, 4) genetic correlations between 
grow-out survival and growth are favourable (mean rG=0.17) 5) health traits are favourably 
related to grow-out survival, and 6) fingerling survival is not genetically related to growth. 
Breeding programs are typically started with selection on few traits such as body weight and 
maturity age. Survival is not usually among the first traits directly selected, but it may 
improve as a correlated genetic response to selection for other traits. Thus, after establishing 
the genetic associations of survival with other traits, the first question asked here was: 1) 
How effective is the current indirect selection of sea grow-out survival via other traits in 
Finnish national rainbow trout breeding program? In rainbow trout farming, sea survival is 
economically important. However, breeding takes place in freshwater nucleus and G x E has 
to be taken into account. That only alive fish can be selected and the binary nature further 
complicate the selection of sea survival. Thus, the second question of this study was: 2) How 
much can the accuracy of sea survival be elevated by utilising information from other traits? 
Materials and methods  
The data originates from the Finnish national rainbow trout breeding programme. The 
freshwater breeding nucleus is held at FGFRI Tervo station in Central Finland. The breeding 
population has been established in 1992 and pedigree is known until a base population in 
1989 (Kause et al. (2005)). 
 
Traits recorded. Eight traits selected in the programme (Kause et al. (2005)) were analysed. 
Traits were recorded after second growing season (fingerling period at freshwater + one 
grow-out season in either freshwater or sea). Freshwater traits: body weight (Weight2), 
survival (Survival2), deformed skeletal structures (Deformation2), eye cataracts (Cataract2), 
skin colour (Skin colour2), and sex-specific maturity trait (Female maturity2). Sea traits: body 
weight (Weight2sea) and survival (Survival2sea). Causative agents of deformations have not 
been examined in our population but deformations may be caused by high water temperature, 
                                                 
* MTT Agrifood Research Finland, Biotechnology and Food Research, FI-31600 Jokioinen, Finland 
† Wageningen University and Research Centre, Animal Breeding and Genomics Centre, The Netherlands 
‡ Finnish Game and Fisheries Research Institute (FGFRI), Tervo Fisheries Research and Aquaculture, Finland 
diseases, deficient composition of a diet, or management (Kause et al. (2007)). The fish eye 
cataracts are caused by parasitic Diplostomum spp. eye fluke (Kuukka et al. (2010)).  
 
Evaluation of sea survival improvement. To assess the degree to which currently used 
selection indices indirectly improve sea survival, correlations between sea survival EBV, 
current indices, and other trait EBV were calculated using data from latest year-class (birth 
year 2007). To assess how much selection accuracy of sea survival can be improved by 
utilising information from other seven traits accuracies of sea survival were calculated when 
it is selected directly or indirectly together with alternative trait combinations. The accuracies 
were calculated with SelAction program (Rutten et al. (2002)). The population parameters 
for SelAction were 100 male and 300 female parents, 15 male and 15 female offspring per 
female parent, 35 fish from each fullsib family left to nucleus and 15 + 15 sent to sea  
stations. The 30 offspring per females represent the next generation candidates (35 fish – 5 
dying before maturation).  
 
Estimation of genetic (co)variances and breeding values. The data used for (co)variance 
estimation had 200,173 individuals with observations and a total of 1,159 ancestors without 
observations. The fish originated from ten year-classes belonging to four generations. Each 
year-class consisted of 109-341 families of 48-168 sires and 79-272 dams, mated using either 
nested paternal or partial factorial designs. Phenotypic and genetic parameters were 
estimated using restricted maximum likelihood and multitrait animal linear mixed models 
(DMU-AI software; Madsen and Jensen (2008)). The breeding values were estimated using 
whole breeding programme data starting from the base population (16 year-classes, 7 
generations) using MiX99 program (Lidauer and Strandén (1999)). The current selection 
indices are based on estimated breeding values (EBV) of growth, maturity age, external 
appearance, skeletal deformations, fillet colour and cataract (Kause et al. (2005)). From year-
class 2001 onward the breeding program was divided into two lines: GROWTH and 
DELAYED MATURITY (Ritola et al. (2006)). The selection index of GROWTH-line 
(hereafter IndexGrowth) is weighed mainly for rapid growth (around 50 % of index weight on 
growth and 10 % on maturity), and fish are used to produce both fillet and caviar. Index for 
DELAYED MATURITY-line (IndexDelMat) is designed especially for selection of late 
maturity (around 50 % of index weight on maturity and 10 % on growth), and fish are used 
for high quality fillet production.  
Results and discussion 
1) Indirect selection of sea survival by current indices. The correlations between sea 
survival EBV and indices of two selection lines differed quite dramatically (table 1). The 
association of sea survival EBV and IndexGrowth was favourable (r=0.36) suggesting that sea 
survival is enhanced via selection on growth traits (50 % of index weight on growth and 10 
% on maturity). Indeed, the correlations between individual trait EBV revealed that positive 
association comes mainly via sea body weight. In contrast, the correlation between sea 
survival EBV and IndexDelMat was unfavourable (r=-0.18) implicating that sea survival in this 
line is indirectly selected downwards. This is because body weights are not heavily weighed 
(50 % of index weight on maturity and 10 % on growth). In addition, the main unfavourable 
association of individual trait EBV comes via selection for late maturity (r from -0.18 to -
0.35, table 1.). Taken together, current indirect selection of sea survival is fairly effective 
with IndexGrowth, but serious attention must be paid on the moderate unfavourable association 
of sea survival and IndexDelMat. 
 
Table 1: Correlations between sea survival EBV, total indices and separate trait EBV in 
the two selection lines. 
 GROWTH DELAYED MATURITY 
IndexGrowth   0.36 - 
IndexDelMat - -0.18 
Weight2sea   0.31   0.32 
Cataract2   0.11   0.03 
Skin colour2   0.01   0.08 
Deformation2   0.01 -0.01 
Weight2 -0.01 -0.07 
Female maturity2 -0.18 -0.35 
 
2) Accuracy of sea survival in alternative selection indices. Direct selection only for sea 
survival resulted in accuracy of 0.392 (table 2a). Accuracy is substantially elevated (up to 
0.479) when all traits are added into index (table 2a). This represents 22% increase, which 
could be achieved by adding both survival traits into current index. The most effective traits 
were Survival2 and Skin colour2, which both alone elevated the accuracy by 7 % and together 
by 14%. Including Weight2, Female maturity2 and Deformation2 provided almost no extra 
gain (increase in accuracy from 0.001 to 0.002.) Indirect selection (table 2b) for currently 
selected freshwater traits resulted in maximum accuracy of 0.244 for sea survival, which is 
62 % of accuracy gained by direct selection only for sea survival. Addition of Weight2sea to 
index raised accuracy to 0.341. This represents 87 % accuracy of direct selection for sea 
survival and is the maximum accuracy achieved indirectly with currently selected traits. 
Favourable association suggests that fast growing fish are also resistant to mortality factors. 
In the indirect selection, Survival2 was found to be very effective. If it is included to index 
together with all currently selected traits, the sea survival accuracy was 9 % higher (0.428) 
than accuracy gained by direct selection only for sea survival. Also the substantial effect of 
skin colour on accuracy suggests that the appearance traits can viewed as general vigour 
indicators. To sum up, the results of alternative indices show that clearly the best option 
would be to directly select for sea survival. Furthermore, the benefits gained via including 
other traits are substantial (22 % increase in accuracy). However, if there is no possibility to 
record sea survival, the next best thing would be to include freshwater survival to index as it 
elevates accuracy of indirect selection greatly (50 % with only freshwater traits and 25 % 
with all traits). 
 
Table 2. Accuracies of alternative selection indices for improving sea survival 
(Survival2sea).  
Traits  Accuracy of index 
a) Direct selection for sea survival   
Survival2sea 0.392 
Survival2sea + Cataract2 0.399 
Survival2sea + Weight2sea 0.402 
Survival2sea + Survival2 0.418 
Survival2sea + Skin colour2 0.420 
Survival2sea + All other traits 0.479 
b) Indirect selection for sea survival   
Weight2 0.025 
Female maturity2 0.027 
Deformation2 0.042 
Cataract2 0.100 
Skin colour2 0.224 
All currently selected freshwater traits 0.244 
Survival2 0.266 
All currently selected freshwater traits + Survival2 0.366 
Weight2sea 0.181 
All currently selected freshwater traits + Weight2sea  0.341 
All currently selected freshwater traits + Weight2sea+ Survival2 0.428 
Conclusion 
The current indirect selection for sea survival is moderately effective in the GROWTH-line. 
In contrast, the weighing of traits in current selection index for DELAYED MATURITY-line 
results in unfavourable association of index and sea survival, and this line would particularly 
benefit from inclusion of sea survival into index. The alternative indices studied revealed that 
accuracy for sea survival selection can be elevated substantially (9 % in indirect and 22 % in 
direct selection) by having correlated traits in the index.  
References 
Kause, A., Ritola, O., Paananen, T. et al. (2005). Aquaculture 247 : 177-187. 
Kause, A., Ritola, O., and Paananen, T. (2007). Genet. Sel. Evol. 39: 529-543.  
Kuukka-Anttila, H., Peuhkuri, N., Kolari, I. et al. (2010) Heredity 104:20-27.  
Lidauer, M. and Strandén, I. (1999). J. Dairy Sci. 82: 2779-2787. 
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Madsen, P. and Jensen, J. (2008). DMU - a package for analysing multivariate mixed 
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Ritola, O., Paananen, T., and Kause, A. (2006). Proceedings of the 8th World Congress on 
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DMU - a package for analysing multivariate mixed models, version 6. www.dmu.agrsci.dk

Comparison of direct and indirect selection for rainbow trout sea grow-out survival

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Comparison of direct and indirect selection for rainbow trout sea grow-out survival

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