We used digital images of rainbow trout (lateral view) to fit an ellipse around the circumference of the fish. The values for L and H, obtained from the ellipse, were used to calculate ellipticity as (L-H)/(L+H), and the surface area of the fish as p*1/2 L*1/2H. Heritability of ellipticity and surface area at age 8 months was 0.23 and 0.21. Surface area had near-unity genetic correlation with body weight at same age. Genetic correlations of ellipticity with body weight and surface area were -0.55 and -0.56. Genetic correlation of ellipticity with harvest weight at 14 months was -0.49. Estimates of ellipticity are comparable with those of Nile tilapia and common sole. We conclude that when shape is important, ellipticity should be included in the breeding goal, with a weight that reflects the desired direction of change in shap

Blonk, R.J.W.

Komen, J.

Sae-Lim, P.

Wageningen Yield 

 
Proceedings, 10th World Congress of Genetics Applied to Livestock Production 
 
Genetic Analysis of Shape in Trout, using image analysis  
 
H. Komen1, R.J.W. Blonk2 , P. Sae-Lim3. 
1Animal Breeding and Genomics Centre, Wageningen University, Wageningen, the Netherlands,  
2IMARES, Wageningen UR, the Netherlands, 3 Nofima, Ås, Norway. 
 
 
ABSTRACT: We used digital images of rainbow trout 
(lateral view) to fit an ellipse around the circumference of 
the fish. The values for L and H, obtained from the ellipse, 
were used to calculate ellipticity as (L-H)/(L+H), and the 
surface area of the fish as π*1/2 L*1/2H. Heritability of 
ellipticity and surface area at age 8 months was 0.23 and 
0.21. Surface area had near-unity genetic correlation with 
body weight at same age. Genetic correlations of ellipticity 
with body weight and surface area were -0.55 and -0.56. 
Genetic correlation of ellipticity with harvest weight at 14 
months was -0.49. Estimates of ellipticity are comparable 
with those of Nile tilapia and common sole. We conclude 
that when shape is important, ellipticity should be included 
in the breeding goal, with a weight that reflects the desired 
direction of change in shape.  
Keywords: fish; shape; ellipse; image analysis; genetics 
 
 
Introduction 
 
The main trait for genetic improvement in fish 
breeding programs is increased harvest weight, or ‘growth’. 
Results from long-term breeding programs have shown that 
considerable improvement in growth can be achieved. In 
several species, the increase in growth averages ~14 % per 
generation (reviewed in Gjedrem et al. (2012)). In the GIFT 
breeding program (genetically improved farmed tilapia) for 
Nile tilapia, the increase in harvest weight was ~7% per 
generation. After 20 generations of selection, commercial 
harvest weight had increased from 200 grams to over 1.4 
kg, after 7-9 months of on growing (reviewed in Komen 
and Trong (2014)).   
 
Selection for growth in livestock species has led to 
marked changes in body conformation. In pigs, beef cattle 
and broilers, selection for growth and/or slaughter weight 
has led to correlated responses in carcass length and muscle 
development (Hill (2008)). In fish, shape can be an 
important trait when fish are sold whole. Selection for 
growth is suspected to result in correlated responses in 
shape but evidence is only anecdotal. Simple indicator traits 
that can accurately predict a change in shape due to 
selection are currently lacking. Previously, we showed that 
the shape of common sole (Solea solea) can be 
approximated by using digital image analysis to fit an 
ellipse which follows the circumference of the image of a 
fish (Blonk et al. (2010)). Ellipticity is defined as (L-
H)/(L+H), with L the long axis and H the short axis of the 
corresponding ellipse. Values for Ellipticity vary between 0 
and 1, with higher values indicating more elongated shapes. 
Ellipses can also be used to calculate surface area, which 
can be used as a predictor of body weight. The objective of 
this study was to estimate genetic parameters for ellipticity 
and surface area in rainbow trout. Estimates of ellipticity 
were compared among three commercial species, common 
sole, Nile tilapia and rainbow trout. Results show that both 
ellipticity and surface area are traits with moderate 
heritability. The magnitude of the correlations with harvest 
weight suggests that ellipticity and surface area can be 
useful traits to include in a breeding program.  
 
Materials and Methods 
 
Data. We used data from an experiment described 
in detail in Sae-Lim et al. (2013). In brief, experimental fish 
were produced by Troutlodge (USA) by mating 58 sires 
with 100 dams and randomly selecting 25 eggs from each 
dam. Eggs were pooled and shipped to a commercial farm 
in Germany where they were communally reared under 
commercial conditions. At 6 months after hatching, fish 
were tagged, weighed and stocked together with untagged 
fish in an outdoor pond. At age 8 months (avg. weight 64.1 
g) tagged fish were sorted, photographed (lateral view) and 
weighed. On-growing continued until fish were harvested at 
an age of 14 months (avg. weight 376.4 g) and final harvest 
weight was taken. Pedigree of experimental fish was 
reconstructed by genotyping each fish and all parents with 9 
microsatellite markers. After pedigree reconstruction, data 
were available for 2,091 fish of age 8 months and 1,992 fish 
of age 14 months. The average number of fish per sire 
ranged from 30 to 35; the average number of fish per dam 
ranged from 17 to 20. 
 
Shape. The digital image of each trout was used to 
fit an ellipse around the circumference of the fish (lateral 
view) using ImageJ software (Rasband (2008)). The values 
for L and H, obtained from fitting the ellipse, were used to 
calculate ellipticity as (L-H)/(L+H), and the surface area of 
the fish as π*1/2 L*1/2H. 
 
Statistical analysis.  Heritability, phenotypic (rp) 
and genetic (rg) correlations were estimated using restricted 
maximum likelihood in a multivariate animal model 
(ASReml; Gilmour et al. (2009)). Each trait was modeled 
as: 
Yij = µ + Ai + FSj +eij, 
where yij is the observation of the ith individual from the jth 
full-sib family, µ is the overall mean and A is the random 
additive genetic effect of the ith animal. FS is the random 
full-sib common environmental effect, modelled without a 
pedigree, to correct for environmental effects common to 
full-sibs, e.g. incubator effects and environmental maternal 
effects. e is the random residual term. To estimate genetic 
correlations, the full-sib effect was excluded from the 
model. Potential selection bias due to selective mortality 
was accounted for by including body weight at tagging as 
reference trait (Sae-Lim et al. (2013)). 
 
Results and Discussion 
 
Figure 1 shows the distribution of Ellipticity at 8 
months. Ellipticity values varied between 0.5 and 0.7 and 
followed a normal distribution. Mean ellipticity was 0.63. 
Estimates of heritability are given in Table 1. Heritability of 
ellipticity was moderate, 0.23 ± 0.09. Heritability estimate 
of surface area (0.21) was higher than of body weight at 8 
months (BW8: 0.18), but lower than heritability of BW at 
14 months (0.23). Genetic correlations of ellipticity with 
BW8 and surface area were similar and highly negative 
(Table 2: -0.55). Negative values indicate that at 8 months, 
fish with higher weights will have lower ellipticity values, 
i.e. they are ‘rounder’ or less elongated.  Ellipticity values 
at 8 months also showed a negative correlation (-0.49) with 
harvest weight at 14 months. This suggests that fish with 
lower ellipticity values at 8 months will have higher final 
harvest weight. Larger trout tend to be more round and 
breeders will favor more elongated salmon-like phenotypes 
for selection (Haffray et. al. (2013)). Kause et al. (2003) 
estimated the genetic correlation between body weight at 2 
and 3 years and shape, defined as a categorical trait: 
‘slender’, ‘medium’ or ‘rotund’. Genetic correlations were 
positive (0.37 - 0.57) showing that larger fish will be more 
rotund. This correlation is undesirable when trout is 
marketed as whole carcass. Our results confirm the 
observations from Kause et al., (2003) and show that 
ellipticity can be used already at an early age to correct for 
shape during selection.  
 
 
Figure 1. Distribution of shape measured with digital 
image analysis of rainbow trout during sorting. 
Table 1. Genetic parameter estimates for traits 
measured at age 8 months and 14 months. 
Trait Va Ve h2 c2 
BW8 51.0 216.4 0.18 ± 0.11 0.07 ± 0.04 
ELLIP8 0.0001 0.0002 0.23 ± 0.09 0.02 ± 0.03 
SURFS8 238824 820126 0.21 ± 0.11 0.05 ± 0.04 
BW14 1736.3 5087.1 0.25 ± 0.10 0.00 ± 0.03 
Va = additive genetic variance, Ve = residual variance, h2 = heritability, c2 
= common environmental effect, BW8 = body weight at 8 months, ELLIP8 
= ellipticty at 8 months, SURFS8 = surface area at 8 months, BW14 = body 
weight at harvest (14 months). SE in superscript. 
 
 
Table 2. Genetic (lower diagonal) and phenotypic 
correlations (upper diagonal). 
Trait BW8 ELIPS8 SURFS8 BW14 
BW8  -0.33 ±0.03 0.95 ±0.03 0.64 ±0.02 
ELLIP8 -0.56 ±0.11  -0.44 ±0.02 -0.22 ±0.03 
SURFS8 0.99 ±0.01 -0.55 ±0.11  0.65 ±0.02 
BW14 0.73 ±0.07 -0.49 ±0.13 0.77 ±0.07  
BW8 = body weight at 8 months, ELLIP8 = ellipticty at 8 months, SURFS8 
= surface area at 8 months, BW14 = body weight at harvest (14 months). 
SE in superscript. 
 
 
Genetic correlation of body weight at 8 months 
was near unity with surface area, estimated from digital 
images. Furthermore, the genetic correlation of surface area 
with harvest weight was 0.77, higher than that of body 
weight at 8 months with harvest weight (Table 2). These 
results show that image analysis can be used instead of 
body weight to estimate and predict body weights. Image 
analysis is less prone to systematic errors than physical 
weighing, and can be automated with high throughput. 
Image analysis of young fish can be used to discard 
malformed fish, and for selection to correct for undesired 
shapes (Blonk et al. (2010)).  
 
Table 3 summarizes estimates of heritability and 
genetic correlations between Ellipticity and harvest weight 
for rainbow trout, Nile tilapia and common sole. In sole and 
tilapia, ellipticity was measured at harvest. Results show 
that ellipticity is a trait with low to moderate heritability, 
moderately correlated to harvest weight. In both common 
sole and rainbow trout, genetic correlations with harvest 
weight are significant and negative. Negative correlations 
indicate that selection for higher harvest weight will change 
the shape of fish and make them more round. In case of 
sole, this is undesired as sole is marketed whole, and 
customers will pay lower prices for fish that do not have an 
ideal shape. Blonk et al., (2010) showed that including 
shape in the breeding goal resulted in a 13% reduction in 
response in harvest weight when zero change in shape was 
desired. The response in shape is different from what has 
been observed in Nile tilapia. In this study (Trong et al., 
((2013)) ellipticity values were calculated from manual 
measurements on length, height and thickness. Ellipticity 
was then calculated along the three axes (mid-sagittal: L-H, 
transverse: L-T, frontal: H-T). Values are given in Table 3. 
Heritability of ellipticity is lower than for trout and sole, 
which could be due to the fact that manual measurements 
were used instead of image analysis. Genetic correlations 
indicate that Nile tilapia, selected for high harvest weight 
will develop a more elongated and rounder ‘torpedo’-like 
shape. This response in tilapia is different from that 
observed in trout or sole. Juvenile Nile tilapia change shape 
as they reach sexual maturity, growing more in height than 
in length. However, selection has been for considerable 
larger weights at harvest at the same harvest age without 
any apparent correlated response in onset of sexual maturity 
(Komen and Trong (2014)). Hence, the observed 
correlation between shape and harvest weight might 
actually represent selection for a more juvenile phenotype.  
 
Table 3. Summary of genetic parameter estimates for 
ellipticity in fish. 
Species h2 rg rp 
Trout 0.23±0.09 -0.49±0.13 -0.22±0.03 
Sole* 0.34±0.11 -0.44±0.25 -0.30±0.04 
Tilapia** 
EL-H 0.08±0.04 0.47±0.21 0.12±0.03 
EL-T 0.14±0.04 -0.15±0.22 -0.17±0.03 
EH-T 0.08±0.04 -0.42±0.21 -0.22±0.03 
E= Ellipticity; rg and rp: genetic and phenotypic correlations of elliptitcity 
with harvest weight. SE in superscript. * = Blonk et al. (2010) ** = Trong 
et al. (2013) 
 
 
Conclusion 
 
The shape of a fish can be approximated by a set 
of ellipses, fitted along the three main axes: length-height, 
length-thickness, and height-thickness. Heritability for 
ellipticity and surface area in rainbow trout is moderate, 
indicating good prospects for selection. Image analysis can 
be advantageous over physical weighing in situations where 
it is difficult to get accurate measurements. Ellipses, fitted 
on digital images, can be used to calculate ellipticity and 
surface area, which in trout has near-unity genetic 
correlation with body weight (at same age). Genetic 
correlations between ellipticity and surface area or body 
weight are negative, showing that selection for higher 
weights will produce more round fish. In cases where shape 
has commercial value, it is recommended to include shape 
as a trait in the breeding goal, with a weight that reflects the 
desired direction of change. 
 
 
 
 
 
 
 
 
 
 
 
Literature Cited 
 
Blonk, R.J.W., Komen, H., Tenghe, A., et al. (2010). Aquaculture 
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Gilmour, A.R., Gogel, B.J., Cullis, B.R., et al. (2009). ASReml 
user guide 3.0. VSN international Ltd. UK. 
Haffray, P., Bugeon, J., Rivard, Q., et al. (2013). Aquaculture 410-
411: 236-244. 
Hill, W.G. (2008). Lohman Information 43(1):3-20. 
Kause, A., Ritola, O., Paananen, T., et al. (2003). J. Fish Biol. 62: 
610-622.  
Komen, H., and Trong, Q.T. (2014). Isr. J. Aqua. (in press) 
Rasband, W.S. (2008). Natl. Inst. Health, Bethesda, Maryland, 
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Sae-lim, P., Komen, H., Kause, A., et al. (2013). Aquaculture 372-
375: 89-96. 
Trong, T.Q., Mulder, H.A., Arendonk, J.A.M., et al. (2013). 
Aquaculture 384-387:119-127. 
 
 
 
 
 
 
 
 


Genetic Analysis of Shape in Trout, using image analysis

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