Interaction of genotype with production system for slaughter weight in rainbow trout (Oncorhynchus mykiss)

Livestock Production Science, 28 ( 1991 ) 2 5 3 - 2 6 3

253

Elsevier Science Publishers B.V., A m s t e r d a m

Interaction of genotype with production system for slaughter weight in rainbow trout

( Oncorhynchus mykiss ) Susanne Sylv6n a, M o r t e n Rye b a n d H e n n e r Simianer c "Department of A n imal Breeding and Genetics, Swedish University qL4gricultural Sciences, Box 7023, S-750 07 Uppsala, Sweden hAKVAFORSK, Institute of Aquaculture Research, 6600 Sunndalsora, Norway 'Department of A nimal Husbandry and Animal Breeding, University of Hohenheim, PO Box 700562/470, D-7000 Stuttgart 70, German), (Accepted 17 September 1990)

ABSTRACT Sylven, S., Rye, M. and Simianer, H., 1991. Interaction ofgenotype with production system for slaughter weight in rainbow trout (Oncorhynchus mykiss). Livest. Prod. Sci., 28: 253-263. An experiment was carried out to investigate genotypeXenvironment interaction in farmed rainbow trout. In the experiment 35 males and 131 females were mated hierarchically. The individual slaughter weights were recorded of 26 663 offspring reared on eight representative net-pen facilities in Norway and Sweden for approximately a year and a half following the first year in fresh water. The slaughter weights were treated as different traits: I, fresh water facilities in Sweden; 2, brackish water facilities in Sweden; and 3, salt water facilities in Norway. Variance and covariance components for sires, dams within sires and random errors were estimated using residual maximum likelihood (REML). Estimated heritabilities from sire components were 0.27, 0.12 and 0.16 for Traits 1, 2 and 3, respectively, indicating sufficient additive genetic determination to enable selection with sufficient accuracy. Corresponding estimates of genetic correlations were 0.86 for Traits 2 and 3, 0.72 for Traits 1 and 3, and 0.58 for Traits 1 and 2. At least for the latter case g e n o t y p e x e n v i r o n m e n t interaction must be assumed. Genetic parameters derived from dam components indicate considerable influence of non-additive genetic a n d / o r common environment effects. Distinct differences in slaughter weight between Production system 2 and the others were observed. Keywords: rainbow trout; genotype X environment interaction; variance components; slaughter weight.

INTRODUCTION

Interaction between fish genotype and rearing conditions may complicate breeding schemes for important traits and reduce the rate of genetic progress by selection. Genotype X environment interaction (GEI) may be divided into two classes as proposed by Dickerson (1962). The first, termed "true inter0301-6226/91/$03.50

© 1991 - - Elsevier Science Publishers B.V.

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action", affects the variance as well as the ranking of genotypes in different environments. The second class, "pseudo interaction", affects the variance, but not the ranking. The impact of the first class of interaction has to be accounted for in the construction of breeding schemes. Falconer ( 1952 ) presented a formalized GEI test in terms of a genetic correlation by regarding a character recorded in two different environments as two separate traits. A genetic correlation between performance in different environments close to unity implies that "true" GEI is negligible. In contrast, a genetic correlation distinctly less than one indicates GEI. This approach to the GEI test has been discussed by Robertson (1959), James ( 1961 ), Dickerson (1962), Burdon (1977) and Falconer ( 1981 ). Reviews of GEI studies in farmed animals generally conclude that GEI effects are usually of minor importance (Bowman, 1972; Pani and Lasley, 1972; Warwick, 1972; Syrstad, 1989). However, GEI may be important when animals are reared under different conditions covering large environmental variations (Pani and Lasley, 1972 ), or when there are large genotypic differences (Pani and Lasley, 1972; Barlow, 1981; Dunham, 1986 ). The latter consideration explains the tendency of GEI to appear in crossbred fish. Environmental extremes might induce stress conditions and thereby GEI effects (Barlow, 1981; Eisen and Saxton, 1983). When GEI is present, genetic improvement obtained by selection in one environment may not be expressed over a range of other potential rearing environments. However, although significant GEIs in fish have been reported (Sneed, 1971; Moav et al., 1975; Ayles and Baker, 1983), their importance for practical within-breed selection in fish has been considered small (Gunnes and Gjedrem, 1978, 1981 ). The objective of this study is to evaluate the importance of GEI for slaughter weight in rainbow trout (Oncorhynchus mykiss). Performance was recorded on farms in Sweden and Norway representing a variety of rearing conditions with respect to feeding regime, recording system, disease situation, water temperature and salinity. Possible explanations of the results will be discussed. Evaluation of rainbow trout strains for rearing in Sweden, including the one analysed in the present study, is presented in a separate paper by Sylv6n and Elvingson ( 1991 ). MATERIAL

Individual slaughter recordings of 26 663 rainbow trout were analysed. The fish were offspring of 35 males and 131 females (Table 1 ) which were spawned at the Institute of Aquaculture Research, Sunndalsora, Norway, in spring 1984. The genetic line had been selected for four generations for increased slaughter weight at an age of 2.5 years and to have a low frequency of early sexual ma-

255

GENOTYPE× ENVIRONMENTINTERACTION IN RAINBOWTROUT TABLE 1 Analysed production systems No. ofrecorded

Production systems Group

Country

Salinity

Fish

Sires

Dams

Dam/sire

1 2 3

Swedena Swedenb Norway c

0 4-8 28-32 d

4284 3844 18 535

28 28 32

85 85 114

3.0 3.0 3.6

26 663

35

131

3.7

Total

(%)

"Water temperature: winter rain. 1.5 °C, summer max. 22°C. ~Water temperature: winter min. - 0 . 5 °C, summer max. 15 °C. Diseases: vibriosis the summer of 1985, unvaccinated fish. 'Waler temperature: winter min. 3-4 ° C, summer max. 13-14 ° C. The water surface was not covered with ice during winter as in the Swedish environments. All the Norwegian fish were dip-vaccinated against vibriosis during the spring of 1985. dOne farm situated in a 0ord had lower salinity during the spring thaw (April-May).

turity. A hierarchical mating system was carried out in which one to nine dams were mated with each sire. Fish tested in Norway were kept at Sunndalsora until smoltification. The fish tested in Sweden were transported as eyed eggs to the research station of the Swedish National Board of Fisheries in Kaelerne. The full-sib groups were kept in separate tanks within each country until about 8 months after startfeeding; then they were marked by a combination of freeze branding and fin clipping, and mixed with the other groups. The fish were randomly grouped late in the spring of 1985 and transported to eight different fish farms. Of the eight farms, seven were commercial Swedish or Norwegian net-pen facilities in fresh, brackish or salt water and one was a Norwegian research farm. The farms were grouped according to known differences in production systems, i.e., country (recording system), water temperature and salinity (Table 1 ). Following the first year in fresh water, the fish were reared for approximately one and a half years. All the fish were kept in one cage per farm, except in the Norwegian research farm, where four cages were used. The management and feeding regimes were in accordance with c o m m o n practice at the individual farms, representing a variety of commercial rearing conditions. At each farm the fish were slaughtered within a period of 1-2 weeks (September-October) approximately 4-6 months before the onset of the spawning season. Individual slaughter weights were recorded, and the fish were classified as to physiological status of sex/sexual maturity in one of three groups: l, females expected

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to spawn in the following spawning season; 2, males expected to spawn in the following spawning season; and 3, fish not expected to spawn (neuters) in the following spawning season. ANALYSIS OF DATA

Weight at slaughtering in flesh water (Sweden), brackish water (Sweden) and salt water (Norway) facilities, were considered as three different traits Traits 1-3, respectively. An appropriate multi-trait model of analysis is y, = Xi,Sj + Zis, + Widi +el where subscript i pertains to Trait i = 1-3 and y, = vector of phenotypic observations for Trait i; fli = vector of fixed farm X cage X sex/sexual maturity effects; s, = vector of random additive-genetic sire effects; d, = vector of random effects of dams within sires; e, = random error vector; X, Zg and W, = design matrices pertaining to p~, s, and d~ For the random effects s ' = (s'l s'2 s'3 ), d' = (all d2 d3 ), e' = (el e'2 e3 ), the distribution is assumed to be ~1/%10

LLOJLO

°

D*I 0

,ll R*

The dam effects include non-additive genetic environmental effects c o m m o n to one full-sib group. Therefore, only relationships of sires were taken into account via the numerator relationship matrix A. G and D are the sire and dam variance-covariance matrices for the three traits. As full-sib groups were reared in different environments during most of their lives, the residuals are assumed to be uncorrelated and R is a diagonal matrix containing residual variances only. Finally * symbolizes the direct product operator. The structure of the data resembles the structure Schaeffer et al. (1978) dealt with. In view of this similarity the residual maximum likelihood (REML, Patterson and Thompson, 1971) procedure suggested by Schaeffer et al. (1978) could be adopted to estimate the 15 variance and covariance components. Genetic parameters were derived from the estimated sire and dam components. Due to computational restrictions, standard errors of the estimates could not be calculated. However, rough approximations showed that standard errors of heritabilities and genetic correlations were in the range 0.05-0.1. RESULTS

Mean slaughter weights within the three production systems and frequency of males, females and neuters in each separate farm are shown in Table 2. The

GENOTYPE × ENVIRONMENT INTERACTION IN RAINBOW TROUT

257

TABLE 2

Production data for Production systems 1, 2 and 3 Prod. system

Farm

Cage

N

Mean (kg)

s.d." (kg)

CV ~

Proportion (%) of

Females

Males

Neuters

1

1 2c

1 1

2297 1987

2.36 2.96

0.47 0.61

19.9 20.7

37.0 46.6

46.8 44.8

16.2 8.6

2

3 4c

1 1

1912 1932

2.32 2.44

0.48 0.57

20.7 23.2

8.8 52.4

28.3 38.6

62.9 9.0

3

5~

1 2 3 4

2860 2922 2813 3009

4.30 4.44 4.46 4.27

1.09 1.10 1.14 1.05

25.2 24.7 25.5 24.5

37.7 41.0 41.2 42.1

43.2 42.6 43.7 40.6

19.1 16.4 15. I 17.3

6 7 8d

l 1 1

2595 2404 1932

4.24 2.50 2.60

1.09 0.81 0.59

25.8 32.5 22.7

41.9 30.8 37.7

37.7 41.5 43.2

20.4 27.7 19.1

aStandard deviation. bCoefficient of variation ( = s . d . / m e a n . 100). cSex/sexual maturation status. All or part of the fish recorded as round fish ( whole carcass ). dReduced salinity during the spring thaw ( A p r i l - M a y ) .

disease frequency was different between production systems. No losses from vibriosis were reported in the vaccinated population (Production system 3 ) or in the flesh water locality (Production system 1 ). Fish from the Norwegian research farm (Farm 5 ) and one commercial farm (Farm 6 ), both included in the production system of Trait 3, deviated in that they had greater mean weights than fish on the other farms. The coefficients of variation in phenotypic slaughter weights were lower in the Swedish environments (represented by Traits 1 and 2) than in the Norwegian environments (Trait 3). The only Norwegian exception was the fjord farm (Farm 8). Differences were also apparent in the estimated variance and covariance components (Table 3). Since full-sib groups are expected to be affected by non-additive genetic and/or common environment effects, only the estimated sire components may be interpreted in terms of additive genetic parameters. Heritabilities between 0.12 and 0.27 (Table 4) indicate sufficient additive genetic determination to enable selection with sufficient accuracy. The differences in the corresponding estimated sire variance components cannot be exclusively attributed to scale effects. Therefore real differences in proportion of genetic variances in the three environments must be assumed. The genetic coefficients of variation were 9.9%, 6.5% and 9.5% for Traits l, 2 and 3, respectively. The heritabilities calculated from estimated dam com-

258

S. SYLVI~NET AL.

TABLE 3

Estimated error, sire and dam components of variance and covariance for the evaluated traits Variance/covariance components, traits 1

2

3

1 2 3

0.0165

0.0055 0.0056

0.0172 0.0119 0.0345

1 2 3

0.0224

0.0138 0.0152

0.0248 0.0128 0.0762

1

0.2070

Sire

Dam

Error 2 3

0.1639 0.7756

TABLE 4

Estimated heritabilities based on sire ( h l ) and dam (h~) components of variance Trait

h2

h2

1 ~ 3

0.27 0.12 0.16

0.36 0.33 0.34

TABLE 5

Estimated genetic correlations; sire estimates above the diagonal, dam estimates below the diagonal Trait

1

2

3

l 2 3

-0.75 0.60

0.58 -0.38

0.72 0.86 --

ponents (Table 4) indicate that the above assumed that non-additive genetic and/or common environment effects are of considerable magnitude, relative to the additive genetic effects. Additive genetic correlations calculated from sire components (Table 5 ) are moderate to high (0.58-0.86). Since exact standard errors of the estimates are not at hand and since a proper statistical test of a correlation which is different from unity (for a discussion of this problem see Burdon, 1977 ) is

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not known to the authors, definite statements on significance of GEI in this study cannot be made. The correlation of 0.58 between Traits 1 and 2, however, seems to indicate considerable interaction. The low correlation between Traits 2 and 3 calculated from the dam components (Table 5 ), the low heritability for Trait 2, and the lowest genetic coefficient of variation seem to indicate that performance under Production system 2 differs from performance under the other production systems. In a separate analysis, performances on the Norwegian Farms 5, 6 and 7 were considered as different traits and analysed with the same model. Farm 8 was excluded because of differences in salinity between it and the other farms (see Table 2, footnote d ). Heritabilities derived from the estimated sire components were 0.14, 0.13 and 0.26 for performance on Farms 5, 6 and 7, respectively. Thus, the two farms with highest mean performances show the lowest heritabilities. This relationship was caused by increased residual variances while the sire variances were of comparable magnitude. The estimated additive-genetic correlations, however, were 0.82 for performance on Farms 5 and 6, 0.79 for Farms 5 and 7, and 0.86 for Farms 6 and 7. These high correlations within the Norwegian environment, as opposed to the lower genetic correlations found between the three main production systems considered, confirm the validity of the approach used. DISCUSSION

Growth is probably influenced by a large number of genes and the influence of each of the genes involved is small (Dickerson, 1954). This emphasizes the importance of understanding the concept of growth and its interrelationships with other factors, such as appetite, food conversion efficiency and production of lean tissue. This is discussed by several scientists involved in husbandry research, e.g., Fowler et al. ( 1976 ), Smith and Fowler ( 1978 ), Webb and Curran (1986), Cameron et al. (1988). The genetic potential for growth in fish may not necessarily be fully expressed by the trait slaughter weight, as this variable may also be affected by genes controlling traits such as osmoregulatory capacity, disease resistance and age at sexual maturation. Accordingly, the GEI indicated in the present work may partly be ascribed to GEI of traits other than growth. The growth trait in this study is an expression of genetic potential within the respective production systems. According to Robertson (1959), the estimated genetic correlations based on the sire components of variance in our analysis indicate that GEI is of minor practical importance. The importance of maternal and/or common environmental effects affecting the full-sib groups during the fry stage is clearly indicated in the results. The maternal sibs reared in the same tank during their first months correlated more strongly than maternal sibs reared in dif-

260

S. SYLVI~N ET AL.

ferent tanks: 0.75 compared to 0.60 and 0.38. Heritability estimates based on sire and dam components of variance illustrate the effects as well. Pseudo interaction may be caused by variation in production levels between rearing environments (Danell, 1981 ). Scale effect was proposed as a reason for variability in the growth performance of rainbow trout by Ayles ( 1975 ). Klupp et al. ( 1978 ) report a reduction in genetic variability for growth as a consequence of low variation in pond environments. A linear relationship similar to the one between estimated genetic correlations and production levels proposed by Cunningham and O'Byrne (1977) may be used to adjust estimates of correlations for production level. No adjustments of the estimates in the present study were required, because the estimated correlations are independent of scale. Possible causes o f GEI Utilization of the genetic potential for growth may be influenced by mean water temperature and temperature fluctuations in the rearing environment. Periods of extremely low water temperature during the winter and large and frequent changes in water temperature and salinity during the summer in Production system 2 may have led to stress and subsequently made the fish more susceptible to infectious diseases. Genetic variation in disease resistance has been reviewed by Chevassus and Dorson (1990). The water temperature in Production system 3 (salt water, Norway) was the most uniform of the environments studied and allowed growth throughout the year. McKay et al. (1984) have reported that GEI has an important influence on growth rate induced by variation in water temperature. As a consequence of a substantial variation in salinity between rearing environments, variation in osmoregulatory capacity may have contributed to the interaction effect. Reduced growth with increased salinity is reported by McKay and Gjerde ( 1985 ). They proposed that the effects of underlying factors, such as appetite and food conversion efficiency, possibly explained their findings. GEI may be partly ascribed to variation in environmental sensitivity, which tends to be increased by upward selection in a good environment. Hence, selection for increased slaughter weight based on growth performance in salt water facilities in Norway for four generations may have affected the results of the present study. The GEI effects indicated in the present work may partly be due to different recording systems. For example, the classification of sex/sexual maturation status was not consistent among the environments studied. On the Swedish farms these frequencies seem to have been influenced by whether the classification had been done on round (whole carcass) or opened fish. The GEI may also be influenced by differences in sexual maturation in the

GENOTYPE × ENVIRONMENT INTERACTION IN RAINBOW T R O U T

261

different environments. Genetic variation in the age of sexual maturation and its effect on slaughter weight is reported by Gjerde et al. ( 1991 ).

Choice of breeding strategy Our study shows GEI in growth performance between different production systems. Low genetic variation in any production system lowers the precision of the selection work, because when genetic variation is low potential brood fish are more difficult to separate. The genetic coefficients of variation in the three environments were all as high (Trait 2) and higher (Traits 1 and 3) than usually found in breeding programmes with domesticated animals (normally around 5%). However, having a genetic correlation of 0.58 between slaughter weights in different environments means that the GEI effect should be considered. ACKNOWLEDGEMENTS

We are grateful to T. Arnason, O. Danell and O. Syrstad for providing helpful suggestions. We would also like to thank two anonymous referees for their valuable comments, colleagues for positive discussions, and Britt-Marie Gillberg and Lena NordstrSm for skilfully typing the manuscript.

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Dickerson, G.E., 1954. Hereditary mechanisms in animal growth. In: E.J. Boell (Editor), Dynamics of Growth Processes. Princeton Univ. Press, N J, Chapter XII, pp. 242-276. Dickerson, G.E., 1962. Implications of genetic-environment interactions in animal breeding. Anim. Prod., 4: 47-63. Dunham, R.A., 1986. Selection and crossbreeding responses for cultural fish. Third World Congress on Genetics Applied to Livestock Production, Lincoln, NE, Vol. X, pp. 391-400. Eisen, E.J. and Saxton, A.M., 1983. Genotype by environmental interactions and genetic correlations involving two environmental factors. Theor. Appl. Genet., 67: 75-86. Falconer, D.S., 1952. The problem of environment and selection. Am. Nat., 86: 293-298. Falconer, D.S., 1981. Introduction to Quantitative Genetics, 2nd edition. Longman, London, 340 pp. Fowler, V.R., Bichard, M. and Pease, A., 1976. Objectives in pig breeding. Anim. Prod., 23: 365-387. Gjerde, B., Refstie, T. and Simianer, H., 1991. Growth rate in different age periods and age at sexual maturity in Atlantic salmon. Estimates of heritability and genetic and phenotypic correlations. In prep. Gunnes, K. and Gjedrem, T., 1978. Selection experiments with salmon. IV. Growth of Atlantic salmon during two years in the sea. Aquaculture, 15:19-33. Gunnes, K. and Gjedrem, T., 1981. A genetic analysis of body weight and length in rainbow trout reared in seawater for 18 months. Aquaculture, 24:161-174. James, J.W., 1961. Selection in two environments. Heredity, 16: 145-162. Klupp, R., Herl, G. and Pirchner, F., 1978. Effects of interaction between strains and environment on growth traits in rainbow trout (Salmo gairdneri). Aquaculture, 14:271-275. McKay, L.R. and Gjerde, B., 1985. The effect of salinity on growth of rainbow trout. Aquaculture, 49: 325-331. McKay, L.R., Friars, G.W. and Ihssen, P., 1984. Genotype × temperature interactions for growth of rainbow trout. Aquaculture, 41: 131-140. Moav, R., Hulata, G. and Wohlfart, G., 1975. Genetic differences between the Chinese and European races of the common carp. 1. Analysis ofgenotype-environment interactions. Heredity, 34: 323-340. Pani, S.N. and Lasley, J.F., 1972. Genotype×environment interactions in animals. Univ. of Missouri Research Bull. 992, Colombia, MO. Patterson, H.D. and Thompson, R., 1971. Recovery of inter-block information when block sizes are unequal. Biometrika, 58: 545-554. Robertson, A., 1959. The sampling variance of the genetic correlation coefficient. Biometrics, 15: 469-485. Schaeffer, L.R., Wilton, J.W. and Thompson, R., 1978. Simultaneous estimation of variance and covariance components from multitrait mixed model equations. Biometrics, 34: 199208. Smith, C. and Fowler, V.R., 1978. The importance of selection criteria and feeding regimes in the selection and improvement in pigs. Livest. Prod. Sci., 5:415-423. Sneed, K.E., 1971. Some current North American work in hybridization and selection of cultured fishes. In: FAO Seminar/Study Tour in the USSR on Genetic Selection and Hybridization of Cultivated Fishes. Rep. F A O / U N D P - (TA) 2926, pp. 143-150. Sylv6n, S. and Elvingson, P., 1991. Strain X environment interactions, genetic and phenotypic parameters of production traits of rainbow trout (Oncorhynchus mykiss). Submitted. Syrstad, O., 1989. Dairy cattle crossbreeding in the tropics: the importance of genotype X environment interaction. Livest. Prod. Sci., 23: 97-106. Warwick, E.J., 1972. Genotype-environment interactions in cattle. World Rev. Anim. Prod., 8: 33-38. Webb, A.J. and Curran, M.K., 1986. Selection regime by production system interaction in pig improvement: a review of possible causes and solutions. Livest. Prod. Sci., 14:41-54.

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RESUME Sylv6n, S., Rye, M. et Simianer, H., 1991. Interaction entre g6notype et syst6me de production pour le poids d'abattage chez la truite arc-en-ciet (Oncorhynchus mykiss). Livest. Prod. Sci., 28:253-263 (en anglais). Une exp6rience a 6t6 effectu6e pour 6tudier l'interaction g6notype X environnement chez la truite arc-en-ciel d'61evage. Dans cette 6tude, 35 males et 131 femelles ont 6t6 accoupl6s de fagon hi6rarchique. Les poids individuels/l l'abattage ont 6t6 enregistr6s sur 26 663 descendants gard6s dans huit 61evages repr6sentatifs de Norv6ge et de Su6de pendant environ un an et demi aprbs avoir pass6 leur premi6re ann6e en eau douce. On a consid6r6 comme des caract6res diff6rents le poids d'abattage dans trois milieux: 1, 61evages en eau douce en Su6de; 2, 61evages en eaux saum~tres en Su6de; et 3, 61evages en eau sal6e en Norv6ge. Les composantes de variance et de covariance pour les effets p6re, m6re intra-p6re et residuel ont 6t6 estim6es en utilisant le maximum de vraisemblance restreint (REML). Les h6ritabilit6s estim6es/~ partir des composantes paternelles 6taient de 0,27, 0,12 et 0,16 respectivement pour les caract6res 1/l 3, indiquant un d6terminisme g6n6tique additif suffisant pour que la s61ection soit suffisamment pr6cise. Les estimations des corr61ations g6n6tiques correspondantes 6taient de 0,86 entre les caract6res 2 et 3, 0,72 entre les caract6res 1 et 3 et 0,58 entre les caract6res 1 et 2. Au moins dans ce dernier cas, l'existence d'une interaction g6notype X environnement doit 6tre admise. Les param6tres g6n6tique d6rivant des composantes maternelles indiquent une influence consid6rable des effets g6n6tiques non additifs et/ou des effets de milieu commun. Des diff6rences de poids d'abattage ont fit6 constat6es entre le syst6me 2 de production et les autres.

KURZFASSUNG Sylv6n, S., Rye, M. und Simianer, H., 1991. Die Interaktion zwischen Genotyp und Produktionssystem auf dem Schlachtgewicht yon Regenbogenforellen (Oncorhynchus mykiss). Livest. Prod. Sci., 28:253-263 (aufenglisch). Es wurde ein Experiment durchgefiirt, um das Vorliegen von Genotyp × Umwelt Interaktion bei der Regenbogenforelle zu untersuchen. Es wurden 35 m~innliche und 131 weibliche Tiere hierarchisch angepaart, nach 2,5 Jahren wurde das Schlachtgewicht von 26 663 Nachkommen erfal3t. Acht repr~isentative Fischfarmen in Norwegen und Schweden wurden ausgew/ihlt, wo die Fische nach dem ersten Jahr in StiBwasser 18 Monate gem~istet wurden. Die Farmen wurden nach Produktionssystem aufgeteilt, welche in der Analyse als unterschiedliche Merkmale behandelt wurden: l, SiiBwasserfarmen in Schweden; 2, Brackwasserfarmen in Schweden; und 3, Salzwasserfarmen in Norwegen. Die Varianz-Kovarianzkomponenten f'dr V~iter, Mfitter innerhalb V/iter und die Residuen wurden mittels Residual Maximum Likelihood (REML) gesch~itzt. Die gesch~itzten Heritabiliditen aus V~iterkomponenten f'tir Merkmal 1-3 waren 0.27, 0.12 und 0.16, so dab ausreichend genetische Variation fiir eine ziichterische Bearbeitung des Merkmals vorliegt. Die additiv-genetischen Korrelationen waren 0.86 fiir Merkmal 2 und 3, 0.72 ftir Merkmal 1 und 3 und 0.58 ftir Merkmal l u n d 2. Zumindest ftir die letztgenannte Kombination muB eine signifikante Genotyp X Umwelt Interaktion vermutet werden. Die gesch~itzten Mutterkomponenten zeigen, dab nicht-additive Genwirkungen und/oder gemeinsame Umwelteffekte in betr~ichtlichem Umfang vorliegen. Produktionssystem 2 scheint sich von den anderen beiden Produktionssystemen zu unterscheiden.