Breeding salmonids for feed efficiency in current fishmeal and future plant-based diet environments

Genet. Sel. Evol. 39 (2007) 431–446 Available online at: c INRA, EDP Sciences, 2007 www.gse-journal.org DOI: 10.1051/gse:2007013 Original article Breeding salmonids for feed efficiency in current fishmeal and future plant-based diet environments Cheryl D. Qa∗, Antti Ka, Juha Kb, Ossi Rc aMTT Agrifood Research Finland, Biotechnology and Food Research, Biometrical Genetics, 31600 Jokioinen, Finland bFinnish Game and Fisheries Research Institute Jyväskylä, Survontie 9, 40500 Jyväskylä, Finland cFinnish Game and Fisheries Research Institute, Tervo Fisheries Research and Aquaculture, 72210 Tervo, Finland (Received 9 November 2006; accepted 20 January 2007) Abstract – The aquaculture industry is increasingly replacing fishmeal in feeds for carnivorous fish with soybean meal (SBM). This diet change presents a potential for genotype-environment (G ×E) interactions. We tested whether current salmonid breeding programmes that evaluate and select within fishmeal diets also improve growth and efficiency on potential future SBM diets. A total of 1680 European whitefish from 70 families were reared with either fishmealor SBM-based diets in a split-family design. Individual daily gain (DG), daily feed intake (DFI) and feed efficiency (FE) were recorded. Traits displayed only weak G ×E interactions as variances and heritabilities did not differ substantially between the diets, and cross-diet genetic correlations were near unity. In both diets, DFI exhibited moderate heritability and had very high genetic correlation with DG whereas FE had low heritability. Predicted genetic responses demonstrated that selection to increase DG and FE on the fishmeal diet lead to favourable responses on the SBM diet. Selection for FE based on an index including DG and DFI achieved at least double FE gain versus selection on DG alone. Therefore, current breeding programmes are improving the biological ability of salmonids to use novel plant-based diets, and aiding the aquaculture industry to reduce fishmeal use. feed efficiency /genotype-environment interaction /selection /aquaculture /Coregonus lavaretus ∗Corresponding author: [email protected] Article published by EDP Sciences and available at http://www.gse-journal.org or http://dx.doi.org/10.1051/gse:2007013 432 C.D. Quinton et al. 1. INTRODUCTION The use of fishmeal in aquaculture feeds has become a major issue as the global industry continues to grow [10]. Fishmeal is mostly produced from wild-caught small pelagic fish species, and is considered to be the superior protein source for teleost fishes [18]. It is currently the major component of diets for farmed carnivorous salmonids such as rainbow trout (Oncorhynchus mykiss) and Atlantic salmon (Salmo salar) whose grower diets typically contain 30–50% fishmeal (2000 statistics [35,45]). However, several environmental and economic reasons exist for reducing fishmeal use. These include the decline and fluctuation of wild stocks harvested for fishmeal production due to overfishing and natural environmental factors, instability and predicted increases in fishmeal prices, and negative consumer perception of the safety of fishmeal-fed products [10,34]. Accordingly, feed manufacturers aim to replace fishmeal with alternatives such as plant products. Soybean meal is one source of protein that can be successfully substituted in part for fishmeal in carnivorous fishes’ feeds [18,23,36,41,43]. Some major feed companies have pledged to reduce fishmeal use by at least 50% by 2010, and it has been predicted that within 10 years, fish diets will be close to 100% vegetarian [39]. Aquaculture genetic improvement programmes aim to complement such production system changes. Breeding programmes exist for all major farmed carnivorous fish species, and have achieved improvements in growth, feed efficiency, disease resistance and product quality traits [12]. Improved feed efficiency in particular is a goal of most fish breeding programmes, but genetic parameter estimates for feed consumption and efficiency are rare [21,22,24,42]. This is because recording methods for individual fish feed intake have only recently been implemented in large-scale genetics research [21, 22, 42]; thus no current breeding programmes select directly for feed efficiency. Furthermore, all breeding programmes evaluate and select broodstock based on performance with current fishmeal-based diets. Future plant-based diets may impact fish breeding programmes if genotype-environment (G ×E) interactions occur. G ×E interactions may occur in the form of genotype re-ranking across environments, or scaling effects as indicated by environmental differences in trait genetic variation [9,30]. In a worst-case scenario, superior genotypes on a fishmeal diet would actually be inferior on a plant-based diet. This would be shown by a negative genetic correlation between diets [8]. In this case, current selection on fishmeal diets would compromise performance on future plantbased diets. In this study, the impact of a novel soybean protein-based diet on selective improvement of growth, feed intake and feed efficiency was investigated in Breeding fish for novel diet efficiency 433 European whitefish (Coregonus lavaretus L.), a salmonid now farmed commercially in Finland. A breeding programme has recently been established to improve growth and feed efficiency in this species. To assess the degree to which these traits are affected by G ×E interactions when reared with either a traditional fishmeal diet or a potential future soybean meal-based diet, we tested whether these traits express diet-specific phenotypic and genetic variation, and estimated trait genetic correlations between the diets. To quantify the impact of G ×E interaction on selection response, we predicted genetic changes occurring on both diets in response to alternative strategies of selection for growth or feed efficiency on either fishmeal or soybean meal-based diets. 2. MATERIALS AND METHODS 2.1. Diet formulations Two practical isonitrogenous and isocaloric diets were formulated (Tab. I). In the fishmeal (FM) diet, fishmeal supplied 100% of the dietary protein. This diet represents a typical commercial diet used in whitefish farming. In the soybean meal (SBM) diet, 50% of the dietary protein was replaced with SBMderived protein. This diet represents a realistic future diet. Methionine, lysine and phosphorus supplements were added to the SBM diet to balance these levels with the FM diet. Ingredients were mixed in a Hobart-type mixer, extruded (Clextral BC 45, FR) to 3.5 mm pellets, re-dried at 40–45 ◦C, top dressed with fish oil and stored in a freezer until use. 2.2. Population and experimental design Whitefish in the experiment originated from the breeding programme based at the Tervo station of the Finnish Game and Fisheries Research Institute (FGFRI). The original broodstock was established in 1998 by mating 50 wild males and 150 wild females originating from the Kokemäki River, Finland. In October 2003, the generation for the current experiment was established: 45 sires were mated with 52 dams in a partial factorial design to create 70 fulland half-sib families. Each sire was mated to an average of 1.6 dams and each dam to an average of 1.3 sires (both ranges 1–2). Genetic relationships among sires and dams are unknown. Families were kept separate during incubation and early rearing. At the eyed-egg stage (January 2004), the families were transported to the FGFRI 434 C.D. Quinton et al. Table I. Formulation and analysed nutrient composition of the experimental fishmeal and soybean meal diets. Fishmeal (g·kg−1) Soybean meal (g·kg−1) Ingredient Fishmeal, capelina480 255 Soybean meal, defatted, hulls 0 411 removed by sievea Fish oil, sandeela160 180 Wheat glutena20 20 Wheat meala267.5 34.0 Wheat starch, pregelatinizeda52.5 52.5 Mono calcium phosphateb016 L-lysinec07 DL-methionined0.0 4.5 Vitamin and mineral mixe20 20 Analysed composition Water 23 27 Crude protein 398 404 Crude lipid 210 210 Crude fibre 10 22 N-free extractsf359 337 Gross energy (MJ·kg−1) 22.6 22.3 Sources: aRaisio, FI; bKemira, FI; cBASF, DE; dWelding GmbH, DE. eAdded to supply (per kg diet): retinol acetate 8000 IU, cholecalciferol 3000 IU, allrace-α-tocopheryl acetate 300 IU, menadione sodium bisulfite 10 mg, thiamine HCl 21 mg, riboflavin 30 mg, calcium d-pantothenate 92 mg, biotin 0.3 mg, folic acid 6 mg, vitamin B12 0.04 mg, niacin 120 mg, pyridoxineHCl 20 mg, ascorbic acid (35% Stay C) 900 mg, inositol 200 mg, manganese oxide (62% Mn) 100 mg, zinc oxide (74% Zn) 200 mg, potassium iodide (76% I) 6 mg. fCalculated as 1000 – (water +protein +lipid +fibre). Laukaa Research Station and incubated (water 4–6 ◦C). After hatching (February 2004), 100 to 150 fish per family were held in indoor 150 L fibreglass tanks (water 13–15 ◦C) and fed commercial dry diets (larvae: AgloNorse, EWOS Ltd., NO; juveniles: Nutra Parr and Royal Silver, Raisio Ltd., FI). In June 2004, approximately 40 random individuals from each family were tagged by injecting a passive integrated transponder into the body cavity. To give all the fish the same initial nutritional environment and to identify if tagging harmed Breeding fish for novel diet efficiency 435 any fish, all fish were fed with a 1:1 mixture of the two experimental diets for four weeks prior to the trial. Twenty-four tagged fish per family were randomly sampled for the diet trial. To construct a split-family design, each family was first randomly split into two groups to be reared with the alternative diets. Each group was evenly distributed over 6 round 0.6 m3replicate tanks (12 experimental tanks in total). Consequently, the diet trial began with a total of 1680 fish, each tank containing 140 fish (two fish from each family). The trial was conducted from July 29 to October 21, 2004, during which fish tripled in weight. Fish were fed 6 h·d−1using belt feeders. Fish were counted and bulk-weighed biweekly and feeding was adjusted according to average weight and tank biomass. To ensure excess feeding, the feed amounts supplied were adjusted to be 1.3 times higher than predicted by Koskela [27]. Tanks were supplied with fresh water (14.8–15.1 ◦C; flow rate 8–16 L·min−1; outlet water O2level >80% saturation) and 24 h light was provided with ceiling fluorescent tubes. The experimental conditions were standardized to permit accurate comparison of families across diets. This test environment deviates from commercial circumstances where fish are reared in large outdoor net cages under naturally varying environmental conditions. 2.3. Traits recorded Individual body weights were recorded to the nearest g at the beginning and end of the trial (Tab. II). Daily feed intake was measured by X-radiography [17] 5 times per individual, with 2-week intervals between measurements. On feed intake measurement days, fish were fed with the same methods and quantity as during normal days, but feed pellets included lead glass beads (Ballotini size 8.5; Jencons Ltd., UK) visible in X-ray. Afterward, fish were anaesthetized, identified, and X-rayed (Bennett HFQ 3000P X-ray machine, US). To transform the number of glass beads fed to the amount of feed ingested, predictive regression models were established in a separate study. For each diet, 16 samples of known weights were taken from the bead-labelled pellets and X-rayed. The number of beads present in each sample was counted, and diet-specific regression equations were obtained (R2=0.97−0.98). Feed intake (g) for each individual was predicted using these equations from the number of beads observed under X-ray [17]. Traits analysed were individual daily weight gain (DG), average daily feed intake (DFI) and feed efficiency (FE). Daily gain was calculated as the difference between the initial and the final body weights, divided by the number 436 C.D. Quinton et al. of days in the trial (77–80 d, depending on the tank). Individual DFI was calculated by fitting repeated measures analysis of variance with measurement time (1–5) as the random repeated factor, and then calculating least squares means for each individual (MIXED procedure, SAS 9.1; SAS Inst. Inc., US). This was done separately within each experimental tank. Feed efficiency was calculated as the ratio of DG to DFI. 2.4. Statistical analysis of diet effects Diet effects on the means of body weights, DG, DFI and FE were tested with analysis of variance (MIXED procedure). Statistical models included diet as a fixed effect, and replicate tank nested within diet, family, and diet-family interaction as random effects. For all traits, variance due to random experimental tank-family interaction was zero and thus was excluded. Standard errors and degrees of freedom for the F-tests of the fixed effects were calculated using the Kenward and Roger option. Additional analyses were performed to standardize DG and DFI to a common body weight by adding initial weight as a regression covariate to the above model. 2.5. Genetic analysis In order to examine G ×E interactions, observations recorded under each diet treatment were treated as separate traits. For instance, DG recorded on FM (DGFM) and SBM diets (DGSBM) were defined as different traits. Phenotypic and genetic parameters of DG, DFI and FE were estimated using multiple-trait animal models with DMU software, applying restricted maximum likelihood and average information methods [31]. Models contained experimental tank as a fixed effect, and full-sib family, individual genetic and residual error as random effects. The individual genetic effect included additive genetic effects and parts of potential dominance effects. The random full-sib family effect contained (co)variance due to common incubation and early rearing of full sibs, as well as parts of potential dominance (co)variances. Residual covariances between traits measured in different diets were set to zero. Standard errors of (co)variances were obtained by a first-order Taylor series expansion of the average information matrix of the estimated (co)variances. Heritability (h2) was calculated as the ratio of genetic variance to total phenotypic variance. Full-sib family effect (c2) was calculated as the ratio of full-sib family variance to total phenotypic variance. When calculating trait correlations within diets, we did not estimate correlations between FE and its component Breeding fish for novel diet efficiency 437 traits because this practice can be considered statistically vague due to autocorrelation effects. To aid the reader in perceiving the results, we do however present diet means, variances and heritabilities for FE. Daily gain and feed intake are commonly expressed relative to body weight. Thus, the analysis for DG and DFI was also carried out using a model that included initial body weight as a covariate. For this study, we use the terms “absolute” and “relative” to refer to traits analysed without and with initial body weight as a covariate, respectively. 2.6. Prediction of genetic responses to selection Selection index calculations were used to predict responses in DG, DFI and FE on both FM and SBM diets to phenotypic selection [15]. Selection was not practiced directly for FE because when selecting on a ratio, genetic changes in the individual component traits are very difficult to control [14]. Four selection strategies were compared: (a) selection for DGFM; (b) selection for maximum FEFM where increased DGFM and decreased DFIFM were selected for simultaneously and selection index weights were set to obtain maximum genetic change in FEFM; (c) selection for DGSBM; and (d) selection for maximum FESBM where increased DGSBM and decreased DFISBM were selected to obtain maximum genetic change in FESBM. Strategy (a) is comparable to current aquaculture breeding programmes that only select for growth rate. Strategies (c) and (d) predict the effects of selection performed on the potential future soybean diets. Direct and correlated genetic responses to one generation of selection were calculated by R=i(bG)(bPb)−1 2,whereRis the vector of responses, i is intensity of selection (set to 1), bis the vector of relative index weights which sum to 1, Gis the genetic covariance matrix and Pis the phenotypic covariance matrix. Gand Pwere results from the 4-trait genetic parameter estimation model for DGFM,DFI FM,DG SBM and DFISBM described above. The parameters for FE were not needed because all results for this ratio can be predicted from its component traits DG and DFI [14]. To generate the alternative selection strategies, relative index weights were modified as follows. To obtain strategy (a), the index weight for DGFM was set to 1, while the weights for the three remaining traits were zero. Similarly, for strategy (c), the index weight for DGSBM was set to one. For the selection strategies to maximise FE (b and d), the maximum genetic response in FE on both diets was obtained when half of the index weight was on DG and half against DFI. 438 C.D. Quinton et al. Table II. Least squares means (±s.e.), and statistical tests for the diet effect for traits recorded on fishmeal and soybean meal diets. Denominator degrees of freedom (ddf) shown; all numerator df =1. Fishmeal Soybean meal Test statistics Trait nmean ±s.e. nmean ±s.e. ddf FP Initial body weight (g) 818 40.9 ±0.85 829 40.6 ±0.87 10.0 0.78 0.3984 Final body weight (g) 765 131.2 ±2.38 768 125.8 ±2.24 9.7 9.36 0.0125 Daily gain (g·d−1) 765 1.145 ±0.0212 768 1.091 ±0.0201 9.5 20.0 0.0014 Daily feed intake (g·d−1) 817 0.948 ±0.0236 829 1.027 ±0.0285 9.6 7.23 0.0235 Feed efficiency 765 1.208 ±0.0150 768 1.078 ±0.0240 8.5 23.2 0.0011 To calculate genetic response in FE, mean FE was first calculated for each diet from the data, i.e., before selection. Then, genetic responses to selection were calculated for DG and DFI, and the new mean FE was calculated from these [22]. 3. RESULTS 3.1. Diet differences As expected, whitefish performed better on the FM diet. Fish fed the FM diet had significantly higher final body weight and DG, lower DFI, and better FE than those fed the SBM diet (Tab. II). These diet differences remained (results not shown) when DG and DFI were standardised by including initial body weight as a covariate (P<0.0001) in the statistical models. 3.2. Phenotypic and genetic (co)variation There was no evidence for diet differences in trait phenotypic or genetic variation. In both diets, DG and DFI showed moderate heritability whereas FE showed very low heritability that did not differ from zero (Tab. III). For a given trait recorded on both diets, the differences in heritabilities between diets were relatively small (mean absolute difference =0.07) and confidence intervals overlapped considerably. Because heritabilities may remain constant even if the underlying genetic and residual variations change, diet-specific coefficients of variation were also calculated for each trait. Diet differences between coefficients of phenotypic variation for pairs of traits were small (mean absolute difference =1.1%) as well. Coefficients of genetic variation for absolute and relative DG, DFI and FE were only slightly higher on the FM diet (mean difference =2.9%). Breeding fish for novel diet efficiency 439 Table III. Phenotypic variance (VP), coefficients of genetic (CVG) and phenotypic variation (CVP), heritability (h2±s.e.) and full-sib family effect (c2±s.e.) for absolute and relative (Rel.) traits measured on fish reared with fishmeal and soybean meal diets. Diet,Trait VPCVGCVPh2±s.e.c2±s.e. Fishmeal Daily gain 0.134 16.2 31.9 0.26 ±0.18 0.13 ±0.09 Daily feed intake 0.095 15.6 32.5 0.23 ±0.15 0.08 ±0.07 Feed efficiency 0.044 4.3 17.4 0.06 ±0.10 0.04 ±0.05 Rel. daily gain 0.090 17.0 26.1 0.42 ±0.17 0.04 ±0.07 Rel. daily feed intake 0.068 15.6 27.6 0.32 ±0.14 0.03 ±0.06 Soybean meal Daily gain 0.100 13.1 29.0 0.20 ±0.15 0.08 ±0.08 Daily feed intake 0.104 12.8 31.3 0.17 ±0.15 0.09 ±0.08 Feed efficiency 0.033 4.3 16.9 0.07 ±0.11 0.07 ±0.06 Rel. daily gain 0.076 14.3 25.2 0.32 ±0.14 0.04 ±0.06 Rel. daily feed intake 0.081 12.6 27.8 0.21 ±0.15 0.10 ±0.08 Genetic correlations between the same traits recorded in each diet indicated very little re-ranking of families across the diets. Genetic correlations (±s.e.) between the diets for DG (0.97±0.21), DFI (0.93±0.28) and FE (1.00±0.95) were all close to unity. The large standard error of the FE correlation was likely caused by the low heritability of this trait. Genetic correlations between the diets for relative DG (0.99 ±0.13) and relative DFI (0.97 ±0.22) were also very high. Within-diet trait correlations were similar in both diets. Phenotypic (rP)and genetic correlations (rG±s.e.) between DG and DFI were high and positive on both FM (rP=0.88, rG=0.97 ±0.05) and SBM diets (rP=0.86, rG= 0.93 ±0.10). Correlations between relative DG and DFI (FM rP=0.82, rG= 0.97 ±0.05; SBM rP=0.82, rG=0.96 ±0.10) were similar to those for absolute traits. 3.3. Prediction of selection responses The selection index calculations showed that current selection on fishmeal diets will lead to strong favourable correlated genetic changes for performance on SBM diets (Fig. 1). Little difference was observed between the diets for genetic response to selection. We emphasize that although the genetic and phenotypic parameter point estimates used in the selection index differed 446 C.D. Quinton et al. 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