# Staff Publications

## Staff Publications

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 Uniformity in birth weight is heritable in Norwegian White SheepSae-Lim, Panya ; Jakobsen, Jette H. ; Mulder, H.A. - \ 2018In: Proceedings of the 11th World Congress on Genetics Applied to Livestock Production. - WCGALP - 6 p. Sheep - Birth weight - Uniformity - Maternal genetic effect - DHGLM Birth weight is an optimum trait where very high and very low birth weights are undesirable as they may cause issues, such as dystocia, stillbirths and diminished lamb vigor. Due to economic and welfare concerns, selection for more uniform birth weight is therefore desirable at all litter sizes. If uniformity in birth weight is heritable, selection against very high and very low birth weights can be conducted. The aim of the current study was to investigate if direct and maternal genetic variances in uniformity in birth weight exist in Norwegian White Sheep (NWS). Data composed birth weights of 136,992 NWS lambs born between 2000 and 2017 and corresponding sire-maternal grand sire pedigree. The double hierarchical generalized linear mixed model (DHGLM) was fitted. The direct and maternal heritability for uniformity of birth weight were 0.08 and 0.11, respectively, and larger than for many other uniformity traits in livestock. Furthermore, the direct (57.8%) and maternal (69.4%) genetic coefficients of variation for uniformity were substantial, revealing large potential for selection for more uniform birth weight in NWS lambs. Genetic correlations between direct and maternal genetic effects on birth weight and uniformity were 0.39 and 0.12, respectively, indicating that that selection for more uniform birth weight may reduce the average birth weight genetically. Single-step genomic evaluation for uniformity of growth in Atlantic salmon (Salmo salar)Sae-Lim, Panya ; Kause, Antti ; Lillehammer, M. ; Mulder, H.A. - \ 2017In: Book of Abstracts of the 68th Annual Meeting of the European Federation of Animal Science. - Wageningen : Wageningen Academic Publishers (Book of abstracts 23) - ISBN 9789086863129 - p. 231 - 231. The heritability for uniformity of body weight is low, indicating that also accuracy of estimated breeding values (EBV) can be low. The use of genomic information could be one way to increase the accuracy and, hence, obtain higher response to selection. Genomic information can be merged with pedigree information to construct a combined relationship matrix (H matrix) for a single-step genomic evaluation (ssGBLUP), allowing realized relationships of the genotyped animals to be exploited, in addition to the numerator pedigree relationships for ngenotyped animals (A matrix). We compared the predictive ability of EBV for uniformity of body weight in Atlantic salmon, when implementing either A or H matrix in the genetic evaluation. We used double hierarchical generalized linear models based on an animal model (animal DHGLM) for both body weight and its uniformity. With the animal DHGLM, the use of H instead of A significantly increased the correlation between the predicted EBV and adjusted phenotypes, which is a measure of predictive ability, for both body weight and its uniformity (41.1 to 78.1%). When log-transformed body weights were used to account for a scale effect, the use of H instead of A produced a small and non-significant increase (1.3 to 13.9%) in predictive ability. The use of H significantly increased the predictive ability of EBV for uniformity when using the animal DHGLM for untransformed body weight. When using logtransformed body weights, the increase in predictive ability was only minor likely due to the lower heritability foruniformity of transformed body weight, a lower genetic correlation between transformed body weights and their uniformities. In conclusion, the use of ssGBLUP increases the accuracy of breeding values for uniformity of harvestweight and therefore is expected to increase response to selection in uniformity. Breeding and genetics symposium : Climate change and selective breeding in aquacultureSae-Lim, P. ; Kause, A. ; Mulder, H.A. ; Olesen, I. - \ 2017Journal of Animal Science 95 (2017)4. - ISSN 0021-8812 - p. 1801 - 1812. Adaptation - Climate change - Resilience - Robustness - Selective breeding Aquaculture is the fastest growing food production sector and it contributes significantly to global food security. Based on Food and Agriculture Organization (FAO) of the United Nations, aquaculture production must increase significantly to meet the future global demand for aquatic foods in 2050. According to Intergovernmental Panel on Climate Change (IPCC) and FAO, climate change may result in global warming, sea level rise, changes of ocean productivity, freshwater shortage, and more frequent extreme climate events. Consequently, climate change may affect aquaculture to various extents depending on climatic zones, geographical areas, rearing systems, and species farmed. There are 2 major challenges for aquaculture caused by climate change. First, the current fish, adapted to the prevailing environmental conditions, may be suboptimal under future conditions. Fish species are often poikilothermic and, therefore, may be particularly vulnerable to temperature changes. This will make low sensitivity to temperature more important for fish than for livestock and other terrestrial species. Second, climate change may facilitate outbreaks of existing and new pathogens or parasites. To cope with the challenges above, 3 major adaptive strategies are identified. First, general ‘robustness’ will become a key trait in aquaculture, whereby fish will be less vulnerable to current and new diseases while at the same time thriving in a wider range of temperatures. Second, aquaculture activities, such as input power, transport, and feed production contribute to greenhouse gas emissions. Selection for feed efficiency as well as defining a breeding goal that minimizes greenhouse gas emissions will reduce impacts of aquaculture on climate change. Finally, the limited adoption of breeding programs in aquaculture is a major concern. This implies inefficient use of resources for feed, water, and land. Consequently, the carbon footprint per kg fish produced is greater than when fish from breeding programs would be more heavily used. Aquaculture should use genetically improved and robust organisms not suffering from inbreeding depression. This will require using fish from well-managed selective breeding programs with proper inbreeding control and breeding goals. Policymakers and breeding organizations should provide incentives to boost selective breeding programs in aquaculture for more robust fish tolerating climatic change. Estimation of breeding values for uniformity of growth in Atlantic salmon (Salmo salar) using pedigree relationships or single-step genomic evaluationSae-Lim, Panya ; Kause, Antti ; Lillehammer, Marie ; Mulder, Herman - \ 2017Genetics, Selection, Evolution 49 (2017)1. - ISSN 0999-193X Background: In farmed Atlantic salmon, heritability for uniformity of body weight is low, indicating that the accuracy of estimated breeding values (EBV) may be low. The use of genomic information could be one way to increase accuracy and, hence, obtain greater response to selection. Genomic information can be merged with pedigree information to construct a combined relationship matrix ($${\mathbf{H}}$$ H matrix) for a single-step genomic evaluation (ssGBLUP), allowing realized relationships of the genotyped animals to be exploited, in addition to numerator pedigree relationships ($${\mathbf{A}}$$ A matrix). We compared the predictive ability of EBV for uniformity of body weight in Atlantic salmon, when implementing either the $${\mathbf{A}}$$ A or $${\mathbf{H}}$$ H matrix in the genetic evaluation. We used double hierarchical generalized linear models (DHGLM) based either on a sire-dam (sire-dam DHGLM) or an animal model (animal DHGLM) for both body weight and its uniformity. Results: With the animal DHGLM, the use of $${\mathbf{H}}$$ H instead of $${\mathbf{A}}$$ A significantly increased the correlation between the predicted EBV and adjusted phenotypes, which is a measure of predictive ability, for both body weight and its uniformity (41.1 to 78.1%). When log-transformed body weights were used to account for a scale effect, the use of $${\mathbf{H}}$$ H instead of $${\mathbf{A}}$$ A produced a small and non-significant increase (1.3 to 13.9%) in predictive ability. The sire-dam DHGLM had lower predictive ability for uniformity compared to the animal DHGLM. Conclusions: Use of the combined numerator and genomic relationship matrix ($${\mathbf{H}}$$ H) significantly increased the predictive ability of EBV for uniformity when using the animal DHGLM for untransformed body weight. The increase was only minor when using log-transformed body weights, which may be due to the lower heritability of scaled uniformity, the lower genetic correlation of transformed body weight with its uniformity compared to the untransformed traits, and the small number of genotyped animals in the reference population. This study shows that ssGBLUP increases the accuracy of EBV for uniformity of body weight and is expected to increase response to selection in uniformity. Selective breeding in aquaculture for future environments under climate changeMulder, H.A. ; Sae-Lim, P. ; Kause, A. ; Olesen, I. - \ 2016 Aquaculture - Climate change - environmental sensitivity Aquaculture is the fastest growing food production sector that contributes significantly to global food security. Based on FAO reports, aquaculture production has to increase by 42.9% to meet the future global demand for aquatic foods in 2020. According to the reports by IPCC and FAO, climate change may result in global warming, sea level rise, changes of ocean productivity, freshwater shortage, and more frequent extreme climate events. Consequently, climate change may affect aquaculture to various extents depending on climatic zones, geographical areas (inland or coastal), type of aquaculture systems, and species farmed. Climate change may introduce opportunities as well as several challenges: Opportunities may arise at certain locations and geographical areas; for instance, a rise of temperature may prolong growth period, increase fish growth rate, allow new and more efficient farming systems, and new-farmed species. Spatial planning will enable the identification of locations with optimal conditions for farming. Challenges; There are two major challenges caused by climate change. Firstly, the current fish material, adapted to the prevailing environmental conditions, may be suboptimal under future conditions. Similarly, breeding programmes selecting for genotypes with current superior performance, may not be the optimal genotypes in the future. Genotype-by- environment interaction (GxE) is a phenomenon by which animals respond differently to changes in environment. The presence of GxE indicates that there is genetic variation in environmental sensitivity and it is possible to select for fish that can adapt to the changing environments. For instance, rainbow trout (Oncorhynchus mykiss), a very popular farmed salmonid worldwide, has a narrow optimal temperature range. Strong GxE in growth performance of rainbow trout in different temperatures has been reported; hence, utilisation of selective breeding can be advantageous for breeding rainbow trout that are best adapted to the temperature changes induced by climate change. Secondly, climate change may facilitate outbreaks of existing pathogens or parasites. Moreover, change in water temperature may promote dispersal of new diseases. Disease prevalence increases with physical stress, e.g., that associated with a change in temperature, due to reduction in host resistance and increasing growth of pathogens. Many diseases of farmed fish can potentially become a greater problem at higher temperatures. Thus, mortality rates will increase and production from aquaculture will reduce. In Australia, farmed abalone (Haliotis laevigata) has experienced 25% summer mortality due to elevated water temperature, leading to AU\$1.75 million loss of profit. To cope with the challenges above, adaptive measures must be addressed through both a reduction of environmental impacts from greenhouse gas (GHG) emissions and selective breeding strategies. Adaptive strategies. Three major adaptive strategies are identified: 1. Fish species are often poikilothermic, and may therefore be particularly vulnerable to temperature changes. This will make low sensitivity to temperature more important for fish than for livestock and other terrestrial species. Hence, general “robustness” will become a key trait in aquaculture, whereby fish will be less vulnerable to current and new diseases and parasites while at the same time thriving in a wider range of temperatures. Breeding goals may change toward prioritising robustness. Nevertheless, knowledge of, and implementation of genetic adaptation to fish breeding is limited and has not received much attention. 2. The limited adoption of breeding programmes in aquaculture (<10%) is a major concern. Aquaculture based on wild stocks that are not adapted to the farm environment, or farmed animals from breeding programmes without proper selection and/or control of inbreeding, will lead to poor performance and survival compared to genetically improved or well-managed stocks. This implies low aquaculture production and inefficient use of resources for feed and land. Consequently, a higher carbon footprint with a negative impact on climate change per kg fish produced is expected. Aquaculture should use genetically improved and robust species not suffering from inbreeding depression. This will imply using fish materials from well-managed selective breeding programmes with proper breeding goals and a controlled rate of inbreeding. Policy makers should provide incentives and public support to boost selective breeding programmes in aquaculture for more robust fish tolerating climatic changes. 3. Although aquatic organisms do not emit GHGs as ruminants do, aquaculture activities, such as input power, transport, and feed production contribute to GHG emissions. Life cycle analysis (LCA) is a method to quantify the use of resources and emission of pollutants of the entire production chain for a product. Selective breeding for increased production is expected to enhance efficiency of resource utilisation (feed, energy and land) of a production system, through correlated changes in feed efficiency or shorter production period. Applications of LCA to define breeding goals that maximise production while minimising environmental impacts can be one solution, as already demonstrated in African catfish. Conclusions: Climate change poses opportunities and challenges to aquaculture production. Selective breeding is a long-term, cost-effective strategy that can best minimise the detrimental effects of climate change on aquaculture. Empirical studies are required to estimate the potential of increasing robustness of fish by selection methods. Applying selective breeding to develop robust animals will become more important under climate change, and dissemination of genetically improved stocks will in-turn efficiently increase aquaculture production and reduce environmental load, including GHG-emissions. Established selective breeding programmes are a prerequisite to apply genomic information for further genetic improvement of aquaculture production. Hence, stakeholders should support the adoption and development of selective breeding by disseminating genetically improved materials and knowledge of selective breeding at all levels of the aquaculture sector worldwide, to ensure food security for the growing human population under climate change. A review of genotype-by-environment interaction and micro-environmental sensitivity in aquaculture speciesSae-Lim, P. ; Gjerde, B. ; Nielsen, H.M. ; Mulder, H.A. ; Kause, A. - \ 2016Reviews in Aquaculture 8 (2016)4. - ISSN 1753-5123 - p. 369 - 393. Generating breeding programmes that effectively improve farmed fish performance across multiple environments and make fish more uniform within production environments would aid farmers to produce food under diverse environments. We review genotype-by-environment interaction leading to re-ranking of genotypes across environments, that is non-unity genetic correlation between traits measured in different environments, and micro-environmental sensitivity leading to a change in environmental variance of a trait. A quantitative review across 38 species showed that (i) genotype-by-environment interaction studies are lacking for many economically important traits. (ii) Re-ranking is moderate for growth (average genetic correlation = 0.72) and survival (average genetic correlation = 0.54). Significant re-ranking is of concern because selection in a nucleus leads to lower genetic responses in commercial environments compared to a case when re-ranking does not exist. (iii) Re-ranking is weak for age-at-sexual-maturity and fish appearance (average genetic correlation = 0.86), implying that genetic improvement in one environment is expected to be effective in the other environments. Future research should provide guidelines for how to account for genotype-by-environment interaction when collecting data, estimating breeding values and optimising the structure of the breeding programme. (iv) Coefficient of genetic variation for sensitivity against unknown micro-environmental factors within a single environment for body weight is high. Hence, genetic improvement towards less sensitive fish, resulting in more uniform production, is possible, but a large number of relatives with phenotypes is needed for obtaining moderate accuracy of selection. This review elucidates needs for further research on genotype-by-environment interaction and micro-environmental sensitivity in economically important traits and species. Comparison of designs for estimating genetic parameters and obtaining response to selection for social interaction traits in aquacultureSae-Lim, P. ; Bijma, P. - \ 2016Aquaculture 451 (2016). - ISSN 0044-8486 - p. 330 - 339. Social interactions among individuals may affect individual productivity and welfare in aquaculture. Since social effects may have a genetic component, known as an indirect genetic effect (IGE), genetic selection may be a promising tool to simultaneously improve welfare and productivity in aquaculture. Here we compare two experimental designs that have been previously proposed for the genetic improvement of socially affected traits. We used stochastic simulations to compare a design where each group consisted of members of two families (2-FAM) with a design where each group consisted of members of three families (3-FAM). The 2-FAM and 3-FAM designs were compared using an equal number of groups (96 groups). The group size, i.e., number of individuals within each group, was either 30 or 60 individuals. Both designs were compared for the precision of estimated direct and social genetic parameters and for response to selection, either with or without a restriction on the rate of inbreeding. Four different schemes with a low variance (heritability for social effects or hS 2=0.1), a moderate variance (hS 2=0.3), or a high variance (hS 2=0.5) of the social genetic effects, and a negative correlation between direct and social genetic effects (rAD,S=-0.6) were compared. The negative rAD,S indicates competition between group mates. Differences in precision of estimated genetic parameters between both designs were small. At low hS 2 and group size of 30, the 2-FAM design was superior with respect to precision of social additive genetic variance. When the social genetic variance was small, a larger group size, e.g. 60 is recommended. The 2-FAM design resulted in a higher accuracy of selection for social and total genetic effects, but also in a higher rate of inbreeding compared to the 3-FAM design. When the rate of inbreeding was restricted to ~2%, the total response to selection was significantly higher for the 3-FAM design. In conclusion, the 2-FAM and 3-FAM designs differ little in accuracy of the direct and social genetic parameter estimates, while the 3-FAM design is superior with respect to the response to selection at a fixed rate of inbreeding. This study is particularly important for making decisions regarding experimental design when breeding for social genetic effects in aquaculture. Statement of relevance: Communal or family rearing with a large number of animals in a group is common practice in aquaculture. Large variation in body size, which may inflate competition for feed, is generally observed in the communal rearing tank. In addition, cannibalistic or aggressive behaviour is frequently observed in a large variety of fish species. So far, the issue is addressed by management measures, such as size grading, which may be labour intensive. Sustainable solutions through selective breeding have not been tested.Previous studies compared two experimental designs for social interaction traits; groups composed of two families (2-FAM) and three families (3-FAM) with groups composed at random and found that the 2-FAM and 3-FAM designs were better with respect to genetic parameter estimates than designs with groups composed at random. It is however still unknown whether the 2-FAM or the 3-FAM design is better. Previous studies did not consider the rate of inbreeding when studying the designs. This paper describes the results from a comparison of the two experimental designs for estimating genetic parameters and obtaining response to selection, taking into account the rate of inbreeding. As such, this paper should be of interest to a broad readership including those interested in aquaculture genetics Genotype-by-environment interaction for uniformity of growth in rainbow trout (Oncorhynchus mykiss)Sae-Lim, P. ; Kause, A. ; Janhunen, M. ; Vehvilainen, H. ; Koskinen, H. ; Gjerde, B. ; Lillehammer, M. ; Mulder, H.A. - \ 2015In: Abstract Book ISGA XII - The International Symposium on Genetics in AquacultureI. - - p. 57 - 57. When a rainbow trout stock from a single breeding program is reared in diverse production environments, genotype-by-environment interaction (GxE) may present itself. Growth and its uniformity are considered as two of the most important traits by trout producers worldwide. However, GxE for uniformity of growth has not been studied. Using a double hierarchical generalized linear model and data from the Finnish breeding program, we quantified the genetic variance and correlation of body weight (BW) and its uniformity, as well as the degree of GxE for uniformity of BW in a breeding (BE) and a production (PE) environment. To investigate whether scale effect (high variance related to high mean) affected the estimated parameter, the data were also log-transformed. Although heritability for uniformity () in the BE (0.014) and in the PE (0.012) was low and of similar magnitude, the genetic coefficient of variation for uniformity was 19 and 21%, respectively, revealing high potential for response to selection. The low heritability for uniformity implies that a large number of relatives are needed to obtain moderate accuracy of selection. Genotype re-ranking of uniformity was moderate (rg = 0.56) but became strong after log-transformation (rg = -0.08), indicating independent ranking of genotypes in uniformity across the two environments when the scale effect was accounted for. Due to the strong GxE, especially after log-transformation, the use of sib-testing in the PE is recommended when uniformity is required to be improved across environments. The genetic correlation between BW and uniformity was 0.30 in the BE and 0.79 in the PE, but for the log-transformed BW, the genetic correlations were switched to -0.83 in the BE and -0.62 in the PE. The opposite sign of genetic correlations between BW and uniformity from the raw and log-transformed BW data, respectively, indicate that increased BW is genetically related to increased variance of BW, but to decreased variance of BW after accounting for the scale effect. Hence, the scale effect substantially influences the genetic parameters of uniformity, especially the sign and magnitude of its genetic correlations. Genetic (co)variance of rainbow trout (Oncorhynchus mykiss) body weight and its uniformity across production environmentsSae-Lim, P. ; Kause, A. ; Janhunen, M. ; Vehvilainen, H. ; Koskinen, H. ; Gjerde, B. ; Lillehammer, M. ; Mulder, H.A. - \ 2015Genetics, Selection, Evolution 47 (2015). - ISSN 0999-193X - 10 p. Background: When rainbow trout from a single breeding program are introduced into various production environments, genotype-by-environment (GxE) interaction may occur. Although growth and its uniformity are two of the most important traits for trout producers worldwide, GxE interaction on uniformity of growth has not been studied. Our objectives were to quantify the genetic variance in body weight (BW) and its uniformity and the genetic correlation (r g) between these traits, and to investigate the degree of GxE interaction on uniformity of BW in breeding (BE) and production (PE) environments using double hierarchical generalized linear models. Log-transformed data were also used to investigate whether the genetic variance in uniformity of BW, GxE interaction on uniformity of BW, and r g between BW and its uniformity were influenced by a scale effect. Results: Although heritability estimates for uniformity of BW were low and of similar magnitude in BE (0.014) and PE (0.012), the corresponding coefficients of genetic variation reached 19 and 21%, which indicated a high potential for response to selection. The genetic re-ranking for uniformity of BW (r g¿=¿0.56) between BE and PE was moderate but greater after log-transformation, as expressed by the low r g (-0.08) between uniformity in BE and PE, which indicated independent genetic rankings for uniformity in the two environments when the scale effect was accounted for. The r g between BW and its uniformity were 0.30 for BE and 0.79 for PE but with log-transformed BW, these values switched to -0.83 and -0.62, respectively. Conclusions: Genetic variance exists for uniformity of BW in both environments but its low heritability implies that a large number of relatives are needed to reach even moderate accuracy of selection. GxE interaction on uniformity is present for both environments and sib-testing in PE is recommended when the aim is to improve uniformity across environments. Positive and negative r g between BW and its uniformity estimated with original and log-transformed BW data, respectively, indicate that increased BW is genetically associated with increased variance in BW but with a decrease in the coefficient of variation. Thus, the scale effect substantially influences the genetic parameters of uniformity, especially the sign and magnitude of its r g. Genetics of growth reaction norms in farmed rainbow troutSae-Lim, P. ; Mulder, H.A. ; Gjerde, B. ; Koskinen, H. ; Lillehammer, M. ; Kause, A. - \ 2015PLoS ONE 10 (2015)8. - ISSN 1932-6203 - 17 p. Rainbow trout is farmed globally under diverse uncontrollable environments. Fish with low macroenvironmental sensitivity (ES) of growth is important to thrive and grow under these uncontrollable environments. The ES may evolve as a correlated response to selection for growth in one environment when the genetic correlation between ES and growth is nonzero. The aims of this study were to quantify additive genetic variance for ES of body weight (BW), defined as the slope of reaction norm across breeding environment (BE) and production environment (PE), and to estimate the genetic correlation (rg(int, sl)) between BW and ES. To estimate heritable variance of ES, the coheritability of ES was derived using selection index theory. The BW records from 43,040 rainbow trout performing either in freshwater or seawater were analysed using a reaction norm model. High additive genetic variance for ES (9584) was observed, inferring that genetic changes in ES can be expected. The coheritability for ES was either -0.06 (intercept at PE) or -0.08 (intercept at BE), suggesting that BW observation in either PE or BE results in low accuracy of selection for ES. Yet, the rg(int, sl) was negative (-0.41 to -0.33) indicating that selection for BW in one environment is expected to result in more sensitive fish. To avoid an increase of ES while selecting for BW, it is possible to have equal genetic gain in BW in both environments so that ES is maintained stable. Experimental Designs for Genetic Parameters and Response to Selection of Social Interaction Traits in aquacultureSae-Lim, P. ; Bijma, P. - \ 2014 more info ... Genetic Analysis of Shape in Trout, using image analysisKomen, J. ; Blonk, R.J.W. ; Sae-Lim, P. - \ 2014 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 shape Identifying environmental variables explaining genotype-by-environment interaction for body weight of rainbow trout (Onchorynchus mykiss): reaction norm and factor analytic modelsSae-Lim, P. ; Komen, J. ; Kause, A. ; Mulder, H.A. - \ 2014Genetics, Selection, Evolution 46 (2014). - ISSN 0999-193X polymorphic microsatellite markers - random regression-models - expressed sequence tags - oncorhynchus-mykiss - parental allocation - genetic-parameters - breeding programs - bilinear models - selection - trials Background: Identifying the relevant environmental variables that cause GxE interaction is often difficult when they cannot be experimentally manipulated. Two statistical approaches can be applied to address this question. When data on candidate environmental variables are available, GxE interaction can be quantified as a function of specific environmental variables using a reaction norm model. Alternatively, a factor analytic model can be used to identify the latent common factor that explains GxE interaction. This factor can be correlated with known environmental variables to identify those that are relevant. Previously, we reported a significant GxE interaction for body weight at harvest in rainbow trout reared on three continents. Here we explore their possible causes. Methods: Reaction norm and factor analytic models were used to identify which environmental variables (age at harvest, water temperature, oxygen, and photoperiod) may have caused the observed GxE interaction. Data on body weight at harvest was recorded on 8976 offspring reared in various locations: (1) a breeding environment in the USA (nucleus), (2) a recirculating aquaculture system in the Freshwater Institute in West Virginia, USA, (3) a high-altitude farm in Peru, and (4) a low-water temperature farm in Germany. Akaike and Bayesian information criteria were used to compare models. Results: The combination of days to harvest multiplied with daily temperature (Day*Degree) and photoperiod were identified by the reaction norm model as the environmental variables responsible for the GxE interaction. The latent common factor that was identified by the factor analytic model showed the highest correlation with Day*Degree. Day*Degree and photoperiod were the environmental variables that differed most between Peru and other environments. Akaike and Bayesian information criteria indicated that the factor analytical model was more parsimonious than the reaction norm model. Conclusions: Day*Degree and photoperiod were identified as environmental variables responsible for the strong GxE interaction for body weight at harvest in rainbow trout across four environments. Both the reaction norm and the factor analytic models can help identify the environmental variables responsible for GxE interaction. A factor analytic model is preferred over a reaction norm model when limited information on differences in environmental variables between farms is available Genotype-by-environment interaction of growth traits in rainbow trout (Oncorhynchus mykiss): A continental scale study.Sae-Lim, P. ; Kause, A. ; Mulder, H.A. ; Martin, K.E. ; Barfoot, A.J. ; Arendonk, J.A.M. van; Komen, J. - \ 2013Journal of Animal Science 91 (2013)12. - ISSN 0021-8812 - p. 5572 - 5581. plant-based diets - body-weight - parental allocation - genetic-parameters - breeding programs - water temperature - sexual-maturity - dairy-cattle - selection - variance Rainbow trout is a globally important fish species for aquaculture. However, fish for most farms worldwide are produced by only a few breeding companies. Selection based solely on fish performance recorded at a nucleus may lead to lower-than-expected genetic gains in other production environments when genotype-by-environment (G × E) interaction exists. The aim was to quantify the magnitude of G × E interaction of growth traits (tagging weight; BWT, harvest weight; BWH, and growth rate; TGC) measured across 4 environments, located in 3 different continents, by estimating genetic correlations between environments. A total of 100 families, of at least 25 in size, were produced from the mating 58 sires and 100 dams. In total, 13,806 offspring were reared at the nucleus (selection environment) in Washington State (NUC) and in 3 other environments: a recirculating aquaculture system in Freshwater Institute (FI), West Virginia; a high-altitude farm in Peru (PE), and a cold-water farm in Germany (GER). To account for selection bias due to selective mortality, a multitrait multienvironment animal mixed model was applied to analyze the performance data in different environments as different traits. Genetic correlation (rg) of a trait measured in different environments and rg of different traits measured in different environments were estimated. The results show that heterogeneity of additive genetic variances was mainly found for BWH measured in FI and PE. Additive genetic coefficient of variation for BWH in NUC, FI, PE, and GER were 7.63, 8.36, 8.64, and 9.75, respectively. Genetic correlations between the same trait in different environments were low, indicating strong reranking (BWT: rg = 0.15 to 0.37, BWH: rg = 0.19 to 0.48, TGC: rg = 0.31 to 0.36) across environments. The rg between BWT in NUC and BWH in both FI (0.31) and GER (0.36) were positive, which was also found between BWT in NUC and TGC in both FI (0.10) and GER (0.20). However, rg were negative between BWT in NUC and both BWH (–0.06) and TGC (–0.20) in PE. Correction for selection bias resulted in higher additive genetic variances. In conclusion, strong G × E interaction was found for BWT, BWH, and TGC. Accounting for G × E interaction in the breeding program, either by using sib information from testing stations or environment-specific breeding programs, would increase genetic gains for environments that differ significantly from NUC. One size fits all? : optimization of rainbow trout breeding program under diverse preferences and genotype-by-environment interactionSae-Lim, P. - \ 2013Wageningen University. Promotor(en): Johan van Arendonk, co-promotor(en): Hans Komen; A. Kause. - S.l. : s.n. - ISBN 9789461734648 - 200 regenboogforel - dierveredeling - veredelingsprogramma's - genotype-milieu interactie - optimalisatie - kenmerken - genetische winst - selectief fokken - simulatie - visteelt - aquacultuur - rainbow trout - animal breeding - breeding programmes - genotype environment interaction - optimization - traits - genetic gain - selective breeding - simulation - fish culture - aquaculture Global fish breeders distribute improved animal material to several continents to be farmed under diverse environments, and for very different market conditions. When establishing a global breeding program, there is a need to assess whether or not a single breeding objective satisfies the markets across different countries. It may be challenging to develop a single fish stock that performs well across all environments due to genotype-by-environment interaction (GxE). GxE is a phenomenon describing the possibility that different genotypes have a different sensitivity to changes in an environment. The objective of this thesis was to develop an optimized global breeding program for rainbow trout (Oncorhynchus mykiss) in terms of a balanced breeding goal that satisfies preferences of trout producers and maximized genetic gains across environments in the presence of GxE in production traits. Analytic hierarchy process (AHP) was used to estimate preferences, which can be aggregated to consensus preference values using weighted goal programming (WGP). The analysis revealed that the 6 most important traits were thermal growth coefficient (TGC), survival (Surv), feed conversion ratio (FCR), condition factor (CF), fillet percentage (FIL%), and late maturation (LMat). Individual trait preferences are different for farmers having different farming environments and producing different end-products. Calculating consensus preference values resulted in consensus desired genetic gains. To satisfy most farmers, consensus desired genetic gains can be taken into account in a global breeding strategy. Strong genotype re-ranking was found for all growth traits across environments. Based on simulation, re-location of breeding program led to highest total genetic gain for body weight at harvest. Alternatively, including sib performance into selection index increased genetic gain in all environments. Finally, environment-specific program can be used, but this is costly. There is a possibility of a conflict between 2 profits: from a breeding company and fish farmers and an optimum solution for that conflict can be found by using macroeconomics and cost-benefit analysis. Enhancing selective breeding for growth, slaughter traits and overall survival in rainbow trout (Oncorhynchus mykiss)Sae-Lim, P. ; Komen, J. ; Kause, A. ; Martin, K.E. ; Crooijmans, R.P.M.A. ; Arendonk, J.A.M. van; Parsons, J.E. - \ 2013Aquaculture 372-375 (2013). - ISSN 0044-8486 - p. 89 - 96. polymorphic microsatellite markers - oreochromis-niloticus l. - cultured atlantic salmon - expressed sequence tags - carcass quality traits - genetic-parameters - body-weight - parental allocation - water temperature - 2-stage selection Enhancing selection using two-stage selection is normally implemented by pre-selection for tagging weight (BWT) and by final selection for ungutted harvest weight (BWH) and thermal growth coefficient from tagging to harvest (TGCTH). However, selection on harvest traits, i.e., gutted weight (GBWH), visceral percentage (VISW%), condition factor (CFH), and overall survival (SURV), can be enhanced by exploiting correlated traits. It can be hypothesized that the efficiency of two-stage selection on genetic response in BWH and TGCTH is dependent on their genetic (rg) and phenotypic (rp) correlations with BWT and therefore dependent on the time point of pre-selection. The aims of this study were, first, to estimate genetic parameters (heritability: h2, rp, and rg) for BWT (7 months), BWS (weight at sorting, 9 months), BWH (14 months), TGCTH, GBWH, VISW%, CFH, and SURV. Second, these genetic parameters were used in two deterministic simulation studies; i) one- and two-stage selections to compare genetic responses in BWH and TGCTH, and ii) alternative selection indices using correlated traits to compare corresponding accuracy of selection (rIH) for slaughter traits, CFH, and SURV. Genetic parameters were estimated using an animal mixed model in ASReml on 2,041 fish records. The main results showed that, first, rg of BWT was 0.35 with BWH but - 0.25 with TGCTH, whereas the rg of BWS was 0.72 with BWH but 0.39 with TGCTH. Pre-selection for BWS led to genetic response of 54.15 g in BWH which was higher than the genetic response from pre-selection for BWT (51.90 g). Similarly, pre-selection on BWS enhanced correlated genetic response in TGCTH to 0.30 g(1/3)/°C*day. In contrast, pre-selection for BWT resulted in lower correlated genetic response in TGCTH of 0.20 g(1/3)/°C*day. It can be concluded that genetic improvement of BWH and TGCTH can be enhanced by postponing pre-selection to a later age. However, an optimal time point for tagging and pre-selection should be found to minimize common environmental effects and rearing costs during communal rearing of full-sibs. Second, including GBWH in a selection index can reduce unfavourable selection responses in VISW%. The GBWH is highly genetically correlated with BWH and can be easily indirectly selected. TGCTH is a good predictor for selection for lower VISW%, and higher SURV, but not for higher CFH. To control genetic changes in the condition factor, it should be included to the selection index. Genotype-by-environment interaction for growth traits in rainbow trout: a continental scale studySae-Lim, P. ; Mulder, H.A. ; Komen, J. ; Kause, A. ; Arendonk, J.A.M. van; Martin, K.E. ; Parsons, J.E. - \ 2012In: Book of Abstracts of the International symposium on genetics in aquaculture XI, 24-30 june 2012, Auburn University, Alabama, USA. - - p. 36 - 36. more info ... Defining desired genetic gains for rainbow trout breeding objective using analytic hierarchy processSae-Lim, P. ; Komen, J. ; Kause, A. ; Arendonk, J.A.M. van; Barfoot, A.J. ; Martin, K.E. ; Parsons, A.J. - \ 2012Journal of Animal Science 90 (2012)6. - ISSN 0021-8812 - p. 1766 - 1776. goal-programming approach - salmon salmo-salar - oncorhynchus-mykiss - atlantic salmon - environment interactions - sexual-maturity - body traits - fuzzy ahp - parameters - growth Distributing animals from a single breeding program to a global market may not satisfy all producers, as they may differ in market objectives and farming environments. Analytic hierarchy process (AHP) is used to estimate preferences, which can be aggregated to consensus preference values using weighted goal programming (WGP). The aim of this study was to use an AHP-WGP based approach to derive desired genetic gains for rainbow trout breeding, and to study whether breeding trait preferences vary depending on commercial products and farming environments. Two questionnaires were sent out, Q-A and Q-B. Q-A was distributed to 178 farmers from 5 continents and used to collect information on commercial products and farming environments. In this questionnaire, farmers were asked to rank the 6 most important traits for genetic improvement from a list of 13 traits. Questionnaire B (Q-B) was sent to all farmers who responded to Q-A (53 in total). For Q-B, preferences of the 6 traits were obtained using pairwise comparison. Preference intensity was given in order to quantify (in % of a trait mean; G%) the degree to which one trait is preferred over the other. Individual preferences (Ind-P), social preferences (Soc-P), and consensus preferences (Con-P) were estimated using AHP and WGP. Desired gains were constructed by multiplying Con-P with G%. The analysis revealed that the 6 most important traits were thermal growth coefficient (TGC), survival (Surv), feed conversion ratio (FCR), condition factor (CF), fillet percentage (F%), and late maturation (LMat). Ranking of traits based on average Con-P values were Surv (0.271), FCR (0.246), TGC (0.246), LMat (0.090), F% (0.081), and CF (0.067). Corresponding desired genetic gains (in % of trait mean) were 1.63%, 1.87%, 1.67%, 1.29%, 0.06%, and 0.33%, respectively. The results from Con-P values show that trait preferences may vary for different types of commercial production or farming environments. This study demonstrated that combination of AHP and WGP can be used to derive desired gains for a breeding program, and to quantify differences due to variations market demand or production environment Desired genetic gains for a breeding objective: A novel participatory approachSae-Lim, P. ; Komen, J. ; Kause, A. ; Arendonk, J.A.M. van; Barfoot, A.J. ; Martin, K.E. ; Parsons, J. - \ 2011 more info ... Bias and precision of estimates of genotype-by-environment interaction: A simulation studySae-Lim, P. ; Komen, J. ; Kause, A. - \ 2010Aquaculture 310 (2010)1-2. - ISSN 0044-8486 - p. 66 - 73. trout oncorhynchus-mykiss - body-composition traits - large rainbow-trout - genetic-parameters - breeding schemes - fish-meal - selection - growth - l. - variance Re-ranking of genotypes across environments is a form of genotype-by-environment (G x E) interaction with serious consequences for breeding programmes. The degree of such G x E interaction can be estimated using the genetic correlation (r(g)) between measurements in two environments for a given trait. When r(g) is lower than 0.8, G x E interaction is commonly considered to be biologically significant. Here a stochastic simulation was used to study the impact of population structure on bias and precision of genetic correlation estimates between two environments. Simulated populations resulted from a nested mating design (1 sire to 2 dams). Simulated r(g) was 0.0, 0.5, or 0.8. A trait with heritability (h(2)) of either 0.3 or 0.1 in both environments was simulated. Simulation results show that genetic correlation estimates are biased downward especially when the simulated rg is 0.8, heritability is 0.1, and family size is less than 10. A downward biased genetic correlation estimate incorrectly suggests the existence of G x E interaction. This can lead to the erroneous conclusion that a multi-environment breeding programme is needed. The optimal design with the lowest mean square error fort., for a trait with low h(2) requires a large family size (20-25) and a low number of families (100-80 or 50-40 for population size fixed to 2000 and 1000 animals, respectively). For traits with moderate h(2), the optimal family size is 10 with 200 or 100 families for population size fixed to 2000 and 1000, respectively. We also studied the effect of selective mortality on G x E estimates. However, schemes with unequal family sizes due to differences between families in survival produced similar results for the optimum design as schemes with equal family sizes. Equal-family-size design can thus be used to determine the optimal design for estimating G x E interaction. Our study can be used as a guideline for estimating a genetic correlation for practical breeding programmes