Genetics of growth reaction norms in farmed rainbow trout

RESEARCH ARTICLE Genetics of Growth Reaction Norms in Farmed Rainbow Trout Panya Sae-Lim 1 *, Han Mulder 2 , Bjarne Gjerde 1 , Heikki Koskinen 3 , Marie Lillehammer 1 , Antti Kause 4 1Aquaculture and Genetics, Nofima, Osloveien 1, Ås, Norway, 2Animal Breeding and Genomics Centre, Wageningen University, Wageningen, the Netherlands, 3Aquaculture Unit, Natural Resources Institute Finland, Tervo, Finland, 4Biometrical Genetics, Natural Resources Institute Finland, Jokioinen, Finland *[email protected] Abstract 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 (r g(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 r g(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. Introduction The performance of organisms is influenced by the surrounding environmental conditions, leading to phenotypically plastic responses to environmental changes. Such plastic responses have been observed, for example, as adaptive plasticity in the neck teeth of Daphnia (water fleas) which develops as a protective response to the chemical cues of a predatory Chaoborus present in the water [1]. In fish species, phenotypic plasticity has been explored especially from ecological and evolutionary points of view. There is evidence for genetic basis of phenotypic plasticity, for example in salmonids [2], Trinidadian guppies (Poecilia reticulata)[3,4] and pupfishes (Cyprinodon nevadensis)[5]. PLOS ONE | DOI:10.1371/journal.pone.0135133 August 12, 2015 1/17 OPEN ACCESS Citation: Sae-Lim P, Mulder H, Gjerde B, Koskinen H, Lillehammer M, Kause A (2015) Genetics of Growth Reaction Norms in Farmed Rainbow Trout. PLoS ONE 10(8): e0135133. doi:10.1371/journal. pone.0135133 Editor: Gen Hua Yue, Temasek Life Sciences Laboratory, SINGAPORE Received: February 6, 2015 Accepted: July 18, 2015 Published: August 12, 2015 Copyright: © 2015 Sae-Lim et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: Natural Resources Institute Finland (Luke) own the data underlying this paper. Please send requests for the data to petri. [email protected]. Funding: This study is funded by Norwegian Research Council (NRC: 234144/E40), http://www. forskningsradet.no/en/Home_page/1177315753906. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. Among animal breeders, phenotypic plasticity is termed macroenvironmental sensitivity (ES) [6]. It has been of interest for animal breeders because of its connection with animal’s performance across environments [7,8] and to the robustness and welfare of animals [9]. For a genotype, such as a clone, family, population, or a species, macroenvironmental sensitivity can be defined by its slope of reaction norm across environments. Assuming a linear reaction norm, the degree of macroenvironmental sensitivity can be quantified by the regression slope of a genotype's performance, such as growth, against an environmental gradient [10–12]. Rainbow trout Oncorhynchus mykiss (Walbaum 1792) is one of the main fish species farmed under diverse environmental conditions across continents. Rapid growth is one of the most important traits for profitable trout farming. However, fish may not be able to maintain high growth when rearing conditions are suboptimal. Therefore, a more robust fish with high stability of growth is important to thrive under variable environmental conditions. To quantify the potential for changing macroenvironmental sensitivity through selection, an estimate of genetic variance in macroenvironmental sensitivity is required. The genetic variation in the macroenvironmental sensitivity is known as non-parallel reaction norms, causing genotypeby-environment interaction (GxE) [13]. Evidences of GxE in growth of rainbow trout have been reported [14–20]. However, so far most studies use multi-trait model in which GxE is quantified as the genetic correlation between the records of the same trait measured in different environments. Such genetic correlation expresses the magnitude of re-ranking of families with respect to their breeding value, but it does not provide an explanation on how macroenvironmental sensitivity can evolve across environments. The concept of macroenvironmental sensitivity has never been applied to breeding in aquaculture before. In Finland, the national breeding programme for rainbow trout breeds especially for improved growth performance in commercial production environment at the Baltic Sea [21]. However, the stock is also reared in inland freshwater production environments, and exported to Russia and Asia where the production environment differs from Finland substantially. Hence, the macroenvironmental sensitivity is considered as an important trait. The aims of this study were two-fold. Firstly, we quantify the genetic variance for ES, defined as the slope of reaction norm across seawater and freshwater production environments in Finland, using a reaction norm model. Secondly, to study whether selection for fast growth in one environment will change ES, we estimate the genetic correlation between ES and body weight in one environment, defined as the intercept of reaction norm. In addition, we derived the coheritability for ES. Although, the genetic covariance matrix from reaction norm and multi-trait models is interchangeable [7,22–23], the reaction norm model is chosen as the method in this study because it provides the genetic parameters for ES and body weight directly without interchanging. To be able to compare our results with previous studies, we exploit this interchangeable property to calculate genetic variance in ES and its genetic correlation with intercept in aquaculture GxE studies that all have used a multi-trait model. Materials and Methods Ethics Statement All procedures involving animals were approved by the animal care committee of the Natural Resources Institute Finland. To enhance animal welfare and ameliorate suffering during all fish handling, the fish were always first anaesthetized using MS-222. Data source All fish used in this study were obtained from the Finnish national breeding programme. Breeding candidates are held at the Tervo fish farm in central Finland (freshwater nucleus Genetics of Macroenvironmental Sensitivity PLOS ONE | DOI:10.1371/journal.pone.0135133 August 12, 2015 2/17 station) and the sibs of the breeding candidates are tested at commercial sea stations located at the Baltic Sea. The phenotypic data had 53,638 records of body weight at tagging from four year classes and belonged to two subpopulations, one with year classes of 1996 and 1999 and the other with 1997 and 2000. Both of these subpopulations were established from the parents of year class 1993. Sires were mated to dams using either paternal nested mating or partial factorial mating designs. Each year class consisted of 94 to 197 full-sib families established from the mating of 37 to 95 sires with 79 to 129 dams. After hatching, fingerlings from the same full-sib family were held in one or more family tanks until the fingerlings reached tagging size (mean body weight of approximately 50 g). During the tagging, full-sibs from each family were randomly sampled and divided into two or three batches that were reared either at the freshwater nucleus station (defined as “breeding environment”or BE) or at one or two seawater stations (defined as “production environment” or PE) at the Baltic Sea. When the fish were 2-year-old, they were individually weighed at BE (trait: BW BE , in g) and PE (trait: BW PE , in g) stations. The total number of records analysed was 22,175 individuals for BW BE and 20,865 individuals for BW PE (Table 1). The average BW BE (SD) and BW PE (SD) were 1094 (363.9) g. and 1050.0 (334.5) g, respectively. The pedigree was traced back to the parents (the founders) of the 1990 year class. The ancestors back to the founder population of the 1990 year class were included in the pedigree. Genetic Analysis Reaction norm model. A reaction norm model was used to estimate genetic (co)variance for ES (regression slope) for body weights recorded on 2-year-old fish. (Co)variance components of all analyses were estimated using restricted maximum likelihood in ASReml version 3.0 [24]. Approximate standard errors were calculated with ASReml following Fisher et al. [25]. In addition to the analysis of observed body weights, the analysis was also performed with log-transformed body weights. This was to test the hypothesis that genetic variance in ES may be influenced by a scale effect, typically observed for body weight in fish species, i.e., increasing variance for BW with increasing mean for BW. For instance, parallel reaction norms for genotypes (no genetic variance for slopes) with different intercepts are in fact translated into different magnitudes of sensitivity if change in body weight is calculated as a percentage change in the trait mean. The log-transformation reduces such scale effect [26]. Table 1. Population structure. Subpopulation I Subpopulation II 1996 1999 1997 2000 Population structure Sires, dams 57, 129 37, 94 65, 79 95, 121 Sires per dam, mean (range) 1.00 (1–1) 1.00 (1–1) 2.41 (1–3) 1.63 (1–3) Dams per sire, mean (range) 2.26 (1–4) 2.54 (1–4) 2.93 (1–5) 2.06 (1–5) Full-sib families, family tanks 129, 129 94, 135 191, 259 197, 197 Number of fish with records Freshwater nucleus station 4994 3084 8099 5998 Fish per full-sib family 38.7 32.8 42.4 30.4 Seawater station 2573 2442 8351 7499 Fish per full-sib family 19.9 26.0 43.7 38.1 doi:10.1371/journal.pone.0135133.t001 Genetics of Macroenvironmental Sensitivity PLOS ONE | DOI:10.1371/journal.pone.0135133 August 12, 2015 3/17 The reaction norm model was: yhijklmn ¼bint þbslXhþYC SITE SEX MATijklþ am;int þam;slXhþcn;int þcn;slXhþehijklmn;ð1Þ where yis the observation (body weight or log body weight) of the m th individual. The β int and β sl are the fixed regression coefficients for the population intercept (int) and slope (sl), respectively. The X h is the regressor for the environments (X h = 0 and 1) in which the intercept was placed at X h = 0. The fixed effect YC×SITE×SEX×MAT was included in the model to correct for the interaction of the i th year class (YC,i= 1996, 1997, 1999, 2000), the j th test station (SITE,j= 1: BE, 2 to 4: sea-test stations), the k th sex (SEX,k= 1: male, 2: female, or 9: unknown), and the l th maturity (MAT,l= 2: mature at 2-year-old, 3: mature at 3-year-old, 9: unknown). The ais the random additive genetic effect of intercept (int) and slope (sl) of reaction norm, aint asl "# ~ MVN[0,AG RN ], where Ais the additive genetic relationship matrix, G RN is genetic covariance matrix from the reaction norm model, and MVN is multivariate normal distribution. The c n is the random full-sib tank effect (unique numbers in different year classes), explaining an effect common to full-sibs other than additive genetics (tank effect due to the separate rearing of the families prior to tagging and non-additive genetic effect), cint csl "# ~ MVN[0,IC RN ], where C RN is common environmental covariance matrix and Iis the identity matrix. The e~N(0, Is2 e1 0 0Is2 e2 "# ) is the random residual effect of an animal min environment hwith for each environment a different residual variance. The sire’s and offspring’s estimated breeding values (EBVs) for slope obtained from model (1) were used to illustrate the range of additive genetic values of slope available for selection. The magnitude and the sign of a genetic correlation between the slope and intercept, and genetic variance for the intercept, can change depending on which environment the intercept is defined. Hence, the model was run twice, either with PE (Xh1=0)orBE(Xh2= 0) as the intercept environment. To illustrate the covariance between EBVs of slope and intercept, sire’s EBVs for the slope when the intercept were placed at PE were ranked and a total of fifteen sires with the highest, close to zero, and the lowest EBVs for the slope were chosen for plotting the reaction norm. Genetic characteristics of macroenvironmental sensitivity. The strict sense of heritability for ES is the ratio between additive genetic variance of a slope to phenotypic variance of the slope. Due to the lack of phenotypic variance of the slope, it is not possible to calculate the heritability for ES. Three alternative parameters were used here to describe genetic characteristics of ES. Following Scheiner [27], heritability for ES (h2 ES) was calculated as: h2 ES ¼s2 GxE s2 P;ANOVA ;ð2Þ where s2 GxE is genotype by environment interaction variance. The s2 GxE is equal to the standardized additive genetic variance of the slope (^ s2 a;sl ^ s2 X) that is independent from different scales of an environmental variable (X). The ^ s2 Xis the variance of X[28], i.e., ^ s2 Xis 0.5 in this study, as the possible values of Xin this study are 0 and 1. The s2 P;ANOVA is total phenotypic variance across environments, in Scheiner's approach calculated from an analysis of variance (ANOVA) [27]. Because s2 P;ANOVA may not be available from the reaction norm model, we adopted Scheiner’s heritability by Genetics of Macroenvironmental Sensitivity PLOS ONE | DOI:10.1371/journal.pone.0135133 August 12, 2015 4/17 replacing the s2 P;ANOVA by ^ s2 P;Total ¼ðnBE 1Þ^ s2 PBW;BE þðnPE 1Þ^ s2 PBW;PE þnBE nPEðWBE WPE Þ2=ðnBE þnPE Þ ðnBE þnPE 1Þ  ,wherenis the number of animals with a record for an animal trait, ^ s2 PBW is phenotypic variance of the trait and Wis the mean. Note that neither s2 P;ANOVA nor ^ s2 P;Total is the phenotypic variance of the environmental sensitivity. Thus, h2 ES is more descriptive rather than a predictive parameter [28]. Furthermore, the definition of heritability in Eq (2) does not coincide with the heritability being the regression of breeding value on phenotype. Because the expression in Eq (1) is not predictive for response to selection, we defined a second measure called coheritability following selection index principles. The phenotype (P)ofan individual that includes reaction norm parameters can be defined as (1). We assume no covariances among a,c, and e, because there is no relationship among a,c, and e. The phenotypic variance (s2 P) of a trait is: s2 P¼s2 a;int þ2Xsa;int;sl þX2s2 a;sl þs2 c;int þ2Xsc;int;sl þX2s2 c;sl þs2 eð3Þ Estimated additive genetic effect of slope (^ asl) is equal to the regression on Pdeviated from the population mean, or ^ asl ¼bðPmÞ. The regression coefficient (b) of the breeding value of slope on phenotype is: b¼covðasl;PÞ s2 P¼sa;int;sl þXs2 a;sl s2 Pð4Þ The bin Eq (4)is“coheritability”for ES. The term coheritability is used instead of heritability because coheritability defines the inheritance of association between ES and BW in one environment. The additive genetic covariance between intercept and slope changes along the levels of the environmental factor (X). Hence, the magnitude and sign of coheritability is dependent on the value of X. A negative coheritability is possible if the absolute of −σ a,int, sl is greater than Xs2 a;sl and/or Xis negative and absolute Xs2 a;sl is greater than σ a,int, sl . The sign of the coheritability explains the change in correlated response of ES when mass selection for higher phenotypic values is performed. When intercept is placed to the environment, in which selection is practised on P,Xbecomes zero, leading to: b¼sa;int;sl s2 a;int þs2 c;int þs2 e;int ¼sa;int;sl s2 Ph ;ð5Þ where s2 Phis the phenotypic variance of BW in the selection environment h. This value is equal to s2 P;int described above. Finally, to understand the potential genetic response in ES, the accuracy (r IH ) of predicting breeding value for ES when a selection criterion is BW in one of the environments is equal to: rIH ¼ffiffiffiffiffiffiffi bg s2 a;sl s¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ðsa;int;sl þXs2 a;slÞ2 s2 Ps2 a;sl s¼sa;int;sl þXs2 a;sl sPsa;sl ;ð6Þ where g is sa;int;sl þXs2 a;sl. The Eq (6) is equivalent to the equation derived by Kolmodin and Bijma [29]. Coheritability of ES changes depending on a degree and forms of GxE. To demonstrate the relationship between coheritability and GxE in both forms, i.e. genotype re-ranking and Genetics of Macroenvironmental Sensitivity PLOS ONE | DOI:10.1371/journal.pone.0135133 August 12, 2015 5/17 heterogeneity of variances, Eq (5) is rearranged as (see S1 Appendix): b¼ sa;ðE1;E2Þs2 a;int s2 P;int ¼rgsa;E1sa;E2s2 a;int s2 P;int ;ð7Þ where r g is the genetic correlation between traits measured in two different environments (E 1 and E 2 ). The r g different from unity indicates a present of genotype re-ranking. The rg¼sa;ðE1;E2Þ sa;E1sa;E2 , where sa;ðE1;E2Þ,sa;E1and sa;E2are additive genetic covariance and additive genetic standard deviation in E 1 and E 2 , respectively. Assume that there is no heterogeneity of additive genetic variances (s2 a;E1=s2 a;E2=s2 a;int) and s2 P;E1=s2 P;E2=s2 P;int,Eq(7) is equal to: b¼h2rgh2¼h2ðrg1Þð8Þ Eq (8) shows regression of coheritability on genotype re-ranking, where the slope and intercept is equal to the h 2 of a trait. If the h 2 = 0.3 and r g varies from -1 to 1, the magnitude of coheritability, regardless of the sign increases when the genetic correlation differs from the unity and the coheritability is at maximum when the genetic correlation equals -1. Placing the intercept (X= 0) in either E 1 or E 2 does not influence the magnitude of the coheritability (Fig 1). Assume there is heterogeneity of additive genetic variances (s2 a;E16¼ s2 a;E2), Eq (7) is equal to: b¼ðhE1hE2Þrgh2 int ð9Þ In contrast to Eq (8), Eq (9) shows that heterogeneity of additive genetic variances results in different values of coheritability because h 2int changes, depending on the intercept (X=0) which is placed in either E 1 or E 2 as shown in Fig 2. Calculation of reaction norm parameters For the intercept of reaction norms (body weight at the intercept environment), heritability (h2 int) and common environmental effect (c2 int) were calculated as: h2 int ¼^ s2 a;int=^ s2 P;int, c2 int ¼^ s2 c;int=^ s2 P;int,where^ s2 P;int is equal to ^ s2 a;int þ^ s2 c;int þ^ s2 e;int, which is the phenotypic variance of BW in the intercept environment when X= 0. For the slope of reaction norms, the h2 ES was calculated using Eq (1) while the coheritability was calculated using Eq (5), assuming that intercept is placed in the selection environment (X=0)asitreflects actual situation of selective breeding in aquaculture. The coheritability was calculated twice, either having BE or PE as the selection environment. The genetic correlation between intercept and slope (r g(int, sl) ) was calculated as: rgðint;slÞ¼^ saint;sl ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ^ s2 a;int^ s2 a;sl p. Comparison to previous studies. There are no previous studies using reaction norm model to study environmental sensitivity in aquaculture. Hence, to compare the reaction norm parameters of the present study to the previous GxE studies, (co)variance components of the previous studies calculated using multi-trait model were used to calculate the (co)variance components for reaction norm parameters (see S1 and S2 Appendixes). The h2 ES, coheritability and r g,(int,sl) were calculated. The choice of GxE papers in aquaculture species was based on the following: growth traits as the studied trait, at least 30 full-sib families, and providing all the parameters needed for the calculations. In total, 17 studies were found, the species covering Arctic charr (Salvelinus alpinus)[30], Atlantic cod (Gadus morhua)[31], Common carp Genetics of Macroenvironmental Sensitivity PLOS ONE | DOI:10.1371/journal.pone.0135133 August 12, 2015 6/17 (Cyprinus carpio)[32], European whitefish (Coregonus lavaretus)[33], European sea bass (Dicentrarchus labrax)[34], Nile tilapia (Oreochromis niloticus)[35,36], Pacific white shrimp (Litopenaeus vannamei)[37], Rainbow trout [14,16–21,38], Shiranus tilapia (Oreochromis shiranus)[39]. Results In the Finnish data, GxE of BW existed in both forms; re-ranking as indicating by r g of BW between BE and PE was 0.73, and heterogeneity of genetic variances (Table 2). Both phenomena induce genetic variation for ES. Genetic variance for macroenvironmental sensitivity The additive genetic variance of slope of BW (9584) was considerable and the h2 int was moderate in both environments (0.23 for PE and 0.25 for BE), the h2 ES was low (0.07) implying the additive genetic variance of ES explains only a small proportion relative to total phenotypic variance of BW across environments. Similarly, the coheritability for ES was low and negative, i.e., -0.06 for PE and -0.08 for BE. Thus the accuracy of selection for ES of BW is very low when applying individual selection for BW in one of the environments. Fig 1. Relationship between coheritability and the genetic correlation between environments. The input parameters are a trait with phenotypic variance of 1 and heritability of 0.3 which are the same across two environments. The genetic correlation ranges from -1 to 1. doi:10.1371/journal.pone.0135133.g001 Genetics of Macroenvironmental Sensitivity PLOS ONE | DOI:10.1371/journal.pone.0135133 August 12, 2015 7/17 The magnitude of heritability for ES of log-transformed BW was similar to the heritability for ES of observed BW. The additive genetic variance of slope of log-transformed BW was 69% in PE and 65% in BE of the additive genetic variance of intercept, relatively slightly higher than on the observed scale (57% in PE and 53% in BE). This indicates that simple scale effects did not generate genetic variation for macroenvironmental sensitivity. When PE was the intercept, the slope EBVs for sires (-234.5 to 228.8) and animals (-210.6 to 199.8) ranged from strongly negative to positive (Fig 3). A positive slope implies that EBVs for BW are elevated in BE as compared to EBVs for BW in the intercept environment PE. If BE was the intercept environment, the EBVs of slope would change sign. Genetic correlation between intercept and slope The significant r g(int, sl) (SE) between BW in a given environment and ES ranged from -0.33 (0.10) to -0.41 (0.10), depending on the environment used as the intercept environment (Table 2). The sires with steep slope EBVs had high intercept (at PE or BE) (Fig 4). The sires with flat slope EBV had low intercept EBV. The negative correlations from log-transformed Fig 2. Relationship between coheritability, heterogeneity of additive genetic variances and the genetic correlation between environments. The input parameters are a trait that has different magnitudes of heritability; 0.1 (line with circles) and 0.5 (line with squares) in two different environments (E 1 or E 2 ) and phenotypic variances are equal to 1. The genetic correlation ranged from -1 to 1. Line graphs show that heterogeneity of additive genetic variances results in different values of coheritability because h 2int (0.1 or 0.5) changes, depending on the intercept which is placed in either E 1 or E 2 . doi:10.1371/journal.pone.0135133.g002 Genetics of Macroenvironmental Sensitivity PLOS ONE | DOI:10.1371/journal.pone.0135133 August 12, 2015 8/17 data (-0.40 to -0.42) remained similar to untransformed data. This shows that rapid growth in one environment is genetically related to elevated sensitivity across environments. Genetic parameters calculated from the previous GxE studies In the previous aquaculture studies on GxE in growth, the h2 ES ranged from 0.010 to 0.207 (median = 0.110) while the coheritability ranged from -0.600 to 0.500 (median = -0.011; intercept at E 1 and = -0.078; intercept at E 2 )(Fig 5). The r g(int, sl) between growth traits and ES varied from -1.00 to 0.94 (median = -0.386) as shown in Fig 6. Discussion Genetic variation for macroenvironmental sensitivity Substantial additive genetic variance of macroenvironmental sensitivity (ES) for both observed and log-transformed body weight (BW) indicates potential for genetic response to selection on ES. After the log-transformation of BW, the variance components of ES were reduced but h2 ES remained similar to the one estimated from the untransformed data. This indicates that scale effects (high variance depending on high mean) do not explain the genetic effects for ES. Table 2. Variance components and genetic correlations between intercept and slope from the reaction norm (RN) models. Parameter Intercept Production Breeding Body weight s ^ a;int 216754.9 18040.0 s ^ a;sl 29584.3 9584.3 s ^ c;int 23041.1 3227.3 s ^ c;sl 23822.7 3822.7 s ^ e;int 253197.8 51092.8 s ^ P;Total 273168.5 73168.5 h2 int 0.23 (0.03) 0.25 (0.03) h2 ES 0.07 (0.03) 0.07 (0.03) c2 int 0.04 (0.01) 0.04 (0.01) Coheritability -0.06 (0.02) -0.08 (0.02) r g(int, sl) -0.33 (0.10) -0.41 (0.10) Log(body weight) s ^ a;int 20.016 0.017 s ^ a;sl 20.011 0.011 s ^ c;int 20.003 0.003 s ^ c;sl 20.004 0.004 s ^ e;int 20.092 0.071 s ^ P;Total 20.102 0.102 h2 int 0.15 (0.02) 0.18 (0.03) h2 ES 0.06 (0.02) 0.06 (0.02) c2 int 0.03 (0.01) 0.04 (0.01) r g(int, sl) -0.40 (0.10) -0.42 (0.10) doi:10.1371/journal.pone.0135133.t002 Genetics of Macroenvironmental Sensitivity PLOS ONE | DOI:10.1371/journal.pone.0135133 August 12, 2015 9/17 9. Ellen E, Star L, Uitdehaag K, Brom F, KlopčičM, Reents R, et al. (2009) Robustness as a breeding goal and its relation with health, welfare and integrity. In: Klopcic M, Reents R, Philipsson J, Kuipers A, editors. Breeding for robustness in cattle. Wageningen Academic Publishers. pp. 45–53. 10. De Jong G (1990) Quantitative genetics of reaction norms. J Evolution Biol 3: 447–468. 11. Schmalhausen II (1949) Factors of evolution: the theory of stabilizing selection. Oxford, English: Blakiston. 327 p. 12. Finlay K, Wilkinson G (1963) The analysis of adaptation in a plant-breeding programme. Crop Pasture Sci 14: 742–754. 13. Falconer DS, Mackay TFC (1996) Introduction to quantitative genetics. 4th Edition ed. UK: Longman, Essex. pp. 464 pp. 14. Fishback AG, Danzmann RG, Ferguson MM, Gibson JP (2002) Estimates of genetic parameters and genotype by environment interactions for growth traits of rainbow trout (Oncorhynchus mykiss)as inferred using molecular pedigrees. Aquaculture 206: 137–150. 15. Kause A, Ritola O, Paananen T (2004) Breeding for improved appearance of large rainbow trout in two production environments. Aquac Res 35: 924–930. 16. Kause A, Ritola O, Paananen T, Mäntysaari E, Eskelinen U (2003) Selection against early maturity in large rainbow trout Oncorhynchus mykiss: the quantitative genetics of sexual dimorphism and genotype-by-environment interactions. Aquaculture 228: 53–68. 17. Le Boucher R, Quillet E, Vandeputte M, Lecalvez JM, Goardon L, Chatain B, et al. (2011) Plant-based diet in rainbow trout (Oncorhynchus mykiss Walbaum): Are there genotype-diet interactions for main production traits when fish are fed marine vs. plant-based diets from the first meal? Aquaculture 321: 41–48. 18. Pierce LR, Palti Y, Silverstein JT, Barrows FT, Hallerman EM, Parsons J (2008) Family growth response to fishmeal and plant-based diets shows genotype× diet interaction in rainbow trout (Oncorhynchus mykiss). Aquaculture 278: 37–42. 19. Sae-Lim P, Kause A, Mulder HA, Martin KE, Barfoot AJ, Parsons J, et al. (2013) Genotype-by-environment interaction of growth traits in rainbow trout (Oncorhynchus mykiss): A continental scale study. J Anim Sci 91: 5572–5581. doi: 10.2527/jas.2012-5949 PMID: 24085417 20. Sylvén S, Rye M, Simianer H (1991) Interaction of genotype with production system for slaughter weight in rainbow trout (Oncorhynchus mykiss). Livest Prod Sci 28: 253–263. 21. Kause A, Ritola O, Paananen T, Wahlroos H, Mäntysaari EA (2005) Genetic trends in growth, sexual maturity and skeletal deformations, and rate of inbreeding in a breeding programme for rainbow trout (Oncorhynchus mykiss). Aquaculture 247: 177–187. 22. Via S, Gomulkiewicz R, De Jong G, Scheiner SM, Schlichting CD, Van Tienderen PH (1995) Adaptive phenotypic plasticity: consensus and controversy. Trends Ecol Evol 10: 212–217. PMID: 21237012 23. Van Tienderen PH, Koelewijn HP (1994) Selection on reaction norms, genetic correlations and constraints. Genet Res 64: 115–125. PMID: 7813902 24. Gilmour AR, Gogel BJ, Cullis BR, Thompson R (2009) ASReml User Guide Release 3.0. NSW Department of Industry and Investment. 25. Fischer T, Gilmour A, Werf J (2004) Computing approximate standard errors for genetic parameters derived from random regression models fitted by average information REML. Genet Sel Evol 36: 363– 369. PMID: 15107271 26. Lande R (1979) Quantitative genetic analysis of multivariate evolution, applied to brain: body size allometry. Evolution: 402–416. 27. Scheiner SM, Lyman RF (1989) The genetics of phenotypic plasticity I. Heritability. J Evolution Biol 2: 95–107. 28. Scheiner SM (1993) Genetics and evolution of phenotypic plasticity. Annu Rev Ecol Syst 24: 35–68. 29. Kolmodin R, Bijma P (2004) Response to mass selection when the genotype by environment interaction is modelled as a linear reaction norm. Genet Sel Evol 36: 435–454. PMID: 15231233 30. Nilsson J (1990) Heritability estimates of growth-related traits in Arctic charr (Salvelinus alpinus). Aquaculture 84: 211–217. 31. Kolstad K, Thorland I, Refstie T, Gjerde B (2006) Genetic variation and genotype by location interaction in body weight, spinal deformity and sexual maturity in Atlantic cod (Gadus morhua) reared at different locations off Norway. Aquaculture 259: 66–73. 32. Ninh NH, Ponzoni RW, Nguyen NH, Woolliams JA, Taggart JB, McAndrew BJ, et al. (2011) A comparison of communal and separate rearing of families in selective breeding of common carp (Cyprinus carpio): Estimation of genetic parameters. Aquaculture 322: 39–46. Genetics of Macroenvironmental Sensitivity PLOS ONE | DOI:10.1371/journal.pone.0135133 August 12, 2015 16 / 17 33. Cheryl D, Antti K, Juha K (2007) Breeding salmonids for feed efficiency in current fishmeal and future plant-based diet environments. Genet Sel Evol 39: 431–446. PMID: 17612482 34. Dupont-Nivet M, Vandeputte M, Vergnet A, Merdy O, Haffray P, Chavanne H, et al. (2008) Heritabilities and GxE interactions for growth in the European sea bass (Dicentrarchus labrax L.) using a markerbased pedigree. Aquaculture 275: 81–87. 35. Charo-Karisa H, Komen H, Reynolds S, Rezk MA, Ponzoni RW, Bovenhuis H (2006) Genetic and environmental factors affecting growth of Nile tilapia (Oreochromis niloticus) juveniles: Modelling spatial correlations between hapas. Aquaculture 255: 586–596. 36. Khaw HL, Ponzoni RW, Hamzah A, Abu-Bakar KR, Bijma P (2012) Genotype by production environment interaction in the GIFT strain of Nile tilapia (Oreochromis niloticus). Aquaculture 326: 53–60. 37. Castillo-Juárez H, Casares JCQ, Campos-Montes G, Villela CC, Ortega AM, Montaldo HH (2007) Heritability for body weight at harvest size in the Pacific white shrimp, Penaeus (Litopenaeus)vannamei, from a multi-environment experiment using univariate and multivariate animal models. Aquaculture 273: 42–49. 38. Tobin D, Kause A, Mäntysaari EA, Martin SA, Houlihan DF, Dobly A, et al. (2006) Fat or lean? The quantitative genetic basis for selection strategies of muscle and body composition traits in breeding schemes of rainbow trout (Oncorhynchus mykiss). Aquaculture 261: 510–521. 39. Maluwa AO, Gjerde B, Ponzoni RW (2006) Genetic parameters and genotype by environment interaction for body weight of Oreochromis shiranus. Aquaculture 259: 47–55. 40. Janssens M (1979) Coheritability: its relation to correlated response, linkage, and pleiotropy in cases of polygenic inheritance. Euphytica 28: 601–608. 41. Hill W, Mulder H (2010) Genetic analysis of environmental variation. Genet Res 92: 381–395. 42. Janhunen M, Kause A, Vehvilainen H, Jarvisalo O (2012) Genetics of microenvironmental sensitivity of body weight in rainbow trout (Oncorhynchus mykiss) selected for improved growth. PLoS One 7: e38766. doi: 10.1371/journal.pone.0038766 PMID: 22701708 43. Mulder H, Bijma P, Hill W (2007) Prediction of breeding values and selection response with genetic heterogeneity of environmental variance. Genetics 175: 1895–1910. PMID: 17277375 44. Sae-Lim P, Kause A, Janhunen M, Vehviläinen H, Koskinen H, Gjerde B, et al. (2015) Genetic (co) variance of rainbow trout (Oncorhynchus mykiss) body weight and its uniformity across production environments. Genet Sel Evol 47: 46. doi: 10.1186/s12711-015-0122-8 PMID: 25986847 45. Sae-Lim P, Gjerde B, Nielsen HM, Mulder H, Kause A (2015) A review of genotype-by-environment interaction and micro-environmental sensitivity in aquaculture species. Rev Aquaculture: In press. 46. Jinks J, Connolly V (1973) Selection for specific and general response to environmental differences. Heredity 30: 33–40. 47. Walsh B, Lynch M (2013) Evolution and selection of quantitative traits: II. Advanced topics in breeding and evolution. Available: http://nitro.biosci.arizona.edu/zbook/NewVolume_2/newvol2.html. Accessed 12 July 2013. 48. Rosielle A, Hamblin J (1981) Theoretical aspects of selection for yield in stress and non-stress environment. Crop Sci 21: 943–946. 49. Brascamp E. Selection indices with constraints; 1984. pp. 645–654. 50. Sae-Lim P, Komen H, Kause A, Mulder H (2014) Identifying environmental variables explaining genotype-by-environment interaction for body weight of rainbow trout (Onchorynchus mykiss): reaction norm and factor analytic models. Genet Sel Evol 46: 16. doi: 10.1186/1297-9686-46-16 PMID: 24571451 51. Flaherty M, Szuster B, Miller P (2000) Low salinity inland shrimp farming in Thailand. Ambio 29: 174– 179. Genetics of Macroenvironmental Sensitivity PLOS ONE | DOI:10.1371/journal.pone.0135133 August 12, 2015 17 / 17