Genotype-by-environment interaction of growth traits in rainbow trout (Oncorhynchus mykiss): A continental scale study1

Abstract

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.

Estimating genetic parameters and genotype-by-environment interactions in body traits of turbot in two different rearing environments

“…Furthermore, it is optimum to record the sib's performance in the production environment (IFSLT environment or other environment similar to the IFS environment) to increase the accuracy of selection and for future genetic evaluations of later generations. The genetic correlation across environments for K (0.78) was near the break-even correlation (≥ 0.70) in aquaculture, as suggested by Sae-Lim et al (2013). The break-even genetic correlation is the intersection of genetic correlations where the genetic gains from different breeding strategies are identical and can be used as a criterion to select a proper breeding strategy (Mulder et al, 2006).…”

“…A sea bass experiment was performed under two water temperatures by Saillant et al (2006), and a significant G × E interaction was reported. Similar G × E interactions between different environments have been discovered in other aquaculture species (Evans and Langdon, 2006;Kvingedal et al, 2010;Sae-Lim et al, 2013;Trọng et al, 2013). Thus, a G × E interaction may occur in turbot when they are reared in different environments under different water temperatures.…”

“…The G × E interaction is a phenomenon in which different genotypes respond differently to changes in an environment, which results in different consequences, including the re-ranking effect, the scaling effect, heterogeneous heritability, and heterogeneous correlations between traits (measured within environment) across environments (Calus, 2006;Lynch and Walsh, 1998;Mulder, 2007;Sae-Lim et al, 2013). The presence of a re-ranking effect can change the ranking of genotypes in different environments (Sae-Lim et al, 2015).…”

“…LRT = −2[ln(L) r − ln(L) f ], where ln(L) r and ln(L) f are the natural logarithms of likelihood from the reduced model (without additive genetic effect or effect common to full-sib families) and the full model (with additive genetic effect or effect common to full-sib families), respectively (Lynch and Walsh, 1998). The asymptotic distribution of the likelihood ratio follows the chi-square (χ 2 ) distribution with a 50:50 mixture of degrees of freedom between 0 and 1 (Sae- Lim et al, 2013;Stram and Lee, 1994). The χ 2 value at the 5% significance level is 2.71.…”

Rapid assessment of genotype-by-environment interactions and heritability for growth rate in aquaculture species using in vitro fertilisation and DNA tagging

“…Significant GxE interactions for growth in aquaculture have occurred due to differences in temperature, salinity, stocking density and between recirculation, pond and cage facilities (Sylven et al, 1991;Myers et al, 2001;Ponzoni et al, 2005;Saillant et al, 2006;Eknath 2007;Khaw et al, 2009;Mas-Muñoz et al, 2013) with estimates for body weight at harvest in different environments as low as 0.19+0.13 (Sae-Lim et al, 2013). There is also an emerging need for GxE assessment of growth rate in newly formulated diets with significant re-ranking of family performances between diets observed by Pierce et al (2008), Dupont-Nivet et al…”