Genotype-by-environment interaction of growth traits in rainbow trout (Oncorhynchus mykiss): a continental scale study.

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.

Defining desired genetic gains for rainbow trout breeding objective using analytic hierarchy process.

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. Questionnaire-A (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 to quantify (in % of a trait mean; G%) the degree to which 1 trait is preferred over the other. Individual preferences, social preferences, and consensus preferences (Con-P) were estimated using AHP and WGP. Desired gains were constructed by multiplying Con-P by 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 (FIL%), 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), FIL% (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.