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ARS Home » Northeast Area » Leetown, West Virginia » Cool and Cold Water Aquaculture Research » Research » Publications at this Location » Publication #304862
Title: Genotype-by-environment interaction of growth traits in rainbow trout (Oncorhynchus mykiss): A continental scale study

Author
item SAE-LIM, P - Wageningen Agricultural University
item KASUE, A - Mtt Agrifood Research Finland
item MULDER, H - Wageningen Agricultural University
item MARTIN, K - Troutlodge, Inc
item BARFOOT, A - Troutlodge, Inc
item PARSONS, J - Troutlodge, Inc
item DAVIDSON, J - Freshwater Institute
item Rexroad, Caird
item VAN ARENDONK, J.A. - Wageningen Agricultural University
item KOMEN, H - Wageningen Agricultural University

Submitted to: Journal of Animal Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/11/2013
Publication Date: 10/1/2013
Citation: Sae-Lim, P., Kasue, A., Mulder, H.A., Martin, K.E., Barfoot, A.J., Parsons, J.E., Davidson, J., Rexroad III, C.E., Van Arendonk, J.M., Komen, H. 2013. Genotype-by-environment interaction of growth traits in rainbow trout (Oncorhynchus mykiss): A continental scale study. Journal of Animal Science. 91:5572-5581. DOI:10.2527/jas2012-5949.

Interpretive Summary: Fish performance in terms of growth is heavily dependent on genetics, environment, and the interactions of genetics and environment. Our aim was to evaluate the performance of rainbow trout from 100 families from a single population in 4 environments on 3 different continents. Performance sites included the location of the selection program in Washington State, a recirculation aquaculture system in West Virginia, a high-altitude farm in Peru, and a cold-water farm in Germany. Strong genetic x environment interactions were observed for early body weight at tagging, body weight at harvest, and the rate of growth in this interval. Accounting for these interactions in selective breeding will positively impact genetic gain in environments outside the site of selection.

Technical 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.