Genetic (co)variance of rainbow trout (Oncorhynchus mykiss) body weight and its uniformity across production environments.

Panya Sae-Lim, Antti Kause, Matti Janhunen, Harri Vehviläinen, Heikki Koskinen, Bjarne Gjerde, Marie Lillehammer, Han A Mulder
Author Information
  1. Panya Sae-Lim: Nofima Ås, Osloveien 1, P.O. Box 210, NO-1431 Ås, Norway. panya.sae-lim@nofima.no.
  2. Antti Kause: Natural Resources Institute Finland (LUKE), Biometrical Genetics, FI-31600, Jokioinen, Finland. antti.kause@luke.fi.
  3. Matti Janhunen: Natural Resources Institute Finland (LUKE), Biometrical Genetics, FI-31600, Jokioinen, Finland. matti.janhunen@luke.fi.
  4. Harri Vehviläinen: Natural Resources Institute Finland (LUKE), Biometrical Genetics, FI-31600, Jokioinen, Finland. harri.vehvilainen@luke.fi.
  5. Heikki Koskinen: Natural Resources Institute Finland (LUKE), Aquaculture Unit, FI-72210, Tervo, Finland. heikki.koskinen@luke.fi.
  6. Bjarne Gjerde: Nofima Ås, Osloveien 1, P.O. Box 210, NO-1431 Ås, Norway. bjarne.gjerde@nofima.no.
  7. Marie Lillehammer: Nofima Ås, Osloveien 1, P.O. Box 210, NO-1431 Ås, Norway. marie.lillehammer@nofima.no.
  8. Han A Mulder: Animal Breeding and Genomics Centre, Wageningen University, P.O. Box 338, 6700 AH, Wageningen, the Netherlands. han.mulder@wur.nl.

Abstract

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 (rg) 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 rg 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 (rg = 0.56) between BE and PE was moderate but greater after log-transformation, as expressed by the low rg (-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 rg 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 rg 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 rg.

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MeSH Term

Animals
Body Weight
Gene-Environment Interaction
Genetic Variation
Oncorhynchus mykiss
Phenotype

Word Cloud

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