Search

Background: A genotype-by-environment (G × E) interaction is defined as genotypes responding differently to different environments. In salmonids, G × E interactions can occur in different rearing conditions, including changes in salinity or temperature. However, water flow, an important variable that can influence metabolism, has yet to be considered for potential G × E interactions, although water flows differ across production stages. The salmonid industry is now manipulating flow in tanks to improve welfare and production performance, and expanding sea pen farming offshore, where flow dynamics are substantially greater. Therefore, there is a need to test whether G × E interactions occur under low and higher flow regimes to determine if industry should consider modifying their performance evaluation and selection criteria to account for different flow environments. Here, we used genotype-by-sequencing to create a genomic-relationship matrix of 37 Chinook salmon, Oncorhynchus tshawytscha, families to assess possible G × E interactions for production performance under two flow environments: a low flow regime (0.3 body lengths per second; bl s -1 ) and a moderate flow regime (0.8 bl s -1 )., Results: Genetic correlations for the same production performance trait between flow regimes suggest there is minimal evidence of a G × E interaction between the low and moderate flow regimes tested in this study, for Chinook salmon reared from 82.9 ± 16.8 g ( x ¯  ± s.d.) to 583.2 ± 117.1 g ( x ¯  ± s.d.). Estimates of genetic and phenotypic correlations between traits did not reveal any unfavorable trait correlations for size- (weight and condition factor) and growth-related traits, regardless of the flow regime, but did suggest measuring feed intake would be the preferred approach to improve feed efficiency because of the strong correlations between feed intake and feed efficiency, consistent with previous studies., Conclusion: This new information suggests that Chinook salmon families do not need to be selected separately for performance across different flow regimes. However, further studies are needed to confirm this across a wider range of fish sizes and flows. This information is key for breeding programs to determine if separate evaluation groups are required for different flow regimes that are used for production (e.g., hatchery, post smolt recirculating aquaculture system, or offshore)., (© 2024. The Author(s).) more...

The aim of this study was to estimate genetic parameters for lactation yields of milk (MY), fat (FY), protein (PY), and somatic cell score (SCS) of New Zealand dairy goats. The analysis used 64,604 lactation records from 23,583 does, kidding between 2004 and 2017, distributed in 21 flocks and representing 915 bucks. Estimates of genetic and residual (co) variances, heritabilities, and repeatabilities were obtained using a multiple-trait repeatability animal model. The model included the fixed effects of contemporary group (does kidding in the same flock and year), age of the doe (in years), and as covariates, kidding day, proportion of Alpine, Nubian, Toggenburg, and "unknown" breeds (Saanen was used as the base breed), and heterosis. Random effects included additive animal genetic and doe permanent environmental effects. Estimates of heritabilities were 0.25 for MY, 0.24 for FY, 0.24 for PY, and 0.21 for SCS. The phenotypic correlations between MY, FY, and PY ranged from 0.90 to 0.96, and the genetic correlations ranged from 0.81 to 0.93. These results indicate lactation yield traits exhibit useful heritable variation and that multiple trait selection for these traits could improve milk revenue produced from successive generations of New Zealand dairy goats., (© 2019 Japanese Society of Animal Science.) more...