P1010 Genotype by environment interaction and genetic heterogeneity of environmental variance of body weight at harvest in genetically improved farmed tilapia (Oreochromis niloticus) reared in 3 different countries
Traditionally genotype by environment interaction studies have dealt with changes in external environment. In this experiment an attempt was made to alter internal environment and keep external environment constant. Cockerels from each of six different commercial stocks were injected with 0,1,2 and 4 mgs hydrocortisone acetate per 100 gms body weight at 14 days of age. This type of hormonal treatment was shown to release additional variability in growth without producing any stock-treatment interaction at the level of means. The results indicate a possible new avenue for future research.
Chapter 8’s focus is on the genetic architecture of complex traits determined jointly by multiple genes and environmental factors. Sometimes called quantitative genetics, the basic concepts include components of genetic and environmental variance, genotype-by-environment interaction, genotype-by-environment association, correlation between relatives, and broad-sense and narrow-sense heritability. It distinguishes between physiological and statistical epistasis, and it shows why the former can be large while the latter may be negligible. Various types of artificial selection are considered, and individual truncation selection is examined in detail, culminating in the famous prediction equation R = h
2
S. Special topics include genomic selection, correlated response, selection limits, and the heritability of liability of threshold traits.
AbstractThe systematic use of the same genotype in several different environments provides information that can be used to estimate genotype by environment interaction (G ✕ E) variances and parameters. Data from the UK Suffolk Sire Referencing Scheme Ltd were used to investigate a range of sire and dam by environment interactions in lamb weight (at 8 weeks and scanning) and body composition traits (muscle and fat depth). These interactions were calculated in a DFREML mixed model containing direct additive, maternal additive, maternal environmental random variance components and the covariance between direct and maternal additive effects. Sire interactions with year, flock and flock-year and dam effects within and between litters were investigated. The addition of all G ✕ E (co)variance components resulted in an improved fit of the model for all traits. Sire interactions accounted for between 2 and 3% of the phenotypic variance in all traits, usually at the expense of both additive effects. Maternal litter environmental variance components ranged from 10% (fat depth and muscle depth) to 20% (8-week weight) of phenotypic variance. Most of this variation was found in the residual component of variance when the term was omitted from the model. When fitting sire G✕ E components in a model the covariance between direct and maternal additive genetic effects, as a proportion of phenotypic variance, was reduced to a low level (from –0·36 to –0·08 for 8-week weight). Genotype by environment interactions form a significant source of variation in lamb growth and composition traits and reduce the high negative correlation between additive effects found previously in these traits.
Breeding for robustness: investigating the genotype-by-environment interaction and micro-environmental sensitivity of Genetically Improved Farmed Tilapia (Oreochromis niloticus
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