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Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 407471
Title Bias and precision of estimates of genotype-by-environment interaction: A simulation study
Author(s) Sae-Lim, P.; Komen, J.; Kause, A.
Source Aquaculture 310 (2010)1-2. - ISSN 0044-8486 - p. 66 - 73.
Department(s) Animal Breeding and Genetics
Publication type Refereed Article in a scientific journal
Publication year 2010
Keyword(s) trout oncorhynchus-mykiss - body-composition traits - large rainbow-trout - genetic-parameters - breeding schemes - fish-meal - selection - growth - l. - variance
Abstract Re-ranking of genotypes across environments is a form of genotype-by-environment (G x E) interaction with serious consequences for breeding programmes. The degree of such G x E interaction can be estimated using the genetic correlation (r(g)) between measurements in two environments for a given trait. When r(g) is lower than 0.8, G x E interaction is commonly considered to be biologically significant. Here a stochastic simulation was used to study the impact of population structure on bias and precision of genetic correlation estimates between two environments. Simulated populations resulted from a nested mating design (1 sire to 2 dams). Simulated r(g) was 0.0, 0.5, or 0.8. A trait with heritability (h(2)) of either 0.3 or 0.1 in both environments was simulated. Simulation results show that genetic correlation estimates are biased downward especially when the simulated rg is 0.8, heritability is 0.1, and family size is less than 10. A downward biased genetic correlation estimate incorrectly suggests the existence of G x E interaction. This can lead to the erroneous conclusion that a multi-environment breeding programme is needed. The optimal design with the lowest mean square error fort., for a trait with low h(2) requires a large family size (20-25) and a low number of families (100-80 or 50-40 for population size fixed to 2000 and 1000 animals, respectively). For traits with moderate h(2), the optimal family size is 10 with 200 or 100 families for population size fixed to 2000 and 1000, respectively. We also studied the effect of selective mortality on G x E estimates. However, schemes with unequal family sizes due to differences between families in survival produced similar results for the optimum design as schemes with equal family sizes. Equal-family-size design can thus be used to determine the optimal design for estimating G x E interaction. Our study can be used as a guideline for estimating a genetic correlation for practical breeding programmes
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