Phenotypically Selective Genotyping Realizes More Genetic Gains in a Rainbow Trout Breeding Program in the Presence of Genotype-by-Environment Interactions
| Autor: | Torben Nielsen, Anders Christian Sørensen, Guosheng Su, Thinh Tuan Chu, Mogens Sandø Lund, Kristian Meier |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
Animal breeding Breeding program lcsh:QH426-470 selective genotyping Pedigree chart Biology genomic selection 03 medical and health sciences 0302 clinical medicine Genotype Genetics breeding program design Genotyping genotype-by-environment interactions Genetics (clinical) Selection (genetic algorithm) Original Research fish rainbow trout lcsh:Genetics 030104 developmental biology 030220 oncology & carcinogenesis Selective genotyping Molecular Medicine Rainbow trout |
| Zdroj: | Frontiers in Genetics, Vol 11 (2020) Chu, T T, Sørensen, A C, Lund, M S, Meier, K, Nielsen, T & Su, G 2020, ' Phenotypically Selective Genotyping Realizes More Genetic Gains in a Rainbow Trout Breeding Program in the Presence of Genotype-by-Environment Interactions ', Frontiers in Genetics, vol. 11, 866 . https://doi.org/10.3389/fgene.2020.00866 Frontiers in Genetics |
| ISSN: | 1664-8021 |
| DOI: | 10.3389/fgene.2020.00866/full |
| Popis: | Selective genotyping of phenotypically superior animals may lead to bias and less accurate genomic breeding values (GEBV). Performing selective genotyping based on phenotypes measured in the breeding environment (B) is not necessarily a good strategy when the aim of a breeding program is to improve animals’ performance in the commercial environment (C). Our simulation study compared different genotyping strategies for selection candidates and for fish in C in a breeding program for rainbow trout in the presence of genotype-by-environment interactions when the program had limited genotyping resources and unregistered pedigrees of individuals. For the reference population, selective genotyping of top and bottom individuals in C based on phenotypes measured in C led to the highest genetic gains, followed by random genotyping and then selective genotyping of top individuals in C. For selection candidates, selective genotyping of top individuals in B based on phenotypes measured in B led to the highest genetic gains, followed by selective genotyping of top and bottom individuals and then random genotyping. Selective genotyping led to bias in predicting GEBV. However, in scenarios that used selective genotyping of top fish in B and random genotyping of fish in C, predictions of GEBV were unbiased, with genetic correlations of 0.2 and 0.5 between traits measured in B and C. Estimates of variance components were sensitive to genotyping strategy, with an overestimation of the variance with selective genotyping of top and bottom fish and an underestimation of the variance with selective genotyping of top fish. Unbiased estimates of variance components were obtained when fish in B and C were genotyped at random. In conclusion, we recommend phenotypic genotyping of top and bottom fish in C and top fish in B for the purpose of selecting breeding animals and random genotyping of individuals in B and C for the purpose of estimating variance components when a genomic breeding program for rainbow trout aims to improve animals’ performance in C. |
| Databáze: | OpenAIRE |
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