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Combined Multistage Linear Genomic Selection Indices To Predict the Net Genetic Merit in Plant Breeding.

ABSTRACT: A combined multistage linear genomic selection index (CMLGSI) is a linear combination of phenotypic and genomic estimated breeding values useful for predicting the individual net genetic merit, which in turn is a linear combination of the true unobservable breeding values of the traits weighted by their respective economic values. The CMLGSI is a cost-saving strategy for improving multiple traits because the breeder does not need to measure all traits at each stage. The optimum (OCMLGSI) and decorrelated (DCMLGSI) indices are the main CMLGSIs. Whereas the OCMLGSI takes into consideration the index correlation values among stages, the DCMLGSI imposes the restriction that the index correlation values among stages be zero. Using real and simulated datasets, we compared the efficiency of both indices in a two-stage context. The criteria we applied to compare the efficiency of both indices were that the total selection response of each index must be lower than or equal to the single-stage combined linear genomic selection index (CLGSI) response and that the correlation of each index with the net genetic merit should be maximum. Using four different total proportions for the real dataset, the estimated total OCMLGSI and DCMLGSI responses explained 97.5% and 90%, respectively, of the estimated single-stage CLGSI selection response. In addition, at stage two, the estimated correlations of the OCMLGSI and the DCMLGSI with the net genetic merit were 0.84 and 0.63, respectively. We found similar results for the simulated datasets. Thus, we recommend using the OCMLGSI when performing multistage selection.

SUBMITTER: Ceron-Rojas JJ

PROVIDER: S-EPMC7263695 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Combined Multistage Linear Genomic Selection Indices To Predict the Net Genetic Merit in Plant Breeding.

Cerón-Rojas J Jesus JJ Crossa Jose J

G3 (Bethesda, Md.) 20200601 6

A combined multistage linear genomic selection index (CMLGSI) is a linear combination of phenotypic and genomic estimated breeding values useful for predicting the individual net genetic merit, which in turn is a linear combination of the true unobservable breeding values of the traits weighted by their respective economic values. The CMLGSI is a cost-saving strategy for improving multiple traits because the breeder does not need to measure all traits at each stage. The <i>optimum</i> (OCMLGSI) ...[more]

PMID: 32312840

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Project description:IntroductionForestry in many parts of the world depends on exotic species, making this industry a source of invasions in some countries. Among others, plantations of the genus Pinus, Eucalyptus, Acacia, Populus, and Pseudotsuga underpin the forestry industry and are a vital component of many countries economies. Among woody plants, the cosmopolitan genus Acacia includes some of the most commonly planted trees worldwide. In order to prevent, manage and control invasive plant species, one of the most used tools is species distribution models. The output of these models can also be used to obtain information about population characteristics, such as spatial abundance patterns or species performance. Although ecological theory suggests a direct link between fitness and suitability, this link is often absent. The reasons behind the lack of this relationship are multiple. Chile is one of the countries where Acacia species, in particular, A. dealbata and A. melanoxylon, have become invaders.MethodsHere, we used climatic and edaphic variables to predict thepotentially suitable habitats for A. dealbata and A. melanoxylon in continental Chile and evaluate if the suitability indices obtained from these models are associated with the observed performance of the trees along the country.ResultsOur models show that variable importance showed significant similarities between the variables that characterize each species' niche. However, despite the high accuracy of our models, we did not observe an association between suitability and tree growth.DiscussionThis disconnection between suitability and performance can result from multiple causes, from structural limitations, like the lack of biotic interactions in the models, to methodological issues, like the usefulness of the performance metric used. Whatever the scenario, our results suggest that plans to control invasive species should be cautious in assuming this relationship in their design and consider other indicators such as species establishment success.

Dataset Information

Combined Multistage Linear Genomic Selection Indices To Predict the Net Genetic Merit in Plant Breeding.

Publications

Combined Multistage Linear Genomic Selection Indices To Predict the Net Genetic Merit in Plant Breeding.

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