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A singular value decomposition Bayesian multiple-trait and multiple-environment genomic model

Author: Montesinos-Lopez, O.A.
Author: Montesinos-Lopez, A.
Author: Crossa, J.
Author: Kismiantini
Author: Ramirez-Alcaraz, J.M.
Author: Singh, R.P.
Author: Mondal, S.
Author: Juliana, P.
Year: 2018
ISSN: 1365-2540
ISSN: ISSN: 0018-067X
ISSN: ESSN: 1365-2540
URI: https://hdl.handle.net/10883/19788
Descriptors: Bayesian theory
Descriptors: Genomics
Descriptors: Breeding
Abstract: Today, breeders perform genomic-assisted breeding to improve more than one trait. However, frequently there are several traits under study at one time, and the implementation of current genomic multiple-trait and multiple-environment models is challenging. Consequently, we propose a four-stage analysis for multiple-trait data in this paper. In the first stage, we perform singular value decomposition (SVD) on the resulting matrix of trait responses; in the second stage, we perform multiple trait analysis on transformed responses. In stages three and four, we collect and transform the traits back to their original state and obtain the parameter estimates and the predictions on these scale variables prior to transformation. The results of the proposed method are compared, in terms of parameter estimation and prediction accuracy, with the results of the Bayesian multiple-trait and multiple-environment model (BMTME) previously described in the literature. We found that the proposed method based on SVD produced similar results, in terms of parameter estimation and prediction accuracy, to those obtained with the BMTME model. Moreover, the proposed multiple-trait method is atractive because it can be implemented using current single-trait genomic prediction software, which yields a more efficient algorithm in terms of computation.
Language: English
Publisher: Springer Nature
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Type: Article
Place: United Kingdom
Pages: 381-401
Journal: Heredity
Journal Volume: 122
DOI: 10.1038/s41437-018-0109-7
Keywords: Genome Evolution
Keywords: Tropical Ecology
Agrovoc: BAYESIAN THEORY
Agrovoc: BIOINFORMATICS
Agrovoc: GENOMICS
Agrovoc: AGRICULTURAL SCIENCES AND BIOTECHNOLOGY
Agrovoc: AGRICULTURE
Related Datasets: https://static-content.springer.com/esm/art%3A10.1038%2Fs41437-018-0109-7/MediaObjects/41437_2018_109_MOESM1_ESM.docx


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    Genetic Resources including germplasm collections, wild relatives, genotyping, genomics, and IP

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