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Bayesian multitrait kernel methods improve multienvironment genome-based prediction

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Relation http://oar.icrisat.org/12645/
https://academic.oup.com/g3journal/article/12/2/jkab406/6446035
https://doi.org/10.1093/g3journal/jkab406
 
Title Bayesian multitrait kernel methods improve multienvironment genome-based prediction
 
Creator Montesinos-López, O A
Montesinos-López, J C
Montesinos-Lopez, A
Ramírez-Alcaraz, J M
Poland, J
Singh, R
Dreisigacker, S
Crespo, L
Mondal, S
Govidan, V
Juliana, P
Espino, J H
Shrestha, S
Varshney, R K
Crossa, J
 
Subject Plant Breeding
Genetics and Genomics
 
Description When multitrait data are available, the preferred models are those that are able to account for correlations between phenotypic traits because when the degree of correlation is moderate or large, this increases the genomic prediction accuracy. For this reason, in this article, we explore Bayesian multitrait kernel methods for genomic prediction and we illustrate the power of these models with three-real datasets. The kernels under study were the linear, Gaussian, polynomial, and sigmoid kernels; they were compared with the conventional Ridge regression and GBLUP multitrait models. The results show that, in general, the Gaussian kernel method outperformed conventional Bayesian Ridge and GBLUP multitrait linear models by 2.2–17.45% (datasets 1–3) in terms of prediction performance based on the mean square error of prediction. This improvement in terms of prediction performance of the Bayesian multitrait kernel method can be attributed to the fact that the proposed model is able to capture nonlinear patterns more efficiently than linear multitrait models. However, not all kernels perform well in the datasets used for evaluation, which is why more than one kernel should be evaluated to be able to choose the best kernel.
 
Publisher Genetics Society of America (GSA)
 
Date 2021-11-29
 
Type Article
PeerReviewed
 
Format application/pdf
 
Language en
 
Rights cc_attribution
 
Identifier http://oar.icrisat.org/12645/1/G3_12_2_1-17_2022.pdf
Montesinos-López, O A and Montesinos-López, J C and Montesinos-Lopez, A and Ramírez-Alcaraz, J M and Poland, J and Singh, R and Dreisigacker, S and Crespo, L and Mondal, S and Govidan, V and Juliana, P and Espino, J H and Shrestha, S and Varshney, R K and Crossa, J (2021) Bayesian multitrait kernel methods improve multienvironment genome-based prediction. G3: Genes, Genomes, Genetics (TSI), 12 (2). pp. 1-17. ISSN 2160-1836