Bayesian multitrait kernel methods improve multienvironment genome-based prediction. Issue 2 (29th November 2021)

• Record Type: Journal Article
• Authors: Montesinos-López, Osval Antonio; Montesinos-López, José Cricelio; Montesinos-López, Abelardo; Ramírez-Alcaraz, Juan Manuel; Poland, Jesse; Singh, Ravi; Dreisigacker, Susanne; Crespo, Leonardo; Mondal, Sushismita; Govidan, Velu; Juliana, Philomin; Espino, Julio Huerta; Shrestha, Sandesh; Varshney, Rajeev K; Crossa, José
• Editors: Lipka, A
• Is Part Of: G3. Volume 12:Issue 2(2022)
• Publication Date: 2021-11-29
• Subjects: multitrait -- kernel methods -- plant breeding -- genomic-enabled prediction -- genomic prediction -- GenPred -- shared data resources; Genetics -- Research -- Periodicals; Genomics -- Periodicals; Genetics; Genomics; Genes; Genetics -- Research; Genomics; Electronic journals; Periodical; Periodicals; Fulltext; Internet Resources; Periodicals; 572.8
• DOI: 10.1093/g3journal/jkab406
• Languages: English
• ISSNs: 2160-1836

Abstract

Abstract: 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.