Genome-Based Genotype × Environment Prediction Enhances Potato (Solanum tuberosum L.) Improvement Using Pseudo-Diploid and Polysomic Tetraploid Modeling
Potato breeding must improve its efficiency by increasing the reliability of selection as well as identifying a promising germplasm for crossing. This study shows the prediction accuracy of genomic-estimated breeding values for several potato (Solanum tuberosum L.) breeding clones and the released c...
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Frontiers Media S.A.
2022-02-01
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Series: | Frontiers in Plant Science |
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Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2022.785196/full |
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author | Rodomiro Ortiz José Crossa Fredrik Reslow Paulino Perez-Rodriguez Jaime Cuevas |
author_facet | Rodomiro Ortiz José Crossa Fredrik Reslow Paulino Perez-Rodriguez Jaime Cuevas |
author_sort | Rodomiro Ortiz |
collection | DOAJ |
description | Potato breeding must improve its efficiency by increasing the reliability of selection as well as identifying a promising germplasm for crossing. This study shows the prediction accuracy of genomic-estimated breeding values for several potato (Solanum tuberosum L.) breeding clones and the released cultivars that were evaluated at three locations in northern and southern Sweden for various traits. Three dosages of marker alleles [pseudo-diploid (A), additive tetrasomic polyploidy (B), and additive-non-additive tetrasomic polyploidy (C)] were considered in the genome-based prediction models, for single environments and multiple environments (accounting for the genotype-by-environment interaction or G × E), and for comparing two kernels, the conventional linear, Genomic Best Linear Unbiased Prediction (GBLUP) (GB), and the non-linear Gaussian kernel (GK), when used with the single-kernel genetic matrices of A, B, C, or when employing two-kernel genetic matrices in the model using the kernels from B and C for a single environment (models 1 and 2, respectively), and for multi-environments (models 3 and 4, respectively). Concerning the single site analyses, the trait with the highest prediction accuracy for all sites under A, B, C for model 1, model 2, and for GB and GK methods was tuber starch percentage. Another trait with relatively high prediction accuracy was the total tuber weight. Results show an increase in prediction accuracy of model 2 over model 1. Non-linear Gaussian kernel (GK) did not show any clear advantage over the linear kernel GBLUP (GB). Results from the multi-environments had prediction accuracy estimates (models 3 and 4) higher than those obtained from the single-environment analyses. Model 4 with GB was the best method in combination with the marker structure B for predicting most of the tuber traits. Most of the traits gave relatively high prediction accuracy under this combination of marker structure (A, B, C, and B-C), and methods GB and GK combined with the multi-environment with G × E model. |
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language | English |
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spelling | doaj.art-ffa2f5114ad74c15bf51d83c6d3f1ab02022-12-22T04:10:34ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2022-02-011310.3389/fpls.2022.785196785196Genome-Based Genotype × Environment Prediction Enhances Potato (Solanum tuberosum L.) Improvement Using Pseudo-Diploid and Polysomic Tetraploid ModelingRodomiro Ortiz0José Crossa1Fredrik Reslow2Paulino Perez-Rodriguez3Jaime Cuevas4Department of Plant Breeding, Swedish University of Agricultural Sciences (SLU), Lomma, SwedenInternational Maize and Wheat Improvement Center (CIMMYT), Texcoco, MexicoDepartment of Plant Breeding, Swedish University of Agricultural Sciences (SLU), Lomma, SwedenColegio de Postgraduados, Montecillos, MexicoDivisión de Ciencias, Ingeniería y Tecnologías, Universidad de Quintana Roo, Chetumal, MexicoPotato breeding must improve its efficiency by increasing the reliability of selection as well as identifying a promising germplasm for crossing. This study shows the prediction accuracy of genomic-estimated breeding values for several potato (Solanum tuberosum L.) breeding clones and the released cultivars that were evaluated at three locations in northern and southern Sweden for various traits. Three dosages of marker alleles [pseudo-diploid (A), additive tetrasomic polyploidy (B), and additive-non-additive tetrasomic polyploidy (C)] were considered in the genome-based prediction models, for single environments and multiple environments (accounting for the genotype-by-environment interaction or G × E), and for comparing two kernels, the conventional linear, Genomic Best Linear Unbiased Prediction (GBLUP) (GB), and the non-linear Gaussian kernel (GK), when used with the single-kernel genetic matrices of A, B, C, or when employing two-kernel genetic matrices in the model using the kernels from B and C for a single environment (models 1 and 2, respectively), and for multi-environments (models 3 and 4, respectively). Concerning the single site analyses, the trait with the highest prediction accuracy for all sites under A, B, C for model 1, model 2, and for GB and GK methods was tuber starch percentage. Another trait with relatively high prediction accuracy was the total tuber weight. Results show an increase in prediction accuracy of model 2 over model 1. Non-linear Gaussian kernel (GK) did not show any clear advantage over the linear kernel GBLUP (GB). Results from the multi-environments had prediction accuracy estimates (models 3 and 4) higher than those obtained from the single-environment analyses. Model 4 with GB was the best method in combination with the marker structure B for predicting most of the tuber traits. Most of the traits gave relatively high prediction accuracy under this combination of marker structure (A, B, C, and B-C), and methods GB and GK combined with the multi-environment with G × E model.https://www.frontiersin.org/articles/10.3389/fpls.2022.785196/fullgenomic-enabled predictionsmulti-environment trialspotato breedingSolanum tuberosumgenetic gains in plant breeding |
spellingShingle | Rodomiro Ortiz José Crossa Fredrik Reslow Paulino Perez-Rodriguez Jaime Cuevas Genome-Based Genotype × Environment Prediction Enhances Potato (Solanum tuberosum L.) Improvement Using Pseudo-Diploid and Polysomic Tetraploid Modeling Frontiers in Plant Science genomic-enabled predictions multi-environment trials potato breeding Solanum tuberosum genetic gains in plant breeding |
title | Genome-Based Genotype × Environment Prediction Enhances Potato (Solanum tuberosum L.) Improvement Using Pseudo-Diploid and Polysomic Tetraploid Modeling |
title_full | Genome-Based Genotype × Environment Prediction Enhances Potato (Solanum tuberosum L.) Improvement Using Pseudo-Diploid and Polysomic Tetraploid Modeling |
title_fullStr | Genome-Based Genotype × Environment Prediction Enhances Potato (Solanum tuberosum L.) Improvement Using Pseudo-Diploid and Polysomic Tetraploid Modeling |
title_full_unstemmed | Genome-Based Genotype × Environment Prediction Enhances Potato (Solanum tuberosum L.) Improvement Using Pseudo-Diploid and Polysomic Tetraploid Modeling |
title_short | Genome-Based Genotype × Environment Prediction Enhances Potato (Solanum tuberosum L.) Improvement Using Pseudo-Diploid and Polysomic Tetraploid Modeling |
title_sort | genome based genotype environment prediction enhances potato solanum tuberosum l improvement using pseudo diploid and polysomic tetraploid modeling |
topic | genomic-enabled predictions multi-environment trials potato breeding Solanum tuberosum genetic gains in plant breeding |
url | https://www.frontiersin.org/articles/10.3389/fpls.2022.785196/full |
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