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...

Full description

Bibliographic Details
Main Authors: Rodomiro Ortiz, José Crossa, Fredrik Reslow, Paulino Perez-Rodriguez, Jaime Cuevas
Format: Article
Language:English
Published: Frontiers Media S.A. 2022-02-01
Series:Frontiers in Plant Science
Subjects:
Online Access:https://www.frontiersin.org/articles/10.3389/fpls.2022.785196/full
_version_ 1798024243236044800
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.
first_indexed 2024-04-11T17:59:19Z
format Article
id doaj.art-ffa2f5114ad74c15bf51d83c6d3f1ab0
institution Directory Open Access Journal
issn 1664-462X
language English
last_indexed 2024-04-11T17:59:19Z
publishDate 2022-02-01
publisher Frontiers Media S.A.
record_format Article
series Frontiers in Plant Science
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
work_keys_str_mv AT rodomiroortiz genomebasedgenotypeenvironmentpredictionenhancespotatosolanumtuberosumlimprovementusingpseudodiploidandpolysomictetraploidmodeling
AT josecrossa genomebasedgenotypeenvironmentpredictionenhancespotatosolanumtuberosumlimprovementusingpseudodiploidandpolysomictetraploidmodeling
AT fredrikreslow genomebasedgenotypeenvironmentpredictionenhancespotatosolanumtuberosumlimprovementusingpseudodiploidandpolysomictetraploidmodeling
AT paulinoperezrodriguez genomebasedgenotypeenvironmentpredictionenhancespotatosolanumtuberosumlimprovementusingpseudodiploidandpolysomictetraploidmodeling
AT jaimecuevas genomebasedgenotypeenvironmentpredictionenhancespotatosolanumtuberosumlimprovementusingpseudodiploidandpolysomictetraploidmodeling