Home About Browse Search
Svenska


Genome-Based Genotype × Environment Prediction Enhances Potato (Solanum tuberosum L.) Improvement Using Pseudo-Diploid and Polysomic Tetraploid Modeling

Ortiz, Rodomiro and Crossa, Jose and Reslow, Fredrik and Pérez-Rodríguez, Paulino and Cuevas, Jaime (2022). Genome-Based Genotype × Environment Prediction Enhances Potato (Solanum tuberosum L.) Improvement Using Pseudo-Diploid and Polysomic Tetraploid Modeling. Frontiers in Plant Science. 13 , 785196
[Research article]

[img] PDF
1MB

Abstract

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.

Authors/Creators:Ortiz, Rodomiro and Crossa, Jose and Reslow, Fredrik and Pérez-Rodríguez, Paulino and Cuevas, Jaime
Title:Genome-Based Genotype × Environment Prediction Enhances Potato (Solanum tuberosum L.) Improvement Using Pseudo-Diploid and Polysomic Tetraploid Modeling
Series Name/Journal:Frontiers in Plant Science
Year of publishing :2022
Volume:13
Article number:785196
Number of Pages:17
ISSN:1664-462X
Language:English
Publication Type:Research article
Article category:Scientific peer reviewed
Version:Published version
Copyright:Creative Commons: Attribution 4.0
Full Text Status:Public
Subjects:(A) Swedish standard research categories 2011 > 4 Agricultural Sciences > 401 Agricultural, Forestry and Fisheries > Horticulture
(A) Swedish standard research categories 2011 > 4 Agricultural Sciences > 401 Agricultural, Forestry and Fisheries > Agricultural Science
(A) Swedish standard research categories 2011 > 4 Agricultural Sciences > 404 Agricultural Biotechnology > Genetics and Breeding
Keywords:genomic-enabled predictions, multi-environment trials, potato breeding, Solanum tuberosum, genetic gains in plant breeding
URN:NBN:urn:nbn:se:slu:epsilon-p-115883
Permanent URL:
http://urn.kb.se/resolve?urn=urn:nbn:se:slu:epsilon-p-115883
Additional ID:
Type of IDID
DOI10.3389/fpls.2022.785196
ID Code:27234
Faculty:LTV - Fakulteten för landskapsarkitektur, trädgårds- och växtproduktionsvetenskap
Department:(LTJ, LTV) > Plant Breeding and Biotechnology (until 121231)
(LTJ, LTV) > Department of Plant Breeding (from 130101)
Deposited By: SLUpub Connector
Deposited On:01 Mar 2022 10:25
Metadata Last Modified:01 Mar 2022 10:31

Repository Staff Only: item control page

Downloads

Downloads per year (since September 2012)

View more statistics

Downloads
Hits