Genomic prediction of seed nutritional traits in biparental families of oat (Avena sativa).


Journal

The plant genome
ISSN: 1940-3372
Titre abrégé: Plant Genome
Pays: United States
ID NLM: 101273919

Informations de publication

Date de publication:
04 Aug 2023
Historique:
revised: 01 05 2023
received: 18 08 2022
accepted: 20 06 2023
medline: 4 8 2023
pubmed: 4 8 2023
entrez: 4 8 2023
Statut: aheadofprint

Résumé

Selection for more nutritious crop plants is an important goal of plant breeding to improve food quality and contribute to human health outcomes. While there are efforts to integrate genomic prediction to accelerate breeding progress, an ongoing challenge is identifying strategies to improve accuracy when predicting within biparental populations in breeding programs. We tested multiple genomic prediction methods for 12 seed fatty acid content traits in oat (Avena sativa L.), as unsaturated fatty acids are a key nutritional trait in oat. Using two well-characterized oat germplasm panels and other biparental families as training populations, we predicted family mean and individual values within families. Genomic prediction of family mean exceeded a mean accuracy of 0.40 and 0.80 using an unrelated and related germplasm panel, respectively, where the related germplasm panel outperformed prediction based on phenotypic means (0.54). Within family prediction accuracy was more variable: training on the related germplasm had higher accuracy than the unrelated panel (0.14-0.16 and 0.05-0.07, respectively), but variability between families was not easily predicted by parent relatedness, segregation of a locus detected by a genome-wide association study in the panel, or other characteristics. When using other families as training populations, prediction accuracies were comparable to the related germplasm panel (0.11-0.23), and families that had half-sib families in the training set had higher prediction accuracy than those that did not. Overall, this work provides an example of genomic prediction of family means and within biparental families for an important nutritional trait and suggests that using related germplasm panels as training populations can be effective.

Identifiants

pubmed: 37539632
doi: 10.1002/tpg2.20370
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

e20370

Subventions

Organisme : National Institute of Food and Agriculture
ID : 2017-67007-26502
Organisme : Agricultural Research Service
ID : 8062-21000-045-000-D

Informations de copyright

© 2023 The Authors. The Plant Genome published by Wiley Periodicals LLC on behalf of Crop Science Society of America.

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Auteurs

Lauren J Brzozowski (LJ)

Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, New York, USA.
USDA-ARS, Robert W. Holley Center for Agriculture and Health, Ithaca, New York, USA.

Malachy T Campbell (MT)

Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, New York, USA.

Haixiao Hu (H)

Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, New York, USA.

Linxing Yao (L)

Analytical Resources Core-Bioanalysis and Omics, Colorado State University, Fort Collins, Colorado, USA.

Melanie Caffe (M)

Department of Agronomy, Horticulture & Plant Science, South Dakota State University, Brookings, South Dakota, USA.

Lucı A Gutiérrez (LA)

Department of Agronomy, University of Wisconsin-Madison, Madison, Wisconsin, USA.

Kevin P Smith (KP)

Department of Agronomy & Plant Genetics, University of Minnesota, Saint Paul, Minnesota, USA.

Mark E Sorrells (ME)

Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, New York, USA.

Michael A Gore (MA)

Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, New York, USA.

Jean-Luc Jannink (JL)

Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, New York, USA.
USDA-ARS, Robert W. Holley Center for Agriculture and Health, Ithaca, New York, USA.

Classifications MeSH