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. 2023 Jan;136(1):23.
doi: 10.1007/s00122-023-04249-6. Epub 2023 Jan 24.

Comparison of linear and semi-parametric models incorporating genomic, pedigree, and associated loci information for the prediction of resistance to stripe rust in an Austrian winter wheat breeding program

Laura Morales  1 , Christian Ametz  2 , Hermann Gregor Dallinger  3   2 , Franziska Löschenberger  2 , Anton Neumayer  2 , Simone Zimmerl  3 , Hermann Buerstmayr  3
Affiliations

Comparison of linear and semi-parametric models incorporating genomic, pedigree, and associated loci information for the prediction of resistance to stripe rust in an Austrian winter wheat breeding program

Laura Morales et al. Theor Appl Genet. 2023 Jan.

Erratum in

  • Correction to volume 136 issue 1.
    [No authors listed] [No authors listed] Theor Appl Genet. 2023 Mar 23;136(4):84. doi: 10.1007/s00122-023-04323-z. Theor Appl Genet. 2023. PMID: 36952001 Free PMC article. No abstract available.

Abstract

We used a historical dataset on stripe rust resistance across 11 years in an Austrian winter wheat breeding program to evaluate genomic and pedigree-based linear and semi-parametric prediction methods. Stripe rust (yellow rust) is an economically important foliar disease of wheat (Triticum aestivum L.) caused by the fungus Puccinia striiformis f. sp. tritici. Resistance to stripe rust is controlled by both qualitative (R-genes) and quantitative (small- to medium-effect quantitative trait loci, QTL) mechanisms. Genomic and pedigree-based prediction methods can accelerate selection for quantitative traits such as stripe rust resistance. Here we tested linear and semi-parametric models incorporating genomic, pedigree, and QTL information for cross-validated, forward, and pairwise prediction of adult plant resistance to stripe rust across 11 years (2008-2018) in an Austrian winter wheat breeding program. Semi-parametric genomic modeling had the greatest predictive ability and genetic variance overall, but differences between models were small. Including QTL as covariates improved predictive ability in some years where highly significant QTL had been detected via genome-wide association analysis. Predictive ability was moderate within years (cross-validated) but poor in cross-year frameworks.

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Conflict of interest statement

CA, HGD, FL, and AN were employed by the company Saatzucht Donau GmbH & CoKG. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Fig. 1
Fig. 1
Between-year predictive ability of GBLUP and GRKHS models in which lines present in both the training and test sets were excluded from the test set (GBLUP–no overlap and GRKHS–no overlap) and in which all training and test set lines were included (GBLUP–overlap and GRKHS–overlap)
Fig. 2
Fig. 2
Forward predictive ability of GBLUP and GRKHS models in which lines present in both the training and test sets were excluded from the test set (GBLUP–no overlap and GRKHS–no overlap) and in which all training and test set lines were included (GBLUP–overlap and GRKHS–overlap). For each test year, the training sets comprised progressive sets of subsequent years
See this image and copyright information in PMC

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