Genomic signatures of selection for resistance to stripe rust in Austrian winter wheat

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Abstract

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We combined quantitative and population genetic methods to identify loci under selection for adult plant resistance to stripe rust in an Austrian winter wheat breeding population from 2008 to 2018.

Abstract

Resistance to stripe rust, a foliar disease caused by the fungus P. striiformis f. sp. tritici, in wheat ( Triticum aestivum L.) is both qualitatively and quantitatively controlled. Resistance genes confer complete, race-specific resistance but are easily overcome by evolving pathogen populations, while quantitative resistance is controlled by many small- to medium-effect loci that provide incomplete yet more durable protection. Data on resistance loci can be applied in marker-assisted selection and genomic prediction frameworks. We employed genome-wide association to detect loci associated with stripe rust and selection testing to identify regions of the genome that underwent selection for stripe rust resistance in an Austrian winter wheat breeding program from 2008 to 2018. Genome-wide association mapping identified 150 resistance loci, 62 of which showed significant evidence of selection over time. The breeding population also demonstrated selection for resistance at the genome-wide level.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00122-021-03882-3.

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Steve Walker, Martin Mächler, Ben Bolker … (2022)

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A Robust, Simple Genotyping-by-Sequencing (GBS) Approach for High Diversity Species

Robert Elshire, Jeffrey C. Glaubitz, Qi-ying Sun … (2011)

Advances in next generation technologies have driven the costs of DNA sequencing down to the point that genotyping-by-sequencing (GBS) is now feasible for high diversity, large genome species. Here, we report a procedure for constructing GBS libraries based on reducing genome complexity with restriction enzymes (REs). This approach is simple, quick, extremely specific, highly reproducible, and may reach important regions of the genome that are inaccessible to sequence capture approaches. By using methylation-sensitive REs, repetitive regions of genomes can be avoided and lower copy regions targeted with two to three fold higher efficiency. This tremendously simplifies computationally challenging alignment problems in species with high levels of genetic diversity. The GBS procedure is demonstrated with maize (IBM) and barley (Oregon Wolfe Barley) recombinant inbred populations where roughly 200,000 and 25,000 sequence tags were mapped, respectively. An advantage in species like barley that lack a complete genome sequence is that a reference map need only be developed around the restriction sites, and this can be done in the process of sample genotyping. In such cases, the consensus of the read clusters across the sequence tagged sites becomes the reference. Alternatively, for kinship analyses in the absence of a reference genome, the sequence tags can simply be treated as dominant markers. Future application of GBS to breeding, conservation, and global species and population surveys may allow plant breeders to conduct genomic selection on a novel germplasm or species without first having to develop any prior molecular tools, or conservation biologists to determine population structure without prior knowledge of the genome or diversity in the species.

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MissForest--non-parametric missing value imputation for mixed-type data.

Daniel Stekhoven, Peter Bühlmann (2012)

Modern data acquisition based on high-throughput technology is often facing the problem of missing data. Algorithms commonly used in the analysis of such large-scale data often depend on a complete set. Missing value imputation offers a solution to this problem. However, the majority of available imputation methods are restricted to one type of variable only: continuous or categorical. For mixed-type data, the different types are usually handled separately. Therefore, these methods ignore possible relations between variable types. We propose a non-parametric method which can cope with different types of variables simultaneously. We compare several state of the art methods for the imputation of missing values. We propose and evaluate an iterative imputation method (missForest) based on a random forest. By averaging over many unpruned classification or regression trees, random forest intrinsically constitutes a multiple imputation scheme. Using the built-in out-of-bag error estimates of random forest, we are able to estimate the imputation error without the need of a test set. Evaluation is performed on multiple datasets coming from a diverse selection of biological fields with artificially introduced missing values ranging from 10% to 30%. We show that missForest can successfully handle missing values, particularly in datasets including different types of variables. In our comparative study, missForest outperforms other methods of imputation especially in data settings where complex interactions and non-linear relations are suspected. The out-of-bag imputation error estimates of missForest prove to be adequate in all settings. Additionally, missForest exhibits attractive computational efficiency and can cope with high-dimensional data. The package missForest is freely available from http://stat.ethz.ch/CRAN/. stekhoven@stat.math.ethz.ch; buhlmann@stat.math.ethz.ch

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Author and article information

Contributors

Laura Morales:

ORCID: http://orcid.org/0000-0002-0403-035X

laura.morales@boku.ac.at

Journal

Journal ID (nlm-ta): Theor Appl Genet

Journal ID (iso-abbrev): Theor Appl Genet

Title: TAG. Theoretical and Applied Genetics. Theoretische Und Angewandte Genetik

Publisher: Springer Berlin Heidelberg (Berlin/Heidelberg )

ISSN (Print): 0040-5752

ISSN (Electronic): 1432-2242

Publication date (Electronic): 14 June 2021

Publication date PMC-release: 14 June 2021

Publication date (Print): 2021

Volume: 134

Issue: 9

Pages: 3111-3121

Affiliations

[1 ]GRID grid.5173.0, ISNI 0000 0001 2298 5320, Institute of Biotechnology in Plant Production, Department of Agrobiotechnology, , University of Natural Resources and Life Sciences Vienna, ; Tulln, Austria

[2 ]Saatzucht Donau GmbH & CoKG, Probstdorf, Austria

Author notes

Communicated by Susanne Dreisigacker.

Author information

Laura Morales http://orcid.org/0000-0002-0403-035X

Sebastian Michel http://orcid.org/0000-0001-6636-2694

Hermann Gregor Dallinger http://orcid.org/0000-0002-4101-7402

Franziska Löschenberger http://orcid.org/0000-0003-2984-4461

Hermann Buerstmayr http://orcid.org/0000-0002-0748-2351

Article

Publisher ID: 3882

DOI: 10.1007/s00122-021-03882-3

PMC ID: 8354948

PubMed ID: 34125246

SO-VID: ed4d1e16-d547-4b36-b807-8b6f9caa51f7

License:

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

History

Date received : 8 March 2021

Date accepted : 2 June 2021

Funding

Funded by: Austrian Federal Ministry of Agriculture, Regions and Tourism

Award ID: DaFNE-101402

Award Recipient : Hermann Buerstmayr

Funded by: University of Natural Resources and Life Sciences Vienna (BOKU)

Open Access :

Open access funding provided by University of Natural Resources and Life Sciences Vienna (BOKU).

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ScienceOpen disciplines: Genetics

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ScienceOpen disciplines: Genetics

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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
Authors: Laura Morales, Christian Ametz, Hermann Gregor Dallinger …

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Genomic signatures of selection for resistance to stripe rust in Austrian winter wheat

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Abstract

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Genome Integrity

Most cited references 48

Fitting Linear Mixed-Effects Models Usinglme4

A Robust, Simple Genotyping-by-Sequencing (GBS) Approach for High Diversity Species

MissForest--non-parametric missing value imputation for mixed-type data.

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