Pyramiding of scald resistance genes in four spring barley MAGIC populations

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Abstract

Genome-Wide Association Studies (GWAS) of four Multi-parent Advanced Generation Inter-Cross (MAGIC) populations identified nine regions on chromosomes 1H, 3H, 4H, 5H, 6H and 7H associated with resistance against barley scald disease. Three of these regions are putatively novel resistance Quantitative Trait Loci (QTL). Barley scald is caused by Rhynchosporium commune, one of the most important barley leaf diseases that are prevalent in most barley-growing regions. Up to 40% yield losses can occur in susceptible barley cultivars. Four MAGIC populations were generated in a Nordic Public–Private Pre-breeding of spring barley project (PPP Barley) to introduce resistance to several important diseases. Here, these MAGIC populations consisting of six to eight founders each were tested for scald resistance in field trials in Finland and Iceland. Eight different model covariate combinations were compared for GWAS studies, and the models that deviated the least from the expected p-values were selected. For all QTL, candidate genes were identified that are predicted to be involved in pathogen defence. The MAGIC progenies contained new haplotypes of significant SNP-markers with high resistance levels. The lines with successfully pyramided resistance against scald and mildew and the significant markers are now distributed among Nordic plant breeders and will benefit development of disease-resistant cultivars.

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The online version of this article (10.1007/s00122-021-03930-y) contains supplementary material, which is available to authorized users.

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Efficient methods to compute genomic predictions.

P VanRaden (2008)

Efficient methods for processing genomic data were developed to increase reliability of estimated breeding values and to estimate thousands of marker effects simultaneously. Algorithms were derived and computer programs tested with simulated data for 2,967 bulls and 50,000 markers distributed randomly across 30 chromosomes. Estimation of genomic inbreeding coefficients required accurate estimates of allele frequencies in the base population. Linear model predictions of breeding values were computed by 3 equivalent methods: 1) iteration for individual allele effects followed by summation across loci to obtain estimated breeding values, 2) selection index including a genomic relationship matrix, and 3) mixed model equations including the inverse of genomic relationships. A blend of first- and second-order Jacobi iteration using 2 separate relaxation factors converged well for allele frequencies and effects. Reliability of predicted net merit for young bulls was 63% compared with 32% using the traditional relationship matrix. Nonlinear predictions were also computed using iteration on data and nonlinear regression on marker deviations; an additional (about 3%) gain in reliability for young bulls increased average reliability to 66%. Computing times increased linearly with number of genotypes. Estimation of allele frequencies required 2 processor days, and genomic predictions required <1 d per trait, and traits were processed in parallel. Information from genotyping was equivalent to about 20 daughters with phenotypic records. Actual gains may differ because the simulation did not account for linkage disequilibrium in the base population or selection in subsequent generations.

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GAPIT: genome association and prediction integrated tool.

Alexander Lipka, Feng Tian, Qishan Wang … (2012)

Software programs that conduct genome-wide association studies and genomic prediction and selection need to use methodologies that maximize statistical power, provide high prediction accuracy and run in a computationally efficient manner. We developed an R package called Genome Association and Prediction Integrated Tool (GAPIT) that implements advanced statistical methods including the compressed mixed linear model (CMLM) and CMLM-based genomic prediction and selection. The GAPIT package can handle large datasets in excess of 10 000 individuals and 1 million single-nucleotide polymorphisms with minimal computational time, while providing user-friendly access and concise tables and graphs to interpret results. http://www.maizegenetics.net/GAPIT. zhiwu.zhang@cornell.edu Supplementary data are available at Bioinformatics online.

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An efficient multi-locus mixed model approach for genome-wide association studies in structured populations

Vincent Segura, Bjarni J. Vilhjalmsson, Alexander Platt … (2012)

Population structure causes genome-wide linkage disequilibrium between unlinked loci, leading to statistical confounding in genome-wide association studies. Mixed models have been shown to handle the confounding effects of a diffuse background of large numbers of loci of small effect well, but do not always account for loci of larger effect. Here we propose a multi-locus mixed model as a general method for mapping complex traits in structured populations. Simulations suggest that our method outperforms existing methods, in terms of power as well as false discovery rate. We apply our method to human and Arabidopsis thaliana data, identifying novel associations in known candidates as well as evidence for allelic heterogeneity. We also demonstrate how a priori knowledge from an A. thaliana linkage mapping study can be integrated into our method using a Bayesian approach. Our implementation is computationally efficient, making the analysis of large datasets (n > 10000) practicable.

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Author and article information

Contributors

Therése Bengtsson:

ORCID: http://orcid.org/0000-0003-4784-1723

therese.bengtsson@slu.se

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): 4 August 2021

Publication date PMC-release: 4 August 2021

Publication date (Print): 2021

Volume: 134

Issue: 12

Pages: 3829-3843

Affiliations

[1 ]GRID grid.22642.30, ISNI 0000 0004 4668 6757, Natural Resources Institute Finland (Luke), ; Survontie 9, 40500 Jyväskylä, Finland

[2 ]GRID grid.6341.0, ISNI 0000 0000 8578 2742, Department of Plant Breeding, , Swedish University of Agricultural Sciences, ; P.O. Box 190, 234 22 Lomma, Sweden

[3 ]GRID grid.22642.30, ISNI 0000 0004 4668 6757, Natural Resources Institute Finland (Luke), ; Tietotie 4, 31600 Jokioinen, Finland

[4 ]GRID grid.432856.e, ISNI 0000 0001 1014 8912, Faculty of Land and Animal Resources, , The Agricultural University of Iceland, ; Hvanneyri, 311 Borgarnes Iceland

[5 ]Boreal Plant Breeding Ltd., Myllytie 10, 31600 Jokioinen, Norway

[6 ]Graminor Ltd. Hommelstadvegen 60, 2322 Ridabu, Norway

[7 ]Sejet Plant Breeding, Nørremarksvej 67, 8700 Horsens, Norway

[8 ]Nordic Seed A/S, Kornmarken 1, 8464 Galten, Denmark

Author notes

Communicated by Thomas Miedaner.

Author information

Juho Hautsalo http://orcid.org/0000-0003-2501-2297

Fluturë Novakazi http://orcid.org/0000-0001-7151-6811

Magnus Göransson http://orcid.org/0000-0002-0081-2207

Outi Manninen http://orcid.org/0000-0001-9641-6657

Therése Bengtsson http://orcid.org/0000-0003-4784-1723

Article

Publisher ID: 3930

DOI: 10.1007/s00122-021-03930-y

PMC ID: 8580920

PubMed ID: 34350474

SO-VID: 505896f9-15d0-47a1-ad47-67b0d7298157

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 : 14 May 2021

Date accepted : 27 July 2021

Funding

Funded by: FundRef http://dx.doi.org/10.13039/501100004182, Nordisk Ministerråd;

Award ID: PPP-1802, PPP-1502

Award Recipient : Ahmed Jahoor

Funded by: Swedish University of Agricultural Sciences

Open Access :

Open access funding provided by Swedish University of Agricultural Sciences.

Custom metadata

ScienceOpen disciplines: Genetics

Keywords: rhynchosporium commune,gwas,hordeum vulgare l.,multi-parent advanced generation inter-cross,farmcpu,blink

Data availability:

ScienceOpen disciplines: Genetics

Keywords: rhynchosporium commune, gwas, hordeum vulgare l., multi-parent advanced generation inter-cross, farmcpu, blink

Pyramiding of scald resistance genes in four spring barley MAGIC populations

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Genes & Diseases

Most cited references 62

Efficient methods to compute genomic predictions.

GAPIT: genome association and prediction integrated tool.

An efficient multi-locus mixed model approach for genome-wide association studies in structured populations

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