Literature DB >> 24965887

Genomic prediction for rust resistance in diverse wheat landraces.

Hans D Daetwyler1, Urmil K Bansal, Harbans S Bariana, Matthew J Hayden, Ben J Hayes.   

Abstract

KEY MESSAGE: We have demonstrated that genomic selection in diverse wheat landraces for resistance to leaf, stem and strip rust is possible, as genomic breeding values were moderately accurate. Markers with large effects in the Bayesian analysis confirmed many known genes, while also discovering many previously uncharacterised genome regions associated with rust scores. Genomic selection, where selection decisions are based on genomic estimated breeding values (GEBVs) derived from genome-wide DNA markers, could accelerate genetic progress in plant breeding. In this study, we assessed the accuracy of GEBVs for rust resistance in 206 hexaploid wheat (Triticum aestivum) landraces from the Watkins collection of phenotypically diverse wheat genotypes from 32 countries. The landraces were genotyped for 5,568 SNPs using an Illumina iSelect 9 K bead chip assay and phenotyped for field-based leaf rust (Lr), stem rust (Sr) and stripe rust (Yr) responses across multiple years. Genomic Best Linear Unbiased Prediction (GBLUP) and a Bayesian Regression method (BayesR) were used to predict GEBVs. Based on fivefold cross-validation, the accuracy of genomic prediction averaged across years was 0.35, 0.27 and 0.44 for Lr, Sr and Yr using GBLUP and 0.33, 0.38 and 0.30 for Lr, Sr and Yr using BayesR, respectively. Inclusion of PCR-predicted genotypes for known rust resistance genes increased accuracy more substantially when the marker was diagnostic (Lr34/Sr57/Yr18) for the presence-absence of the gene rather than just linked (Sr2). Investigation of the impact of genetic relatedness between validation and reference lines on accuracy of genomic prediction showed that accuracy will be higher when each validation line had at least one close relationship to the reference lines. Overall, the prediction accuracies achieved in this study are encouraging, and confirm the feasibility of genomic selection in wheat. In several instances, estimated marker effects were confirmed by published literature and results of mapping experiments using Watkins accessions.

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Year:  2014        PMID: 24965887     DOI: 10.1007/s00122-014-2341-8

Source DB:  PubMed          Journal:  Theor Appl Genet        ISSN: 0040-5752            Impact factor:   5.699


  20 in total

1.  The impact of genetic architecture on genome-wide evaluation methods.

Authors:  Hans D Daetwyler; Ricardo Pong-Wong; Beatriz Villanueva; John A Woolliams
Journal:  Genetics       Date:  2010-04-20       Impact factor: 4.562

2.  The impact of genetic relationship information on genome-assisted breeding values.

Authors:  D Habier; R L Fernando; J C M Dekkers
Journal:  Genetics       Date:  2007-12       Impact factor: 4.562

3.  Increased accuracy of artificial selection by using the realized relationship matrix.

Authors:  B J Hayes; P M Visscher; M E Goddard
Journal:  Genet Res (Camb)       Date:  2009-02       Impact factor: 1.588

4.  Genomic selection: prediction of accuracy and maximisation of long term response.

Authors:  Mike Goddard
Journal:  Genetica       Date:  2008-08-14       Impact factor: 1.082

5.  Prediction of genetic values of quantitative traits in plant breeding using pedigree and molecular markers.

Authors:  José Crossa; Gustavo de Los Campos; Paulino Pérez; Daniel Gianola; Juan Burgueño; José Luis Araus; Dan Makumbi; Ravi P Singh; Susanne Dreisigacker; Jianbing Yan; Vivi Arief; Marianne Banziger; Hans-Joachim Braun
Journal:  Genetics       Date:  2010-09-02       Impact factor: 4.562

6.  Common SNPs explain a large proportion of the heritability for human height.

Authors:  Jian Yang; Beben Benyamin; Brian P McEvoy; Scott Gordon; Anjali K Henders; Dale R Nyholt; Pamela A Madden; Andrew C Heath; Nicholas G Martin; Grant W Montgomery; Michael E Goddard; Peter M Visscher
Journal:  Nat Genet       Date:  2010-06-20       Impact factor: 38.330

7.  Reliability of direct genomic values for animals with different relationships within and to the reference population.

Authors:  M Pszczola; T Strabel; H A Mulder; M P L Calus
Journal:  J Dairy Sci       Date:  2012-01       Impact factor: 4.034

8.  Genetic diversity and linkage disequilibrium in Chinese bread wheat (Triticum aestivum L.) revealed by SSR markers.

Authors:  Chenyang Hao; Lanfen Wang; Hongmei Ge; Yuchen Dong; Xueyong Zhang
Journal:  PLoS One       Date:  2011-02-18       Impact factor: 3.240

9.  A function accounting for training set size and marker density to model the average accuracy of genomic prediction.

Authors:  Malena Erbe; Birgit Gredler; Franz Reinhold Seefried; Beat Bapst; Henner Simianer
Journal:  PLoS One       Date:  2013-12-05       Impact factor: 3.240

10.  Imputation of unordered markers and the impact on genomic selection accuracy.

Authors:  Jessica E Rutkoski; Jesse Poland; Jean-Luc Jannink; Mark E Sorrells
Journal:  G3 (Bethesda)       Date:  2013-03-01       Impact factor: 3.154

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  34 in total

1.  Mapping of a new stem rust resistance gene Sr49 in chromosome 5B of wheat.

Authors:  Urmil K Bansal; Sher Muhammad; Kerrie L Forrest; Matthew J Hayden; Harbans S Bariana
Journal:  Theor Appl Genet       Date:  2015-07-12       Impact factor: 5.699

Review 2.  Wheat genetic resources in the post-genomics era: promise and challenges.

Authors:  Awais Rasheed; Abdul Mujeeb-Kazi; Francis Chuks Ogbonnaya; Zhonghu He; Sanjaya Rajaram
Journal:  Ann Bot       Date:  2018-03-14       Impact factor: 4.357

3.  Diversity analysis and genomic prediction of Sclerotinia resistance in sunflower using a new 25 K SNP genotyping array.

Authors:  Maren Livaja; Sandra Unterseer; Wiltrud Erath; Christina Lehermeier; Ralf Wieseke; Jörg Plieske; Andreas Polley; Hartmut Luerßen; Silke Wieckhorst; Martin Mascher; Volker Hahn; Milena Ouzunova; Chris-Carolin Schön; Martin W Ganal
Journal:  Theor Appl Genet       Date:  2015-11-04       Impact factor: 5.699

4.  Identification of a new source of stripe rust resistance Yr82 in wheat.

Authors:  Kandiah Pakeerathan; Harbans Bariana; Naeela Qureshi; Debbie Wong; Matthew Hayden; Urmil Bansal
Journal:  Theor Appl Genet       Date:  2019-08-28       Impact factor: 5.699

Review 5.  From markers to genome-based breeding in wheat.

Authors:  Awais Rasheed; Xianchun Xia
Journal:  Theor Appl Genet       Date:  2019-01-23       Impact factor: 5.699

6.  Detection and validation of genomic regions associated with resistance to rust diseases in a worldwide hexaploid wheat landrace collection using BayesR and mixed linear model approaches.

Authors:  Raj K Pasam; Urmil Bansal; Hans D Daetwyler; Kerrie L Forrest; Debbie Wong; Joanna Petkowski; Nicholas Willey; Mandeep Randhawa; Mumta Chhetri; Hanif Miah; Josquin Tibbits; Harbans Bariana; Matthew J Hayden
Journal:  Theor Appl Genet       Date:  2017-03-02       Impact factor: 5.699

7.  Accelerating wheat breeding for end-use quality with multi-trait genomic predictions incorporating near infrared and nuclear magnetic resonance-derived phenotypes.

Authors:  B J Hayes; J Panozzo; C K Walker; A L Choy; S Kant; D Wong; J Tibbits; H D Daetwyler; S Rochfort; M J Hayden; G C Spangenberg
Journal:  Theor Appl Genet       Date:  2017-08-24       Impact factor: 5.699

8.  Genomic prediction of agronomic traits in wheat using different models and cross-validation designs.

Authors:  Teketel A Haile; Sean Walkowiak; Amidou N'Diaye; John M Clarke; Pierre J Hucl; Richard D Cuthbert; Ron E Knox; Curtis J Pozniak
Journal:  Theor Appl Genet       Date:  2020-11-01       Impact factor: 5.699

9.  High-resolution genome-wide association study and genomic prediction for disease resistance and cold tolerance in wheat.

Authors:  Yunlong Pang; Yuye Wu; Chunxia Liu; Wenhui Li; Paul St Amand; Amy Bernardo; Danfeng Wang; Lei Dong; Xiufang Yuan; Huirui Zhang; Meng Zhao; Linzhi Li; Liming Wang; Fang He; Yunlong Liang; Qiang Yan; Yue Lu; Yu Su; Hongming Jiang; Jiajie Wu; Anfei Li; Lingrang Kong; Guihua Bai; Shubing Liu
Journal:  Theor Appl Genet       Date:  2021-06-01       Impact factor: 5.699

10.  Unlocking new alleles for leaf rust resistance in the Vavilov wheat collection.

Authors:  Adnan Riaz; Naveenkumar Athiyannan; Sambasivam K Periyannan; Olga Afanasenko; Olga P Mitrofanova; Gregory J Platz; Elizabeth A B Aitken; Rod J Snowdon; Evans S Lagudah; Lee T Hickey; Kai P Voss-Fels
Journal:  Theor Appl Genet       Date:  2017-10-04       Impact factor: 5.699

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