Literature DB >> 27858103

Validating the prediction accuracies of marker-assisted and genomic selection of Fusarium head blight resistance in wheat using an independent sample.

Yong Jiang1, Albert Wilhelm Schulthess1, Bernd Rodemann2, Jie Ling1, Jörg Plieske3, Sonja Kollers4, Erhard Ebmeyer4, Viktor Korzun4, Odile Argillier5, Gunther Stiewe6, Martin W Ganal3, Marion S Röder1, Jochen C Reif7.   

Abstract

KEY MESSAGE: Compared with independent validation, cross-validation simultaneously sampling genotypes and environments provided similar estimates of accuracy for genomic selection, but inflated estimates for marker-assisted selection. Estimates of prediction accuracy of marker-assisted (MAS) and genomic selection (GS) require validations. The main goal of our study was to compare the prediction accuracies of MAS and GS validated in an independent sample with results obtained from fivefold cross-validation using genomic and phenotypic data for Fusarium head blight resistance in wheat. In addition, the applicability of the reliability criterion, a concept originally developed in the context of classic animal breeding and GS, was explored for MAS. We observed that prediction accuracies of MAS were overestimated by 127% using cross-validation sampling genotype and environments in contrast to independent validation. In contrast, prediction accuracies of GS determined in independent samples are similar to those estimated with cross-validation sampling genotype and environments. This can be explained by small population differentiation between the training and validation sets in our study. For European wheat breeding, which is so far characterized by a slow temporal dynamic in allele frequencies, this assumption seems to be realistic. Thus, GS models used to improve European wheat populations are expected to possess a long-lasting validity. Since quantitative trait loci information can be exploited more precisely if the predicted genotype is more related to the training population, the reliability criterion is also a valuable tool to judge the level of prediction accuracy of individual genotypes in MAS.

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Year:  2016        PMID: 27858103     DOI: 10.1007/s00122-016-2827-7

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


  35 in total

1.  Best linear unbiased estimation and prediction under a selection model.

Authors:  C R Henderson
Journal:  Biometrics       Date:  1975-06       Impact factor: 2.571

2.  Efficient methods to compute genomic predictions.

Authors:  P M VanRaden
Journal:  J Dairy Sci       Date:  2008-11       Impact factor: 4.034

3.  Relatedness severely impacts accuracy of marker-assisted selection for disease resistance in hybrid wheat.

Authors:  M Gowda; Y Zhao; T Würschum; C F H Longin; T Miedaner; E Ebmeyer; R Schachschneider; E Kazman; J Schacht; J-P Martinant; M F Mette; J C Reif
Journal:  Heredity (Edinb)       Date:  2013-12-18       Impact factor: 3.821

4.  Quantitative trait locus (QTL) mapping using different testers and independent population samples in maize reveals low power of QTL detection and large bias in estimates of QTL effects.

Authors:  A E Melchinger; H F Utz; C C Schön
Journal:  Genetics       Date:  1998-05       Impact factor: 4.562

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

Review 6.  Raising yield potential of wheat. I. Overview of a consortium approach and breeding strategies.

Authors:  Matthew Reynolds; David Bonnett; Scott C Chapman; Robert T Furbank; Yann Manès; Diane E Mather; Martin A J Parry
Journal:  J Exp Bot       Date:  2010-10-15       Impact factor: 6.992

7.  Meta-analysis of the effects of triazole-based fungicides on wheat yield and test weight as influenced by Fusarium head blight intensity.

Authors:  P A Paul; M P McMullen; D E Hershman; L V Madden
Journal:  Phytopathology       Date:  2010-02       Impact factor: 4.025

Review 8.  Invited review: Genomic selection in dairy cattle: progress and challenges.

Authors:  B J Hayes; P J Bowman; A J Chamberlain; M E Goddard
Journal:  J Dairy Sci       Date:  2009-02       Impact factor: 4.034

9.  Whole genome association mapping of Fusarium head blight resistance in European winter wheat (Triticum aestivum L.).

Authors:  Sonja Kollers; Bernd Rodemann; Jie Ling; Viktor Korzun; Erhard Ebmeyer; Odile Argillier; Maike Hinze; Jörg Plieske; Dagmar Kulosa; Martin W Ganal; Marion S Röder
Journal:  PLoS One       Date:  2013-02-22       Impact factor: 3.240

10.  Genomic Prediction of Testcross Performance in Canola (Brassica napus).

Authors:  Habib U Jan; Amine Abbadi; Sophie Lücke; Richard A Nichols; Rod J Snowdon
Journal:  PLoS One       Date:  2016-01-29       Impact factor: 3.240

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  14 in total

Review 1.  Fusarium head blight in wheat: contemporary status and molecular approaches.

Authors:  Mohd Kamran Khan; Anamika Pandey; Tabinda Athar; Saumya Choudhary; Ravi Deval; Sait Gezgin; Mehmet Hamurcu; Ali Topal; Emel Atmaca; Pamela Aracena Santos; Makbule Rumeysa Omay; Hatice Suslu; Kamer Gulcan; Merve Inanc; Mahinur S Akkaya; Abdullah Kahraman; George Thomas
Journal:  3 Biotech       Date:  2020-03-18       Impact factor: 2.406

2.  Extension of a haplotype-based genomic prediction model to manage multi-environment wheat data using environmental covariates.

Authors:  Sang He; Rebecca Thistlethwaite; Kerrie Forrest; Fan Shi; Matthew J Hayden; Richard Trethowan; Hans D Daetwyler
Journal:  Theor Appl Genet       Date:  2019-08-21       Impact factor: 5.699

Review 3.  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

4.  Genetic insights into underground responses to Fusarium graminearum infection in wheat.

Authors:  Kai P Voss-Fels; Lunwen Qian; Iulian Gabur; Christian Obermeier; Lee T Hickey; Christian R Werner; Stefan Kontowski; Matthias Frisch; Wolfgang Friedt; Rod J Snowdon; Sven Gottwald
Journal:  Sci Rep       Date:  2018-09-03       Impact factor: 4.379

5.  Genomic Prediction for 25 Agronomic and Quality Traits in Alfalfa (Medicago sativa).

Authors:  Congjun Jia; Fuping Zhao; Xuemin Wang; Jianlin Han; Haiming Zhao; Guibo Liu; Zan Wang
Journal:  Front Plant Sci       Date:  2018-08-20       Impact factor: 5.753

6.  Exploring and exploiting the genetic variation of Fusarium head blight resistance for genomic-assisted breeding in the elite durum wheat gene pool.

Authors:  Barbara Steiner; Sebastian Michel; Marco Maccaferri; Marc Lemmens; Roberto Tuberosa; Hermann Buerstmayr
Journal:  Theor Appl Genet       Date:  2018-12-01       Impact factor: 5.699

7.  Genetic Mapping and Prediction Analysis of FHB Resistance in a Hard Red Spring Wheat Breeding Population.

Authors:  Yuan Liu; Evan Salsman; Jason D Fiedler; Justin B Hegstad; Andrew Green; Mohamed Mergoum; Shaobin Zhong; Xuehui Li
Journal:  Front Plant Sci       Date:  2019-08-06       Impact factor: 5.753

8.  Characterization of the Powdery Mildew Resistance Gene in the Elite Wheat Cultivar Jimai 23 and Its Application in Marker-Assisted Selection.

Authors:  Mengshu Jia; Hongxing Xu; Cheng Liu; Ruixi Mao; Haosheng Li; Jianjun Liu; Wenxiao Du; Wenrui Wang; Xu Zhang; Ran Han; Xiaolu Wang; Liru Wu; Xiao Liang; Jiancheng Song; Huagang He; Pengtao Ma
Journal:  Front Genet       Date:  2020-04-02       Impact factor: 4.599

9.  Grain number and grain yield distribution along the spike remain stable despite breeding for high yield in winter wheat.

Authors:  Norman Philipp; Heiko Weichert; Utkarsh Bohra; Winfriede Weschke; Albert Wilhelm Schulthess; Hans Weber
Journal:  PLoS One       Date:  2018-10-10       Impact factor: 3.240

10.  Comparing the Potential of Marker-Assisted Selection and Genomic Prediction for Improving Rust Resistance in Hybrid Wheat.

Authors:  Ulrike Beukert; Patrick Thorwarth; Yusheng Zhao; C Friedrich H Longin; Albrecht Serfling; Frank Ordon; Jochen C Reif
Journal:  Front Plant Sci       Date:  2020-10-28       Impact factor: 5.753

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