Literature DB >> 35451791

Genomic Prediction: Progress and Perspectives for Rice Improvement.

Jérôme Bartholomé1,2,3, Parthiban Thathapalli Prakash4, Joshua N Cobb5.   

Abstract

Genomic prediction can be a powerful tool to achieve greater rates of genetic gain for quantitative traits if thoroughly integrated into a breeding strategy. In rice as in other crops, the interest in genomic prediction is very strong with a number of studies addressing multiple aspects of its use, ranging from the more conceptual to the more practical. In this chapter, we review the literature on rice (Oryza sativa) and summarize important considerations for the integration of genomic prediction in breeding programs. The irrigated breeding program at the International Rice Research Institute is used as a concrete example on which we provide data and R scripts to reproduce the analysis but also to highlight practical challenges regarding the use of predictions. The adage "To someone with a hammer, everything looks like a nail" describes a common psychological pitfall that sometimes plagues the integration and application of new technologies to a discipline. We have designed this chapter to help rice breeders avoid that pitfall and appreciate the benefits and limitations of applying genomic prediction, as it is not always the best approach nor the first step to increasing the rate of genetic gain in every context.
© 2022. The Author(s).

Entities:  

Keywords:  Breeding program; Genomic prediction; Genomic selection; Oryza sativa; Rice

Mesh:

Year:  2022        PMID: 35451791     DOI: 10.1007/978-1-0716-2205-6_21

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  109 in total

1.  Marker-assisted selection to introgress rice QTLs controlling root traits into an Indian upland rice variety.

Authors:  K A Steele; A H Price; H E Shashidhar; J R Witcombe
Journal:  Theor Appl Genet       Date:  2005-10-06       Impact factor: 5.699

Review 2.  Genomic selection.

Authors:  M E Goddard; B J Hayes
Journal:  J Anim Breed Genet       Date:  2007-12       Impact factor: 2.380

3.  Invited review: reliability of genomic predictions for North American Holstein bulls.

Authors:  P M VanRaden; C P Van Tassell; G R Wiggans; T S Sonstegard; R D Schnabel; J F Taylor; F S Schenkel
Journal:  J Dairy Sci       Date:  2009-01       Impact factor: 4.034

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.  Genomic prediction unifies animal and plant breeding programs to form platforms for biological discovery.

Authors:  John M Hickey; Tinashe Chiurugwi; Ian Mackay; Wayne Powell
Journal:  Nat Genet       Date:  2017-08-30       Impact factor: 38.330

Review 6.  Genomic Selection in Plant Breeding: Methods, Models, and Perspectives.

Authors:  José Crossa; Paulino Pérez-Rodríguez; Jaime Cuevas; Osval Montesinos-López; Diego Jarquín; Gustavo de Los Campos; Juan Burgueño; Juan M González-Camacho; Sergio Pérez-Elizalde; Yoseph Beyene; Susanne Dreisigacker; Ravi Singh; Xuecai Zhang; Manje Gowda; Manish Roorkiwal; Jessica Rutkoski; Rajeev K Varshney
Journal:  Trends Plant Sci       Date:  2017-09-28       Impact factor: 18.313

Review 7.  Enhancing genetic gain in the era of molecular breeding.

Authors:  Yunbi Xu; Ping Li; Cheng Zou; Yanli Lu; Chuanxiao Xie; Xuecai Zhang; Boddupalli M Prasanna; Michael S Olsen
Journal:  J Exp Bot       Date:  2017-05-17       Impact factor: 6.992

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.  Isozymes and classification of Asian rice varieties.

Authors:  J C Glaszmann
Journal:  Theor Appl Genet       Date:  1987-05       Impact factor: 5.699

Review 10.  Back to the future: revisiting MAS as a tool for modern plant breeding.

Authors:  Joshua N Cobb; Partha S Biswas; J Damien Platten
Journal:  Theor Appl Genet       Date:  2018-12-17       Impact factor: 5.699

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