Literature DB >> 30569365

Accelerating crop genetic gains with genomic selection.

Kai Peter Voss-Fels1, Mark Cooper1, Ben John Hayes2.   

Abstract

KEY MESSAGE: Genomic prediction based on additive genetic effects can accelerate genetic gain. There are opportunities for further improvement by including non-additive effects that access untapped sources of genetic diversity. Several studies have reported a worrying gap between the projected global future demand for plant-based products and the current annual rates of production increase, indicating that enhancing the rate of genetic gain might be critical for future food security. Therefore, new breeding technologies and strategies are required to significantly boost genetic improvement of future crop cultivars. Genomic selection (GS) has delivered considerable genetic gain in animal breeding and is becoming an essential component of many modern plant breeding programmes as well. In this paper, we review the lessons learned from implementing GS in livestock and the impact of GS on crop breeding, and discuss important features for the success of GS under different breeding scenarios. We highlight major challenges associated with GS including rapid genotyping, phenotyping, genotype-by-environment interaction and non-additivity and give examples for opportunities to overcome these issues. Finally, the potential of combining GS with other modern technologies in order to maximise the rate of crop genetic improvement is discussed, including the potential of increasing prediction accuracy by integration of crop growth models in GS frameworks.

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Year:  2018        PMID: 30569365     DOI: 10.1007/s00122-018-3270-8

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


  89 in total

1.  Prediction of total genetic value using genome-wide dense marker maps.

Authors:  T H Meuwissen; B J Hayes; M E Goddard
Journal:  Genetics       Date:  2001-04       Impact factor: 4.562

2.  The GP problem: quantifying gene-to-phenotype relationships.

Authors:  Mark Cooper; Scott C Chapman; Dean W Podlich; Graeme L Hammer
Journal:  In Silico Biol       Date:  2002

Review 3.  21st century wheat breeding: plot selection or plate detection?

Authors:  Robert M D Koebner; Richard W Summers
Journal:  Trends Biotechnol       Date:  2003-02       Impact factor: 19.536

Review 4.  Structure of linkage disequilibrium in plants.

Authors:  Sherry A Flint-Garcia; Jeffry M Thornsberry; Edward S Buckler
Journal:  Annu Rev Plant Biol       Date:  2003       Impact factor: 26.379

Review 5.  Models for navigating biological complexity in breeding improved crop plants.

Authors:  Graeme Hammer; Mark Cooper; François Tardieu; Stephen Welch; Bruce Walsh; Fred van Eeuwijk; Scott Chapman; Dean Podlich
Journal:  Trends Plant Sci       Date:  2006-11-07       Impact factor: 18.313

6.  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

7.  A mixed-model quantitative trait loci (QTL) analysis for multiple-environment trial data using environmental covariables for QTL-by-environment interactions, with an example in maize.

Authors:  Martin P Boer; Deanne Wright; Lizhi Feng; Dean W Podlich; Lang Luo; Mark Cooper; Fred A van Eeuwijk
Journal:  Genetics       Date:  2007-10-18       Impact factor: 4.562

8.  Accuracy of predicting the genetic risk of disease using a genome-wide approach.

Authors:  Hans D Daetwyler; Beatriz Villanueva; John A Woolliams
Journal:  PLoS One       Date:  2008-10-14       Impact factor: 3.240

9.  Rapid SNP discovery and genetic mapping using sequenced RAD markers.

Authors:  Nathan A Baird; Paul D Etter; Tressa S Atwood; Mark C Currey; Anthony L Shiver; Zachary A Lewis; Eric U Selker; William A Cresko; Eric A Johnson
Journal:  PLoS One       Date:  2008-10-13       Impact factor: 3.240

Review 10.  Data and theory point to mainly additive genetic variance for complex traits.

Authors:  William G Hill; Michael E Goddard; Peter M Visscher
Journal:  PLoS Genet       Date:  2008-02-29       Impact factor: 5.917

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  53 in total

Review 1.  Omics-Facilitated Crop Improvement for Climate Resilience and Superior Nutritive Value.

Authors:  Tinashe Zenda; Songtao Liu; Anyi Dong; Jiao Li; Yafei Wang; Xinyue Liu; Nan Wang; Huijun Duan
Journal:  Front Plant Sci       Date:  2021-12-01       Impact factor: 5.753

2.  Can we harness digital technologies and physiology to hasten genetic gain in US maize breeding?

Authors:  Christine H Diepenbrock; Tom Tang; Michael Jines; Frank Technow; Sara Lira; Dean Podlich; Mark Cooper; Carlos Messina
Journal:  Plant Physiol       Date:  2022-02-04       Impact factor: 8.340

3.  Enviromics in breeding: applications and perspectives on envirotypic-assisted selection.

Authors:  Rafael T Resende; Hans-Peter Piepho; Guilherme J M Rosa; Orzenil B Silva-Junior; Fabyano F E Silva; Marcos Deon V de Resende; Dario Grattapaglia
Journal:  Theor Appl Genet       Date:  2020-09-22       Impact factor: 5.699

Review 4.  Hotter, drier, CRISPR: the latest edit on climate change.

Authors:  Karen Massel; Yasmine Lam; Albert C S Wong; Lee T Hickey; Andrew K Borrell; Ian D Godwin
Journal:  Theor Appl Genet       Date:  2021-01-08       Impact factor: 5.699

5.  Genome-Wide Association Analysis and Genomic Prediction for Adult-Plant Resistance to Septoria Tritici Blotch and Powdery Mildew in Winter Wheat.

Authors:  Admas Alemu; Gintaras Brazauskas; David S Gaikpa; Tina Henriksson; Bulat Islamov; Lise Nistrup Jørgensen; Mati Koppel; Reine Koppel; Žilvinas Liatukas; Jan T Svensson; Aakash Chawade
Journal:  Front Genet       Date:  2021-05-12       Impact factor: 4.599

Review 6.  Scaling up high-throughput phenotyping for abiotic stress selection in the field.

Authors:  Daniel T Smith; Andries B Potgieter; Scott C Chapman
Journal:  Theor Appl Genet       Date:  2021-06-02       Impact factor: 5.699

Review 7.  Tackling G × E × M interactions to close on-farm yield-gaps: creating novel pathways for crop improvement by predicting contributions of genetics and management to crop productivity.

Authors:  Mark Cooper; Kai P Voss-Fels; Carlos D Messina; Tom Tang; Graeme L Hammer
Journal:  Theor Appl Genet       Date:  2021-03-18       Impact factor: 5.699

8.  Unlocking big data doubled the accuracy in predicting the grain yield in hybrid wheat.

Authors:  Yusheng Zhao; Patrick Thorwarth; Yong Jiang; Norman Philipp; Albert W Schulthess; Mario Gils; Philipp H G Boeven; C Friedrich H Longin; Johannes Schacht; Erhard Ebmeyer; Viktor Korzun; Vilson Mirdita; Jost Dörnte; Ulrike Avenhaus; Ralf Horbach; Hilmar Cöster; Josef Holzapfel; Ludwig Ramgraber; Simon Kühnle; Pierrick Varenne; Anne Starke; Friederike Schürmann; Sebastian Beier; Uwe Scholz; Fang Liu; Renate H Schmidt; Jochen C Reif
Journal:  Sci Adv       Date:  2021-06-11       Impact factor: 14.136

9.  Multi-Species Genomics-Enabled Selection for Improving Agroecosystems Across Space and Time.

Authors:  Marnin D Wolfe; Jean-Luc Jannink; Michael B Kantar; Nicholas Santantonio
Journal:  Front Plant Sci       Date:  2021-06-23       Impact factor: 5.753

Review 10.  Advances in Cereal Crop Genomics for Resilience under Climate Change.

Authors:  Tinashe Zenda; Songtao Liu; Anyi Dong; Huijun Duan
Journal:  Life (Basel)       Date:  2021-05-29
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