Literature DB >> 34724078

Genome-based prediction of agronomic traits in spring wheat under conventional and organic management systems.

Kassa Semagn1, Muhammad Iqbal1, José Crossa2, Diego Jarquin3, Reka Howard3, Hua Chen1,4, Darcy H Bemister1, Brian L Beres5, Harpinder Randhawa5, Amidou N'Diaye6, Curtis Pozniak6, Dean Spaner7.   

Abstract

KEY MESSAGE: Using phenotype data of three spring wheat populations evaluated at 6-15 environments under two management systems, we found moderate to very high prediction accuracies across seven traits. The phenotype data collected under an organic management system effectively predicted the performance of lines in the conventional management and vice versa. There is growing interest in developing wheat cultivars specifically for organic agriculture, but we are not aware of the effect of organic management on the predictive ability of genomic selection (GS). Here, we evaluated within populations prediction accuracies of four GS models, four combinations of training and testing sets, three reaction norm models, and three random cross-validations (CV) schemes in three populations phenotyped under organic and conventional management systems. Our study was based on a total of 578 recombinant inbred lines and varieties from three spring wheat populations, which were evaluated for seven traits at 3-9 conventionally and 3-6 organically managed field environments and genotyped either with the wheat 90 K SNP array or DArTseq. We predicted the management systems (CV0M) or environments (CV0), a subset of lines that have been evaluated in either management (CV2M) or some environments (CV2), and the performance of newly developed lines in either management (CV1M) or environments (CV1). The average prediction accuracies of the model that incorporated genotype × environment interactions with CV0 and CV2 schemes varied from 0.69 to 0.97. In the CV1 and CV1M schemes, prediction accuracies ranged from - 0.12 to 0.77 depending on the reaction norm models, the traits, and populations. In most cases, grain protein showed the highest prediction accuracies. The phenotype data collected under the organic management effectively predicted the performance of lines under conventional management and vice versa. This is the first comprehensive GS study that investigated the effect of the organic management system in wheat.
© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

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Year:  2021        PMID: 34724078     DOI: 10.1007/s00122-021-03982-0

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


  41 in total

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Authors:  Filippo M Bassi; Alison R Bentley; Gilles Charmet; Rodomiro Ortiz; Jose Crossa
Journal:  Plant Sci       Date:  2015-09-06       Impact factor: 4.729

2.  TASSEL: software for association mapping of complex traits in diverse samples.

Authors:  Peter J Bradbury; Zhiwu Zhang; Dallas E Kroon; Terry M Casstevens; Yogesh Ramdoss; Edward S Buckler
Journal:  Bioinformatics       Date:  2007-06-22       Impact factor: 6.937

3.  A pseudo-response regulator is misexpressed in the photoperiod insensitive Ppd-D1a mutant of wheat (Triticum aestivum L.).

Authors:  James Beales; Adrian Turner; Simon Griffiths; John W Snape; David A Laurie
Journal:  Theor Appl Genet       Date:  2007-07-19       Impact factor: 5.699

4.  Different models of genetic variation and their effect on genomic evaluation.

Authors:  Samuel A Clark; John M Hickey; Julius H J van der Werf
Journal:  Genet Sel Evol       Date:  2011-05-17       Impact factor: 4.297

Review 5.  Conventional breeding, marker-assisted selection, genomic selection and inbreeding in clonally propagated crops: a case study for cassava.

Authors:  Hernán Ceballos; Robert S Kawuki; Vernon E Gracen; G Craig Yencho; Clair H Hershey
Journal:  Theor Appl Genet       Date:  2015-06-21       Impact factor: 5.699

6.  Molecular characterization of vernalization and response genes in bread wheat from the Yellow and Huai Valley of China.

Authors:  Feng Chen; Manxia Gao; Jianghua Zhang; Aihui Zuo; Xiaoli Shang; Dangqun Cui
Journal:  BMC Plant Biol       Date:  2013-12-05       Impact factor: 4.215

Review 7.  Enhancing the rate of genetic gain in public-sector plant breeding programs: lessons from the breeder's equation.

Authors:  Joshua N Cobb; Roselyne U Juma; Partha S Biswas; Juan D Arbelaez; Jessica Rutkoski; Gary Atlin; Tom Hagen; Michael Quinn; Eng Hwa Ng
Journal:  Theor Appl Genet       Date:  2019-03-01       Impact factor: 5.699

8.  Identification of quantitative trait loci associated with nitrogen use efficiency in winter wheat.

Authors:  Kyle Brasier; Brian Ward; Jared Smith; John Seago; Joseph Oakes; Maria Balota; Paul Davis; Myron Fountain; Gina Brown-Guedira; Clay Sneller; Wade Thomason; Carl Griffey
Journal:  PLoS One       Date:  2020-02-24       Impact factor: 3.240

9.  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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  5 in total

1.  Comparison of single-trait and multi-trait genomic predictions on agronomic and disease resistance traits in spring wheat.

Authors:  Kassa Semagn; José Crossa; Jaime Cuevas; Muhammad Iqbal; Izabela Ciechanowska; Maria Antonia Henriquez; Harpinder Randhawa; Brian L Beres; Reem Aboukhaddour; Brent D McCallum; Anita L Brûlé-Babel; Amidou N'Diaye; Curtis Pozniak; Dean Spaner
Journal:  Theor Appl Genet       Date:  2022-06-23       Impact factor: 5.574

2.  Genomic Prediction Accuracy of Stripe Rust in Six Spring Wheat Populations by Modeling Genotype by Environment Interaction.

Authors:  Kassa Semagn; Muhammad Iqbal; Diego Jarquin; Harpinder Randhawa; Reem Aboukhaddour; Reka Howard; Izabela Ciechanowska; Momna Farzand; Raman Dhariwal; Colin W Hiebert; Amidou N'Diaye; Curtis Pozniak; Dean Spaner
Journal:  Plants (Basel)       Date:  2022-06-30

3.  Genomic Predictions for Common Bunt, FHB, Stripe Rust, Leaf Rust, and Leaf Spotting Resistance in Spring Wheat.

Authors:  Kassa Semagn; Muhammad Iqbal; Diego Jarquin; José Crossa; Reka Howard; Izabela Ciechanowska; Maria Antonia Henriquez; Harpinder Randhawa; Reem Aboukhaddour; Brent D McCallum; Anita L Brûlé-Babel; Alireza Navabi; Amidou N'Diaye; Curtis Pozniak; Dean Spaner
Journal:  Genes (Basel)       Date:  2022-03-23       Impact factor: 4.141

4.  Genome-based prediction of agronomic traits in spring wheat under conventional and organic management systems.

Authors:  Kassa Semagn; Muhammad Iqbal; José Crossa; Diego Jarquin; Reka Howard; Hua Chen; Darcy H Bemister; Brian L Beres; Harpinder Randhawa; Amidou N'Diaye; Curtis Pozniak; Dean Spaner
Journal:  Theor Appl Genet       Date:  2021-11-01       Impact factor: 5.699

5.  Identification of Spring Wheat with Superior Agronomic Performance under Contrasting Nitrogen Managements Using Linear Phenotypic Selection Indices.

Authors:  Muhammad Iqbal; Kassa Semagn; J Jesus Céron-Rojas; José Crossa; Diego Jarquin; Reka Howard; Brian L Beres; Klaus Strenzke; Izabela Ciechanowska; Dean Spaner
Journal:  Plants (Basel)       Date:  2022-07-20
  5 in total

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