1
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Burridge AJ, Winfield M, Przewieslik-Allen A, Edwards KJ, Siddique I, Barral-Arca R, Griffiths S, Cheng S, Huang Z, Feng C, Dreisigacker S, Bentley AR, Brown-Guedira G, Barker GL. Development of a next generation SNP genotyping array for wheat. Plant Biotechnol J 2024. [PMID: 38520342 DOI: 10.1111/pbi.14341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 03/05/2024] [Accepted: 03/06/2024] [Indexed: 03/25/2024]
Abstract
High-throughput genotyping arrays have provided a cost-effective, reliable and interoperable system for genotyping hexaploid wheat and its relatives. Existing, highly cited arrays including our 35K Wheat Breeder's array and the Illumina 90K array were designed based on a limited amount of varietal sequence diversity and with imperfect knowledge of SNP positions. Recent progress in wheat sequencing has given us access to a vast pool of SNP diversity, whilst technological improvements have allowed us to fit significantly more probes onto a 384-well format Axiom array than previously possible. Here we describe a novel Axiom genotyping array, the 'Triticum aestivum Next Generation' array (TaNG), largely derived from whole genome skim sequencing of 204 elite wheat lines and 111 wheat landraces taken from the Watkins 'Core Collection'. We used a novel haplotype optimization approach to select SNPs with the highest combined varietal discrimination and a design iteration step to test and replace SNPs which failed to convert to reliable markers. The final design with 43 372 SNPs contains a combination of haplotype-optimized novel SNPs and legacy cross-platform markers. We show that this design has an improved distribution of SNPs compared to previous arrays and can be used to generate genetic maps with a significantly higher number of distinct bins than our previous array. We also demonstrate the improved performance of TaNGv1.1 for Genome-wide association studies (GWAS) and its utility for Copy Number Variation (CNV) analysis. The array is commercially available with supporting marker annotations and initial genotyping results freely available.
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Affiliation(s)
| | - Mark Winfield
- School of Biological Sciences, University of Bristol, Bristol, UK
| | | | - Keith J Edwards
- School of Biological Sciences, University of Bristol, Bristol, UK
| | - Imteaz Siddique
- Thermo Fisher Scientific, 3450 Central Expressway, Santa Clara, CA, USA
| | - Ruth Barral-Arca
- Thermo Fisher Scientific, 3450 Central Expressway, Santa Clara, CA, USA
| | | | - Shifeng Cheng
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Zejian Huang
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Cong Feng
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | | | | | - Gina Brown-Guedira
- Plant Science Research Unit, USDA Agricultural Research Service, Raleigh, NC, USA
| | - Gary L Barker
- School of Biological Sciences, University of Bristol, Bristol, UK
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2
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Wright TIC, Horsnell R, Love B, Burridge AJ, Gardner KA, Jackson R, Leigh FJ, Ligeza A, Heuer S, Bentley AR, Howell P. A new winter wheat genetic resource harbors untapped diversity from synthetic hexaploid wheat. Theor Appl Genet 2024; 137:73. [PMID: 38451354 PMCID: PMC10920491 DOI: 10.1007/s00122-024-04577-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Accepted: 02/06/2024] [Indexed: 03/08/2024]
Abstract
KEY MESSAGE The NIAB_WW_SHW_NAM population, a large nested association mapping panel, is a useful resource for mapping QTL from synthetic hexaploid wheat that can improve modern elite wheat cultivars. The allelic richness harbored in progenitors of hexaploid bread wheat (Triticum aestivum L.) is a useful resource for addressing the genetic diversity bottleneck in modern cultivars. Synthetic hexaploid wheat (SHW) is created through resynthesis of the hybridisation events between the tetraploid (Triticum turgidum subsp. durum Desf.) and diploid (Aegilops tauschii Coss.) bread wheat progenitors. We developed a large and diverse winter wheat nested association mapping (NAM) population (termed the NIAB_WW_SHW_NAM) consisting of 3241 genotypes derived from 54 nested back-cross 1 (BC1) populations, each formed via back-crossing a different primary SHW into the UK winter wheat cultivar 'Robigus'. The primary SHW lines were created using 15 T. durum donors and 47 Ae. tauschii accessions that spanned the lineages and geographical range of the species. Primary SHW parents were typically earlier flowering, taller and showed better resistance to yellow rust infection (Yr) than 'Robigus'. The NIAB_WW_SHW_NAM population was genotyped using a single nucleotide polymorphism (SNP) array and 27 quantitative trait loci (QTLs) were detected for flowering time, plant height and Yr resistance. Across multiple field trials, a QTL for Yr resistance was found on chromosome 4D that corresponded to the Yr28 resistance gene previously reported in other SHW lines. These results demonstrate the value of the NIAB_WW_SHW_NAM population for genetic mapping and provide the first evidence of Yr28 working in current UK environments and genetic backgrounds. These examples, coupled with the evidence of commercial wheat breeders selecting promising genotypes, highlight the potential value of the NIAB_WW_SHW_NAM to variety improvement.
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Affiliation(s)
- Tally I C Wright
- The John Bingham Laboratory, NIAB, 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK.
| | - Richard Horsnell
- The John Bingham Laboratory, NIAB, 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK
| | - Bethany Love
- The John Bingham Laboratory, NIAB, 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK
| | | | - Keith A Gardner
- The John Bingham Laboratory, NIAB, 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK
- International Maize and Wheat Improvement Center (CIMMYT), El Batan, Mexico
| | - Robert Jackson
- The John Bingham Laboratory, NIAB, 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK
| | - Fiona J Leigh
- The John Bingham Laboratory, NIAB, 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK
| | - Aleksander Ligeza
- The John Bingham Laboratory, NIAB, 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK
- Processors and Growers Research Organization (PGRO), The Research Station, Thornhaugh, Peterborough, PE8 6HJ, UK
| | - Sigrid Heuer
- The John Bingham Laboratory, NIAB, 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK
| | - Alison R Bentley
- The John Bingham Laboratory, NIAB, 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK
- Research School of Biology, Australian National University, Canberra, ACT, 2600, Australia
| | - Philip Howell
- The John Bingham Laboratory, NIAB, 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK
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3
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Horsnell R, Leigh FJ, Wright TIC, Burridge AJ, Ligeza A, Przewieslik-Allen AM, Howell P, Uauy C, Edwards KJ, Bentley AR. A wheat chromosome segment substitution line series supports characterization and use of progenitor genetic variation. Plant Genome 2024; 17:e20288. [PMID: 36718796 DOI: 10.1002/tpg2.20288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 10/20/2022] [Indexed: 06/18/2023]
Abstract
Genome-wide introgression and substitution lines have been developed in many plant species, enhancing mapping precision, gene discovery, and the identification and exploitation of variation from wild relatives. Created over multiple generations of crossing and/or backcrossing accompanied by marker-assisted selection, the resulting introgression lines are a fixed genetic resource. In this study we report the development of spring wheat (Triticum aestivum L.) chromosome segment substitution lines (CSSLs) generated to systematically capture genetic variation from tetraploid (T. turgidum ssp. dicoccoides) and diploid (Aegilops tauschii) progenitor species. Generated in a common genetic background over four generations of backcrossing, this is a base resource for the mapping and characterization of wheat progenitor variation. To facilitate further exploitation the final population was genetically characterized using a high-density genotyping array and a range of agronomic and grain traits assessed to demonstrate the potential use of the populations for trait localization in wheat.
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Affiliation(s)
- Richard Horsnell
- The John Bingham Laboratory, NIAB, 93 Lawrence Weaver Road, Cambridge, UK
| | - Fiona J Leigh
- The John Bingham Laboratory, NIAB, 93 Lawrence Weaver Road, Cambridge, UK
| | - Tally I C Wright
- The John Bingham Laboratory, NIAB, 93 Lawrence Weaver Road, Cambridge, UK
| | | | - Aleksander Ligeza
- The John Bingham Laboratory, NIAB, 93 Lawrence Weaver Road, Cambridge, UK
| | | | - Philip Howell
- The John Bingham Laboratory, NIAB, 93 Lawrence Weaver Road, Cambridge, UK
| | - Cristobal Uauy
- John Innes Centre, Norwich Research Park, Norwich, NR4 7UH, UK
| | | | - Alison R Bentley
- The John Bingham Laboratory, NIAB, 93 Lawrence Weaver Road, Cambridge, UK
- International Maize and Wheat Improvement Center (CIMMYT), El Batan, Mexico
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4
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Fradgley NS, Bentley AR, Gardner KA, Swarbreck SM, Kerton M. Maintenance of UK bread baking quality: Trends in wheat quality traits over 50 years of breeding and potential for future application of genomic-assisted selection. Plant Genome 2023; 16:e20326. [PMID: 37057385 DOI: 10.1002/tpg2.20326] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 02/22/2023] [Accepted: 02/28/2023] [Indexed: 06/19/2023]
Abstract
Improved selection of wheat varieties with high end-use quality contributes to sustainable food systems by ensuring productive crops are suitable for human consumption end-uses. Here, we investigated the genetic control and genomic prediction of milling and baking quality traits in a panel of 379 historic and elite, high-quality UK bread wheat (Triticum eastivum L.) varieties and breeding lines. Analysis of the panel showed that genetic diversity has not declined over recent decades of selective breeding while phenotypic analysis found a clear trend of increased loaf baking quality of modern milling wheats despite declining grain protein content. Genome-wide association analysis identified 24 quantitative trait loci (QTL) across all quality traits, many of which had pleiotropic effects. Changes in the frequency of positive alleles of QTL over recent decades reflected trends in trait variation and reveal where progress has historically been made for improved baking quality traits. It also demonstrates opportunities for marker-assisted selection for traits such as Hagberg falling number and specific weight that do not appear to have been improved by recent decades of phenotypic selection. We demonstrate that applying genomic prediction in a commercial wheat breeding program for expensive late-stage loaf baking quality traits outperforms phenotypic selection based on early-stage predictive quality traits. Finally, trait-assisted genomic prediction combining both phenotypic and genomic selection enabled slightly higher prediction accuracy, but genomic prediction alone was the most cost-effective selection strategy considering genotyping and phenotyping costs per sample.
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Affiliation(s)
- Nick S Fradgley
- Genetics and Pre-Breeding Department, National Institute of Agricultural Botany (NIAB), 93 Lawrence Weaver Road, Cambridge, UK
| | - Alison R Bentley
- Genetics and Pre-Breeding Department, National Institute of Agricultural Botany (NIAB), 93 Lawrence Weaver Road, Cambridge, UK
- International Maize and Wheat Improvement Center (CIMMYT), Carretera México-Veracruz, México
| | - Keith A Gardner
- Genetics and Pre-Breeding Department, National Institute of Agricultural Botany (NIAB), 93 Lawrence Weaver Road, Cambridge, UK
- International Maize and Wheat Improvement Center (CIMMYT), Carretera México-Veracruz, México
| | - Stéphanie M Swarbreck
- Genetics and Pre-Breeding Department, National Institute of Agricultural Botany (NIAB), 93 Lawrence Weaver Road, Cambridge, UK
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5
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Montesinos-López OA, Crespo-Herrera L, Saint Pierre C, Bentley AR, de la Rosa-Santamaria R, Ascencio-Laguna JA, Agbona A, Gerard GS, Montesinos-López A, Crossa J. Do feature selection methods for selecting environmental covariables enhance genomic prediction accuracy? Front Genet 2023; 14:1209275. [PMID: 37554404 PMCID: PMC10405933 DOI: 10.3389/fgene.2023.1209275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 07/03/2023] [Indexed: 08/10/2023] Open
Abstract
Genomic selection (GS) is transforming plant and animal breeding, but its practical implementation for complex traits and multi-environmental trials remains challenging. To address this issue, this study investigates the integration of environmental information with genotypic information in GS. The study proposes the use of two feature selection methods (Pearson's correlation and Boruta) for the integration of environmental information. Results indicate that the simple incorporation of environmental covariates may increase or decrease prediction accuracy depending on the case. However, optimal incorporation of environmental covariates using feature selection significantly improves prediction accuracy in four out of six datasets between 14.25% and 218.71% under a leave one environment out cross validation scenario in terms of Normalized Root Mean Squared Error, but not relevant gain was observed in terms of Pearson´s correlation. In two datasets where environmental covariates are unrelated to the response variable, feature selection is unable to enhance prediction accuracy. Therefore, the study provides empirical evidence supporting the use of feature selection to improve the prediction power of GS.
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Affiliation(s)
| | | | | | - Alison R. Bentley
- International Maize and Wheat Improvement Center (CIMMYT), El Battan, Mexico
| | | | | | - Afolabi Agbona
- International Institute of Tropical Agriculture (IITA), Ibadan, Nigeria
- Molecular & Environmental Plant Sciences, Texas A&M University, College Station, TX, United States
| | - Guillermo S. Gerard
- International Maize and Wheat Improvement Center (CIMMYT), El Battan, Mexico
| | - Abelardo Montesinos-López
- Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), Universidad de Guadalajara, Guadalajara, JA, Mexico
| | - José Crossa
- International Maize and Wheat Improvement Center (CIMMYT), El Battan, Mexico
- Colegio de Postgraduados, Campus Montecillos, Montecillos, Mexico
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6
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Jackson R, Buntjer JB, Bentley AR, Lage J, Byrne E, Burt C, Jack P, Berry S, Flatman E, Poupard B, Smith S, Hayes C, Barber T, Love B, Gaynor RC, Gorjanc G, Howell P, Mackay IJ, Hickey JM, Ober ES. Phenomic and genomic prediction of yield on multiple locations in winter wheat. Front Genet 2023; 14:1164935. [PMID: 37229190 PMCID: PMC10203586 DOI: 10.3389/fgene.2023.1164935] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 04/20/2023] [Indexed: 05/27/2023] Open
Abstract
Genomic selection has recently become an established part of breeding strategies in cereals. However, a limitation of linear genomic prediction models for complex traits such as yield is that these are unable to accommodate Genotype by Environment effects, which are commonly observed over trials on multiple locations. In this study, we investigated how this environmental variation can be captured by the collection of a large number of phenomic markers using high-throughput field phenotyping and whether it can increase GS prediction accuracy. For this purpose, 44 winter wheat (Triticum aestivum L.) elite populations, comprising 2,994 lines, were grown on two sites over 2 years, to approximate the size of trials in a practical breeding programme. At various growth stages, remote sensing data from multi- and hyperspectral cameras, as well as traditional ground-based visual crop assessment scores, were collected with approximately 100 different data variables collected per plot. The predictive power for grain yield was tested for the various data types, with or without genome-wide marker data sets. Models using phenomic traits alone had a greater predictive value (R2 = 0.39-0.47) than genomic data (approximately R2 = 0.1). The average improvement in predictive power by combining trait and marker data was 6%-12% over the best phenomic-only model, and performed best when data from one full location was used to predict the yield on an entire second location. The results suggest that genetic gain in breeding programmes can be increased by utilisation of large numbers of phenotypic variables using remote sensing in field trials, although at what stage of the breeding cycle phenomic selection could be most profitably applied remains to be answered.
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Affiliation(s)
- Robert Jackson
- The John Bingham Laboratory, NIAB, Cambridge, United Kingdom
| | - Jaap B. Buntjer
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Scotland, United Kingdom
| | | | - Jacob Lage
- KWS UK Ltd, Thriplow, Royston, Cambridgeshire, United Kingdom
| | - Ed Byrne
- KWS UK Ltd, Thriplow, Royston, Cambridgeshire, United Kingdom
| | - Chris Burt
- RAGT UK, Ickleton, Saffron Walden, Cambridgeshire, United Kingdom
| | - Peter Jack
- RAGT UK, Ickleton, Saffron Walden, Cambridgeshire, United Kingdom
| | - Simon Berry
- Limagrain UK Ltd, Rothwell, Market Rasen, Lincolnshire, United Kingdom
| | - Edward Flatman
- Limagrain UK Ltd, Rothwell, Market Rasen, Lincolnshire, United Kingdom
| | - Bruno Poupard
- Limagrain UK Ltd, Rothwell, Market Rasen, Lincolnshire, United Kingdom
| | - Stephen Smith
- Elsoms Wheat Limited, Spalding, Linconshire, United Kingdom
| | | | - Tobias Barber
- The John Bingham Laboratory, NIAB, Cambridge, United Kingdom
| | - Bethany Love
- The John Bingham Laboratory, NIAB, Cambridge, United Kingdom
| | - R. Chris Gaynor
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Scotland, United Kingdom
| | - Gregor Gorjanc
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Scotland, United Kingdom
| | - Phil Howell
- The John Bingham Laboratory, NIAB, Cambridge, United Kingdom
| | - Ian J. Mackay
- The John Bingham Laboratory, NIAB, Cambridge, United Kingdom
| | - John M. Hickey
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Scotland, United Kingdom
| | - Eric S. Ober
- The John Bingham Laboratory, NIAB, Cambridge, United Kingdom
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7
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Montesinos-López OA, Bentley AR, Pierre CS, Crespo-Herrera L, Rebollar-Ruellas L, Valladares-Celis PE, Lillemo M, Montesinos-López A, Crossa J. Efficacy of plant breeding using genomic information. Plant Genome 2023:e20346. [PMID: 37139645 DOI: 10.1002/tpg2.20346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Revised: 04/05/2023] [Accepted: 04/07/2023] [Indexed: 05/05/2023]
Abstract
Genomic selection (GS) proposed by Meuwissen et al. more than 20 years ago, is revolutionizing plant and animal breeding. Although GS has been widely accepted and applied to plant and animal breeding, there are many factors affecting its efficacy. We studied 14 real datasets to respond to the practical question of whether the accuracy of genomic prediction increases when considering genomic as compared with not using genomic. We found across traits, environments, datasets, and metrics, that the average gain in prediction accuracy when genomic information is considered was 26.31%, while only in terms of Pearson's correlation the gain was of 46.1%, while only in terms of normalized root mean squared error the gain was of 6.6%. If the quality of the makers and relatedness of the individuals increase, major gains in prediction accuracy can be obtained, but if these two factors decrease, a lower increase is possible. Finally, our findings reinforce genomic is vital for improving the prediction accuracy and, therefore, the realized genetic gain in genomic assisted plant breeding programs.
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Affiliation(s)
| | - Alison R Bentley
- International Maize and Wheat Improvement Center (CIMMYT), Estado de México, México
| | | | | | | | | | - Morten Lillemo
- Department of Plant Sciences, Norwegian University of Life Sciences, Ås, Norway
| | - Abelardo Montesinos-López
- Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), Universidad de Guadalajara, Guadalajara, Jalisco, México
| | - José Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Estado de México, México
- Colegio de Postgraduados, Montecillos, Estado de México, México
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8
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Montesinos-López OA, Saint Pierre C, Gezan SA, Bentley AR, Mosqueda-González BA, Montesinos-López A, van Eeuwijk F, Beyene Y, Gowda M, Gardner K, Gerard GS, Crespo-Herrera L, Crossa J. Optimizing Sparse Testing for Genomic Prediction of Plant Breeding Crops. Genes (Basel) 2023; 14:genes14040927. [PMID: 37107685 PMCID: PMC10137724 DOI: 10.3390/genes14040927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 04/07/2023] [Accepted: 04/13/2023] [Indexed: 04/29/2023] Open
Abstract
While sparse testing methods have been proposed by researchers to improve the efficiency of genomic selection (GS) in breeding programs, there are several factors that can hinder this. In this research, we evaluated four methods (M1-M4) for sparse testing allocation of lines to environments under multi-environmental trails for genomic prediction of unobserved lines. The sparse testing methods described in this study are applied in a two-stage analysis to build the genomic training and testing sets in a strategy that allows each location or environment to evaluate only a subset of all genotypes rather than all of them. To ensure a valid implementation, the sparse testing methods presented here require BLUEs (or BLUPs) of the lines to be computed at the first stage using an appropriate experimental design and statistical analyses in each location (or environment). The evaluation of the four cultivar allocation methods to environments of the second stage was done with four data sets (two large and two small) under a multi-trait and uni-trait framework. We found that the multi-trait model produced better genomic prediction (GP) accuracy than the uni-trait model and that methods M3 and M4 were slightly better than methods M1 and M2 for the allocation of lines to environments. Some of the most important findings, however, were that even under a scenario where we used a training-testing relation of 15-85%, the prediction accuracy of the four methods barely decreased. This indicates that genomic sparse testing methods for data sets under these scenarios can save considerable operational and financial resources with only a small loss in precision, which can be shown in our cost-benefit analysis.
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Affiliation(s)
| | - Carolina Saint Pierre
- International Maize and Wheat Improvement Center (CIMMYT), Km 45, Carretera Mexico-Veracruz, El Batan, Texcoco 56237, Mexico
| | | | - Alison R Bentley
- International Maize and Wheat Improvement Center (CIMMYT), Km 45, Carretera Mexico-Veracruz, El Batan, Texcoco 56237, Mexico
| | - Brandon A Mosqueda-González
- Centro de Investigación en Computación (CIC), Instituto Politécnico Nacional (IPN), Mexico City 07738, Mexico
| | - Abelardo Montesinos-López
- Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), Universidad de Guadalajara, Guadalajara 44430, Mexico
| | - Fred van Eeuwijk
- Department of Plant Science Mathematical and Statistical Methods-Biometrics, P.O. Box 16, 6700AA Wageningen, The Netherlands
| | - Yoseph Beyene
- International Maize and Wheat Improvement Center (CIMMYT), Km 45, Carretera Mexico-Veracruz, El Batan, Texcoco 56237, Mexico
| | - Manje Gowda
- International Maize and Wheat Improvement Center (CIMMYT), Km 45, Carretera Mexico-Veracruz, El Batan, Texcoco 56237, Mexico
| | - Keith Gardner
- International Maize and Wheat Improvement Center (CIMMYT), Km 45, Carretera Mexico-Veracruz, El Batan, Texcoco 56237, Mexico
| | - Guillermo S Gerard
- International Maize and Wheat Improvement Center (CIMMYT), Km 45, Carretera Mexico-Veracruz, El Batan, Texcoco 56237, Mexico
| | - Leonardo Crespo-Herrera
- International Maize and Wheat Improvement Center (CIMMYT), Km 45, Carretera Mexico-Veracruz, El Batan, Texcoco 56237, Mexico
| | - José Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Km 45, Carretera Mexico-Veracruz, El Batan, Texcoco 56237, Mexico
- Colegio de Postgraduados, Montecillos 56230, Mexico
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9
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Dreisigacker S, Pérez-Rodríguez P, Crespo-Herrera L, Bentley AR, Crossa J. Results from rapid-cycle recurrent genomic selection in spring bread wheat. G3 (Bethesda) 2023; 13:jkad025. [PMID: 36702618 PMCID: PMC10085763 DOI: 10.1093/g3journal/jkad025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 01/18/2023] [Accepted: 01/19/2023] [Indexed: 01/28/2023]
Abstract
Genomic selection (GS) in wheat breeding programs is of great interest for predicting the genotypic values of individuals, where both additive and nonadditive effects determine the final breeding value of lines. While several simulation studies have shown the efficiency of rapid-cycling GS strategies for parental selection or population improvement, their practical implementations are still lacking in wheat and other crops. In this study, we demonstrate the potential of rapid-cycle recurrent GS (RCRGS) to increase genetic gain for grain yield (GY) in wheat. Our results showed a consistent realized genetic gain for GY after 3 cycles of recombination (C1, C2, and C3) of bi-parental F1s, when summarized across 2 years of phenotyping. For both evaluation years combined, genetic gain through RCRGS reached 12.3% from cycle C0 to C3 and realized gain was 0.28 ton ha-1 per cycle with a GY from C0 (6.88 ton ha-1) to C3 (7.73 ton ha-1). RCRGS was also associated with some changes in important agronomic traits that were measured (days to heading, days to maturity, and plant height) but not selected for. To account for these changes, we recommend implementing GS together with multi-trait prediction models.
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Affiliation(s)
- Susanne Dreisigacker
- International Maize and Wheat Improvement Center (CIMMYT), Km 45 Carretera México-Veracruz, Texcoco, Edo. de México, CP 56100, México
| | | | - Leonardo Crespo-Herrera
- International Maize and Wheat Improvement Center (CIMMYT), Km 45 Carretera México-Veracruz, Texcoco, Edo. de México, CP 56100, México
| | - Alison R Bentley
- International Maize and Wheat Improvement Center (CIMMYT), Km 45 Carretera México-Veracruz, Texcoco, Edo. de México, CP 56100, México
| | - José Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Km 45 Carretera México-Veracruz, Texcoco, Edo. de México, CP 56100, México
- Colegio de Postgraduados, Montecillos, Edo. de México, CP 56264, México
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10
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Latorre SM, Were VM, Foster AJ, Langner T, Malmgren A, Harant A, Asuke S, Reyes-Avila S, Gupta DR, Jensen C, Ma W, Mahmud NU, Mehebub MS, Mulenga RM, Muzahid ANM, Paul SK, Rabby SMF, Rahat AAM, Ryder L, Shrestha RK, Sichilima S, Soanes DM, Singh PK, Bentley AR, Saunders DGO, Tosa Y, Croll D, Lamour KH, Islam T, Tembo B, Win J, Talbot NJ, Burbano HA, Kamoun S. Genomic surveillance uncovers a pandemic clonal lineage of the wheat blast fungus. PLoS Biol 2023; 21:e3002052. [PMID: 37040332 PMCID: PMC10089362 DOI: 10.1371/journal.pbio.3002052] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 02/24/2023] [Indexed: 04/12/2023] Open
Abstract
Wheat, one of the most important food crops, is threatened by a blast disease pandemic. Here, we show that a clonal lineage of the wheat blast fungus recently spread to Asia and Africa following two independent introductions from South America. Through a combination of genome analyses and laboratory experiments, we show that the decade-old blast pandemic lineage can be controlled by the Rmg8 disease resistance gene and is sensitive to strobilurin fungicides. However, we also highlight the potential of the pandemic clone to evolve fungicide-insensitive variants and sexually recombine with African lineages. This underscores the urgent need for genomic surveillance to track and mitigate the spread of wheat blast outside of South America and to guide preemptive wheat breeding for blast resistance.
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Affiliation(s)
- Sergio M Latorre
- Centre for Life's Origins and Evolution, Department of Genetics, Evolution and Environment, University College London, London, United Kingdom
| | - Vincent M Were
- The Sainsbury Laboratory, University of East Anglia, Norwich Research Park, Norwich, United Kingdom
| | - Andrew J Foster
- The Sainsbury Laboratory, University of East Anglia, Norwich Research Park, Norwich, United Kingdom
| | - Thorsten Langner
- The Sainsbury Laboratory, University of East Anglia, Norwich Research Park, Norwich, United Kingdom
| | - Angus Malmgren
- The Sainsbury Laboratory, University of East Anglia, Norwich Research Park, Norwich, United Kingdom
| | - Adeline Harant
- The Sainsbury Laboratory, University of East Anglia, Norwich Research Park, Norwich, United Kingdom
| | - Soichiro Asuke
- Graduate School of Agricultural Science, Kobe University, Kobe, Japan
| | - Sarai Reyes-Avila
- The Sainsbury Laboratory, University of East Anglia, Norwich Research Park, Norwich, United Kingdom
| | - Dipali Rani Gupta
- Institute of Biotechnology and Genetic Engineering, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur, Bangladesh
| | - Cassandra Jensen
- John Innes Centre, Norwich Research Park, Norwich, United Kingdom
| | - Weibin Ma
- The Sainsbury Laboratory, University of East Anglia, Norwich Research Park, Norwich, United Kingdom
| | - Nur Uddin Mahmud
- Institute of Biotechnology and Genetic Engineering, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur, Bangladesh
| | - Md Shåbab Mehebub
- Institute of Biotechnology and Genetic Engineering, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur, Bangladesh
| | - Rabson M Mulenga
- Zambia Agricultural Research Institute, Mt. Makulu Central Research Station, Lusaka, Zambia
| | - Abu Naim Md Muzahid
- Institute of Biotechnology and Genetic Engineering, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur, Bangladesh
| | - Sanjoy Kumar Paul
- Institute of Biotechnology and Genetic Engineering, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur, Bangladesh
| | - S M Fajle Rabby
- Institute of Biotechnology and Genetic Engineering, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur, Bangladesh
| | - Abdullah Al Mahbub Rahat
- Institute of Biotechnology and Genetic Engineering, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur, Bangladesh
| | - Lauren Ryder
- The Sainsbury Laboratory, University of East Anglia, Norwich Research Park, Norwich, United Kingdom
| | - Ram-Krishna Shrestha
- The Sainsbury Laboratory, University of East Anglia, Norwich Research Park, Norwich, United Kingdom
| | - Suwilanji Sichilima
- Zambia Agricultural Research Institute, Mt. Makulu Central Research Station, Lusaka, Zambia
| | - Darren M Soanes
- Department of Biosciences, University of Exeter, Exeter, United Kingdom
| | - Pawan Kumar Singh
- International Maize and Wheat Improvement Center, (CIMMYT), Texcoco, Mexico
| | - Alison R Bentley
- International Maize and Wheat Improvement Center, (CIMMYT), Texcoco, Mexico
| | | | - Yukio Tosa
- Graduate School of Agricultural Science, Kobe University, Kobe, Japan
| | - Daniel Croll
- Laboratory of Evolutionary Genetics, Institute of Biology, University of Neuchâtel, Neuchâtel, Switzerland
| | - Kurt H Lamour
- Department of Entomology and Plant Pathology, University of Tennessee, Knoxville, Tennessee, United States of America
| | - Tofazzal Islam
- Institute of Biotechnology and Genetic Engineering, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur, Bangladesh
| | - Batiseba Tembo
- Zambia Agricultural Research Institute, Mt. Makulu Central Research Station, Lusaka, Zambia
| | - Joe Win
- The Sainsbury Laboratory, University of East Anglia, Norwich Research Park, Norwich, United Kingdom
| | - Nicholas J Talbot
- The Sainsbury Laboratory, University of East Anglia, Norwich Research Park, Norwich, United Kingdom
| | - Hernán A Burbano
- Centre for Life's Origins and Evolution, Department of Genetics, Evolution and Environment, University College London, London, United Kingdom
| | - Sophien Kamoun
- The Sainsbury Laboratory, University of East Anglia, Norwich Research Park, Norwich, United Kingdom
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11
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Latorre SM, Were VM, Foster AJ, Langner T, Malmgren A, Harant A, Asuke S, Reyes-Avila S, Gupta DR, Jensen C, Ma W, Mahmud NU, Mehebub MS, Mulenga RM, Muzahid ANM, Paul SK, Rabby SMF, Rahat AAM, Ryder L, Shrestha RK, Sichilima S, Soanes DM, Singh PK, Bentley AR, Saunders DGO, Tosa Y, Croll D, Lamour KH, Islam T, Tembo B, Win J, Talbot NJ, Burbano HA, Kamoun S. Genomic surveillance uncovers a pandemic clonal lineage of the wheat blast fungus. PLoS Biol 2023; 21:e3002052. [PMID: 37040332 DOI: 10.1101/2022.06.06.494979] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 02/24/2023] [Indexed: 05/21/2023] Open
Abstract
Wheat, one of the most important food crops, is threatened by a blast disease pandemic. Here, we show that a clonal lineage of the wheat blast fungus recently spread to Asia and Africa following two independent introductions from South America. Through a combination of genome analyses and laboratory experiments, we show that the decade-old blast pandemic lineage can be controlled by the Rmg8 disease resistance gene and is sensitive to strobilurin fungicides. However, we also highlight the potential of the pandemic clone to evolve fungicide-insensitive variants and sexually recombine with African lineages. This underscores the urgent need for genomic surveillance to track and mitigate the spread of wheat blast outside of South America and to guide preemptive wheat breeding for blast resistance.
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Affiliation(s)
- Sergio M Latorre
- Centre for Life's Origins and Evolution, Department of Genetics, Evolution and Environment, University College London, London, United Kingdom
| | - Vincent M Were
- The Sainsbury Laboratory, University of East Anglia, Norwich Research Park, Norwich, United Kingdom
| | - Andrew J Foster
- The Sainsbury Laboratory, University of East Anglia, Norwich Research Park, Norwich, United Kingdom
| | - Thorsten Langner
- The Sainsbury Laboratory, University of East Anglia, Norwich Research Park, Norwich, United Kingdom
| | - Angus Malmgren
- The Sainsbury Laboratory, University of East Anglia, Norwich Research Park, Norwich, United Kingdom
| | - Adeline Harant
- The Sainsbury Laboratory, University of East Anglia, Norwich Research Park, Norwich, United Kingdom
| | - Soichiro Asuke
- Graduate School of Agricultural Science, Kobe University, Kobe, Japan
| | - Sarai Reyes-Avila
- The Sainsbury Laboratory, University of East Anglia, Norwich Research Park, Norwich, United Kingdom
| | - Dipali Rani Gupta
- Institute of Biotechnology and Genetic Engineering, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur, Bangladesh
| | - Cassandra Jensen
- John Innes Centre, Norwich Research Park, Norwich, United Kingdom
| | - Weibin Ma
- The Sainsbury Laboratory, University of East Anglia, Norwich Research Park, Norwich, United Kingdom
| | - Nur Uddin Mahmud
- Institute of Biotechnology and Genetic Engineering, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur, Bangladesh
| | - Md Shåbab Mehebub
- Institute of Biotechnology and Genetic Engineering, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur, Bangladesh
| | - Rabson M Mulenga
- Zambia Agricultural Research Institute, Mt. Makulu Central Research Station, Lusaka, Zambia
| | - Abu Naim Md Muzahid
- Institute of Biotechnology and Genetic Engineering, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur, Bangladesh
| | - Sanjoy Kumar Paul
- Institute of Biotechnology and Genetic Engineering, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur, Bangladesh
| | - S M Fajle Rabby
- Institute of Biotechnology and Genetic Engineering, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur, Bangladesh
| | - Abdullah Al Mahbub Rahat
- Institute of Biotechnology and Genetic Engineering, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur, Bangladesh
| | - Lauren Ryder
- The Sainsbury Laboratory, University of East Anglia, Norwich Research Park, Norwich, United Kingdom
| | - Ram-Krishna Shrestha
- The Sainsbury Laboratory, University of East Anglia, Norwich Research Park, Norwich, United Kingdom
| | - Suwilanji Sichilima
- Zambia Agricultural Research Institute, Mt. Makulu Central Research Station, Lusaka, Zambia
| | - Darren M Soanes
- Department of Biosciences, University of Exeter, Exeter, United Kingdom
| | - Pawan Kumar Singh
- International Maize and Wheat Improvement Center, (CIMMYT), Texcoco, Mexico
| | - Alison R Bentley
- International Maize and Wheat Improvement Center, (CIMMYT), Texcoco, Mexico
| | | | - Yukio Tosa
- Graduate School of Agricultural Science, Kobe University, Kobe, Japan
| | - Daniel Croll
- Laboratory of Evolutionary Genetics, Institute of Biology, University of Neuchâtel, Neuchâtel, Switzerland
| | - Kurt H Lamour
- Department of Entomology and Plant Pathology, University of Tennessee, Knoxville, Tennessee, United States of America
| | - Tofazzal Islam
- Institute of Biotechnology and Genetic Engineering, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur, Bangladesh
| | - Batiseba Tembo
- Zambia Agricultural Research Institute, Mt. Makulu Central Research Station, Lusaka, Zambia
| | - Joe Win
- The Sainsbury Laboratory, University of East Anglia, Norwich Research Park, Norwich, United Kingdom
| | - Nicholas J Talbot
- The Sainsbury Laboratory, University of East Anglia, Norwich Research Park, Norwich, United Kingdom
| | - Hernán A Burbano
- Centre for Life's Origins and Evolution, Department of Genetics, Evolution and Environment, University College London, London, United Kingdom
| | - Sophien Kamoun
- The Sainsbury Laboratory, University of East Anglia, Norwich Research Park, Norwich, United Kingdom
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12
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Fradgley NS, Bacon J, Bentley AR, Costa‐Neto G, Cottrell A, Crossa J, Cuevas J, Kerton M, Pope E, Swarbreck SM, Gardner KA. Prediction of near-term climate change impacts on UK wheat quality and the potential for adaptation through plant breeding. Glob Chang Biol 2023; 29:1296-1313. [PMID: 36482280 PMCID: PMC10108302 DOI: 10.1111/gcb.16552] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 11/17/2022] [Accepted: 11/29/2022] [Indexed: 05/26/2023]
Abstract
Wheat is a major crop worldwide, mainly cultivated for human consumption and animal feed. Grain quality is paramount in determining its value and downstream use. While we know that climate change threatens global crop yields, a better understanding of impacts on wheat end-use quality is also critical. Combining quantitative genetics with climate model outputs, we investigated UK-wide trends in genotypic adaptation for wheat quality traits. In our approach, we augmented genomic prediction models with environmental characterisation of field trials to predict trait values and climate effects in historical field trial data between 2001 and 2020. Addition of environmental covariates, such as temperature and rainfall, successfully enabled prediction of genotype by environment interactions (G × E), and increased prediction accuracy of most traits for new genotypes in new year cross validation. We then extended predictions from these models to much larger numbers of simulated environments using climate scenarios projected under Representative Concentration Pathways 8.5 for 2050-2069. We found geographically varying climate change impacts on wheat quality due to contrasting associations between specific weather covariables and quality traits across the UK. Notably, negative impacts on quality traits were predicted in the East of the UK due to increased summer temperatures while the climate in the North and South-west may become more favourable with increased summer temperatures. Furthermore, by projecting 167,040 simulated future genotype-environment combinations, we found only limited potential for breeding to exploit predictable G × E to mitigate year-to-year environmental variability for most traits except Hagberg falling number. This suggests low adaptability of current UK wheat germplasm across future UK climates. More generally, approaches demonstrated here will be critical to enable adaptation of global crops to near-term climate change.
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Affiliation(s)
| | | | - Alison R. Bentley
- NIABCambridgeUK
- International Maize and Wheat Improvement Center (CIMMYT)Carretera México‐VeracruzMexico
| | | | | | - Jose Crossa
- International Maize and Wheat Improvement Center (CIMMYT)Carretera México‐VeracruzMexico
| | - Jaime Cuevas
- Universidad Autonoma del Estado de Quintana RooChetumalQuintana RooMexico
| | | | | | | | - Keith A. Gardner
- NIABCambridgeUK
- International Maize and Wheat Improvement Center (CIMMYT)Carretera México‐VeracruzMexico
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13
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Costa-Neto G, Crespo-Herrera L, Fradgley N, Gardner K, Bentley AR, Dreisigacker S, Fritsche-Neto R, Montesinos-López OA, Crossa J. Envirome-wide associations enhance multi-year genome-based prediction of historical wheat breeding data. G3 (Bethesda) 2022; 13:6861853. [PMID: 36454213 PMCID: PMC9911085 DOI: 10.1093/g3journal/jkac313] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 11/02/2022] [Accepted: 11/03/2022] [Indexed: 12/03/2022]
Abstract
Linking high-throughput environmental data (enviromics) to genomic prediction (GP) is a cost-effective strategy for increasing selection intensity under genotype-by-environment interactions (G × E). This study developed a data-driven approach based on Environment-Phenotype Association (EPA) aimed at recycling important G × E information from historical breeding data. EPA was developed in two applications: (1) scanning a secondary source of genetic variation, weighted from the shared reaction-norms of past-evaluated genotypes and (2) pinpointing weights of the similarity among trial-sites (locations), given the historical impact of each envirotyping data variable for a given site. These results were then used as a dimensionality reduction strategy, integrating historical data to feed multi-environment GP models, which led to the development of four new G × E kernels considering genomics, enviromics, and EPA outcomes. The wheat trial data used included 36 locations, 8 years, and three target populations of environments (TPEs) in India. Four prediction scenarios and six kernel models within/across TPEs were tested. Our results suggest that the conventional GBLUP, without enviromic data or when omitting EPA, is inefficient in predicting the performance of wheat lines in future years. Nevertheless, when EPA was introduced as an intermediary learning step to reduce the dimensionality of the G × E kernels while connecting phenotypic and environmental-wide variation, a significant enhancement of G × E prediction accuracy was evident. EPA revealed that the effect of seasonality makes strategies such as "covariable selection" unfeasible because G × E is year-germplasm specific. We propose that the EPA effectively serves as a "reinforcement learner" algorithm capable of uncovering the effect of seasonality over the reaction-norms, with the benefits of better forecasting the similarities between past and future trialing sites. EPA combines the benefits of dimensionality reduction while reducing the uncertainty of genotype-by-year predictions and increasing the resolution of GP for the genotype-specific level.
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Affiliation(s)
- Germano Costa-Neto
- Institute for Genomics Diversity, Cornell University, Ithaca, NY 14853, USA
| | - Leonardo Crespo-Herrera
- International Maize and Wheat Improvement Center (CIMMYT), Km 45 Carretera México-Veracruz, El Batan, Edo. de México 5623, Mexico
| | - Nick Fradgley
- NIAB, 93 Lawrence Weaver Road, Cambridge CB3 0LE, UK
| | - Keith Gardner
- International Maize and Wheat Improvement Center (CIMMYT), Km 45 Carretera México-Veracruz, El Batan, Edo. de México 5623, Mexico
| | - Alison R Bentley
- International Maize and Wheat Improvement Center (CIMMYT), Km 45 Carretera México-Veracruz, El Batan, Edo. de México 5623, Mexico
| | - Susanne Dreisigacker
- International Maize and Wheat Improvement Center (CIMMYT), Km 45 Carretera México-Veracruz, El Batan, Edo. de México 5623, Mexico
| | | | - Osval A Montesinos-López
- Corresponding authors: Facultad de Telematica, Universidad de Colima, Mexico. ; and International Maize and Wheat Improvement Center (CIMMYT) and Colegio de Post-Graduados, Mexico.
| | - Jose Crossa
- Corresponding authors: Facultad de Telematica, Universidad de Colima, Mexico. ; and International Maize and Wheat Improvement Center (CIMMYT) and Colegio de Post-Graduados, Mexico.
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14
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Lopez-Cruz M, Dreisigacker S, Crespo-Herrera L, Bentley AR, Singh R, Poland J, Shrestha S, Huerta-Espino J, Govindan V, Juliana P, Mondal S, Pérez-Rodríguez P, Crossa J. Sparse kernel models provide optimization of training set design for genomic prediction in multiyear wheat breeding data. Plant Genome 2022; 15:e20254. [PMID: 36043341 DOI: 10.1002/tpg2.20254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2021] [Accepted: 07/17/2022] [Indexed: 06/15/2023]
Abstract
The success of genomic selection (GS) in breeding schemes relies on its ability to provide accurate predictions of unobserved lines at early stages. Multigeneration data provides opportunities to increase the training data size and thus, the likelihood of extracting useful information from ancestors to improve prediction accuracy. The genomic best linear unbiased predictions (GBLUPs) are performed by borrowing information through kinship relationships between individuals. Multigeneration data usually becomes heterogeneous with complex family relationship patterns that are increasingly entangled with each generation. Under these conditions, historical data may not be optimal for model training as the accuracy could be compromised. The sparse selection index (SSI) is a method for training set (TRN) optimization, in which training individuals provide predictions to some but not all predicted subjects. We added an additional trimming process to the original SSI (trimmed SSI) to remove less important training individuals for prediction. Using a large multigeneration (8 yr) wheat (Triticum aestivum L.) grain yield dataset (n = 68,836), we found increases in accuracy as more years are included in the TRN, with improvements of ∼0.05 in the GBLUP accuracy when using 5 yr of historical data relative to when using only 1 yr. The SSI method showed a small gain over the GBLUP accuracy but with an important reduction on the TRN size. These reduced TRNs were formed with a similar number of subjects from each training generation. Our results suggest that the SSI provides a more stable ranking of genotypes than the GBLUP as the TRN becomes larger.
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Affiliation(s)
- Marco Lopez-Cruz
- Dep. of Epidemiology and Biostatistics, Michigan State Univ., East Lansing, MI, USA
| | - Susanne Dreisigacker
- Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Leonardo Crespo-Herrera
- Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Alison R Bentley
- Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Ravi Singh
- Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Jesse Poland
- Dep. of Agronomy, Kansas State Univ., Manhattan, KS, USA
| | | | - Julio Huerta-Espino
- Campo Experimental Valle de Mexico, Instituto Nacional de Investigaciones Forestales, Agricolas y Pecuarias (INIFAP), Chapingo, Mexico
| | - Velu Govindan
- Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Philomin Juliana
- Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Suchismita Mondal
- Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | | | - Jose Crossa
- Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
- Colegio de Postgraduados, Montecillos, Mexico
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15
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Hussain B, Akpınar BA, Alaux M, Algharib AM, Sehgal D, Ali Z, Aradottir GI, Batley J, Bellec A, Bentley AR, Cagirici HB, Cattivelli L, Choulet F, Cockram J, Desiderio F, Devaux P, Dogramaci M, Dorado G, Dreisigacker S, Edwards D, El-Hassouni K, Eversole K, Fahima T, Figueroa M, Gálvez S, Gill KS, Govta L, Gul A, Hensel G, Hernandez P, Crespo-Herrera LA, Ibrahim A, Kilian B, Korzun V, Krugman T, Li Y, Liu S, Mahmoud AF, Morgounov A, Muslu T, Naseer F, Ordon F, Paux E, Perovic D, Reddy GVP, Reif JC, Reynolds M, Roychowdhury R, Rudd J, Sen TZ, Sukumaran S, Ozdemir BS, Tiwari VK, Ullah N, Unver T, Yazar S, Appels R, Budak H. Capturing Wheat Phenotypes at the Genome Level. Front Plant Sci 2022; 13:851079. [PMID: 35860541 PMCID: PMC9289626 DOI: 10.3389/fpls.2022.851079] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Accepted: 05/19/2022] [Indexed: 06/15/2023]
Abstract
Recent technological advances in next-generation sequencing (NGS) technologies have dramatically reduced the cost of DNA sequencing, allowing species with large and complex genomes to be sequenced. Although bread wheat (Triticum aestivum L.) is one of the world's most important food crops, efficient exploitation of molecular marker-assisted breeding approaches has lagged behind that achieved in other crop species, due to its large polyploid genome. However, an international public-private effort spanning 9 years reported over 65% draft genome of bread wheat in 2014, and finally, after more than a decade culminated in the release of a gold-standard, fully annotated reference wheat-genome assembly in 2018. Shortly thereafter, in 2020, the genome of assemblies of additional 15 global wheat accessions was released. As a result, wheat has now entered into the pan-genomic era, where basic resources can be efficiently exploited. Wheat genotyping with a few hundred markers has been replaced by genotyping arrays, capable of characterizing hundreds of wheat lines, using thousands of markers, providing fast, relatively inexpensive, and reliable data for exploitation in wheat breeding. These advances have opened up new opportunities for marker-assisted selection (MAS) and genomic selection (GS) in wheat. Herein, we review the advances and perspectives in wheat genetics and genomics, with a focus on key traits, including grain yield, yield-related traits, end-use quality, and resistance to biotic and abiotic stresses. We also focus on reported candidate genes cloned and linked to traits of interest. Furthermore, we report on the improvement in the aforementioned quantitative traits, through the use of (i) clustered regularly interspaced short-palindromic repeats/CRISPR-associated protein 9 (CRISPR/Cas9)-mediated gene-editing and (ii) positional cloning methods, and of genomic selection. Finally, we examine the utilization of genomics for the next-generation wheat breeding, providing a practical example of using in silico bioinformatics tools that are based on the wheat reference-genome sequence.
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Affiliation(s)
- Babar Hussain
- Department of Biological Sciences, Middle East Technical University, Ankara, Turkey
- Department of Biotechnology, Faculty of Life Sciences, University of Central Punjab, Lahore, Pakistan
| | | | - Michael Alaux
- Université Paris-Saclay, INRAE, URGI, Versailles, France
| | - Ahmed M. Algharib
- Department of Environment and Bio-Agriculture, Faculty of Agriculture, Al-Azhar University, Cairo, Egypt
| | - Deepmala Sehgal
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Zulfiqar Ali
- Institute of Plant Breeding and Biotechnology, MNS University of Agriculture, Multan, Pakistan
| | - Gudbjorg I. Aradottir
- Department of Pathology, The National Institute of Agricultural Botany, Cambridge, United Kingdom
| | - Jacqueline Batley
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, Australia
| | - Arnaud Bellec
- French Plant Genomic Resource Center, INRAE-CNRGV, Castanet Tolosan, France
| | - Alison R. Bentley
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Halise B. Cagirici
- Crop Improvement and Genetics Research, USDA, Agricultural Research Service, Albany, CA, United States
| | - Luigi Cattivelli
- Council for Agricultural Research and Economics-Research Centre for Genomics and Bioinformatics, Fiorenzuola d’Arda, Italy
| | - Fred Choulet
- French National Research Institute for Agriculture, Food and the Environment, INRAE, GDEC, Clermont-Ferrand, France
| | - James Cockram
- The John Bingham Laboratory, The National Institute of Agricultural Botany, Cambridge, United Kingdom
| | - Francesca Desiderio
- Council for Agricultural Research and Economics-Research Centre for Genomics and Bioinformatics, Fiorenzuola d’Arda, Italy
| | - Pierre Devaux
- Research & Innovation, Florimond Desprez Group, Cappelle-en-Pévèle, France
| | - Munevver Dogramaci
- USDA, Agricultural Research Service, Edward T. Schafer Agricultural Research Center, Fargo, ND, United States
| | - Gabriel Dorado
- Department of Bioquímica y Biología Molecular, Campus Rabanales C6-1-E17, Campus de Excelencia Internacional Agroalimentario (ceiA3), Universidad de Córdoba, Córdoba, Spain
| | | | - David Edwards
- University of Western Australia, Perth, WA, Australia
| | - Khaoula El-Hassouni
- State Plant Breeding Institute, The University of Hohenheim, Stuttgart, Germany
| | - Kellye Eversole
- International Wheat Genome Sequencing Consortium (IWGSC), Bethesda, MD, United States
| | - Tzion Fahima
- Institute of Evolution and Department of Environmental and Evolutionary Biology, University of Haifa, Haifa, Israel
| | - Melania Figueroa
- Commonwealth Scientific and Industrial Research Organization, Agriculture and Food, Canberra, ACT, Australia
| | - Sergio Gálvez
- Department of Languages and Computer Science, ETSI Informática, Campus de Teatinos, Universidad de Málaga, Andalucía Tech, Málaga, Spain
| | - Kulvinder S. Gill
- Department of Crop Science, Washington State University, Pullman, WA, United States
| | - Liubov Govta
- Institute of Evolution and Department of Environmental and Evolutionary Biology, University of Haifa, Haifa, Israel
| | - Alvina Gul
- Atta-ur-Rahman School of Applied Biosciences, National University of Sciences and Technology, Islamabad, Pakistan
| | - Goetz Hensel
- Center of Plant Genome Engineering, Heinrich-Heine-Universität, Düsseldorf, Germany
- Division of Molecular Biology, Centre of Region Haná for Biotechnological and Agriculture Research, Czech Advanced Technology and Research Institute, Palacký University, Olomouc, Czechia
| | - Pilar Hernandez
- Institute for Sustainable Agriculture (IAS-CSIC), Consejo Superior de Investigaciones Científicas (CSIC), Córdoba, Spain
| | | | - Amir Ibrahim
- Crop and Soil Science, Texas A&M University, College Station, TX, United States
| | | | | | - Tamar Krugman
- Institute of Evolution and Department of Environmental and Evolutionary Biology, University of Haifa, Haifa, Israel
| | - Yinghui Li
- Institute of Evolution and Department of Environmental and Evolutionary Biology, University of Haifa, Haifa, Israel
| | - Shuyu Liu
- Crop and Soil Science, Texas A&M University, College Station, TX, United States
| | - Amer F. Mahmoud
- Department of Plant Pathology, Faculty of Agriculture, Assiut University, Assiut, Egypt
| | - Alexey Morgounov
- Food and Agriculture Organization of the United Nations, Riyadh, Saudi Arabia
| | - Tugdem Muslu
- Molecular Biology, Genetics and Bioengineering, Sabanci University, Istanbul, Turkey
| | - Faiza Naseer
- Atta-ur-Rahman School of Applied Biosciences, National University of Sciences and Technology, Islamabad, Pakistan
| | - Frank Ordon
- Institute for Resistance Research and Stress Tolerance, Julius Kühn Institute, Quedlinburg, Germany
| | - Etienne Paux
- French National Research Institute for Agriculture, Food and the Environment, INRAE, GDEC, Clermont-Ferrand, France
| | - Dragan Perovic
- Institute for Resistance Research and Stress Tolerance, Julius Kühn Institute, Quedlinburg, Germany
| | - Gadi V. P. Reddy
- USDA-Agricultural Research Service, Southern Insect Management Research Unit, Stoneville, MS, United States
| | - Jochen Christoph Reif
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany
| | - Matthew Reynolds
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Rajib Roychowdhury
- Institute of Evolution and Department of Environmental and Evolutionary Biology, University of Haifa, Haifa, Israel
| | - Jackie Rudd
- Crop and Soil Science, Texas A&M University, College Station, TX, United States
| | - Taner Z. Sen
- Crop Improvement and Genetics Research, USDA, Agricultural Research Service, Albany, CA, United States
| | | | | | | | - Naimat Ullah
- Institute of Biological Sciences (IBS), Gomal University, D. I. Khan, Pakistan
| | - Turgay Unver
- Ficus Biotechnology, Ostim Teknopark, Ankara, Turkey
| | - Selami Yazar
- General Directorate of Research, Ministry of Agriculture, Ankara, Turkey
| | | | - Hikmet Budak
- Montana BioAgriculture, Inc., Missoula, MT, United States
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16
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Bentley AR, Donovan J, Sonder K, Baudron F, Lewis JM, Voss R, Rutsaert P, Poole N, Kamoun S, Saunders DGO, Hodson D, Hughes DP, Negra C, Ibba MI, Snapp S, Sida TS, Jaleta M, Tesfaye K, Becker-Reshef I, Govaerts B. Near- to long-term measures to stabilize global wheat supplies and food security. Nat Food 2022; 3:483-486. [PMID: 37117944 DOI: 10.1038/s43016-022-00559-y] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/30/2023]
Affiliation(s)
- Alison R Bentley
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico.
| | - Jason Donovan
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Kai Sonder
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Frédéric Baudron
- International Maize and Wheat Improvement Center (CIMMYT), Harare, Zimbabwe
- University of KwaZulu-Natal, Pietermaritzburg, South Africa
| | - Janet M Lewis
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Rachel Voss
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Pieter Rutsaert
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | | | | | | | - David Hodson
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - David P Hughes
- Current and Emerging Threats to Crops Innovation Lab, The Pennsylvania State University, Pennsylvania, PA, USA
| | | | - Maria Itria Ibba
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Sieglinde Snapp
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | | | - Moti Jaleta
- International Maize and Wheat Improvement Center (CIMMYT), Addis Ababa, Ethiopia
| | - Kindie Tesfaye
- International Maize and Wheat Improvement Center (CIMMYT), Addis Ababa, Ethiopia
| | - Inbal Becker-Reshef
- Department of Geographical Sciences, University of Maryland, College Park, MD, USA
| | - Bram Govaerts
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
- Cornell University, Ithaca, NY, USA
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17
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Atanda SA, Govindan V, Singh R, Robbins KR, Crossa J, Bentley AR. Sparse testing using genomic prediction improves selection for breeding targets in elite spring wheat. Theor Appl Genet 2022; 135:1939-1950. [PMID: 35348821 PMCID: PMC9205816 DOI: 10.1007/s00122-022-04085-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 03/16/2022] [Indexed: 06/08/2023]
Abstract
Sparse testing using genomic prediction can be efficiently used to increase the number of testing environments while maintaining selection intensity in the early yield testing stage without increasing the breeding budget. Sparse testing using genomic prediction enables expanded use of selection environments in early-stage yield testing without increasing phenotyping cost. We evaluated different sparse testing strategies in the yield testing stage of a CIMMYT spring wheat breeding pipeline characterized by multiple populations each with small family sizes of 1-9 individuals. Our results indicated that a substantial overlap between lines across environments should be used to achieve optimal prediction accuracy. As sparse testing leverages information generated within and across environments, the genetic correlations between environments and genomic relationships of lines across environments were the main drivers of prediction accuracy in multi-environment yield trials. Including information from previous evaluation years did not consistently improve the prediction performance. Genomic best linear unbiased prediction was found to be the best predictor of true breeding value, and therefore, we propose that it should be used as a selection decision metric in the early yield testing stages. We also propose it as a proxy for assessing prediction performance to mirror breeder's advancement decisions in a breeding program so that it can be readily applied for advancement decisions by breeding programs.
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Affiliation(s)
| | - Velu Govindan
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Ravi Singh
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Kelly R Robbins
- Section of Plant Breeding and Genetics, School of Integrative Plant Sciences, Cornell University, Ithaca, NY, USA
| | - Jose Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Alison R Bentley
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico.
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18
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Bentley AR, Chen C, D’Agostino N. Editorial: Genome Wide Association Studies and Genomic Selection for Crop Improvement in the Era of Big Data. Front Genet 2022; 13:873060. [PMID: 35669194 PMCID: PMC9164124 DOI: 10.3389/fgene.2022.873060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 05/04/2022] [Indexed: 12/02/2022] Open
Affiliation(s)
- Alison R. Bentley
- International Wheat and Maize Improvement Center (CIMMYT), El Batan, Mexico
- NIAB, Cambridge, United Kingdom
- *Correspondence: Alison R. Bentley, ; Charles Chen, ; Nunzio D’Agostino,
| | - Charles Chen
- Department of Biochemistry and Molecular Biology, 246 Noble Research Center, Oklahoma State University, Stillwater, OK, United States
- *Correspondence: Alison R. Bentley, ; Charles Chen, ; Nunzio D’Agostino,
| | - Nunzio D’Agostino
- Department of Agricultural Sciences, University of Naples Federico II, Portici, Italy
- *Correspondence: Alison R. Bentley, ; Charles Chen, ; Nunzio D’Agostino,
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19
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Leigh FJ, Wright TIC, Horsnell RA, Dyer S, Bentley AR. Progenitor species hold untapped diversity for potential climate-responsive traits for use in wheat breeding and crop improvement. Heredity (Edinb) 2022; 128:291-303. [PMID: 35383318 PMCID: PMC9076643 DOI: 10.1038/s41437-022-00527-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Revised: 03/13/2022] [Accepted: 03/15/2022] [Indexed: 01/07/2023] Open
Abstract
Climate change will have numerous impacts on crop production worldwide necessitating a broadening of the germplasm base required to source and incorporate novel traits. Major variation exists in crop progenitor species for seasonal adaptation, photosynthetic characteristics, and root system architecture. Wheat is crucial for securing future food and nutrition security and its evolutionary history and progenitor diversity offer opportunities to mine favourable functional variation in the primary gene pool. Here we provide a review of the status of characterisation of wheat progenitor variation and the potential to use this knowledge to inform the use of variation in other cereal crops. Although significant knowledge of progenitor variation has been generated, we make recommendations for further work required to systematically characterise underlying genetics and physiological mechanisms and propose steps for effective use in breeding. This will enable targeted exploitation of useful variation, supported by the growing portfolio of genomics and accelerated breeding approaches. The knowledge and approaches generated are also likely to be useful across wider crop improvement.
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Affiliation(s)
- Fiona J Leigh
- The John Bingham Laboratory, NIAB, 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK
| | - Tally I C Wright
- The John Bingham Laboratory, NIAB, 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK
| | - Richard A Horsnell
- The John Bingham Laboratory, NIAB, 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK
| | - Sarah Dyer
- The John Bingham Laboratory, NIAB, 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK.,European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Alison R Bentley
- The John Bingham Laboratory, NIAB, 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK. .,International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico.
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20
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Grover G, Sharma A, Mackay I, Srivastava P, Kaur S, Kaur J, Burridge A, Allen SP, Bentley AR, Chhuneja P, Bains NS. Identification of a novel stripe rust resistance gene from the European winter wheat cultivar 'Acienda': A step towards rust proofing wheat cultivation. PLoS One 2022; 17:e0264027. [PMID: 35171951 PMCID: PMC8849526 DOI: 10.1371/journal.pone.0264027] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Accepted: 01/31/2022] [Indexed: 11/18/2022] Open
Abstract
All stage resistance to stripe rust races prevalent in India was investigated in the European winter wheat cultivar 'Acienda'. In order to dissect the genetic basis of the resistance, a backcross population was developed between 'Acienda' and the stripe rust susceptible Indian spring wheat cultivar 'HD 2967'. Inheritance studies revealed segregation for a dominant resistant gene. High density SNP genotyping was used to map stripe rust resistance and marker regression analysis located stripe rust resistance to the distal end of wheat chromosome 1A. Interval mapping located this region between the SNP markers AX-95162217 and AX-94540853, at a LOD score of 15.83 with a phenotypic contribution of 60%. This major stripe rust resistance locus from 'Acienda' has been temporarily designated as Yraci. A candidate gene search in the 2.76 Mb region carrying Yraci on chromosome 1A identified 18 NBS-LRR genes based on wheat RefSeqv1.0 annotations. Our results indicate that as there is no major gene reported in the Yraci chromosome region, it is likely to be a novel stripe rust resistance locus and offers potential for deployment, using the identified markers, to confer all stage stripe rust resistance.
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Affiliation(s)
- Gomti Grover
- Punjab Agricultural University, Ludhiana, India
- * E-mail:
| | | | - Ian Mackay
- IMPlant Consultancy Ltd., Chelmsford, United Kingdom
| | | | | | - Jaspal Kaur
- Punjab Agricultural University, Ludhiana, India
| | - Amanda Burridge
- Life Sciences, University of Bristol, Bristol, United Kingdom
| | | | | | | | - N. S. Bains
- Punjab Agricultural University, Ludhiana, India
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21
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Bandyopadhyay T, Swarbreck SM, Jaiswal V, Maurya J, Gupta R, Bentley AR, Griffiths H, Prasad M. GWAS identifies genetic loci underlying nitrogen responsiveness in the climate resilient C 4 model Setaria italica (L.). J Adv Res 2022; 42:249-261. [PMID: 36513416 PMCID: PMC9788950 DOI: 10.1016/j.jare.2022.01.010] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 01/21/2022] [Accepted: 01/23/2022] [Indexed: 12/27/2022] Open
Abstract
INTRODUCTION N responsiveness is the capacity to perceive and induce morpho-physiological adaptation to external and internal Nitrogen (N). Crop productivity is propelled by N fertilizer and requires the breeding/selection of cultivars with intrinsically high N responsiveness. This trait has many advantages in being more meaningful in commercial/environmental context, facilitating in-season N management and not being inversely correlated with N availability over processes regulating NUE. Current lack of its understanding at the physio-genetic basis is an impediment to select for cultivars with a predictably high N response. OBJECTIVES To dissect physio-genetic basis of N responsiveness in 142 diverse population of foxtail millet, Setaria italica (L.) by employing contrasting N fertilizer nutrition regimes. METHODS We phenotyped S. italica accessions for major yield related traits under low (N10, N25) and optimal (N100) growth conditions and genotyped them to subsequently perform a genome-wide association study to identify genetic loci associated with nitrogen responsiveness trait. Groups of accessions showing contrasting trait performance and allelic forms of specific linked genetic loci (showing haplotypes) were further accessed for N dependent transcript abundances of their proximal genes. RESULTS Our study show that N dependent yield rise in S. italica is driven by grain number whose responsiveness to N availability is genetically underlined. We identify 22 unique SNP loci strongly associated with this trait out of which six exhibit haplotypes and consistent allelic variation between lines with contrasting N dependent grain number response and panicle architectures. Furthermore, differential transcript abundances of specific genes proximally linked to these SNPs in same lines is indicative of their N dependence in a genotype specific manner. CONCLUSION The study demonstrates the value/ potential of N responsiveness as a selection trait and identifies key genetic components underlying the trait in S. italica. This has major implications for improving crop N sustainability and food security.
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Affiliation(s)
| | - Stéphanie M Swarbreck
- The John Bingham Laboratory, NIAB, 93 Lawrence Weaver Rd, Cambridge CB3 0LE, United Kingdom,Department of Plant Sciences, University of Cambridge, Downing Street, Cambridge CB2 3EA, United Kingdom
| | - Vandana Jaiswal
- CSIR-Institute of Himalayan Bioresource Technology, Palampur, Himachal Pradesh 176061, India
| | - Jyoti Maurya
- National Institute of Plant Genome Research, Aruna Asaf Ali Marg, New Delhi 110067, India
| | - Rajeev Gupta
- Cereal Crops Research Unit, US Department of Agriculture (USDA) Agricultural Research Service (ARS), Fargo, ND, United States,International Crop Research Institute for the Semi -arid Tropics, Patancheru, Hyderabad, Telangana 502324, India
| | - Alison R. Bentley
- The John Bingham Laboratory, NIAB, 93 Lawrence Weaver Rd, Cambridge CB3 0LE, United Kingdom,International Maize and Wheat Improvement Center, Texcoco, México
| | - Howard Griffiths
- Department of Plant Sciences, University of Cambridge, Downing Street, Cambridge CB2 3EA, United Kingdom
| | - Manoj Prasad
- National Institute of Plant Genome Research, Aruna Asaf Ali Marg, New Delhi 110067, India,Corresponding author at: National Institute of Plant Genome Research, Aruna Asaf Ali Marg, New Delhi 110067, India.
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22
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Montesinos-López OA, Montesinos-López A, Mosqueda-González BA, Bentley AR, Lillemo M, Varshney RK, Crossa J. A New Deep Learning Calibration Method Enhances Genome-Based Prediction of Continuous Crop Traits. Front Genet 2021; 12:798840. [PMID: 34976026 PMCID: PMC8718701 DOI: 10.3389/fgene.2021.798840] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Accepted: 11/18/2021] [Indexed: 11/13/2022] Open
Abstract
Genomic selection (GS) has the potential to revolutionize predictive plant breeding. A reference population is phenotyped and genotyped to train a statistical model that is used to perform genome-enabled predictions of new individuals that were only genotyped. In this vein, deep neural networks, are a type of machine learning model and have been widely adopted for use in GS studies, as they are not parametric methods, making them more adept at capturing nonlinear patterns. However, the training process for deep neural networks is very challenging due to the numerous hyper-parameters that need to be tuned, especially when imperfect tuning can result in biased predictions. In this paper we propose a simple method for calibrating (adjusting) the prediction of continuous response variables resulting from deep learning applications. We evaluated the proposed deep learning calibration method (DL_M2) using four crop breeding data sets and its performance was compared with the standard deep learning method (DL_M1), as well as the standard genomic Best Linear Unbiased Predictor (GBLUP). While the GBLUP was the most accurate model overall, the proposed deep learning calibration method (DL_M2) helped increase the genome-enabled prediction performance in all data sets when compared with the traditional DL method (DL_M1). Taken together, we provide evidence for extending the use of the proposed calibration method to evaluate its potential and consistency for predicting performance in the context of GS applied to plant breeding.
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Affiliation(s)
| | - Abelardo Montesinos-López
- Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), Universidad de Guadalajara, Guadalajara, Mexico
- *Correspondence: Abelardo Montesinos-López, ; Rajeev K. Varshney, ; José Crossa,
| | - Brandon A. Mosqueda-González
- Centro de Investigación en Computación (CIC), Instituto Politécnico Nacional (IPN), Esq. Miguel Othón de Mendizábal, Mexico city, Mexico
| | - Alison R. Bentley
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Morten Lillemo
- Department of Plant Sciences, Norwegian University of Life Sciences, IHA/CIGENE, As, Norway
| | - Rajeev K. Varshney
- Centre of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
- State Agricultural Biotechnology Centre, Centre for Crop and Food Innovation, Murdoch University, Perth, WA, Australia
- *Correspondence: Abelardo Montesinos-López, ; Rajeev K. Varshney, ; José Crossa,
| | - José Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
- Colegio de Postgraduados, Montecillo, Mexico
- *Correspondence: Abelardo Montesinos-López, ; Rajeev K. Varshney, ; José Crossa,
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23
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Ivić M, Grljušić S, Plavšin I, Dvojković K, Lovrić A, Rajković B, Maričević M, Černe M, Popović B, Lončarić Z, Bentley AR, Swarbreck SM, Šarčević H, Novoselović D. Variation for Nitrogen Use Efficiency Traits in Wheat Under Contrasting Nitrogen Treatments in South-Eastern Europe. Front Plant Sci 2021; 12:682333. [PMID: 34868096 PMCID: PMC8636685 DOI: 10.3389/fpls.2021.682333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Accepted: 10/18/2021] [Indexed: 05/06/2023]
Abstract
Wheat cultivars differ in their response to nitrogen (N) fertilizer, both in terms of its uptake and utilization. Characterizing this variation is an important step in improving the N use efficiency (NUE) of future cultivars while maximizing production (yield) potential. In this study, we compared the agronomic performance of 48 diverse wheat cultivars released between 1936 and 2016 at low and high N input levels in field conditions to assess the relationship between NUE and its components. Agronomic trait values were significantly lower in the low N treatment, and the cultivars tested showed a significant variation for all traits (apart from the N remobilization efficiency), indicating that response is genotype-dependent, although significant genotype × environment effects were also observed. Overall, we show a varietal improvement in NUE over time of 0.33 and 0.30% year-1 at low and high N, respectively, and propose that this is driven predominantly by varietal selection for increased yield. More complete understanding of the components of these improvements will inform future targeted breeding and selection strategies to support a reduction in fertilizer use while maintaining productivity.
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Affiliation(s)
- Marko Ivić
- Agricultural Institute Osijek, Osijek, Croatia
| | | | - Ivana Plavšin
- Agricultural Institute Osijek, Osijek, Croatia
- Centre of Excellence for Biodiversity and Molecular Plant Breeding (CroP-BioDiv), Zagreb, Croatia
| | | | - Ana Lovrić
- Faculty of Agriculture Zagreb, University of Zagreb, Zagreb, Croatia
| | - Bruno Rajković
- Centre of Excellence for Biodiversity and Molecular Plant Breeding (CroP-BioDiv), Zagreb, Croatia
- Faculty of Agriculture Zagreb, University of Zagreb, Zagreb, Croatia
| | - Marko Maričević
- BC Institute for Breeding and Production of Field Crops Zagreb, Rugvica, Croatia
| | - Marko Černe
- Centre of Excellence for Biodiversity and Molecular Plant Breeding (CroP-BioDiv), Zagreb, Croatia
- Institute for Agriculture and Tourism Poreč, Poreč, Croatia
| | - Brigita Popović
- Faculty of Agrobiotechnical Sciences, University of Josip Juraj Strossmayer in Osijek, Osijek, Croatia
| | - Zdenko Lončarić
- Faculty of Agrobiotechnical Sciences, University of Josip Juraj Strossmayer in Osijek, Osijek, Croatia
| | - Alison R. Bentley
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Stéphanie M. Swarbreck
- The John Bingham Lab, National Institute of Agricultural Botany (NIAB), Cambridge, United Kingdom
| | - Hrvoje Šarčević
- Centre of Excellence for Biodiversity and Molecular Plant Breeding (CroP-BioDiv), Zagreb, Croatia
- Faculty of Agriculture Zagreb, University of Zagreb, Zagreb, Croatia
| | - Dario Novoselović
- Agricultural Institute Osijek, Osijek, Croatia
- Centre of Excellence for Biodiversity and Molecular Plant Breeding (CroP-BioDiv), Zagreb, Croatia
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24
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Montesinos-López A, Runcie DE, Ibba MI, Pérez-Rodríguez P, Montesinos-López OA, Crespo LA, Bentley AR, Crossa J. Multi-trait genomic-enabled prediction enhances accuracy in multi-year wheat breeding trials. G3 (Bethesda) 2021; 11:6332007. [PMID: 34568924 PMCID: PMC8496321 DOI: 10.1093/g3journal/jkab270] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Accepted: 07/25/2021] [Indexed: 11/14/2022]
Abstract
Implementing genomic-based prediction models in genomic selection requires an understanding of the measures for evaluating prediction accuracy from different models and methods using multi-trait data. In this study, we compared prediction accuracy using six large multi-trait wheat data sets (quality and grain yield). The data were used to predict 1 year (testing) from the previous year (training) to assess prediction accuracy using four different prediction models. The results indicated that the conventional Pearson’s correlation between observed and predicted values underestimated the true correlation value, whereas the corrected Pearson’s correlation calculated by fitting a bivariate model was higher than the division of the Pearson’s correlation by the squared root of the heritability across traits, by 2.53–11.46%. Across the datasets, the corrected Pearson’s correlation was higher than the uncorrected by 5.80–14.01%. Overall, we found that for grain yield the prediction performance was highest using a multi-trait compared to a single-trait model. The higher the absolute genetic correlation between traits the greater the benefits of multi-trait models for increasing the genomic-enabled prediction accuracy of traits.
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Affiliation(s)
- Abelardo Montesinos-López
- Departamento de Matemáticas, Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), Universidad de Guadalajara, Guadalajara 44430, Mexico
| | - Daniel E Runcie
- Department of Plant Sciences, College of Agricultural & Environmental Sciences, University of California Davis, Davis CA 95616, USA
| | - Maria Itria Ibba
- International Maize and Wheat Improvement Center (CIMMYT), Carretera México-Veracruz, México
| | | | | | - Leonardo A Crespo
- International Maize and Wheat Improvement Center (CIMMYT), Carretera México-Veracruz, México
| | - Alison R Bentley
- International Maize and Wheat Improvement Center (CIMMYT), Carretera México-Veracruz, México
| | - José Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Carretera México-Veracruz, México.,Colegio de Postgraduados (COLPOS), Montecillos, Edo. de México, México
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25
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Scott MF, Fradgley N, Bentley AR, Brabbs T, Corke F, Gardner KA, Horsnell R, Howell P, Ladejobi O, Mackay IJ, Mott R, Cockram J. Limited haplotype diversity underlies polygenic trait architecture across 70 years of wheat breeding. Genome Biol 2021; 22:137. [PMID: 33957956 PMCID: PMC8101041 DOI: 10.1186/s13059-021-02354-7] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Accepted: 04/16/2021] [Indexed: 11/25/2022] Open
Abstract
Background Selection has dramatically shaped genetic and phenotypic variation in bread wheat. We can assess the genomic basis of historical phenotypic changes, and the potential for future improvement, using experimental populations that attempt to undo selection through the randomizing effects of recombination. Results We bred the NIAB Diverse MAGIC multi-parent population comprising over 500 recombinant inbred lines, descended from sixteen historical UK bread wheat varieties released between 1935 and 2004. We sequence the founders’ genes and promoters by capture, and the MAGIC population by low-coverage whole-genome sequencing. We impute 1.1 M high-quality SNPs that are over 99% concordant with array genotypes. Imputation accuracy only marginally improves when including the founders’ genomes as a haplotype reference panel. Despite capturing 73% of global wheat genetic polymorphism, 83% of genes cluster into no more than three haplotypes. We phenotype 47 agronomic traits over 2 years and map 136 genome-wide significant associations, concentrated at 42 genetic loci with large and often pleiotropic effects. Around half of these overlap known quantitative trait loci. Most traits exhibit extensive polygenicity, as revealed by multi-locus shrinkage modelling. Conclusions Our results are consistent with a gene pool of low haplotypic diversity, containing few novel loci of large effect. Most past, and projected future, phenotypic changes arising from existing variation involve fine-scale shuffling of a few haplotypes to recombine dozens of polygenic alleles of small effect. Moreover, extensive pleiotropy means selection on one trait will have unintended consequences, exemplified by the negative trade-off between yield and protein content, unless selection and recombination can break unfavorable trait-trait associations. Supplementary Information The online version contains supplementary material available at 10.1186/s13059-021-02354-7.
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Affiliation(s)
- Michael F Scott
- University College London (UCL) Genetics Institute, Gower St, London, WC1E 6BT, UK.,Current address: School of Biological Sciences, University of East Anglia, Norwich Research Park, Norwich, NR4 7TJ, UK
| | - Nick Fradgley
- National Institute for Agricultural Botany (NIAB), 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK
| | - Alison R Bentley
- National Institute for Agricultural Botany (NIAB), 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK.,Current address: International Maize and Wheat Improvement Center (CIMMYT), El Batán, Texcoco, Mexico
| | | | - Fiona Corke
- The National Plant Phenomics Centre, Institute of Biological, Rural and Environmental Sciences (IBERS), Aberystwyth University, Gogerddan, Aberystwyth, SY23 3EE, UK
| | - Keith A Gardner
- National Institute for Agricultural Botany (NIAB), 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK
| | - Richard Horsnell
- National Institute for Agricultural Botany (NIAB), 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK
| | - Phil Howell
- National Institute for Agricultural Botany (NIAB), 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK
| | | | - Ian J Mackay
- National Institute for Agricultural Botany (NIAB), 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK.,Current address: SRUC, Peter Wilson Building King's Buildings, W Mains Rd, Edinburgh, EH9 3JG, UK
| | - Richard Mott
- University College London (UCL) Genetics Institute, Gower St, London, WC1E 6BT, UK.
| | - James Cockram
- National Institute for Agricultural Botany (NIAB), 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK.
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26
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Schaart JG, Salentijn EMJ, Goryunova SV, Chidzanga C, Esselink DG, Gosman N, Bentley AR, Gilissen LJWJ, Smulders MJM. Exploring the alpha-gliadin locus: the 33-mer peptide with six overlapping coeliac disease epitopes in Triticum aestivum is derived from a subgroup of Aegilops tauschii. Plant J 2021; 106:86-94. [PMID: 33369792 PMCID: PMC8248119 DOI: 10.1111/tpj.15147] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Revised: 12/13/2020] [Accepted: 12/17/2020] [Indexed: 05/28/2023]
Abstract
Most alpha-gliadin genes of the Gli-D2 locus on the D genome of hexaploid bread wheat (Triticum aestivum) encode for proteins with epitopes that can trigger coeliac disease (CD), and several contain a 33-mer peptide with six partly overlapping copies of three epitopes, which is regarded as a remarkably potent T-cell stimulator. To increase genetic diversity in the D genome, synthetic hexaploid wheat lines are being made by hybridising accessions of Triticum turgidum (AB genome) and Aegilops tauschii (the progenitor of the D genome). The diversity of alpha-gliadins in A. tauschii has not been studied extensively. We analysed the alpha-gliadin transcriptome of 51 A. tauschii accessions representative of the diversity in A. tauschii. We extracted RNA from developing seeds and performed 454 amplicon sequencing of the first part of the alpha-gliadin genes. The expression profile of allelic variants of the alpha-gliadins was different between accessions, and also between accessions of the Western and Eastern clades of A. tauschii. Generally, both clades expressed many allelic variants not found in bread wheat. In contrast to earlier studies, we detected the 33-mer peptide in some A. tauschii accessions, indicating that it was introduced along with the D genome into bread wheat. In these accessions, transcripts with the 33-mer peptide were present at lower frequencies than in bread wheat varieties. In most A. tauschii accessions, however, the alpha-gliadins do not contain the epitope, and this may be exploited, through synthetic hexaploid wheats, to breed bread wheat varieties with fewer or no coeliac disease epitopes.
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Affiliation(s)
- Jan G. Schaart
- Plant BreedingWageningen University and ResearchDroevendaalsesteeg 1NL‐6708 PB Wageningenthe Netherlands
| | - Elma M. J. Salentijn
- Plant BreedingWageningen University and ResearchDroevendaalsesteeg 1NL‐6708 PB Wageningenthe Netherlands
| | - Svetlana V. Goryunova
- Plant BreedingWageningen University and ResearchDroevendaalsesteeg 1NL‐6708 PB Wageningenthe Netherlands
- Present address:
FSBSI Lorch Potato Research InstituteKraskovo140051Russia
- Present address:
Institute of General GeneticsRussian Academy of ScienceMoscow119333Russia
| | - Charity Chidzanga
- Plant BreedingWageningen University and ResearchDroevendaalsesteeg 1NL‐6708 PB Wageningenthe Netherlands
- Present address:
University of AdelaideSchool of Agriculture, Food and WineWaite CampusUrrbraeSouth Australia5064Australia
| | - Danny G. Esselink
- Plant BreedingWageningen University and ResearchDroevendaalsesteeg 1NL‐6708 PB Wageningenthe Netherlands
| | - Nick Gosman
- The John Bingham LaboratoryNIAB93 Lawrence Weaver RoadCambridgeCB3 0LEUK
- Present address:
Gosman AssociatesAg‐Biotech Consultingthe StreetBressingham, DissIP22 2BLUK
| | - Alison R. Bentley
- The John Bingham LaboratoryNIAB93 Lawrence Weaver RoadCambridgeCB3 0LEUK
- Present address:
International Maize and Wheat Improvement Center (CIMMYT)TexcocoMexico
| | - Luud J. W. J. Gilissen
- Plant BreedingWageningen University and ResearchDroevendaalsesteeg 1NL‐6708 PB Wageningenthe Netherlands
- BioscienceWageningen University and ResearchDroevendaalsesteeg 1NL‐6708 PB Wageningenthe Netherlands
- Allergy Consortium WageningenDroevendaalsesteeg 1NL‐6708 PB Wageningenthe Netherlands
| | - Marinus J. M. Smulders
- Plant BreedingWageningen University and ResearchDroevendaalsesteeg 1NL‐6708 PB Wageningenthe Netherlands
- Allergy Consortium WageningenDroevendaalsesteeg 1NL‐6708 PB Wageningenthe Netherlands
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27
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Móring A, Hooda S, Raghuram N, Adhya TK, Ahmad A, Bandyopadhyay SK, Barsby T, Beig G, Bentley AR, Bhatia A, Dragosits U, Drewer J, Foulkes J, Ghude SD, Gupta R, Jain N, Kumar D, Kumar RM, Ladha JK, Mandal PK, Neeraja CN, Pandey R, Pathak H, Pawar P, Pellny TK, Poole P, Price A, Rao DLN, Reay DS, Singh NK, Sinha SK, Srivastava RK, Shewry P, Smith J, Steadman CE, Subrahmanyam D, Surekha K, Venkatesh K, Varinderpal-Singh, Uwizeye A, Vieno M, Sutton MA. Nitrogen Challenges and Opportunities for Agricultural and Environmental Science in India. Front Sustain Food Syst 2021. [DOI: 10.3389/fsufs.2021.505347] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023] Open
Abstract
In the last six decades, the consumption of reactive nitrogen (Nr) in the form of fertilizer in India has been growing rapidly, whilst the nitrogen use efficiency (NUE) of cropping systems has been decreasing. These trends have led to increasing environmental losses of Nr, threatening the quality of air, soils, and fresh waters, and thereby endangering climate-stability, ecosystems, and human-health. Since it has been suggested that the fertilizer consumption of India may double by 2050, there is an urgent need for scientific research to support better nitrogen management in Indian agriculture. In order to share knowledge and to develop a joint vision, experts from the UK and India came together for a conference and workshop on “Challenges and Opportunities for Agricultural Nitrogen Science in India.” The meeting concluded with three core messages: (1) Soil stewardship is essential and legumes need to be planted in rotation with cereals to increase nitrogen fixation in areas of limited Nr availability. Synthetic symbioses and plastidic nitrogen fixation are possibly disruptive technologies, but their potential and implications must be considered. (2) Genetic diversity of crops and new technologies need to be shared and exploited to reduce N losses and support productive, sustainable agriculture livelihoods. (3) The use of leaf color sensing shows great potential to reduce nitrogen fertilizer use (by 10–15%). This, together with the usage of urease inhibitors in neem-coated urea, and better management of manure, urine, and crop residues, could result in a 20–25% improvement in NUE of India by 2030.
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28
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Przewieslik-Allen AM, Wilkinson PA, Burridge AJ, Winfield MO, Dai X, Beaumont M, King J, Yang CY, Griffiths S, Wingen LU, Horsnell R, Bentley AR, Shewry P, Barker GLA, Edwards KJ. The role of gene flow and chromosomal instability in shaping the bread wheat genome. Nat Plants 2021; 7:172-183. [PMID: 33526912 DOI: 10.1038/s41477-020-00845-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Accepted: 12/18/2020] [Indexed: 05/02/2023]
Abstract
Bread wheat (Triticum aestivum) is one of the world's most important crops; however, a low level of genetic diversity within commercial breeding accessions can significantly limit breeding potential. In contrast, wheat relatives exhibit considerable genetic variation and so potentially provide a valuable source of novel alleles for use in breeding new cultivars. Historically, gene flow between wheat and its relatives may have contributed novel alleles to the bread wheat pangenome. To assess the contribution made by wheat relatives to genetic diversity in bread wheat, we used markers based on single nucleotide polymorphisms to compare bread wheat accessions, created in the past 150 years, with 45 related species. We show that many bread wheat accessions share near-identical haplotype blocks with close relatives of wheat's diploid and tetraploid progenitors, while some show evidence of introgressions from more distant species and structural variation between accessions. Hence, introgressions and chromosomal rearrangements appear to have made a major contribution to genetic diversity in cultivar collections. As gene flow from relatives to bread wheat is an ongoing process, we assess the impact that introgressions might have on future breeding strategies.
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Affiliation(s)
| | - Paul A Wilkinson
- Life Sciences, University of Bristol, Bristol, UK
- Institute of Systems, Molecular & Integrative Biology, University of Liverpool, Liverpool, UK
| | | | | | - Xiaoyang Dai
- Life Sciences, University of Bristol, Bristol, UK
| | | | - Julie King
- Plant Sciences Building, School of Biosciences, The University of Nottingham, Sutton Bonington, UK
| | - Cai-Yun Yang
- Plant Sciences Building, School of Biosciences, The University of Nottingham, Sutton Bonington, UK
| | | | | | | | - Alison R Bentley
- International Maize and Wheat Improvement Center (CIMMYT), El Batán, Mexico
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29
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Crossa J, Fritsche-Neto R, Montesinos-Lopez OA, Costa-Neto G, Dreisigacker S, Montesinos-Lopez A, Bentley AR. The Modern Plant Breeding Triangle: Optimizing the Use of Genomics, Phenomics, and Enviromics Data. Front Plant Sci 2021; 12:651480. [PMID: 33936136 PMCID: PMC8085545 DOI: 10.3389/fpls.2021.651480] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2021] [Accepted: 02/11/2021] [Indexed: 05/04/2023]
Affiliation(s)
- Jose Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Carretera México-Veracruz, de Mexico, Mexico
- Colegio de Postgraduados, Montecillo, Edo. de Mexico, Mexico
| | - Roberto Fritsche-Neto
- Department of Genetics, “Luiz de Queiroz” Agriculture College, University of São Paulo, São Paulo, Brazil
| | | | - Germano Costa-Neto
- Department of Genetics, “Luiz de Queiroz” Agriculture College, University of São Paulo, São Paulo, Brazil
| | - Susanne Dreisigacker
- International Maize and Wheat Improvement Center (CIMMYT), Carretera México-Veracruz, de Mexico, Mexico
| | - Abelardo Montesinos-Lopez
- Departamento de Matemáticas, Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), Universidad de Guadalajara, Guadalajara, Mexico
| | - Alison R. Bentley
- International Maize and Wheat Improvement Center (CIMMYT), Carretera México-Veracruz, de Mexico, Mexico
- *Correspondence: Alison R. Bentley
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30
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Scott MF, Ladejobi O, Amer S, Bentley AR, Biernaskie J, Boden SA, Clark M, Dell'Acqua M, Dixon LE, Filippi CV, Fradgley N, Gardner KA, Mackay IJ, O'Sullivan D, Percival-Alwyn L, Roorkiwal M, Singh RK, Thudi M, Varshney RK, Venturini L, Whan A, Cockram J, Mott R. Multi-parent populations in crops: a toolbox integrating genomics and genetic mapping with breeding. Heredity (Edinb) 2020; 125:396-416. [PMID: 32616877 PMCID: PMC7784848 DOI: 10.1038/s41437-020-0336-6] [Citation(s) in RCA: 68] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Revised: 06/16/2020] [Accepted: 06/16/2020] [Indexed: 11/21/2022] Open
Abstract
Crop populations derived from experimental crosses enable the genetic dissection of complex traits and support modern plant breeding. Among these, multi-parent populations now play a central role. By mixing and recombining the genomes of multiple founders, multi-parent populations combine many commonly sought beneficial properties of genetic mapping populations. For example, they have high power and resolution for mapping quantitative trait loci, high genetic diversity and minimal population structure. Many multi-parent populations have been constructed in crop species, and their inbred germplasm and associated phenotypic and genotypic data serve as enduring resources. Their utility has grown from being a tool for mapping quantitative trait loci to a means of providing germplasm for breeding programmes. Genomics approaches, including de novo genome assemblies and gene annotations for the population founders, have allowed the imputation of rich sequence information into the descendent population, expanding the breadth of research and breeding applications of multi-parent populations. Here, we report recent successes from crop multi-parent populations in crops. We also propose an ideal genotypic, phenotypic and germplasm 'package' that multi-parent populations should feature to optimise their use as powerful community resources for crop research, development and breeding.
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Affiliation(s)
| | | | - Samer Amer
- University of Reading, Reading, RG6 6AH, UK
- Faculty of Agriculture, Alexandria University, Alexandria, 23714, Egypt
| | - Alison R Bentley
- The John Bingham Laboratory, NIAB, 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK
| | - Jay Biernaskie
- Department of Plant Sciences, University of Oxford, South Parks Road, Oxford, OX1 3RB, UK
| | - Scott A Boden
- School of Agriculture, Food and Wine, University of Adelaide, Glen Osmond, SA, 5064, Australia
| | | | | | - Laura E Dixon
- Faculty of Biological Sciences, University of Leeds, Leeds, LS2 9JT, UK
| | - Carla V Filippi
- Instituto de Agrobiotecnología y Biología Molecular (IABIMO), INTA-CONICET, Nicolas Repetto y Los Reseros s/n, 1686, Hurlingham, Buenos Aires, Argentina
| | - Nick Fradgley
- The John Bingham Laboratory, NIAB, 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK
| | - Keith A Gardner
- The John Bingham Laboratory, NIAB, 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK
| | - Ian J Mackay
- SRUC, West Mains Road, Kings Buildings, Edinburgh, EH9 3JG, UK
| | | | | | - Manish Roorkiwal
- Center of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
| | - Rakesh Kumar Singh
- International Center for Biosaline Agriculture, Academic City, Dubai, United Arab Emirates
| | - Mahendar Thudi
- Center of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
| | - Rajeev Kumar Varshney
- Center of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
| | | | - Alex Whan
- CSIRO, GPO Box 1700, Canberra, ACT, 2601, Australia
| | - James Cockram
- The John Bingham Laboratory, NIAB, 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK
| | - Richard Mott
- UCL Genetics Institute, Gower Street, London, WC1E 6BT, UK
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31
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Bentley AR, Compton LJ. Theory into practice: opportunities & applications of quantitative genetics in plants. Heredity (Edinb) 2020; 125:373-374. [PMID: 33168960 DOI: 10.1038/s41437-020-00374-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Accepted: 09/13/2020] [Indexed: 11/09/2022] Open
Affiliation(s)
| | - Lindsey J Compton
- School of Biosciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.
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32
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Adamski NM, Borrill P, Brinton J, Harrington SA, Marchal C, Bentley AR, Bovill WD, Cattivelli L, Cockram J, Contreras-Moreira B, Ford B, Ghosh S, Harwood W, Hassani-Pak K, Hayta S, Hickey LT, Kanyuka K, King J, Maccaferrri M, Naamati G, Pozniak CJ, Ramirez-Gonzalez RH, Sansaloni C, Trevaskis B, Wingen LU, Wulff BBH, Uauy C. A roadmap for gene functional characterisation in crops with large genomes: Lessons from polyploid wheat. eLife 2020; 9:e55646. [PMID: 32208137 PMCID: PMC7093151 DOI: 10.7554/elife.55646] [Citation(s) in RCA: 54] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Accepted: 03/12/2020] [Indexed: 02/04/2023] Open
Abstract
Understanding the function of genes within staple crops will accelerate crop improvement by allowing targeted breeding approaches. Despite their importance, a lack of genomic information and resources has hindered the functional characterisation of genes in major crops. The recent release of high-quality reference sequences for these crops underpins a suite of genetic and genomic resources that support basic research and breeding. For wheat, these include gene model annotations, expression atlases and gene networks that provide information about putative function. Sequenced mutant populations, improved transformation protocols and structured natural populations provide rapid methods to study gene function directly. We highlight a case study exemplifying how to integrate these resources. This review provides a helpful guide for plant scientists, especially those expanding into crop research, to capitalise on the discoveries made in Arabidopsis and other plants. This will accelerate the improvement of crops of vital importance for food and nutrition security.
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Affiliation(s)
| | - Philippa Borrill
- School of Biosciences, University of BirminghamBirminghamUnited Kingdom
| | - Jemima Brinton
- John Innes Centre, Norwich Research ParkNorwichUnited Kingdom
| | | | | | | | - William D Bovill
- Commonwealth Scientific and Industrial Research Organisation, Agriculture and Food (CSIRO)CanberraAustralia
| | - Luigi Cattivelli
- Council for Agricultural Research and Economics, Research Centre for Genomics and BioinformaticsFiorenzuola d'ArdaItaly
| | | | - Bruno Contreras-Moreira
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome CampusHinxtonUnited Kingdom
| | - Brett Ford
- Commonwealth Scientific and Industrial Research Organisation, Agriculture and Food (CSIRO)CanberraAustralia
| | - Sreya Ghosh
- John Innes Centre, Norwich Research ParkNorwichUnited Kingdom
| | - Wendy Harwood
- John Innes Centre, Norwich Research ParkNorwichUnited Kingdom
| | | | - Sadiye Hayta
- John Innes Centre, Norwich Research ParkNorwichUnited Kingdom
| | - Lee T Hickey
- Queensland Alliance for Agriculture and Food Innovation, The University of QueenslandSt LuciaAustralia
| | | | - Julie King
- Division of Plant and Crop Sciences, The University of Nottingham, Sutton Bonington CampusLoughboroughUnited Kingdom
| | - Marco Maccaferrri
- Department of Agricultural and Food Sciences (DISTAL), Alma Mater Studiorum - Università di Bologna (University of Bologna)BolognaItaly
| | - Guy Naamati
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome CampusHinxtonUnited Kingdom
| | - Curtis J Pozniak
- Crop Development Centre, University of SaskatchewanSaskatoonCanada
| | | | | | - Ben Trevaskis
- Commonwealth Scientific and Industrial Research Organisation, Agriculture and Food (CSIRO)CanberraAustralia
| | - Luzie U Wingen
- John Innes Centre, Norwich Research ParkNorwichUnited Kingdom
| | - Brande BH Wulff
- John Innes Centre, Norwich Research ParkNorwichUnited Kingdom
| | - Cristobal Uauy
- John Innes Centre, Norwich Research ParkNorwichUnited Kingdom
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33
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Mellers G, Mackay I, Cowan S, Griffiths I, Martinez‐Martin P, Poland JA, Bekele W, Tinker NA, Bentley AR, Howarth CJ. Implementing within-cross genomic prediction to reduce oat breeding costs. Plant Genome 2020; 13:e20004. [PMID: 33016630 PMCID: PMC8638661 DOI: 10.1002/tpg2.20004] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Accepted: 11/26/2019] [Indexed: 05/23/2023]
Abstract
A barrier to the adoption of genomic prediction in small breeding programs is the initial cost of genotyping material. Although decreasing, marker costs are usually higher than field trial costs. In this study we demonstrate the utility of stratifying a narrow-base biparental oat population genotyped with a modest number of markers to employ genomic prediction at early and later generations. We also show that early generation genotyping data can reduce the number of lines for later phenotyping based on selections of siblings to progress. Using sets of small families selected at an early generation could enable the use of genomic prediction for adaptation to multiple target environments at an early stage in the breeding program. In addition, we demonstrate that mixed marker data can be effectively integrated to combine cheap dominant marker data (including legacy data) with more expensive but higher density codominant marker data in order to make within generation and between lineage predictions based on genotypic information. Taken together, our results indicate that small programs can test and initiate genomic predictions using sets of stratified, narrow-base populations and incorporating low density legacy genotyping data. This can then be scaled to include higher density markers and a broadened population base.
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Affiliation(s)
- Greg Mellers
- The John Bingham LaboratoryNIABCambridgeUnited Kingdom
| | - Ian Mackay
- IMplant Consultancy Ltd.ChelmsfordUnited Kingdom
| | - Sandy Cowan
- Institute of Biological, Environmental and Rural Sciences, Plas GogerddanAberystwyth UniversityAberystwythUnited Kingdom
| | - Irene Griffiths
- Institute of Biological, Environmental and Rural Sciences, Plas GogerddanAberystwyth UniversityAberystwythUnited Kingdom
| | - Pilar Martinez‐Martin
- Institute of Biological, Environmental and Rural Sciences, Plas GogerddanAberystwyth UniversityAberystwythUnited Kingdom
| | - Jesse A. Poland
- Wheat Genetics Resource Center, Department of Plant PathologyKansas State UniversityManhattanKSUSA
| | - Wubishet Bekele
- Ottawa Research and Development Centre, Agriculture and Agri‐Food CanadaOttawaCanada
| | - Nicholas A. Tinker
- Ottawa Research and Development Centre, Agriculture and Agri‐Food CanadaOttawaCanada
| | | | - Catherine J. Howarth
- Institute of Biological, Environmental and Rural Sciences, Plas GogerddanAberystwyth UniversityAberystwythUnited Kingdom
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Ladejobi O, Mackay IJ, Poland J, Praud S, Hibberd JM, Bentley AR. Reference Genome Anchoring of High-Density Markers for Association Mapping and Genomic Prediction in European Winter Wheat. Front Plant Sci 2019; 10:1278. [PMID: 31781130 PMCID: PMC6857554 DOI: 10.3389/fpls.2019.01278] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2018] [Accepted: 09/12/2019] [Indexed: 05/28/2023]
Abstract
In this study, we anchored genotyping-by-sequencing data to the International Wheat Genome Sequencing Consortium Reference Sequence v1.0 assembly to generate over 40,000 high quality single nucleotide polymorphism markers on a panel of 376 elite European winter wheat varieties released between 1946 and 2007. We compared association mapping and genomic prediction accuracy for a range of productivity traits with previous results based on lower density dominant DArT markers. The results demonstrate that the availability of RefSeq v1.0 supports higher precision trait mapping and provides the density of markers required to obtain accurate predictions of traits controlled by multiple small effect loci, including grain yield.
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Affiliation(s)
- Olufunmilayo Ladejobi
- The John Bingham Laboratory, NIAB, Cambridge, United Kingdom
- Department of Plant Sciences, The University of Cambridge, Cambridge, United Kingdom
| | - Ian J. Mackay
- The John Bingham Laboratory, NIAB, Cambridge, United Kingdom
- IMplant Consultancy Ltd., Chelmsford, United Kingdom
| | - Jesse Poland
- Wheat Genetics Resource Center, Department of Plant Pathology, Kansas State University, Manhattan, KS, United States
| | | | - Julian M. Hibberd
- Department of Plant Sciences, The University of Cambridge, Cambridge, United Kingdom
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35
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Swarbreck SM, Wang M, Wang Y, Kindred D, Sylvester-Bradley R, Shi W, Bentley AR, Griffiths H. A Roadmap for Lowering Crop Nitrogen Requirement. Trends Plant Sci 2019; 24:892-904. [PMID: 31285127 DOI: 10.1016/j.tplants.2019.06.006] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/10/2018] [Revised: 05/31/2019] [Accepted: 06/06/2019] [Indexed: 05/03/2023]
Abstract
Increasing nitrogen fertilizer applications have sustained a growing world population in the 20th century. However, to avoid any further associated environmental damage, new sustainable agronomic practices together with new cultivars must be developed. To date the concept of nitrogen use efficiency (NUE) has been useful in quantifying the processes of nitrogen uptake and utilization, but we propose a shift in focus to consider nitrogen responsiveness as a more appropriate trait to select varieties with lower nitrogen requirements. We provide a roadmap to integrate the regulation of nitrogen uptake and assimilation into varietal selection and crop breeding programs. The overall goal is to reduce nitrogen inputs by farmers growing crops in contrasting cropping systems around the world, while sustaining yields and reducing greenhouse gas (GHG) emissions.
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Affiliation(s)
| | - Meng Wang
- State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
| | - Yuan Wang
- State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
| | | | | | - Weiming Shi
- State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
| | - Alison R Bentley
- The John Bingham Laboratory, NIAB, Huntingdon Road, Cambridge CB3 0LE, UK
| | - Howard Griffiths
- Department of Plant Sciences, University of Cambridge, Cambridge CB2 3EA, UK
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36
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Edwards SM, Buntjer JB, Jackson R, Bentley AR, Lage J, Byrne E, Burt C, Jack P, Berry S, Flatman E, Poupard B, Smith S, Hayes C, Gaynor RC, Gorjanc G, Howell P, Ober E, Mackay IJ, Hickey JM. The effects of training population design on genomic prediction accuracy in wheat. Theor Appl Genet 2019; 132:1943-1952. [PMID: 30888431 PMCID: PMC6588656 DOI: 10.1007/s00122-019-03327-y] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/15/2018] [Accepted: 03/11/2019] [Indexed: 05/22/2023]
Abstract
Genomic selection offers several routes for increasing the genetic gain or efficiency of plant breeding programmes. In various species of livestock, there is empirical evidence of increased rates of genetic gain from the use of genomic selection to target different aspects of the breeder's equation. Accurate predictions of genomic breeding value are central to this, and the design of training sets is in turn central to achieving sufficient levels of accuracy. In summary, small numbers of close relatives and very large numbers of distant relatives are expected to enable predictions with higher accuracy. To quantify the effect of some of the properties of training sets on the accuracy of genomic selection in crops, we performed an extensive field-based winter wheat trial. In summary, this trial involved the construction of 44 F2:4 bi- and tri-parental populations, from which 2992 lines were grown on four field locations and yield was measured. For each line, genotype data were generated for 25 K segregating SNP markers. The overall heritability of yield was estimated to 0.65, and estimates within individual families ranged between 0.10 and 0.85. Genomic prediction accuracies of yield BLUEs were 0.125-0.127 using two different cross-validation approaches and generally increased with training set size. Using related crosses in training and validation sets generally resulted in higher prediction accuracies than using unrelated crosses. The results of this study emphasise the importance of the training panel design in relation to the genetic material to which the resulting prediction model is to be applied.
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Affiliation(s)
- Stefan McKinnon Edwards
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Easter Bush, Midlothian, Scotland, UK
| | - Jaap B Buntjer
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Easter Bush, Midlothian, Scotland, UK
| | - Robert Jackson
- The John Bingham Laboratory, NIAB, Huntingdon Road, Cambridge, CB3 0LE, UK
| | - Alison R Bentley
- The John Bingham Laboratory, NIAB, Huntingdon Road, Cambridge, CB3 0LE, UK
| | - Jacob Lage
- KWS UK Ltd, 56 Church Street, Hertfordshire, SG8 7RE, UK
| | - Ed Byrne
- KWS UK Ltd, 56 Church Street, Hertfordshire, SG8 7RE, UK
| | - Chris Burt
- RAGT UK, Grange Rd, Saffron Walden, CB10 1TA, UK
| | - Peter Jack
- RAGT UK, Grange Rd, Saffron Walden, CB10 1TA, UK
| | - Simon Berry
- Limagrain UK Ltd, Rothwell, Market Rasen, Lincolnshire, LN7 6DT, UK
| | - Edward Flatman
- Limagrain UK Ltd, Rothwell, Market Rasen, Lincolnshire, LN7 6DT, UK
| | - Bruno Poupard
- Limagrain UK Ltd, Rothwell, Market Rasen, Lincolnshire, LN7 6DT, UK
| | - Stephen Smith
- Elsoms Wheat Limited, Pinchbeck Road, Spalding, Linconshire, PE11 1QG, UK
| | - Charlotte Hayes
- Elsoms Wheat Limited, Pinchbeck Road, Spalding, Linconshire, PE11 1QG, UK
| | - R Chris Gaynor
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Easter Bush, Midlothian, Scotland, UK
| | - Gregor Gorjanc
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Easter Bush, Midlothian, Scotland, UK
| | - Phil Howell
- The John Bingham Laboratory, NIAB, Huntingdon Road, Cambridge, CB3 0LE, UK
| | - Eric Ober
- The John Bingham Laboratory, NIAB, Huntingdon Road, Cambridge, CB3 0LE, UK
| | | | - John M Hickey
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Easter Bush, Midlothian, Scotland, UK.
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37
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Arora S, Steuernagel B, Gaurav K, Chandramohan S, Long Y, Matny O, Johnson R, Enk J, Periyannan S, Singh N, Asyraf Md Hatta M, Athiyannan N, Cheema J, Yu G, Kangara N, Ghosh S, Szabo LJ, Poland J, Bariana H, Jones JDG, Bentley AR, Ayliffe M, Olson E, Xu SS, Steffenson BJ, Lagudah E, Wulff BBH. Resistance gene cloning from a wild crop relative by sequence capture and association genetics. Nat Biotechnol 2019; 37:139-143. [PMID: 30718880 DOI: 10.1038/s41587-018-0007-9] [Citation(s) in RCA: 183] [Impact Index Per Article: 36.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2018] [Accepted: 12/12/2018] [Indexed: 01/17/2023]
Abstract
Disease resistance (R) genes from wild relatives could be used to engineer broad-spectrum resistance in domesticated crops. We combined association genetics with R gene enrichment sequencing (AgRenSeq) to exploit pan-genome variation in wild diploid wheat and rapidly clone four stem rust resistance genes. AgRenSeq enables R gene cloning in any crop that has a diverse germplasm panel.
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Affiliation(s)
- Sanu Arora
- John Innes Centre, Norwich Research Park, Norwich, UK
| | | | - Kumar Gaurav
- John Innes Centre, Norwich Research Park, Norwich, UK
| | - Sutha Chandramohan
- Commonwealth Scientific and Industrial Research Organization Agriculture and Food, Canberra, Australian Capital Territory, Australia
| | - Yunming Long
- Department of Plant Sciences, North Dakota State University, Fargo, ND, USA
| | - Oadi Matny
- Department of Plant Pathology, University of Minnesota, St. Paul, MN, USA
| | - Ryan Johnson
- Department of Plant Pathology, University of Minnesota, St. Paul, MN, USA
| | - Jacob Enk
- Arbor Biosciences, Ann Arbor, MI, USA
| | - Sambasivam Periyannan
- Commonwealth Scientific and Industrial Research Organization Agriculture and Food, Canberra, Australian Capital Territory, Australia
| | - Narinder Singh
- Wheat Genetics Resource Center, Department of Plant Pathology, Kansas State University, Manhattan, KS, USA
| | - M Asyraf Md Hatta
- John Innes Centre, Norwich Research Park, Norwich, UK.,Faculty of Agriculture, Universiti Putra Malaysia, Serdang, Malaysia
| | - Naveenkumar Athiyannan
- Commonwealth Scientific and Industrial Research Organization Agriculture and Food, Canberra, Australian Capital Territory, Australia.,Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, Queensland, Australia
| | | | - Guotai Yu
- John Innes Centre, Norwich Research Park, Norwich, UK
| | | | - Sreya Ghosh
- John Innes Centre, Norwich Research Park, Norwich, UK
| | - Les J Szabo
- US Department of Agriculture, Agriculture Research Service, Cereal Disease Laboratory, St. Paul, MN, USA
| | - Jesse Poland
- Wheat Genetics Resource Center, Department of Plant Pathology, Kansas State University, Manhattan, KS, USA
| | - Harbans Bariana
- The University of Sydney Plant Breeding Institute, Cobbitty, New South Wales, Australia
| | | | | | - Mick Ayliffe
- Commonwealth Scientific and Industrial Research Organization Agriculture and Food, Canberra, Australian Capital Territory, Australia
| | - Eric Olson
- Michigan State University, East Lansing, MI, USA
| | - Steven S Xu
- US Department of Agriculture, Agriculture Research Service, Northern Crop Science Laboratory, Cereal Crops Research Unit, Red River Valley Agricultural Research Center, Fargo, ND, USA
| | - Brian J Steffenson
- Department of Plant Pathology, University of Minnesota, St. Paul, MN, USA
| | - Evans Lagudah
- Commonwealth Scientific and Industrial Research Organization Agriculture and Food, Canberra, Australian Capital Territory, Australia
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38
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Clifton-Brown JC, Senior H, Purdy SJ, Horsnell R, Lankamp B, Müennekhoff AK, Virk D, Guillemois E, Chetty V, Cookson A, Girdwood S, Clifton-Brown G, Tan MLMC, Awty-Carroll D, Bentley AR. Investigating the potential of novel non-woven fabrics for efficient pollination control in plant breeding. PLoS One 2018; 13:e0204728. [PMID: 30265713 PMCID: PMC6161889 DOI: 10.1371/journal.pone.0204728] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2018] [Accepted: 09/13/2018] [Indexed: 11/20/2022] Open
Abstract
Plant breeding is achieved through the controlled self- or cross-pollination of individuals and typically involves isolation of floral parts from selected parental plants. Paper, cellulose or synthetic materials are used to avoid self pollination or cross contamination. Low seed set limits the rate of breeding progress and increases costs. We hypothesized that a novel ‘non-woven’ fabric optimal for both pollination and seed set in multiple plant species could be developed. After determining the baseline pollen characteristics and usage requirements we established iterative three phase development and biological testing. This determined (1) that white fabric gave superior seed return and informed the (2) development of three non-woven materials using different fibre and layering techniques. We tested their performance in selfing and hybridisation experiments recording differences in performance by material type within species. Finally we (3) developed further advanced fabrics with increased air permeability and tested biological performance. An interaction between material type and species was observed and environmental decoupling investigated, showing that the non-woven fabrics had superior water vapour transmission and temperature regulation compared to controls. Overall, non-woven fabrics outperformed existing materials for both pollination and seed set and we found that different materials can optimize species-specific, rather than species-generic performance.
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Affiliation(s)
- John C. Clifton-Brown
- Institute of Biological, Environmental and Rural Sciences, Plas Gogerddan, Aberystwyth University, Aberystwyth, United Kingdom
| | | | - Sarah J. Purdy
- Institute of Biological, Environmental and Rural Sciences, Plas Gogerddan, Aberystwyth University, Aberystwyth, United Kingdom
| | | | | | | | - Daljit Virk
- PBS International, Scarborough, United Kingdom
| | | | - Vera Chetty
- Nonwovens Innovation & Research Institute Ltd, Leeds, United Kingdom
| | - Alan Cookson
- Institute of Biological, Environmental and Rural Sciences, Plas Gogerddan, Aberystwyth University, Aberystwyth, United Kingdom
| | - Sarah Girdwood
- Institute of Biological, Environmental and Rural Sciences, Plas Gogerddan, Aberystwyth University, Aberystwyth, United Kingdom
| | - Gabi Clifton-Brown
- Institute of Biological, Environmental and Rural Sciences, Plas Gogerddan, Aberystwyth University, Aberystwyth, United Kingdom
| | | | - Danny Awty-Carroll
- Institute of Biological, Environmental and Rural Sciences, Plas Gogerddan, Aberystwyth University, Aberystwyth, United Kingdom
| | - Alison R. Bentley
- The John Bingham Laboratory, NIAB, Cambridge, United Kingdom
- * E-mail:
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39
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Borah P, Das A, Milner MJ, Ali A, Bentley AR, Pandey R. Long Non-Coding RNAs as Endogenous Target Mimics and Exploration of Their Role in Low Nutrient Stress Tolerance in Plants. Genes (Basel) 2018; 9:E459. [PMID: 30223541 PMCID: PMC6162444 DOI: 10.3390/genes9090459] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2018] [Revised: 09/05/2018] [Accepted: 09/07/2018] [Indexed: 12/14/2022] Open
Abstract
Long non-coding RNA (lncRNA) research in plants has recently gained momentum taking cues from studies in animals systems. The availability of next-generation sequencing has enabled genome-wide identification of lncRNA in several plant species. Some lncRNAs are inhibitors of microRNA expression and have a function known as target mimicry with the sequestered transcript known as an endogenous target mimic (eTM). The lncRNAs identified to date show diverse mechanisms of gene regulation, most of which remain poorly understood. In this review, we discuss the role of identified putative lncRNAs that may act as eTMs for nutrient-responsive microRNAs (miRNAs) in plants. If functionally validated, these putative lncRNAs would enhance current understanding of the role of lncRNAs in nutrient homeostasis in plants.
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Affiliation(s)
- Priyanka Borah
- Mineral Nutrition Laboratory, Division of Plant Physiology, ICAR-Indian Agricultural Research Institute, New Delhi 110 012, India.
- Department of Biosciences, Jamia Millia Islamia, New Delhi 110025, India.
| | - Antara Das
- ICAR-National Research Centre on Plant Biotechnology, New Delhi 110012, India.
| | - Matthew J Milner
- The John Bingham Laboratory, National Institute of Agricultural Botany (NIAB), Huntingdon Road, Cambridge CB30LE, UK.
| | - Arif Ali
- Department of Biosciences, Jamia Millia Islamia, New Delhi 110025, India.
| | - Alison R Bentley
- The John Bingham Laboratory, National Institute of Agricultural Botany (NIAB), Huntingdon Road, Cambridge CB30LE, UK.
| | - Renu Pandey
- Mineral Nutrition Laboratory, Division of Plant Physiology, ICAR-Indian Agricultural Research Institute, New Delhi 110 012, India.
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40
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Camargo AV, Mackay I, Mott R, Han J, Doonan JH, Askew K, Corke F, Williams K, Bentley AR. Functional Mapping of Quantitative Trait Loci (QTLs) Associated With Plant Performance in a Wheat MAGIC Mapping Population. Front Plant Sci 2018; 9:887. [PMID: 30038630 PMCID: PMC6047115 DOI: 10.3389/fpls.2018.00887] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/05/2018] [Accepted: 06/07/2018] [Indexed: 05/18/2023]
Abstract
In crop genetic studies, the mapping of longitudinal data describing the spatio-temporal nature of agronomic traits can elucidate the factors influencing their formation and development. Here, we combine the mapping power and precision of a MAGIC wheat population with robust computational methods to track the spatio- temporal dynamics of traits associated with wheat performance. NIAB MAGIC lines were phenotyped throughout their lifecycle under smart house conditions. Growth models were fitted to the data describing growth trajectories of plant area, height, water use and senescence and fitted parameters were mapped as quantitative traits. Trait data from single time points were also mapped to determine when and how markers became and ceased to be significant. Assessment of temporal dynamics allowed the identification of marker-trait associations and tracking of trait development against the genetic contribution of key markers. We establish a data-driven approach for understanding complex agronomic traits and accelerate research in plant breeding.
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Affiliation(s)
- Anyela V. Camargo
- The John Bingham Laboratory, National Institute of Agricultural Botany, Cambridge, United Kingdom
- *Correspondence: Anyela V. Camargo
| | - Ian Mackay
- The John Bingham Laboratory, National Institute of Agricultural Botany, Cambridge, United Kingdom
| | - Richard Mott
- Division of Bioscience, Genetics Institute, University College London, London, United Kingdom
| | - Jiwan Han
- National Plant Phenomics Centre, Institute of Biological Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, United Kingdom
| | - John H. Doonan
- National Plant Phenomics Centre, Institute of Biological Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, United Kingdom
| | - Karen Askew
- National Plant Phenomics Centre, Institute of Biological Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, United Kingdom
| | - Fiona Corke
- National Plant Phenomics Centre, Institute of Biological Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, United Kingdom
| | - Kevin Williams
- National Plant Phenomics Centre, Institute of Biological Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, United Kingdom
| | - Alison R. Bentley
- The John Bingham Laboratory, National Institute of Agricultural Botany, Cambridge, United Kingdom
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41
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Allen AM, Winfield MO, Burridge AJ, Downie RC, Benbow HR, Barker GLA, Wilkinson PA, Coghill J, Waterfall C, Davassi A, Scopes G, Pirani A, Webster T, Brew F, Bloor C, Griffiths S, Bentley AR, Alda M, Jack P, Phillips AL, Edwards KJ. Characterization of a Wheat Breeders' Array suitable for high-throughput SNP genotyping of global accessions of hexaploid bread wheat (Triticum aestivum). Plant Biotechnol J 2017; 15:390-401. [PMID: 27627182 PMCID: PMC5316916 DOI: 10.1111/pbi.12635] [Citation(s) in RCA: 103] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2016] [Revised: 09/02/2016] [Accepted: 09/09/2016] [Indexed: 05/18/2023]
Abstract
Targeted selection and inbreeding have resulted in a lack of genetic diversity in elite hexaploid bread wheat accessions. Reduced diversity can be a limiting factor in the breeding of high yielding varieties and crucially can mean reduced resilience in the face of changing climate and resource pressures. Recent technological advances have enabled the development of molecular markers for use in the assessment and utilization of genetic diversity in hexaploid wheat. Starting with a large collection of 819 571 previously characterized wheat markers, here we describe the identification of 35 143 single nucleotide polymorphism-based markers, which are highly suited to the genotyping of elite hexaploid wheat accessions. To assess their suitability, the markers have been validated using a commercial high-density Affymetrix Axiom® genotyping array (the Wheat Breeders' Array), in a high-throughput 384 microplate configuration, to characterize a diverse global collection of wheat accessions including landraces and elite lines derived from commercial breeding communities. We demonstrate that the Wheat Breeders' Array is also suitable for generating high-density genetic maps of previously uncharacterized populations and for characterizing novel genetic diversity produced by mutagenesis. To facilitate the use of the array by the wheat community, the markers, the associated sequence and the genotype information have been made available through the interactive web site 'CerealsDB'.
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Affiliation(s)
| | | | | | - Rowena C. Downie
- Life SciencesUniversity of BristolBristolUK
- The John Bingham LaboratoryNIABCambridgeUK
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42
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Ladejobi O, Elderfield J, Gardner KA, Gaynor RC, Hickey J, Hibberd JM, Mackay IJ, Bentley AR. Maximizing the potential of multi-parental crop populations. Appl Transl Genom 2016; 11:9-17. [PMID: 28018845 PMCID: PMC5167364 DOI: 10.1016/j.atg.2016.10.002] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2016] [Revised: 10/22/2016] [Accepted: 10/24/2016] [Indexed: 11/03/2022]
Abstract
Most agriculturally significant crop traits are quantitatively inherited which limits the ease and efficiency of trait dissection. Multi-parent populations overcome the limitations of traditional trait mapping and offer new potential to accurately define the genetic basis of complex crop traits. The increasing popularity and use of nested association mapping (NAM) and multi-parent advanced generation intercross (MAGIC) populations raises questions about the optimal design and allocation of resources in their creation. In this paper we review strategies for the creation of multi-parent populations and describe two complementary in silico studies addressing the design and construction of NAM and MAGIC populations. The first simulates the selection of diverse founder parents and the second the influence of multi-parent crossing schemes (and number of founders) on haplotype creation and diversity. We present and apply two open software resources to simulate alternate strategies for the development of multi-parent populations.
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Affiliation(s)
- Olufunmilayo Ladejobi
- The John Bingham Laboratory, NIAB, Huntingdon Road, Cambridge CB3 0LE, United Kingdom
- Department of Plant Sciences, The University of Cambridge, Downing Street, Cambridge CB2 3EA, United Kingdom
| | - James Elderfield
- Department of Plant Sciences, The University of Cambridge, Downing Street, Cambridge CB2 3EA, United Kingdom
| | - Keith A. Gardner
- The John Bingham Laboratory, NIAB, Huntingdon Road, Cambridge CB3 0LE, United Kingdom
| | - R. Chris Gaynor
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush Research Centre, Midlothian EH25 9RG, United Kingdom
| | - John Hickey
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush Research Centre, Midlothian EH25 9RG, United Kingdom
| | - Julian M. Hibberd
- Department of Plant Sciences, The University of Cambridge, Downing Street, Cambridge CB2 3EA, United Kingdom
| | - Ian J. Mackay
- The John Bingham Laboratory, NIAB, Huntingdon Road, Cambridge CB3 0LE, United Kingdom
| | - Alison R. Bentley
- The John Bingham Laboratory, NIAB, Huntingdon Road, Cambridge CB3 0LE, United Kingdom
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43
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Winfield MO, Allen AM, Burridge AJ, Barker GLA, Benbow HR, Wilkinson PA, Coghill J, Waterfall C, Davassi A, Scopes G, Pirani A, Webster T, Brew F, Bloor C, King J, West C, Griffiths S, King I, Bentley AR, Edwards KJ. High-density SNP genotyping array for hexaploid wheat and its secondary and tertiary gene pool. Plant Biotechnol J 2016; 14:1195-206. [PMID: 26466852 PMCID: PMC4950041 DOI: 10.1111/pbi.12485] [Citation(s) in RCA: 120] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/07/2015] [Revised: 08/21/2015] [Accepted: 09/07/2015] [Indexed: 05/15/2023]
Abstract
In wheat, a lack of genetic diversity between breeding lines has been recognized as a significant block to future yield increases. Species belonging to bread wheat's secondary and tertiary gene pools harbour a much greater level of genetic variability, and are an important source of genes to broaden its genetic base. Introgression of novel genes from progenitors and related species has been widely employed to improve the agronomic characteristics of hexaploid wheat, but this approach has been hampered by a lack of markers that can be used to track introduced chromosome segments. Here, we describe the identification of a large number of single nucleotide polymorphisms that can be used to genotype hexaploid wheat and to identify and track introgressions from a variety of sources. We have validated these markers using an ultra-high-density Axiom(®) genotyping array to characterize a range of diploid, tetraploid and hexaploid wheat accessions and wheat relatives. To facilitate the use of these, both the markers and the associated sequence and genotype information have been made available through an interactive web site.
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Affiliation(s)
| | | | | | | | | | | | - Jane Coghill
- Life Sciences, University of Bristol, Bristol, UK
| | | | | | | | | | | | | | | | - Julie King
- School of Biosciences, Sutton Bonington, Leicestershire, UK
| | - Claire West
- John Innes Centre, Norwich Research Park, Norwich, Norfolk, UK
| | - Simon Griffiths
- John Innes Centre, Norwich Research Park, Norwich, Norfolk, UK
| | - Ian King
- School of Biosciences, Sutton Bonington, Leicestershire, UK
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44
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Halliwell J, Borrill P, Gordon A, Kowalczyk R, Pagano ML, Saccomanno B, Bentley AR, Uauy C, Cockram J. Systematic Investigation of FLOWERING LOCUS T-Like Poaceae Gene Families Identifies the Short-Day Expressed Flowering Pathway Gene, TaFT3 in Wheat (Triticum aestivum L.). Front Plant Sci 2016; 7:857. [PMID: 27458461 PMCID: PMC4937749 DOI: 10.3389/fpls.2016.00857] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2016] [Accepted: 05/31/2016] [Indexed: 05/20/2023]
Abstract
To date, a small number of major flowering time loci have been identified in the related Triticeae crops, bread wheat (Triticum aestivum), durum wheat (T. durum), and barley (Hordeum vulgare). Natural genetic variants at these loci result in major phenotypic changes which have adapted crops to the novel environments encountered during the spread of agriculture. The polyploid nature of bread and durum wheat means that major flowering time loci in which recessive alleles confer adaptive advantage in related diploid species have not been readily identified. One such example is the PPD-H2 flowering time locus encoded by FLOWERING LOCUS T 3 (HvFT3) in the diploid crop barley, for which recessive mutant alleles confer delayed flowering under short day (SD) photoperiods. In autumn-sown barley, such alleles aid the repression of flowering over the winter, which help prevent the development of cold-sensitive floral organs until the onset of inductive long day (LD) photoperiods the following spring. While the identification of orthologous loci in wheat could provide breeders with alternative mechanisms to fine tune flowering time, systematic identification of wheat orthologs of HvFT3 has not been reported. Here, we characterize the FT gene families in six Poaceae species, identifying novel members in all taxa investigated, as well as FT3 homoeologs from the A, B and D genomes of hexaploid (TaFT3) and tetraploid wheat. Sequence analysis shows TaFT3 homoeologs display high similarity to the HvFT3 coding region (95-96%) and predicted protein (96-97%), with conservation of intron/exon structure across the five cereal species investigated. Genetic mapping and comparative analyses in hexaploid and tetraploid wheat find TaFT3 homoeologs map to the long arms of the group 1 chromosomes, collinear to HvFT3 in barley and FT3 orthologs in rice, foxtail millet and brachypodium. Genome-specific expression analyses show FT3 homoeologs in tetraploid and hexaploid wheat are upregulated under SD photoperiods, but not under LDs, analogous to the expression of HvFT3. Collectively, these results indicate that functional wheat orthologs of HvFT3 have been identified. The molecular resources generated here provide the foundation for engineering a novel major flowering time locus in wheat using forward or reverse genetics approaches.
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Affiliation(s)
- Joanna Halliwell
- Crop Genetics Department, John Innes CentreNorwich, UK
- John Bingham Laboratory, National Institute of Agricultural BotanyCambridge, UK
| | | | - Anna Gordon
- John Bingham Laboratory, National Institute of Agricultural BotanyCambridge, UK
| | - Radoslaw Kowalczyk
- John Bingham Laboratory, National Institute of Agricultural BotanyCambridge, UK
- Faculty of Life Sciences, University of ManchesterManchester, UK
| | - Marina L. Pagano
- John Bingham Laboratory, National Institute of Agricultural BotanyCambridge, UK
- Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, University of MessinaMessina, Italy
| | | | - Alison R. Bentley
- John Bingham Laboratory, National Institute of Agricultural BotanyCambridge, UK
| | - Cristobal Uauy
- Crop Genetics Department, John Innes CentreNorwich, UK
- John Bingham Laboratory, National Institute of Agricultural BotanyCambridge, UK
| | - James Cockram
- John Bingham Laboratory, National Institute of Agricultural BotanyCambridge, UK
- *Correspondence: James Cockram
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45
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Camargo AV, Mott R, Gardner KA, Mackay IJ, Corke F, Doonan JH, Kim JT, Bentley AR. Determining Phenological Patterns Associated with the Onset of Senescence in a Wheat MAGIC Mapping Population. Front Plant Sci 2016; 7:1540. [PMID: 27822218 PMCID: PMC5075575 DOI: 10.3389/fpls.2016.01540] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/15/2016] [Accepted: 09/30/2016] [Indexed: 05/19/2023]
Abstract
The appropriate timing of developmental transitions is critical for adapting many crops to their local climatic conditions. Therefore, understanding the genetic basis of different aspects of phenology could be useful in highlighting mechanisms underpinning adaptation, with implications in breeding for climate change. For bread wheat (Triticum aestivum), the transition from vegetative to reproductive growth, the start and rate of leaf senescence and the relative timing of different stages of flowering and grain filling all contribute to plant performance. In this study we screened under Smart house conditions a large, multi-founder "NIAB elite MAGIC" wheat population, to evaluate the genetic elements that influence the timing of developmental stages in European elite varieties. This panel of recombinant inbred lines was derived from eight parents that are or recently have been grown commercially in the UK and Northern Europe. We undertook a detailed temporal phenotypic analysis under Smart house conditions of the population and its parents, to try to identify known or novel Quantitative Trait Loci associated with variation in the timing of key phenological stages in senescence. This analysis resulted in the detection of QTL interactions with novel traits such the time between "half of ear emergence above flag leaf ligule" and the onset of senescence at the flag leaf as well as traits associated with plant morphology such as stem height. In addition, strong correlations between several traits and the onset of senescence of the flag leaf were identified. This work establishes the value of systematically phenotyping genetically unstructured populations to reveal the genetic architecture underlying morphological variation in commercial wheat.
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Affiliation(s)
- Anyela V. Camargo
- National Plant Phenomics Centre, Institute of Biological Environmental and Rural Sciences, Aberystwyth UniversityAberystwyth, UK
- *Correspondence: Anyela V. Camargo
| | - Richard Mott
- UCL Genetics InstituteUniversity College London, UK
| | - Keith A. Gardner
- The John Bingham Laboratory, National Institute of Agricultural BotanyCambridge, UK
| | - Ian J. Mackay
- The John Bingham Laboratory, National Institute of Agricultural BotanyCambridge, UK
| | - Fiona Corke
- National Plant Phenomics Centre, Institute of Biological Environmental and Rural Sciences, Aberystwyth UniversityAberystwyth, UK
| | - John H. Doonan
- National Plant Phenomics Centre, Institute of Biological Environmental and Rural Sciences, Aberystwyth UniversityAberystwyth, UK
| | | | - Alison R. Bentley
- The John Bingham Laboratory, National Institute of Agricultural BotanyCambridge, UK
- Alison R. Bentley
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Bassi FM, Bentley AR, Charmet G, Ortiz R, Crossa J. Breeding schemes for the implementation of genomic selection in wheat (Triticum spp.). Plant Sci 2016; 242:23-36. [PMID: 26566822 DOI: 10.1016/j.plantsci.2015.08.021] [Citation(s) in RCA: 136] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/16/2015] [Revised: 08/23/2015] [Accepted: 08/27/2015] [Indexed: 05/18/2023]
Abstract
In the last decade the breeding technology referred to as 'genomic selection' (GS) has been implemented in a variety of species, with particular success in animal breeding. Recent research shows the potential of GS to reshape wheat breeding. Many authors have concluded that the estimated genetic gain per year applying GS is several times that of conventional breeding. GS is, however, a new technology for wheat breeding and many programs worldwide are still struggling to identify the best strategy for its implementation. This article provides practical guidelines on the key considerations when implementing GS. A review of the existing GS literature for a range of species is provided and used to prime breeder-oriented considerations on the practical applications of GS. Furthermore, this article discusses potential breeding schemes for GS, genotyping considerations, and methods for effective training population design. The components of selection intensity, progress toward inbreeding in half- or full-sibs recurrent schemes, and the generation of selection are also presented.
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Affiliation(s)
- Filippo M Bassi
- International Center for Agricultural Research in the Dry Areas (ICARDA), Av. Mohamed Alaoui, Rabat 10000, Morocco
| | - Alison R Bentley
- The John Bingham Laboratory, NIAB, Huntingdon Road, Cambridge CB3 0LE, United Kingdom
| | - Gilles Charmet
- Institut national de la Recherche Agronomique, INRA UMR1095 GDEC Clermont-Ferrand, 5 chemin de Beaulieu, 63039 Clermont-Ferrand, France
| | - Rodomiro Ortiz
- Swedish University of Agricultural Sciences (SLU), Sundsvagen 14 Box 101, SE23453, Alnarp, Sweden.
| | - Jose Crossa
- Biometrics and Statistics Unit, International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, 06600 Mexico DF, Mexico
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Novoselović D, Bentley AR, Šimek R, Dvojković K, Sorrells ME, Gosman N, Horsnell R, Drezner G, Šatović Z. Characterizing Croatian Wheat Germplasm Diversity and Structure in a European Context by DArT Markers. Front Plant Sci 2016; 7:184. [PMID: 26941756 PMCID: PMC4761793 DOI: 10.3389/fpls.2016.00184] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2015] [Accepted: 02/03/2016] [Indexed: 05/08/2023]
Abstract
Narrowing the genetic base available for future genetic progress is a major concern to plant breeders. In order to avoid this, strategies to characterize and protect genetic diversity in regional breeding pools are required. In this study, 89 winter wheat cultivars released in Croatia between 1936 and 2006 were genotyped using 1,229 DArT (diversity array technology) markers to assess the diversity and population structure. In order to place Croatian breeding pool (CBP) in a European context, Croatian wheat cultivars were compared to 523 European cultivars from seven countries using a total of 166 common DArT markers. The results show higher genetic diversity in the wheat breeding pool from Central Europe (CE) as compared to that from Northern and Western European (NWE) countries. The most of the genetic diversity was attributable to the differences among cultivars within countries. When the geographical criterion (CE vs. NWE) was applied, highly significant difference between regions was obtained that accounted for 16.19% of the total variance, revealing that the CBP represents genetic variation not currently captured in elite European wheat. The current study emphasizes the important contribution made by plant breeders to maintaining wheat genetic diversity and suggests that regional breeding is essential to the maintenance of this diversity. The usefulness of open-access wheat datasets is also highlighted.
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Affiliation(s)
- Dario Novoselović
- Department for Breeding & Genetics of Small Cereal Crops, Agricultural Institute OsijekOsijek, Croatia
- Centre of Excellence for Biodiversity and Molecular Plant BreedingZagreb, Croatia
| | - Alison R. Bentley
- The John Bingham Laboratory, National Institute of Agricultural BotanyCambridge, UK
| | - Ruđer Šimek
- Department for Breeding & Genetics of Small Cereal Crops, Agricultural Institute OsijekOsijek, Croatia
- *Correspondence: Ruđer Šimek,
| | - Krešimir Dvojković
- Department for Breeding & Genetics of Small Cereal Crops, Agricultural Institute OsijekOsijek, Croatia
| | - Mark E. Sorrells
- Department of Plant Breeding and Genetics, Cornell University, IthacaNY, USA
| | | | - Richard Horsnell
- The John Bingham Laboratory, National Institute of Agricultural BotanyCambridge, UK
| | - Georg Drezner
- Department for Breeding & Genetics of Small Cereal Crops, Agricultural Institute OsijekOsijek, Croatia
| | - Zlatko Šatović
- Centre of Excellence for Biodiversity and Molecular Plant BreedingZagreb, Croatia
- Faculty of Agriculture, University of ZagrebZagreb, Croatia
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48
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Bentley AR, Scutari M, Gosman N, Faure S, Bedford F, Howell P, Cockram J, Rose GA, Barber T, Irigoyen J, Horsnell R, Pumfrey C, Winnie E, Schacht J, Beauchêne K, Praud S, Greenland A, Balding D, Mackay IJ. Applying association mapping and genomic selection to the dissection of key traits in elite European wheat. Theor Appl Genet 2014; 127:2619-33. [PMID: 25273129 DOI: 10.1007/s00122-014-2403-y] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2014] [Accepted: 09/20/2014] [Indexed: 05/18/2023]
Abstract
We show the application of association mapping and genomic selection for key breeding targets using a large panel of elite winter wheat varieties and a large volume of agronomic data. The heightening urgency to increase wheat production in line with the needs of a growing population, and in the face of climatic uncertainty, mean new approaches, including association mapping (AM) and genomic selection (GS) need to be validated and applied in wheat breeding. Key adaptive responses are the cornerstone of regional breeding. There is evidence that new ideotypes for long-standing traits such as flowering time may be required. In order to detect targets for future marker-assisted improvement and validate the practical application of GS for wheat breeding we genotyped 376 elite wheat varieties with 3,046 DArT, single nucleotide polymorphism and gene markers and measured seven traits in replicated yield trials over 2 years in France, Germany and the UK. The scale of the phenotyping exceeds the breadth of previous AM and GS studies in these key economic wheat production regions of Northern Europe. Mixed-linear modelling (MLM) detected significant marker-trait associations across and within regions. Genomic prediction using elastic net gave low to high prediction accuracies depending on the trait, and could be experimentally increased by modifying the constituents of the training population (TP). We also tested the use of differentially penalised regression to integrate candidate gene and genome-wide markers to predict traits, demonstrating the validity and simplicity of this approach. Overall, our results suggest that whilst AM offers potential for application in both research and breeding, GS represents an exciting opportunity to select key traits, and that optimisation of the TP is crucial to its successful implementation.
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Affiliation(s)
- Alison R Bentley
- The John Bingham Laboratory, NIAB, Huntingdon Road, Cambridge, CB3 0LE, UK,
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49
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Jones H, Gosman N, Horsnell R, Rose GA, Everest LA, Bentley AR, Tha S, Uauy C, Kowalski A, Novoselovic D, Simek R, Kobiljski B, Kondic-Spika A, Brbaklic L, Mitrofanova O, Chesnokov Y, Bonnett D, Greenland A. Strategy for exploiting exotic germplasm using genetic, morphological, and environmental diversity: the Aegilops tauschii Coss. example. Theor Appl Genet 2013; 126:1793-808. [PMID: 23558983 DOI: 10.1007/s00122-013-2093-x] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2012] [Accepted: 03/21/2013] [Indexed: 05/09/2023]
Abstract
Hexaploid bread wheat evolved from a rare hybridisation, which resulted in a loss of genetic diversity in the wheat D-genome with respect to the ancestral donor, Aegilops tauschii. Novel genetic variation can be introduced into modern wheat by recreating the above hybridisation; however, the information associated with the Ae. tauschii accessions in germplasm collections is limited, making rational selection of accessions into a re-synthesis programme difficult. We describe methodologies to identify novel diversity from Ae. tauschii accessions that combines Bayesian analysis of genotypic data, sub-species diversity and geographic information that summarises variation in climate and habitat at the collection point for each accession. Comparisons were made between diversity discovered amongst a panel of Ae. tauschii accessions, bread wheat varieties and lines from the CIMMYT synthetic hexaploid wheat programme. The selection of Ae. tauschii accessions based on differing approaches had significant effect on diversity within each set. Our results suggest that a strategy that combines several criteria will be most effective in maximising the sampled variation across multiple parameters. The analysis of multiple layers of variation in ex situ Ae. tauschii collections allows for an informed and rational approach to the inclusion of wild relatives into crop breeding programmes.
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Affiliation(s)
- H Jones
- NIAB, Huntingdon Road, Cambridge, CB1 0LE, UK.
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Bentley AR, Horsnell R, Werner CP, Turner AS, Rose GA, Bedard C, Howell P, Wilhelm EP, Mackay IJ, Howells RM, Greenland A, Laurie DA, Gosman N. Short, natural, and extended photoperiod response in BC2F4 lines of bread wheat with different photoperiod-1 (Ppd-1) alleles. J Exp Bot 2013; 64:1783-93. [PMID: 23420880 DOI: 10.1093/jxb/ert038] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
Flowering is a critical period in the life cycle of flowering plant species, resulting in an irreversible commitment of significant resources. Wheat is photoperiod sensitive, flowering only when daylength surpasses a critical length; however, photoperiod insensitivity (PI) has been selected by plant breeders for >40 years to enhance yield in certain environments. Control of flowering time has been greatly facilitated by the development of molecular markers for the Photoperiod-1 (Ppd-1) homeoloci, on the group 2 chromosomes. In the current study, an allelic series of BC2F4 lines in the winter wheat cultivars 'Robigus' and 'Alchemy' was developed to elucidate the influence on flowering of eight gene variants from the B- and D-genomes of bread wheat and the A-genome of durum wheat. Allele effects were tested in short, natural, and extended photoperiods in the field and controlled environments. Across genetic background and treatment, the D-genome PI allele, Ppd-D1a, had a more potent effect on reducing flowering time than Ppd-B1a. However, there was significant donor allele effect for both Ppd-D1a and Ppd-B1a, suggesting the presence of linked modifier genes and/or additional sources of latent sensitivity. Development of Ppd-A1a BC2F4 lines derived from synthetic hexaploid wheat provided an opportunity to compare directly the flowering time effect of the A-genome allele from durum with the B- and D-genome variants from bread wheat for the first time. Analyses indicated that the reducing effect of Ppd-A1a is comparable with that of Ppd-D1a, confirming it as a useful alternative source of PI.
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Affiliation(s)
- A R Bentley
- The John Bingham Laboratory, NIAB, Huntingdon Road, Cambridge CB3 0LE, UK.
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