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Dreisigacker S, Martini JWR, Cuevas J, Pérez-Rodríguez P, Lozano-Ramírez N, Huerta J, Singh P, Crespo-Herrera L, Bentley AR, Crossa J. Genomic prediction of synthetic hexaploid wheat upon tetraploid durum and diploid Aegilops parental pools. THE PLANT GENOME 2024; 17:e20464. [PMID: 38764312 DOI: 10.1002/tpg2.20464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 04/04/2024] [Accepted: 04/09/2024] [Indexed: 05/21/2024]
Abstract
Bread wheat (Triticum aestivum L.) is a globally important food crop, which was domesticated about 8-10,000 years ago. Bread wheat is an allopolyploid, and it evolved from two hybridization events of three species. To widen the genetic base in breeding, bread wheat has been re-synthesized by crossing durum wheat (Triticum turgidum ssp. durum) and goat grass (Aegilops tauschii Coss), leading to so-called synthetic hexaploid wheat (SHW). We applied the quantitative genetics tools of "hybrid prediction"-originally developed for the prediction of wheat hybrids generated from different heterotic groups - to a situation of allopolyploidization. Our use-case predicts the phenotypes of SHW for three quantitatively inherited global wheat diseases, namely tan spot (TS), septoria nodorum blotch (SNB), and spot blotch (SB). Our results revealed prediction abilities comparable to studies in 'traditional' elite or hybrid wheat. Prediction abilities were highest using a marker model and performing random cross-validation, predicting the performance of untested SHW (0.483 for SB to 0.730 for TS). When testing parents not necessarily used in SHW, combination prediction abilities were slightly lower (0.378 for SB to 0.718 for TS), yet still promising. Despite the limited phenotypic data, our results provide a general example for predictive models targeting an allopolyploidization event and a method that can guide the use of genetic resources available in gene banks.
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Affiliation(s)
| | | | - Jaime Cuevas
- Universidad Autónoma del Estado de Quintana Roo, Chetumal, México
| | | | | | - Julio Huerta
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, México
| | - Pawan Singh
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, México
| | | | - Alison R Bentley
- Australian National University, Research School of Biology, Canberra, Australia
| | - Jose Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, México
- Colegio de Postgraduados, Campus Montecillos, Texcoco, México
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2
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Ma X, Wang H, Wu S, Han B, Cui D, Liu J, Zhang Q, Xia X, Song P, Tang C, Geng L, Yang Y, Yan S, Zhou K, Han L. DeepCCR: large-scale genomics-based deep learning method for improving rice breeding. PLANT BIOTECHNOLOGY JOURNAL 2024. [PMID: 38805625 DOI: 10.1111/pbi.14384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 03/31/2024] [Accepted: 05/07/2024] [Indexed: 05/30/2024]
Affiliation(s)
- Xiaoding Ma
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Hao Wang
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | | | - Bing Han
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Di Cui
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Jin Liu
- Jiangxi Research Center of Crop Germplasm Resources, Rice Research Institute, Jiangxi Academy of Agricultural Sciences, Nanchang, China
| | - Qiang Zhang
- Jilin Provincial Laboratory of Crop Germplasm Resources, Rice Research Institute, Jilin Academy of Agricultural Sciences, Changchun, China
| | - Xiuzhong Xia
- Guangxi Key Laboratory of Rice Genetics and Breeding, Rice Research Institute, Guangxi Academy of Agricultural Sciences, Nanning, China
| | - Peng Song
- National Key Laboratory of Crop Genetic Improvement, College of Plant Science & Technology, Huazhong Agricultural University, Wuhan, China
| | - Cuifeng Tang
- Key Lab of Southwestern Crop Gene Resources and Germplasm Innovation, Biotechnology and Germplasm Resources Institute, Kunming, China
| | - Leiyue Geng
- Institute of Coastal Agriculture, Hebei Academy of Agriculture and Forestry Sciences, Tangshan, China
| | - Yaolong Yang
- State Key Laboratory of Rice Biology and Breeding, China National Rice Research Institute, Hangzhou, China
| | - Shen Yan
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Kunneng Zhou
- Anhui Province Key Laboratory of Rice Germplasm Innovation and Molecular Improvement, Rice Research Institute, Anhui Academy of Agricultural Sciences, Hefei, China
| | - Longzhi Han
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
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3
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Kapoor C, Anamika, Mukesh Sankar S, Singh SP, Singh N, Kumar S. Omics-driven utilization of wild relatives for empowering pre-breeding in pearl millet. PLANTA 2024; 259:155. [PMID: 38750378 DOI: 10.1007/s00425-024-04423-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/17/2023] [Accepted: 04/25/2024] [Indexed: 05/23/2024]
Abstract
MAIN CONCLUSION Pearl millet wild relatives harbour novel alleles which could be utilized to broaden genetic base of cultivated species. Genomics-informed pre-breeding is needed to speed up introgression from wild to cultivated gene pool in pearl millet. Rising episodes of intense biotic and abiotic stresses challenge pearl millet production globally. Wild relatives provide a wide spectrum of novel alleles which could address challenges posed by climate change. Pre-breeding holds potential to introgress novel diversity in genetically narrow cultivated Pennisetum glaucum from diverse gene pool. Practical utilization of gene pool diversity remained elusive due to genetic intricacies. Harnessing promising traits from wild pennisetum is limited by lack of information on underlying candidate genes/QTLs. Next-Generation Omics provide vast scope to speed up pre-breeding in pearl millet. Genomic resources generated out of draft genome sequence and improved genome assemblies can be employed to utilize gene bank accessions effectively. The article highlights genetic richness in pearl millet and its utilization with a focus on harnessing next-generation Omics to empower pre-breeding.
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Affiliation(s)
- Chandan Kapoor
- ICAR-Indian Agricultural Research Institute, New Delhi, 110012, India.
| | - Anamika
- ICAR-Indian Agricultural Research Institute, New Delhi, 110012, India
| | - S Mukesh Sankar
- ICAR-Indian Institute of Spices Research, Kozhikode, Kerala, 673012, India
| | - S P Singh
- ICAR-Indian Agricultural Research Institute, New Delhi, 110012, India
| | - Nirupma Singh
- ICAR-Indian Agricultural Research Institute, New Delhi, 110012, India
| | - Sudhir Kumar
- ICAR-Indian Agricultural Research Institute, New Delhi, 110012, India
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4
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Mascher M, Marone MP, Schreiber M, Stein N. Are cereal grasses a single genetic system? NATURE PLANTS 2024; 10:719-731. [PMID: 38605239 DOI: 10.1038/s41477-024-01674-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 03/17/2024] [Indexed: 04/13/2024]
Abstract
In 1993, a passionate and provocative call to arms urged cereal researchers to consider the taxon they study as a single genetic system and collaborate with each other. Since then, that group of scientists has seen their discipline blossom. In an attempt to understand what unity of genetic systems means and how the notion was borne out by later research, we survey the progress and prospects of cereal genomics: sequence assemblies, population-scale sequencing, resistance gene cloning and domestication genetics. Gene order may not be as extraordinarily well conserved in the grasses as once thought. Still, several recurring themes have emerged. The same ancestral molecular pathways defining plant architecture have been co-opted in the evolution of different cereal crops. Such genetic convergence as much as cross-fertilization of ideas between cereal geneticists has led to a rich harvest of genes that, it is hoped, will lead to improved varieties.
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Affiliation(s)
- Martin Mascher
- Leibniz Institute of Plant Genetics and Crop Plant Research, Gatersleben, Germany.
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany.
| | - Marina Püpke Marone
- Leibniz Institute of Plant Genetics and Crop Plant Research, Gatersleben, Germany
| | - Mona Schreiber
- University of Marburg, Department of Biology, Marburg, Germany
| | - Nils Stein
- Leibniz Institute of Plant Genetics and Crop Plant Research, Gatersleben, Germany.
- Martin Luther University Halle-Wittenberg, Halle (Saale), Germany.
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Romanov MN, Shakhin AV, Abdelmanova AS, Volkova NA, Efimov DN, Fisinin VI, Korshunova LG, Anshakov DV, Dotsev AV, Griffin DK, Zinovieva NA. Dissecting Selective Signatures and Candidate Genes in Grandparent Lines Subject to High Selection Pressure for Broiler Production and in a Local Russian Chicken Breed of Ushanka. Genes (Basel) 2024; 15:524. [PMID: 38674458 PMCID: PMC11050503 DOI: 10.3390/genes15040524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Revised: 04/16/2024] [Accepted: 04/20/2024] [Indexed: 04/28/2024] Open
Abstract
Breeding improvements and quantitative trait genetics are essential to the advancement of broiler production. The impact of artificial selection on genomic architecture and the genetic markers sought remains a key area of research. Here, we used whole-genome resequencing data to analyze the genomic architecture, diversity, and selective sweeps in Cornish White (CRW) and Plymouth Rock White (PRW) transboundary breeds selected for meat production and, comparatively, in an aboriginal Russian breed of Ushanka (USH). Reads were aligned to the reference genome bGalGal1.mat.broiler.GRCg7b and filtered to remove PCR duplicates and low-quality reads using BWA-MEM2 and bcftools software; 12,563,892 SNPs were produced for subsequent analyses. Compared to CRW and PRW, USH had a lower diversity and a higher genetic distinctiveness. Selective sweep regions and corresponding candidate genes were examined based on ZFST, hapFLK, and ROH assessment procedures. Twenty-seven prioritized chicken genes and the functional projection from human homologs suggest their importance for selection signals in the studied breeds. These genes have a functional relationship with such trait categories as body weight, muscles, fat metabolism and deposition, reproduction, etc., mainly aligned with the QTLs in the sweep regions. This information is pivotal for further executing genomic selection to enhance phenotypic traits.
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Affiliation(s)
- Michael N. Romanov
- L. K. Ernst Federal Research Center for Animal Husbandry, Dubrovitsy, Podolsk 142132, Moscow Oblast, Russia; (A.V.S.); (A.S.A.); (N.A.V.); (A.V.D.)
- School of Biosciences, University of Kent, Canterbury CT2 7NJ, UK;
| | - Alexey V. Shakhin
- L. K. Ernst Federal Research Center for Animal Husbandry, Dubrovitsy, Podolsk 142132, Moscow Oblast, Russia; (A.V.S.); (A.S.A.); (N.A.V.); (A.V.D.)
| | - Alexandra S. Abdelmanova
- L. K. Ernst Federal Research Center for Animal Husbandry, Dubrovitsy, Podolsk 142132, Moscow Oblast, Russia; (A.V.S.); (A.S.A.); (N.A.V.); (A.V.D.)
| | - Natalia A. Volkova
- L. K. Ernst Federal Research Center for Animal Husbandry, Dubrovitsy, Podolsk 142132, Moscow Oblast, Russia; (A.V.S.); (A.S.A.); (N.A.V.); (A.V.D.)
| | - Dmitry N. Efimov
- Federal State Budget Scientific Institution Federal Scientific Center “All-Russian Research and Technological Poultry Institute”, Sergiev Posad 141311, Moscow Oblast, Russia; (D.N.E.); (V.I.F.); (L.G.K.)
| | - Vladimir I. Fisinin
- Federal State Budget Scientific Institution Federal Scientific Center “All-Russian Research and Technological Poultry Institute”, Sergiev Posad 141311, Moscow Oblast, Russia; (D.N.E.); (V.I.F.); (L.G.K.)
| | - Liudmila G. Korshunova
- Federal State Budget Scientific Institution Federal Scientific Center “All-Russian Research and Technological Poultry Institute”, Sergiev Posad 141311, Moscow Oblast, Russia; (D.N.E.); (V.I.F.); (L.G.K.)
| | - Dmitry V. Anshakov
- Breeding and Genetic Center “Zagorsk Experimental Breeding Farm”—Branch of the Federal Research Center “All-Russian Poultry Research and Technological Institute”, Russian Academy of Sciences, Sergiev Posad 141311, Moscow Oblast, Russia;
| | - Arsen V. Dotsev
- L. K. Ernst Federal Research Center for Animal Husbandry, Dubrovitsy, Podolsk 142132, Moscow Oblast, Russia; (A.V.S.); (A.S.A.); (N.A.V.); (A.V.D.)
| | | | - Natalia A. Zinovieva
- L. K. Ernst Federal Research Center for Animal Husbandry, Dubrovitsy, Podolsk 142132, Moscow Oblast, Russia; (A.V.S.); (A.S.A.); (N.A.V.); (A.V.D.)
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Awan MJA, Farooq MA, Naqvi RZ, Karamat U, Bukhari SAR, Waqas MAB, Mahmood MA, Buzdar MI, Rasheed A, Amin I, Saeed NA, Mansoor S. Deciphering the differential expression patterns of yield-related negative regulators in hexaploid wheat cultivars and hybrids at different growth stages. Mol Biol Rep 2024; 51:537. [PMID: 38642174 DOI: 10.1007/s11033-024-09454-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Accepted: 03/18/2024] [Indexed: 04/22/2024]
Abstract
BACKGROUND Hexaploid bread wheat underwent a series of polyploidization events through interspecific hybridizations that conferred adaptive plasticity and resulted in duplication and neofunctionalization of major agronomic genes. The genetic architecture of polyploid wheat not only confers adaptive plasticity but also offers huge genetic diversity. However, the contribution of different gene copies (homeologs) encoded from different subgenomes (A, B, D) at different growth stages remained unexplored. METHODS In this study, hybrid of elite cultivars of wheat were developed via reciprocal crosses (cytoplasm swapping) and phenotypically evaluated. We assessed differential expression profiles of yield-related negative regulators in these cultivars and their F1 hybrids and identified various cis-regulatory signatures by employing bioinformatics tools. Furthermore, the preferential expression patterns of the syntenic triads encoded from A, B, and D subgenomes were assessed to decipher their functional redundancy at six different growth stages. RESULTS Hybrid progenies showed better heterosis such as up to 17% increase in the average number of grains and up to 50% increase in average thousand grains weight as compared to mid-parents. Based on the expression profiling, our results indicated significant dynamic transcriptional expression patterns, portraying the different homeolog-dominance at the same stage in the different cultivars and their hybrids. Albeit belonging to same syntenic triads, a dynamic trend was observed in the regulatory signatures of these genes that might be influencing their expression profiles. CONCLUSION These findings can substantially contribute and provide insights for the selective introduction of better cultivars into traditional and hybrid breeding programs which can be harnessed for the improvement of future wheat.
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Affiliation(s)
- Muhammad Jawad Akbar Awan
- Agricultural Biotechnology Division, National Institute for Biotechnology and Genetic Engineering (NIBGE), Constituent College of Pakistan Institute of Engineering and Applied Sciences, Jhang Road, Faisalabad, Pakistan
| | - Muhammad Awais Farooq
- Agricultural Biotechnology Division, National Institute for Biotechnology and Genetic Engineering (NIBGE), Constituent College of Pakistan Institute of Engineering and Applied Sciences, Jhang Road, Faisalabad, Pakistan
- State Key Laboratory of North China Crop Improvement and Regulation, Key Laboratory of Vegetable Germplasm Innovation and Utilization of Hebei, Collaborative Innovation Center of Vegetable Industry in Hebei, College of Horticulture, Hebei Agricultural University, Baoding, China
| | - Rubab Zahra Naqvi
- Agricultural Biotechnology Division, National Institute for Biotechnology and Genetic Engineering (NIBGE), Constituent College of Pakistan Institute of Engineering and Applied Sciences, Jhang Road, Faisalabad, Pakistan
| | - Umer Karamat
- State Key Laboratory of North China Crop Improvement and Regulation, Key Laboratory of Vegetable Germplasm Innovation and Utilization of Hebei, Collaborative Innovation Center of Vegetable Industry in Hebei, College of Horticulture, Hebei Agricultural University, Baoding, China
- Guangdong Key Laboratory for New Technology Research of Vegetables, Vegetable Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou, China
| | - Sayyad Ali Raza Bukhari
- School of Natural Sciences, University of Tasmania, Hobart, TAS, Australia
- Department of Biotechnology, University of Sargodha, Sargodha, Pakistan
| | - Muhammad Abu Bakar Waqas
- Agricultural Biotechnology Division, National Institute for Biotechnology and Genetic Engineering (NIBGE), Constituent College of Pakistan Institute of Engineering and Applied Sciences, Jhang Road, Faisalabad, Pakistan
| | - Muhammad Arslan Mahmood
- Agricultural Biotechnology Division, National Institute for Biotechnology and Genetic Engineering (NIBGE), Constituent College of Pakistan Institute of Engineering and Applied Sciences, Jhang Road, Faisalabad, Pakistan
| | - Muhammad Ismail Buzdar
- Agricultural Biotechnology Division, National Institute for Biotechnology and Genetic Engineering (NIBGE), Constituent College of Pakistan Institute of Engineering and Applied Sciences, Jhang Road, Faisalabad, Pakistan
| | - Awais Rasheed
- Department of Plant Sciences, Quaid-i-Azam University, Islamabad, 45320, Pakistan
- Institute of Crop Science, Chinese Academy of Agricultural Sciences (CAAS) & CIMMYT-China office, 12 Zhongguancun South Street, Beijing, 100081, China
| | - Imran Amin
- Agricultural Biotechnology Division, National Institute for Biotechnology and Genetic Engineering (NIBGE), Constituent College of Pakistan Institute of Engineering and Applied Sciences, Jhang Road, Faisalabad, Pakistan
| | - Nasir A Saeed
- Agricultural Biotechnology Division, National Institute for Biotechnology and Genetic Engineering (NIBGE), Constituent College of Pakistan Institute of Engineering and Applied Sciences, Jhang Road, Faisalabad, Pakistan
| | - Shahid Mansoor
- Agricultural Biotechnology Division, National Institute for Biotechnology and Genetic Engineering (NIBGE), Constituent College of Pakistan Institute of Engineering and Applied Sciences, Jhang Road, Faisalabad, Pakistan.
- International Center for Chemical and Biological Sciences, University of Karachi, Karachi, Pakistan.
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7
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Alemu A, Åstrand J, Montesinos-López OA, Isidro Y Sánchez J, Fernández-Gónzalez J, Tadesse W, Vetukuri RR, Carlsson AS, Ceplitis A, Crossa J, Ortiz R, Chawade A. Genomic selection in plant breeding: Key factors shaping two decades of progress. MOLECULAR PLANT 2024; 17:552-578. [PMID: 38475993 DOI: 10.1016/j.molp.2024.03.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 01/22/2024] [Accepted: 03/08/2024] [Indexed: 03/14/2024]
Abstract
Genomic selection, the application of genomic prediction (GP) models to select candidate individuals, has significantly advanced in the past two decades, effectively accelerating genetic gains in plant breeding. This article provides a holistic overview of key factors that have influenced GP in plant breeding during this period. We delved into the pivotal roles of training population size and genetic diversity, and their relationship with the breeding population, in determining GP accuracy. Special emphasis was placed on optimizing training population size. We explored its benefits and the associated diminishing returns beyond an optimum size. This was done while considering the balance between resource allocation and maximizing prediction accuracy through current optimization algorithms. The density and distribution of single-nucleotide polymorphisms, level of linkage disequilibrium, genetic complexity, trait heritability, statistical machine-learning methods, and non-additive effects are the other vital factors. Using wheat, maize, and potato as examples, we summarize the effect of these factors on the accuracy of GP for various traits. The search for high accuracy in GP-theoretically reaching one when using the Pearson's correlation as a metric-is an active research area as yet far from optimal for various traits. We hypothesize that with ultra-high sizes of genotypic and phenotypic datasets, effective training population optimization methods and support from other omics approaches (transcriptomics, metabolomics and proteomics) coupled with deep-learning algorithms could overcome the boundaries of current limitations to achieve the highest possible prediction accuracy, making genomic selection an effective tool in plant breeding.
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Affiliation(s)
- Admas Alemu
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden.
| | - Johanna Åstrand
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden; Lantmännen Lantbruk, Svalöv, Sweden
| | | | - Julio Isidro Y Sánchez
- Centro de Biotecnología y Genómica de Plantas (CBGP, UPM-INIA), Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Campus de Montegancedo-UPM, 28223 Madrid, Spain
| | - Javier Fernández-Gónzalez
- Centro de Biotecnología y Genómica de Plantas (CBGP, UPM-INIA), Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Campus de Montegancedo-UPM, 28223 Madrid, Spain
| | - Wuletaw Tadesse
- International Center for Agricultural Research in the Dry Areas (ICARDA), Rabat, Morocco
| | - Ramesh R Vetukuri
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden
| | - Anders S Carlsson
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden
| | | | - José Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Km 45, Carretera México-Veracruz, Texcoco, México 52640, Mexico
| | - Rodomiro Ortiz
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden.
| | - Aakash Chawade
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden
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8
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Bassi FM, Sanchez-Garcia M, Ortiz R. What plant breeding may (and may not) look like in 2050? THE PLANT GENOME 2024; 17:e20368. [PMID: 37455348 DOI: 10.1002/tpg2.20368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 06/23/2023] [Accepted: 06/26/2023] [Indexed: 07/18/2023]
Abstract
At the turn of 2000 many authors envisioned future plant breeding. Twenty years after, which of those authors' visions became reality or not, and which ones may become so in the years to come. After two decades of debates, climate change is a "certainty," food systems shifted from maximizing farm production to reducing environmental impact, and hopes placed into GMOs are mitigated by their low appreciation by consumers. We revise herein how plant breeding may raise or reduce genetic gains based on the breeder's equation. "Accuracy of Selection" has significantly improved by many experimental-scale field and laboratory implements, but also by vulgarizing statistical models, and integrating DNA markers into selection. Pre-breeding has really promoted the increase of useful "Genetic Variance." Shortening "Recycling Time" has seen great progression, to the point that achieving a denominator equal to "1" is becoming a possibility. Maintaining high "Selection Intensity" remains the biggest challenge, since adding any technology results in a higher cost per progeny, despite the steady reduction in cost per datapoint. Furthermore, the concepts of variety and seed enterprise might change with the advent of cheaper genomic tools to monitor their use and the promotion of participatory or citizen science. The technological and societal changes influence the new generation of plant breeders, moving them further away from field work, emphasizing instead the use of genomic-based selection methods relying on big data. We envisage what skills plant breeders of tomorrow might need to address challenges, and whether their time in the field may dwindle.
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Affiliation(s)
- Filippo M Bassi
- International Center for Agricultural Research in the Dry Areas (ICARDA), Rabat, Morocco
| | - Miguel Sanchez-Garcia
- International Center for Agricultural Research in the Dry Areas (ICARDA), Rabat, Morocco
| | - Rodomiro Ortiz
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Lomma, Sweden
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9
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Hoque A, Anderson JV, Rahman M. Genomic prediction for agronomic traits in a diverse Flax (Linum usitatissimum L.) germplasm collection. Sci Rep 2024; 14:3196. [PMID: 38326469 PMCID: PMC10850546 DOI: 10.1038/s41598-024-53462-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 01/31/2024] [Indexed: 02/09/2024] Open
Abstract
Breeding programs require exhaustive phenotyping of germplasms, which is time-demanding and expensive. Genomic prediction helps breeders harness the diversity of any collection to bypass phenotyping. Here, we examined the genomic prediction's potential for seed yield and nine agronomic traits using 26,171 single nucleotide polymorphism (SNP) markers in a set of 337 flax (Linum usitatissimum L.) germplasm, phenotyped in five environments. We evaluated 14 prediction models and several factors affecting predictive ability based on cross-validation schemes. Models yielded significant variation among predictive ability values across traits for the whole marker set. The ridge regression (RR) model covering additive gene action yielded better predictive ability for most of the traits, whereas it was higher for low heritable traits by models capturing epistatic gene action. Marker subsets based on linkage disequilibrium decay distance gave significantly higher predictive abilities to the whole marker set, but for randomly selected markers, it reached a plateau above 3000 markers. Markers having significant association with traits improved predictive abilities compared to the whole marker set when marker selection was made on the whole population instead of the training set indicating a clear overfitting. The correction for population structure did not increase predictive abilities compared to the whole collection. However, stratified sampling by picking representative genotypes from each cluster improved predictive abilities. The indirect predictive ability for a trait was proportionate to its correlation with other traits. These results will help breeders to select the best models, optimum marker set, and suitable genotype set to perform an indirect selection for quantitative traits in this diverse flax germplasm collection.
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Affiliation(s)
- Ahasanul Hoque
- Department of Plant Sciences, North Dakota State University, Fargo, ND, USA
- Department of Genetics and Plant Breeding, Bangladesh Agricultural University, Mymensingh, 2202, Bangladesh
| | - James V Anderson
- USDA-ARS, Edward T. Schafer Agricultural Research Center, Fargo, ND, USA
| | - Mukhlesur Rahman
- Department of Plant Sciences, North Dakota State University, Fargo, ND, USA.
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10
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Knapp SJ, Cole GS, Pincot DDA, Dilla-Ermita CJ, Bjornson M, Famula RA, Gordon TR, Harshman JM, Henry PM, Feldmann MJ. Transgressive segregation, hopeful monsters, and phenotypic selection drove rapid genetic gains and breakthroughs in predictive breeding for quantitative resistance to Macrophomina in strawberry. HORTICULTURE RESEARCH 2024; 11:uhad289. [PMID: 38487295 PMCID: PMC10939388 DOI: 10.1093/hr/uhad289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 12/17/2023] [Indexed: 03/17/2024]
Abstract
Two decades have passed since the strawberry (Fragaria x ananassa) disease caused by Macrophomina phaseolina, a necrotrophic soilborne fungal pathogen, began surfacing in California, Florida, and elsewhere. This disease has since become one of the most common causes of plant death and yield losses in strawberry. The Macrophomina problem emerged and expanded in the wake of the global phase-out of soil fumigation with methyl bromide and appears to have been aggravated by an increase in climate change-associated abiotic stresses. Here we show that sources of resistance to this pathogen are rare in gene banks and that the favorable alleles they carry are phenotypically unobvious. The latter were exposed by transgressive segregation and selection in populations phenotyped for resistance to Macrophomina under heat and drought stress. The genetic gains were immediate and dramatic. The frequency of highly resistant individuals increased from 1% in selection cycle 0 to 74% in selection cycle 2. Using GWAS and survival analysis, we found that phenotypic selection had increased the frequencies of favorable alleles among 10 loci associated with resistance and that favorable alleles had to be accumulated among four or more of these loci for an individual to acquire resistance. An unexpectedly straightforward solution to the Macrophomina disease resistance breeding problem emerged from our studies, which showed that highly resistant cultivars can be developed by genomic selection per se or marker-assisted stacking of favorable alleles among a comparatively small number of large-effect loci.
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Affiliation(s)
- Steven J Knapp
- Department of Plant Sciences, University of California, Davis, One Shields Avenue, Davis, CA 95616, USA
| | - Glenn S Cole
- Department of Plant Sciences, University of California, Davis, One Shields Avenue, Davis, CA 95616, USA
| | - Dominique D A Pincot
- Department of Plant Sciences, University of California, Davis, One Shields Avenue, Davis, CA 95616, USA
| | - Christine Jade Dilla-Ermita
- Department of Plant Sciences, University of California, Davis, One Shields Avenue, Davis, CA 95616, USA
- Crop Improvement and Protection Research, USDA-ARS, 1636 E. Alisal Street, CA 93905, USA
| | - Marta Bjornson
- Department of Plant Sciences, University of California, Davis, One Shields Avenue, Davis, CA 95616, USA
| | - Randi A Famula
- Department of Plant Sciences, University of California, Davis, One Shields Avenue, Davis, CA 95616, USA
| | - Thomas R Gordon
- Department of Plant Pathology, University of California, One Shields Avenue, Davis, CA 95616, USA
| | - Julia M Harshman
- Department of Plant Sciences, University of California, Davis, One Shields Avenue, Davis, CA 95616, USA
| | - Peter M Henry
- Crop Improvement and Protection Research, USDA-ARS, 1636 E. Alisal Street, CA 93905, USA
| | - Mitchell J Feldmann
- Department of Plant Sciences, University of California, Davis, One Shields Avenue, Davis, CA 95616, USA
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11
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Berkner MO, Weise S, Reif JC, Schulthess AW. Genomic prediction reveals unexplored variation in grain protein and lysine content across a vast winter wheat genebank collection. FRONTIERS IN PLANT SCIENCE 2024; 14:1270298. [PMID: 38273944 PMCID: PMC10808176 DOI: 10.3389/fpls.2023.1270298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 10/31/2023] [Indexed: 01/27/2024]
Abstract
Globally, wheat (Triticum aestivum L.) is a major source of proteins in human nutrition despite its unbalanced amino acid composition. The low lysine content in the protein fraction of wheat can lead to protein-energy-malnutrition prominently in developing countries. A promising strategy to overcome this problem is to breed varieties which combine high protein content with high lysine content. Nevertheless, this requires the incorporation of yet undefined donor genotypes into pre-breeding programs. Genebank collections are suspected to harbor the needed genetic diversity. In the 1970s, a large-scale screening of protein traits was conducted for the wheat genebank collection in Gatersleben; however, this data has been poorly mined so far. In the present study, a large historical dataset on protein content and lysine content of 4,971 accessions was curated, strictly corrected for outliers as well as for unreplicated data and consolidated as the corresponding adjusted entry means. Four genomic prediction approaches were compared based on the ability to accurately predict the traits of interest. High-quality phenotypic data of 558 accessions was leveraged by engaging the best performing prediction model, namely EG-BLUP. Finally, this publication incorporates predicted phenotypes of 7,651 accessions of the winter wheat collection. Five accessions were proposed as donor genotypes due to the combination of outstanding high protein content as well as lysine content. Further investigation of the passport data suggested an association of the adjusted lysine content with the elevation of the collecting site. This publicly available information can facilitate future pre-breeding activities.
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Affiliation(s)
- Marcel O. Berkner
- Breeding Research Department, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Seeland, Germany
| | - Stephan Weise
- Genebank Department, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Seeland, Germany
| | - Jochen C. Reif
- Breeding Research Department, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Seeland, Germany
| | - Albert W. Schulthess
- Breeding Research Department, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Seeland, Germany
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12
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Tibbs-Cortes LE, Guo T, Li X, Tanaka R, Vanous AE, Peters D, Gardner C, Magallanes-Lundback M, Deason NT, DellaPenna D, Gore MA, Yu J. Genomic prediction of tocochromanols in exotic-derived maize. THE PLANT GENOME 2023; 16:e20286. [PMID: 36575809 DOI: 10.1002/tpg2.20286] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 10/16/2022] [Indexed: 06/17/2023]
Abstract
Tocochromanols (vitamin E) are an essential part of the human diet. Plant products, including maize (Zea mays L.) grain, are the major dietary source of tocochromanols; therefore, breeding maize with higher vitamin content (biofortification) could improve human nutrition. Incorporating exotic germplasm in maize breeding for trait improvement including biofortification is a promising approach and an important research topic. However, information about genomic prediction of exotic-derived lines using available training data from adapted germplasm is limited. In this study, genomic prediction was systematically investigated for nine tocochromanol traits within both an adapted (Ames Diversity Panel [AP]) and an exotic-derived (Backcrossed Germplasm Enhancement of Maize [BGEM]) maize population. Although prediction accuracies up to 0.79 were achieved using genomic best linear unbiased prediction (gBLUP) when predicting within each population, genomic prediction of BGEM based on an AP training set resulted in low prediction accuracies. Optimal training population (OTP) design methods fast and unique representative subset selection (FURS), maximization of connectedness and diversity (MaxCD), and partitioning around medoids (PAM) were adapted for inbreds and, along with the methods mean coefficient of determination (CDmean) and mean prediction error variance (PEVmean), often improved prediction accuracies compared with random training sets of the same size. When applied to the combined population, OTP designs enabled successful prediction of the rest of the exotic-derived population. Our findings highlight the importance of leveraging genotype data in training set design to efficiently incorporate new exotic germplasm into a plant breeding program.
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Affiliation(s)
| | - Tingting Guo
- Hubei Hongshan Laboratory, Wuhan, China
- Huazhong Agricultural Univ., Wuhan, China
| | - Xianran Li
- USDA ARS, Wheat Health, Genetics, and Quality Research Unit, Pullman, WA, USA
| | - Ryokei Tanaka
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell Univ., Ithaca, NY, USA
| | - Adam E Vanous
- USDA ARS, North Central Regional Plant Introduction Station, Ames, IA, USA
| | - David Peters
- USDA ARS, North Central Regional Plant Introduction Station, Ames, IA, USA
| | - Candice Gardner
- USDA ARS, North Central Regional Plant Introduction Station, Ames, IA, USA
| | | | - Nicholas T Deason
- Dep. of Biochemistry and Molecular Biology, Michigan State Univ., East Lansing, MI, USA
| | - Dean DellaPenna
- Dep. of Biochemistry and Molecular Biology, Michigan State Univ., East Lansing, MI, USA
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell Univ., Ithaca, NY, USA
| | - Jianming Yu
- Dep. of Agronomy, Iowa State Univ., Ames, IA, USA
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13
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Zhang Y, Zhang M, Ye J, Xu Q, Feng Y, Xu S, Hu D, Wei X, Hu P, Yang Y. Integrating genome-wide association study into genomic selection for the prediction of agronomic traits in rice ( Oryza sativa L.). MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2023; 43:81. [PMID: 37965378 PMCID: PMC10641074 DOI: 10.1007/s11032-023-01423-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 10/09/2023] [Indexed: 11/16/2023]
Abstract
Accurately identifying varieties with targeted agronomic traits was thought to contribute to genetic selection and accelerate rice breeding progress. Genomic selection (GS) is a promising technique that uses markers covering the whole genome to predict the genomic-estimated breeding values (GEBV), with the ability to select before phenotypes are measured. To choose the appropriate GS models for breeding work, we analyzed the predictability of nine agronomic traits measured from a population of 459 diverse rice varieties. By the comparison of eight representative GS models, we found that the prediction accuracies ranged from 0.407 to 0.896, with reproducing kernel Hilbert space (RKHS) having the highest predictive ability in most traits. Further results demonstrated the predictivity of GS is altered by several factors. Moreover, we assessed the method of integrating genome-wide association study (GWAS) into various GS models. The predictabilities of GS combined peak-associated markers generated from six different GWAS models were significantly different; a recommendation of Mixed Linear Model (MLM)-RKHS was given for the GWAS-GS-integrated prediction. Finally, based on the above result, we experimented with applying the P-values obtained from optimal GWAS models into ridge regression best linear unbiased prediction (rrBLUP), which benefited the low predictive traits in rice. Supplementary Information The online version contains supplementary material available at 10.1007/s11032-023-01423-y.
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Affiliation(s)
- Yuanyuan Zhang
- Zhejiang Lab, Hangzhou, 311121 China
- CNRRI-Zhejiang Lab Computational Breeding Joint Laboratory, China National Rice Research Institute, Hangzhou, China
| | - Mengchen Zhang
- Zhejiang Lab, Hangzhou, 311121 China
- CNRRI-Zhejiang Lab Computational Breeding Joint Laboratory, China National Rice Research Institute, Hangzhou, China
- National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya, 572024 China
| | - Junhua Ye
- CNRRI-Zhejiang Lab Computational Breeding Joint Laboratory, China National Rice Research Institute, Hangzhou, China
| | - Qun Xu
- CNRRI-Zhejiang Lab Computational Breeding Joint Laboratory, China National Rice Research Institute, Hangzhou, China
| | - Yue Feng
- CNRRI-Zhejiang Lab Computational Breeding Joint Laboratory, China National Rice Research Institute, Hangzhou, China
- National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya, 572024 China
| | - Siliang Xu
- CNRRI-Zhejiang Lab Computational Breeding Joint Laboratory, China National Rice Research Institute, Hangzhou, China
| | - Dongxiu Hu
- CNRRI-Zhejiang Lab Computational Breeding Joint Laboratory, China National Rice Research Institute, Hangzhou, China
| | - Xinghua Wei
- Zhejiang Lab, Hangzhou, 311121 China
- CNRRI-Zhejiang Lab Computational Breeding Joint Laboratory, China National Rice Research Institute, Hangzhou, China
- National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya, 572024 China
| | - Peisong Hu
- Zhejiang Lab, Hangzhou, 311121 China
- CNRRI-Zhejiang Lab Computational Breeding Joint Laboratory, China National Rice Research Institute, Hangzhou, China
| | - Yaolong Yang
- Zhejiang Lab, Hangzhou, 311121 China
- CNRRI-Zhejiang Lab Computational Breeding Joint Laboratory, China National Rice Research Institute, Hangzhou, China
- National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya, 572024 China
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14
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Pixley KV, Cairns JE, Lopez-Ridaura S, Ojiewo CO, Dawud MA, Drabo I, Mindaye T, Nebie B, Asea G, Das B, Daudi H, Desmae H, Batieno BJ, Boukar O, Mukankusi CTM, Nkalubo ST, Hearne SJ, Dhugga KS, Gandhi H, Snapp S, Zepeda-Villarreal EA. Redesigning crop varieties to win the race between climate change and food security. MOLECULAR PLANT 2023; 16:1590-1611. [PMID: 37674314 DOI: 10.1016/j.molp.2023.09.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Revised: 08/17/2023] [Accepted: 09/03/2023] [Indexed: 09/08/2023]
Abstract
Climate change poses daunting challenges to agricultural production and food security. Rising temperatures, shifting weather patterns, and more frequent extreme events have already demonstrated their effects on local, regional, and global agricultural systems. Crop varieties that withstand climate-related stresses and are suitable for cultivation in innovative cropping systems will be crucial to maximize risk avoidance, productivity, and profitability under climate-changed environments. We surveyed 588 expert stakeholders to predict current and novel traits that may be essential for future pearl millet, sorghum, maize, groundnut, cowpea, and common bean varieties, particularly in sub-Saharan Africa. We then review the current progress and prospects for breeding three prioritized future-essential traits for each of these crops. Experts predict that most current breeding priorities will remain important, but that rates of genetic gain must increase to keep pace with climate challenges and consumer demands. Importantly, the predicted future-essential traits include innovative breeding targets that must also be prioritized; for example, (1) optimized rhizosphere microbiome, with benefits for P, N, and water use efficiency, (2) optimized performance across or in specific cropping systems, (3) lower nighttime respiration, (4) improved stover quality, and (5) increased early vigor. We further discuss cutting-edge tools and approaches to discover, validate, and incorporate novel genetic diversity from exotic germplasm into breeding populations with unprecedented precision, accuracy, and speed. We conclude that the greatest challenge to developing crop varieties to win the race between climate change and food security might be our innovativeness in defining and boldness to breed for the traits of tomorrow.
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Affiliation(s)
- Kevin V Pixley
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico.
| | - Jill E Cairns
- International Maize and Wheat Improvement Center (CIMMYT), Harare, Zimbabwe
| | | | - Chris O Ojiewo
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | | | - Inoussa Drabo
- International Maize and Wheat Improvement Center (CIMMYT), Dakar, Senegal
| | - Taye Mindaye
- Ethiopian Institute of Agricultural Research (EIAR), Addis Ababa, Ethiopia
| | - Baloua Nebie
- International Maize and Wheat Improvement Center (CIMMYT), Dakar, Senegal
| | - Godfrey Asea
- National Agricultural Research Organization (NARO), Kampala, Uganda
| | - Biswanath Das
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Happy Daudi
- Tanzania Agricultural Research Institute (TARI), Naliendele, Tanzania
| | - Haile Desmae
- International Maize and Wheat Improvement Center (CIMMYT), Dakar, Senegal
| | - Benoit Joseph Batieno
- Institut de l'Environnement et de Recherches Agricoles (INERA), Ouagadougou, Burkina Faso
| | - Ousmane Boukar
- International Institute of Tropicl Agriculture (IITA), Kano, Nigeria
| | | | | | - Sarah J Hearne
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Kanwarpal S Dhugga
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Harish Gandhi
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Sieglinde Snapp
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
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15
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Escudero-Martinez C, Bulgarelli D. Engineering the Crop Microbiota Through Host Genetics. ANNUAL REVIEW OF PHYTOPATHOLOGY 2023; 61:257-277. [PMID: 37196364 DOI: 10.1146/annurev-phyto-021621-121447] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/19/2023]
Abstract
The microbiota populating the plant-soil continuum defines an untapped resource for sustainable crop production. The host plant is a driver for the taxonomic composition and function of these microbial communities. In this review, we illustrate how the host genetic determinants of the microbiota have been shaped by plant domestication and crop diversification. We discuss how the heritable component of microbiota recruitment may represent, at least partially, a selection for microbial functions underpinning the growth, development, and health of their host plants and how the magnitude of this heritability is influenced by the environment. We illustrate how host-microbiota interactions can be treated as an external quantitative trait and review recent studies associating crop genetics with microbiota-based quantitative traits. We also explore the results of reductionist approaches, including synthetic microbial communities, to establish causal relationships between microbiota and plant phenotypes. Lastly, we propose strategies to integrate microbiota manipulation into crop selection programs. Although a detailed understanding of when and how heritability for microbiota composition can be deployed for breeding purposes is still lacking, we argue that advances in crop genomics are likely to accelerate wider applications of plant-microbiota interactions in agriculture.
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Affiliation(s)
| | - Davide Bulgarelli
- Plant Sciences, School of Life Sciences, University of Dundee, Dundee, United Kingdom; ,
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16
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El Hanafi S, Jiang Y, Kehel Z, Schulthess AW, Zhao Y, Mascher M, Haupt M, Himmelbach A, Stein N, Amri A, Reif JC. Genomic predictions to leverage phenotypic data across genebanks. FRONTIERS IN PLANT SCIENCE 2023; 14:1227656. [PMID: 37701801 PMCID: PMC10493331 DOI: 10.3389/fpls.2023.1227656] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 08/07/2023] [Indexed: 09/14/2023]
Abstract
Genome-wide prediction is a powerful tool in breeding. Initial results suggest that genome-wide approaches are also promising for enhancing the use of the genebank material: predicting the performance of plant genetic resources can unlock their hidden potential and fill the information gap in genebanks across the world and, hence, underpin prebreeding programs. As a proof of concept, we evaluated the power of across-genebank prediction for extensive germplasm collections relying on historical data on flowering/heading date, plant height, and thousand kernel weight of 9,344 barley (Hordeum vulgare L.) plant genetic resources from the German Federal Ex situ Genebank for Agricultural and Horticultural Crops (IPK) and of 1,089 accessions from the International Center for Agriculture Research in the Dry Areas (ICARDA) genebank. Based on prediction abilities for each trait, three scenarios for predictive characterization were compared: 1) a benchmark scenario, where test and training sets only contain ICARDA accessions, 2) across-genebank predictions using IPK as training and ICARDA as test set, and 3) integrated genebank predictions that include IPK with 30% of ICARDA accessions as a training set to predict the rest of ICARDA accessions. Within the population of ICARDA accessions, prediction abilities were low to moderate, which was presumably caused by a limited number of accessions used to train the model. Interestingly, ICARDA prediction abilities were boosted up to ninefold by using training sets composed of IPK plus 30% of ICARDA accessions. Pervasive genotype × environment interactions (GEIs) can become a potential obstacle to train robust genome-wide prediction models across genebanks. This suggests that the potential adverse effect of GEI on prediction ability was counterbalanced by the augmented training set with certain connectivity to the test set. Therefore, across-genebank predictions hold the promise to improve the curation of the world's genebank collections and contribute significantly to the long-term development of traditional genebanks toward biodigital resource centers.
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Affiliation(s)
- Samira El Hanafi
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany
| | - Yong Jiang
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany
| | - Zakaria Kehel
- International Center for Agricultural Research in Dry Areas (ICARDA), Rabat, Morocco
| | - Albert W. Schulthess
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany
| | - Yusheng Zhao
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany
| | - Martin Mascher
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany
| | - Max Haupt
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany
| | - Axel Himmelbach
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany
| | - Nils Stein
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany
- Center for Integrated Breeding Research (CiBreed), Georg-August-University, Göttingen, Germany
| | - Ahmed Amri
- International Center for Agricultural Research in Dry Areas (ICARDA), Rabat, Morocco
| | - Jochen C. Reif
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany
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17
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Shimono H, Abe A, Kim CH, Sato C, Iwata H. Upcycling rice yield trial data using a weather-driven crop growth model. Commun Biol 2023; 6:764. [PMID: 37479731 PMCID: PMC10362053 DOI: 10.1038/s42003-023-05145-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 07/13/2023] [Indexed: 07/23/2023] Open
Abstract
Efficient plant breeding plays a significant role in increasing crop yields and attaining food security under climate change. Screening new cultivars through yield trials in multi-environments has improved crop yields, but the accumulated data from these trials has not been effectively upcycled. We propose a simple method that quantifies cultivar-specific productivity characteristics using two regression coefficients: yield-ability (β) and yield-plasticity (α). The recorded yields of each cultivar are expressed as a unique linear regression in response to the theoretical potential yield (Yp) calculated by a weather-driven crop growth model, called as the "YpCGM method". We apply this to 72510 independent datasets from yield trials of rice that used 237 cultivars measured at 110 locations in Japan over 38 years. The YpCGM method can upcycle accumulated yield data for use in genetic-gain analysis and genome-wide-association studies to guide future breeding programs for developing new cultivars suitable for the world's changing climate.
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Affiliation(s)
- Hiroyuki Shimono
- Faculty of Agriculture, Iwate University, Morioka, Iwate, 020-8550, Japan.
- Agri-Innovation Center, Iwate University, Morioka, Iwate, 020-8550, Japan.
| | - Akira Abe
- Iwate Biotechnology Research Center, Kitakami, Iwate, 024-0003, Japan
| | - Chyon Hae Kim
- Faculty of Science and Engineering, Iwate University, Morioka, Iwate, 020-8550, Japan
| | - Chikashi Sato
- Ifuu Rinrin, 77-9, Rikuzentakata, Iwate, 029-2205, Japan
| | - Hiroyoshi Iwata
- Laboratory of Biometry and Bioinformatics, University of Tokyo, Bunkyo-ku, Tokyo, 113-8657, Japan
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18
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La Borde N, Rajewski J, Dweikat I. Novel QTL for chilling tolerance at germination and early seedling stages in sorghum. Front Genet 2023; 14:1129460. [PMID: 37007950 PMCID: PMC10052408 DOI: 10.3389/fgene.2023.1129460] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 02/23/2023] [Indexed: 03/17/2023] Open
Abstract
Sorghum (Sorghum bicolor L.) a drought tolerant staple crop for half a billion people in Africa and Asia, an important source of animal feed throughout the world and a biofuel feedstock of growing importanceorghum’s originated from tropical regions rendering the crop to be cold sensitive. Low temperature stresses such as chilling and frost greatly affect the agronomic performance of sorghum and limit its geographical distribution, posing a major problem in temperate environments when sorghum is planted early. Understanding the genetic basis of wide adaptability and of sorghum would facilitate molecular breeding programs and studies of other C4 crops. The objective of this study is to conduct quantitative trait loci analysis using genotying by sequencing for early seed germination and seedling cold tolerance in two sorghum recombinant inbred lines populations. To accomplish that, we used two populations of recombinant inbred lines (RIL) developed from crosses between cold-tolerant (CT19, ICSV700) and cold-sensitive (TX430, M81E) parents. The derived RIL populations were evaluated for single nucleotide polymorphism (SNP) using genotype-by-sequencing (GBS) in the field and under controlled environments for their response to chilling stress. Linkage maps were constructed with 464 and 875 SNPs for the CT19 X TX430 (C1) and ICSV700 X M81 E (C2) populations respectively. Using quantitative trait loci (QTL) mapping, we identified QTL conferring tolerance to chilling tolerance at the seedling stage. A total of 16 and 39 total QTL were identified in the C1 and C2 populations, respectively. Two major QTL were identified in the C1 population, and three major QTL were mapped in the C2 population. Comparisons between the two populations and with previously identified QTL show a high degree of similarity in QTL locations. Given the amount of co-localization of QTL across traits and the direction of allelic effect supports that these regions have a pleiotropic effect. These QTL regions were also identified to be highly enriched for genes encoding chilling stress and hormonal response genes. This identified QTL can be useful in developing tools for molecular breeding of sorghums with improved low-temperature germinability.
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19
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Li G, Wang Z, Meng Y, Fu ZQ, Wang D, Zhang K. A new phase of treasure hunting in plant genebanks. MOLECULAR PLANT 2023; 16:503-505. [PMID: 36709410 DOI: 10.1016/j.molp.2023.01.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 01/15/2023] [Accepted: 01/19/2023] [Indexed: 06/18/2023]
Affiliation(s)
- Guangwei Li
- State Key Laboratory of Wheat and Maize Crop Science, Center for Crop Genome Engineering, and College of Agronomy, Henan Agricultural University, Zhengzhou, Henan 450002, China; The Shennong Laboratory, Zhengzhou, Henan 450002, China
| | - Zhiyong Wang
- State Key Laboratory of Wheat and Maize Crop Science, Center for Crop Genome Engineering, and College of Agronomy, Henan Agricultural University, Zhengzhou, Henan 450002, China; The Shennong Laboratory, Zhengzhou, Henan 450002, China
| | - Yuxuan Meng
- State Key Laboratory of Wheat and Maize Crop Science, Center for Crop Genome Engineering, and College of Agronomy, Henan Agricultural University, Zhengzhou, Henan 450002, China
| | - Zheng Qing Fu
- Department of Biological Sciences, University of South Carolina, Columbia, SC 29208, USA
| | - Daowen Wang
- State Key Laboratory of Wheat and Maize Crop Science, Center for Crop Genome Engineering, and College of Agronomy, Henan Agricultural University, Zhengzhou, Henan 450002, China; The Shennong Laboratory, Zhengzhou, Henan 450002, China.
| | - Kunpu Zhang
- State Key Laboratory of Wheat and Maize Crop Science, Center for Crop Genome Engineering, and College of Agronomy, Henan Agricultural University, Zhengzhou, Henan 450002, China; The Shennong Laboratory, Zhengzhou, Henan 450002, China.
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20
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Bandillo NB, Jarquin D, Posadas LG, Lorenz AJ, Graef GL. Genomic selection performs as effectively as phenotypic selection for increasing seed yield in soybean. THE PLANT GENOME 2023; 16:e20285. [PMID: 36447395 DOI: 10.1002/tpg2.20285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Accepted: 10/08/2022] [Indexed: 05/10/2023]
Abstract
Increasing the rate of genetic gain for seed yield remains the primary breeding objective in both public and private soybean [Glycine max (L.) Merr.] breeding programs. Genomic selection (GS) has the potential to accelerate the rate of genetic gain for soybean seed yield. Limited studies to date have validated GS accuracy and directly compared GS with phenotypic selection (PS), and none have been reported in soybean. This study conducted the first empirical validation of GS for increasing seed yield using over 1,500 lines and over 7 yr (2010-2016) of replicated experiments in the University of Nebraska-Lincoln soybean breeding program. The study was designed to capture the varying genetic relatedness of the training population to three validation sets: two large biparental populations (TBP-1 and TBP-2) and a large validation set comprised of 457 preselected advanced lines derived from 45 biparental populations (TMP). We found that prediction accuracy (.54) realized in our validation experiments was comparable with what we obtained from a series of cross-validation experiments (.64). Both GS and PS were more effective for increasing the population mean performance compared with random selection (RS). We found a selection advantage of GS over PS, where higher genetic gain and identification of top-performing lines was maximized at 10-20% selected proportion. Genomic selection led to small increases in genetic similarity when compared with PS and RS presumably because of a significant shift on allelic frequencies toward the extremes, suggesting that it could erode genetic diversity more quickly. Overall, we found that GS can perform as effectively as PS but that measures should be considered to protect against loss of genetic variance when using GS.
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Affiliation(s)
- Nonoy B Bandillo
- Dep. of Agronomy and Horticulture, Univ. of Nebraska, 363 Keim Hall, Lincoln, NE, 68583, USA
- Dep. of Plant Sciences, North Dakota State Univ., NDSU Dep. 7670, P.O. Box 6050, Fargo, ND, 58108-6050, USA
| | - Diego Jarquin
- Dep. of Agronomy and Horticulture, Univ. of Nebraska, 363 Keim Hall, Lincoln, NE, 68583, USA
- Agronomy Dep., Univ. of Florida, 2089 McCarthy Hall B, Gainesville, FL, 32611, USA
| | - Luis G Posadas
- Dep. of Agronomy and Horticulture, Univ. of Nebraska, 363 Keim Hall, Lincoln, NE, 68583, USA
| | - Aaron J Lorenz
- Dep. of Agronomy and Horticulture, Univ. of Nebraska, 363 Keim Hall, Lincoln, NE, 68583, USA
- Dep. of Agronomy and Plant Genetics, Univ. of Minnesota, St. Paul, MN, 55108-6026, USA
| | - George L Graef
- Dep. of Agronomy and Horticulture, Univ. of Nebraska, 363 Keim Hall, Lincoln, NE, 68583, USA
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21
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Guo T, Li X. Machine learning for predicting phenotype from genotype and environment. Curr Opin Biotechnol 2023; 79:102853. [PMID: 36463837 DOI: 10.1016/j.copbio.2022.102853] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 11/01/2022] [Accepted: 11/07/2022] [Indexed: 12/03/2022]
Abstract
Predicting phenotype with genomic and environmental information is critically needed and challenging. Machine learning methods have emerged as powerful tools to make accurate predictions from large and complex biological data. Here, we review the progress of phenotype prediction models enabled or improved by machine learning methods. We categorized the applications into three scenarios: prediction with genotypic information, with environmental information, and with both. In each scenario, we illustrate the practicality of prediction models, the advantages of machine learning, and the challenges of modeling complex relationships. We discuss the promising potential of leveraging machine learning and genetics theories to develop models that can predict phenotype and also interpret the biological consequences of changes in genotype and environment.
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Affiliation(s)
- Tingting Guo
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Hubei Hongshan Laboratory, Wuhan 430070, China.
| | - Xianran Li
- USDA, Agricultural Research Service, Wheat Health, Genetics, and Quality Research Unit, Pullman, WA 99164, USA; Department of Crop and Soil Sciences, Washington State University, Pullman, WA 99164, USA.
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22
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Berkner MO, Schulthess AW, Zhao Y, Jiang Y, Oppermann M, Reif JC. Choosing the right tool: Leveraging of plant genetic resources in wheat (Triticum aestivum L.) benefits from selection of a suitable genomic prediction model. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:4391-4407. [PMID: 36182979 PMCID: PMC9734214 DOI: 10.1007/s00122-022-04227-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 09/17/2022] [Indexed: 06/16/2023]
Abstract
Genomic prediction of genebank accessions benefits from the consideration of additive-by-additive epistasis and subpopulation-specific marker effects. Wheat (Triticum aestivum L.) and other species of the Triticum genus are well represented in genebank collections worldwide. The substantial genetic diversity harbored by more than 850,000 accessions can be explored for their potential use in modern plant breeding. Characterization of these large number of accessions is constrained by the required resources, and this fact limits their use so far. This limitation might be overcome by engaging genomic prediction. The present study compared ten different genomic prediction approaches to the prediction of four traits, namely flowering time, plant height, thousand grain weight, and yellow rust resistance, in a diverse set of 7745 accession samples from Germany's Federal ex situ genebank at the Leibniz Institute of Plant Genetics and Crop Plant Research in Gatersleben. Approaches were evaluated based on prediction ability and robustness to the confounding influence of strong population structure. The authors propose the wide application of extended genomic best linear unbiased prediction due to the observed benefit of incorporating additive-by-additive epistasis. General and subpopulation-specific additive ridge regression best linear unbiased prediction, which accounts for subpopulation-specific marker-effects, was shown to be a good option if contrasting clusters are encountered in the analyzed collection. The presented findings reaffirm that the trait's genetic architecture as well as the composition and relatedness of the training set and test set are major driving factors for the accuracy of genomic prediction.
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Affiliation(s)
- Marcel O Berkner
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466, Stadt Seeland, Germany
| | - Albert W Schulthess
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466, Stadt Seeland, Germany
| | - Yusheng Zhao
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466, Stadt Seeland, Germany
| | - Yong Jiang
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466, Stadt Seeland, Germany
| | - Markus Oppermann
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466, Stadt Seeland, Germany
| | - Jochen C Reif
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466, Stadt Seeland, Germany.
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23
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Röckel F, Schreiber T, Schüler D, Braun U, Krukenberg I, Schwander F, Peil A, Brandt C, Willner E, Gransow D, Scholz U, Kecke S, Maul E, Lange M, Töpfer R. PhenoApp: A mobile tool for plant phenotyping to record field and greenhouse observations. F1000Res 2022; 11:12. [PMID: 36636476 PMCID: PMC9813448 DOI: 10.12688/f1000research.74239.1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 12/20/2021] [Indexed: 01/21/2023] Open
Abstract
With the ongoing cost decrease of genotyping and sequencing technologies, accurate and fast phenotyping remains the bottleneck in the utilizing of plant genetic resources for breeding and breeding research. Although cost-efficient high-throughput phenotyping platforms are emerging for specific traits and/or species, manual phenotyping is still widely used and is a time- and money-consuming step. Approaches that improve data recording, processing or handling are pivotal steps towards the efficient use of genetic resources and are demanded by the research community. Therefore, we developed PhenoApp, an open-source Android app for tablets and smartphones to facilitate the digital recording of phenotypical data in the field and in greenhouses. It is a versatile tool that offers the possibility to fully customize the descriptors/scales for any possible scenario, also in accordance with international information standards such as MIAPPE (Minimum Information About a Plant Phenotyping Experiment) and FAIR (Findable, Accessible, Interoperable, and Reusable) data principles. Furthermore, PhenoApp enables the use of pre-integrated ready-to-use BBCH (Biologische Bundesanstalt für Land- und Forstwirtschaft, Bundessortenamt und CHemische Industrie) scales for apple, cereals, grapevine, maize, potato, rapeseed and rice. Additional BBCH scales can easily be added. The simple and adaptable structure of input and output files enables an easy data handling by either spreadsheet software or even the integration in the workflow of laboratory information management systems (LIMS). PhenoApp is therefore a decisive contribution to increase efficiency of digital data acquisition in genebank management but also contributes to breeding and breeding research by accelerating the labour intensive and time-consuming acquisition of phenotyping data.
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Affiliation(s)
- Franco Röckel
- Julius Kühn Institute (JKI) - Federal Research Centre for Cultivated Plants, Institute for Grapevine Breeding Geilweilerhof, Siebeldingen, 76833, Germany,
| | - Toni Schreiber
- Julius Kühn Institute (JKI) - Federal Research Centre for Cultivated Plants, Data Processing Department, Erwin-Baur-Straße 27, Quedlinburg, 06484, Germany
| | - Danuta Schüler
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Corrensstraße 3, Seeland, 06466, Germany
| | - Ulrike Braun
- Julius Kühn Institute (JKI) - Federal Research Centre for Cultivated Plants, Institute for Grapevine Breeding Geilweilerhof, Siebeldingen, 76833, Germany
| | - Ina Krukenberg
- Julius Kühn Institute (JKI) - Federal Research Centre for Cultivated Plants, Data Processing Department, Königin-Luise-Strasse 19, Berlin, 14195, Germany
| | - Florian Schwander
- Julius Kühn Institute (JKI) - Federal Research Centre for Cultivated Plants, Institute for Grapevine Breeding Geilweilerhof, Siebeldingen, 76833, Germany
| | - Andreas Peil
- Julius Kühn Institute (JKI) - Federal Research Centre for Cultivated Plants, Institute for Breeding Research on Fruit Crops, Pillnitzer Platz 3a, Dresden/Pillnitz, 01326, Germany
| | - Christine Brandt
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), The Satellite Collections North, Parkweg 3a, Sanitz, 18190, Germany
| | - Evelin Willner
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), The Satellite Collections North, Inselstraße 9, Malchow/Poel, 23999, Germany
| | - Daniel Gransow
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), The Satellite Collections North, Inselstraße 9, Malchow/Poel, 23999, Germany
| | - Uwe Scholz
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Corrensstraße 3, Seeland, 06466, Germany
| | - Steffen Kecke
- Julius Kühn Institute (JKI) - Federal Research Centre for Cultivated Plants, Data Processing Department, Erwin-Baur-Straße 27, Quedlinburg, 06484, Germany
| | - Erika Maul
- Julius Kühn Institute (JKI) - Federal Research Centre for Cultivated Plants, Institute for Grapevine Breeding Geilweilerhof, Siebeldingen, 76833, Germany
| | - Matthias Lange
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Corrensstraße 3, Seeland, 06466, Germany
| | - Reinhard Töpfer
- Julius Kühn Institute (JKI) - Federal Research Centre for Cultivated Plants, Institute for Grapevine Breeding Geilweilerhof, Siebeldingen, 76833, Germany
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24
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Röckel F, Schreiber T, Schüler D, Braun U, Krukenberg I, Schwander F, Peil A, Brandt C, Willner E, Gransow D, Scholz U, Kecke S, Maul E, Lange M, Töpfer R. PhenoApp: A mobile tool for plant phenotyping to record field and greenhouse observations. F1000Res 2022; 11:12. [PMID: 36636476 PMCID: PMC9813448 DOI: 10.12688/f1000research.74239.2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/25/2022] [Indexed: 11/29/2022] Open
Abstract
With the ongoing cost decrease of genotyping and sequencing technologies, accurate and fast phenotyping remains the bottleneck in the utilizing of plant genetic resources for breeding and breeding research. Although cost-efficient high-throughput phenotyping platforms are emerging for specific traits and/or species, manual phenotyping is still widely used and is a time- and money-consuming step. Approaches that improve data recording, processing or handling are pivotal steps towards the efficient use of genetic resources and are demanded by the research community. Therefore, we developed PhenoApp, an open-source Android app for tablets and smartphones to facilitate the digital recording of phenotypical data in the field and in greenhouses. It is a versatile tool that offers the possibility to fully customize the descriptors/scales for any possible scenario, also in accordance with international information standards such as MIAPPE (Minimum Information About a Plant Phenotyping Experiment) and FAIR (Findable, Accessible, Interoperable, and Reusable) data principles. Furthermore, PhenoApp enables the use of pre-integrated ready-to-use BBCH (Biologische Bundesanstalt für Land- und Forstwirtschaft, Bundessortenamt und CHemische Industrie) scales for apple, cereals, grapevine, maize, potato, rapeseed and rice. Additional BBCH scales can easily be added. The simple and adaptable structure of input and output files enables an easy data handling by either spreadsheet software or even the integration in the workflow of laboratory information management systems (LIMS). PhenoApp is therefore a decisive contribution to increase efficiency of digital data acquisition in genebank management but also contributes to breeding and breeding research by accelerating the labour intensive and time-consuming acquisition of phenotyping data.
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Affiliation(s)
- Franco Röckel
- Julius Kühn Institute (JKI) - Federal Research Centre for Cultivated Plants, Institute for Grapevine Breeding Geilweilerhof, Siebeldingen, 76833, Germany,
| | - Toni Schreiber
- Julius Kühn Institute (JKI) - Federal Research Centre for Cultivated Plants, Data Processing Department, Erwin-Baur-Straße 27, Quedlinburg, 06484, Germany
| | - Danuta Schüler
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Corrensstraße 3, Seeland, 06466, Germany
| | - Ulrike Braun
- Julius Kühn Institute (JKI) - Federal Research Centre for Cultivated Plants, Institute for Grapevine Breeding Geilweilerhof, Siebeldingen, 76833, Germany
| | - Ina Krukenberg
- Julius Kühn Institute (JKI) - Federal Research Centre for Cultivated Plants, Data Processing Department, Königin-Luise-Strasse 19, Berlin, 14195, Germany
| | - Florian Schwander
- Julius Kühn Institute (JKI) - Federal Research Centre for Cultivated Plants, Institute for Grapevine Breeding Geilweilerhof, Siebeldingen, 76833, Germany
| | - Andreas Peil
- Julius Kühn Institute (JKI) - Federal Research Centre for Cultivated Plants, Institute for Breeding Research on Fruit Crops, Pillnitzer Platz 3a, Dresden/Pillnitz, 01326, Germany
| | - Christine Brandt
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), The Satellite Collections North, Parkweg 3a, Sanitz, 18190, Germany
| | - Evelin Willner
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), The Satellite Collections North, Inselstraße 9, Malchow/Poel, 23999, Germany
| | - Daniel Gransow
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), The Satellite Collections North, Inselstraße 9, Malchow/Poel, 23999, Germany
| | - Uwe Scholz
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Corrensstraße 3, Seeland, 06466, Germany
| | - Steffen Kecke
- Julius Kühn Institute (JKI) - Federal Research Centre for Cultivated Plants, Data Processing Department, Erwin-Baur-Straße 27, Quedlinburg, 06484, Germany
| | - Erika Maul
- Julius Kühn Institute (JKI) - Federal Research Centre for Cultivated Plants, Institute for Grapevine Breeding Geilweilerhof, Siebeldingen, 76833, Germany
| | - Matthias Lange
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Corrensstraße 3, Seeland, 06466, Germany
| | - Reinhard Töpfer
- Julius Kühn Institute (JKI) - Federal Research Centre for Cultivated Plants, Institute for Grapevine Breeding Geilweilerhof, Siebeldingen, 76833, Germany
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25
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Genomics-informed prebreeding unlocks the diversity in genebanks for wheat improvement. Nat Genet 2022; 54:1544-1552. [DOI: 10.1038/s41588-022-01189-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 08/18/2022] [Indexed: 11/06/2022]
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26
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Ramstein GP, Buckler ES. Prediction of evolutionary constraint by genomic annotations improves functional prioritization of genomic variants in maize. Genome Biol 2022; 23:183. [PMID: 36050782 PMCID: PMC9438327 DOI: 10.1186/s13059-022-02747-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Accepted: 08/15/2022] [Indexed: 11/10/2022] Open
Abstract
Background Crop improvement through cross-population genomic prediction and genome editing requires identification of causal variants at high resolution, within fewer than hundreds of base pairs. Most genetic mapping studies have generally lacked such resolution. In contrast, evolutionary approaches can detect genetic effects at high resolution, but they are limited by shifting selection, missing data, and low depth of multiple-sequence alignments. Here we use genomic annotations to accurately predict nucleotide conservation across angiosperms, as a proxy for fitness effect of mutations. Results Using only sequence analysis, we annotate nonsynonymous mutations in 25,824 maize gene models, with information from bioinformatics and deep learning. Our predictions are validated by experimental information: within-species conservation, chromatin accessibility, and gene expression. According to gene ontology and pathway enrichment analyses, predicted nucleotide conservation points to genes in central carbon metabolism. Importantly, it improves genomic prediction for fitness-related traits such as grain yield, in elite maize panels, by stringent prioritization of fewer than 1% of single-site variants. Conclusions Our results suggest that predicting nucleotide conservation across angiosperms may effectively prioritize sites most likely to impact fitness-related traits in crops, without being limited by shifting selection, missing data, and low depth of multiple-sequence alignments. Our approach—Prediction of mutation Impact by Calibrated Nucleotide Conservation (PICNC)—could be useful to select polymorphisms for accurate genomic prediction, and candidate mutations for efficient base editing. The trained PICNC models and predicted nucleotide conservation at protein-coding SNPs in maize are publicly available in CyVerse (10.25739/hybz-2957). Supplementary Information The online version contains supplementary material available at 10.1186/s13059-022-02747-2.
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Affiliation(s)
- Guillaume P Ramstein
- Center for Quantitative Genetics and Genomics, Aarhus University, 8000, Aarhus, Denmark. .,Institute for Genomic Diversity, Cornell University, Ithaca, NY, 14853, USA.
| | - Edward S Buckler
- Institute for Genomic Diversity, Cornell University, Ithaca, NY, 14853, USA.,USDA-ARS, Ithaca, NY, 14853, USA
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27
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Muvunyi BP, Zou W, Zhan J, He S, Ye G. Multi-Trait Genomic Prediction Models Enhance the Predictive Ability of Grain Trace Elements in Rice. Front Genet 2022; 13:883853. [PMID: 35812754 PMCID: PMC9257107 DOI: 10.3389/fgene.2022.883853] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 05/05/2022] [Indexed: 11/13/2022] Open
Abstract
Multi-trait (MT) genomic prediction models enable breeders to save phenotyping resources and increase the prediction accuracy of unobserved target traits by exploiting available information from non-target or auxiliary traits. Our study evaluated different MT models using 250 rice accessions from Asian countries genotyped and phenotyped for grain content of zinc (Zn), iron (Fe), copper (Cu), manganese (Mn), and cadmium (Cd). The predictive performance of MT models compared to a traditional single trait (ST) model was assessed by 1) applying different cross-validation strategies (CV1, CV2, and CV3) inferring varied phenotyping patterns and budgets; 2) accounting for local epistatic effects along with the main additive effect in MT models; and 3) using a selective marker panel composed of trait-associated SNPs in MT models. MT models were not statistically significantly (p < 0.05) superior to ST model under CV1, where no phenotypic information was available for the accessions in the test set. After including phenotypes from auxiliary traits in both training and test sets (MT-CV2) or simply in the test set (MT-CV3), MT models significantly (p < 0.05) outperformed ST model for all the traits. The highest increases in the predictive ability of MT models relative to ST models were 11.1% (Mn), 11.5 (Cd), 33.3% (Fe), 95.2% (Cu) and 126% (Zn). Accounting for the local epistatic effects using a haplotype-based model further improved the predictive ability of MT models by 4.6% (Cu), 3.8% (Zn), and 3.5% (Cd) relative to MT models with only additive effects. The predictive ability of the haplotype-based model was not improved after optimizing the marker panel by only considering the markers associated with the traits. This study first assessed the local epistatic effects and marker optimization strategies in the MT genomic prediction framework and then illustrated the power of the MT model in predicting trace element traits in rice for the effective use of genetic resources to improve the nutritional quality of rice grain.
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Affiliation(s)
- Blaise Pascal Muvunyi
- CAAS-IRRI Joint Laboratory for Genomics-Assisted Germplasm Enhancement, Agricultural Genomics Institute in Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Wenli Zou
- CAAS-IRRI Joint Laboratory for Genomics-Assisted Germplasm Enhancement, Agricultural Genomics Institute in Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Junhui Zhan
- CAAS-IRRI Joint Laboratory for Genomics-Assisted Germplasm Enhancement, Agricultural Genomics Institute in Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Sang He
- CAAS-IRRI Joint Laboratory for Genomics-Assisted Germplasm Enhancement, Agricultural Genomics Institute in Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
- *Correspondence: Sang He, ; Guoyou Ye,
| | - Guoyou Ye
- CAAS-IRRI Joint Laboratory for Genomics-Assisted Germplasm Enhancement, Agricultural Genomics Institute in Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
- Rice Breeding Innovations Platform, International Rice Research Institute, Los Baños, Philippines
- *Correspondence: Sang He, ; Guoyou Ye,
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28
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Renzi JP, Coyne CJ, Berger J, von Wettberg E, Nelson M, Ureta S, Hernández F, Smýkal P, Brus J. How Could the Use of Crop Wild Relatives in Breeding Increase the Adaptation of Crops to Marginal Environments? FRONTIERS IN PLANT SCIENCE 2022; 13:886162. [PMID: 35783966 PMCID: PMC9243378 DOI: 10.3389/fpls.2022.886162] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 05/11/2022] [Indexed: 06/01/2023]
Abstract
Alongside the use of fertilizer and chemical control of weeds, pests, and diseases modern breeding has been very successful in generating cultivars that have increased agricultural production several fold in favorable environments. These typically homogeneous cultivars (either homozygous inbreds or hybrids derived from inbred parents) are bred under optimal field conditions and perform well when there is sufficient water and nutrients. However, such optimal conditions are rare globally; indeed, a large proportion of arable land could be considered marginal for agricultural production. Marginal agricultural land typically has poor fertility and/or shallow soil depth, is subject to soil erosion, and often occurs in semi-arid or saline environments. Moreover, these marginal environments are expected to expand with ongoing climate change and progressive degradation of soil and water resources globally. Crop wild relatives (CWRs), most often used in breeding as sources of biotic resistance, often also possess traits adapting them to marginal environments. Wild progenitors have been selected over the course of their evolutionary history to maintain their fitness under a diverse range of stresses. Conversely, modern breeding for broad adaptation has reduced genetic diversity and increased genetic vulnerability to biotic and abiotic challenges. There is potential to exploit genetic heterogeneity, as opposed to genetic uniformity, in breeding for the utilization of marginal lands. This review discusses the adaptive traits that could improve the performance of cultivars in marginal environments and breeding strategies to deploy them.
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Affiliation(s)
- Juan Pablo Renzi
- Instituto Nacional de Tecnología Agropecuaria, Hilario Ascasubi, Argentina
- CERZOS, Departamento de Agronomía, Universidad Nacional del Sur (CONICET), Bahía Blanca, Argentina
| | | | - Jens Berger
- Agriculture and Food, Commonwealth Scientific and Industrial Research Organisation, Wembley, WA, Australia
| | - Eric von Wettberg
- Department of Plant and Soil Science, Gund Institute for Environment, University of Vermont, Burlington, VT, United States
- Department of Applied Mathematics, Peter the Great St. Petersburg Polytechnic University, Saint Petersburg, Russia
| | - Matthew Nelson
- Agriculture and Food, Commonwealth Scientific and Industrial Research Organisation, Wembley, WA, Australia
- The UWA Institute of Agriculture, University of Western Australia, Crawley, WA, Australia
| | - Soledad Ureta
- CERZOS, Departamento de Agronomía, Universidad Nacional del Sur (CONICET), Bahía Blanca, Argentina
| | - Fernando Hernández
- CERZOS, Departamento de Agronomía, Universidad Nacional del Sur (CONICET), Bahía Blanca, Argentina
| | - Petr Smýkal
- Department of Botany, Faculty of Science, Palacký University, Olomouc, Czechia
| | - Jan Brus
- Department of Geoinformatics, Faculty of Sciences, Palacký University, Olomouc, Czechia
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29
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Singh M, Nara U. Genetic insights in pearl millet breeding in the genomic era: challenges and prospects. PLANT BIOTECHNOLOGY REPORTS 2022; 17:15-37. [PMID: 35692233 PMCID: PMC9169599 DOI: 10.1007/s11816-022-00767-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 04/30/2022] [Accepted: 05/17/2022] [Indexed: 05/28/2023]
Abstract
Pearl millet, a vital staple food and an important cereal, is emerging as crop having various end-uses as feed, food as well as fodder. Advancement in high-throughput sequencing technology has boosted up pearl millet genomic research in past few years. The available draft genome of pearl millet providing an insight into the advancement of several breeding lines. Comparative and functional genomics have untangled several loci and genes regulating adaptive and agronomic traits in pearl millet. Additionally, the knowledge achieved has far away from being applicable in real breeding practices. We believe that the best path ahead is to adopt genome-based approaches for tailored designing of pearl millet as multi-functional crop with outstanding agronomic traits for various end uses. Presently review highlight several novel concepts and techniques in crop breeding, and summarize the recent advances in pearl millet genomic research, peculiarly genome-wide association dissections of several novel alleles and genes for agronomically important traits.
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Affiliation(s)
- Mandeep Singh
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, Punjab 141004 India
| | - Usha Nara
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, Punjab 141004 India
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30
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Sharwood RE, Quick WP, Sargent D, Estavillo GM, Silva-Perez V, Furbank RT. Mining for allelic gold: finding genetic variation in photosynthetic traits in crops and wild relatives. JOURNAL OF EXPERIMENTAL BOTANY 2022; 73:3085-3108. [PMID: 35274686 DOI: 10.1093/jxb/erac081] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 02/28/2022] [Indexed: 06/14/2023]
Abstract
Improvement of photosynthetic traits in crops to increase yield potential and crop resilience has recently become a major breeding target. Synthetic biology and genetic technologies offer unparalleled opportunities to create new genetics for photosynthetic traits driven by existing fundamental knowledge. However, large 'gene bank' collections of germplasm comprising historical collections of crop species and their relatives offer a wealth of opportunities to find novel allelic variation in the key steps of photosynthesis, to identify new mechanisms and to accelerate genetic progress in crop breeding programmes. Here we explore the available genetic resources in food and fibre crops, strategies to selectively target allelic variation in genes underpinning key photosynthetic processes, and deployment of this variation via gene editing in modern elite material.
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Affiliation(s)
- Robert E Sharwood
- Hawkesbury Institute for the Environment, Western Sydney University, Richmond, NSW, Australia
- ARC Centre of Excellence for Translational Photosynthesis, Research School of Biology, Australian National University, Canberra, ACT, Australia
| | - W Paul Quick
- ARC Centre of Excellence for Translational Photosynthesis, Research School of Biology, Australian National University, Canberra, ACT, Australia
- International Rice Research Institute, Los Baños, Laguna, Philippines
| | - Demi Sargent
- Hawkesbury Institute for the Environment, Western Sydney University, Richmond, NSW, Australia
| | | | | | - Robert T Furbank
- ARC Centre of Excellence for Translational Photosynthesis, Research School of Biology, Australian National University, Canberra, ACT, Australia
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31
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Meher PK, Rustgi S, Kumar A. Performance of Bayesian and BLUP alphabets for genomic prediction: analysis, comparison and results. Heredity (Edinb) 2022; 128:519-530. [PMID: 35508540 DOI: 10.1038/s41437-022-00539-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Revised: 04/19/2022] [Accepted: 04/19/2022] [Indexed: 11/09/2022] Open
Abstract
We evaluated the performances of three BLUP and five Bayesian methods for genomic prediction by using nine actual and 54 simulated datasets. The genomic prediction accuracy was measured using Pearson's correlation coefficient between the genomic estimated breeding value (GEBV) and the observed phenotypic data using a fivefold cross-validation approach with 100 replications. The Bayesian alphabets performed better for the traits governed by a few genes/QTLs with relatively larger effects. On the contrary, the BLUP alphabets (GBLUP and CBLUP) exhibited higher genomic prediction accuracy for the traits controlled by several small-effect QTLs. Additionally, Bayesian methods performed better for the highly heritable traits and, for other traits, performed at par with the BLUP methods. Further, genomic BLUP (GBLUP) was identified as the least biased method for the GEBV estimation. Among the Bayesian methods, the Bayesian ridge regression and Bayesian LASSO were less biased than other Bayesian alphabets. Nonetheless, genomic prediction accuracy increased with an increase in trait heritability, irrespective of the sample size, marker density, and the QTL type (major/minor effect). In sum, this study provides valuable information regarding the choice of the selection method for genomic prediction in different breeding programs.
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Affiliation(s)
- Prabina Kumar Meher
- Division of Statistical Genetics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi-12, India.
| | - Sachin Rustgi
- Department of Plant and Environmental Sciences, Clemson University Pee Dee Research and Education Center, Darlington, SC, USA.
| | - Anuj Kumar
- Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi-12, India
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Phenomic Selection: A New and Efficient Alternative to Genomic Selection. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2022; 2467:397-420. [PMID: 35451784 DOI: 10.1007/978-1-0716-2205-6_14] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Recently, it has been proposed to switch molecular markers to near-infrared (NIR) spectra for inferring relationships between individuals and further performing phenomic selection (PS), analogous to genomic selection (GS). The PS concept is similar to genomic-like omics-based (GLOB) selection, in which molecular markers are replaced by endophenotypes, such as metabolites or transcript levels, except that the phenomic information obtained for instance by near-infrared spectroscopy (NIRS ) has usually a much lower cost than other omics. Though NIRS has been routinely used in breeding for several decades, especially to deal with end-product quality traits, its use to predict other traits of interest and further make selections is new. Since the seminal paper on PS , several publications have advocated the use of spectral acquisition (including NIRS and hyperspectral imaging) in plant breeding towards PS , potentially providing a scope of what is possible. In the present chapter, we first come back to the concept of PS as originally proposed and provide a classification of selected papers related to the use of phenomics in breeding. We further provide a review of the selected literature concerning the type of technology used, the preprocessing of the spectra, and the statistical modeling to make predictions. We discuss the factors that likely affect the efficiency of PS and compare it to GS in terms of predictive ability. Finally, we propose several prospects for future work and application of PS in the context of plant breeding.
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Robert P, Auzanneau J, Goudemand E, Oury FX, Rolland B, Heumez E, Bouchet S, Le Gouis J, Rincent R. Phenomic selection in wheat breeding: identification and optimisation of factors influencing prediction accuracy and comparison to genomic selection. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:895-914. [PMID: 34988629 DOI: 10.1007/s00122-021-04005-8] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 11/23/2021] [Indexed: 05/15/2023]
Abstract
Phenomic selection is a promising alternative or complement to genomic selection in wheat breeding. Models combining spectra from different environments maximise the predictive ability of grain yield and heading date of wheat breeding lines. Phenomic selection (PS) is a recent breeding approach similar to genomic selection (GS) except that genotyping is replaced by near-infrared (NIR) spectroscopy. PS can potentially account for non-additive effects and has the major advantage of being low cost and high throughput. Factors influencing GS predictive abilities have been intensively studied, but little is known about PS. We tested and compared the abilities of PS and GS to predict grain yield and heading date from several datasets of bread wheat lines corresponding to the first or second years of trial evaluation from two breeding companies and one research institute in France. We evaluated several factors affecting PS predictive abilities including the possibility of combining spectra collected in different environments. A simple H-BLUP model predicted both traits with prediction ability from 0.26 to 0.62 and with an efficient computation time. Our results showed that the environments in which lines are grown had a crucial impact on predictive ability based on the spectra acquired and was specific to the trait considered. Models combining NIR spectra from different environments were the best PS models and were at least as accurate as GS in most of the datasets. Furthermore, a GH-BLUP model combining genotyping and NIR spectra was the best model of all (prediction ability from 0.31 to 0.73). We demonstrated also that as for GS, the size and the composition of the training set have a crucial impact on predictive ability. PS could therefore replace or complement GS for efficient wheat breeding programs.
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Affiliation(s)
- Pauline Robert
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE - Le Moulon, 91190, Gif-sur-Yvette, France
- INRAE-Université Clermont-Auvergne, UMR1095, GDEC, 5 chemin de Beaulieu, 63000, ClermontFerrand, France
- Agri-Obtentions, Ferme de Gauvilliers, 78660, Orsonville, France
- Florimond-Desprez Veuve & Fils SAS, 3 rue Florimond-Desprez, BP 41, 59242, Cappelle-en-Pévèle, France
| | - Jérôme Auzanneau
- Agri-Obtentions, Ferme de Gauvilliers, 78660, Orsonville, France
| | - Ellen Goudemand
- Florimond-Desprez Veuve & Fils SAS, 3 rue Florimond-Desprez, BP 41, 59242, Cappelle-en-Pévèle, France
| | - François-Xavier Oury
- INRAE-Université Clermont-Auvergne, UMR1095, GDEC, 5 chemin de Beaulieu, 63000, ClermontFerrand, France
| | - Bernard Rolland
- INRAE-Agrocampus Ouest-Université Rennes 1, UMR1349, IGEPP, Domaine de la Motte, 35653, Le Rheu, France
| | - Emmanuel Heumez
- INRAE, UE 972, Grandes Cultures Innovation Environnement, 2 Chaussée Brunehaut, 80200, EstréesMons, France
| | - Sophie Bouchet
- INRAE-Université Clermont-Auvergne, UMR1095, GDEC, 5 chemin de Beaulieu, 63000, ClermontFerrand, France
| | - Jacques Le Gouis
- INRAE-Université Clermont-Auvergne, UMR1095, GDEC, 5 chemin de Beaulieu, 63000, ClermontFerrand, France
| | - Renaud Rincent
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE - Le Moulon, 91190, Gif-sur-Yvette, France.
- INRAE-Université Clermont-Auvergne, UMR1095, GDEC, 5 chemin de Beaulieu, 63000, ClermontFerrand, France.
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Bari MAA, Zheng P, Viera I, Worral H, Szwiec S, Ma Y, Main D, Coyne CJ, McGee RJ, Bandillo N. Harnessing Genetic Diversity in the USDA Pea Germplasm Collection Through Genomic Prediction. Front Genet 2022; 12:707754. [PMID: 35003202 PMCID: PMC8740293 DOI: 10.3389/fgene.2021.707754] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Accepted: 10/26/2021] [Indexed: 11/13/2022] Open
Abstract
Phenotypic evaluation and efficient utilization of germplasm collections can be time-intensive, laborious, and expensive. However, with the plummeting costs of next-generation sequencing and the addition of genomic selection to the plant breeder's toolbox, we now can more efficiently tap the genetic diversity within large germplasm collections. In this study, we applied and evaluated genomic prediction's potential to a set of 482 pea (Pisum sativum L.) accessions-genotyped with 30,600 single nucleotide polymorphic (SNP) markers and phenotyped for seed yield and yield-related components-for enhancing selection of accessions from the USDA Pea Germplasm Collection. Genomic prediction models and several factors affecting predictive ability were evaluated in a series of cross-validation schemes across complex traits. Different genomic prediction models gave similar results, with predictive ability across traits ranging from 0.23 to 0.60, with no model working best across all traits. Increasing the training population size improved the predictive ability of most traits, including seed yield. Predictive abilities increased and reached a plateau with increasing number of markers presumably due to extensive linkage disequilibrium in the pea genome. Accounting for population structure effects did not significantly boost predictive ability, but we observed a slight improvement in seed yield. By applying the best genomic prediction model (e.g., RR-BLUP), we then examined the distribution of genotyped but nonphenotyped accessions and the reliability of genomic estimated breeding values (GEBV). The distribution of GEBV suggested that none of the nonphenotyped accessions were expected to perform outside the range of the phenotyped accessions. Desirable breeding values with higher reliability can be used to identify and screen favorable germplasm accessions. Expanding the training set and incorporating additional orthogonal information (e.g., transcriptomics, metabolomics, physiological traits, etc.) into the genomic prediction framework can enhance prediction accuracy.
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Affiliation(s)
- Md Abdullah Al Bari
- Department of Plant Sciences, North Dakota State University, Fargo, ND, United States
| | - Ping Zheng
- Department of Horticulture, Washington State University, Pullman, WA, United States
| | - Indalecio Viera
- Department of Plant Sciences, North Dakota State University, Fargo, ND, United States
| | - Hannah Worral
- NDSU North Central Research Extension Center, Minot, ND, United States
| | - Stephen Szwiec
- NDSU North Central Research Extension Center, Minot, ND, United States
| | - Yu Ma
- Department of Horticulture, Washington State University, Pullman, WA, United States
| | - Dorrie Main
- Department of Horticulture, Washington State University, Pullman, WA, United States
| | - Clarice J Coyne
- USDA-ARS Plant Germplasm Introduction and Testing, Washington State University, Pullman, WA, United States
| | - Rebecca J McGee
- USDA-ARS Grain Legume Genetics and Physiology Research, Pullman, WA, United States
| | - Nonoy Bandillo
- Department of Plant Sciences, North Dakota State University, Fargo, ND, United States
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Bartholomé J, Prakash PT, Cobb JN. Genomic Prediction: Progress and Perspectives for Rice Improvement. Methods Mol Biol 2022; 2467:569-617. [PMID: 35451791 DOI: 10.1007/978-1-0716-2205-6_21] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Genomic prediction can be a powerful tool to achieve greater rates of genetic gain for quantitative traits if thoroughly integrated into a breeding strategy. In rice as in other crops, the interest in genomic prediction is very strong with a number of studies addressing multiple aspects of its use, ranging from the more conceptual to the more practical. In this chapter, we review the literature on rice (Oryza sativa) and summarize important considerations for the integration of genomic prediction in breeding programs. The irrigated breeding program at the International Rice Research Institute is used as a concrete example on which we provide data and R scripts to reproduce the analysis but also to highlight practical challenges regarding the use of predictions. The adage "To someone with a hammer, everything looks like a nail" describes a common psychological pitfall that sometimes plagues the integration and application of new technologies to a discipline. We have designed this chapter to help rice breeders avoid that pitfall and appreciate the benefits and limitations of applying genomic prediction, as it is not always the best approach nor the first step to increasing the rate of genetic gain in every context.
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Affiliation(s)
- Jérôme Bartholomé
- CIRAD, UMR AGAP Institut, Montpellier, France.
- AGAP Institut, Univ Montpellier, CIRAD, INRAE, Montpellier SupAgro, Montpellier, France.
- Rice Breeding Platform, International Rice Research Institute, Manila, Philippines.
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Lange M, Alako BTF, Cochrane G, Ghaffar M, Mascher M, Habekost PK, Hillebrand U, Scholz U, Schorch F, Freitag J, Scholz AH. Quantitative monitoring of nucleotide sequence data from genetic resources in context of their citation in the scientific literature. Gigascience 2021; 10:giab084. [PMID: 34966925 PMCID: PMC8716361 DOI: 10.1093/gigascience/giab084] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Revised: 08/04/2021] [Accepted: 11/29/2021] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Linking nucleotide sequence data (NSD) to scientific publication citations can enhance understanding of NSD provenance, scientific use, and reuse in the community. By connecting publications with NSD records, NSD geographical provenance information, and author geographical information, it becomes possible to assess the contribution of NSD to infer trends in scientific knowledge gain at the global level. FINDINGS We extracted and linked records from the European Nucleotide Archive to citations in open-access publications aggregated at Europe PubMed Central. A total of 8,464,292 ENA accessions with geographical provenance information were associated with publications. We conducted a data quality review to uncover potential issues in publication citation information extraction and author affiliation tagging and developed and implemented best-practice recommendations for citation extraction. We constructed flat data tables and a data warehouse with an interactive web application to enable ad hoc exploration of NSD use and summary statistics. CONCLUSIONS The extraction and linking of NSD with associated publication citations enables transparency. The quality review contributes to enhanced text mining methods for identifier extraction and use. Furthermore, the global provision and use of NSD enable scientists worldwide to join literature and sequence databases in a multidimensional fashion. As a concrete use case, we visualized statistics of country clusters concerning NSD access in the context of discussions around digital sequence information under the United Nations Convention on Biological Diversity.
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Affiliation(s)
- Matthias Lange
- Leibniz Institute of Plant Genetics and Crop Plant Research, Department Breeding Research, OT Gatersleben, Corrensstrasse 3, 06466 Seeland, Germany
| | - Blaise T F Alako
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Guy Cochrane
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Mehmood Ghaffar
- Leibniz Institute of Plant Genetics and Crop Plant Research, Department Breeding Research, OT Gatersleben, Corrensstrasse 3, 06466 Seeland, Germany
| | - Martin Mascher
- Leibniz Institute of Plant Genetics and Crop Plant Research, Department Breeding Research, OT Gatersleben, Corrensstrasse 3, 06466 Seeland, Germany
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Puschstraße 4, 04103 Leipzig, Germany
| | - Pia-Katharina Habekost
- Leibniz Institute of Plant Genetics and Crop Plant Research, Department Breeding Research, OT Gatersleben, Corrensstrasse 3, 06466 Seeland, Germany
- The Harz University of Applied Science, Department of Automation and Computer Science, Friedrichstraße 57, 38855 Wernigerode, Germany
| | - Upneet Hillebrand
- Leibniz Institute DSMZ-German Collection of Microorganisms and Cell Cultures GmbH, Department Research - Microbial Ecology and Diversity, Inhoffenstraße 7B, 38124 Braunschweig, Germany
| | - Uwe Scholz
- Leibniz Institute of Plant Genetics and Crop Plant Research, Department Breeding Research, OT Gatersleben, Corrensstrasse 3, 06466 Seeland, Germany
| | - Florian Schorch
- Leibniz Institute of Plant Genetics and Crop Plant Research, Department Breeding Research, OT Gatersleben, Corrensstrasse 3, 06466 Seeland, Germany
- The Harz University of Applied Science, Department of Automation and Computer Science, Friedrichstraße 57, 38855 Wernigerode, Germany
| | - Jens Freitag
- Leibniz Institute of Plant Genetics and Crop Plant Research, Department Breeding Research, OT Gatersleben, Corrensstrasse 3, 06466 Seeland, Germany
| | - Amber Hartman Scholz
- Leibniz Institute DSMZ-German Collection of Microorganisms and Cell Cultures GmbH, Department Research - Microbial Ecology and Diversity, Inhoffenstraße 7B, 38124 Braunschweig, Germany
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Cazenave X, Petit B, Lateur M, Nybom H, Sedlak J, Tartarini S, Laurens F, Durel CE, Muranty H. Combining genetic resources and elite material populations to improve the accuracy of genomic prediction in apple. G3 (BETHESDA, MD.) 2021; 12:6459174. [PMID: 34893831 PMCID: PMC9210277 DOI: 10.1093/g3journal/jkab420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/29/2021] [Accepted: 11/29/2021] [Indexed: 11/12/2022]
Abstract
Genomic selection is an attractive strategy for apple breeding that could reduce the length of breeding cycles. A possible limitation to the practical implementation of this approach lies in the creation of a training set large and diverse enough to ensure accurate predictions. In this study, we investigated the potential of combining two available populations, i.e., genetic resources and elite material, in order to obtain a large training set with a high genetic diversity. We compared the predictive ability of genomic predictions within-population, across-population or when combining both populations, and tested a model accounting for population-specific marker effects in this last case. The obtained predictive abilities were moderate to high according to the studied trait and small increases in predictive ability could be obtained for some traits when the two populations were combined into a unique training set. We also investigated the potential of such a training set to predict hybrids resulting from crosses between the two populations, with a focus on the method to design the training set and the best proportion of each population to optimize predictions. The measured predictive abilities were very similar for all the proportions, except for the extreme cases where only one of the two populations was used in the training set, in which case predictive abilities could be lower than when using both populations. Using an optimization algorithm to choose the genotypes in the training set also led to higher predictive abilities than when the genotypes were chosen at random. Our results provide guidelines to initiate breeding programs that use genomic selection when the implementation of the training set is a limitation.
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Affiliation(s)
- Xabi Cazenave
- Univ Angers, INRAE, Institut Agro, IRHS, SFR QuaSaV, F-49000 Angers, France
| | - Bernard Petit
- Univ Angers, INRAE, Institut Agro, IRHS, SFR QuaSaV, F-49000 Angers, France
| | - Marc Lateur
- Plant Breeding and Biodiversity, Centre Wallon de Recherches Agronomiques, Gembloux, Belgium
| | - Hilde Nybom
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Kristianstad, Sweden
| | - Jiri Sedlak
- Výzkumný a Šlechtitelský ústav Ovocnářský Holovousy s.r.o, Holovousy, Czech Republic
| | - Stefano Tartarini
- Department of Agricultural Sciences, University of Bologna, Bologna, Italy
| | - François Laurens
- Univ Angers, INRAE, Institut Agro, IRHS, SFR QuaSaV, F-49000 Angers, France
| | - Charles-Eric Durel
- Univ Angers, INRAE, Institut Agro, IRHS, SFR QuaSaV, F-49000 Angers, France
| | - Hélène Muranty
- Univ Angers, INRAE, Institut Agro, IRHS, SFR QuaSaV, F-49000 Angers, France,Corresponding author:
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Dzievit MJ, Guo T, Li X, Yu J. Comprehensive analytical and empirical evaluation of genomic prediction across diverse accessions in maize. THE PLANT GENOME 2021; 14:e20160. [PMID: 34661990 DOI: 10.1002/tpg2.20160] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2021] [Accepted: 08/23/2021] [Indexed: 06/13/2023]
Abstract
Efficiently exploiting natural genetic diversity captured by accessions stored in genebanks is crucial to genetic improvement of major crops. Selecting accessions of interest from genebanks has traditionally required information from extensive and expensive evaluation; however, low-cost genotyping combined with genomic prediction have enabled us to generate predicted genetic merits for the entire set with targeted phenotypic evaluation of representative subsets. To explore this general approach, analytical assessment and empirical validation of the maize (Zea mays L.) association population (MAP) as a training population were conducted in the present study. Cross-validation within the MAP revealed mostly modest to strong predictive ability for 36 traits, generally in parallel with the square root of heritability. The MAP was then used to train the prediction models to generate genomic estimated breeding values (GEBVs) for the Ames Diversity Panel. Empirical validation conducted for nine traits across two validation populations confirmed the accuracy level indicated by the cross-validation of the training population. An upper bound for reliability (U value) was calculated for the accessions in the prediction population using genotypic data. The group of accessions with high U values generally had high predictive ability, even though the range of observed trait values was similar to the group of accessions with low U values. Our comprehensive analysis validated the general approach of turbocharging genebanks with genomics and genomic prediction. In addition, breeders and researchers can consider both GEBVs and U values to balance the needs of improving specific traits and broadening genetic diversity when selecting accessions from genebanks.
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Affiliation(s)
| | - Tingting Guo
- Dep. of Agronomy, Iowa State Univ., Ames, IA, 50011, USA
| | - Xianran Li
- Dep. of Agronomy, Iowa State Univ., Ames, IA, 50011, USA
| | - Jianming Yu
- Dep. of Agronomy, Iowa State Univ., Ames, IA, 50011, USA
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Volk GM, Byrne PF, Coyne CJ, Flint-Garcia S, Reeves PA, Richards C. Integrating Genomic and Phenomic Approaches to Support Plant Genetic Resources Conservation and Use. PLANTS (BASEL, SWITZERLAND) 2021; 10:2260. [PMID: 34834625 PMCID: PMC8619436 DOI: 10.3390/plants10112260] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 10/20/2021] [Accepted: 10/20/2021] [Indexed: 05/17/2023]
Abstract
Plant genebanks provide genetic resources for breeding and research programs worldwide. These programs benefit from having access to high-quality, standardized phenotypic and genotypic data. Technological advances have made it possible to collect phenomic and genomic data for genebank collections, which, with the appropriate analytical tools, can directly inform breeding programs. We discuss the importance of considering genebank accession homogeneity and heterogeneity in data collection and documentation. Citing specific examples, we describe how well-documented genomic and phenomic data have met or could meet the needs of plant genetic resource managers and users. We explore future opportunities that may emerge from improved documentation and data integration among plant genetic resource information systems.
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Affiliation(s)
- Gayle M. Volk
- United States Department of Agriculture, Agricultural Research Service, National Laboratory for Genetic Resources Preservation, Fort Collins, CO 80521, USA; (P.A.R.); (C.R.)
| | - Patrick F. Byrne
- Department of Soil and Crop Sciences, Colorado State University, Fort Collins, CO 80523, USA;
| | - Clarice J. Coyne
- United States Department of Agriculture, Agricultural Research Service, Western Regional Plant Introduction Station, Pullman, WA 99164, USA;
| | - Sherry Flint-Garcia
- Plant Genetics Research Unit, United States Department of Agriculture, Agricultural Research Service, Columbia, MO 65211, USA;
| | - Patrick A. Reeves
- United States Department of Agriculture, Agricultural Research Service, National Laboratory for Genetic Resources Preservation, Fort Collins, CO 80521, USA; (P.A.R.); (C.R.)
| | - Chris Richards
- United States Department of Agriculture, Agricultural Research Service, National Laboratory for Genetic Resources Preservation, Fort Collins, CO 80521, USA; (P.A.R.); (C.R.)
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Cheng Q, Jiang S, Xu F, Wang Q, Xiao Y, Zhang R, Zhao J, Yan J, Ma C, Wang X. Genome optimization via virtual simulation to accelerate maize hybrid breeding. Brief Bioinform 2021; 23:6407728. [PMID: 34676389 DOI: 10.1093/bib/bbab447] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 09/24/2021] [Accepted: 09/28/2021] [Indexed: 11/13/2022] Open
Abstract
The employment of doubled-haploid (DH) technology in maize has vastly accelerated the efficiency of developing inbred lines. The selection of superior lines has to rely on genotypes with genomic selection (GS) model, rather than phenotypes due to the high expense of field phenotyping. In this work, we implemented 'genome optimization via virtual simulation (GOVS)' using the genotype and phenotype data of 1404 maize lines and their F1 progeny. GOVS simulates a virtual genome encompassing the most abundant 'optimal genotypes' or 'advantageous alleles' in a genetic pool. Such a virtually optimized genome, although can never be developed in reality, may help plot the optimal route to direct breeding decisions. GOVS assists in the selection of superior lines based on the genomic fragments that a line contributes to the simulated genome. The assumption is that the more fragments of optimal genotypes a line contributes to the assembly, the higher the likelihood of the line favored in the F1 phenotype, e.g. grain yield. Compared to traditional GS method, GOVS-assisted selection may avoid using an arbitrary threshold for the predicted F1 yield to assist selection. Additionally, the selected lines contributed complementary sets of advantageous alleles to the virtual genome. This feature facilitates plotting the optimal route for DH production, whereby the fewest lines and F1 combinations are needed to pyramid a maximum number of advantageous alleles in the new DH lines. In summary, incorporation of DH production, GS and genome optimization will ultimately improve genomically designed breeding in maize. Short abstract: Doubled-haploid (DH) technology has been widely applied in maize breeding industry, as it greatly shortens the period of developing homozygous inbred lines via bypassing several rounds of self-crossing. The current challenge is how to efficiently screen the large volume of inbred lines based on genotypes. We present the toolbox of genome optimization via virtual simulation (GOVS), which complements the traditional genomic selection model. GOVS simulates a virtual genome encompassing the most abundant 'optimal genotypes' in a breeding population, and then assists in selection of superior lines based on the genomic fragments that a line contributes to the simulated genome. Availability of GOVS (https://govs-pack.github.io/) to the public may ultimately facilitate genomically designed breeding in maize.
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Affiliation(s)
- Qian Cheng
- State Key Laboratory of Crop Stress Biology for Arid Areas, Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Shaanxi, China
| | - Shuqing Jiang
- National Maize Improvement Center of China Agricultural University, Beijing, China
| | - Feng Xu
- National Maize Improvement Center of China Agricultural University, Beijing, China
| | - Qian Wang
- National Maize Improvement Center of China Agricultural University, Beijing, China
| | - Yingjie Xiao
- National Key Laboratory of Crop Genetic Improvement, College of Plant Sciences and Technology at Huazhong Agricultural University, Wuhan, China
| | - Ruyang Zhang
- Maize Research Center at Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Jiuran Zhao
- Maize Research Center at Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Jianbing Yan
- National Key Laboratory of Crop Genetic Improvement, College of Plant Sciences and Technology at Huazhong Agricultural University, Wuhan, China
| | - Chuang Ma
- State Key Laboratory of Crop Stress Biology for Arid Areas, Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Shaanxi, China
| | - Xiangfeng Wang
- Sanya Institute of China Agricultural University, Hainan, China
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Tanaka R, Lui-King J, Mandaharisoa ST, Rakotondramanana M, Ranaivo HN, Pariasca-Tanaka J, Kanegae HK, Iwata H, Wissuwa M. From gene banks to farmer's fields: using genomic selection to identify donors for a breeding program in rice to close the yield gap on smallholder farms. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:3397-3410. [PMID: 34264372 PMCID: PMC8440315 DOI: 10.1007/s00122-021-03909-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 07/06/2021] [Indexed: 06/13/2023]
Abstract
KEY MESSAGE Despite phenotyping the training set under unfavorable conditions on smallholder farms in Madagascar, we were able to successfully apply genomic prediction to select donors among gene bank accessions. Poor soil fertility and low fertilizer application rates are main reasons for the large yield gap observed for rice produced in sub-Saharan Africa. Traditional varieties that are preserved in gene banks were shown to possess traits and alleles that would improve the performance of modern variety under such low-input conditions. How to accelerate the utilization of gene bank resources in crop improvement is an unresolved question and here our objective was to test whether genomic prediction could aid in the selection of promising donors. A subset of the 3,024 sequenced accessions from the IRRI rice gene bank was phenotyped for yield and agronomic traits for two years in unfertilized farmers' fields in Madagascar, and based on these data, a genomic prediction model was developed. This model was applied to predict the performance of the entire set of 3024 accessions, and the top predicted performers were sent to Madagascar for confirmatory trials. The prediction accuracies ranged from 0.10 to 0.30 for grain yield, from 0.25 to 0.63 for straw biomass, to 0.71 for heading date. Two accessions have subsequently been utilized as donors in rice breeding programs in Madagascar. Despite having conducted phenotypic evaluations under challenging conditions on smallholder farms, our results are encouraging as the prediction accuracy realized in on-farm experiments was in the range of accuracies achieved in on-station studies. Thus, we could provide clear empirical evidence on the value of genomic selection in identifying suitable genetic resources for crop improvement, if genotypic data are available.
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Affiliation(s)
- Ryokei Tanaka
- Department of Agricultural and Environmental Biology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo, Tokyo, 113-8657, Japan
| | - James Lui-King
- International Program in Agricultural Development Studies, Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo, Tokyo, 113-8657, Japan
- Crop, Livestock and Environment Division, Japan International Research Center for Agricultural Sciences (JIRCAS), 1-1 Ohwashi, Tsukuba, Ibaraki, 305-8686, Japan
| | - Sarah Tojo Mandaharisoa
- Rice Research Department, The National Center for Applied Research On Rural Development (FOFIFA), Antananarivo, 101, Madagascar
| | - Mbolatantely Rakotondramanana
- Rice Research Department, The National Center for Applied Research On Rural Development (FOFIFA), Antananarivo, 101, Madagascar
| | - Harisoa Nicole Ranaivo
- Rice Research Department, The National Center for Applied Research On Rural Development (FOFIFA), Antananarivo, 101, Madagascar
| | - Juan Pariasca-Tanaka
- Crop, Livestock and Environment Division, Japan International Research Center for Agricultural Sciences (JIRCAS), 1-1 Ohwashi, Tsukuba, Ibaraki, 305-8686, Japan
| | - Hiromi Kajiya Kanegae
- Department of Agricultural and Environmental Biology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo, Tokyo, 113-8657, Japan
| | - Hiroyoshi Iwata
- Department of Agricultural and Environmental Biology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo, Tokyo, 113-8657, Japan
| | - Matthias Wissuwa
- Crop, Livestock and Environment Division, Japan International Research Center for Agricultural Sciences (JIRCAS), 1-1 Ohwashi, Tsukuba, Ibaraki, 305-8686, Japan.
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Mahadevaiah C, Appunu C, Aitken K, Suresha GS, Vignesh P, Mahadeva Swamy HK, Valarmathi R, Hemaprabha G, Alagarasan G, Ram B. Genomic Selection in Sugarcane: Current Status and Future Prospects. FRONTIERS IN PLANT SCIENCE 2021; 12:708233. [PMID: 34646284 PMCID: PMC8502939 DOI: 10.3389/fpls.2021.708233] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Accepted: 08/24/2021] [Indexed: 05/18/2023]
Abstract
Sugarcane is a C4 and agro-industry-based crop with a high potential for biomass production. It serves as raw material for the production of sugar, ethanol, and electricity. Modern sugarcane varieties are derived from the interspecific and intergeneric hybridization between Saccharum officinarum, Saccharum spontaneum, and other wild relatives. Sugarcane breeding programmes are broadly categorized into germplasm collection and characterization, pre-breeding and genetic base-broadening, and varietal development programmes. The varietal identification through the classic breeding programme requires a minimum of 12-14 years. The precise phenotyping in sugarcane is extremely tedious due to the high propensity of lodging and suckering owing to the influence of environmental factors and crop management practices. This kind of phenotyping requires data from both plant crop and ratoon experiments conducted over locations and seasons. In this review, we explored the feasibility of genomic selection schemes for various breeding programmes in sugarcane. The genetic diversity analysis using genome-wide markers helps in the formation of core set germplasm representing the total genomic diversity present in the Saccharum gene bank. The genome-wide association studies and genomic prediction in the Saccharum gene bank are helpful to identify the complete genomic resources for cane yield, commercial cane sugar, tolerances to biotic and abiotic stresses, and other agronomic traits. The implementation of genomic selection in pre-breeding, genetic base-broadening programmes assist in precise introgression of specific genes and recurrent selection schemes enhance the higher frequency of favorable alleles in the population with a considerable reduction in breeding cycles and population size. The integration of environmental covariates and genomic prediction in multi-environment trials assists in the prediction of varietal performance for different agro-climatic zones. This review also directed its focus on enhancing the genetic gain over time, cost, and resource allocation at various stages of breeding programmes.
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Affiliation(s)
| | - Chinnaswamy Appunu
- Division of Crop Improvement, ICAR-Sugarcane Breeding Institute, Coimbatore, India
| | - Karen Aitken
- CSIRO (Commonwealth Scientific and Industrial Research Organization), St. Lucia, QLD, Australia
| | | | - Palanisamy Vignesh
- Division of Crop Improvement, ICAR-Sugarcane Breeding Institute, Coimbatore, India
| | | | | | - Govind Hemaprabha
- Division of Crop Improvement, ICAR-Sugarcane Breeding Institute, Coimbatore, India
| | - Ganesh Alagarasan
- Division of Crop Improvement, ICAR-Sugarcane Breeding Institute, Coimbatore, India
| | - Bakshi Ram
- Division of Crop Improvement, ICAR-Sugarcane Breeding Institute, Coimbatore, India
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Wynberg R, Andersen R, Laird S, Kusena K, Prip C, Westengen OT. Farmers' Rights and Digital Sequence Information: Crisis or Opportunity to Reclaim Stewardship Over Agrobiodiversity? FRONTIERS IN PLANT SCIENCE 2021; 12:686728. [PMID: 34484258 PMCID: PMC8409524 DOI: 10.3389/fpls.2021.686728] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Accepted: 07/09/2021] [Indexed: 06/13/2023]
Abstract
Contestations about the way in which digital sequence information is used and regulated have created stumbling blocks across multiple international policy processes. Such schisms have profound implications for the way in which we manage and conceptualize agrobiodiversity and its benefits. This paper explores the relationship between farmers' rights, as recognized in the International Treaty on Plant Genetic Resources for Food and Agriculture, and the dematerialization of genetic resources. Using concepts of "stewardship" and "ownership" we emphasize the need to move away from viewing agrobiodiversity as a commodity that can be owned, toward a strengthened, proactive and expansive stewardship approach that recognizes plant genetic resources for food and agriculture as a public good which should be governed as such. Through this lens we analyze the relationship between digital sequence information and different elements of farmers' rights to compare and contrast implications for the governance of digital sequence information. Two possible parallel pathways are presented, the first envisaging an enhanced multilateral system that includes digital sequence information and which promotes and enhances the realization of farmers' rights; and the second a more radical approach that folds together concepts of stewardship, farmers' rights, and open source science. Farmers' rights, we suggest, may well be the linchpin for finding fair and equitable solutions for digital sequence information beyond the bilateral and transactional approach that has come to characterize access and benefit sharing under the Convention on Biological Diversity. Existing policy uncertainties could be seized as an unexpected but serendipitous opportunity to chart an alternative and visionary pathway for the rights of farmers and other custodians of plant genetic resources.
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Affiliation(s)
- Rachel Wynberg
- Department of Environmental and Geographical Science, University of Cape Town, Cape Town, South Africa
| | | | - Sarah Laird
- People and Plants International, Bristol, VT, United States
| | - Kudzai Kusena
- Department of Environmental and Geographical Science, University of Cape Town, Cape Town, South Africa
- Department of Research and Specialist Services, National Genebank of Zimbabwe, Harare, Zimbabwe
| | | | - Ola Tveitereid Westengen
- Department of International Environment and Development Studies (Noragric), Norwegian University of Life Sciences, Aas, Norway
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Martini JWR, Molnar TL, Crossa J, Hearne SJ, Pixley KV. Opportunities and Challenges of Predictive Approaches for Harnessing the Potential of Genetic Resources. FRONTIERS IN PLANT SCIENCE 2021; 12:674036. [PMID: 34276731 PMCID: PMC8281018 DOI: 10.3389/fpls.2021.674036] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Accepted: 05/17/2021] [Indexed: 06/13/2023]
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Gonzalez MY, Zhao Y, Jiang Y, Stein N, Habekuss A, Reif JC, Schulthess AW. Genomic prediction models trained with historical records enable populating the German ex situ genebank bio-digital resource center of barley (Hordeum sp.) with information on resistances to soilborne barley mosaic viruses. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:2181-2196. [PMID: 33768281 PMCID: PMC8263548 DOI: 10.1007/s00122-021-03815-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Accepted: 03/10/2021] [Indexed: 05/04/2023]
Abstract
Genomic prediction with special weight of major genes is a valuable tool to populate bio-digital resource centers. Phenotypic information of crop genetic resources is a prerequisite for an informed selection that aims to broaden the genetic base of the elite breeding pools. We investigated the potential of genomic prediction based on historical screening data of plant responses against the Barley yellow mosaic viruses for populating the bio-digital resource center of barley. Our study includes dense marker data for 3838 accessions of winter barley, and historical screening data of 1751 accessions for Barley yellow mosaic virus (BaYMV) and of 1771 accessions for Barley mild mosaic virus (BaMMV). Linear mixed models were fitted by considering combinations for the effects of genotypes, years, and locations. The best linear unbiased estimations displayed a broad spectrum of plant responses against BaYMV and BaMMV. Prediction abilities, computed as correlations between predictions and observed phenotypes of accessions, were low for the marker-assisted selection approach amounting to 0.42. In contrast, prediction abilities of genomic best linear unbiased predictions were high, with values of 0.62 for BaYMV and 0.64 for BaMMV. Prediction abilities of genomic prediction were improved by up to ~ 5% using W-BLUP, in which more weight is given to markers with significant major effects found by association mapping. Our results outline the utility of historical screening data and W-BLUP model to predict the performance of the non-phenotyped individuals in genebank collections. The presented strategy can be considered as part of the different approaches used in genebank genomics to valorize genetic resources for their usage in disease resistance breeding and research.
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Affiliation(s)
- Maria Y Gonzalez
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466, Gatersleben, Germany
| | - Yusheng Zhao
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466, Gatersleben, Germany
| | - Yong Jiang
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466, Gatersleben, Germany
| | - Nils Stein
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466, Gatersleben, Germany
- Department of Crop Sciences, Center for Integrated Breeding Research (CiBreed), Georg-August-University, Göttingen, Germany
| | - Antje Habekuss
- Julius Kühn Institute (Federal Research Centre for Cultivated Plants), Quedlinburg, Germany
| | - Jochen C Reif
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466, Gatersleben, Germany.
| | - Albert W Schulthess
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466, Gatersleben, Germany
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46
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Hao H, Li Z, Leng C, Lu C, Luo H, Liu Y, Wu X, Liu Z, Shang L, Jing HC. Sorghum breeding in the genomic era: opportunities and challenges. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:1899-1924. [PMID: 33655424 PMCID: PMC7924314 DOI: 10.1007/s00122-021-03789-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Accepted: 02/05/2021] [Indexed: 05/04/2023]
Abstract
The importance and potential of the multi-purpose crop sorghum in global food security have not yet been fully exploited, and the integration of the state-of-art genomics and high-throughput technologies into breeding practice is required. Sorghum, a historically vital staple food source and currently the fifth most important major cereal, is emerging as a crop with diverse end-uses as food, feed, fuel and forage and a model for functional genetics and genomics of tropical grasses. Rapid development in high-throughput experimental and data processing technologies has significantly speeded up sorghum genomic researches in the past few years. The genomes of three sorghum lines are available, thousands of genetic stocks accessible and various genetic populations, including NAM, MAGIC, and mutagenised populations released. Functional and comparative genomics have elucidated key genetic loci and genes controlling agronomical and adaptive traits. However, the knowledge gained has far away from being translated into real breeding practices. We argue that the way forward is to take a genome-based approach for tailored designing of sorghum as a multi-functional crop combining excellent agricultural traits for various end uses. In this review, we update the new concepts and innovation systems in crop breeding and summarise recent advances in sorghum genomic researches, especially the genome-wide dissection of variations in genes and alleles for agronomically important traits. Future directions and opportunities for sorghum breeding are highlighted to stimulate discussion amongst sorghum academic and industrial communities.
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Affiliation(s)
- Huaiqing Hao
- Key Laboratory of Plant Resources, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China.
| | - Zhigang Li
- Key Laboratory of Plant Resources, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
| | - Chuanyuan Leng
- Key Laboratory of Plant Resources, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
| | - Cheng Lu
- Key Laboratory of Plant Resources, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Hong Luo
- Key Laboratory of Plant Resources, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
| | - Yuanming Liu
- Key Laboratory of Plant Resources, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Xiaoyuan Wu
- Key Laboratory of Plant Resources, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
| | - Zhiquan Liu
- Key Laboratory of Plant Resources, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
| | - Li Shang
- Key Laboratory of Plant Resources, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
| | - Hai-Chun Jing
- Key Laboratory of Plant Resources, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China.
- Engineering Laboratory for Grass-based Livestock Husbandry, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
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Jaikumar NS, Stutz SS, Fernandes SB, Leakey ADB, Bernacchi CJ, Brown PJ, Long SP. Can improved canopy light transmission ameliorate loss of photosynthetic efficiency in the shade? An investigation of natural variation in Sorghum bicolor. JOURNAL OF EXPERIMENTAL BOTANY 2021; 72:4965-4980. [PMID: 33914063 PMCID: PMC8219039 DOI: 10.1093/jxb/erab176] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2020] [Accepted: 04/28/2021] [Indexed: 05/29/2023]
Abstract
Previous studies have found that maximum quantum yield of CO2 assimilation (Φ CO2,max,app) declines in lower canopies of maize and miscanthus, a maladaptive response to self-shading. These observations were limited to single genotypes, leaving it unclear whether the maladaptive shade response is a general property of this C4 grass tribe, the Andropogoneae. We explored the generality of this maladaptation by testing the hypothesis that erect leaf forms (erectophiles), which allow more light into the lower canopy, suffer less of a decline in photosynthetic efficiency than drooping leaf (planophile) forms. On average, Φ CO2,max,app declined 27% in lower canopy leaves across 35 accessions, but the decline was over twice as great in planophiles than in erectophiles. The loss of photosynthetic efficiency involved a decoupling between electron transport and assimilation. This was not associated with increased bundle sheath leakage, based on 13C measurements. In both planophiles and erectophiles, shaded leaves had greater leaf absorptivity and lower activities of key C4 enzymes than sun leaves. The erectophile form is considered more productive because it allows a more effective distribution of light through the canopy to support photosynthesis. We show that in sorghum, it provides a second benefit, maintenance of higher Φ CO2,max,app to support efficient use of that light resource.
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Affiliation(s)
- Nikhil S Jaikumar
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Samantha S Stutz
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Samuel B Fernandes
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Andrew D B Leakey
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Carl J Bernacchi
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
- USDA ARS Global Change and Photosynthesis Research Unit, Urbana, IL 61801, USA
| | - Patrick J Brown
- Department of Plant Sciences, University of California at Davis, Davis, CA 95616, USA
| | - Stephen P Long
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
- Lancaster Environment Centre, University of Lancaster, Lancaster LA1 4YQ, UK
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Germplasm Conservation: Instrumental in Agricultural Biodiversity—A Review. SUSTAINABILITY 2021. [DOI: 10.3390/su13126743] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Germplasm is a valuable natural resource that provides knowledge about the genetic composition of a species and is crucial for conserving plant diversity. Germplasm protection strategies not only involve rescuing plant species threatened with extinction, but also help preserve all essential plants, on which rests the survival of all organisms. The successful use of genetic resources necessitates their diligent collection, storage, analysis, documentation, and exchange. Slow growth cultures, cryopreservation, pollen and DNA banks, botanical gardens, genetic reserves, and farmers’ fields are a few germplasm conservation techniques being employed. However, the adoption of in-vitro techniques with any chance of genetic instability could lead to the destruction of the entire substance, but the improved understanding of basic regeneration biology would, in turn, undoubtedly increase the capacity to regenerate new plants, thus expanding selection possibilities. Germplasm conservation seeks to conserve endangered and vulnerable plant species worldwide for future proliferation and development; it is also the bedrock of agricultural production.
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Li D, Xu Z, Gu R, Wang P, Xu J, Du D, Fu J, Wang J, Zhang H, Wang G. Genomic Prediction across Structured Hybrid Populations and Environments in Maize. PLANTS 2021; 10:plants10061174. [PMID: 34207722 PMCID: PMC8227059 DOI: 10.3390/plants10061174] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 06/01/2021] [Accepted: 06/02/2021] [Indexed: 11/17/2022]
Abstract
Genomic prediction (GP) across different populations and environments should be enhanced to increase the efficiency of crop breeding. In this study, four populations were constructed and genotyped with DNA chips containing 55,000 SNPs. These populations were testcrossed to a common tester, generating four hybrid populations. Yields of the four hybrid populations were evaluated in three environments. We demonstrated by using real data that the prediction accuracies of GP across structured hybrid populations were lower than those of within-population GP. Including relatives of the validation population in the training population could increase the prediction accuracies of GP across structured hybrid populations drastically. G × E models (including main and genotype-by-environment effect) had better performance than single environment (within environment) and across environment (including only main effect) GP models in the structured hybrid population, especially in the environment where yields had higher heritability. GP by implementing G × E models in two cross-validation schemes indicated that, to increase the prediction accuracy of a new hybrid line, it would be better to field-test the hybrid line in at least one environment. Our results would be helpful for designing training population and planning field testing in hybrid breeding.
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Affiliation(s)
- Dongdong Li
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; (D.L.); (P.W.); (J.X.); (J.F.)
| | - Zhenxiang Xu
- Center for Seed Science and Technology, College of Agronomy and Biotechnology, China Agricultural University, Beijing 100193, China; (Z.X.); (R.G.); (J.W.)
| | - Riliang Gu
- Center for Seed Science and Technology, College of Agronomy and Biotechnology, China Agricultural University, Beijing 100193, China; (Z.X.); (R.G.); (J.W.)
| | - Pingxi Wang
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; (D.L.); (P.W.); (J.X.); (J.F.)
| | - Jialiang Xu
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; (D.L.); (P.W.); (J.X.); (J.F.)
| | - Dengxiang Du
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China;
| | - Junjie Fu
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; (D.L.); (P.W.); (J.X.); (J.F.)
| | - Jianhua Wang
- Center for Seed Science and Technology, College of Agronomy and Biotechnology, China Agricultural University, Beijing 100193, China; (Z.X.); (R.G.); (J.W.)
| | - Hongwei Zhang
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; (D.L.); (P.W.); (J.X.); (J.F.)
- Correspondence: (H.Z.); (G.W.)
| | - Guoying Wang
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; (D.L.); (P.W.); (J.X.); (J.F.)
- Correspondence: (H.Z.); (G.W.)
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Varshney RK, Bohra A, Yu J, Graner A, Zhang Q, Sorrells ME. Designing Future Crops: Genomics-Assisted Breeding Comes of Age. TRENDS IN PLANT SCIENCE 2021; 26:631-649. [PMID: 33893045 DOI: 10.1016/j.tplants.2021.03.010] [Citation(s) in RCA: 150] [Impact Index Per Article: 50.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Revised: 03/16/2021] [Accepted: 03/17/2021] [Indexed: 05/18/2023]
Abstract
Over the past decade, genomics-assisted breeding (GAB) has been instrumental in harnessing the potential of modern genome resources and characterizing and exploiting allelic variation for germplasm enhancement and cultivar development. Sustaining GAB in the future (GAB 2.0) will rely upon a suite of new approaches that fast-track targeted manipulation of allelic variation for creating novel diversity and facilitate their rapid and efficient incorporation in crop improvement programs. Genomic breeding strategies that optimize crop genomes with accumulation of beneficial alleles and purging of deleterious alleles will be indispensable for designing future crops. In coming decades, GAB 2.0 is expected to play a crucial role in breeding more climate-smart crop cultivars with higher nutritional value in a cost-effective and timely manner.
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Affiliation(s)
- Rajeev K Varshney
- Center of Excellence in Genomics and Systems Biology (CEGSB), International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, India; State Agricultural Biotechnology Centre, Centre for Crop and Food Innovation, Food Futures Institute, Murdoch University, Murdoch, Western Australia, Australia.
| | - Abhishek Bohra
- Crop Improvement Division, ICAR- Indian Institute of Pulses Research (ICAR- IIPR), Kanpur, India
| | - Jianming Yu
- Department of Agronomy, Iowa State University, Ames, IA, USA
| | - Andreas Graner
- Leibniz Institute of Plant Genetics and Crops Plant Research (IPK), Gatersleben, Germany
| | - Qifa Zhang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
| | - Mark E Sorrells
- Department of Plant Breeding and Genetics, Cornell University, Ithaca, NY, USA
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